FL_RankingsV2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import hockey_rink\n",
    "import hockey_scraper\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noe have shift data for the nhl 2023-24 season as well as the play by play data (pbp) in separate csv files. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading in pbp data\n",
    "pbp=pd.read_csv(\"nhl_pbp_20232024.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 323164 entries, 1 to 445636\n",
      "Data columns (total 57 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   Unnamed: 0       323164 non-null  int64  \n",
      " 1   Game_Id          323164 non-null  int64  \n",
      " 2   Date             323164 non-null  object \n",
      " 3   Period           323164 non-null  int64  \n",
      " 4   Event            323164 non-null  object \n",
      " 5   Description      323164 non-null  object \n",
      " 6   Time_Elapsed     323164 non-null  object \n",
      " 7   Seconds_Elapsed  323164 non-null  float64\n",
      " 8   Strength         323164 non-null  object \n",
      " 9   Ev_Zone          270144 non-null  object \n",
      " 10  Type             139605 non-null  object \n",
      " 11  Ev_Team          270069 non-null  object \n",
      " 12  Home_Zone        270144 non-null  object \n",
      " 13  Away_Team        323164 non-null  object \n",
      " 14  Home_Team        323164 non-null  object \n",
      " 15  p1_name          270001 non-null  object \n",
      " 16  p1_ID            263648 non-null  float64\n",
      " 17  p2_name          145390 non-null  object \n",
      " 18  p2_ID            142442 non-null  float64\n",
      " 19  p3_name          5823 non-null    object \n",
      " 20  p3_ID            5685 non-null    float64\n",
      " 21  awayPlayer1      320324 non-null  object \n",
      " 22  awayPlayer1_id   315519 non-null  float64\n",
      " 23  awayPlayer2      319587 non-null  object \n",
      " 24  awayPlayer2_id   310771 non-null  float64\n",
      " 25  awayPlayer3      319535 non-null  object \n",
      " 26  awayPlayer3_id   313392 non-null  float64\n",
      " 27  awayPlayer4      319496 non-null  object \n",
      " 28  awayPlayer4_id   310338 non-null  float64\n",
      " 29  awayPlayer5      315872 non-null  object \n",
      " 30  awayPlayer5_id   309431 non-null  float64\n",
      " 31  awayPlayer6      288120 non-null  object \n",
      " 32  awayPlayer6_id   287609 non-null  float64\n",
      " 33  homePlayer1      320284 non-null  object \n",
      " 34  homePlayer1_id   314849 non-null  float64\n",
      " 35  homePlayer2      319545 non-null  object \n",
      " 36  homePlayer2_id   310584 non-null  float64\n",
      " 37  homePlayer3      319498 non-null  object \n",
      " 38  homePlayer3_id   313916 non-null  float64\n",
      " 39  homePlayer4      319470 non-null  object \n",
      " 40  homePlayer4_id   310014 non-null  float64\n",
      " 41  homePlayer5      316117 non-null  object \n",
      " 42  homePlayer5_id   309638 non-null  float64\n",
      " 43  homePlayer6      289560 non-null  object \n",
      " 44  homePlayer6_id   289085 non-null  float64\n",
      " 45  Away_Players     323164 non-null  int64  \n",
      " 46  Home_Players     323164 non-null  int64  \n",
      " 47  Away_Score       323164 non-null  int64  \n",
      " 48  Home_Score       323164 non-null  int64  \n",
      " 49  Away_Goalie      315177 non-null  object \n",
      " 50  Away_Goalie_Id   314679 non-null  float64\n",
      " 51  Home_Goalie      315123 non-null  object \n",
      " 52  Home_Goalie_Id   314650 non-null  float64\n",
      " 53  xC               323164 non-null  float64\n",
      " 54  yC               323164 non-null  float64\n",
      " 55  Home_Coach       323164 non-null  object \n",
      " 56  Away_Coach       323164 non-null  object \n",
      "dtypes: float64(20), int64(7), object(30)\n",
      "memory usage: 143.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Drop rows where xC or yC is NaN\n",
    "pbp = pbp.dropna(subset=['xC', 'yC'])\n",
    "pbp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: Data Cleansing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definitely do not need many of these variables so going to drop a bunch of them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright looks like players 1,2, and 3 are the forwards. 4 and 5 are the defensemen and 6 is the goalie, So i only need players 1-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbp=pbp.drop(['Away_Coach','Home_Coach','Home_Goalie_Id','Home_Goalie','Away_Goalie_Id','Away_Goalie', 'Unnamed: 0','awayPlayer4','awayPlayer4_id','awayPlayer5','awayPlayer5_id','awayPlayer6','awayPlayer6_id','homePlayer4','homePlayer4_id','homePlayer5','homePlayer5_id','homePlayer6','homePlayer6_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "WRIST SHOT                              77163\n",
       "SNAP SHOT                               18419\n",
       "SLAP SHOT                               16066\n",
       "TIP-IN                                   9190\n",
       "BACKHAND                                 8148\n",
       "                                        ...  \n",
       "AR #77 DEANGELO Slashing(2 min)             1\n",
       "OS #61 MAROON High-sticking(2 min)          1\n",
       "AL #3 TANEV Delay Game(2 min)               1\n",
       "AL #3 TANEV High-sticking(2 min)            1\n",
       "YR #96 ROSLOVIC High-sticking(2 min)        1\n",
       "Name: count, Length: 204, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp.Type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Got rid of a ton of columns not needed. All columns now should be useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Game_Id', 'Date', 'Period', 'Event', 'Description', 'Time_Elapsed',\n",
       "       'Seconds_Elapsed', 'Strength', 'Ev_Zone', 'Type', 'Ev_Team',\n",
       "       'Home_Zone', 'Away_Team', 'Home_Team', 'p1_name', 'p1_ID', 'p2_name',\n",
       "       'p2_ID', 'p3_name', 'p3_ID', 'awayPlayer1', 'awayPlayer1_id',\n",
       "       'awayPlayer2', 'awayPlayer2_id', 'awayPlayer3', 'awayPlayer3_id',\n",
       "       'homePlayer1', 'homePlayer1_id', 'homePlayer2', 'homePlayer2_id',\n",
       "       'homePlayer3', 'homePlayer3_id', 'Away_Players', 'Home_Players',\n",
       "       'Away_Score', 'Home_Score', 'xC', 'yC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables we want, many of which need to be modified or created:\n",
    "\n",
    "    1)xC (modified) (Done)\n",
    "\n",
    "    2)yC (Modified) (Done)\n",
    "\n",
    "    3)Type (modified) (Done)\n",
    "\n",
    "    4)shotDistance (Create)\n",
    "\n",
    "    5)shotAngle (Create)\n",
    "\n",
    "    6)timeSinceLastEvent (Create) (Done)\n",
    "\n",
    "    7)distanceFromLastEvent (Create) (Have to calculate before transposing the x and y coordinates, cuz what if back to back shots happen between the two teams?) (Done)\n",
    "\n",
    "    8)PowerPlay (Create) (Done)\n",
    "\n",
    "    9) Goal (Create) - TARGET VARIABLE (Done)\n",
    "\n",
    "-Date needs to be last of reg season, not last of entire season including playoffs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the distanceFromLastEvent prior to transposing x and y coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the difference in x and y coordinates between consecutive events\n",
    "pbp['x_diff'] = pbp['xC'].diff()\n",
    "pbp['y_diff'] = pbp['yC'].diff()\n",
    "\n",
    "# Calculate the Euclidean distance between consecutive events\n",
    "pbp['distanceFromLastEvent'] = np.sqrt(pbp['x_diff']**2 + pbp['y_diff']**2)\n",
    "\n",
    "# Fill NaN values that may result from the first row\n",
    "pbp['distanceFromLastEvent'] = pbp['distanceFromLastEvent'].fillna(0)\n",
    "\n",
    "# Optionally, drop the intermediate x_diff and y_diff columns if no longer needed\n",
    "pbp = pbp.drop(columns=['x_diff', 'y_diff'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making all shots occur on the right side of the rink, modifying the xC and yC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First making all shots appear on the positive (right side of the rink)\n",
    "pbp['xC']=np.where(pbp['xC']<0, pbp['xC']*-1, pbp['xC'])\n",
    "pbp['yC']=np.where(pbp['xC']<0, pbp['yC']*-1, pbp['yC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making new column for PowerPlay, 1 means yes there is a pp and 0 means no pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a new column for whether or not its a power play goal. \n",
    "pbp['Power Play'] = np.where( ((pbp.Ev_Team.equals(pbp.Home_Team)) & (pbp.Strength.str[0] > pbp.Strength.str[2])) | ((pbp.Ev_Team.equals(pbp.Away_Team)) & (pbp.Strength.str[0] < pbp.Strength.str[2])), 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting time since last event (Right now have seconds elapsed, which is just time elapsed from period. So to get time elapsed since last event, Just have to subtract seconds elased on previous row/event from the current row/event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbp['timeSinceLastEvent']= pbp['Seconds_Elapsed'].diff()\n",
    "pbp['timeSinceLastEvent'] = pbp['timeSinceLastEvent'].fillna(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the each shot type into individual binary value columns using one hot encoding (Then keeping the columns created that represents shots only: WRIST SHOT,SNAP SHOT,SLAP SHOT,TIP-IN,BACKHAND, Done at the very end of data cleaning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "WRIST SHOT                              77163\n",
       "SNAP SHOT                               18419\n",
       "SLAP SHOT                               16066\n",
       "TIP-IN                                   9190\n",
       "BACKHAND                                 8148\n",
       "                                        ...  \n",
       "AR #77 DEANGELO Slashing(2 min)             1\n",
       "OS #61 MAROON High-sticking(2 min)          1\n",
       "AL #3 TANEV Delay Game(2 min)               1\n",
       "AL #3 TANEV High-sticking(2 min)            1\n",
       "YR #96 ROSLOVIC High-sticking(2 min)        1\n",
       "Name: count, Length: 204, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im going to keep only the values of type above not any other crazy ones. I am also going to convert to string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbp['Type'] = pbp['Type'].astype(str)\n",
    "pbp=pbp[(pbp['Type']=='WRIST SHOT') | (pbp['Type']=='SNAP SHOT') | (pbp['Type']=='SLAP SHOT') | (pbp['Type']=='TIP-IN') | (pbp['Type']=='BACKHAND') | (pbp['Type']=='DEFLECTED') | (pbp['Type']=='WRAP-AROUND') ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "WRIST SHOT     77163\n",
       "SNAP SHOT      18419\n",
       "SLAP SHOT      16066\n",
       "TIP-IN          9190\n",
       "BACKHAND        8148\n",
       "DEFLECTED       2074\n",
       "WRAP-AROUND      919\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking \n",
    "pbp['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure the Type column is clean (e.g., no leading/trailing spaces)\n",
    "pbp['Type'] = pbp['Type'].str.strip()\n",
    "\n",
    "# Identify unique categories\n",
    "unique_types = pbp['Type'].unique()\n",
    "\n",
    "# Manually one-hot encode\n",
    "for shot_type in unique_types:\n",
    "    pbp[shot_type] = (pbp['Type'] == shot_type).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Game_Id', 'Date', 'Period', 'Event', 'Description', 'Time_Elapsed',\n",
       "       'Seconds_Elapsed', 'Strength', 'Ev_Zone', 'Type', 'Ev_Team',\n",
       "       'Home_Zone', 'Away_Team', 'Home_Team', 'p1_name', 'p1_ID', 'p2_name',\n",
       "       'p2_ID', 'p3_name', 'p3_ID', 'awayPlayer1', 'awayPlayer1_id',\n",
       "       'awayPlayer2', 'awayPlayer2_id', 'awayPlayer3', 'awayPlayer3_id',\n",
       "       'homePlayer1', 'homePlayer1_id', 'homePlayer2', 'homePlayer2_id',\n",
       "       'homePlayer3', 'homePlayer3_id', 'Away_Players', 'Home_Players',\n",
       "       'Away_Score', 'Home_Score', 'xC', 'yC', 'distanceFromLastEvent',\n",
       "       'Power Play', 'timeSinceLastEvent', 'WRIST SHOT', 'TIP-IN', 'SNAP SHOT',\n",
       "       'SLAP SHOT', 'BACKHAND', 'DEFLECTED', 'WRAP-AROUND'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking columns\n",
    "pbp.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping shots from behind the net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbp.drop(pbp[pbp['xC'] > 89].index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting goals column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a binary column for Goals \n",
    "pbp['goal'] = np.where(pbp.Event == 'GOAL', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "goal\n",
       "0    114067\n",
       "1      6846\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp['goal'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Event\n",
       "SHOT     59390\n",
       "BLOCK    30792\n",
       "MISS     23885\n",
       "GOAL      6846\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp['Event'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can drop all rows where event is not equal to shot or goal because we already calculated the distance from last event and the time since last event which were the variables that needed to have every event in the dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbp=pbp[(pbp['Event']=='SHOT') | (pbp['Event']=='GOAL') | (pbp['Event']=='MISS')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, lets get a sense on where out dataframe is at, how many rows contain na values for the x and y coordinates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NaN values in xC: 0\n",
      "Number of NaN values in yC: 0\n"
     ]
    }
   ],
   "source": [
    "# Count the number of NaN values in the xC and yC columns\n",
    "xC_nan_count = pbp['xC'].isna().sum()\n",
    "yC_nan_count = pbp['yC'].isna().sum()\n",
    "\n",
    "# Print the counts\n",
    "print(f\"Number of NaN values in xC: {xC_nan_count}\")\n",
    "print(f\"Number of NaN values in yC: {yC_nan_count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many total values are in our dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90121, 49)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting shot angles using a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function calculates the angle to the center of the net at (89, 0) in radians and degrees.\n",
    "def angles(x, y):\n",
    "    num = math.sqrt(((89.0 - x) * (89.0 - x)) + ((y) * (y)))\n",
    "    if num==0:\n",
    "        radians= 0.0\n",
    "        degrees= 0.0\n",
    "    else:\n",
    "        radians = np.arcsin(y/num)\n",
    "        degrees = (radians * 180.0) / 3.14\n",
    "        \n",
    "    arr = [radians, degrees]\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using that function to place angles into dataframe and also get shot distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiliaze empty columns\n",
    "pbp['shotAngle_Radians'] = ''\n",
    "pbp['shotAngle_Degrees'] = ''\n",
    "pbp['shotDistance'] = ''\n",
    "\n",
    "# Add values to columns \n",
    "for index, row in pbp.iterrows():\n",
    "    x = row['xC']\n",
    "    y = row['yC']\n",
    "    all_angles = angles(x, y)\n",
    "    pbp.at[index, 'shotAngle_Radians'] = all_angles[0]\n",
    "    pbp.at[index, 'shotAngle_Degrees'] = all_angles[1]\n",
    "    pbp.at[index, 'shotDistance'] = np.sqrt((y - 0)**2 + (x - 89.0)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last step is to limit games to only regular season games. Last day of the regular season was 04-18. Get rid of all rows with a date after that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n"
     ]
    }
   ],
   "source": [
    "dtype = pbp['Date'].dtype\n",
    "print(dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "445623    2024-06-24\n",
       "445626    2024-06-24\n",
       "445627    2024-06-24\n",
       "445628    2024-06-24\n",
       "445634    2024-06-24\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp['Date'].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright to accomplish getting rid of playoff data, have to convert the date column to datetime format, and then filter for rows before 2024-04-18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert 'date' column to datetime format\n",
    "pbp['Date'] = pd.to_datetime(pbp['Date'])\n",
    "\n",
    "# Define the cutoff date\n",
    "cutoff_date = pd.to_datetime('2024-04-18')\n",
    "\n",
    "# Filter rows where the date is after the cutoff date\n",
    "pbp = pbp[pbp['Date'] <= cutoff_date]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright now to only keep the variables we want. Dropping all other columns. Then our data should be good to go for modeling!!\n",
    "\n",
    "Shot types:  'WRIST SHOT', 'TIP-IN', 'SNAP SHOT',\n",
    "       'SLAP SHOT', 'BACKHAND', 'DEFLECTED', 'WRAP-AROUND'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbp=pbp[['Ev_Zone','Ev_Team','Home_Team','Away_Team','Event', 'awayPlayer1','awayPlayer2','awayPlayer3','homePlayer1','homePlayer2','homePlayer3','Description','Strength', 'Seconds_Elapsed', 'timeSinceLastEvent','goal','distanceFromLastEvent','Power Play','xC','yC','shotAngle_Radians','shotAngle_Degrees','shotDistance','BACKHAND', 'DEFLECTED', 'SLAP SHOT', 'SNAP SHOT', 'TIP-IN', 'WRAP-AROUND', 'WRIST SHOT','Description','Type']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, now that we got rid of a good number of columns we don't need, let's think about the problem at hand. I want to evaluate who are the best 20 forward lines in the nhl. How should I accomplish this? I think the best way from a hollistic standpoint is expected goals %. Expected Goals % is the percent of all expected goals the player's team gets while the player is on the ice compared to the other team. For example, if a player's team gets 0.6 expected goals and the opposing team gets 0.4 expected goals while the player is on the ice, the player's expected goals % is 60%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On MoneyPuck, an xGoals% of above 60% gets a forward line inside the top 10 (Limiting to lines who played 200 minutes or more together).An xGoals% of 50% and above gets a FL inside the top 60 lines. \n",
    "\n",
    "I also want to look at each lines expected goals against and their expected goals for, rather than the percentage of expected goal they own. An expected goals for of 28 or above gets you in the top 10, and an xG For of 20 gets you inside the top 25. \n",
    "\n",
    "For xG against, 8.5 or less gets you inside the top 10, 11 or less gets you inside the top 25. \n",
    "\n",
    "By looking at these xG for and xG against rather than xG%, I can get the best offensive forward lines and the best defensive lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, at the very end of this project my goal is to have 3 different rankings:\n",
    "1) The best 15 lines in hockey (using xG%)\n",
    "2) The best 15 offensive lines in hockey (using xG For)\n",
    "3) The best 15 defensive lines in hockey (using xG Against)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: Building the model and features to be passed into the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pbp=pbp[['Ev_Zone','Ev_Team','Home_Team','Away_Team','Event', 'awayPlayer1','awayPlayer2','awayPlayer3','homePlayer1','homePlayer2','homePlayer3','Description','Strength', 'Seconds_Elapsed', 'timeSinceLastEvent','goal','distanceFromLastEvent','Power Play','xC','yC','shotAngle_Radians','shotAngle_Degrees','shotDistance','BACKHAND', 'DEFLECTED', 'SLAP SHOT', 'SNAP SHOT', 'TIP-IN', 'WRAP-AROUND', 'WRIST SHOT','Description','Type']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, in the expected goals model I am building, these variables will be features with variable 15 being the target:\n",
    "    1)xC\n",
    "    2)yC\n",
    "    3)BACKHAND\n",
    "    4)DEFLECTED\n",
    "    5)SLAP SHOT\n",
    "    6)SNAP SHOT\n",
    "    7)TIP-IN\n",
    "    8)WRAP-AROUND\n",
    "    9)WRIST SHOT\n",
    "    10)shotAngle_Degrees\n",
    "    11)timeSinceLastEvent\n",
    "    12)shotDistance\n",
    "    13)distanceFromLastEvent\n",
    "    14)Power Play\n",
    "\n",
    "    15)goal (Target variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the data types of our variables. All variables being passed into model need to be int/double/float. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 84546 entries, 6 to 413852\n",
      "Data columns (total 32 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Ev_Zone                84546 non-null  object \n",
      " 1   Ev_Team                84546 non-null  object \n",
      " 2   Home_Team              84546 non-null  object \n",
      " 3   Away_Team              84546 non-null  object \n",
      " 4   Event                  84546 non-null  object \n",
      " 5   awayPlayer1            84546 non-null  object \n",
      " 6   awayPlayer2            84060 non-null  object \n",
      " 7   awayPlayer3            84038 non-null  object \n",
      " 8   homePlayer1            84546 non-null  object \n",
      " 9   homePlayer2            84063 non-null  object \n",
      " 10  homePlayer3            84039 non-null  object \n",
      " 11  Description            84546 non-null  object \n",
      " 12  Strength               84546 non-null  object \n",
      " 13  Seconds_Elapsed        84546 non-null  float64\n",
      " 14  timeSinceLastEvent     84546 non-null  float64\n",
      " 15  goal                   84546 non-null  int64  \n",
      " 16  distanceFromLastEvent  84546 non-null  float64\n",
      " 17  Power Play             84546 non-null  int64  \n",
      " 18  xC                     84546 non-null  float64\n",
      " 19  yC                     84546 non-null  float64\n",
      " 20  shotAngle_Radians      84546 non-null  object \n",
      " 21  shotAngle_Degrees      84546 non-null  object \n",
      " 22  shotDistance           84546 non-null  object \n",
      " 23  BACKHAND               84546 non-null  int64  \n",
      " 24  DEFLECTED              84546 non-null  int64  \n",
      " 25  SLAP SHOT              84546 non-null  int64  \n",
      " 26  SNAP SHOT              84546 non-null  int64  \n",
      " 27  TIP-IN                 84546 non-null  int64  \n",
      " 28  WRAP-AROUND            84546 non-null  int64  \n",
      " 29  WRIST SHOT             84546 non-null  int64  \n",
      " 30  Description            84546 non-null  object \n",
      " 31  Type                   84546 non-null  object \n",
      "dtypes: float64(5), int64(9), object(18)\n",
      "memory usage: 21.3+ MB\n"
     ]
    }
   ],
   "source": [
    "pbp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to convert shotDistance, and both shotAngle variables to float64 instead of object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbp['shotDistance'] = pd.to_numeric(pbp['shotDistance'], errors='coerce').astype('float64')\n",
    "pbp['shotAngle_Degrees'] = pd.to_numeric(pbp['shotAngle_Degrees'], errors='coerce').astype('float64')\n",
    "pbp['shotAngle_Radians'] = pd.to_numeric(pbp['shotAngle_Radians'], errors='coerce').astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking to make sure it worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 84546 entries, 6 to 413852\n",
      "Data columns (total 32 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Ev_Zone                84546 non-null  object \n",
      " 1   Ev_Team                84546 non-null  object \n",
      " 2   Home_Team              84546 non-null  object \n",
      " 3   Away_Team              84546 non-null  object \n",
      " 4   Event                  84546 non-null  object \n",
      " 5   awayPlayer1            84546 non-null  object \n",
      " 6   awayPlayer2            84060 non-null  object \n",
      " 7   awayPlayer3            84038 non-null  object \n",
      " 8   homePlayer1            84546 non-null  object \n",
      " 9   homePlayer2            84063 non-null  object \n",
      " 10  homePlayer3            84039 non-null  object \n",
      " 11  Description            84546 non-null  object \n",
      " 12  Strength               84546 non-null  object \n",
      " 13  Seconds_Elapsed        84546 non-null  float64\n",
      " 14  timeSinceLastEvent     84546 non-null  float64\n",
      " 15  goal                   84546 non-null  int64  \n",
      " 16  distanceFromLastEvent  84546 non-null  float64\n",
      " 17  Power Play             84546 non-null  int64  \n",
      " 18  xC                     84546 non-null  float64\n",
      " 19  yC                     84546 non-null  float64\n",
      " 20  shotAngle_Radians      84546 non-null  float64\n",
      " 21  shotAngle_Degrees      84546 non-null  float64\n",
      " 22  shotDistance           84546 non-null  float64\n",
      " 23  BACKHAND               84546 non-null  int64  \n",
      " 24  DEFLECTED              84546 non-null  int64  \n",
      " 25  SLAP SHOT              84546 non-null  int64  \n",
      " 26  SNAP SHOT              84546 non-null  int64  \n",
      " 27  TIP-IN                 84546 non-null  int64  \n",
      " 28  WRAP-AROUND            84546 non-null  int64  \n",
      " 29  WRIST SHOT             84546 non-null  int64  \n",
      " 30  Description            84546 non-null  object \n",
      " 31  Type                   84546 non-null  object \n",
      "dtypes: float64(8), int64(9), object(15)\n",
      "memory usage: 21.3+ MB\n"
     ]
    }
   ],
   "source": [
    "pbp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ev_Zone</th>\n",
       "      <th>Ev_Team</th>\n",
       "      <th>Home_Team</th>\n",
       "      <th>Away_Team</th>\n",
       "      <th>Event</th>\n",
       "      <th>awayPlayer1</th>\n",
       "      <th>awayPlayer2</th>\n",
       "      <th>awayPlayer3</th>\n",
       "      <th>homePlayer1</th>\n",
       "      <th>homePlayer2</th>\n",
       "      <th>...</th>\n",
       "      <th>shotDistance</th>\n",
       "      <th>BACKHAND</th>\n",
       "      <th>DEFLECTED</th>\n",
       "      <th>SLAP SHOT</th>\n",
       "      <th>SNAP SHOT</th>\n",
       "      <th>TIP-IN</th>\n",
       "      <th>WRAP-AROUND</th>\n",
       "      <th>WRIST SHOT</th>\n",
       "      <th>Description</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413826</th>\n",
       "      <td>Off</td>\n",
       "      <td>L.A</td>\n",
       "      <td>L.A</td>\n",
       "      <td>CHI</td>\n",
       "      <td>MISS</td>\n",
       "      <td>JASON DICKINSON</td>\n",
       "      <td>PHILIPP KURASHEV</td>\n",
       "      <td>CONNOR BEDARD</td>\n",
       "      <td>BLAKE LIZOTTE</td>\n",
       "      <td>TREVOR LEWIS</td>\n",
       "      <td>...</td>\n",
       "      <td>14.142136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LAK #22 FIALA, Wrist, Wide Right, Off. Zone, 5...</td>\n",
       "      <td>WRIST SHOT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413835</th>\n",
       "      <td>Off</td>\n",
       "      <td>L.A</td>\n",
       "      <td>L.A</td>\n",
       "      <td>CHI</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>ANDREAS ATHANASIOU</td>\n",
       "      <td>TYLER JOHNSON</td>\n",
       "      <td>LUKAS REICHEL</td>\n",
       "      <td>BLAKE LIZOTTE</td>\n",
       "      <td>TREVOR LEWIS</td>\n",
       "      <td>...</td>\n",
       "      <td>41.677332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LAK ONGOAL - #84 GAVRIKOV, Slap , Off. Zone, 6...</td>\n",
       "      <td>SLAP SHOT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413838</th>\n",
       "      <td>Off</td>\n",
       "      <td>L.A</td>\n",
       "      <td>L.A</td>\n",
       "      <td>CHI</td>\n",
       "      <td>MISS</td>\n",
       "      <td>JASON DICKINSON</td>\n",
       "      <td>PHILIPP KURASHEV</td>\n",
       "      <td>CONNOR BEDARD</td>\n",
       "      <td>PHILLIP DANAULT</td>\n",
       "      <td>PIERRE-LUC DUBOIS</td>\n",
       "      <td>...</td>\n",
       "      <td>11.401754</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LAK #24 DANAULT, Tip-In, Wide Right, Off. Zone...</td>\n",
       "      <td>TIP-IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413845</th>\n",
       "      <td>Off</td>\n",
       "      <td>L.A</td>\n",
       "      <td>L.A</td>\n",
       "      <td>CHI</td>\n",
       "      <td>GOAL</td>\n",
       "      <td>JASON DICKINSON</td>\n",
       "      <td>NICK FOLIGNO</td>\n",
       "      <td>SETH JONES</td>\n",
       "      <td>ANZE KOPITAR</td>\n",
       "      <td>ADRIAN KEMPE</td>\n",
       "      <td>...</td>\n",
       "      <td>56.586217</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>LAK #33 ARVIDSSON(6), Wrist , Off. Zone, 16 ft...</td>\n",
       "      <td>WRIST SHOT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413852</th>\n",
       "      <td>Off</td>\n",
       "      <td>L.A</td>\n",
       "      <td>L.A</td>\n",
       "      <td>CHI</td>\n",
       "      <td>GOAL</td>\n",
       "      <td>PHILIPP KURASHEV</td>\n",
       "      <td>CONNOR BEDARD</td>\n",
       "      <td>KEVIN KORCHINSKI</td>\n",
       "      <td>ANZE KOPITAR</td>\n",
       "      <td>ADRIAN KEMPE</td>\n",
       "      <td>...</td>\n",
       "      <td>46.065171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LAK #9 KEMPE(28), Snap , Off. Zone, 32 ft.</td>\n",
       "      <td>SNAP SHOT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Ev_Zone Ev_Team Home_Team Away_Team Event         awayPlayer1  \\\n",
       "413826     Off     L.A       L.A       CHI  MISS     JASON DICKINSON   \n",
       "413835     Off     L.A       L.A       CHI  SHOT  ANDREAS ATHANASIOU   \n",
       "413838     Off     L.A       L.A       CHI  MISS     JASON DICKINSON   \n",
       "413845     Off     L.A       L.A       CHI  GOAL     JASON DICKINSON   \n",
       "413852     Off     L.A       L.A       CHI  GOAL    PHILIPP KURASHEV   \n",
       "\n",
       "             awayPlayer2       awayPlayer3      homePlayer1  \\\n",
       "413826  PHILIPP KURASHEV     CONNOR BEDARD    BLAKE LIZOTTE   \n",
       "413835     TYLER JOHNSON     LUKAS REICHEL    BLAKE LIZOTTE   \n",
       "413838  PHILIPP KURASHEV     CONNOR BEDARD  PHILLIP DANAULT   \n",
       "413845      NICK FOLIGNO        SETH JONES     ANZE KOPITAR   \n",
       "413852     CONNOR BEDARD  KEVIN KORCHINSKI     ANZE KOPITAR   \n",
       "\n",
       "              homePlayer2  ... shotDistance BACKHAND DEFLECTED  SLAP SHOT  \\\n",
       "413826       TREVOR LEWIS  ...    14.142136        0         0          0   \n",
       "413835       TREVOR LEWIS  ...    41.677332        0         0          1   \n",
       "413838  PIERRE-LUC DUBOIS  ...    11.401754        0         0          0   \n",
       "413845       ADRIAN KEMPE  ...    56.586217        0         0          0   \n",
       "413852       ADRIAN KEMPE  ...    46.065171        0         0          0   \n",
       "\n",
       "        SNAP SHOT  TIP-IN  WRAP-AROUND  WRIST SHOT  \\\n",
       "413826          0       0            0           1   \n",
       "413835          0       0            0           0   \n",
       "413838          0       1            0           0   \n",
       "413845          0       0            0           1   \n",
       "413852          1       0            0           0   \n",
       "\n",
       "                                              Description        Type  \n",
       "413826  LAK #22 FIALA, Wrist, Wide Right, Off. Zone, 5...  WRIST SHOT  \n",
       "413835  LAK ONGOAL - #84 GAVRIKOV, Slap , Off. Zone, 6...   SLAP SHOT  \n",
       "413838  LAK #24 DANAULT, Tip-In, Wide Right, Off. Zone...      TIP-IN  \n",
       "413845  LAK #33 ARVIDSSON(6), Wrist , Off. Zone, 16 ft...  WRIST SHOT  \n",
