ArsenalUnited.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "272706eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mplsoccer import Pitch,Sbopen\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1713a408",
   "metadata": {},
   "outputs": [],
   "source": [
    "parser= Sbopen()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ff613f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, related, freeze, tactics = parser.event(3754171)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "976f3428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'index', 'period', 'timestamp', 'minute', 'second', 'possession',\n",
       "       'duration', 'match_id', 'type_id', 'type_name', 'possession_team_id',\n",
       "       'possession_team_name', 'play_pattern_id', 'play_pattern_name',\n",
       "       'team_id', 'team_name', 'tactics_formation', 'player_id', 'player_name',\n",
       "       'position_id', 'position_name', 'pass_recipient_id',\n",
       "       'pass_recipient_name', 'pass_length', 'pass_angle', 'pass_height_id',\n",
       "       'pass_height_name', 'end_x', 'end_y', 'body_part_id', 'body_part_name',\n",
       "       'sub_type_id', 'sub_type_name', 'x', 'y', 'under_pressure',\n",
       "       'outcome_id', 'outcome_name', 'foul_won_defensive', 'pass_cross',\n",
       "       'aerial_won', 'out', 'technique_id', 'technique_name',\n",
       "       'pass_assisted_shot_id', 'pass_shot_assist', 'shot_statsbomb_xg',\n",
       "       'end_z', 'shot_key_pass_id', 'goalkeeper_position_id',\n",
       "       'goalkeeper_position_name', 'off_camera', 'foul_committed_advantage',\n",
       "       'foul_won_advantage', 'pass_cut_back', 'pass_goal_assist',\n",
       "       'shot_first_time', 'counterpress', 'pass_switch', 'pass_deflected',\n",
       "       'block_deflection', 'ball_recovery_recovery_failure',\n",
       "       'foul_committed_card_id', 'foul_committed_card_name', 'dribble_overrun',\n",
       "       'substitution_replacement_id', 'substitution_replacement_name',\n",
       "       'dribble_nutmeg', 'pass_no_touch', 'shot_one_on_one'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8f1feb6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['team_name']=='Arsenal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "efce990f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Incomplete', 'Complete', 'Unknown', 'Off T', 'Goal',\n",
       "       'Lost Out', 'Won', 'Out', 'Success In Play', 'Success',\n",
       "       'Pass Offside', 'Lost In Play', 'In Play Safe', 'Blocked',\n",
       "       'Touched Out', 'Success To Team', 'Saved', 'Tactical', 'Injury',\n",
       "       'Post'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['outcome_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "eb71320b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Starting XI', 'Half Start', 'Pass', 'Ball Receipt', 'Carry',\n",
       "       'Dribble', 'Foul Won', 'Dispossessed', 'Shot', 'Clearance', 'Duel',\n",
       "       'Block', 'Pressure', 'Foul Committed', 'Miscontrol',\n",
       "       'Ball Recovery', 'Dribbled Past', 'Interception', 'Goal Keeper',\n",
       "       'Half End', '50/50', 'Substitution', 'Shield'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b9ed475b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   nan,  3668.,  3496., 11386.,  3517.,  3475.,  3511.,  3437.,\n",
       "        6746.,  3385.,  3498.,  3461.,  3604.,  3525.,  3528.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['player_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1bb085d",
   "metadata": {},
   "outputs": [],
   "source": [
    "passes = df[df['type_name']=='Pass']\n",
    "filtered = passes[['player_name','x','y','end_x', 'end_y','pass_recipient_name','outcome_id','outcome_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "112f5d60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>end_x</th>\n",
       "      <th>end_y</th>\n",
       "      <th>pass_recipient_name</th>\n",
       "      <th>outcome_id</th>\n",
       "      <th>outcome_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Theo Walcott</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.1</td>\n",
       "      <td>37.7</td>\n",
       "      <td>Mesut Özil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mesut Özil</td>\n",
       "      <td>59.8</td>\n",
       "      <td>37.5</td>\n",
       "      <td>45.9</td>\n",
       "      <td>43.1</td>\n",
       "      <td>Santiago Cazorla González</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mesut Özil</td>\n",
       "      <td>77.3</td>\n",
       "      <td>46.9</td>\n",
       "      <td>84.7</td>\n",
       "      <td>55.3</td>\n",
       "      <td>Aaron Ramsey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Aaron Ramsey</td>\n",
       "      <td>73.9</td>\n",
       "      <td>61.1</td>\n",
       "      <td>48.2</td>\n",
       "      <td>60.5</td>\n",
       "      <td>Per Mertesacker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Per Mertesacker</td>\n",
       "      <td>48.4</td>\n",
       "      <td>60.5</td>\n",
       "      <td>56.2</td>\n",
       "      <td>71.6</td>\n",
       "      <td>Aaron Ramsey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3666</th>\n",
       "      <td>Héctor Bellerín Moruno</td>\n",
       "      <td>56.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>Per Mertesacker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3669</th>\n",
       "      <td>Per Mertesacker</td>\n",
       "      <td>44.7</td>\n",
       "      <td>65.2</td>\n",
       "      <td>55.8</td>\n",
       "      <td>78.2</td>\n",
       "      <td>Héctor Bellerín Moruno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3671</th>\n",
       "      <td>Héctor Bellerín Moruno</td>\n",
       "      <td>55.8</td>\n",
       "      <td>77.3</td>\n",
       "      <td>68.8</td>\n",
       "      <td>76.8</td>\n",
       "      <td>Alex Oxlade-Chamberlain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3673</th>\n",
       "      <td>Alex Oxlade-Chamberlain</td>\n",
       "      <td>71.4</td>\n",
       "      <td>77.5</td>\n",
       "      <td>76.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>Héctor Bellerín Moruno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3752</th>\n",
       "      <td>Héctor Bellerín Moruno</td>\n",
       "      <td>76.3</td>\n",
       "      <td>61.1</td>\n",
       "      <td>111.5</td>\n",
       "      <td>56.3</td>\n",
       "      <td>Alex Oxlade-Chamberlain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>332 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  player_name     x     y  end_x  end_y  \\\n",
       "4                Theo Walcott  60.0  40.0   60.1   37.7   \n",
       "7                  Mesut Özil  59.8  37.5   45.9   43.1   \n",
       "15                 Mesut Özil  77.3  46.9   84.7   55.3   \n",
       "20               Aaron Ramsey  73.9  61.1   48.2   60.5   \n",
       "23            Per Mertesacker  48.4  60.5   56.2   71.6   \n",
       "...                       ...   ...   ...    ...    ...   \n",
       "3666   Héctor Bellerín Moruno  56.0  70.0   43.0   66.2   \n",
       "3669          Per Mertesacker  44.7  65.2   55.8   78.2   \n",
       "3671   Héctor Bellerín Moruno  55.8  77.3   68.8   76.8   \n",
       "3673  Alex Oxlade-Chamberlain  71.4  77.5   76.0   66.6   \n",
       "3752   Héctor Bellerín Moruno  76.3  61.1  111.5   56.3   \n",
       "\n",
       "            pass_recipient_name  outcome_id outcome_name  \n",
       "4                    Mesut Özil         NaN          NaN  \n",
       "7     Santiago Cazorla González         NaN          NaN  \n",
       "15                 Aaron Ramsey         NaN          NaN  \n",
       "20              Per Mertesacker         NaN          NaN  \n",
       "23                 Aaron Ramsey         NaN          NaN  \n",
       "...                         ...         ...          ...  \n",
       "3666            Per Mertesacker         NaN          NaN  \n",
       "3669     Héctor Bellerín Moruno         NaN          NaN  \n",
       "3671    Alex Oxlade-Chamberlain         NaN          NaN  \n",
       "3673     Héctor Bellerín Moruno         NaN          NaN  \n",
       "3752    Alex Oxlade-Chamberlain         NaN          NaN  \n",
       "\n",
       "[332 rows x 8 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered[filtered['outcome_name'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2e18d475",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lineup = parser.lineup(3754171)\n",
    "jersey_data=df_lineup[['player_id','jersey_number']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "39853961",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lamsa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:1203: RuntimeWarning: invalid value encountered in cast\n",
      "  if not (lk == lk.astype(rk.dtype))[~np.isnan(lk)].all():\n"
     ]
    }
   ],
   "source": [
    "df= pd.merge(df,jersey_data,on='player_id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "85f59ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#player id is the jersey number\n",
    "df['passer'] = df['jersey_number']\n",
    "passes = df[df['type_name']=='Pass']\n",
    "successful = passes[passes['outcome_name'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f51b2212",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lamsa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\frame.py:5039: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().rename(\n"
     ]
    },
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       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>211743e6-39c5-4ac6-8679-b22f523cb1b7</td>\n",
       "      <td>3344</td>\n",
       "      <td>2</td>\n",
       "      <td>00:36:16.633000</td>\n",
       "      <td>81</td>\n",
       "      <td>16</td>\n",
       "      <td>151</td>\n",
       "      <td>3.962653</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>a79bd8bb-d1c4-4919-9ffb-f082a640cae8</td>\n",
       "      <td>3541</td>\n",
       "      <td>2</td>\n",
       "      <td>00:41:20.017000</td>\n",
       "      <td>86</td>\n",
       "      <td>20</td>\n",
       "      <td>170</td>\n",
       "      <td>3.686247</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>7733c3a1-9587-43f4-a39e-7de90ed6c868</td>\n",
       "      <td>3734</td>\n",
       "      <td>2</td>\n",
       "      <td>00:46:29.279000</td>\n",
       "      <td>91</td>\n",
       "      <td>29</td>\n",
       "      <td>179</td>\n",
       "      <td>3.993361</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>ac915371-319b-48a2-81a1-801cb7c5d67b</td>\n",
       "      <td>3402</td>\n",
       "      <td>2</td>\n",
       "      <td>00:38:10.775000</td>\n",
       "      <td>83</td>\n",
       "      <td>10</td>\n",
       "      <td>155</td>\n",
       "      <td>0.181173</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>5011b4a1-267d-4dd9-9216-b926255079ff</td>\n",
       "      <td>3506</td>\n",
       "      <td>2</td>\n",
       "      <td>00:40:03.937000</td>\n",
       "      <td>85</td>\n",
       "      <td>3</td>\n",
       "      <td>158</td>\n",
       "      <td>0.857388</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>393 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id  index  period        timestamp  \\\n",
       "0    5b94f3b2-d60a-4aad-be61-fa1925244d05      5       1  00:00:00.886000   \n",
       "1    905afc0d-2384-448c-910c-c39b7b68a2b5    232       1  00:06:26.565000   \n",
       "2    1ef570b6-68a1-44e4-ae8e-258424733376     66       1  00:00:52.391000   \n",
       "3    cbccf91e-4ef8-4518-9f69-3a976fec60e1    313       1  00:09:01.188000   \n",
       "4    1504c27e-e168-4894-8758-c9b3e1abbb32    833       1  00:22:24.112000   \n",
       "..                                    ...    ...     ...              ...   \n",
       "388  211743e6-39c5-4ac6-8679-b22f523cb1b7   3344       2  00:36:16.633000   \n",
       "389  a79bd8bb-d1c4-4919-9ffb-f082a640cae8   3541       2  00:41:20.017000   \n",
       "390  7733c3a1-9587-43f4-a39e-7de90ed6c868   3734       2  00:46:29.279000   \n",
       "391  ac915371-319b-48a2-81a1-801cb7c5d67b   3402       2  00:38:10.775000   \n",
       "392  5011b4a1-267d-4dd9-9216-b926255079ff   3506       2  00:40:03.937000   \n",
       "\n",
       "     minute  second  possession  duration  match_id  type_id  ...  \\\n",
       "0         0       0           2  0.342259   3754171       30  ...   \n",
       "1         6      26           9  0.592175   3754171       30  ...   \n",
       "2         0      52           2  1.143783   3754171       30  ...   \n",
       "3         9       1          15  1.024242   3754171       30  ...   \n",
       "4        22      24          34  0.746196   3754171       30  ...   \n",
       "..      ...     ...         ...       ...       ...      ...  ...   \n",
       "388      81      16         151  3.962653   3754171       30  ...   \n",
       "389      86      20         170  3.686247   3754171       30  ...   \n",
       "390      91      29         179  3.993361   3754171       30  ...   \n",
       "391      83      10         155  0.181173   3754171       30  ...   \n",
       "392      85       3         158  0.857388   3754171       30  ...   \n",
       "\n",
       "    foul_committed_card_name  dribble_overrun substitution_replacement_id  \\\n",
       "0                        NaN              NaN                         NaN   \n",
       "1                        NaN              NaN                         NaN   \n",
       "2                        NaN              NaN                         NaN   \n",
       "3                        NaN              NaN                         NaN   \n",
       "4                        NaN              NaN                         NaN   \n",
       "..                       ...              ...                         ...   \n",
       "388                      NaN              NaN                         NaN   \n",
       "389                      NaN              NaN                         NaN   \n",
       "390                      NaN              NaN                         NaN   \n",
       "391                      NaN              NaN                         NaN   \n",
       "392                      NaN              NaN                         NaN   \n",
       "\n",
       "     substitution_replacement_name dribble_nutmeg  pass_no_touch  \\\n",