       "413852        LAK #9 KEMPE(28), Snap , Off. Zone, 32 ft.    SNAP SHOT  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First step, is splitting the feature/predictor variables from the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pbp[['timeSinceLastEvent','distanceFromLastEvent','Power Play','xC','yC','shotAngle_Degrees','shotDistance','BACKHAND', 'DEFLECTED', 'SLAP SHOT', 'SNAP SHOT', 'TIP-IN', 'WRAP-AROUND', 'WRIST SHOT']]\n",
    "y=pbp.goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting data into train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train,y_test= train_test_split(X,y,test_size=0.2, random_state=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost Start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attempt at Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'max_depth': [3, 7, 12],          # Different values for max depth of a tree\n",
    "    'learning_rate': [0.01, 0.1, 0.3],  # Learning rate (eta)\n",
    "    'n_estimators': [100, 500, 1000],    # Number of boosting rounds\n",
    "    'subsample': [0.8, 1.0]            # Subsample ratio of the training instances\n",
    "    #'scale_pos_weight': [1,7,18]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize XGBoost model\n",
    "xgb_model = XGBClassifier(objective='binary:logistic', eval_metric='auc', use_label_encoder=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up Grid Search\n",
    "grid_search = GridSearchCV(estimator=xgb_model, param_grid=param_grid, scoring='roc_auc', cv=5, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[13:56:41] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:42] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:42] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:42] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:46] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:47] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:49] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:56:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:03] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:10] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:17] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:26] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:26] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:27] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:30] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:35] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:38] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:42] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:45] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:49] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:57:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:37] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:49] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:58:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:09] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:17] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:19] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:23] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:27] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:37] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:45] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[13:59:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:00:03] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:00:12] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:00:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:00:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:00:37] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:00:45] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:00:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:01:09] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:01:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:01:41] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:01:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:02:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:02:27] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:02:40] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:02:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:21] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:23] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:23] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:30] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:33] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:35] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:38] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:40] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:41] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:46] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:03:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:10] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:19] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:26] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:27] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:30] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:32] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:33] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:34] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:38] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:42] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:46] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:04:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:32] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:40] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:48] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:05:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:18] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:26] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:33] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:34] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:06:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:07:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:07:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:07:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:07:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:07:33] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:07:39] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:07:44] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:08:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:08:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:08:32] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:08:48] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:33] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:09:58] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:03] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:06] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:12] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:14] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:17] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:19] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:29] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:34] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:39] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:46] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:10:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:00] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:03] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:06] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:11] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:15] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:20] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:28] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:34] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:37] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:40] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:43] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:11:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:24] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:30] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:35] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:41] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:46] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:12:59] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:06] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:08] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:16] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:23] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:31] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:39] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:47] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:13:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:14:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:14:07] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:14:12] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:14:27] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:14:42] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:14:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:15:12] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:15:27] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:15:36] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:15:45] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:15:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:16:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m[14:16:13] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False,\n",
       "                                     eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "                                     gamma=None, grow_policy=None,\n",
       "                                     importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None...\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.3],\n",
       "                         &#x27;max_depth&#x27;: [3, 7, 12],\n",
       "                         &#x27;n_estimators&#x27;: [100, 500, 1000],\n",
       "                         &#x27;subsample&#x27;: [0.8, 1.0]},\n",
       "             scoring=&#x27;roc_auc&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False,\n",
       "                                     eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "                                     gamma=None, grow_policy=None,\n",
       "                                     importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None...\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.3],\n",
       "                         &#x27;max_depth&#x27;: [3, 7, 12],\n",
       "                         &#x27;n_estimators&#x27;: [100, 500, 1000],\n",
       "                         &#x27;subsample&#x27;: [0.8, 1.0]},\n",
       "             scoring=&#x27;roc_auc&#x27;, verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False,\n",
       "                                     eval_metric='auc', feature_types=None,\n",
       "                                     gamma=None, grow_policy=None,\n",
       "                                     importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None...\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             param_grid={'learning_rate': [0.01, 0.1, 0.3],\n",
       "                         'max_depth': [3, 7, 12],\n",
       "                         'n_estimators': [100, 500, 1000],\n",
       "                         'subsample': [0.8, 1.0]},\n",
       "             scoring='roc_auc', verbose=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "grid_search.fit(X_train, y_train)\n",
    "#Takes a long time, going to run at night"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 1000, 'subsample': 0.8}\n"
     ]
    }
   ],
   "source": [
    "# Get the best parameters from the grid search\n",
    "best_params = grid_search.best_params_\n",
    "print(f\"Best parameters: {best_params}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[14:18:04] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "\u001b[0m"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;auc&#x27;, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric='auc', feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the final model using the best parameters\n",
    "best_model = grid_search.best_estimator_\n",
    "best_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright calculating the auc score for the training data to see if there is a signifcant difference from the auc score on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.6444969460647043\n"
     ]
    }
   ],
   "source": [
    "# Predict probabilities for the training set\n",
    "y_train_pred_prob = best_model.predict_proba(X_train)[:, 1]  # Probabilities for the positive class\n",
    "\n",
    "# Evaluate the AUC score\n",
    "roc_auc = roc_auc_score(y_train, y_train_pred_prob)\n",
    "print(f'ROC AUC: {roc_auc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on the test data\n",
    "y_pred = best_model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0629987 , 0.04032657, 0.07972875, ..., 0.07938475, 0.04909216,\n",
       "       0.12149397], dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.6139955285032432\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "print(f'ROC AUC: {roc_auc}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC AUC Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "\n",
    "# Step 2: Calculate ROC curve and AUC for both models\n",
    "fpr_xgb, tpr_xgb, _ = roc_curve(y_test, y_pred)\n",
    "roc_auc_xgb = auc(fpr_xgb, tpr_xgb)\n",
    "\n",
    "\n",
    "# Step 3: Plot the ROC curves\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(fpr_xgb, tpr_xgb, color='blue', lw=2, label=f'XGBoost (AUC = {roc_auc_xgb:.2f})')\n",
    "\n",
    "# Plotting the random guess line\n",
    "plt.plot([0, 1], [0, 1], color='red', linestyle='--')\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve Comparison')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature importance\n",
    "from sklearn.inspection import permutation_importance\n",
    "feature_importance = best_model.get_booster().get_score(importance_type='weight')\n",
    "\n",
    "# Convert to array for plotting\n",
    "sorted_idx = np.argsort(list(feature_importance.values()))\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "plt.barh(range(len(sorted_idx)), np.array(list(feature_importance.values()))[sorted_idx], align='center')\n",
    "plt.yticks(range(len(sorted_idx)), np.array(list(feature_importance.keys()))[sorted_idx])\n",
    "plt.title('Feature Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once Model is built, then since we are trying to find best forward lines, Group by player1, player2, player3. Sum their expected goals. Take top 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_varBoost= pbp[['timeSinceLastEvent','distanceFromLastEvent','Power Play','xC','yC','shotAngle_Degrees','shotDistance','BACKHAND', 'DEFLECTED', 'SLAP SHOT', 'SNAP SHOT', 'TIP-IN', 'WRAP-AROUND', 'WRIST SHOT']]\n",
    "\n",
    "# Predict using the best_model\n",
    "xG = best_model.predict_proba(pred_varBoost)[:, 1]\n",
    "\n",
    "# Add the predictions to your DataFrame\n",
    "pbp['xG'] = xG\n",
    "\n",
    "\n",
    "# dtest=xgb.DMatrix(pred_varBoost)\n",
    "# xG=bst.predict(dtest)\n",
    "# pbp['xG']=xG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ev_Zone</th>\n",
       "      <th>Ev_Team</th>\n",
       "      <th>Home_Team</th>\n",
       "      <th>Away_Team</th>\n",
       "      <th>Event</th>\n",
       "      <th>awayPlayer1</th>\n",
       "      <th>awayPlayer2</th>\n",
       "      <th>awayPlayer3</th>\n",
       "      <th>homePlayer1</th>\n",
       "      <th>homePlayer2</th>\n",
       "      <th>...</th>\n",
       "      <th>BACKHAND</th>\n",
       "      <th>DEFLECTED</th>\n",
       "      <th>SLAP SHOT</th>\n",
       "      <th>SNAP SHOT</th>\n",
       "      <th>TIP-IN</th>\n",
       "      <th>WRAP-AROUND</th>\n",
       "      <th>WRIST SHOT</th>\n",
       "      <th>Description</th>\n",
       "      <th>Type</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Off</td>\n",
       "      <td>T.B</td>\n",
       "      <td>T.B</td>\n",
       "      <td>NSH</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>COLTON SISSONS</td>\n",
       "      <td>YAKOV TRENIN</td>\n",
       "      <td>COLE SMITH</td>\n",
       "      <td>BRAYDEN POINT</td>\n",
       "      <td>STEVEN STAMKOS</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>TBL ONGOAL - #43 RADDYSH, Wrist , Off. Zone, 3...</td>\n",
       "      <td>WRIST SHOT</td>\n",
       "      <td>0.098621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Off</td>\n",
       "      <td>T.B</td>\n",
       "      <td>T.B</td>\n",
       "      <td>NSH</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>PHILIP TOMASINO</td>\n",
       "      <td>RYAN O'REILLY</td>\n",
       "      <td>LUKE EVANGELISTA</td>\n",
       "      <td>BRAYDEN POINT</td>\n",
       "      <td>STEVEN STAMKOS</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>TBL ONGOAL - #77 HEDMAN, Wrist , Off. Zone, 63...</td>\n",
       "      <td>WRIST SHOT</td>\n",
       "      <td>0.064260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Off</td>\n",
       "      <td>T.B</td>\n",
       "      <td>T.B</td>\n",
       "      <td>NSH</td>\n",
       "      <td>GOAL</td>\n",
       "      <td>JUUSO PARSSINEN</td>\n",
       "      <td>RYAN O'REILLY</td>\n",
       "      <td>FILIP FORSBERG</td>\n",
       "      <td>BRAYDEN POINT</td>\n",
       "      <td>STEVEN STAMKOS</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TBL #86 KUCHEROV(1), Slap , Off. Zone, 42 ft.A...</td>\n",
       "      <td>SLAP SHOT</td>\n",
       "      <td>0.062019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Neu</td>\n",
       "      <td>T.B</td>\n",
       "      <td>T.B</td>\n",
       "      <td>NSH</td>\n",
       "      <td>MISS</td>\n",
       "      <td>JUUSO PARSSINEN</td>\n",
       "      <td>RYAN O'REILLY</td>\n",
       "      <td>FILIP FORSBERG</td>\n",
       "      <td>TYLER MOTTE</td>\n",
       "      <td>ANTHONY CIRELLI</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>TBL #64 MOTTE, Wrist, Wide Right, Neu. Zone, 8...</td>\n",
       "      <td>WRIST SHOT</td>\n",
       "      <td>0.053883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Off</td>\n",
       "      <td>T.B</td>\n",
       "      <td>T.B</td>\n",
       "      <td>NSH</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>COLTON SISSONS</td>\n",
       "      <td>YAKOV TRENIN</td>\n",
       "      <td>COLE SMITH</td>\n",
       "      <td>LUKE GLENDENING</td>\n",
       "      <td>WALTTERI MERELA</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TBL ONGOAL - #81 CERNAK, Snap , Off. Zone, 62 ...</td>\n",
       "      <td>SNAP SHOT</td>\n",
       "      <td>0.082742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ev_Zone Ev_Team Home_Team Away_Team Event      awayPlayer1    awayPlayer2  \\\n",
       "6      Off     T.B       T.B       NSH  SHOT   COLTON SISSONS   YAKOV TRENIN   \n",
       "40     Off     T.B       T.B       NSH  SHOT  PHILIP TOMASINO  RYAN O'REILLY   \n",
       "41     Off     T.B       T.B       NSH  GOAL  JUUSO PARSSINEN  RYAN O'REILLY   \n",
       "44     Neu     T.B       T.B       NSH  MISS  JUUSO PARSSINEN  RYAN O'REILLY   \n",
       "47     Off     T.B       T.B       NSH  SHOT   COLTON SISSONS   YAKOV TRENIN   \n",
       "\n",
       "         awayPlayer3      homePlayer1      homePlayer2  ... BACKHAND  \\\n",
       "6         COLE SMITH    BRAYDEN POINT   STEVEN STAMKOS  ...        0   \n",
       "40  LUKE EVANGELISTA    BRAYDEN POINT   STEVEN STAMKOS  ...        0   \n",
       "41    FILIP FORSBERG    BRAYDEN POINT   STEVEN STAMKOS  ...        0   \n",
       "44    FILIP FORSBERG      TYLER MOTTE  ANTHONY CIRELLI  ...        0   \n",
       "47        COLE SMITH  LUKE GLENDENING  WALTTERI MERELA  ...        0   \n",
       "\n",
       "   DEFLECTED SLAP SHOT  SNAP SHOT  TIP-IN  WRAP-AROUND  WRIST SHOT  \\\n",
       "6          0         0          0       0            0           1   \n",
       "40         0         0          0       0            0           1   \n",
       "41         0         1          0       0            0           0   \n",
       "44         0         0          0       0            0           1   \n",
       "47         0         0          1       0            0           0   \n",
       "\n",
       "                                          Description        Type        xG  \n",
       "6   TBL ONGOAL - #43 RADDYSH, Wrist , Off. Zone, 3...  WRIST SHOT  0.098621  \n",
       "40  TBL ONGOAL - #77 HEDMAN, Wrist , Off. Zone, 63...  WRIST SHOT  0.064260  \n",
       "41  TBL #86 KUCHEROV(1), Slap , Off. Zone, 42 ft.A...   SLAP SHOT  0.062019  \n",
       "44  TBL #64 MOTTE, Wrist, Wide Right, Neu. Zone, 8...  WRIST SHOT  0.053883  \n",
       "47  TBL ONGOAL - #81 CERNAK, Snap , Off. Zone, 62 ...   SNAP SHOT  0.082742  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright going to merge in nhl team logos so that I can display nicely in final dataframe once I group the forward lines. That way you can see which team the forward line belongs to. This requires me to load in the nhl logos csv, and also map the abbreviations so that I can merge onto the pbp dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhl_logos=pd.read_csv(\"nhl_logos.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to see the abbreviations from the pbp so I can create the mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['T.B', 'NSH', 'PIT', 'CHI', 'SEA', 'VGK', 'OTT', 'CAR', 'TOR',\n",
       "       'MTL', 'BOS', 'WPG', 'CGY', 'COL', 'L.A', 'EDM', 'VAN', 'NYR',\n",
       "       'BUF', 'CBJ', 'PHI', 'DET', 'N.J', 'DAL', 'STL', 'FLA', 'MIN',\n",
       "       'S.J', 'ARI', 'WSH', 'NYI', 'ANA'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp['Ev_Team'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CAR', 'CHI', 'CBJ', 'DAL', 'DET', 'FLA', 'NSH', 'TBL', 'BOS',\n",
       "       'BUF', 'NJD', 'NYI', 'NYR', 'PHI', 'PIT', 'WSH', 'CGY', 'EDM',\n",
       "       'MTL', 'OTT', 'TOR', 'VAN', 'WPG', 'ANA', 'ARI', 'COL', 'LAK',\n",
       "       'MIN', 'SJS', 'STL', 'VEG', 'SEA'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhl_logos['refe_abrName'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhl_logos=nhl_logos[['refe_abrName','espn_logo']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mapping dictionary\n",
    "abbrev_map = {\n",
    "    'T.B': 'TBL',   \n",
    "    'NSH': 'NSH',\n",
    "    'PIT': 'PIT',\n",
    "    'CHI': 'CHI',  # Example: Golden Knights\n",
    "    'SEA': 'SEA',\n",
    "    'VGK': 'VEG',\n",
    "    'OTT': 'OTT',\n",
    "    'CAR': 'CAR',\n",
    "    'TOR': 'TOR',\n",
    "    'MTL': 'MTL',\n",
    "    'BOS': 'BOS',\n",
    "    'WPG': 'WPG',\n",
    "    'CGY': 'CGY',\n",
    "    'COL': 'COL',\n",
    "    'L.A': 'LAK',\n",
    "    'EDM': 'EDM',\n",
    "    'VAN': 'VAN',\n",
    "    'NYR': 'NYR',\n",
    "    'BUF': 'BUF',\n",
    "    'CBJ':'CBJ',\n",
    "    'PHI':'PHI',\n",
    "    'DET': 'DET',\n",
    "    'N.J': 'NJD',\n",
    "    'DAL': 'DAL',\n",
    "    'STL': 'STL',\n",
    "    'FLA': 'FLA',\n",
    "    'MIN': 'MIN',\n",
    "    'S.J': 'SJS',\n",
    "    'ARI': 'ARI',\n",
    "    'WSH':'WSH',\n",
    "    'NYI':'NYI',\n",
    "    'ANA':'ANA'\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbp['Ev_Team'] = pbp['Ev_Team'].map(abbrev_map).fillna(pbp['Ev_Team'])\n",
    "pbp['Home_Team'] = pbp['Home_Team'].map(abbrev_map).fillna(pbp['Home_Team'])\n",
    "pbp['Away_Team'] = pbp['Away_Team'].map(abbrev_map).fillna(pbp['Away_Team'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge for EV_Team logos\n",
    "pbp_with_logos = pd.merge(pbp, nhl_logos, left_on='Ev_Team', right_on='refe_abrName', how='left')\n",
    "pbp_with_logos.rename(columns={'espn_logo':'EV_espn_logo'}, inplace=True)\n",
    "pbp_with_logos.drop(columns=['refe_abrName'], inplace=True)\n",
    "\n",
    "# Merge for Home_Team logos\n",
    "pbp_with_logos = pd.merge(pbp_with_logos, nhl_logos[['refe_abrName', 'espn_logo']], left_on='Home_Team', right_on='refe_abrName', how='left')\n",
    "pbp_with_logos.rename(columns={'espn_logo': 'Home_Team_Logo'}, inplace=True)\n",
    "pbp_with_logos.drop(columns=['refe_abrName'], inplace=True)  # Drop the merged abbreviation column if no longer needed\n",
    "\n",
    "# Merge for Away_Team logos\n",
    "pbp_with_logos = pd.merge(pbp_with_logos, nhl_logos[['refe_abrName', 'espn_logo']], left_on='Away_Team', right_on='refe_abrName', how='left')\n",
    "pbp_with_logos.rename(columns={'espn_logo': 'Away_Team_Logo'}, inplace=True)\n",
    "pbp_with_logos.drop(columns=['refe_abrName'], inplace=True)  # Drop the merged abbreviation column if no longer needed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ev_Zone</th>\n",
       "      <th>Ev_Team</th>\n",
       "      <th>Home_Team</th>\n",
       "      <th>Away_Team</th>\n",
       "      <th>Event</th>\n",
       "      <th>awayPlayer1</th>\n",
       "      <th>awayPlayer2</th>\n",
       "      <th>awayPlayer3</th>\n",
       "      <th>homePlayer1</th>\n",
       "      <th>homePlayer2</th>\n",
       "      <th>...</th>\n",
       "      <th>SNAP SHOT</th>\n",
       "      <th>TIP-IN</th>\n",
       "      <th>WRAP-AROUND</th>\n",
       "      <th>WRIST SHOT</th>\n",
       "      <th>Description</th>\n",
       "      <th>Type</th>\n",
       "      <th>xG</th>\n",
       "      <th>EV_espn_logo</th>\n",
       "      <th>Home_Team_Logo</th>\n",
       "      <th>Away_Team_Logo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Off</td>\n",
       "      <td>TBL</td>\n",
       "      <td>TBL</td>\n",
       "      <td>NSH</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>COLTON SISSONS</td>\n",
       "      <td>YAKOV TRENIN</td>\n",
       "      <td>COLE SMITH</td>\n",
       "      <td>BRAYDEN POINT</td>\n",
       "      <td>STEVEN STAMKOS</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>TBL ONGOAL - #43 RADDYSH, Wrist , Off. Zone, 3...</td>\n",
       "      <td>WRIST SHOT</td>\n",
       "      <td>0.098621</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Off</td>\n",
       "      <td>TBL</td>\n",
       "      <td>TBL</td>\n",
       "      <td>NSH</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>PHILIP TOMASINO</td>\n",
       "      <td>RYAN O'REILLY</td>\n",
       "      <td>LUKE EVANGELISTA</td>\n",
       "      <td>BRAYDEN POINT</td>\n",
       "      <td>STEVEN STAMKOS</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>TBL ONGOAL - #77 HEDMAN, Wrist , Off. Zone, 63...</td>\n",
       "      <td>WRIST SHOT</td>\n",
       "      <td>0.064260</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Off</td>\n",
       "      <td>TBL</td>\n",
       "      <td>TBL</td>\n",
       "      <td>NSH</td>\n",
       "      <td>GOAL</td>\n",
       "      <td>JUUSO PARSSINEN</td>\n",
       "      <td>RYAN O'REILLY</td>\n",
       "      <td>FILIP FORSBERG</td>\n",
       "      <td>BRAYDEN POINT</td>\n",
       "      <td>STEVEN STAMKOS</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TBL #86 KUCHEROV(1), Slap , Off. Zone, 42 ft.A...</td>\n",
       "      <td>SLAP SHOT</td>\n",
       "      <td>0.062019</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Neu</td>\n",
       "      <td>TBL</td>\n",
       "      <td>TBL</td>\n",
       "      <td>NSH</td>\n",
       "      <td>MISS</td>\n",
       "      <td>JUUSO PARSSINEN</td>\n",
       "      <td>RYAN O'REILLY</td>\n",
       "      <td>FILIP FORSBERG</td>\n",
       "      <td>TYLER MOTTE</td>\n",
       "      <td>ANTHONY CIRELLI</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>TBL #64 MOTTE, Wrist, Wide Right, Neu. Zone, 8...</td>\n",
       "      <td>WRIST SHOT</td>\n",
       "      <td>0.053883</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Off</td>\n",
       "      <td>TBL</td>\n",
       "      <td>TBL</td>\n",
       "      <td>NSH</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>COLTON SISSONS</td>\n",
       "      <td>YAKOV TRENIN</td>\n",
       "      <td>COLE SMITH</td>\n",
       "      <td>LUKE GLENDENING</td>\n",
       "      <td>WALTTERI MERELA</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TBL ONGOAL - #81 CERNAK, Snap , Off. Zone, 62 ...</td>\n",
       "      <td>SNAP SHOT</td>\n",
       "      <td>0.082742</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ev_Zone Ev_Team Home_Team Away_Team Event      awayPlayer1    awayPlayer2  \\\n",
       "0     Off     TBL       TBL       NSH  SHOT   COLTON SISSONS   YAKOV TRENIN   \n",
       "1     Off     TBL       TBL       NSH  SHOT  PHILIP TOMASINO  RYAN O'REILLY   \n",
       "2     Off     TBL       TBL       NSH  GOAL  JUUSO PARSSINEN  RYAN O'REILLY   \n",
       "3     Neu     TBL       TBL       NSH  MISS  JUUSO PARSSINEN  RYAN O'REILLY   \n",
       "4     Off     TBL       TBL       NSH  SHOT   COLTON SISSONS   YAKOV TRENIN   \n",
       "\n",
       "        awayPlayer3      homePlayer1      homePlayer2  ... SNAP SHOT TIP-IN  \\\n",
       "0        COLE SMITH    BRAYDEN POINT   STEVEN STAMKOS  ...         0      0   \n",
       "1  LUKE EVANGELISTA    BRAYDEN POINT   STEVEN STAMKOS  ...         0      0   \n",
       "2    FILIP FORSBERG    BRAYDEN POINT   STEVEN STAMKOS  ...         0      0   \n",
       "3    FILIP FORSBERG      TYLER MOTTE  ANTHONY CIRELLI  ...         0      0   \n",
       "4        COLE SMITH  LUKE GLENDENING  WALTTERI MERELA  ...         1      0   \n",
       "\n",
       "  WRAP-AROUND  WRIST SHOT                                        Description  \\\n",
       "0           0           1  TBL ONGOAL - #43 RADDYSH, Wrist , Off. Zone, 3...   \n",
       "1           0           1  TBL ONGOAL - #77 HEDMAN, Wrist , Off. Zone, 63...   \n",
       "2           0           0  TBL #86 KUCHEROV(1), Slap , Off. Zone, 42 ft.A...   \n",
       "3           0           1  TBL #64 MOTTE, Wrist, Wide Right, Neu. Zone, 8...   \n",
       "4           0           0  TBL ONGOAL - #81 CERNAK, Snap , Off. Zone, 62 ...   \n",
       "\n",
       "         Type        xG                                      EV_espn_logo  \\\n",
       "0  WRIST SHOT  0.098621  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "1  WRIST SHOT  0.064260  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "2   SLAP SHOT  0.062019  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "3  WRIST SHOT  0.053883  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "4   SNAP SHOT  0.082742  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "\n",
       "                                     Home_Team_Logo  \\\n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "1  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "2  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "3  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "\n",
       "                                      Away_Team_Logo  \n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  \n",
       "1  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  \n",
       "2  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  \n",
       "3  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbp_with_logos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright time to group by forward lines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADAM ERNE</td>\n",
       "      <td>JAMES HAMBLIN</td>\n",
       "      <td>BRETT KULAK</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/edm.png</td>\n",