       "0                              NaN            NaN            NaN   \n",
       "1                              NaN            NaN            NaN   \n",
       "2                              NaN            NaN            NaN   \n",
       "3                              NaN            NaN            NaN   \n",
       "4                              NaN            NaN            NaN   \n",
       "..                             ...            ...            ...   \n",
       "388                            NaN            NaN            NaN   \n",
       "389                            NaN            NaN            NaN   \n",
       "390                            NaN            NaN            NaN   \n",
       "391                            NaN            NaN            NaN   \n",
       "392                            NaN            NaN            NaN   \n",
       "\n",
       "    shot_one_on_one jersey_number  passer pass_recipient  \n",
       "0               NaN            14      14             11  \n",
       "1               NaN            14      14             11  \n",
       "2               NaN            19      19             11  \n",
       "3               NaN            19      19             11  \n",
       "4               NaN            19      19             11  \n",
       "..              ...           ...     ...            ...  \n",
       "388             NaN            33      33             12  \n",
       "389             NaN            33      33             12  \n",
       "390             NaN            33      33             12  \n",
       "391             NaN            15      15             12  \n",
       "392             NaN            15      15             12  \n",
       "\n",
       "[393 rows x 74 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec= pd.to_numeric(successful['pass_recipient_id'],downcast='integer')\n",
    "jersey_data.rename(columns={'player_id':'pass_recipient_id'},inplace=True)\n",
    "jersey_data.rename(columns={'jersey_number':'pass_recipient'},inplace=True)\n",
    "successful=pd.merge(df,jersey_data,on='pass_recipient_id')\n",
    "successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ca4a8beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "subs = df[df['type_name']=='Substitution']\n",
    "subs = subs['minute']\n",
    "firstSub=subs.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d364334b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firstSub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "026bb486",
   "metadata": {},
   "outputs": [],
   "source": [
    "successful = successful[successful['minute']<firstSub]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "333b4761",
   "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>id</th>\n",
       "      <th>index</th>\n",
       "      <th>period</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>possession</th>\n",
       "      <th>duration</th>\n",
       "      <th>match_id</th>\n",
       "      <th>type_id</th>\n",
       "      <th>...</th>\n",
       "      <th>foul_committed_card_name</th>\n",
       "      <th>dribble_overrun</th>\n",
       "      <th>substitution_replacement_id</th>\n",
       "      <th>substitution_replacement_name</th>\n",
       "      <th>dribble_nutmeg</th>\n",
       "      <th>pass_no_touch</th>\n",
       "      <th>shot_one_on_one</th>\n",
       "      <th>jersey_number</th>\n",
       "      <th>passer</th>\n",
       "      <th>pass_recipient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5b94f3b2-d60a-4aad-be61-fa1925244d05</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:00.886000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.342259</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>905afc0d-2384-448c-910c-c39b7b68a2b5</td>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>00:06:26.565000</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>0.592175</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1ef570b6-68a1-44e4-ae8e-258424733376</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:52.391000</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>1.143783</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cbccf91e-4ef8-4518-9f69-3a976fec60e1</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "      <td>00:09:01.188000</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1.024242</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1504c27e-e168-4894-8758-c9b3e1abbb32</td>\n",
       "      <td>833</td>\n",
       "      <td>1</td>\n",
       "      <td>00:22:24.112000</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>0.746196</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>84bdf6e1-e125-4243-a4c1-3f4dea386030</td>\n",
       "      <td>568</td>\n",
       "      <td>1</td>\n",
       "      <td>00:15:07.030000</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>2.004195</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>ea82f503-82eb-4fae-bb95-abbd044e3b80</td>\n",
       "      <td>1192</td>\n",
       "      <td>1</td>\n",
       "      <td>00:29:44.257000</td>\n",
       "      <td>29</td>\n",
       "      <td>44</td>\n",
       "      <td>47</td>\n",
       "      <td>2.461485</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>83895ef7-553a-4313-9ab4-cd1a8ae1b0a9</td>\n",
       "      <td>2281</td>\n",
       "      <td>2</td>\n",
       "      <td>00:09:42.722000</td>\n",
       "      <td>54</td>\n",
       "      <td>42</td>\n",
       "      <td>99</td>\n",
       "      <td>3.566446</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>0eee7fb5-a9f3-41ab-be51-1a224f417beb</td>\n",
       "      <td>2921</td>\n",
       "      <td>2</td>\n",
       "      <td>00:24:41.089000</td>\n",
       "      <td>69</td>\n",
       "      <td>41</td>\n",
       "      <td>129</td>\n",
       "      <td>0.061137</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>6e8ab69d-fa36-4a5a-8959-63946d2f52a6</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>00:13:09.635000</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>3.679844</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id  index  period        timestamp  \\\n",
       "0    5b94f3b2-d60a-4aad-be61-fa1925244d05      5       1  00:00:00.886000   \n",
       "1    905afc0d-2384-448c-910c-c39b7b68a2b5    232       1  00:06:26.565000   \n",
       "2    1ef570b6-68a1-44e4-ae8e-258424733376     66       1  00:00:52.391000   \n",
       "3    cbccf91e-4ef8-4518-9f69-3a976fec60e1    313       1  00:09:01.188000   \n",
       "4    1504c27e-e168-4894-8758-c9b3e1abbb32    833       1  00:22:24.112000   \n",
       "..                                    ...    ...     ...              ...   \n",
       "380  84bdf6e1-e125-4243-a4c1-3f4dea386030    568       1  00:15:07.030000   \n",
       "381  ea82f503-82eb-4fae-bb95-abbd044e3b80   1192       1  00:29:44.257000   \n",
       "382  83895ef7-553a-4313-9ab4-cd1a8ae1b0a9   2281       2  00:09:42.722000   \n",
       "383  0eee7fb5-a9f3-41ab-be51-1a224f417beb   2921       2  00:24:41.089000   \n",
       "384  6e8ab69d-fa36-4a5a-8959-63946d2f52a6    500       1  00:13:09.635000   \n",
       "\n",
       "     minute  second  possession  duration  match_id  type_id  ...  \\\n",
       "0         0       0           2  0.342259   3754171       30  ...   \n",
       "1         6      26           9  0.592175   3754171       30  ...   \n",
       "2         0      52           2  1.143783   3754171       30  ...   \n",
       "3         9       1          15  1.024242   3754171       30  ...   \n",
       "4        22      24          34  0.746196   3754171       30  ...   \n",
       "..      ...     ...         ...       ...       ...      ...  ...   \n",
       "380      15       7          26  2.004195   3754171       30  ...   \n",
       "381      29      44          47  2.461485   3754171       30  ...   \n",
       "382      54      42          99  3.566446   3754171       30  ...   \n",
       "383      69      41         129  0.061137   3754171       30  ...   \n",
       "384      13       9          23  3.679844   3754171       30  ...   \n",
       "\n",
       "    foul_committed_card_name  dribble_overrun substitution_replacement_id  \\\n",
       "0                        NaN              NaN                         NaN   \n",
       "1                        NaN              NaN                         NaN   \n",
       "2                        NaN              NaN                         NaN   \n",
       "3                        NaN              NaN                         NaN   \n",
       "4                        NaN              NaN                         NaN   \n",
       "..                       ...              ...                         ...   \n",
       "380                      NaN              NaN                         NaN   \n",
       "381                      NaN              NaN                         NaN   \n",
       "382                      NaN              NaN                         NaN   \n",
       "383                      NaN              NaN                         NaN   \n",
       "384                      NaN              NaN                         NaN   \n",
       "\n",
       "     substitution_replacement_name dribble_nutmeg  pass_no_touch  \\\n",
       "0                              NaN            NaN            NaN   \n",
       "1                              NaN            NaN            NaN   \n",
       "2                              NaN            NaN            NaN   \n",
       "3                              NaN            NaN            NaN   \n",
       "4                              NaN            NaN            NaN   \n",
       "..                             ...            ...            ...   \n",
       "380                            NaN            NaN            NaN   \n",
       "381                            NaN            NaN            NaN   \n",
       "382                            NaN            NaN            NaN   \n",
       "383                            NaN            NaN            NaN   \n",
       "384                            NaN            NaN            NaN   \n",
       "\n",
       "    shot_one_on_one jersey_number  passer pass_recipient  \n",
       "0               NaN            14      14             11  \n",
       "1               NaN            14      14             11  \n",
       "2               NaN            19      19             11  \n",
       "3               NaN            19      19             11  \n",
       "4               NaN            19      19             11  \n",
       "..              ...           ...     ...            ...  \n",
       "380             NaN             5       5             33  \n",
       "381             NaN             5       5             33  \n",
       "382             NaN             5       5             33  \n",
       "383             NaN             5       5             33  \n",
       "384             NaN            17      17             33  \n",
       "\n",
       "[351 rows x 74 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1236ca3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                 object\n",
       "index               int64\n",
       "period              int64\n",
       "timestamp          object\n",
       "minute              int64\n",
       "                    ...  \n",
       "pass_no_touch      object\n",
       "shot_one_on_one    object\n",
       "jersey_number       int64\n",
       "passer              int64\n",
       "pass_recipient      int64\n",
       "Length: 74, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "successful.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0deae4a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "63824fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>id</th>\n",
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       "      <th>pass_no_touch</th>\n",
       "      <th>shot_one_on_one</th>\n",
       "      <th>jersey_number</th>\n",
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       "      <th>pass_recipient</th>\n",
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       "      <th>0</th>\n",
       "      <td>5b94f3b2-d60a-4aad-be61-fa1925244d05</td>\n",
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       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>905afc0d-2384-448c-910c-c39b7b68a2b5</td>\n",
       "      <td>232</td>\n",
       "      <td>1</td>\n",
       "      <td>00:06:26.565000</td>\n",
       "      <td>6</td>\n",
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       "      <td>14</td>\n",
       "      <td>14</td>\n",
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       "      <th>2</th>\n",
       "      <td>1ef570b6-68a1-44e4-ae8e-258424733376</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:52.391000</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cbccf91e-4ef8-4518-9f69-3a976fec60e1</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "      <td>00:09:01.188000</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1.024242</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1504c27e-e168-4894-8758-c9b3e1abbb32</td>\n",
       "      <td>833</td>\n",
       "      <td>1</td>\n",
       "      <td>00:22:24.112000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>380</th>\n",
       "      <td>84bdf6e1-e125-4243-a4c1-3f4dea386030</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>ea82f503-82eb-4fae-bb95-abbd044e3b80</td>\n",
       "      <td>1192</td>\n",
       "      <td>1</td>\n",
       "      <td>00:29:44.257000</td>\n",
       "      <td>29</td>\n",
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       "      <td>47</td>\n",
       "      <td>2.461485</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>83895ef7-553a-4313-9ab4-cd1a8ae1b0a9</td>\n",
       "      <td>2281</td>\n",
       "      <td>2</td>\n",
       "      <td>00:09:42.722000</td>\n",
       "      <td>54</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>0eee7fb5-a9f3-41ab-be51-1a224f417beb</td>\n",
       "      <td>2921</td>\n",
       "      <td>2</td>\n",
       "      <td>00:24:41.089000</td>\n",
       "      <td>69</td>\n",
       "      <td>41</td>\n",
       "      <td>129</td>\n",
       "      <td>0.061137</td>\n",
       "      <td>3754171</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>6e8ab69d-fa36-4a5a-8959-63946d2f52a6</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>00:13:09.635000</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