       "      <td>0.054142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADAM ERNE</td>\n",
       "      <td>JAMES HAMBLIN</td>\n",
       "      <td>EVANDER KANE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/edm.png</td>\n",
       "      <td>0.090595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADAM ERNE</td>\n",
       "      <td>WARREN FOEGELE</td>\n",
       "      <td>JAMES HAMBLIN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/edm.png</td>\n",
       "      <td>0.053301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADAM FANTILLI</td>\n",
       "      <td>ALEXANDRE TEXIER</td>\n",
       "      <td>DMITRI VORONKOV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cbj.png</td>\n",
       "      <td>0.062565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADAM FANTILLI</td>\n",
       "      <td>ALEXANDRE TEXIER</td>\n",
       "      <td>EMIL BEMSTROM</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cbj.png</td>\n",
       "      <td>1.598034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Player1           Player2          Player3  \\\n",
       "0      ADAM ERNE     JAMES HAMBLIN      BRETT KULAK   \n",
       "1      ADAM ERNE     JAMES HAMBLIN     EVANDER KANE   \n",
       "2      ADAM ERNE    WARREN FOEGELE    JAMES HAMBLIN   \n",
       "3  ADAM FANTILLI  ALEXANDRE TEXIER  DMITRI VORONKOV   \n",
       "4  ADAM FANTILLI  ALEXANDRE TEXIER    EMIL BEMSTROM   \n",
       "\n",
       "                                                Team        xG  \n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/edm.png  0.054142  \n",
       "1  https://a.espncdn.com/i/teamlogos/nhl/500/edm.png  0.090595  \n",
       "2  https://a.espncdn.com/i/teamlogos/nhl/500/edm.png  0.053301  \n",
       "3  https://a.espncdn.com/i/teamlogos/nhl/500/cbj.png  0.062565  \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/cbj.png  1.598034  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Create separate columns for each player based on Ev_Team\n",
    "pbp_with_logos['Player1'] = pbp_with_logos.apply(lambda row: row['awayPlayer1'] if row['Ev_Team'] == row['Away_Team'] else row['homePlayer1'], axis=1)\n",
    "pbp_with_logos['Player2'] = pbp_with_logos.apply(lambda row: row['awayPlayer2'] if row['Ev_Team'] == row['Away_Team'] else row['homePlayer2'], axis=1)\n",
    "pbp_with_logos['Player3'] = pbp_with_logos.apply(lambda row: row['awayPlayer3'] if row['Ev_Team'] == row['Away_Team'] else row['homePlayer3'], axis=1)\n",
    "pbp_with_logos['Team'] = pbp_with_logos.apply(lambda row: row['Away_Team_Logo'] if row['Ev_Team'] == row['Away_Team'] else row['Home_Team_Logo'], axis=1)\n",
    "\n",
    "# Step 2: Group by the three player columns and sum the xG\n",
    "xg_ForwardLine_For = pbp_with_logos.groupby(['Player1', 'Player2', 'Player3','Team'])['xG'].sum().reset_index()\n",
    "\n",
    "\n",
    "# View the result\n",
    "xg_ForwardLine_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moment of truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_ForwardLine_For=xg_ForwardLine_For.sort_values(by='xG', ascending=False)\n",
    "xg_ForwardLine_For=xg_ForwardLine_For.reset_index(drop=True)\n",
    "# Step 2: Add a Rank column\n",
    "#xg_ForwardLine['Rank'] = xg_ForwardLine['xG'].rank(ascending=False, method='min').astype(int)\n",
    "\n",
    "xgFor_Top15=xg_ForwardLine_For.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I want to calculate the best defensive lines in hockey by xG Against."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    " #Step 1: Create separate columns for each opposing player based on Ev_Team\n",
    "pbp_with_logos['Opp_Player1'] = pbp_with_logos.apply(lambda row: row['homePlayer1'] if row['Ev_Team'] == row['Away_Team'] else row['awayPlayer1'], axis=1)\n",
    "pbp_with_logos['Opp_Player2'] = pbp_with_logos.apply(lambda row: row['homePlayer2'] if row['Ev_Team'] == row['Away_Team'] else row['awayPlayer2'], axis=1)\n",
    "pbp_with_logos['Opp_Player3'] = pbp_with_logos.apply(lambda row: row['homePlayer3'] if row['Ev_Team'] == row['Away_Team'] else row['awayPlayer3'], axis=1)\n",
    "pbp_with_logos['Team'] = pbp_with_logos.apply(lambda row: row['Home_Team_Logo'] if row['Ev_Team'] == row['Away_Team'] else row['Away_Team_Logo'], axis=1)\n",
    "\n",
    "# Step 2: Group by the three opposing player columns and sum the xG\n",
    "xg_ForwardLine_Against = pbp_with_logos.groupby(['Opp_Player1', 'Opp_Player2', 'Opp_Player3','Team'])['xG'].sum().reset_index()\n",
    "\n",
    "# Step 3: Sort the results by xG Against in descending order to find the highest xG Against lines\n",
    "xg_ForwardLine_Against = xg_ForwardLine_Against.sort_values(by='xG', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7361</th>\n",
       "      <td>MIKE HOFFMAN</td>\n",
       "      <td>KEVIN LABANC</td>\n",
       "      <td>ALEXANDER BARABANOV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sj.png</td>\n",
       "      <td>0.015034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3436</th>\n",
       "      <td>DYLAN LARKIN</td>\n",
       "      <td>J.T. COMPHER</td>\n",
       "      <td>DAVID PERRON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/det.png</td>\n",
       "      <td>0.017409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11605</th>\n",
       "      <td>VLADISLAV NAMESTNIKOV</td>\n",
       "      <td>NIKOLAJ EHLERS</td>\n",
       "      <td>JOSH MORRISSEY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png</td>\n",
       "      <td>0.018511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9347</th>\n",
       "      <td>ROSS COLTON</td>\n",
       "      <td>VALERI NICHUSHKIN</td>\n",
       "      <td>TOMAS TATAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>0.020398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8290</th>\n",
       "      <td>NILS AMAN</td>\n",
       "      <td>BROCK BOESER</td>\n",
       "      <td>ILYA MIKHEYEV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/van.png</td>\n",
       "      <td>0.020788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>ADAM LOWRY</td>\n",
       "      <td>MASON APPLETON</td>\n",
       "      <td>NINO NIEDERREITER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png</td>\n",
       "      <td>25.430140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11312</th>\n",
       "      <td>TYLER SEGUIN</td>\n",
       "      <td>MATT DUCHENE</td>\n",
       "      <td>MASON MARCHMENT</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>28.860943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7902</th>\n",
       "      <td>NICK SUZUKI</td>\n",
       "      <td>COLE CAUFIELD</td>\n",
       "      <td>JURAJ SLAFKOVSKY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/mtl.png</td>\n",
       "      <td>30.473534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</th>\n",
       "      <td>GUSTAV NYQUIST</td>\n",
       "      <td>RYAN O'REILLY</td>\n",
       "      <td>FILIP FORSBERG</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>31.398317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11437</th>\n",
       "      <td>VINCENT TROCHECK</td>\n",
       "      <td>ARTEMI PANARIN</td>\n",
       "      <td>ALEXIS LAFRENIÈRE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>33.872913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11941 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Opp_Player1        Opp_Player2          Opp_Player3  \\\n",
       "7361            MIKE HOFFMAN       KEVIN LABANC  ALEXANDER BARABANOV   \n",
       "3436            DYLAN LARKIN       J.T. COMPHER         DAVID PERRON   \n",
       "11605  VLADISLAV NAMESTNIKOV     NIKOLAJ EHLERS       JOSH MORRISSEY   \n",
       "9347             ROSS COLTON  VALERI NICHUSHKIN          TOMAS TATAR   \n",
       "8290               NILS AMAN       BROCK BOESER        ILYA MIKHEYEV   \n",
       "...                      ...                ...                  ...   \n",
       "201               ADAM LOWRY     MASON APPLETON    NINO NIEDERREITER   \n",
       "11312           TYLER SEGUIN       MATT DUCHENE      MASON MARCHMENT   \n",
       "7902             NICK SUZUKI      COLE CAUFIELD     JURAJ SLAFKOVSKY   \n",
       "4020          GUSTAV NYQUIST      RYAN O'REILLY       FILIP FORSBERG   \n",
       "11437       VINCENT TROCHECK     ARTEMI PANARIN    ALEXIS LAFRENIÈRE   \n",
       "\n",
       "                                                    Team         xG  \n",
       "7361    https://a.espncdn.com/i/teamlogos/nhl/500/sj.png   0.015034  \n",
       "3436   https://a.espncdn.com/i/teamlogos/nhl/500/det.png   0.017409  \n",
       "11605  https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png   0.018511  \n",
       "9347   https://a.espncdn.com/i/teamlogos/nhl/500/col.png   0.020398  \n",
       "8290   https://a.espncdn.com/i/teamlogos/nhl/500/van.png   0.020788  \n",
       "...                                                  ...        ...  \n",
       "201    https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png  25.430140  \n",
       "11312  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  28.860943  \n",
       "7902   https://a.espncdn.com/i/teamlogos/nhl/500/mtl.png  30.473534  \n",
       "4020   https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  31.398317  \n",
       "11437  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png  33.872913  \n",
       "\n",
       "[11941 rows x 5 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_ForwardLine_Against"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I have to get the GF%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to create final dataframe of xG%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we need to get the true best lines in hockey by implementing a minimum TOI requirement. Im going to do this by loading in data from moneypuck. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "monpuck= pd.read_csv(\"Temp.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>847793384801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Reinhart</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>7819</td>\n",
       "      <td>172</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>Luostarinen</td>\n",
       "      <td>Lundell</td>\n",
       "      <td>Reinhart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>847579884793168480821</td>\n",
       "      <td>Kunin-Granlund-Zadina</td>\n",
       "      <td>SJS</td>\n",
       "      <td>line</td>\n",
       "      <td>1073</td>\n",
       "      <td>65</td>\n",
       "      <td>7</td>\n",
       "      <td>5on5</td>\n",
       "      <td>Kunin</td>\n",
       "      <td>Granlund</td>\n",
       "      <td>Zadina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>847793984784838479318</td>\n",
       "      <td>Marner-Matthews-Nylander</td>\n",
       "      <td>TOR</td>\n",
       "      <td>line</td>\n",
       "      <td>2425</td>\n",
       "      <td>346</td>\n",
       "      <td>31</td>\n",
       "      <td>5on5</td>\n",
       "      <td>Marner</td>\n",
       "      <td>Matthews</td>\n",
       "      <td>Nylander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>847595884816568482148</td>\n",
       "      <td>Protas-Lapierre-Sgarbossa</td>\n",
       "      <td>WSH</td>\n",
       "      <td>line</td>\n",
       "      <td>821</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>5on5</td>\n",
       "      <td>Protas</td>\n",
       "      <td>Lapierre</td>\n",
       "      <td>Sgarbossa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>847821184784218479291</td>\n",
       "      <td>Greer-Rooney-Hunt</td>\n",
       "      <td>CGY</td>\n",
       "      <td>line</td>\n",
       "      <td>1808</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>5on5</td>\n",
       "      <td>Greer</td>\n",
       "      <td>Rooney</td>\n",
       "      <td>Hunt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 lineId                          name team  \\\n",
       "0           0  847793384801858482113  Luostarinen-Lundell-Reinhart  FLA   \n",
       "1           2  847579884793168480821         Kunin-Granlund-Zadina  SJS   \n",
       "2           4  847793984784838479318      Marner-Matthews-Nylander  TOR   \n",
       "3           5  847595884816568482148     Protas-Lapierre-Sgarbossa  WSH   \n",
       "4           8  847821184784218479291             Greer-Rooney-Hunt  CGY   \n",
       "\n",
       "  position  icetime  iceTimeRank  games_played situation      Player1  \\\n",
       "0     line     7819          172            24      5on5  Luostarinen   \n",
       "1     line     1073           65             7      5on5        Kunin   \n",
       "2     line     2425          346            31      5on5       Marner   \n",
       "3     line      821           30             4      5on5       Protas   \n",
       "4     line     1808           23             5      5on5        Greer   \n",
       "\n",
       "    Player2    Player3  \n",
       "0   Lundell   Reinhart  \n",
       "1  Granlund     Zadina  \n",
       "2  Matthews   Nylander  \n",
       "3  Lapierre  Sgarbossa  \n",
       "4    Rooney       Hunt  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monpuck.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "monpuck now has the same player1, player2, player3 format as the pbp data. just have to convert to uppercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "monpuck[['Player1', 'Player2', 'Player3']] = monpuck[['Player1', 'Player2', 'Player3']].apply(lambda x: x.str.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>847793384801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Reinhart</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>7819</td>\n",
       "      <td>172</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>REINHART</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>847579884793168480821</td>\n",
       "      <td>Kunin-Granlund-Zadina</td>\n",
       "      <td>SJS</td>\n",
       "      <td>line</td>\n",
       "      <td>1073</td>\n",
       "      <td>65</td>\n",
       "      <td>7</td>\n",
       "      <td>5on5</td>\n",
       "      <td>KUNIN</td>\n",
       "      <td>GRANLUND</td>\n",
       "      <td>ZADINA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>847793984784838479318</td>\n",
       "      <td>Marner-Matthews-Nylander</td>\n",
       "      <td>TOR</td>\n",
       "      <td>line</td>\n",
       "      <td>2425</td>\n",
       "      <td>346</td>\n",
       "      <td>31</td>\n",
       "      <td>5on5</td>\n",
       "      <td>MARNER</td>\n",
       "      <td>MATTHEWS</td>\n",
       "      <td>NYLANDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>847595884816568482148</td>\n",
       "      <td>Protas-Lapierre-Sgarbossa</td>\n",
       "      <td>WSH</td>\n",
       "      <td>line</td>\n",
       "      <td>821</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>5on5</td>\n",
       "      <td>PROTAS</td>\n",
       "      <td>LAPIERRE</td>\n",
       "      <td>SGARBOSSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>847821184784218479291</td>\n",
       "      <td>Greer-Rooney-Hunt</td>\n",
       "      <td>CGY</td>\n",
       "      <td>line</td>\n",
       "      <td>1808</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>5on5</td>\n",
       "      <td>GREER</td>\n",
       "      <td>ROONEY</td>\n",
       "      <td>HUNT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 lineId                          name team  \\\n",
       "0           0  847793384801858482113  Luostarinen-Lundell-Reinhart  FLA   \n",
       "1           2  847579884793168480821         Kunin-Granlund-Zadina  SJS   \n",
       "2           4  847793984784838479318      Marner-Matthews-Nylander  TOR   \n",
       "3           5  847595884816568482148     Protas-Lapierre-Sgarbossa  WSH   \n",
       "4           8  847821184784218479291             Greer-Rooney-Hunt  CGY   \n",
       "\n",
       "  position  icetime  iceTimeRank  games_played situation      Player1  \\\n",
       "0     line     7819          172            24      5on5  LUOSTARINEN   \n",
       "1     line     1073           65             7      5on5        KUNIN   \n",
       "2     line     2425          346            31      5on5       MARNER   \n",
       "3     line      821           30             4      5on5       PROTAS   \n",
       "4     line     1808           23             5      5on5        GREER   \n",
       "\n",
       "    Player2    Player3  \n",
       "0   LUNDELL   REINHART  \n",
       "1  GRANLUND     ZADINA  \n",
       "2  MATTHEWS   NYLANDER  \n",
       "3  LAPIERRE  SGARBOSSA  \n",
       "4    ROONEY       HUNT  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monpuck.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VINCENT TROCHECK</td>\n",
       "      <td>ARTEMI PANARIN</td>\n",
       "      <td>ALEXIS LAFRENIÈRE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>42.190269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GUSTAV NYQUIST</td>\n",
       "      <td>RYAN O'REILLY</td>\n",
       "      <td>FILIP FORSBERG</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>38.678131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANZE KOPITAR</td>\n",
       "      <td>ADRIAN KEMPE</td>\n",
       "      <td>QUINTON BYFIELD</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>36.434795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRAYDEN POINT</td>\n",
       "      <td>STEVEN STAMKOS</td>\n",
       "      <td>NIKITA KUCHEROV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>34.906006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JOE PAVELSKI</td>\n",
       "      <td>ROOPE HINTZ</td>\n",
       "      <td>JASON ROBERTSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>33.453220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Player1         Player2            Player3  \\\n",
       "0  VINCENT TROCHECK  ARTEMI PANARIN  ALEXIS LAFRENIÈRE   \n",
       "1    GUSTAV NYQUIST   RYAN O'REILLY     FILIP FORSBERG   \n",
       "2      ANZE KOPITAR    ADRIAN KEMPE    QUINTON BYFIELD   \n",
       "3     BRAYDEN POINT  STEVEN STAMKOS    NIKITA KUCHEROV   \n",
       "4      JOE PAVELSKI     ROOPE HINTZ    JASON ROBERTSON   \n",
       "\n",
       "                                                Team         xG  \n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png  42.190269  \n",
       "1  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  38.678131  \n",
       "2   https://a.espncdn.com/i/teamlogos/nhl/500/la.png  36.434795  \n",
       "3   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png  34.906006  \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  33.453220  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_ForwardLine_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have to make sure xg_ForwardLine is just last names of players before we sort. Since the monpuck is just lastnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to extract the last name\n",
    "def get_last_name(full_name):\n",
    "    return full_name.split()[-1] if pd.notna(full_name) else ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "xg_ForwardLine_For[['Player1', 'Player2', 'Player3']] = xg_ForwardLine_For[['Player1', 'Player2', 'Player3']].applymap(get_last_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TROCHECK</td>\n",
       "      <td>PANARIN</td>\n",
       "      <td>LAFRENIÈRE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>42.190269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NYQUIST</td>\n",
       "      <td>O'REILLY</td>\n",
       "      <td>FORSBERG</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>38.678131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KOPITAR</td>\n",
       "      <td>KEMPE</td>\n",
       "      <td>BYFIELD</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>36.434795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT</td>\n",
       "      <td>STAMKOS</td>\n",
       "      <td>KUCHEROV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>34.906006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAVELSKI</td>\n",
       "      <td>HINTZ</td>\n",
       "      <td>ROBERTSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>33.453220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player1   Player2     Player3  \\\n",
       "0  TROCHECK   PANARIN  LAFRENIÈRE   \n",
       "1   NYQUIST  O'REILLY    FORSBERG   \n",
       "2   KOPITAR     KEMPE     BYFIELD   \n",
       "3     POINT   STAMKOS    KUCHEROV   \n",
       "4  PAVELSKI     HINTZ   ROBERTSON   \n",
       "\n",
       "                                                Team         xG  \n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png  42.190269  \n",
       "1  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  38.678131  \n",
       "2   https://a.espncdn.com/i/teamlogos/nhl/500/la.png  36.434795  \n",
       "3   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png  34.906006  \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  33.453220  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_ForwardLine_For.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_ForwardLine_For = xg_ForwardLine_For.replace('LAFRENIÈRE', 'LAFRENIERE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TROCHECK</td>\n",
       "      <td>PANARIN</td>\n",
       "      <td>LAFRENIERE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>42.190269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NYQUIST</td>\n",
       "      <td>O'REILLY</td>\n",
       "      <td>FORSBERG</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>38.678131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KOPITAR</td>\n",
       "      <td>KEMPE</td>\n",
       "      <td>BYFIELD</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>36.434795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT</td>\n",
       "      <td>STAMKOS</td>\n",
       "      <td>KUCHEROV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>34.906006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAVELSKI</td>\n",
       "      <td>HINTZ</td>\n",
       "      <td>ROBERTSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>33.453220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player1   Player2     Player3  \\\n",
       "0  TROCHECK   PANARIN  LAFRENIERE   \n",
       "1   NYQUIST  O'REILLY    FORSBERG   \n",
       "2   KOPITAR     KEMPE     BYFIELD   \n",
       "3     POINT   STAMKOS    KUCHEROV   \n",
       "4  PAVELSKI     HINTZ   ROBERTSON   \n",
       "\n",
       "                                                Team         xG  \n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png  42.190269  \n",
       "1  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  38.678131  \n",
       "2   https://a.espncdn.com/i/teamlogos/nhl/500/la.png  36.434795  \n",
       "3   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png  34.906006  \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  33.453220  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_ForwardLine_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now sort each line alphabetically so that we can successfully compare and merge the two dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "monpuck[['Player1', 'Player2', 'Player3']] = monpuck[['Player1', 'Player2', 'Player3']].apply(lambda row: pd.Series(sorted(row)), axis=1)\n",
    "xg_ForwardLine_For[['Player1', 'Player2', 'Player3']] = xg_ForwardLine_For[['Player1', 'Player2', 'Player3']].apply(lambda row: pd.Series(sorted(row)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>847793384801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Reinhart</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>7819</td>\n",
       "      <td>172</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>REINHART</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>847579884793168480821</td>\n",
       "      <td>Kunin-Granlund-Zadina</td>\n",
       "      <td>SJS</td>\n",
       "      <td>line</td>\n",
       "      <td>1073</td>\n",
       "      <td>65</td>\n",
       "      <td>7</td>\n",
       "      <td>5on5</td>\n",
       "      <td>GRANLUND</td>\n",
       "      <td>KUNIN</td>\n",
       "      <td>ZADINA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>847793984784838479318</td>\n",
       "      <td>Marner-Matthews-Nylander</td>\n",
       "      <td>TOR</td>\n",
       "      <td>line</td>\n",
       "      <td>2425</td>\n",
       "      <td>346</td>\n",
       "      <td>31</td>\n",
       "      <td>5on5</td>\n",
       "      <td>MARNER</td>\n",
       "      <td>MATTHEWS</td>\n",
       "      <td>NYLANDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>847595884816568482148</td>\n",
       "      <td>Protas-Lapierre-Sgarbossa</td>\n",
       "      <td>WSH</td>\n",
       "      <td>line</td>\n",
       "      <td>821</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LAPIERRE</td>\n",
       "      <td>PROTAS</td>\n",
       "      <td>SGARBOSSA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>847821184784218479291</td>\n",
       "      <td>Greer-Rooney-Hunt</td>\n",
       "      <td>CGY</td>\n",
       "      <td>line</td>\n",
       "      <td>1808</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>5on5</td>\n",
       "      <td>GREER</td>\n",
       "      <td>HUNT</td>\n",
       "      <td>ROONEY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 lineId                          name team  \\\n",
       "0           0  847793384801858482113  Luostarinen-Lundell-Reinhart  FLA   \n",
       "1           2  847579884793168480821         Kunin-Granlund-Zadina  SJS   \n",
       "2           4  847793984784838479318      Marner-Matthews-Nylander  TOR   \n",
       "3           5  847595884816568482148     Protas-Lapierre-Sgarbossa  WSH   \n",
       "4           8  847821184784218479291             Greer-Rooney-Hunt  CGY   \n",
       "\n",
       "  position  icetime  iceTimeRank  games_played situation   Player1  \\\n",
       "0     line     7819          172            24      5on5   LUNDELL   \n",
       "1     line     1073           65             7      5on5  GRANLUND   \n",
       "2     line     2425          346            31      5on5    MARNER   \n",
       "3     line      821           30             4      5on5  LAPIERRE   \n",
       "4     line     1808           23             5      5on5     GREER   \n",
       "\n",
       "       Player2    Player3  \n",
       "0  LUOSTARINEN   REINHART  \n",
       "1        KUNIN     ZADINA  \n",
       "2     MATTHEWS   NYLANDER  \n",
       "3       PROTAS  SGARBOSSA  \n",
       "4         HUNT     ROONEY  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monpuck.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LAFRENIERE</td>\n",
       "      <td>PANARIN</td>\n",
       "      <td>TROCHECK</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>42.190269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FORSBERG</td>\n",
       "      <td>NYQUIST</td>\n",
       "      <td>O'REILLY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>38.678131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BYFIELD</td>\n",
       "      <td>KEMPE</td>\n",
       "      <td>KOPITAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>36.434795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KUCHEROV</td>\n",
       "      <td>POINT</td>\n",
       "      <td>STAMKOS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>34.906006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HINTZ</td>\n",
       "      <td>PAVELSKI</td>\n",
       "      <td>ROBERTSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>33.453220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Player1   Player2    Player3  \\\n",
       "0  LAFRENIERE   PANARIN   TROCHECK   \n",
       "1    FORSBERG   NYQUIST   O'REILLY   \n",
       "2     BYFIELD     KEMPE    KOPITAR   \n",
       "3    KUCHEROV     POINT    STAMKOS   \n",
       "4       HINTZ  PAVELSKI  ROBERTSON   \n",
       "\n",
       "                                                Team         xG  \n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png  42.190269  \n",
       "1  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  38.678131  \n",
       "2   https://a.espncdn.com/i/teamlogos/nhl/500/la.png  36.434795  \n",
       "3   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png  34.906006  \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  33.453220  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_ForwardLine_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, its time to merge the two dataframes. Lets see what we get..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the DataFrames based on the player columns\n",
    "merged_df_XG_For = pd.merge(monpuck, xg_ForwardLine_For, on=['Player1', 'Player2', 'Player3'], how='left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>847793384801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Reinhart</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>7819</td>\n",
       "      <td>172</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>REINHART</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>6.498608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>847579884793168480821</td>\n",
       "      <td>Kunin-Granlund-Zadina</td>\n",
       "      <td>SJS</td>\n",
       "      <td>line</td>\n",
       "      <td>1073</td>\n",
       "      <td>65</td>\n",
       "      <td>7</td>\n",
       "      <td>5on5</td>\n",
       "      <td>GRANLUND</td>\n",
       "      <td>KUNIN</td>\n",
       "      <td>ZADINA</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sj.png</td>\n",
       "      <td>0.689780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>847793984784838479318</td>\n",
       "      <td>Marner-Matthews-Nylander</td>\n",
       "      <td>TOR</td>\n",
       "      <td>line</td>\n",
       "      <td>2425</td>\n",
       "      <td>346</td>\n",
       "      <td>31</td>\n",
       "      <td>5on5</td>\n",
       "      <td>MARNER</td>\n",
       "      <td>MATTHEWS</td>\n",
       "      <td>NYLANDER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tor.png</td>\n",
       "      <td>4.962447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>847595884816568482148</td>\n",
       "      <td>Protas-Lapierre-Sgarbossa</td>\n",
       "      <td>WSH</td>\n",
       "      <td>line</td>\n",
       "      <td>821</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LAPIERRE</td>\n",
       "      <td>PROTAS</td>\n",
       "      <td>SGARBOSSA</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wsh.png</td>\n",
       "      <td>0.326855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>847821184784218479291</td>\n",
       "      <td>Greer-Rooney-Hunt</td>\n",
       "      <td>CGY</td>\n",
       "      <td>line</td>\n",
       "      <td>1808</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>5on5</td>\n",
       "      <td>GREER</td>\n",
       "      <td>HUNT</td>\n",
       "      <td>ROONEY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png</td>\n",
       "      <td>1.214996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 lineId                          name team  \\\n",
       "0           0  847793384801858482113  Luostarinen-Lundell-Reinhart  FLA   \n",
       "1           2  847579884793168480821         Kunin-Granlund-Zadina  SJS   \n",
       "2           4  847793984784838479318      Marner-Matthews-Nylander  TOR   \n",
       "3           5  847595884816568482148     Protas-Lapierre-Sgarbossa  WSH   \n",
       "4           8  847821184784218479291             Greer-Rooney-Hunt  CGY   \n",
       "\n",
       "  position  icetime  iceTimeRank  games_played situation   Player1  \\\n",
       "0     line     7819          172            24      5on5   LUNDELL   \n",
       "1     line     1073           65             7      5on5  GRANLUND   \n",
       "2     line     2425          346            31      5on5    MARNER   \n",
       "3     line      821           30             4      5on5  LAPIERRE   \n",
       "4     line     1808           23             5      5on5     GREER   \n",
       "\n",
       "       Player2    Player3                                               Team  \\\n",
       "0  LUOSTARINEN   REINHART  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "1        KUNIN     ZADINA   https://a.espncdn.com/i/teamlogos/nhl/500/sj.png   \n",
       "2     MATTHEWS   NYLANDER  https://a.espncdn.com/i/teamlogos/nhl/500/tor.png   \n",
       "3       PROTAS  SGARBOSSA  https://a.espncdn.com/i/teamlogos/nhl/500/wsh.png   \n",
       "4         HUNT     ROONEY  https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png   \n",
       "\n",
       "         xG  \n",
       "0  6.498608  \n",
       "1  0.689780  \n",
       "2  4.962447  \n",
       "3  0.326855  \n",
       "4  1.214996  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should be all good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_For=merged_df_XG_For.sort_values(by='xG',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>1202</td>\n",
       "      <td>847638984785508482109</td>\n",
       "      <td>Lafreniere-Trocheck-Panarin</td>\n",
       "      <td>NYR</td>\n",
       "      <td>line</td>\n",
       "      <td>51785</td>\n",
       "      <td>97</td>\n",
       "      <td>74</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LAFRENIERE</td>\n",
       "      <td>PANARIN</td>\n",
       "      <td>TROCHECK</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>42.190269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>932</td>\n",
       "      <td>847467984751588476887</td>\n",
       "      <td>Forsberg-O'Reilly-Nyquist</td>\n",
       "      <td>NSH</td>\n",
       "      <td>line</td>\n",
       "      <td>45988</td>\n",
       "      <td>117</td>\n",
       "      <td>78</td>\n",
       "      <td>5on5</td>\n",
       "      <td>FORSBERG</td>\n",
       "      <td>NYQUIST</td>\n",
       "      <td>O'REILLY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>38.678131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>1095</td>\n",
       "      <td>847168584779608482124</td>\n",
       "      <td>Kempe-Kopitar-Byfield</td>\n",
       "      <td>LAK</td>\n",
       "      <td>line</td>\n",
       "      <td>38321</td>\n",
       "      <td>194</td>\n",
       "      <td>71</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BYFIELD</td>\n",
       "      <td>KEMPE</td>\n",
       "      <td>KOPITAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>36.434795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>1154</td>\n",
       "      <td>847456484764538478010</td>\n",
       "      <td>Stamkos-Point-Kucherov</td>\n",
       "      <td>TBL</td>\n",
       "      <td>line</td>\n",
       "      <td>18840</td>\n",
       "      <td>256</td>\n",
       "      <td>49</td>\n",
       "      <td>5on5</td>\n",
       "      <td>KUCHEROV</td>\n",
       "      <td>POINT</td>\n",
       "      <td>STAMKOS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>34.906006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>1210</td>\n",
       "      <td>847079484784498480027</td>\n",
       "      <td>Robertson-Pavelski-Hintz</td>\n",
       "      <td>DAL</td>\n",
       "      <td>line</td>\n",
       "      <td>43125</td>\n",
       "      <td>188</td>\n",
       "      <td>75</td>\n",
       "      <td>5on5</td>\n",
       "      <td>HINTZ</td>\n",
       "      <td>PAVELSKI</td>\n",
       "      <td>ROBERTSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>33.453220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>1886</td>\n",
       "      <td>848001884815408483515</td>\n",
       "      <td>Slafkovsky-Suzuki-Caufield</td>\n",
       "      <td>MTL</td>\n",
       "      <td>line</td>\n",
       "      <td>45044</td>\n",
       "      <td>142</td>\n",
       "      <td>64</td>\n",
       "      <td>5on5</td>\n",
       "      <td>CAUFIELD</td>\n",
       "      <td>SLAFKOVSKY</td>\n",
       "      <td>SUZUKI</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/mtl.png</td>\n",
       "      <td>32.089432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>2366</td>\n",
       "      <td>847749284775018478420</td>\n",
       "      <td>Nichushkin-Mackinnon-Rantanen</td>\n",
       "      <td>COL</td>\n",
       "      <td>line</td>\n",
       "      <td>22920</td>\n",
       "      <td>135</td>\n",
       "      <td>43</td>\n",
       "      <td>5on5</td>\n",
       "      <td>MACKINNON</td>\n",
       "      <td>NICHUSHKIN</td>\n",
       "      <td>RANTANEN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>31.070208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>2437</td>\n",
       "      <td>847749284774948478420</td>\n",
       "      <td>Drouin-Mackinnon-Rantanen</td>\n",
       "      <td>COL</td>\n",
       "      <td>line</td>\n",
       "      <td>29126</td>\n",
       "      <td>257</td>\n",
       "      <td>62</td>\n",
       "      <td>5on5</td>\n",
       "      <td>DROUIN</td>\n",
       "      <td>MACKINNON</td>\n",
       "      <td>RANTANEN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>29.977970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>581</td>\n",
       "      <td>847647984779428479675</td>\n",
       "      <td>Moore-Danault-Fiala</td>\n",
       "      <td>LAK</td>\n",
       "      <td>line</td>\n",
       "      <td>35407</td>\n",
       "      <td>83</td>\n",
       "      <td>54</td>\n",
       "      <td>5on5</td>\n",
       "      <td>DANAULT</td>\n",
       "      <td>FIALA</td>\n",
       "      <td>MOORE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>28.864220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>811</td>\n",
       "      <td>847579984763928478891</td>\n",
       "      <td>Appleton-Lowry-Niederreiter</td>\n",
       "      <td>WPG</td>\n",
       "      <td>line</td>\n",
       "      <td>40922</td>\n",
       "      <td>200</td>\n",
       "      <td>72</td>\n",
       "      <td>5on5</td>\n",
       "      <td>APPLETON</td>\n",
       "      <td>LOWRY</td>\n",
       "      <td>NIEDERREITER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png</td>\n",
       "      <td>28.547495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>1847</td>\n",
       "      <td>847516884757948478975</td>\n",
       "      <td>Marchment-Seguin-Duchene</td>\n",
       "      <td>DAL</td>\n",
       "      <td>line</td>\n",
       "      <td>39783</td>\n",
       "      <td>125</td>\n",
       "      <td>64</td>\n",
       "      <td>5on5</td>\n",
       "      <td>DUCHENE</td>\n",
       "      <td>MARCHMENT</td>\n",
       "      <td>SEGUIN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>28.106392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>2940</td>\n",
       "      <td>847578684764548478402</td>\n",
       "      <td>Hyman-Mcdavid-Nugent-Hopkins</td>\n",
       "      <td>EDM</td>\n",
       "      <td>line</td>\n",
       "      <td>28075</td>\n",
       "      <td>161</td>\n",
       "      <td>49</td>\n",
       "      <td>5on5</td>\n",
       "      <td>HYMAN</td>\n",
       "      <td>MCDAVID</td>\n",
       "      <td>NUGENT-HOPKINS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/edm.png</td>\n",
       "      <td>27.184988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>2695</td>\n",
       "      <td>847740984774938477933</td>\n",
       "      <td>Verhaeghe-Barkov-Reinhart</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>12563</td>\n",
       "      <td>304</td>\n",
       "      <td>46</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BARKOV</td>\n",
       "      <td>REINHART</td>\n",
       "      <td>VERHAEGHE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>26.952290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17</td>\n",
       "      <td>847515184757548478115</td>\n",
       "      <td>Engvall-Nelson-Palmieri</td>\n",
       "      <td>NYI</td>\n",
       "      <td>line</td>\n",
       "      <td>33812</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ENGVALL</td>\n",
       "      <td>NELSON</td>\n",
       "      <td>PALMIERI</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyi.png</td>\n",
       "      <td>25.712502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>1716</td>\n",
       "      <td>847415084763998478233</td>\n",
       "      <td>Mangiapane-Backlund-Coleman</td>\n",
       "      <td>CGY</td>\n",
       "      <td>line</td>\n",
       "      <td>28968</td>\n",
       "      <td>162</td>\n",
       "      <td>51</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BACKLUND</td>\n",
       "      <td>COLEMAN</td>\n",
       "      <td>MANGIAPANE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png</td>\n",
       "      <td>24.752289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>490</td>\n",
       "      <td>847531484775008478445</td>\n",
       "      <td>Lee-Horvat-Barzal</td>\n",
       "      <td>NYI</td>\n",
       "      <td>line</td>\n",
       "      <td>28957</td>\n",
       "      <td>261</td>\n",
       "      <td>59</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BARZAL</td>\n",
       "      <td>HORVAT</td>\n",
       "      <td>LEE</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyi.png</td>\n",
       "      <td>24.369635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>2651</td>\n",
       "      <td>847749384779338478542</td>\n",
       "      <td>Reinhart-Barkov-Rodrigues</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>23367</td>\n",
       "      <td>127</td>\n",
       "      <td>44</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BARKOV</td>\n",
       "      <td>REINHART</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>23.837475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>1254</td>\n",
       "      <td>847646884784448480012</td>\n",
       "      <td>Pettersson-Miller-Boeser</td>\n",
       "      <td>VAN</td>\n",
       "      <td>line</td>\n",
       "      <td>7431</td>\n",
       "      <td>338</td>\n",
       "      <td>34</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BOESER</td>\n",
       "      <td>MILLER</td>\n",
       "      <td>PETTERSSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/van.png</td>\n",
       "      <td>23.503174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 lineId                           name team  \\\n",
       "830         1202  847638984785508482109    Lafreniere-Trocheck-Panarin  NYR   \n",
       "640          932  847467984751588476887      Forsberg-O'Reilly-Nyquist  NSH   \n",
       "758         1095  847168584779608482124          Kempe-Kopitar-Byfield  LAK   \n",
       "802         1154  847456484764538478010         Stamkos-Point-Kucherov  TBL   \n",
       "835         1210  847079484784498480027       Robertson-Pavelski-Hintz  DAL   \n",
       "1276        1886  848001884815408483515     Slafkovsky-Suzuki-Caufield  MTL   \n",
       "1611        2366  847749284775018478420  Nichushkin-Mackinnon-Rantanen  COL   \n",
       "1651        2437  847749284774948478420      Drouin-Mackinnon-Rantanen  COL   \n",
       "393          581  847647984779428479675            Moore-Danault-Fiala  LAK   \n",
       "557          811  847579984763928478891    Appleton-Lowry-Niederreiter  WPG   \n",
       "1253        1847  847516884757948478975       Marchment-Seguin-Duchene  DAL   \n",
       "1996        2940  847578684764548478402   Hyman-Mcdavid-Nugent-Hopkins  EDM   \n",
       "1834        2695  847740984774938477933      Verhaeghe-Barkov-Reinhart  FLA   \n",
       "10            17  847515184757548478115        Engvall-Nelson-Palmieri  NYI   \n",
       "1169        1716  847415084763998478233    Mangiapane-Backlund-Coleman  CGY   \n",
       "337          490  847531484775008478445              Lee-Horvat-Barzal  NYI   \n",
       "1801        2651  847749384779338478542      Reinhart-Barkov-Rodrigues  FLA   \n",
       "866         1254  847646884784448480012       Pettersson-Miller-Boeser  VAN   \n",
       "\n",
       "     position  icetime  iceTimeRank  games_played situation     Player1  \\\n",
       "830      line    51785           97            74      5on5  LAFRENIERE   \n",
       "640      line    45988          117            78      5on5    FORSBERG   \n",
       "758      line    38321          194            71      5on5     BYFIELD   \n",
       "802      line    18840          256            49      5on5    KUCHEROV   \n",
       "835      line    43125          188            75      5on5       HINTZ   \n",
       "1276     line    45044          142            64      5on5    CAUFIELD   \n",
       "1611     line    22920          135            43      5on5   MACKINNON   \n",
       "1651     line    29126          257            62      5on5      DROUIN   \n",
       "393      line    35407           83            54      5on5     DANAULT   \n",
       "557      line    40922          200            72      5on5    APPLETON   \n",
       "1253     line    39783          125            64      5on5     DUCHENE   \n",
       "1996     line    28075          161            49      5on5       HYMAN   \n",
       "1834     line    12563          304            46      5on5      BARKOV   \n",
       "10       line    33812          100            55      5on5     ENGVALL   \n",
       "1169     line    28968          162            51      5on5    BACKLUND   \n",
       "337      line    28957          261            59      5on5      BARZAL   \n",
       "1801     line    23367          127            44      5on5      BARKOV   \n",
       "866      line     7431          338            34      5on5      BOESER   \n",
       "\n",
       "         Player2         Player3  \\\n",
       "830      PANARIN        TROCHECK   \n",
       "640      NYQUIST        O'REILLY   \n",
       "758        KEMPE         KOPITAR   \n",
       "802        POINT         STAMKOS   \n",
       "835     PAVELSKI       ROBERTSON   \n",
       "1276  SLAFKOVSKY          SUZUKI   \n",
       "1611  NICHUSHKIN        RANTANEN   \n",
       "1651   MACKINNON        RANTANEN   \n",
       "393        FIALA           MOORE   \n",
       "557        LOWRY    NIEDERREITER   \n",
       "1253   MARCHMENT          SEGUIN   \n",
       "1996     MCDAVID  NUGENT-HOPKINS   \n",
       "1834    REINHART       VERHAEGHE   \n",
       "10        NELSON        PALMIERI   \n",
       "1169     COLEMAN      MANGIAPANE   \n",
       "337       HORVAT             LEE   \n",
       "1801    REINHART       RODRIGUES   \n",
       "866       MILLER      PETTERSSON   \n",
       "\n",
       "                                                   Team         xG  \n",
       "830   https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png  42.190269  \n",
       "640   https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png  38.678131  \n",
       "758    https://a.espncdn.com/i/teamlogos/nhl/500/la.png  36.434795  \n",
       "802    https://a.espncdn.com/i/teamlogos/nhl/500/tb.png  34.906006  \n",
       "835   https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  33.453220  \n",
       "1276  https://a.espncdn.com/i/teamlogos/nhl/500/mtl.png  32.089432  \n",
       "1611  https://a.espncdn.com/i/teamlogos/nhl/500/col.png  31.070208  \n",
       "1651  https://a.espncdn.com/i/teamlogos/nhl/500/col.png  29.977970  \n",
       "393    https://a.espncdn.com/i/teamlogos/nhl/500/la.png  28.864220  \n",
       "557   https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png  28.547495  \n",
       "1253  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  28.106392  \n",
       "1996  https://a.espncdn.com/i/teamlogos/nhl/500/edm.png  27.184988  \n",
       "1834  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  26.952290  \n",
       "10    https://a.espncdn.com/i/teamlogos/nhl/500/nyi.png  25.712502  \n",
       "1169  https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png  24.752289  \n",
       "337   https://a.espncdn.com/i/teamlogos/nhl/500/nyi.png  24.369635  \n",
       "1801  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  23.837475  \n",
       "866   https://a.espncdn.com/i/teamlogos/nhl/500/van.png  23.503174  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_For.head(18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright we are in business. Couple steps \n",
    "1. Create a TOI (in Minutes) Column from the icetime column\n",
    "2. All decimal numbers (TOI and xG) only display 2 digits after decimal point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(74)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_For['xG'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_For.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_For['Rank'] = merged_df_XG_For['xG'].rank(ascending=False, method='max').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>1202</td>\n",
       "      <td>847638984785508482109</td>\n",
       "      <td>Lafreniere-Trocheck-Panarin</td>\n",
       "      <td>NYR</td>\n",
       "      <td>line</td>\n",
       "      <td>51785</td>\n",
       "      <td>97</td>\n",
       "      <td>74</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LAFRENIERE</td>\n",
       "      <td>PANARIN</td>\n",
       "      <td>TROCHECK</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>42.190269</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>932</td>\n",
       "      <td>847467984751588476887</td>\n",
       "      <td>Forsberg-O'Reilly-Nyquist</td>\n",
       "      <td>NSH</td>\n",
       "      <td>line</td>\n",
       "      <td>45988</td>\n",
       "      <td>117</td>\n",
       "      <td>78</td>\n",
       "      <td>5on5</td>\n",
       "      <td>FORSBERG</td>\n",
       "      <td>NYQUIST</td>\n",
       "      <td>O'REILLY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>38.678131</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>1095</td>\n",
       "      <td>847168584779608482124</td>\n",
       "      <td>Kempe-Kopitar-Byfield</td>\n",
       "      <td>LAK</td>\n",
       "      <td>line</td>\n",
       "      <td>38321</td>\n",
       "      <td>194</td>\n",
       "      <td>71</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BYFIELD</td>\n",
       "      <td>KEMPE</td>\n",
       "      <td>KOPITAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>36.434795</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>1154</td>\n",
       "      <td>847456484764538478010</td>\n",
       "      <td>Stamkos-Point-Kucherov</td>\n",
       "      <td>TBL</td>\n",
       "      <td>line</td>\n",
       "      <td>18840</td>\n",
       "      <td>256</td>\n",
       "      <td>49</td>\n",
       "      <td>5on5</td>\n",
       "      <td>KUCHEROV</td>\n",
       "      <td>POINT</td>\n",
       "      <td>STAMKOS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>34.906006</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>1210</td>\n",
       "      <td>847079484784498480027</td>\n",
       "      <td>Robertson-Pavelski-Hintz</td>\n",
       "      <td>DAL</td>\n",
       "      <td>line</td>\n",
       "      <td>43125</td>\n",
       "      <td>188</td>\n",
       "      <td>75</td>\n",
       "      <td>5on5</td>\n",
       "      <td>HINTZ</td>\n",
       "      <td>PAVELSKI</td>\n",
       "      <td>ROBERTSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>33.453220</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                 lineId                         name team  \\\n",
       "830        1202  847638984785508482109  Lafreniere-Trocheck-Panarin  NYR   \n",
       "640         932  847467984751588476887    Forsberg-O'Reilly-Nyquist  NSH   \n",
       "758        1095  847168584779608482124        Kempe-Kopitar-Byfield  LAK   \n",
       "802        1154  847456484764538478010       Stamkos-Point-Kucherov  TBL   \n",
       "835        1210  847079484784498480027     Robertson-Pavelski-Hintz  DAL   \n",
       "\n",
       "    position  icetime  iceTimeRank  games_played situation     Player1  \\\n",
       "830     line    51785           97            74      5on5  LAFRENIERE   \n",
       "640     line    45988          117            78      5on5    FORSBERG   \n",
       "758     line    38321          194            71      5on5     BYFIELD   \n",
       "802     line    18840          256            49      5on5    KUCHEROV   \n",
       "835     line    43125          188            75      5on5       HINTZ   \n",
       "\n",
       "      Player2    Player3                                               Team  \\\n",
       "830   PANARIN   TROCHECK  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png   \n",
       "640   NYQUIST   O'REILLY  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png   \n",
       "758     KEMPE    KOPITAR   https://a.espncdn.com/i/teamlogos/nhl/500/la.png   \n",
       "802     POINT    STAMKOS   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "835  PAVELSKI  ROBERTSON  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "\n",
       "            xG  Rank  \n",
       "830  42.190269     1  \n",
       "640  38.678131     2  \n",
       "758  36.434795     3  \n",
       "802  34.906006     4  \n",
       "835  33.453220     5  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_For.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_For['TOI']=merged_df_XG_For['icetime']/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "      <th>Rank</th>\n",
       "      <th>TOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>1202</td>\n",
       "      <td>847638984785508482109</td>\n",
       "      <td>Lafreniere-Trocheck-Panarin</td>\n",
       "      <td>NYR</td>\n",
       "      <td>line</td>\n",
       "      <td>51785</td>\n",
       "      <td>97</td>\n",
       "      <td>74</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LAFRENIERE</td>\n",
       "      <td>PANARIN</td>\n",
       "      <td>TROCHECK</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>42.190269</td>\n",
       "      <td>1</td>\n",
       "      <td>863.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>932</td>\n",
       "      <td>847467984751588476887</td>\n",
       "      <td>Forsberg-O'Reilly-Nyquist</td>\n",
       "      <td>NSH</td>\n",
       "      <td>line</td>\n",
       "      <td>45988</td>\n",
       "      <td>117</td>\n",
       "      <td>78</td>\n",
       "      <td>5on5</td>\n",
       "      <td>FORSBERG</td>\n",
       "      <td>NYQUIST</td>\n",
       "      <td>O'REILLY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>38.678131</td>\n",
       "      <td>2</td>\n",
       "      <td>766.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>1095</td>\n",
       "      <td>847168584779608482124</td>\n",
       "      <td>Kempe-Kopitar-Byfield</td>\n",
       "      <td>LAK</td>\n",
       "      <td>line</td>\n",
       "      <td>38321</td>\n",
       "      <td>194</td>\n",
       "      <td>71</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BYFIELD</td>\n",
       "      <td>KEMPE</td>\n",
       "      <td>KOPITAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>36.434795</td>\n",
       "      <td>3</td>\n",
       "      <td>638.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>1154</td>\n",
       "      <td>847456484764538478010</td>\n",
       "      <td>Stamkos-Point-Kucherov</td>\n",
       "      <td>TBL</td>\n",
       "      <td>line</td>\n",
       "      <td>18840</td>\n",
       "      <td>256</td>\n",
       "      <td>49</td>\n",
       "      <td>5on5</td>\n",
       "      <td>KUCHEROV</td>\n",
       "      <td>POINT</td>\n",
       "      <td>STAMKOS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>34.906006</td>\n",
       "      <td>4</td>\n",
       "      <td>314.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>1210</td>\n",
       "      <td>847079484784498480027</td>\n",
       "      <td>Robertson-Pavelski-Hintz</td>\n",
       "      <td>DAL</td>\n",
       "      <td>line</td>\n",
       "      <td>43125</td>\n",
       "      <td>188</td>\n",
       "      <td>75</td>\n",
       "      <td>5on5</td>\n",
       "      <td>HINTZ</td>\n",
       "      <td>PAVELSKI</td>\n",
       "      <td>ROBERTSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>33.453220</td>\n",
       "      <td>5</td>\n",
       "      <td>718.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                 lineId                         name team  \\\n",
       "830        1202  847638984785508482109  Lafreniere-Trocheck-Panarin  NYR   \n",
       "640         932  847467984751588476887    Forsberg-O'Reilly-Nyquist  NSH   \n",
       "758        1095  847168584779608482124        Kempe-Kopitar-Byfield  LAK   \n",
       "802        1154  847456484764538478010       Stamkos-Point-Kucherov  TBL   \n",
       "835        1210  847079484784498480027     Robertson-Pavelski-Hintz  DAL   \n",
       "\n",
       "    position  icetime  iceTimeRank  games_played situation     Player1  \\\n",
       "830     line    51785           97            74      5on5  LAFRENIERE   \n",
       "640     line    45988          117            78      5on5    FORSBERG   \n",
       "758     line    38321          194            71      5on5     BYFIELD   \n",
       "802     line    18840          256            49      5on5    KUCHEROV   \n",
       "835     line    43125          188            75      5on5       HINTZ   \n",
       "\n",
       "      Player2    Player3                                               Team  \\\n",
       "830   PANARIN   TROCHECK  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png   \n",
       "640   NYQUIST   O'REILLY  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png   \n",
       "758     KEMPE    KOPITAR   https://a.espncdn.com/i/teamlogos/nhl/500/la.png   \n",
       "802     POINT    STAMKOS   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "835  PAVELSKI  ROBERTSON  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "\n",
       "            xG  Rank         TOI  \n",
       "830  42.190269     1  863.083333  \n",
       "640  38.678131     2  766.466667  \n",
       "758  36.434795     3  638.683333  \n",
       "802  34.906006     4  314.000000  \n",
       "835  33.453220     5  718.750000  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display xG and TOI only to 2 hundreths place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_For['xG']=merged_df_XG_For['xG'].round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_For['TOI']=merged_df_XG_For['TOI'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_For['xG']=merged_df_XG_For['xG'].apply(lambda x: f\"{x:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_For['TOI']=merged_df_XG_For['TOI'].apply(lambda x: f\"{x:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "      <th>Rank</th>\n",
       "      <th>TOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>1202</td>\n",
       "      <td>847638984785508482109</td>\n",
       "      <td>Lafreniere-Trocheck-Panarin</td>\n",
       "      <td>NYR</td>\n",
       "      <td>line</td>\n",
       "      <td>51785</td>\n",
       "      <td>97</td>\n",
       "      <td>74</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LAFRENIERE</td>\n",
       "      <td>PANARIN</td>\n",
       "      <td>TROCHECK</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>42.19</td>\n",
       "      <td>1</td>\n",
       "      <td>863.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>932</td>\n",
       "      <td>847467984751588476887</td>\n",
       "      <td>Forsberg-O'Reilly-Nyquist</td>\n",
       "      <td>NSH</td>\n",
       "      <td>line</td>\n",
       "      <td>45988</td>\n",
       "      <td>117</td>\n",
       "      <td>78</td>\n",
       "      <td>5on5</td>\n",
       "      <td>FORSBERG</td>\n",
       "      <td>NYQUIST</td>\n",
       "      <td>O'REILLY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>38.68</td>\n",
       "      <td>2</td>\n",
       "      <td>766.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>1095</td>\n",
       "      <td>847168584779608482124</td>\n",
       "      <td>Kempe-Kopitar-Byfield</td>\n",
       "      <td>LAK</td>\n",
       "      <td>line</td>\n",
       "      <td>38321</td>\n",
       "      <td>194</td>\n",
       "      <td>71</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BYFIELD</td>\n",
       "      <td>KEMPE</td>\n",
       "      <td>KOPITAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>36.43</td>\n",
       "      <td>3</td>\n",
       "      <td>638.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>1154</td>\n",
       "      <td>847456484764538478010</td>\n",
       "      <td>Stamkos-Point-Kucherov</td>\n",
       "      <td>TBL</td>\n",
       "      <td>line</td>\n",
       "      <td>18840</td>\n",
       "      <td>256</td>\n",
       "      <td>49</td>\n",
       "      <td>5on5</td>\n",
       "      <td>KUCHEROV</td>\n",
       "      <td>POINT</td>\n",
       "      <td>STAMKOS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>34.91</td>\n",
       "      <td>4</td>\n",
       "      <td>314.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>1210</td>\n",
       "      <td>847079484784498480027</td>\n",
       "      <td>Robertson-Pavelski-Hintz</td>\n",
       "      <td>DAL</td>\n",
       "      <td>line</td>\n",
       "      <td>43125</td>\n",
       "      <td>188</td>\n",
       "      <td>75</td>\n",
       "      <td>5on5</td>\n",
       "      <td>HINTZ</td>\n",
       "      <td>PAVELSKI</td>\n",
       "      <td>ROBERTSON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>33.45</td>\n",
       "      <td>5</td>\n",
       "      <td>718.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                 lineId                         name team  \\\n",
       "830        1202  847638984785508482109  Lafreniere-Trocheck-Panarin  NYR   \n",
       "640         932  847467984751588476887    Forsberg-O'Reilly-Nyquist  NSH   \n",
       "758        1095  847168584779608482124        Kempe-Kopitar-Byfield  LAK   \n",
       "802        1154  847456484764538478010       Stamkos-Point-Kucherov  TBL   \n",
       "835        1210  847079484784498480027     Robertson-Pavelski-Hintz  DAL   \n",
       "\n",
       "    position  icetime  iceTimeRank  games_played situation     Player1  \\\n",
       "830     line    51785           97            74      5on5  LAFRENIERE   \n",
       "640     line    45988          117            78      5on5    FORSBERG   \n",
       "758     line    38321          194            71      5on5     BYFIELD   \n",
       "802     line    18840          256            49      5on5    KUCHEROV   \n",
       "835     line    43125          188            75      5on5       HINTZ   \n",
       "\n",
       "      Player2    Player3                                               Team  \\\n",
       "830   PANARIN   TROCHECK  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png   \n",
       "640   NYQUIST   O'REILLY  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png   \n",
       "758     KEMPE    KOPITAR   https://a.espncdn.com/i/teamlogos/nhl/500/la.png   \n",
       "802     POINT    STAMKOS   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "835  PAVELSKI  ROBERTSON  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "\n",
       "        xG  Rank     TOI  \n",
       "830  42.19     1  863.08  \n",
       "640  38.68     2  766.47  \n",
       "758  36.43     3  638.68  \n",
       "802  34.91     4  314.00  \n",