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       "      <td>3.679844</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id  index  period        timestamp  \\\n",
       "0    5b94f3b2-d60a-4aad-be61-fa1925244d05      5       1  00:00:00.886000   \n",
       "1    905afc0d-2384-448c-910c-c39b7b68a2b5    232       1  00:06:26.565000   \n",
       "2    1ef570b6-68a1-44e4-ae8e-258424733376     66       1  00:00:52.391000   \n",
       "3    cbccf91e-4ef8-4518-9f69-3a976fec60e1    313       1  00:09:01.188000   \n",
       "4    1504c27e-e168-4894-8758-c9b3e1abbb32    833       1  00:22:24.112000   \n",
       "..                                    ...    ...     ...              ...   \n",
       "380  84bdf6e1-e125-4243-a4c1-3f4dea386030    568       1  00:15:07.030000   \n",
       "381  ea82f503-82eb-4fae-bb95-abbd044e3b80   1192       1  00:29:44.257000   \n",
       "382  83895ef7-553a-4313-9ab4-cd1a8ae1b0a9   2281       2  00:09:42.722000   \n",
       "383  0eee7fb5-a9f3-41ab-be51-1a224f417beb   2921       2  00:24:41.089000   \n",
       "384  6e8ab69d-fa36-4a5a-8959-63946d2f52a6    500       1  00:13:09.635000   \n",
       "\n",
       "     minute  second  possession  duration  match_id  type_id  ...  \\\n",
       "0         0       0           2  0.342259   3754171       30  ...   \n",
       "1         6      26           9  0.592175   3754171       30  ...   \n",
       "2         0      52           2  1.143783   3754171       30  ...   \n",
       "3         9       1          15  1.024242   3754171       30  ...   \n",
       "4        22      24          34  0.746196   3754171       30  ...   \n",
       "..      ...     ...         ...       ...       ...      ...  ...   \n",
       "380      15       7          26  2.004195   3754171       30  ...   \n",
       "381      29      44          47  2.461485   3754171       30  ...   \n",
       "382      54      42          99  3.566446   3754171       30  ...   \n",
       "383      69      41         129  0.061137   3754171       30  ...   \n",
       "384      13       9          23  3.679844   3754171       30  ...   \n",
       "\n",
       "    foul_committed_card_name  dribble_overrun substitution_replacement_id  \\\n",
       "0                        NaN              NaN                         NaN   \n",
       "1                        NaN              NaN                         NaN   \n",
       "2                        NaN              NaN                         NaN   \n",
       "3                        NaN              NaN                         NaN   \n",
       "4                        NaN              NaN                         NaN   \n",
       "..                       ...              ...                         ...   \n",
       "380                      NaN              NaN                         NaN   \n",
       "381                      NaN              NaN                         NaN   \n",
       "382                      NaN              NaN                         NaN   \n",
       "383                      NaN              NaN                         NaN   \n",
       "384                      NaN              NaN                         NaN   \n",
       "\n",
       "     substitution_replacement_name dribble_nutmeg  pass_no_touch  \\\n",
       "0                              NaN            NaN            NaN   \n",
       "1                              NaN            NaN            NaN   \n",
       "2                              NaN            NaN            NaN   \n",
       "3                              NaN            NaN            NaN   \n",
       "4                              NaN            NaN            NaN   \n",
       "..                             ...            ...            ...   \n",
       "380                            NaN            NaN            NaN   \n",
       "381                            NaN            NaN            NaN   \n",
       "382                            NaN            NaN            NaN   \n",
       "383                            NaN            NaN            NaN   \n",
       "384                            NaN            NaN            NaN   \n",
       "\n",
       "    shot_one_on_one jersey_number  passer pass_recipient  \n",
       "0               NaN            14      14             11  \n",
       "1               NaN            14      14             11  \n",
       "2               NaN            19      19             11  \n",
       "3               NaN            19      19             11  \n",
       "4               NaN            19      19             11  \n",
       "..              ...           ...     ...            ...  \n",
       "380             NaN             5       5             33  \n",
       "381             NaN             5       5             33  \n",
       "382             NaN             5       5             33  \n",
       "383             NaN             5       5             33  \n",
       "384             NaN            17      17             33  \n",
       "\n",
       "[351 rows x 74 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b9369ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3496.,    nan,  3437.,  3517.,  3385.,  3511., 11386.,  3668.,\n",
       "        3498.,  6746.,  3475.,  3525.,  3528.,  3461.,  3604.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pass_recipient_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fd821801",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_locations= successful.groupby('passer').agg({'x':['mean'],'y':['mean','count']})\n",
    "average_locations.columns = ['x','y','count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "241b7810",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>x</th>\n",
       "      <th>y</th>\n",
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       "      <th>passer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
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       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
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      ],
      "text/plain": [
       "                x          y  count\n",
       "passer                             \n",
       "4       38.873171  56.746341     41\n",
       "5       32.787879  24.342424     33\n",
       "11      69.744737  43.218421     38\n",
       "14      80.518182  51.427273     11\n",
       "16      53.076744  58.527907     43\n",
       "17      73.800000  22.375000     20\n",
       "18      49.003846  11.050000     26\n",
       "19      50.238462  43.853846     52\n",
       "24      49.976667  69.673333     30\n",
       "33      12.048148  40.218519     27\n",
       "34      54.003333  49.183333     30"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4bbec3a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>85 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    passer  pass_recipient  pass_count\n",
       "0        4               5           5\n",
       "1        4              11           3\n",
       "2        4              14           4\n",
       "3        4              16           7\n",
       "4        4              17           1\n",