       "835  33.45     5  718.75  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now time to get final dataframe and display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "xGFor_FINAL = merged_df_XG_For[['Rank','Team', 'name', 'TOI', 'xG']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "xGFor_DisplayFINAL=xGFor_FINAL.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>name</th>\n",
       "      <th>TOI</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>1</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>Lafreniere-Trocheck-Panarin</td>\n",
       "      <td>863.08</td>\n",
       "      <td>42.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>2</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>Forsberg-O'Reilly-Nyquist</td>\n",
       "      <td>766.47</td>\n",
       "      <td>38.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>3</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>Kempe-Kopitar-Byfield</td>\n",
       "      <td>638.68</td>\n",
       "      <td>36.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>4</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>Stamkos-Point-Kucherov</td>\n",
       "      <td>314.00</td>\n",
       "      <td>34.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>5</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>Robertson-Pavelski-Hintz</td>\n",
       "      <td>718.75</td>\n",
       "      <td>33.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>6</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/mtl.png</td>\n",
       "      <td>Slafkovsky-Suzuki-Caufield</td>\n",
       "      <td>750.73</td>\n",
       "      <td>32.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>7</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>Nichushkin-Mackinnon-Rantanen</td>\n",
       "      <td>382.00</td>\n",
       "      <td>31.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>8</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>Drouin-Mackinnon-Rantanen</td>\n",
       "      <td>485.43</td>\n",
       "      <td>29.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>9</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>Moore-Danault-Fiala</td>\n",
       "      <td>590.12</td>\n",
       "      <td>28.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>10</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png</td>\n",
       "      <td>Appleton-Lowry-Niederreiter</td>\n",
       "      <td>682.03</td>\n",
       "      <td>28.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>11</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>Marchment-Seguin-Duchene</td>\n",
       "      <td>663.05</td>\n",
       "      <td>28.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>12</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/edm.png</td>\n",
       "      <td>Hyman-Mcdavid-Nugent-Hopkins</td>\n",
       "      <td>467.92</td>\n",
       "      <td>27.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>13</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>Verhaeghe-Barkov-Reinhart</td>\n",
       "      <td>209.38</td>\n",
       "      <td>26.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyi.png</td>\n",
       "      <td>Engvall-Nelson-Palmieri</td>\n",
       "      <td>563.53</td>\n",
       "      <td>25.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>15</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png</td>\n",
       "      <td>Mangiapane-Backlund-Coleman</td>\n",
       "      <td>482.80</td>\n",
       "      <td>24.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rank                                               Team  \\\n",
       "830      1  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png   \n",
       "640      2  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png   \n",
       "758      3   https://a.espncdn.com/i/teamlogos/nhl/500/la.png   \n",
       "802      4   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "835      5  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "1276     6  https://a.espncdn.com/i/teamlogos/nhl/500/mtl.png   \n",
       "1611     7  https://a.espncdn.com/i/teamlogos/nhl/500/col.png   \n",
       "1651     8  https://a.espncdn.com/i/teamlogos/nhl/500/col.png   \n",
       "393      9   https://a.espncdn.com/i/teamlogos/nhl/500/la.png   \n",
       "557     10  https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png   \n",
       "1253    11  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "1996    12  https://a.espncdn.com/i/teamlogos/nhl/500/edm.png   \n",
       "1834    13  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "10      14  https://a.espncdn.com/i/teamlogos/nhl/500/nyi.png   \n",
       "1169    15  https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png   \n",
       "\n",
       "                               name     TOI     xG  \n",
       "830     Lafreniere-Trocheck-Panarin  863.08  42.19  \n",
       "640       Forsberg-O'Reilly-Nyquist  766.47  38.68  \n",
       "758           Kempe-Kopitar-Byfield  638.68  36.43  \n",
       "802          Stamkos-Point-Kucherov  314.00  34.91  \n",
       "835        Robertson-Pavelski-Hintz  718.75  33.45  \n",
       "1276     Slafkovsky-Suzuki-Caufield  750.73  32.09  \n",
       "1611  Nichushkin-Mackinnon-Rantanen  382.00  31.07  \n",
       "1651      Drouin-Mackinnon-Rantanen  485.43  29.98  \n",
       "393             Moore-Danault-Fiala  590.12  28.86  \n",
       "557     Appleton-Lowry-Niederreiter  682.03  28.55  \n",
       "1253       Marchment-Seguin-Duchene  663.05  28.11  \n",
       "1996   Hyman-Mcdavid-Nugent-Hopkins  467.92  27.18  \n",
       "1834      Verhaeghe-Barkov-Reinhart  209.38  26.95  \n",
       "10          Engvall-Nelson-Palmieri  563.53  25.71  \n",
       "1169    Mangiapane-Backlund-Coleman  482.80  24.75  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xGFor_DisplayFINAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_1a6c2 caption {\n",
       "  caption-side: top;\n",
       "  font-weight: bold;\n",
       "  font-size: 16px;\n",
       "  color: black;\n",
       "  background-color: white;\n",
       "}\n",
       "#T_1a6c2 th {\n",
       "  font-weight: bold;\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_1a6c2 th.colxG::before {\n",
       "  content: \"xG For\";\n",
       "  font-weight: bold;\n",
       "}\n",
       "#T_1a6c2 td.col0 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_1a6c2 td.col1 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_1a6c2 td.col2 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_1a6c2 td.col3 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_1a6c2_row0_col4 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a6c2_row1_col4 {\n",
       "  background-color: #1764ab;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a6c2_row2_col4 {\n",
       "  background-color: #3686c0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a6c2_row3_col4 {\n",
       "  background-color: #4f9bcb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a6c2_row4_col4 {\n",
       "  background-color: #6caed6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a6c2_row5_col4 {\n",
       "  background-color: #8cc0dd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row6_col4 {\n",
       "  background-color: #a3cce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row7_col4 {\n",
       "  background-color: #b7d4ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row8_col4 {\n",
       "  background-color: #c9ddf0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row9_col4 {\n",
       "  background-color: #cddff1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row10_col4 {\n",
       "  background-color: #d1e2f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row11_col4 {\n",
       "  background-color: #dce9f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row12_col4 {\n",
       "  background-color: #deebf7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row13_col4 {\n",
       "  background-color: #ecf4fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1a6c2_row14_col4 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1a6c2\">\n",
       "  <caption>Top 15 Forward Lines by Expected Goals For (xG For)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1a6c2_level0_col0\" class=\"col_heading level0 col0\" >Rank</th>\n",
       "      <th id=\"T_1a6c2_level0_col1\" class=\"col_heading level0 col1\" >Team</th>\n",
       "      <th id=\"T_1a6c2_level0_col2\" class=\"col_heading level0 col2\" >Forward Line</th>\n",
       "      <th id=\"T_1a6c2_level0_col3\" class=\"col_heading level0 col3\" >TOI</th>\n",
       "      <th id=\"T_1a6c2_level0_col4\" class=\"col_heading level0 col4\" >xG For</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row0\" class=\"row_heading level0 row0\" >830</th>\n",
       "      <td id=\"T_1a6c2_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_1a6c2_row0_col1\" class=\"data row0 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row0_col2\" class=\"data row0 col2\" >Lafreniere-Trocheck-Panarin</td>\n",
       "      <td id=\"T_1a6c2_row0_col3\" class=\"data row0 col3\" >863.08</td>\n",
       "      <td id=\"T_1a6c2_row0_col4\" class=\"data row0 col4\" >42.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row1\" class=\"row_heading level0 row1\" >640</th>\n",
       "      <td id=\"T_1a6c2_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_1a6c2_row1_col1\" class=\"data row1 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row1_col2\" class=\"data row1 col2\" >Forsberg-O'Reilly-Nyquist</td>\n",
       "      <td id=\"T_1a6c2_row1_col3\" class=\"data row1 col3\" >766.47</td>\n",
       "      <td id=\"T_1a6c2_row1_col4\" class=\"data row1 col4\" >38.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row2\" class=\"row_heading level0 row2\" >758</th>\n",
       "      <td id=\"T_1a6c2_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_1a6c2_row2_col1\" class=\"data row2 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/la.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row2_col2\" class=\"data row2 col2\" >Kempe-Kopitar-Byfield</td>\n",
       "      <td id=\"T_1a6c2_row2_col3\" class=\"data row2 col3\" >638.68</td>\n",
       "      <td id=\"T_1a6c2_row2_col4\" class=\"data row2 col4\" >36.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row3\" class=\"row_heading level0 row3\" >802</th>\n",
       "      <td id=\"T_1a6c2_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_1a6c2_row3_col1\" class=\"data row3 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/tb.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row3_col2\" class=\"data row3 col2\" >Stamkos-Point-Kucherov</td>\n",
       "      <td id=\"T_1a6c2_row3_col3\" class=\"data row3 col3\" >314.00</td>\n",
       "      <td id=\"T_1a6c2_row3_col4\" class=\"data row3 col4\" >34.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row4\" class=\"row_heading level0 row4\" >835</th>\n",
       "      <td id=\"T_1a6c2_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_1a6c2_row4_col1\" class=\"data row4 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/dal.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row4_col2\" class=\"data row4 col2\" >Robertson-Pavelski-Hintz</td>\n",
       "      <td id=\"T_1a6c2_row4_col3\" class=\"data row4 col3\" >718.75</td>\n",
       "      <td id=\"T_1a6c2_row4_col4\" class=\"data row4 col4\" >33.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row5\" class=\"row_heading level0 row5\" >1276</th>\n",
       "      <td id=\"T_1a6c2_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_1a6c2_row5_col1\" class=\"data row5 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/mtl.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row5_col2\" class=\"data row5 col2\" >Slafkovsky-Suzuki-Caufield</td>\n",
       "      <td id=\"T_1a6c2_row5_col3\" class=\"data row5 col3\" >750.73</td>\n",
       "      <td id=\"T_1a6c2_row5_col4\" class=\"data row5 col4\" >32.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row6\" class=\"row_heading level0 row6\" >1611</th>\n",
       "      <td id=\"T_1a6c2_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_1a6c2_row6_col1\" class=\"data row6 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/col.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row6_col2\" class=\"data row6 col2\" >Nichushkin-Mackinnon-Rantanen</td>\n",
       "      <td id=\"T_1a6c2_row6_col3\" class=\"data row6 col3\" >382.00</td>\n",
       "      <td id=\"T_1a6c2_row6_col4\" class=\"data row6 col4\" >31.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row7\" class=\"row_heading level0 row7\" >1651</th>\n",
       "      <td id=\"T_1a6c2_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_1a6c2_row7_col1\" class=\"data row7 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/col.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row7_col2\" class=\"data row7 col2\" >Drouin-Mackinnon-Rantanen</td>\n",
       "      <td id=\"T_1a6c2_row7_col3\" class=\"data row7 col3\" >485.43</td>\n",
       "      <td id=\"T_1a6c2_row7_col4\" class=\"data row7 col4\" >29.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row8\" class=\"row_heading level0 row8\" >393</th>\n",
       "      <td id=\"T_1a6c2_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_1a6c2_row8_col1\" class=\"data row8 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/la.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row8_col2\" class=\"data row8 col2\" >Moore-Danault-Fiala</td>\n",
       "      <td id=\"T_1a6c2_row8_col3\" class=\"data row8 col3\" >590.12</td>\n",
       "      <td id=\"T_1a6c2_row8_col4\" class=\"data row8 col4\" >28.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row9\" class=\"row_heading level0 row9\" >557</th>\n",
       "      <td id=\"T_1a6c2_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_1a6c2_row9_col1\" class=\"data row9 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row9_col2\" class=\"data row9 col2\" >Appleton-Lowry-Niederreiter</td>\n",
       "      <td id=\"T_1a6c2_row9_col3\" class=\"data row9 col3\" >682.03</td>\n",
       "      <td id=\"T_1a6c2_row9_col4\" class=\"data row9 col4\" >28.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row10\" class=\"row_heading level0 row10\" >1253</th>\n",
       "      <td id=\"T_1a6c2_row10_col0\" class=\"data row10 col0\" >11</td>\n",
       "      <td id=\"T_1a6c2_row10_col1\" class=\"data row10 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/dal.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row10_col2\" class=\"data row10 col2\" >Marchment-Seguin-Duchene</td>\n",
       "      <td id=\"T_1a6c2_row10_col3\" class=\"data row10 col3\" >663.05</td>\n",
       "      <td id=\"T_1a6c2_row10_col4\" class=\"data row10 col4\" >28.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row11\" class=\"row_heading level0 row11\" >1996</th>\n",
       "      <td id=\"T_1a6c2_row11_col0\" class=\"data row11 col0\" >12</td>\n",
       "      <td id=\"T_1a6c2_row11_col1\" class=\"data row11 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/edm.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row11_col2\" class=\"data row11 col2\" >Hyman-Mcdavid-Nugent-Hopkins</td>\n",
       "      <td id=\"T_1a6c2_row11_col3\" class=\"data row11 col3\" >467.92</td>\n",
       "      <td id=\"T_1a6c2_row11_col4\" class=\"data row11 col4\" >27.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row12\" class=\"row_heading level0 row12\" >1834</th>\n",
       "      <td id=\"T_1a6c2_row12_col0\" class=\"data row12 col0\" >13</td>\n",
       "      <td id=\"T_1a6c2_row12_col1\" class=\"data row12 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/fla.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row12_col2\" class=\"data row12 col2\" >Verhaeghe-Barkov-Reinhart</td>\n",
       "      <td id=\"T_1a6c2_row12_col3\" class=\"data row12 col3\" >209.38</td>\n",
       "      <td id=\"T_1a6c2_row12_col4\" class=\"data row12 col4\" >26.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row13\" class=\"row_heading level0 row13\" >10</th>\n",
       "      <td id=\"T_1a6c2_row13_col0\" class=\"data row13 col0\" >14</td>\n",
       "      <td id=\"T_1a6c2_row13_col1\" class=\"data row13 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/nyi.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row13_col2\" class=\"data row13 col2\" >Engvall-Nelson-Palmieri</td>\n",
       "      <td id=\"T_1a6c2_row13_col3\" class=\"data row13 col3\" >563.53</td>\n",
       "      <td id=\"T_1a6c2_row13_col4\" class=\"data row13 col4\" >25.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a6c2_level0_row14\" class=\"row_heading level0 row14\" >1169</th>\n",
       "      <td id=\"T_1a6c2_row14_col0\" class=\"data row14 col0\" >15</td>\n",
       "      <td id=\"T_1a6c2_row14_col1\" class=\"data row14 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_1a6c2_row14_col2\" class=\"data row14 col2\" >Mangiapane-Backlund-Coleman</td>\n",
       "      <td id=\"T_1a6c2_row14_col3\" class=\"data row14 col3\" >482.80</td>\n",
       "      <td id=\"T_1a6c2_row14_col4\" class=\"data row14 col4\" >24.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p style=\"text-align: left; padding-left: 60px; font-size: 14px; font-weight: bold; color: black; background-color: white; margin: 0;\">Created by Nick Sofianakos, *TOI is in minutes </p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom function to render logos as images in the DataFrame\n",
    "def render_logo(logo_url):\n",
    "    return f'<img src=\"{logo_url}\" width=\"40\" height=\"40\">'\n",
    "\n",
    "# Applying the custom rendering and styling\n",
    "styled_df = (\n",
    "    xGFor_DisplayFINAL.style\n",
    "    .set_caption(\"Top 15 Forward Lines by Expected Goals For (xG For)\")  # Title of the DataFrame\n",
    "    .set_table_styles([\n",
    "        # Style for the title\n",
    "        {'selector': 'caption', 'props': [('caption-side', 'top'), ('font-weight', 'bold'), ('font-size', '16px'), ('color', 'black'), ('background-color', 'white')]},\n",
    "        # Mark the table with my name\n",
    "        # Style for the column headers\n",
    "        {'selector': 'th', 'props': [('font-weight', 'bold'), ('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'th.colxG::before', 'props': [('content', '\"xG For\"'), ('font-weight', 'bold')]},\n",
    "        # Style for Player1, Player2, and Player3 columns\n",
    "        {'selector': 'td.col0', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col1', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col2', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col3', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "    ])\n",
    "    .background_gradient(subset=['xG'], cmap='Blues')  # Gradient color for xG column\n",
    "    .format({'Team': render_logo}) # Apply the render_logo function to the Team_Logo column\n",
    ")\n",
    "\n",
    "# Render the DataFrame to HTML\n",
    "html_output = styled_df.to_html()\n",
    "\n",
    "\n",
    "# Replace the 'xG' header with 'xG For'\n",
    "html_output = html_output.replace('>xG<', '>xG For<')\n",
    "html_output = html_output.replace('>name<', '>Forward Line<')\n",
    "\n",
    "# Add a bottom caption directly in HTML\n",
    "bottom_caption = '<p style=\"text-align: left; padding-left: 60px; font-size: 14px; font-weight: bold; color: black; background-color: white; margin: 0;\">Created by Nick Sofianakos, *TOI is in minutes </p>'\n",
    "\n",
    "# Combine the HTML table and bottom caption\n",
    "full_html = html_output + bottom_caption\n",
    "\n",
    "# Display the modified HTML\n",
    "display(HTML(full_html))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright xG For is all done, now lets do xG Against. Need to make sure xg_Against only has last name and also lafreniere is fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7361</th>\n",
       "      <td>BARABANOV</td>\n",
       "      <td>HOFFMAN</td>\n",
       "      <td>LABANC</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sj.png</td>\n",
       "      <td>0.015034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3436</th>\n",
       "      <td>COMPHER</td>\n",
       "      <td>LARKIN</td>\n",
       "      <td>PERRON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/det.png</td>\n",
       "      <td>0.017409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11605</th>\n",
       "      <td>EHLERS</td>\n",
       "      <td>MORRISSEY</td>\n",
       "      <td>NAMESTNIKOV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png</td>\n",
       "      <td>0.018511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9347</th>\n",
       "      <td>COLTON</td>\n",
       "      <td>NICHUSHKIN</td>\n",
       "      <td>TATAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>0.020398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8290</th>\n",
       "      <td>AMAN</td>\n",
       "      <td>BOESER</td>\n",
       "      <td>MIKHEYEV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/van.png</td>\n",
       "      <td>0.020788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Opp_Player1 Opp_Player2  Opp_Player3  \\\n",
       "7361    BARABANOV     HOFFMAN       LABANC   \n",
       "3436      COMPHER      LARKIN       PERRON   \n",
       "11605      EHLERS   MORRISSEY  NAMESTNIKOV   \n",
       "9347       COLTON  NICHUSHKIN        TATAR   \n",
       "8290         AMAN      BOESER     MIKHEYEV   \n",
       "\n",
       "                                                    Team        xG  \n",
       "7361    https://a.espncdn.com/i/teamlogos/nhl/500/sj.png  0.015034  \n",
       "3436   https://a.espncdn.com/i/teamlogos/nhl/500/det.png  0.017409  \n",
       "11605  https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png  0.018511  \n",
       "9347   https://a.espncdn.com/i/teamlogos/nhl/500/col.png  0.020398  \n",
       "8290   https://a.espncdn.com/i/teamlogos/nhl/500/van.png  0.020788  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_ForwardLine_Against.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "xg_ForwardLine_Against[['Opp_Player1', 'Opp_Player2', 'Opp_Player3']] = xg_ForwardLine_Against[['Opp_Player1', 'Opp_Player2', 'Opp_Player3']].applymap(get_last_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7361</th>\n",
       "      <td>BARABANOV</td>\n",
       "      <td>HOFFMAN</td>\n",
       "      <td>LABANC</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sj.png</td>\n",
       "      <td>0.015034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3436</th>\n",
       "      <td>COMPHER</td>\n",
       "      <td>LARKIN</td>\n",
       "      <td>PERRON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/det.png</td>\n",
       "      <td>0.017409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11605</th>\n",
       "      <td>EHLERS</td>\n",
       "      <td>MORRISSEY</td>\n",
       "      <td>NAMESTNIKOV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png</td>\n",
       "      <td>0.018511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9347</th>\n",
       "      <td>COLTON</td>\n",
       "      <td>NICHUSHKIN</td>\n",
       "      <td>TATAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>0.020398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8290</th>\n",
       "      <td>AMAN</td>\n",
       "      <td>BOESER</td>\n",
       "      <td>MIKHEYEV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/van.png</td>\n",
       "      <td>0.020788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Opp_Player1 Opp_Player2  Opp_Player3  \\\n",
       "7361    BARABANOV     HOFFMAN       LABANC   \n",
       "3436      COMPHER      LARKIN       PERRON   \n",
       "11605      EHLERS   MORRISSEY  NAMESTNIKOV   \n",
       "9347       COLTON  NICHUSHKIN        TATAR   \n",
       "8290         AMAN      BOESER     MIKHEYEV   \n",
       "\n",
       "                                                    Team        xG  \n",
       "7361    https://a.espncdn.com/i/teamlogos/nhl/500/sj.png  0.015034  \n",
       "3436   https://a.espncdn.com/i/teamlogos/nhl/500/det.png  0.017409  \n",
       "11605  https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png  0.018511  \n",
       "9347   https://a.espncdn.com/i/teamlogos/nhl/500/col.png  0.020398  \n",
       "8290   https://a.espncdn.com/i/teamlogos/nhl/500/van.png  0.020788  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_ForwardLine_Against.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_ForwardLine_Against = xg_ForwardLine_Against.replace('LAFRENIÈRE', 'LAFRENIERE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now sort the xg_against datframe alphabetically by line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_ForwardLine_Against[['Opp_Player1', 'Opp_Player2', 'Opp_Player3']] = xg_ForwardLine_Against[['Opp_Player1', 'Opp_Player2', 'Opp_Player3']].apply(lambda row: pd.Series(sorted(row)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against = pd.merge(\n",
    "    monpuck,\n",
    "    xg_ForwardLine_Against,\n",
    "    left_on=['Player1', 'Player2', 'Player3'], \n",
    "    right_on=['Opp_Player1', 'Opp_Player2', 'Opp_Player3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>1606</td>\n",
       "      <td>847060484785698480205</td>\n",
       "      <td>Carter-Acciari-Bemstrom</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>1891</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.654905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>966</td>\n",
       "      <td>847856984793448480205</td>\n",
       "      <td>Bemstrom-Acciari-Puljujarvi</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>957</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>0.477964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>2660</td>\n",
       "      <td>847060484741898478569</td>\n",
       "      <td>Eller-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>986</td>\n",
       "      <td>161</td>\n",
       "      <td>17</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.024431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>1713</td>\n",
       "      <td>847060484785698480836</td>\n",
       "      <td>Gruden-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>949</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>GRUDEN</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>GRUDEN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.040314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>355</td>\n",
       "      <td>847060484784248478569</td>\n",
       "      <td>Harkins-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>8435</td>\n",
       "      <td>95</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>HARKINS</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>HARKINS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>4.574167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 lineId                         name team  \\\n",
       "1049        1606  847060484785698480205      Carter-Acciari-Bemstrom  PIT   \n",
       "631          966  847856984793448480205  Bemstrom-Acciari-Puljujarvi  PIT   \n",
       "1733        2660  847060484741898478569         Eller-Acciari-Carter  PIT   \n",
       "1118        1713  847060484785698480836        Gruden-Acciari-Carter  PIT   \n",
       "237          355  847060484784248478569       Harkins-Acciari-Carter  PIT   \n",
       "\n",
       "     position  icetime  iceTimeRank  games_played situation  Player1  \\\n",
       "1049     line     1891           54             6      5on5  ACCIARI   \n",
       "631      line      957           28             4      5on5  ACCIARI   \n",
       "1733     line      986          161            17      5on5  ACCIARI   \n",
       "1118     line      949           15             3      5on5  ACCIARI   \n",
       "237      line     8435           95            24      5on5  ACCIARI   \n",
       "\n",
       "       Player2     Player3 Opp_Player1 Opp_Player2 Opp_Player3  \\\n",
       "1049  BEMSTROM      CARTER     ACCIARI    BEMSTROM      CARTER   \n",
       "631   BEMSTROM  PULJUJARVI     ACCIARI    BEMSTROM  PULJUJARVI   \n",
       "1733    CARTER       ELLER     ACCIARI      CARTER       ELLER   \n",
       "1118    CARTER      GRUDEN     ACCIARI      CARTER      GRUDEN   \n",
       "237     CARTER     HARKINS     ACCIARI      CARTER     HARKINS   \n",
       "\n",
       "                                                   Team        xG  \n",
       "1049  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  1.654905  \n",
       "631   https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  0.477964  \n",
       "1733  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  1.024431  \n",
       "1118  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  1.040314  \n",
       "237   https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  4.574167  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_Against.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get rid of same lines appearing twice. Not sure why we receive duplicates, but know how to fix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Sort the DataFrame by `Player1`, `Player2`, `Player3`, and `xG_Against` in descending order\n",
    "merged_df_XG_Against = merged_df_XG_Against.sort_values(by=['Player1', 'Player2', 'Player3', 'xG'], ascending=[True, True, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Drop duplicates based on `Player1`, `Player2`, and `Player3`, keeping the first occurrence\n",
    "merged_df_XG_Against = merged_df_XG_Against.drop_duplicates(subset=['Player1', 'Player2', 'Player3'], keep='first')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, the dataframe is all good, lets sort the values by smallest xG values since we are looking for best defensive lines which are lines with low xG Against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against=merged_df_XG_Against.sort_values(by='xG',ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>1173</td>\n",
       "      <td>847576584785428478831</td>\n",
       "      <td>Rodrigues-Stenlund-Tarasenko</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>645</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5on5</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>STENLUND</td>\n",
       "      <td>TARASENKO</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>STENLUND</td>\n",
       "      <td>TARASENKO</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>0.061785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>244</td>\n",
       "      <td>847418984764838479344</td>\n",
       "      <td>Rakell-Eller-Puljujarvi</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>801</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>RAKELL</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>RAKELL</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>0.108641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2028</td>\n",
       "      <td>847403484752208480980</td>\n",
       "      <td>Foligno-Dewar-Maroon</td>\n",
       "      <td>MIN</td>\n",
       "      <td>line</td>\n",
       "      <td>647</td>\n",
       "      <td>153</td>\n",
       "      <td>14</td>\n",
       "      <td>5on5</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>FOLIGNO</td>\n",
       "      <td>MAROON</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>FOLIGNO</td>\n",
       "      <td>MAROON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/min.png</td>\n",
       "      <td>0.113940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>1426</td>\n",
       "      <td>847578484815228484145</td>\n",
       "      <td>Skinner-Krebs-Benson</td>\n",
       "      <td>BUF</td>\n",
       "      <td>line</td>\n",
       "      <td>740</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BENSON</td>\n",
       "      <td>KREBS</td>\n",