       "..     ...             ...         ...\n",
       "80      34              16           3\n",
       "81      34              17           3\n",
       "82      34              18           2\n",
       "83      34              19           8\n",
       "84      34              24           2\n",
       "\n",
       "[85 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_between = successful.groupby(['passer','pass_recipient']).id.count().reset_index()\n",
    "pass_between.rename({'id':'pass_count'},axis='columns',inplace=True)\n",
    "pass_between"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fb523285",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>49.183333</td>\n",
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       "      <th>84</th>\n",
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       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    passer  pass_recipient  pass_count          x          y  count\n",
       "0        4               5           5  38.873171  56.746341     41\n",
       "1        4              11           3  38.873171  56.746341     41\n",
       "2        4              14           4  38.873171  56.746341     41\n",
       "3        4              16           7  38.873171  56.746341     41\n",
       "4        4              17           1  38.873171  56.746341     41\n",
       "..     ...             ...         ...        ...        ...    ...\n",
       "80      34              16           3  54.003333  49.183333     30\n",
       "81      34              17           3  54.003333  49.183333     30\n",
       "82      34              18           2  54.003333  49.183333     30\n",
       "83      34              19           8  54.003333  49.183333     30\n",
       "84      34              24           2  54.003333  49.183333     30\n",
       "\n",
       "[85 rows x 6 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_between= pass_between.merge(average_locations,left_on='passer',right_index=True)\n",
    "pass_between\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8403fa84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    passer  pass_recipient  pass_count          x          y  count  \\\n",
       "0        4               5           5  38.873171  56.746341     41   \n",
       "18      11               5           1  69.744737  43.218421     38   \n",
       "32      16               5           4  53.076744  58.527907     43   \n",
       "47      18               5           5  49.003846  11.050000     26   \n",
       "54      19               5           3  50.238462  43.853846     52   \n",
       "..     ...             ...         ...        ...        ...    ...   \n",
       "31      16               4           9  53.076744  58.527907     43   \n",
       "53      19               4           5  50.238462  43.853846     52   \n",
       "62      24               4           1  49.976667  69.673333     30   \n",
       "69      33               4           5  12.048148  40.218519     27   \n",
       "76      34               4           6  54.003333  49.183333     30   \n",
       "\n",
       "        x_end      y_end  count_end  \n",
       "0   32.787879  24.342424         33  \n",
       "18  32.787879  24.342424         33  \n",
       "32  32.787879  24.342424         33  \n",
       "47  32.787879  24.342424         33  \n",
       "54  32.787879  24.342424         33  \n",
       "..        ...        ...        ...  \n",
       "31  38.873171  56.746341         41  \n",
       "53  38.873171  56.746341         41  \n",
       "62  38.873171  56.746341         41  \n",
       "69  38.873171  56.746341         41  \n",
       "76  38.873171  56.746341         41  \n",
       "\n",
       "[85 rows x 9 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_between= pass_between.merge(average_locations,left_on='pass_recipient',right_index=True,suffixes=['','_end'])\n",
    "pass_between"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "891e93bc",
   "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>passer</th>\n",
       "      <th>pass_recipient</th>\n",
       "      <th>pass_count</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>count</th>\n",
       "      <th>x_end</th>\n",
       "      <th>y_end</th>\n",
       "      <th>count_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>19</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    passer  pass_recipient  pass_count          x          y  count  \\\n",
       "0        4               5           5  38.873171  56.746341     41   \n",
       "32      16               5           4  53.076744  58.527907     43   \n",
       "47      18               5           5  49.003846  11.050000     26   \n",
       "33      16              11           5  53.076744  58.527907     43   \n",
       "40      17              11           5  73.800000  22.375000     20   \n",
       "48      18              11           5  49.003846  11.050000     26   \n",
       "55      19              11           8  50.238462  43.853846     52   \n",
       "64      24              11           6  49.976667  69.673333     30   \n",
       "71      33              11           6  12.048148  40.218519     27   \n",
       "78      34              11           4  54.003333  49.183333     30   \n",
       "2        4              14           4  38.873171  56.746341     41   \n",
       "19      11              14           7  69.744737  43.218421     38   \n",
       "41      17              14           4  73.800000  22.375000     20   \n",
       "65      24              14           6  49.976667  69.673333     30   \n",
       "3        4              16           7  38.873171  56.746341     41   \n",
       "20      11              16           5  69.744737  43.218421     38   \n",
       "27      14              16           4  80.518182  51.427273     11   \n",
       "57      19              16          10  50.238462  43.853846     52   \n",
       "66      24              16          12  49.976667  69.673333     30   \n",
       "21      11              17           8  69.744737  43.218421     38   \n",
       "50      18              17           9  49.003846  11.050000     26   \n",
       "58      19              17           6  50.238462  43.853846     52   \n",
       "74      33              17           5  12.048148  40.218519     27   \n",
       "14       5              18           4  32.787879  24.342424     33   \n",
       "22      11              18           5  69.744737  43.218421     38   \n",
       "59      19              18           5  50.238462  43.853846     52   \n",
       "6        4              19           7  38.873171  56.746341     41   \n",
       "15       5              19          11  32.787879  24.342424     33   \n",
       "23      11              19           4  69.744737  43.218421     38   \n",
       "36      16              19           8  53.076744  58.527907     43   \n",
       "44      17              19           4  73.800000  22.375000     20   \n",
       "83      34              19           8  54.003333  49.183333     30   \n",
       "24      11              24           6  69.744737  43.218421     38   \n",
       "37      16              24           8  53.076744  58.527907     43   \n",
       "60      19              24           6  50.238462  43.853846     52   \n",
       "8        4              33           8  38.873171  56.746341     41   \n",
       "16       5              33           4  32.787879  24.342424     33   \n",
       "9        4              34           4  38.873171  56.746341     41   \n",
       "17       5              34           5  32.787879  24.342424     33   \n",
       "61      19              34           6  50.238462  43.853846     52   \n",
       "10       5               4           6  32.787879  24.342424     33   \n",
       "31      16               4           9  53.076744  58.527907     43   \n",
       "53      19               4           5  50.238462  43.853846     52   \n",
       "69      33               4           5  12.048148  40.218519     27   \n",
       "76      34               4           6  54.003333  49.183333     30   \n",
       "\n",
       "        x_end      y_end  count_end  \n",
       "0   32.787879  24.342424         33  \n",
       "32  32.787879  24.342424         33  \n",
       "47  32.787879  24.342424         33  \n",
       "33  69.744737  43.218421         38  \n",
       "40  69.744737  43.218421         38  \n",
       "48  69.744737  43.218421         38  \n",
       "55  69.744737  43.218421         38  \n",
       "64  69.744737  43.218421         38  \n",
       "71  69.744737  43.218421         38  \n",
       "78  69.744737  43.218421         38  \n",
       "2   80.518182  51.427273         11  \n",
       "19  80.518182  51.427273         11  \n",
       "41  80.518182  51.427273         11  \n",
       "65  80.518182  51.427273         11  \n",
       "3   53.076744  58.527907         43  \n",
       "20  53.076744  58.527907         43  \n",
       "27  53.076744  58.527907         43  \n",
       "57  53.076744  58.527907         43  \n",
       "66  53.076744  58.527907         43  \n",
       "21  73.800000  22.375000         20  \n",
       "50  73.800000  22.375000         20  \n",
       "58  73.800000  22.375000         20  \n",
       "74  73.800000  22.375000         20  \n",
       "14  49.003846  11.050000         26  \n",
       "22  49.003846  11.050000         26  \n",
       "59  49.003846  11.050000         26  \n",
       "6   50.238462  43.853846         52  \n",
       "15  50.238462  43.853846         52  \n",
       "23  50.238462  43.853846         52  \n",
       "36  50.238462  43.853846         52  \n",
       "44  50.238462  43.853846         52  \n",
       "83  50.238462  43.853846         52  \n",
       "24  49.976667  69.673333         30  \n",
       "37  49.976667  69.673333         30  \n",
       "60  49.976667  69.673333         30  \n",
       "8   12.048148  40.218519         27  \n",
       "16  12.048148  40.218519         27  \n",
       "9   54.003333  49.183333         30  \n",
       "17  54.003333  49.183333         30  \n",
       "61  54.003333  49.183333         30  \n",
       "10  38.873171  56.746341         41  \n",
       "31  38.873171  56.746341         41  \n",
       "53  38.873171  56.746341         41  \n",
       "69  38.873171  56.746341         41  \n",
       "76  38.873171  56.746341         41  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_between =pass_between[pass_between['pass_count']>3]\n",
    "pass_between.drop_duplicates()\n",
    "pass_between"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fba6c0d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  4,  5,  5,  5,  5,  8,  6,  6,  4,  4,  7,  4,  6,  7,  5,  4,\n",
       "       10, 12,  8,  9,  6,  5,  4,  5,  5,  7, 11,  4,  8,  4,  8,  6,  8,\n",
       "        6,  8,  4,  4,  5,  6,  6,  9,  5,  5,  6], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_locations['count'].values\n",
    "pass_between['pass_count'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0bede89e",
   "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>passer</th>\n",
       "      <th>pass_recipient</th>\n",
       "      <th>pass_count</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>count</th>\n",
       "      <th>x_end</th>\n",
       "      <th>y_end</th>\n",
       "      <th>count_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>80.518182</td>\n",
       "      <td>51.427273</td>\n",
       "      <td>11</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>49.003846</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>73.800000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>20</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>69.744737</td>\n",
       "      <td>43.218421</td>\n",
       "      <td>38</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>49.976667</td>\n",
       "      <td>69.673333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>19</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>32.787879</td>\n",
       "      <td>24.342424</td>\n",
       "      <td>33</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>53.076744</td>\n",
       "      <td>58.527907</td>\n",
       "      <td>43</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>50.238462</td>\n",
       "      <td>43.853846</td>\n",
       "      <td>52</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12.048148</td>\n",
       "      <td>40.218519</td>\n",
       "      <td>27</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>54.003333</td>\n",
       "      <td>49.183333</td>\n",
       "      <td>30</td>\n",
       "      <td>38.873171</td>\n",
       "      <td>56.746341</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    passer  pass_recipient  pass_count          x          y  count  \\\n",
       "0        4               5           5  38.873171  56.746341     41   \n",
       "32      16               5           4  53.076744  58.527907     43   \n",
       "47      18               5           5  49.003846  11.050000     26   \n",
       "33      16              11           5  53.076744  58.527907     43   \n",
       "40      17              11           5  73.800000  22.375000     20   \n",
       "48      18              11           5  49.003846  11.050000     26   \n",
       "55      19              11           8  50.238462  43.853846     52   \n",
       "64      24              11           6  49.976667  69.673333     30   \n",
       "71      33              11           6  12.048148  40.218519     27   \n",
       "78      34              11           4  54.003333  49.183333     30   \n",
       "2        4              14           4  38.873171  56.746341     41   \n",
       "19      11              14           7  69.744737  43.218421     38   \n",
       "41      17              14           4  73.800000  22.375000     20   \n",
       "65      24              14           6  49.976667  69.673333     30   \n",
       "3        4              16           7  38.873171  56.746341     41   \n",
       "20      11              16           5  69.744737  43.218421     38   \n",
       "27      14              16           4  80.518182  51.427273     11   \n",
       "57      19              16          10  50.238462  43.853846     52   \n",
       "66      24              16          12  49.976667  69.673333     30   \n",