       "      <td>SKINNER</td>\n",
       "      <td>BENSON</td>\n",
       "      <td>KREBS</td>\n",
       "      <td>SKINNER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/buf.png</td>\n",
       "      <td>0.117099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>1674</td>\n",
       "      <td>847746484795208480980</td>\n",
       "      <td>Petan-Dewar-Duhaime</td>\n",
       "      <td>MIN</td>\n",
       "      <td>line</td>\n",
       "      <td>646</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5on5</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>DUHAIME</td>\n",
       "      <td>PETAN</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>DUHAIME</td>\n",
       "      <td>PETAN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/min.png</td>\n",
       "      <td>0.118228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 lineId                          name team  \\\n",
       "774         1173  847576584785428478831  Rodrigues-Stenlund-Tarasenko  FLA   \n",
       "165          244  847418984764838479344       Rakell-Eller-Puljujarvi  PIT   \n",
       "1320        2028  847403484752208480980          Foligno-Dewar-Maroon  MIN   \n",
       "938         1426  847578484815228484145          Skinner-Krebs-Benson  BUF   \n",
       "1091        1674  847746484795208480980           Petan-Dewar-Duhaime  MIN   \n",
       "\n",
       "     position  icetime  iceTimeRank  games_played situation    Player1  \\\n",
       "774      line      645           17             3      5on5  RODRIGUES   \n",
       "165      line      801            5             2      5on5      ELLER   \n",
       "1320     line      647          153            14      5on5      DEWAR   \n",
       "938      line      740           51             5      5on5     BENSON   \n",
       "1091     line      646            8             2      5on5      DEWAR   \n",
       "\n",
       "         Player2    Player3 Opp_Player1 Opp_Player2 Opp_Player3  \\\n",
       "774     STENLUND  TARASENKO   RODRIGUES    STENLUND   TARASENKO   \n",
       "165   PULJUJARVI     RAKELL       ELLER  PULJUJARVI      RAKELL   \n",
       "1320     FOLIGNO     MAROON       DEWAR     FOLIGNO      MAROON   \n",
       "938        KREBS    SKINNER      BENSON       KREBS     SKINNER   \n",
       "1091     DUHAIME      PETAN       DEWAR     DUHAIME       PETAN   \n",
       "\n",
       "                                                   Team        xG  \n",
       "774   https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  0.061785  \n",
       "165   https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  0.108641  \n",
       "1320  https://a.espncdn.com/i/teamlogos/nhl/500/min.png  0.113940  \n",
       "938   https://a.espncdn.com/i/teamlogos/nhl/500/buf.png  0.117099  \n",
       "1091  https://a.espncdn.com/i/teamlogos/nhl/500/min.png  0.118228  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_Against.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter lines that played over 100 minutes (6000 seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against['TOI']=merged_df_XG_Against['icetime']/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "      <th>TOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>1173</td>\n",
       "      <td>847576584785428478831</td>\n",
       "      <td>Rodrigues-Stenlund-Tarasenko</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>645</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5on5</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>STENLUND</td>\n",
       "      <td>TARASENKO</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>STENLUND</td>\n",
       "      <td>TARASENKO</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>0.061785</td>\n",
       "      <td>10.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>244</td>\n",
       "      <td>847418984764838479344</td>\n",
       "      <td>Rakell-Eller-Puljujarvi</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>801</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>RAKELL</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>RAKELL</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>0.108641</td>\n",
       "      <td>13.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>2028</td>\n",
       "      <td>847403484752208480980</td>\n",
       "      <td>Foligno-Dewar-Maroon</td>\n",
       "      <td>MIN</td>\n",
       "      <td>line</td>\n",
       "      <td>647</td>\n",
       "      <td>153</td>\n",
       "      <td>14</td>\n",
       "      <td>5on5</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>FOLIGNO</td>\n",
       "      <td>MAROON</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>FOLIGNO</td>\n",
       "      <td>MAROON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/min.png</td>\n",
       "      <td>0.113940</td>\n",
       "      <td>10.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>1426</td>\n",
       "      <td>847578484815228484145</td>\n",
       "      <td>Skinner-Krebs-Benson</td>\n",
       "      <td>BUF</td>\n",
       "      <td>line</td>\n",
       "      <td>740</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BENSON</td>\n",
       "      <td>KREBS</td>\n",
       "      <td>SKINNER</td>\n",
       "      <td>BENSON</td>\n",
       "      <td>KREBS</td>\n",
       "      <td>SKINNER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/buf.png</td>\n",
       "      <td>0.117099</td>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>1674</td>\n",
       "      <td>847746484795208480980</td>\n",
       "      <td>Petan-Dewar-Duhaime</td>\n",
       "      <td>MIN</td>\n",
       "      <td>line</td>\n",
       "      <td>646</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5on5</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>DUHAIME</td>\n",
       "      <td>PETAN</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>DUHAIME</td>\n",
       "      <td>PETAN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/min.png</td>\n",
       "      <td>0.118228</td>\n",
       "      <td>10.766667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 lineId                          name team  \\\n",
       "774         1173  847576584785428478831  Rodrigues-Stenlund-Tarasenko  FLA   \n",
       "165          244  847418984764838479344       Rakell-Eller-Puljujarvi  PIT   \n",
       "1320        2028  847403484752208480980          Foligno-Dewar-Maroon  MIN   \n",
       "938         1426  847578484815228484145          Skinner-Krebs-Benson  BUF   \n",
       "1091        1674  847746484795208480980           Petan-Dewar-Duhaime  MIN   \n",
       "\n",
       "     position  icetime  iceTimeRank  games_played situation    Player1  \\\n",
       "774      line      645           17             3      5on5  RODRIGUES   \n",
       "165      line      801            5             2      5on5      ELLER   \n",
       "1320     line      647          153            14      5on5      DEWAR   \n",
       "938      line      740           51             5      5on5     BENSON   \n",
       "1091     line      646            8             2      5on5      DEWAR   \n",
       "\n",
       "         Player2    Player3 Opp_Player1 Opp_Player2 Opp_Player3  \\\n",
       "774     STENLUND  TARASENKO   RODRIGUES    STENLUND   TARASENKO   \n",
       "165   PULJUJARVI     RAKELL       ELLER  PULJUJARVI      RAKELL   \n",
       "1320     FOLIGNO     MAROON       DEWAR     FOLIGNO      MAROON   \n",
       "938        KREBS    SKINNER      BENSON       KREBS     SKINNER   \n",
       "1091     DUHAIME      PETAN       DEWAR     DUHAIME       PETAN   \n",
       "\n",
       "                                                   Team        xG        TOI  \n",
       "774   https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  0.061785  10.750000  \n",
       "165   https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  0.108641  13.350000  \n",
       "1320  https://a.espncdn.com/i/teamlogos/nhl/500/min.png  0.113940  10.783333  \n",
       "938   https://a.espncdn.com/i/teamlogos/nhl/500/buf.png  0.117099  12.333333  \n",
       "1091  https://a.espncdn.com/i/teamlogos/nhl/500/min.png  0.118228  10.766667  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_Against.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against=merged_df_XG_Against[merged_df_XG_Against['TOI']>=200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "      <th>TOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>73</td>\n",
       "      <td>847854284801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Rodrigues</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>13351</td>\n",
       "      <td>204</td>\n",
       "      <td>40</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>6.604409</td>\n",
       "      <td>222.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>589</td>\n",
       "      <td>847410284779468482078</td>\n",
       "      <td>Perron-Larkin-Raymond</td>\n",
       "      <td>DET</td>\n",
       "      <td>line</td>\n",
       "      <td>12353</td>\n",
       "      <td>146</td>\n",
       "      <td>30</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LARKIN</td>\n",
       "      <td>PERRON</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>LARKIN</td>\n",
       "      <td>PERRON</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/det.png</td>\n",
       "      <td>7.069714</td>\n",
       "      <td>205.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>1335</td>\n",
       "      <td>847702184779518479343</td>\n",
       "      <td>Schmaltz-Kerfoot-Keller</td>\n",
       "      <td>ARI</td>\n",
       "      <td>line</td>\n",
       "      <td>12297</td>\n",
       "      <td>341</td>\n",
       "      <td>42</td>\n",
       "      <td>5on5</td>\n",
       "      <td>KELLER</td>\n",
       "      <td>KERFOOT</td>\n",
       "      <td>SCHMALTZ</td>\n",
       "      <td>KELLER</td>\n",
       "      <td>KERFOOT</td>\n",
       "      <td>SCHMALTZ</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/ari.png</td>\n",
       "      <td>7.493944</td>\n",
       "      <td>204.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>2271</td>\n",
       "      <td>847399484827028482740</td>\n",
       "      <td>Benn-Johnston-Stankoven</td>\n",
       "      <td>DAL</td>\n",
       "      <td>line</td>\n",
       "      <td>13599</td>\n",
       "      <td>61</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BENN</td>\n",
       "      <td>JOHNSTON</td>\n",
       "      <td>STANKOVEN</td>\n",
       "      <td>BENN</td>\n",
       "      <td>JOHNSTON</td>\n",
       "      <td>STANKOVEN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>7.562917</td>\n",
       "      <td>226.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>846</td>\n",
       "      <td>847458684751938482665</td>\n",
       "      <td>Tatar-Beniers-Eberle</td>\n",
       "      <td>SEA</td>\n",
       "      <td>line</td>\n",
       "      <td>12002</td>\n",
       "      <td>112</td>\n",
       "      <td>29</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BENIERS</td>\n",
       "      <td>EBERLE</td>\n",
       "      <td>TATAR</td>\n",
       "      <td>BENIERS</td>\n",
       "      <td>EBERLE</td>\n",
       "      <td>TATAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sea.png</td>\n",
       "      <td>7.670293</td>\n",
       "      <td>200.033333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 lineId                           name team  \\\n",
       "49            73  847854284801858482113  Luostarinen-Lundell-Rodrigues  FLA   \n",
       "386          589  847410284779468482078          Perron-Larkin-Raymond  DET   \n",
       "881         1335  847702184779518479343        Schmaltz-Kerfoot-Keller  ARI   \n",
       "1479        2271  847399484827028482740        Benn-Johnston-Stankoven  DAL   \n",
       "554          846  847458684751938482665           Tatar-Beniers-Eberle  SEA   \n",
       "\n",
       "     position  icetime  iceTimeRank  games_played situation  Player1  \\\n",
       "49       line    13351          204            40      5on5  LUNDELL   \n",
       "386      line    12353          146            30      5on5   LARKIN   \n",
       "881      line    12297          341            42      5on5   KELLER   \n",
       "1479     line    13599           61            24      5on5     BENN   \n",
       "554      line    12002          112            29      5on5  BENIERS   \n",
       "\n",
       "          Player2    Player3 Opp_Player1  Opp_Player2 Opp_Player3  \\\n",
       "49    LUOSTARINEN  RODRIGUES     LUNDELL  LUOSTARINEN   RODRIGUES   \n",
       "386        PERRON    RAYMOND      LARKIN       PERRON     RAYMOND   \n",
       "881       KERFOOT   SCHMALTZ      KELLER      KERFOOT    SCHMALTZ   \n",
       "1479     JOHNSTON  STANKOVEN        BENN     JOHNSTON   STANKOVEN   \n",
       "554        EBERLE      TATAR     BENIERS       EBERLE       TATAR   \n",
       "\n",
       "                                                   Team        xG         TOI  \n",
       "49    https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  6.604409  222.516667  \n",
       "386   https://a.espncdn.com/i/teamlogos/nhl/500/det.png  7.069714  205.883333  \n",
       "881   https://a.espncdn.com/i/teamlogos/nhl/500/ari.png  7.493944  204.950000  \n",
       "1479  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  7.562917  226.650000  \n",
       "554   https://a.espncdn.com/i/teamlogos/nhl/500/sea.png  7.670293  200.033333  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_Against.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_Against['xG'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against['Rank'] = merged_df_XG_Against['xG'].rank(ascending=True, method='max').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "      <th>TOI</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>73</td>\n",
       "      <td>847854284801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Rodrigues</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>13351</td>\n",
       "      <td>204</td>\n",
       "      <td>40</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>6.604409</td>\n",
       "      <td>222.516667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>589</td>\n",
       "      <td>847410284779468482078</td>\n",
       "      <td>Perron-Larkin-Raymond</td>\n",
       "      <td>DET</td>\n",
       "      <td>line</td>\n",
       "      <td>12353</td>\n",
       "      <td>146</td>\n",
       "      <td>30</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LARKIN</td>\n",
       "      <td>PERRON</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>LARKIN</td>\n",
       "      <td>PERRON</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/det.png</td>\n",
       "      <td>7.069714</td>\n",
       "      <td>205.883333</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>1335</td>\n",
       "      <td>847702184779518479343</td>\n",
       "      <td>Schmaltz-Kerfoot-Keller</td>\n",
       "      <td>ARI</td>\n",
       "      <td>line</td>\n",
       "      <td>12297</td>\n",
       "      <td>341</td>\n",
       "      <td>42</td>\n",
       "      <td>5on5</td>\n",
       "      <td>KELLER</td>\n",
       "      <td>KERFOOT</td>\n",
       "      <td>SCHMALTZ</td>\n",
       "      <td>KELLER</td>\n",
       "      <td>KERFOOT</td>\n",
       "      <td>SCHMALTZ</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/ari.png</td>\n",
       "      <td>7.493944</td>\n",
       "      <td>204.950000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>2271</td>\n",
       "      <td>847399484827028482740</td>\n",
       "      <td>Benn-Johnston-Stankoven</td>\n",
       "      <td>DAL</td>\n",
       "      <td>line</td>\n",
       "      <td>13599</td>\n",
       "      <td>61</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BENN</td>\n",
       "      <td>JOHNSTON</td>\n",
       "      <td>STANKOVEN</td>\n",
       "      <td>BENN</td>\n",
       "      <td>JOHNSTON</td>\n",
       "      <td>STANKOVEN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>7.562917</td>\n",
       "      <td>226.650000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>846</td>\n",
       "      <td>847458684751938482665</td>\n",
       "      <td>Tatar-Beniers-Eberle</td>\n",
       "      <td>SEA</td>\n",
       "      <td>line</td>\n",
       "      <td>12002</td>\n",
       "      <td>112</td>\n",
       "      <td>29</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BENIERS</td>\n",
       "      <td>EBERLE</td>\n",
       "      <td>TATAR</td>\n",
       "      <td>BENIERS</td>\n",
       "      <td>EBERLE</td>\n",
       "      <td>TATAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sea.png</td>\n",
       "      <td>7.670293</td>\n",
       "      <td>200.033333</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 lineId                           name team  \\\n",
       "49            73  847854284801858482113  Luostarinen-Lundell-Rodrigues  FLA   \n",
       "386          589  847410284779468482078          Perron-Larkin-Raymond  DET   \n",
       "881         1335  847702184779518479343        Schmaltz-Kerfoot-Keller  ARI   \n",
       "1479        2271  847399484827028482740        Benn-Johnston-Stankoven  DAL   \n",
       "554          846  847458684751938482665           Tatar-Beniers-Eberle  SEA   \n",
       "\n",
       "     position  icetime  iceTimeRank  games_played situation  Player1  \\\n",
       "49       line    13351          204            40      5on5  LUNDELL   \n",
       "386      line    12353          146            30      5on5   LARKIN   \n",
       "881      line    12297          341            42      5on5   KELLER   \n",
       "1479     line    13599           61            24      5on5     BENN   \n",
       "554      line    12002          112            29      5on5  BENIERS   \n",
       "\n",
       "          Player2    Player3 Opp_Player1  Opp_Player2 Opp_Player3  \\\n",
       "49    LUOSTARINEN  RODRIGUES     LUNDELL  LUOSTARINEN   RODRIGUES   \n",
       "386        PERRON    RAYMOND      LARKIN       PERRON     RAYMOND   \n",
       "881       KERFOOT   SCHMALTZ      KELLER      KERFOOT    SCHMALTZ   \n",
       "1479     JOHNSTON  STANKOVEN        BENN     JOHNSTON   STANKOVEN   \n",
       "554        EBERLE      TATAR     BENIERS       EBERLE       TATAR   \n",
       "\n",
       "                                                   Team        xG         TOI  \\\n",
       "49    https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  6.604409  222.516667   \n",
       "386   https://a.espncdn.com/i/teamlogos/nhl/500/det.png  7.069714  205.883333   \n",
       "881   https://a.espncdn.com/i/teamlogos/nhl/500/ari.png  7.493944  204.950000   \n",
       "1479  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  7.562917  226.650000   \n",
       "554   https://a.espncdn.com/i/teamlogos/nhl/500/sea.png  7.670293  200.033333   \n",
       "\n",
       "      Rank  \n",
       "49       1  \n",
       "386      2  \n",
       "881      3  \n",
       "1479     4  \n",
       "554      5  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_Against.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display xG and TOI only to 2 hundreths place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against['xG']=merged_df_XG_Against['xG'].round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against['TOI']=merged_df_XG_Against['TOI'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against['xG']=merged_df_XG_Against['xG'].apply(lambda x: f\"{x:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_XG_Against['TOI']=merged_df_XG_Against['TOI'].apply(lambda x: f\"{x:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "      <th>TOI</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>73</td>\n",
       "      <td>847854284801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Rodrigues</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>13351</td>\n",
       "      <td>204</td>\n",
       "      <td>40</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>6.60</td>\n",
       "      <td>222.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>589</td>\n",
       "      <td>847410284779468482078</td>\n",
       "      <td>Perron-Larkin-Raymond</td>\n",
       "      <td>DET</td>\n",
       "      <td>line</td>\n",
       "      <td>12353</td>\n",
       "      <td>146</td>\n",
       "      <td>30</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LARKIN</td>\n",
       "      <td>PERRON</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>LARKIN</td>\n",
       "      <td>PERRON</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/det.png</td>\n",
       "      <td>7.07</td>\n",
       "      <td>205.88</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>1335</td>\n",
       "      <td>847702184779518479343</td>\n",
       "      <td>Schmaltz-Kerfoot-Keller</td>\n",
       "      <td>ARI</td>\n",
       "      <td>line</td>\n",
       "      <td>12297</td>\n",
       "      <td>341</td>\n",
       "      <td>42</td>\n",
       "      <td>5on5</td>\n",
       "      <td>KELLER</td>\n",
       "      <td>KERFOOT</td>\n",
       "      <td>SCHMALTZ</td>\n",
       "      <td>KELLER</td>\n",
       "      <td>KERFOOT</td>\n",
       "      <td>SCHMALTZ</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/ari.png</td>\n",
       "      <td>7.49</td>\n",
       "      <td>204.95</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>2271</td>\n",
       "      <td>847399484827028482740</td>\n",
       "      <td>Benn-Johnston-Stankoven</td>\n",
       "      <td>DAL</td>\n",
       "      <td>line</td>\n",
       "      <td>13599</td>\n",
       "      <td>61</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BENN</td>\n",
       "      <td>JOHNSTON</td>\n",
       "      <td>STANKOVEN</td>\n",
       "      <td>BENN</td>\n",
       "      <td>JOHNSTON</td>\n",
       "      <td>STANKOVEN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>7.56</td>\n",
       "      <td>226.65</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>846</td>\n",
       "      <td>847458684751938482665</td>\n",
       "      <td>Tatar-Beniers-Eberle</td>\n",
       "      <td>SEA</td>\n",
       "      <td>line</td>\n",
       "      <td>12002</td>\n",
       "      <td>112</td>\n",
       "      <td>29</td>\n",
       "      <td>5on5</td>\n",
       "      <td>BENIERS</td>\n",
       "      <td>EBERLE</td>\n",
       "      <td>TATAR</td>\n",
       "      <td>BENIERS</td>\n",
       "      <td>EBERLE</td>\n",
       "      <td>TATAR</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sea.png</td>\n",
       "      <td>7.67</td>\n",
       "      <td>200.03</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 lineId                           name team  \\\n",
       "49            73  847854284801858482113  Luostarinen-Lundell-Rodrigues  FLA   \n",
       "386          589  847410284779468482078          Perron-Larkin-Raymond  DET   \n",
       "881         1335  847702184779518479343        Schmaltz-Kerfoot-Keller  ARI   \n",
       "1479        2271  847399484827028482740        Benn-Johnston-Stankoven  DAL   \n",
       "554          846  847458684751938482665           Tatar-Beniers-Eberle  SEA   \n",
       "\n",
       "     position  icetime  iceTimeRank  games_played situation  Player1  \\\n",
       "49       line    13351          204            40      5on5  LUNDELL   \n",
       "386      line    12353          146            30      5on5   LARKIN   \n",
       "881      line    12297          341            42      5on5   KELLER   \n",
       "1479     line    13599           61            24      5on5     BENN   \n",
       "554      line    12002          112            29      5on5  BENIERS   \n",
       "\n",
       "          Player2    Player3 Opp_Player1  Opp_Player2 Opp_Player3  \\\n",
       "49    LUOSTARINEN  RODRIGUES     LUNDELL  LUOSTARINEN   RODRIGUES   \n",
       "386        PERRON    RAYMOND      LARKIN       PERRON     RAYMOND   \n",
       "881       KERFOOT   SCHMALTZ      KELLER      KERFOOT    SCHMALTZ   \n",
       "1479     JOHNSTON  STANKOVEN        BENN     JOHNSTON   STANKOVEN   \n",
       "554        EBERLE      TATAR     BENIERS       EBERLE       TATAR   \n",
       "\n",
       "                                                   Team    xG     TOI  Rank  \n",
       "49    https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  6.60  222.52     1  \n",
       "386   https://a.espncdn.com/i/teamlogos/nhl/500/det.png  7.07  205.88     2  \n",
       "881   https://a.espncdn.com/i/teamlogos/nhl/500/ari.png  7.49  204.95     3  \n",
       "1479  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png  7.56  226.65     4  \n",
       "554   https://a.espncdn.com/i/teamlogos/nhl/500/sea.png  7.67  200.03     5  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_XG_Against.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "xGAgainst_FINAL = merged_df_XG_Against[['Rank','Team', 'name', 'TOI', 'xG']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>name</th>\n",
       "      <th>TOI</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>Luostarinen-Lundell-Rodrigues</td>\n",
       "      <td>222.52</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>2</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/det.png</td>\n",
       "      <td>Perron-Larkin-Raymond</td>\n",
       "      <td>205.88</td>\n",
       "      <td>7.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>3</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/ari.png</td>\n",
       "      <td>Schmaltz-Kerfoot-Keller</td>\n",
       "      <td>204.95</td>\n",
       "      <td>7.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>4</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>Benn-Johnston-Stankoven</td>\n",
       "      <td>226.65</td>\n",
       "      <td>7.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>5</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sea.png</td>\n",
       "      <td>Tatar-Beniers-Eberle</td>\n",
       "      <td>200.03</td>\n",
       "      <td>7.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>6</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>Tkachuk-Bennett-Cousins</td>\n",
       "      <td>203.82</td>\n",
       "      <td>7.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>7</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png</td>\n",
       "      <td>Ehlers-Scheifele-Vilardi</td>\n",
       "      <td>207.82</td>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>8</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/chi.png</td>\n",
       "      <td>Blackwell-Dickinson-Anderson</td>\n",
       "      <td>264.05</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>9</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/phi.png</td>\n",
       "      <td>Foerster-Couturier-Konecny</td>\n",
       "      <td>229.10</td>\n",
       "      <td>8.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>10</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>Kreider-Zibanejad-Kakko</td>\n",
       "      <td>222.27</td>\n",
       "      <td>8.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>11</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>Verhaeghe-Barkov-Reinhart</td>\n",
       "      <td>209.38</td>\n",
       "      <td>8.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>12</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nj.png</td>\n",
       "      <td>Palat-Hischier-Bratt</td>\n",
       "      <td>265.80</td>\n",
       "      <td>8.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>13</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/car.png</td>\n",
       "      <td>Teravainen-Aho-Svechnikov</td>\n",
       "      <td>226.43</td>\n",
       "      <td>8.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1812</th>\n",
       "      <td>14</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>Vesey-Goodrow-Pitlick</td>\n",
       "      <td>234.27</td>\n",
       "      <td>8.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>15</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>Sherwood-Mccarron-Smith</td>\n",
       "      <td>256.85</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rank                                               Team  \\\n",
       "49       1  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "386      2  https://a.espncdn.com/i/teamlogos/nhl/500/det.png   \n",
       "881      3  https://a.espncdn.com/i/teamlogos/nhl/500/ari.png   \n",
       "1479     4  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "554      5  https://a.espncdn.com/i/teamlogos/nhl/500/sea.png   \n",
       "877      6  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "1738     7  https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png   \n",
       "343      8  https://a.espncdn.com/i/teamlogos/nhl/500/chi.png   \n",
       "113      9  https://a.espncdn.com/i/teamlogos/nhl/500/phi.png   \n",
       "1481    10  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png   \n",
       "1759    11  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "101     12   https://a.espncdn.com/i/teamlogos/nhl/500/nj.png   \n",
       "431     13  https://a.espncdn.com/i/teamlogos/nhl/500/car.png   \n",
       "1812    14  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png   \n",
       "1312    15  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png   \n",
       "\n",
       "                               name     TOI    xG  \n",
       "49    Luostarinen-Lundell-Rodrigues  222.52  6.60  \n",
       "386           Perron-Larkin-Raymond  205.88  7.07  \n",
       "881         Schmaltz-Kerfoot-Keller  204.95  7.49  \n",
       "1479        Benn-Johnston-Stankoven  226.65  7.56  \n",
       "554            Tatar-Beniers-Eberle  200.03  7.67  \n",
       "877         Tkachuk-Bennett-Cousins  203.82  7.68  \n",
       "1738       Ehlers-Scheifele-Vilardi  207.82  7.74  \n",
       "343    Blackwell-Dickinson-Anderson  264.05  7.80  \n",
       "113      Foerster-Couturier-Konecny  229.10  8.02  \n",
       "1481        Kreider-Zibanejad-Kakko  222.27  8.17  \n",
       "1759      Verhaeghe-Barkov-Reinhart  209.38  8.23  \n",
       "101            Palat-Hischier-Bratt  265.80  8.32  \n",
       "431       Teravainen-Aho-Svechnikov  226.43  8.45  \n",
       "1812          Vesey-Goodrow-Pitlick  234.27  8.54  \n",
       "1312        Sherwood-Mccarron-Smith  256.85  9.14  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xGAgainst_FINAL.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "xGAgainst_DisplayFINAL=xGAgainst_FINAL.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome, now time to display final results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_147b3 caption {\n",
       "  caption-side: top;\n",
       "  font-weight: bold;\n",
       "  font-size: 16px;\n",
       "  color: black;\n",
       "  background-color: white;\n",
       "}\n",
       "#T_147b3 th {\n",
       "  font-weight: bold;\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_147b3 th.colxG::before {\n",
       "  content: \"xG Against\";\n",
       "  font-weight: bold;\n",
       "}\n",
       "#T_147b3 td.col0 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_147b3 td.col1 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_147b3 td.col2 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_147b3 td.col3 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_147b3_row0_col4 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_147b3_row1_col4 {\n",
       "  background-color: #1460a8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_147b3_row2_col4 {\n",
       "  background-color: #3b8bc2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_147b3_row3_col4 {\n",