       "21      11              17           8  69.744737  43.218421     38   \n",
       "50      18              17           9  49.003846  11.050000     26   \n",
       "58      19              17           6  50.238462  43.853846     52   \n",
       "74      33              17           5  12.048148  40.218519     27   \n",
       "14       5              18           4  32.787879  24.342424     33   \n",
       "22      11              18           5  69.744737  43.218421     38   \n",
       "59      19              18           5  50.238462  43.853846     52   \n",
       "6        4              19           7  38.873171  56.746341     41   \n",
       "15       5              19          11  32.787879  24.342424     33   \n",
       "23      11              19           4  69.744737  43.218421     38   \n",
       "36      16              19           8  53.076744  58.527907     43   \n",
       "44      17              19           4  73.800000  22.375000     20   \n",
       "83      34              19           8  54.003333  49.183333     30   \n",
       "24      11              24           6  69.744737  43.218421     38   \n",
       "37      16              24           8  53.076744  58.527907     43   \n",
       "60      19              24           6  50.238462  43.853846     52   \n",
       "8        4              33           8  38.873171  56.746341     41   \n",
       "16       5              33           4  32.787879  24.342424     33   \n",
       "9        4              34           4  38.873171  56.746341     41   \n",
       "17       5              34           5  32.787879  24.342424     33   \n",
       "61      19              34           6  50.238462  43.853846     52   \n",
       "10       5               4           6  32.787879  24.342424     33   \n",
       "31      16               4           9  53.076744  58.527907     43   \n",
       "53      19               4           5  50.238462  43.853846     52   \n",
       "69      33               4           5  12.048148  40.218519     27   \n",
       "76      34               4           6  54.003333  49.183333     30   \n",
       "\n",
       "        x_end      y_end  count_end  \n",
       "0   32.787879  24.342424         33  \n",
       "32  32.787879  24.342424         33  \n",
       "47  32.787879  24.342424         33  \n",
       "33  69.744737  43.218421         38  \n",
       "40  69.744737  43.218421         38  \n",
       "48  69.744737  43.218421         38  \n",
       "55  69.744737  43.218421         38  \n",
       "64  69.744737  43.218421         38  \n",
       "71  69.744737  43.218421         38  \n",
       "78  69.744737  43.218421         38  \n",
       "2   80.518182  51.427273         11  \n",
       "19  80.518182  51.427273         11  \n",
       "41  80.518182  51.427273         11  \n",
       "65  80.518182  51.427273         11  \n",
       "3   53.076744  58.527907         43  \n",
       "20  53.076744  58.527907         43  \n",
       "27  53.076744  58.527907         43  \n",
       "57  53.076744  58.527907         43  \n",
       "66  53.076744  58.527907         43  \n",
       "21  73.800000  22.375000         20  \n",
       "50  73.800000  22.375000         20  \n",
       "58  73.800000  22.375000         20  \n",
       "74  73.800000  22.375000         20  \n",
       "14  49.003846  11.050000         26  \n",
       "22  49.003846  11.050000         26  \n",
       "59  49.003846  11.050000         26  \n",
       "6   50.238462  43.853846         52  \n",
       "15  50.238462  43.853846         52  \n",
       "23  50.238462  43.853846         52  \n",
       "36  50.238462  43.853846         52  \n",
       "44  50.238462  43.853846         52  \n",
       "83  50.238462  43.853846         52  \n",
       "24  49.976667  69.673333         30  \n",
       "37  49.976667  69.673333         30  \n",
       "60  49.976667  69.673333         30  \n",
       "8   12.048148  40.218519         27  \n",
       "16  12.048148  40.218519         27  \n",
       "9   54.003333  49.183333         30  \n",
       "17  54.003333  49.183333         30  \n",
       "61  54.003333  49.183333         30  \n",
       "10  38.873171  56.746341         41  \n",
       "31  38.873171  56.746341         41  \n",
       "53  38.873171  56.746341         41  \n",
       "69  38.873171  56.746341         41  \n",
       "76  38.873171  56.746341         41  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_between"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b4105722",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def create_arrow(row):\n",
    "#     return mpatches.Arrow(1.2*row['x'], 0.8*row['y'], 1.2*row['x_end'], 0.8*row['y_end'], width=row['pass_count']*0.1, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "47728969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pitch = Pitch(pitch_color='#FFFFFF', line_color='#B6BBC4')\n",
    "# Set the figure size\n",
    "fig, ax = pitch.draw(figsize=(8, 6), constrained_layout=True, tight_layout=False)\n",
    "\n",
    "# Set the face color of the figure\n",
    "fig.set_facecolor('#FFFFFF')\n",
    "\n",
    "# Draw arrows and nodes\n",
    "# arrows = pitch.arrows(1.2 * pass_between.x, 0.8 * pass_between.y, 1.2 * pass_between.x_end, 0.8 * pass_between.y_end, ax=ax,color='red', alpha=0.4,width=3)\n",
    "pass_lines = pitch.lines(1.2*pass_between.x, 0.8*pass_between.y, 1.2*pass_between.x_end, 0.8*pass_between.y_end, lw=pass_between.pass_count*0.5,color='red', zorder=1, ax=ax)\n",
    "                 \n",
    "nodes = pitch.scatter(1.2 * average_locations.x, 0.8 * average_locations.y, s=20*average_locations['count'].values, color='white', edgecolors='#a6aab3', linewidth=2, alpha=1, zorder=1, ax=ax)\n",
    "\n",
    "                 \n",
    "# Annotate average_locations\n",
    "for index, row in average_locations.iterrows():\n",
    "    pitch.annotate(index, xy=(1.2 * row.x, 0.8 * row.y), c='#161A30',fontweight='bold', va='center', ha='center', size=8, ax=ax)\n",
    "\n",
    "# Add the endnote\n",
    "ax.text(114, 85, '@samippl1', color='#0E2954', va='bottom', ha='center', fontsize=10)\n",
    "\n",
    "# Add the title\n",
    "ax.set_title('Arsenal 3-0 Manchester United', color='red', va='center', ha='center', fontsize=12,fontweight='bold',pad=20,loc='center')\n",
    "ax.annotate('Premier League 2015/16', xy=(0.5, 1), xytext=(0, 0),\n",
    "             xycoords='axes fraction', textcoords='offset points',\n",
    "             fontsize=10, color='#0E2954', va='top', ha='center')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "cccc0d8d",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'PathCollection' object has no attribute 'unique'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [149], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mnodes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m()\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'PathCollection' object has no attribute 'unique'"
     ]
    }
   ],
   "source": [
    "nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3620e01",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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