       "  background-color: #4292c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_147b3_row4_col4 {\n",
       "  background-color: #519ccc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_147b3_row5_col4 {\n",
       "  background-color: #529dcc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_147b3_row6_col4 {\n",
       "  background-color: #5aa2cf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_147b3_row7_col4 {\n",
       "  background-color: #61a7d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_147b3_row8_col4 {\n",
       "  background-color: #84bcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_147b3_row9_col4 {\n",
       "  background-color: #9cc9e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_147b3_row10_col4 {\n",
       "  background-color: #a4cce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_147b3_row11_col4 {\n",
       "  background-color: #afd1e7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_147b3_row12_col4 {\n",
       "  background-color: #bfd8ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_147b3_row13_col4 {\n",
       "  background-color: #c9ddf0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_147b3_row14_col4 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_147b3\">\n",
       "  <caption>Top 15 Forward Lines by Expected Goals Against (xG Against)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_147b3_level0_col0\" class=\"col_heading level0 col0\" >Rank</th>\n",
       "      <th id=\"T_147b3_level0_col1\" class=\"col_heading level0 col1\" >Team</th>\n",
       "      <th id=\"T_147b3_level0_col2\" class=\"col_heading level0 col2\" >Forward Line</th>\n",
       "      <th id=\"T_147b3_level0_col3\" class=\"col_heading level0 col3\" >TOI</th>\n",
       "      <th id=\"T_147b3_level0_col4\" class=\"col_heading level0 col4\" >xG Against</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row0\" class=\"row_heading level0 row0\" >49</th>\n",
       "      <td id=\"T_147b3_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_147b3_row0_col1\" class=\"data row0 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/fla.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row0_col2\" class=\"data row0 col2\" >Luostarinen-Lundell-Rodrigues</td>\n",
       "      <td id=\"T_147b3_row0_col3\" class=\"data row0 col3\" >222.52</td>\n",
       "      <td id=\"T_147b3_row0_col4\" class=\"data row0 col4\" >6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row1\" class=\"row_heading level0 row1\" >386</th>\n",
       "      <td id=\"T_147b3_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_147b3_row1_col1\" class=\"data row1 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/det.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row1_col2\" class=\"data row1 col2\" >Perron-Larkin-Raymond</td>\n",
       "      <td id=\"T_147b3_row1_col3\" class=\"data row1 col3\" >205.88</td>\n",
       "      <td id=\"T_147b3_row1_col4\" class=\"data row1 col4\" >7.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row2\" class=\"row_heading level0 row2\" >881</th>\n",
       "      <td id=\"T_147b3_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_147b3_row2_col1\" class=\"data row2 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/ari.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row2_col2\" class=\"data row2 col2\" >Schmaltz-Kerfoot-Keller</td>\n",
       "      <td id=\"T_147b3_row2_col3\" class=\"data row2 col3\" >204.95</td>\n",
       "      <td id=\"T_147b3_row2_col4\" class=\"data row2 col4\" >7.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row3\" class=\"row_heading level0 row3\" >1479</th>\n",
       "      <td id=\"T_147b3_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_147b3_row3_col1\" class=\"data row3 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/dal.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row3_col2\" class=\"data row3 col2\" >Benn-Johnston-Stankoven</td>\n",
       "      <td id=\"T_147b3_row3_col3\" class=\"data row3 col3\" >226.65</td>\n",
       "      <td id=\"T_147b3_row3_col4\" class=\"data row3 col4\" >7.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row4\" class=\"row_heading level0 row4\" >554</th>\n",
       "      <td id=\"T_147b3_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_147b3_row4_col1\" class=\"data row4 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/sea.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row4_col2\" class=\"data row4 col2\" >Tatar-Beniers-Eberle</td>\n",
       "      <td id=\"T_147b3_row4_col3\" class=\"data row4 col3\" >200.03</td>\n",
       "      <td id=\"T_147b3_row4_col4\" class=\"data row4 col4\" >7.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row5\" class=\"row_heading level0 row5\" >877</th>\n",
       "      <td id=\"T_147b3_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_147b3_row5_col1\" class=\"data row5 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/fla.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row5_col2\" class=\"data row5 col2\" >Tkachuk-Bennett-Cousins</td>\n",
       "      <td id=\"T_147b3_row5_col3\" class=\"data row5 col3\" >203.82</td>\n",
       "      <td id=\"T_147b3_row5_col4\" class=\"data row5 col4\" >7.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row6\" class=\"row_heading level0 row6\" >1738</th>\n",
       "      <td id=\"T_147b3_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_147b3_row6_col1\" class=\"data row6 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row6_col2\" class=\"data row6 col2\" >Ehlers-Scheifele-Vilardi</td>\n",
       "      <td id=\"T_147b3_row6_col3\" class=\"data row6 col3\" >207.82</td>\n",
       "      <td id=\"T_147b3_row6_col4\" class=\"data row6 col4\" >7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row7\" class=\"row_heading level0 row7\" >343</th>\n",
       "      <td id=\"T_147b3_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_147b3_row7_col1\" class=\"data row7 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/chi.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row7_col2\" class=\"data row7 col2\" >Blackwell-Dickinson-Anderson</td>\n",
       "      <td id=\"T_147b3_row7_col3\" class=\"data row7 col3\" >264.05</td>\n",
       "      <td id=\"T_147b3_row7_col4\" class=\"data row7 col4\" >7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row8\" class=\"row_heading level0 row8\" >113</th>\n",
       "      <td id=\"T_147b3_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_147b3_row8_col1\" class=\"data row8 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/phi.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row8_col2\" class=\"data row8 col2\" >Foerster-Couturier-Konecny</td>\n",
       "      <td id=\"T_147b3_row8_col3\" class=\"data row8 col3\" >229.10</td>\n",
       "      <td id=\"T_147b3_row8_col4\" class=\"data row8 col4\" >8.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row9\" class=\"row_heading level0 row9\" >1481</th>\n",
       "      <td id=\"T_147b3_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_147b3_row9_col1\" class=\"data row9 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row9_col2\" class=\"data row9 col2\" >Kreider-Zibanejad-Kakko</td>\n",
       "      <td id=\"T_147b3_row9_col3\" class=\"data row9 col3\" >222.27</td>\n",
       "      <td id=\"T_147b3_row9_col4\" class=\"data row9 col4\" >8.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row10\" class=\"row_heading level0 row10\" >1759</th>\n",
       "      <td id=\"T_147b3_row10_col0\" class=\"data row10 col0\" >11</td>\n",
       "      <td id=\"T_147b3_row10_col1\" class=\"data row10 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/fla.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row10_col2\" class=\"data row10 col2\" >Verhaeghe-Barkov-Reinhart</td>\n",
       "      <td id=\"T_147b3_row10_col3\" class=\"data row10 col3\" >209.38</td>\n",
       "      <td id=\"T_147b3_row10_col4\" class=\"data row10 col4\" >8.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row11\" class=\"row_heading level0 row11\" >101</th>\n",
       "      <td id=\"T_147b3_row11_col0\" class=\"data row11 col0\" >12</td>\n",
       "      <td id=\"T_147b3_row11_col1\" class=\"data row11 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/nj.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row11_col2\" class=\"data row11 col2\" >Palat-Hischier-Bratt</td>\n",
       "      <td id=\"T_147b3_row11_col3\" class=\"data row11 col3\" >265.80</td>\n",
       "      <td id=\"T_147b3_row11_col4\" class=\"data row11 col4\" >8.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row12\" class=\"row_heading level0 row12\" >431</th>\n",
       "      <td id=\"T_147b3_row12_col0\" class=\"data row12 col0\" >13</td>\n",
       "      <td id=\"T_147b3_row12_col1\" class=\"data row12 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/car.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row12_col2\" class=\"data row12 col2\" >Teravainen-Aho-Svechnikov</td>\n",
       "      <td id=\"T_147b3_row12_col3\" class=\"data row12 col3\" >226.43</td>\n",
       "      <td id=\"T_147b3_row12_col4\" class=\"data row12 col4\" >8.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row13\" class=\"row_heading level0 row13\" >1812</th>\n",
       "      <td id=\"T_147b3_row13_col0\" class=\"data row13 col0\" >14</td>\n",
       "      <td id=\"T_147b3_row13_col1\" class=\"data row13 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row13_col2\" class=\"data row13 col2\" >Vesey-Goodrow-Pitlick</td>\n",
       "      <td id=\"T_147b3_row13_col3\" class=\"data row13 col3\" >234.27</td>\n",
       "      <td id=\"T_147b3_row13_col4\" class=\"data row13 col4\" >8.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_147b3_level0_row14\" class=\"row_heading level0 row14\" >1312</th>\n",
       "      <td id=\"T_147b3_row14_col0\" class=\"data row14 col0\" >15</td>\n",
       "      <td id=\"T_147b3_row14_col1\" class=\"data row14 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_147b3_row14_col2\" class=\"data row14 col2\" >Sherwood-Mccarron-Smith</td>\n",
       "      <td id=\"T_147b3_row14_col3\" class=\"data row14 col3\" >256.85</td>\n",
       "      <td id=\"T_147b3_row14_col4\" class=\"data row14 col4\" >9.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p style=\"text-align: left; padding-left: 60px; font-size: 14px; font-weight: bold; color: black; background-color: white; margin: 0;\">Created by Nick Sofianakos, *TOI is in minutes </p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom function to render logos as images in the DataFrame\n",
    "def render_logo(logo_url):\n",
    "    return f'<img src=\"{logo_url}\" width=\"40\" height=\"40\">'\n",
    "\n",
    "# Applying the custom rendering and styling\n",
    "styled_df = (\n",
    "    xGAgainst_DisplayFINAL.style\n",
    "    .set_caption(\"Top 15 Forward Lines by Expected Goals Against (xG Against)\")  # Title of the DataFrame\n",
    "    .set_table_styles([\n",
    "        # Style for the title\n",
    "        {'selector': 'caption', 'props': [('caption-side', 'top'), ('font-weight', 'bold'), ('font-size', '16px'), ('color', 'black'), ('background-color', 'white')]},\n",
    "        # Mark the table with my name\n",
    "        # Style for the column headers\n",
    "        {'selector': 'th', 'props': [('font-weight', 'bold'), ('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'th.colxG::before', 'props': [('content', '\"xG Against\"'), ('font-weight', 'bold')]},\n",
    "        # Style for Player1, Player2, and Player3 columns\n",
    "        {'selector': 'td.col0', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col1', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col2', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col3', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "    ])\n",
    "    .background_gradient(subset=['xG'], cmap='Blues_r')  # Gradient color for xG column\n",
    "    .format({'Team': render_logo}) # Apply the render_logo function to the Team_Logo column\n",
    ")\n",
    "\n",
    "# Render the DataFrame to HTML\n",
    "html_output = styled_df.to_html()\n",
    "\n",
    "\n",
    "# Replace the 'xG' header with 'xG For'\n",
    "html_output = html_output.replace('>xG<', '>xG Against<')\n",
    "html_output = html_output.replace('>name<', '>Forward Line<')\n",
    "\n",
    "# Add a bottom caption directly in HTML\n",
    "bottom_caption = '<p style=\"text-align: left; padding-left: 60px; font-size: 14px; font-weight: bold; color: black; background-color: white; margin: 0;\">Created by Nick Sofianakos, *TOI is in minutes </p>'\n",
    "\n",
    "# Combine the HTML table and bottom caption\n",
    "full_html = html_output + bottom_caption\n",
    "\n",
    "# Display the modified HTML\n",
    "display(HTML(full_html))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One last table, the ultimate end goal. The best overall forward lines in hockey by xG%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting a combined table with xG For and xG Against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>name</th>\n",
       "      <th>TOI</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>1</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png</td>\n",
       "      <td>Lafreniere-Trocheck-Panarin</td>\n",
       "      <td>863.08</td>\n",
       "      <td>42.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>2</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png</td>\n",
       "      <td>Forsberg-O'Reilly-Nyquist</td>\n",
       "      <td>766.47</td>\n",
       "      <td>38.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>3</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>Kempe-Kopitar-Byfield</td>\n",
       "      <td>638.68</td>\n",
       "      <td>36.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>4</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>Stamkos-Point-Kucherov</td>\n",
       "      <td>314.00</td>\n",
       "      <td>34.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>5</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>Robertson-Pavelski-Hintz</td>\n",
       "      <td>718.75</td>\n",
       "      <td>33.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank                                               Team  \\\n",
       "830     1  https://a.espncdn.com/i/teamlogos/nhl/500/nyr.png   \n",
       "640     2  https://a.espncdn.com/i/teamlogos/nhl/500/nsh.png   \n",
       "758     3   https://a.espncdn.com/i/teamlogos/nhl/500/la.png   \n",
       "802     4   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "835     5  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "\n",
       "                            name     TOI     xG  \n",
       "830  Lafreniere-Trocheck-Panarin  863.08  42.19  \n",
       "640    Forsberg-O'Reilly-Nyquist  766.47  38.68  \n",
       "758        Kempe-Kopitar-Byfield  638.68  36.43  \n",
       "802       Stamkos-Point-Kucherov  314.00  34.91  \n",
       "835     Robertson-Pavelski-Hintz  718.75  33.45  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xGFor_FINAL.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>name</th>\n",
       "      <th>TOI</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>Luostarinen-Lundell-Rodrigues</td>\n",
       "      <td>222.52</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>2</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/det.png</td>\n",
       "      <td>Perron-Larkin-Raymond</td>\n",
       "      <td>205.88</td>\n",
       "      <td>7.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>3</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/ari.png</td>\n",
       "      <td>Schmaltz-Kerfoot-Keller</td>\n",
       "      <td>204.95</td>\n",
       "      <td>7.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>4</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>Benn-Johnston-Stankoven</td>\n",
       "      <td>226.65</td>\n",
       "      <td>7.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>5</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sea.png</td>\n",
       "      <td>Tatar-Beniers-Eberle</td>\n",
       "      <td>200.03</td>\n",
       "      <td>7.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rank                                               Team  \\\n",
       "49       1  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "386      2  https://a.espncdn.com/i/teamlogos/nhl/500/det.png   \n",
       "881      3  https://a.espncdn.com/i/teamlogos/nhl/500/ari.png   \n",
       "1479     4  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "554      5  https://a.espncdn.com/i/teamlogos/nhl/500/sea.png   \n",
       "\n",
       "                               name     TOI    xG  \n",
       "49    Luostarinen-Lundell-Rodrigues  222.52  6.60  \n",
       "386           Perron-Larkin-Raymond  205.88  7.07  \n",
       "881         Schmaltz-Kerfoot-Keller  204.95  7.49  \n",
       "1479        Benn-Johnston-Stankoven  226.65  7.56  \n",
       "554            Tatar-Beniers-Eberle  200.03  7.67  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xGAgainst_FINAL.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now merge each in separate instances witht the moneypuck. First the xg For secondly the xG Against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "Perc_merged_df_XG_For = pd.merge(monpuck, xg_ForwardLine_For, on=['Player1', 'Player2', 'Player3'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "Perc_merged_df_XG_Against = pd.merge(\n",
    "    monpuck,\n",
    "    xg_ForwardLine_Against,\n",
    "    left_on=['Player1', 'Player2', 'Player3'], \n",
    "    right_on=['Opp_Player1', 'Opp_Player2', 'Opp_Player3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing each perc merged df to make sure they are the same,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG_For</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>847793384801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Reinhart</td>\n",
       "      <td>FLA</td>\n",
       "      <td>line</td>\n",
       "      <td>7819</td>\n",
       "      <td>172</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>REINHART</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>6.498608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>847579884793168480821</td>\n",
       "      <td>Kunin-Granlund-Zadina</td>\n",
       "      <td>SJS</td>\n",
       "      <td>line</td>\n",
       "      <td>1073</td>\n",
       "      <td>65</td>\n",
       "      <td>7</td>\n",
       "      <td>5on5</td>\n",
       "      <td>GRANLUND</td>\n",
       "      <td>KUNIN</td>\n",
       "      <td>ZADINA</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sj.png</td>\n",
       "      <td>0.689780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>847793984784838479318</td>\n",
       "      <td>Marner-Matthews-Nylander</td>\n",
       "      <td>TOR</td>\n",
       "      <td>line</td>\n",
       "      <td>2425</td>\n",
       "      <td>346</td>\n",
       "      <td>31</td>\n",
       "      <td>5on5</td>\n",
       "      <td>MARNER</td>\n",
       "      <td>MATTHEWS</td>\n",
       "      <td>NYLANDER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tor.png</td>\n",
       "      <td>4.962447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>847595884816568482148</td>\n",
       "      <td>Protas-Lapierre-Sgarbossa</td>\n",
       "      <td>WSH</td>\n",
       "      <td>line</td>\n",
       "      <td>821</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>5on5</td>\n",
       "      <td>LAPIERRE</td>\n",
       "      <td>PROTAS</td>\n",
       "      <td>SGARBOSSA</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wsh.png</td>\n",
       "      <td>0.326855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>847821184784218479291</td>\n",
       "      <td>Greer-Rooney-Hunt</td>\n",
       "      <td>CGY</td>\n",
       "      <td>line</td>\n",
       "      <td>1808</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>5on5</td>\n",
       "      <td>GREER</td>\n",
       "      <td>HUNT</td>\n",
       "      <td>ROONEY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png</td>\n",
       "      <td>1.214996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 lineId                          name team  \\\n",
       "0           0  847793384801858482113  Luostarinen-Lundell-Reinhart  FLA   \n",
       "1           2  847579884793168480821         Kunin-Granlund-Zadina  SJS   \n",
       "2           4  847793984784838479318      Marner-Matthews-Nylander  TOR   \n",
       "3           5  847595884816568482148     Protas-Lapierre-Sgarbossa  WSH   \n",
       "4           8  847821184784218479291             Greer-Rooney-Hunt  CGY   \n",
       "\n",
       "  position  icetime  iceTimeRank  games_played situation   Player1  \\\n",
       "0     line     7819          172            24      5on5   LUNDELL   \n",
       "1     line     1073           65             7      5on5  GRANLUND   \n",
       "2     line     2425          346            31      5on5    MARNER   \n",
       "3     line      821           30             4      5on5  LAPIERRE   \n",
       "4     line     1808           23             5      5on5     GREER   \n",
       "\n",
       "       Player2    Player3                                               Team  \\\n",
       "0  LUOSTARINEN   REINHART  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "1        KUNIN     ZADINA   https://a.espncdn.com/i/teamlogos/nhl/500/sj.png   \n",
       "2     MATTHEWS   NYLANDER  https://a.espncdn.com/i/teamlogos/nhl/500/tor.png   \n",
       "3       PROTAS  SGARBOSSA  https://a.espncdn.com/i/teamlogos/nhl/500/wsh.png   \n",
       "4         HUNT     ROONEY  https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png   \n",
       "\n",
       "     xG_For  \n",
       "0  6.498608  \n",
       "1  0.689780  \n",
       "2  4.962447  \n",
       "3  0.326855  \n",
       "4  1.214996  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Perc_merged_df_XG_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the xG column in xGFor to xG_For"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "Perc_merged_df_XG_For.rename(columns={'xG':'xG_For'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>icetime</th>\n",
       "      <th>iceTimeRank</th>\n",
       "      <th>games_played</th>\n",
       "      <th>situation</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Opp_Player1</th>\n",
       "      <th>Opp_Player2</th>\n",
       "      <th>Opp_Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>1606</td>\n",
       "      <td>847060484785698480205</td>\n",
       "      <td>Carter-Acciari-Bemstrom</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>1891</td>\n",
       "      <td>54</td>\n",
       "      <td>6</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.654905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>966</td>\n",
       "      <td>847856984793448480205</td>\n",
       "      <td>Bemstrom-Acciari-Puljujarvi</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>957</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>0.477964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>2660</td>\n",
       "      <td>847060484741898478569</td>\n",
       "      <td>Eller-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>986</td>\n",
       "      <td>161</td>\n",
       "      <td>17</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.024431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>1713</td>\n",
       "      <td>847060484785698480836</td>\n",
       "      <td>Gruden-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>949</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>GRUDEN</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>GRUDEN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.040314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>355</td>\n",
       "      <td>847060484784248478569</td>\n",
       "      <td>Harkins-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>line</td>\n",
       "      <td>8435</td>\n",
       "      <td>95</td>\n",
       "      <td>24</td>\n",
       "      <td>5on5</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>HARKINS</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>HARKINS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>4.574167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 lineId                         name team  \\\n",
       "1049        1606  847060484785698480205      Carter-Acciari-Bemstrom  PIT   \n",
       "631          966  847856984793448480205  Bemstrom-Acciari-Puljujarvi  PIT   \n",
       "1733        2660  847060484741898478569         Eller-Acciari-Carter  PIT   \n",
       "1118        1713  847060484785698480836        Gruden-Acciari-Carter  PIT   \n",
       "237          355  847060484784248478569       Harkins-Acciari-Carter  PIT   \n",
       "\n",
       "     position  icetime  iceTimeRank  games_played situation  Player1  \\\n",
       "1049     line     1891           54             6      5on5  ACCIARI   \n",
       "631      line      957           28             4      5on5  ACCIARI   \n",
       "1733     line      986          161            17      5on5  ACCIARI   \n",
       "1118     line      949           15             3      5on5  ACCIARI   \n",
       "237      line     8435           95            24      5on5  ACCIARI   \n",
       "\n",
       "       Player2     Player3 Opp_Player1 Opp_Player2 Opp_Player3  \\\n",
       "1049  BEMSTROM      CARTER     ACCIARI    BEMSTROM      CARTER   \n",
       "631   BEMSTROM  PULJUJARVI     ACCIARI    BEMSTROM  PULJUJARVI   \n",
       "1733    CARTER       ELLER     ACCIARI      CARTER       ELLER   \n",
       "1118    CARTER      GRUDEN     ACCIARI      CARTER      GRUDEN   \n",
       "237     CARTER     HARKINS     ACCIARI      CARTER     HARKINS   \n",
       "\n",
       "                                                   Team        xG  \n",
       "1049  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  1.654905  \n",
       "631   https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  0.477964  \n",
       "1733  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  1.024431  \n",
       "1118  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  1.040314  \n",
       "237   https://a.espncdn.com/i/teamlogos/nhl/500/pit.png  4.574167  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Perc_merged_df_XG_Against.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Duplicates again for some reason. Getting rid of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Sort the DataFrame by `Player1`, `Player2`, `Player3`, and `xG_Against` in descending order\n",
    "Perc_merged_df_XG_Against = Perc_merged_df_XG_Against.sort_values(by=['Player1', 'Player2', 'Player3', 'xG'], ascending=[True, True, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Drop duplicates based on `Player1`, `Player2`, and `Player3`, keeping the first occurrence\n",
    "Perc_merged_df_XG_Against = Perc_merged_df_XG_Against.drop_duplicates(subset=['Player1', 'Player2', 'Player3'], keep='first')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping opp_player columns from xg against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "Perc_merged_df_XG_Against.drop(['Opp_Player1','Opp_Player2','Opp_Player3'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename xG column in xG Against df to xG_Against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "Perc_merged_df_XG_Against.rename(columns={'xG':'xG_Against'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop unwanted columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "Perc_merged_df_XG_Against.drop(['Unnamed: 0','position','situation','iceTimeRank'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>icetime</th>\n",
       "      <th>games_played</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG_Against</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>847060484785698480205</td>\n",
       "      <td>Carter-Acciari-Bemstrom</td>\n",
       "      <td>PIT</td>\n",
       "      <td>1891</td>\n",
       "      <td>6</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.654905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>847856984793448480205</td>\n",
       "      <td>Bemstrom-Acciari-Puljujarvi</td>\n",
       "      <td>PIT</td>\n",
       "      <td>957</td>\n",
       "      <td>4</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>BEMSTROM</td>\n",
       "      <td>PULJUJARVI</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>0.477964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>847060484741898478569</td>\n",
       "      <td>Eller-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>986</td>\n",
       "      <td>17</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>ELLER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.024431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>847060484785698480836</td>\n",
       "      <td>Gruden-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>949</td>\n",
       "      <td>3</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>GRUDEN</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>1.040314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>847060484784248478569</td>\n",
       "      <td>Harkins-Acciari-Carter</td>\n",
       "      <td>PIT</td>\n",
       "      <td>8435</td>\n",
       "      <td>24</td>\n",
       "      <td>ACCIARI</td>\n",
       "      <td>CARTER</td>\n",
       "      <td>HARKINS</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/pit.png</td>\n",
       "      <td>4.574167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lineId                         name team  icetime  \\\n",
       "1049  847060484785698480205      Carter-Acciari-Bemstrom  PIT     1891   \n",
       "631   847856984793448480205  Bemstrom-Acciari-Puljujarvi  PIT      957   \n",
       "1733  847060484741898478569         Eller-Acciari-Carter  PIT      986   \n",
       "1118  847060484785698480836        Gruden-Acciari-Carter  PIT      949   \n",
       "237   847060484784248478569       Harkins-Acciari-Carter  PIT     8435   \n",
       "\n",
       "      games_played  Player1   Player2     Player3  \\\n",
       "1049             6  ACCIARI  BEMSTROM      CARTER   \n",
       "631              4  ACCIARI  BEMSTROM  PULJUJARVI   \n",
       "1733            17  ACCIARI    CARTER       ELLER   \n",
       "1118             3  ACCIARI    CARTER      GRUDEN   \n",
       "237             24  ACCIARI    CARTER     HARKINS   \n",
       "\n",
       "                                                   Team  xG_Against  \n",
       "1049  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png    1.654905  \n",
       "631   https://a.espncdn.com/i/teamlogos/nhl/500/pit.png    0.477964  \n",
       "1733  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png    1.024431  \n",
       "1118  https://a.espncdn.com/i/teamlogos/nhl/500/pit.png    1.040314  \n",
       "237   https://a.espncdn.com/i/teamlogos/nhl/500/pit.png    4.574167  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Perc_merged_df_XG_Against.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "Perc_merged_df_XG_For.drop(['Unnamed: 0','position','situation','iceTimeRank'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>icetime</th>\n",
       "      <th>games_played</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG_For</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>847793384801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Reinhart</td>\n",
       "      <td>FLA</td>\n",
       "      <td>7819</td>\n",
       "      <td>24</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>REINHART</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>6.498608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>847579884793168480821</td>\n",
       "      <td>Kunin-Granlund-Zadina</td>\n",
       "      <td>SJS</td>\n",
       "      <td>1073</td>\n",
       "      <td>7</td>\n",
       "      <td>GRANLUND</td>\n",
       "      <td>KUNIN</td>\n",
       "      <td>ZADINA</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sj.png</td>\n",
       "      <td>0.689780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>847793984784838479318</td>\n",
       "      <td>Marner-Matthews-Nylander</td>\n",
       "      <td>TOR</td>\n",
       "      <td>2425</td>\n",
       "      <td>31</td>\n",
       "      <td>MARNER</td>\n",
       "      <td>MATTHEWS</td>\n",
       "      <td>NYLANDER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tor.png</td>\n",
       "      <td>4.962447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>847595884816568482148</td>\n",
       "      <td>Protas-Lapierre-Sgarbossa</td>\n",
       "      <td>WSH</td>\n",
       "      <td>821</td>\n",
       "      <td>4</td>\n",
       "      <td>LAPIERRE</td>\n",
       "      <td>PROTAS</td>\n",
       "      <td>SGARBOSSA</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wsh.png</td>\n",
       "      <td>0.326855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847821184784218479291</td>\n",
       "      <td>Greer-Rooney-Hunt</td>\n",
       "      <td>CGY</td>\n",
       "      <td>1808</td>\n",
       "      <td>5</td>\n",
       "      <td>GREER</td>\n",
       "      <td>HUNT</td>\n",
       "      <td>ROONEY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png</td>\n",
       "      <td>1.214996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lineId                          name team  icetime  \\\n",
       "0  847793384801858482113  Luostarinen-Lundell-Reinhart  FLA     7819   \n",
       "1  847579884793168480821         Kunin-Granlund-Zadina  SJS     1073   \n",
       "2  847793984784838479318      Marner-Matthews-Nylander  TOR     2425   \n",
       "3  847595884816568482148     Protas-Lapierre-Sgarbossa  WSH      821   \n",
       "4  847821184784218479291             Greer-Rooney-Hunt  CGY     1808   \n",
       "\n",
       "   games_played   Player1      Player2    Player3  \\\n",
       "0            24   LUNDELL  LUOSTARINEN   REINHART   \n",
       "1             7  GRANLUND        KUNIN     ZADINA   \n",
       "2            31    MARNER     MATTHEWS   NYLANDER   \n",
       "3             4  LAPIERRE       PROTAS  SGARBOSSA   \n",
       "4             5     GREER         HUNT     ROONEY   \n",
       "\n",
       "                                                Team    xG_For  \n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  6.498608  \n",
       "1   https://a.espncdn.com/i/teamlogos/nhl/500/sj.png  0.689780  \n",
       "2  https://a.espncdn.com/i/teamlogos/nhl/500/tor.png  4.962447  \n",
       "3  https://a.espncdn.com/i/teamlogos/nhl/500/wsh.png  0.326855  \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png  1.214996  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Perc_merged_df_XG_For.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some reason we are getting duplicate lines in the perc_merged_df_xg dataframe, one line with a really low xG Against which is wrong, and then one row with an xG against that is higher and correct.The later row in the dataframe of the duplicates always contains the correct xG Against. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_plus = pd.merge(\n",
    "    Perc_merged_df_XG_For, \n",
    "    Perc_merged_df_XG_Against, \n",
    "    on=['Player1', 'Player2', 'Player3','Team', 'games_played','icetime','team','name','lineId'], \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>icetime</th>\n",
       "      <th>games_played</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG_For</th>\n",
       "      <th>xG_Against</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>847793384801858482113</td>\n",
       "      <td>Luostarinen-Lundell-Reinhart</td>\n",
       "      <td>FLA</td>\n",
       "      <td>7819</td>\n",
       "      <td>24</td>\n",
       "      <td>LUNDELL</td>\n",
       "      <td>LUOSTARINEN</td>\n",
       "      <td>REINHART</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>6.498608</td>\n",
       "      <td>4.965068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>847579884793168480821</td>\n",
       "      <td>Kunin-Granlund-Zadina</td>\n",
       "      <td>SJS</td>\n",
       "      <td>1073</td>\n",
       "      <td>7</td>\n",
       "      <td>GRANLUND</td>\n",
       "      <td>KUNIN</td>\n",
       "      <td>ZADINA</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/sj.png</td>\n",
       "      <td>0.689780</td>\n",
       "      <td>1.253502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>847793984784838479318</td>\n",
       "      <td>Marner-Matthews-Nylander</td>\n",
       "      <td>TOR</td>\n",
       "      <td>2425</td>\n",
       "      <td>31</td>\n",
       "      <td>MARNER</td>\n",
       "      <td>MATTHEWS</td>\n",
       "      <td>NYLANDER</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tor.png</td>\n",
       "      <td>4.962447</td>\n",
       "      <td>2.785240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>847595884816568482148</td>\n",
       "      <td>Protas-Lapierre-Sgarbossa</td>\n",
       "      <td>WSH</td>\n",
       "      <td>821</td>\n",
       "      <td>4</td>\n",
       "      <td>LAPIERRE</td>\n",
       "      <td>PROTAS</td>\n",
       "      <td>SGARBOSSA</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wsh.png</td>\n",
       "      <td>0.326855</td>\n",
       "      <td>0.354547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847821184784218479291</td>\n",
       "      <td>Greer-Rooney-Hunt</td>\n",
       "      <td>CGY</td>\n",
       "      <td>1808</td>\n",
       "      <td>5</td>\n",
       "      <td>GREER</td>\n",
       "      <td>HUNT</td>\n",
       "      <td>ROONEY</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png</td>\n",
       "      <td>1.214996</td>\n",
       "      <td>0.683377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lineId                          name team  icetime  \\\n",
       "0  847793384801858482113  Luostarinen-Lundell-Reinhart  FLA     7819   \n",
       "1  847579884793168480821         Kunin-Granlund-Zadina  SJS     1073   \n",
       "2  847793984784838479318      Marner-Matthews-Nylander  TOR     2425   \n",
       "3  847595884816568482148     Protas-Lapierre-Sgarbossa  WSH      821   \n",
       "4  847821184784218479291             Greer-Rooney-Hunt  CGY     1808   \n",
       "\n",
       "   games_played   Player1      Player2    Player3  \\\n",
       "0            24   LUNDELL  LUOSTARINEN   REINHART   \n",
       "1             7  GRANLUND        KUNIN     ZADINA   \n",
       "2            31    MARNER     MATTHEWS   NYLANDER   \n",
       "3             4  LAPIERRE       PROTAS  SGARBOSSA   \n",
       "4             5     GREER         HUNT     ROONEY   \n",
       "\n",
       "                                                Team    xG_For  xG_Against  \n",
       "0  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  6.498608    4.965068  \n",
       "1   https://a.espncdn.com/i/teamlogos/nhl/500/sj.png  0.689780    1.253502  \n",
       "2  https://a.espncdn.com/i/teamlogos/nhl/500/tor.png  4.962447    2.785240  \n",
       "3  https://a.espncdn.com/i/teamlogos/nhl/500/wsh.png  0.326855    0.354547  \n",
       "4  https://a.espncdn.com/i/teamlogos/nhl/500/cgy.png  1.214996    0.683377  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_plus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Calculate the xG%\n",
    "xg_plus['xG%'] = xg_plus['xG_For'] / (xg_plus['xG_For'] + xg_plus['xG_Against'])\n",
    "\n",
    "xg_plus = xg_plus.sort_values(by='xG%', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lineId</th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>icetime</th>\n",
       "      <th>games_played</th>\n",
       "      <th>Player1</th>\n",
       "      <th>Player2</th>\n",
       "      <th>Player3</th>\n",
       "      <th>Team</th>\n",
       "      <th>xG_For</th>\n",
       "      <th>xG_Against</th>\n",
       "      <th>xG%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>847403484752208480980</td>\n",
       "      <td>Foligno-Dewar-Maroon</td>\n",
       "      <td>MIN</td>\n",
       "      <td>647</td>\n",
       "      <td>14</td>\n",
       "      <td>DEWAR</td>\n",
       "      <td>FOLIGNO</td>\n",
       "      <td>MAROON</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/min.png</td>\n",
       "      <td>0.865115</td>\n",
       "      <td>0.113940</td>\n",
       "      <td>0.883622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>847579984764808482149</td>\n",
       "      <td>Perfetti-Namestnikov-Niederreiter</td>\n",
       "      <td>WPG</td>\n",
       "      <td>699</td>\n",
       "      <td>22</td>\n",
       "      <td>NAMESTNIKOV</td>\n",
       "      <td>NIEDERREITER</td>\n",
       "      <td>PERFETTI</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png</td>\n",
       "      <td>3.569395</td>\n",
       "      <td>0.520638</td>\n",
       "      <td>0.872706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>847845884808938481716</td>\n",
       "      <td>Roslovic-Voronkov-Marchenko</td>\n",
       "      <td>CBJ</td>\n",
       "      <td>625</td>\n",
       "      <td>2</td>\n",
       "      <td>MARCHENKO</td>\n",
       "      <td>ROSLOVIC</td>\n",
       "      <td>VORONKOV</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/cbj.png</td>\n",
       "      <td>1.165027</td>\n",
       "      <td>0.196859</td>\n",
       "      <td>0.855451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>847576584785428478831</td>\n",
       "      <td>Rodrigues-Stenlund-Tarasenko</td>\n",
       "      <td>FLA</td>\n",
       "      <td>645</td>\n",
       "      <td>3</td>\n",
       "      <td>RODRIGUES</td>\n",
       "      <td>STENLUND</td>\n",
       "      <td>TARASENKO</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>0.357154</td>\n",
       "      <td>0.061785</td>\n",
       "      <td>0.852521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>847574584784988479365</td>\n",
       "      <td>Debrusk-Coyle-Frederic</td>\n",
       "      <td>BOS</td>\n",
       "      <td>1099</td>\n",
       "      <td>20</td>\n",
       "      <td>COYLE</td>\n",
       "      <td>DEBRUSK</td>\n",
       "      <td>FREDERIC</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/bos.png</td>\n",
       "      <td>1.473449</td>\n",
       "      <td>0.267326</td>\n",
       "      <td>0.846433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lineId                               name team  icetime  \\\n",
       "1322  847403484752208480980               Foligno-Dewar-Maroon  MIN      647   \n",
       "1398  847579984764808482149  Perfetti-Namestnikov-Niederreiter  WPG      699   \n",
       "887   847845884808938481716        Roslovic-Voronkov-Marchenko  CBJ      625   \n",
       "774   847576584785428478831       Rodrigues-Stenlund-Tarasenko  FLA      645   \n",
       "209   847574584784988479365             Debrusk-Coyle-Frederic  BOS     1099   \n",
       "\n",
       "      games_played      Player1       Player2    Player3  \\\n",
       "1322            14        DEWAR       FOLIGNO     MAROON   \n",
       "1398            22  NAMESTNIKOV  NIEDERREITER   PERFETTI   \n",
       "887              2    MARCHENKO      ROSLOVIC   VORONKOV   \n",
       "774              3    RODRIGUES      STENLUND  TARASENKO   \n",
       "209             20        COYLE       DEBRUSK   FREDERIC   \n",
       "\n",
       "                                                   Team    xG_For  xG_Against  \\\n",
       "1322  https://a.espncdn.com/i/teamlogos/nhl/500/min.png  0.865115    0.113940   \n",
       "1398  https://a.espncdn.com/i/teamlogos/nhl/500/wpg.png  3.569395    0.520638   \n",
       "887   https://a.espncdn.com/i/teamlogos/nhl/500/cbj.png  1.165027    0.196859   \n",
       "774   https://a.espncdn.com/i/teamlogos/nhl/500/fla.png  0.357154    0.061785   \n",
       "209   https://a.espncdn.com/i/teamlogos/nhl/500/bos.png  1.473449    0.267326   \n",
       "\n",
       "           xG%  \n",
       "1322  0.883622  \n",
       "1398  0.872706  \n",
       "887   0.855451  \n",
       "774   0.852521  \n",
       "209   0.846433  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_plus.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, just filter by icetime. Make it greater than 200 minutes. Create TOI column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_plus['TOI']=xg_plus['icetime']/60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter by lines with more than 100 TOI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_plus=xg_plus[xg_plus['TOI']>=200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_plus['Rank'] = xg_plus['xG%'].rank(ascending=False, method='max').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_plus['xG%']=xg_plus['xG%'].round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_plus['TOI']=xg_plus['TOI'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_plus['xG%']=xg_plus['xG%'].apply(lambda x: f\"{x:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_plus['TOI']=xg_plus['TOI'].apply(lambda x: f\"{x:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reorganize columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "xGPerc_FINAL = xg_plus[['Rank','Team', 'name', 'TOI', 'xG%']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "xGPerc_DisplayFINAL=xGPerc_FINAL.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>name</th>\n",
       "      <th>TOI</th>\n",
       "      <th>xG%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1760</th>\n",
       "      <td>1</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>Verhaeghe-Barkov-Reinhart</td>\n",
       "      <td>209.38</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>2</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tb.png</td>\n",
       "      <td>Stamkos-Point-Kucherov</td>\n",
       "      <td>314.00</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>Luostarinen-Lundell-Rodrigues</td>\n",
       "      <td>222.52</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>4</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/ari.png</td>\n",
       "      <td>Schmaltz-Kerfoot-Keller</td>\n",
       "      <td>204.95</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/nj.png</td>\n",
       "      <td>Palat-Hischier-Bratt</td>\n",
       "      <td>265.80</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>6</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/edm.png</td>\n",
       "      <td>Hyman-Mcdavid-Nugent-Hopkins</td>\n",
       "      <td>467.92</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>7</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>Nichushkin-Mackinnon-Rantanen</td>\n",
       "      <td>382.00</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>8</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/fla.png</td>\n",
       "      <td>Reinhart-Barkov-Rodrigues</td>\n",
       "      <td>389.45</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>9</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/car.png</td>\n",
       "      <td>Martinook-Staal-Fast</td>\n",
       "      <td>389.65</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>10</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/phi.png</td>\n",
       "      <td>Foerster-Couturier-Konecny</td>\n",
       "      <td>229.10</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>11</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/tor.png</td>\n",
       "      <td>Knies-Matthews-Marner</td>\n",
       "      <td>404.33</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>12</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/col.png</td>\n",
       "      <td>Drouin-Mackinnon-Rantanen</td>\n",
       "      <td>485.43</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>13</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/dal.png</td>\n",
       "      <td>Benn-Johnston-Stankoven</td>\n",
       "      <td>226.65</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>14</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>Kempe-Kopitar-Byfield</td>\n",
       "      <td>638.68</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>15</td>\n",
       "      <td>https://a.espncdn.com/i/teamlogos/nhl/500/la.png</td>\n",
       "      <td>Moore-Danault-Fiala</td>\n",
       "      <td>590.12</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rank                                               Team  \\\n",
       "1760     1  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "765      2   https://a.espncdn.com/i/teamlogos/nhl/500/tb.png   \n",
       "48       3  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "882      4  https://a.espncdn.com/i/teamlogos/nhl/500/ari.png   \n",
       "100      5   https://a.espncdn.com/i/teamlogos/nhl/500/nj.png   \n",
       "1919     6  https://a.espncdn.com/i/teamlogos/nhl/500/edm.png   \n",
       "1541     7  https://a.espncdn.com/i/teamlogos/nhl/500/col.png   \n",
       "1727     8  https://a.espncdn.com/i/teamlogos/nhl/500/fla.png   \n",
       "103      9  https://a.espncdn.com/i/teamlogos/nhl/500/car.png   \n",
       "112     10  https://a.espncdn.com/i/teamlogos/nhl/500/phi.png   \n",
       "1408    11  https://a.espncdn.com/i/teamlogos/nhl/500/tor.png   \n",
       "1581    12  https://a.espncdn.com/i/teamlogos/nhl/500/col.png   \n",
       "1482    13  https://a.espncdn.com/i/teamlogos/nhl/500/dal.png   \n",
       "725     14   https://a.espncdn.com/i/teamlogos/nhl/500/la.png   \n",
       "379     15   https://a.espncdn.com/i/teamlogos/nhl/500/la.png   \n",
       "\n",
       "                               name     TOI   xG%  \n",
       "1760      Verhaeghe-Barkov-Reinhart  209.38  0.77  \n",
       "765          Stamkos-Point-Kucherov  314.00  0.68  \n",
       "48    Luostarinen-Lundell-Rodrigues  222.52  0.67  \n",
       "882         Schmaltz-Kerfoot-Keller  204.95  0.66  \n",
       "100            Palat-Hischier-Bratt  265.80  0.65  \n",
       "1919   Hyman-Mcdavid-Nugent-Hopkins  467.92  0.65  \n",
       "1541  Nichushkin-Mackinnon-Rantanen  382.00  0.64  \n",
       "1727      Reinhart-Barkov-Rodrigues  389.45  0.63  \n",
       "103            Martinook-Staal-Fast  389.65  0.62  \n",
       "112      Foerster-Couturier-Konecny  229.10  0.62  \n",
       "1408          Knies-Matthews-Marner  404.33  0.61  \n",
       "1581      Drouin-Mackinnon-Rantanen  485.43  0.61  \n",
       "1482        Benn-Johnston-Stankoven  226.65  0.60  \n",
       "725           Kempe-Kopitar-Byfield  638.68  0.59  \n",
       "379             Moore-Danault-Fiala  590.12  0.59  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xGPerc_DisplayFINAL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying the final table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_cc7a6 caption {\n",
       "  caption-side: top;\n",
       "  font-weight: bold;\n",
       "  font-size: 16px;\n",
       "  color: black;\n",
       "  background-color: white;\n",
       "}\n",
       "#T_cc7a6 th {\n",
       "  font-weight: bold;\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_cc7a6 td.col0 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_cc7a6 td.col1 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_cc7a6 td.col2 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_cc7a6 td.col3 {\n",
       "  background-color: white;\n",
       "  color: black;\n",
       "}\n",
       "#T_cc7a6_row0_col4 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cc7a6_row1_col4 {\n",
       "  background-color: #6aaed6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cc7a6_row2_col4 {\n",
       "  background-color: #82bbdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc7a6_row3_col4 {\n",
       "  background-color: #99c7e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc7a6_row4_col4, #T_cc7a6_row5_col4 {\n",
       "  background-color: #abd0e6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc7a6_row6_col4 {\n",
       "  background-color: #bdd7ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc7a6_row7_col4 {\n",
       "  background-color: #ccdff1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc7a6_row8_col4, #T_cc7a6_row9_col4 {\n",
       "  background-color: #d6e6f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc7a6_row10_col4, #T_cc7a6_row11_col4 {\n",
       "  background-color: #e1edf8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc7a6_row12_col4 {\n",
       "  background-color: #ecf4fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_cc7a6_row13_col4, #T_cc7a6_row14_col4 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cc7a6\">\n",
       "  <caption>Top 15 Forward Lines by Expected Goals Percentage (xG%)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cc7a6_level0_col0\" class=\"col_heading level0 col0\" >Rank</th>\n",
       "      <th id=\"T_cc7a6_level0_col1\" class=\"col_heading level0 col1\" >Team</th>\n",
       "      <th id=\"T_cc7a6_level0_col2\" class=\"col_heading level0 col2\" >Forward Line</th>\n",
       "      <th id=\"T_cc7a6_level0_col3\" class=\"col_heading level0 col3\" >TOI</th>\n",
       "      <th id=\"T_cc7a6_level0_col4\" class=\"col_heading level0 col4\" >xG%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_cc7a6_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_cc7a6_row0_col1\" class=\"data row0 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/fla.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row0_col2\" class=\"data row0 col2\" >Verhaeghe-Barkov-Reinhart</td>\n",
       "      <td id=\"T_cc7a6_row0_col3\" class=\"data row0 col3\" >209.38</td>\n",
       "      <td id=\"T_cc7a6_row0_col4\" class=\"data row0 col4\" >0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_cc7a6_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_cc7a6_row1_col1\" class=\"data row1 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/tb.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row1_col2\" class=\"data row1 col2\" >Stamkos-Point-Kucherov</td>\n",
       "      <td id=\"T_cc7a6_row1_col3\" class=\"data row1 col3\" >314.00</td>\n",
       "      <td id=\"T_cc7a6_row1_col4\" class=\"data row1 col4\" >0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_cc7a6_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_cc7a6_row2_col1\" class=\"data row2 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/fla.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row2_col2\" class=\"data row2 col2\" >Luostarinen-Lundell-Rodrigues</td>\n",
       "      <td id=\"T_cc7a6_row2_col3\" class=\"data row2 col3\" >222.52</td>\n",
       "      <td id=\"T_cc7a6_row2_col4\" class=\"data row2 col4\" >0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_cc7a6_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_cc7a6_row3_col1\" class=\"data row3 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/ari.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row3_col2\" class=\"data row3 col2\" >Schmaltz-Kerfoot-Keller</td>\n",
       "      <td id=\"T_cc7a6_row3_col3\" class=\"data row3 col3\" >204.95</td>\n",
       "      <td id=\"T_cc7a6_row3_col4\" class=\"data row3 col4\" >0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_cc7a6_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_cc7a6_row4_col1\" class=\"data row4 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/nj.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row4_col2\" class=\"data row4 col2\" >Palat-Hischier-Bratt</td>\n",
       "      <td id=\"T_cc7a6_row4_col3\" class=\"data row4 col3\" >265.80</td>\n",
       "      <td id=\"T_cc7a6_row4_col4\" class=\"data row4 col4\" >0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_cc7a6_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_cc7a6_row5_col1\" class=\"data row5 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/edm.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row5_col2\" class=\"data row5 col2\" >Hyman-Mcdavid-Nugent-Hopkins</td>\n",
       "      <td id=\"T_cc7a6_row5_col3\" class=\"data row5 col3\" >467.92</td>\n",
       "      <td id=\"T_cc7a6_row5_col4\" class=\"data row5 col4\" >0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_cc7a6_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_cc7a6_row6_col1\" class=\"data row6 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/col.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row6_col2\" class=\"data row6 col2\" >Nichushkin-Mackinnon-Rantanen</td>\n",
       "      <td id=\"T_cc7a6_row6_col3\" class=\"data row6 col3\" >382.00</td>\n",
       "      <td id=\"T_cc7a6_row6_col4\" class=\"data row6 col4\" >0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_cc7a6_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_cc7a6_row7_col1\" class=\"data row7 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/fla.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row7_col2\" class=\"data row7 col2\" >Reinhart-Barkov-Rodrigues</td>\n",
       "      <td id=\"T_cc7a6_row7_col3\" class=\"data row7 col3\" >389.45</td>\n",
       "      <td id=\"T_cc7a6_row7_col4\" class=\"data row7 col4\" >0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_cc7a6_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_cc7a6_row8_col1\" class=\"data row8 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/car.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row8_col2\" class=\"data row8 col2\" >Martinook-Staal-Fast</td>\n",
       "      <td id=\"T_cc7a6_row8_col3\" class=\"data row8 col3\" >389.65</td>\n",
       "      <td id=\"T_cc7a6_row8_col4\" class=\"data row8 col4\" >0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_cc7a6_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_cc7a6_row9_col1\" class=\"data row9 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/phi.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row9_col2\" class=\"data row9 col2\" >Foerster-Couturier-Konecny</td>\n",
       "      <td id=\"T_cc7a6_row9_col3\" class=\"data row9 col3\" >229.10</td>\n",
       "      <td id=\"T_cc7a6_row9_col4\" class=\"data row9 col4\" >0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_cc7a6_row10_col0\" class=\"data row10 col0\" >11</td>\n",
       "      <td id=\"T_cc7a6_row10_col1\" class=\"data row10 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/tor.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row10_col2\" class=\"data row10 col2\" >Knies-Matthews-Marner</td>\n",
       "      <td id=\"T_cc7a6_row10_col3\" class=\"data row10 col3\" >404.33</td>\n",
       "      <td id=\"T_cc7a6_row10_col4\" class=\"data row10 col4\" >0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_cc7a6_row11_col0\" class=\"data row11 col0\" >12</td>\n",
       "      <td id=\"T_cc7a6_row11_col1\" class=\"data row11 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/col.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row11_col2\" class=\"data row11 col2\" >Drouin-Mackinnon-Rantanen</td>\n",
       "      <td id=\"T_cc7a6_row11_col3\" class=\"data row11 col3\" >485.43</td>\n",
       "      <td id=\"T_cc7a6_row11_col4\" class=\"data row11 col4\" >0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_cc7a6_row12_col0\" class=\"data row12 col0\" >13</td>\n",
       "      <td id=\"T_cc7a6_row12_col1\" class=\"data row12 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/dal.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row12_col2\" class=\"data row12 col2\" >Benn-Johnston-Stankoven</td>\n",
       "      <td id=\"T_cc7a6_row12_col3\" class=\"data row12 col3\" >226.65</td>\n",
       "      <td id=\"T_cc7a6_row12_col4\" class=\"data row12 col4\" >0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_cc7a6_row13_col0\" class=\"data row13 col0\" >14</td>\n",
       "      <td id=\"T_cc7a6_row13_col1\" class=\"data row13 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/la.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row13_col2\" class=\"data row13 col2\" >Kempe-Kopitar-Byfield</td>\n",
       "      <td id=\"T_cc7a6_row13_col3\" class=\"data row13 col3\" >638.68</td>\n",
       "      <td id=\"T_cc7a6_row13_col4\" class=\"data row13 col4\" >0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cc7a6_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_cc7a6_row14_col0\" class=\"data row14 col0\" >15</td>\n",
       "      <td id=\"T_cc7a6_row14_col1\" class=\"data row14 col1\" ><img src=\"https://a.espncdn.com/i/teamlogos/nhl/500/la.png\" width=\"40\" height=\"40\"></td>\n",
       "      <td id=\"T_cc7a6_row14_col2\" class=\"data row14 col2\" >Moore-Danault-Fiala</td>\n",
       "      <td id=\"T_cc7a6_row14_col3\" class=\"data row14 col3\" >590.12</td>\n",
       "      <td id=\"T_cc7a6_row14_col4\" class=\"data row14 col4\" >0.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p style=\"text-align: left; padding-left: 60px; font-size: 14px; font-weight: bold; color: black; background-color: white; margin: 0;\">Created by Nick Sofianakos, *TOI is in minutes </p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom function to render logos as images in the DataFrame\n",
    "def render_logo(logo_url):\n",
    "    return f'<img src=\"{logo_url}\" width=\"40\" height=\"40\">'\n",
    "\n",
    "# Reset the index to remove it from the DataFrame\n",
    "xGPerc_DisplayFINAL_reset = xGPerc_DisplayFINAL.reset_index(drop=True)\n",
    "\n",
    "# Applying the custom rendering and styling\n",
    "styled_df = (\n",
    "    xGPerc_DisplayFINAL_reset.style\n",
    "    .set_caption(\"Top 15 Forward Lines by Expected Goals Percentage (xG%)\")  # Title of the DataFrame\n",
    "    .set_table_styles([\n",
    "        # Style for the title\n",
    "        {'selector': 'caption', 'props': [('caption-side', 'top'), ('font-weight', 'bold'), ('font-size', '16px'), ('color', 'black'), ('background-color', 'white')]},\n",
    "        # Mark the table with my name\n",
    "        # Style for the column headers\n",
    "        {'selector': 'th', 'props': [('font-weight', 'bold'), ('background-color', 'white'), ('color', 'black')]},\n",
    "        #{'selector': 'th.colxG::before', 'props': [('content', '\"xG Against\"'), ('font-weight', 'bold')]},\n",
    "        # Style for Player1, Player2, and Player3 columns\n",
    "        {'selector': 'td.col0', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col1', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col2', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "        {'selector': 'td.col3', 'props': [('background-color', 'white'), ('color', 'black')]},\n",
    "\n",
    "    ])\n",
    "    .background_gradient(subset=['xG%'], cmap='Blues')  # Gradient color for xG column\n",
    "    .format({'Team': render_logo}) # Apply the render_logo function to the Team_Logo column\n",
    ")\n",
    "\n",
    "# Render the DataFrame to HTML\n",
    "html_output = styled_df.to_html(index=False)\n",
    "html_output = html_output.replace('>name<', '>Forward Line<')\n",
    "\n",
    "\n",
    "# Add a bottom caption directly in HTML\n",
    "bottom_caption = '<p style=\"text-align: left; padding-left: 60px; font-size: 14px; font-weight: bold; color: black; background-color: white; margin: 0;\">Created by Nick Sofianakos, *TOI is in minutes </p>'\n",
    "\n",
    "# Combine the HTML table and bottom caption\n",
    "full_html = html_output + bottom_caption\n",
    "\n",
    "# Display the modified HTML\n",
    "display(HTML(full_html))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "penv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}