chris-initial-eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_injury = pd.read_csv('InjuryRecord.csv')\n",
    "df_player = pd.read_csv('PlayerTrackData.csv')\n",
    "df_plays = pd.read_csv('PlayList.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlayerKey</th>\n",
       "      <th>GameID</th>\n",
       "      <th>PlayKey</th>\n",
       "      <th>BodyPart</th>\n",
       "      <th>Surface</th>\n",
       "      <th>DM_M1</th>\n",
       "      <th>DM_M7</th>\n",
       "      <th>DM_M28</th>\n",
       "      <th>DM_M42</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39873</td>\n",
       "      <td>39873-4</td>\n",
       "      <td>39873-4-32</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46074</td>\n",
       "      <td>46074-7</td>\n",
       "      <td>46074-7-26</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Natural</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36557</td>\n",
       "      <td>36557-1</td>\n",
       "      <td>36557-1-70</td>\n",
       "      <td>Ankle</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46646</td>\n",
       "      <td>46646-3</td>\n",
       "      <td>46646-3-30</td>\n",
       "      <td>Ankle</td>\n",
       "      <td>Natural</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43532</td>\n",
       "      <td>43532-5</td>\n",
       "      <td>43532-5-69</td>\n",
       "      <td>Ankle</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>44423</td>\n",
       "      <td>44423-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>31933</td>\n",
       "      <td>31933-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>47285</td>\n",
       "      <td>47285-4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Natural</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>37068</td>\n",
       "      <td>37068-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Natural</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>36696</td>\n",
       "      <td>36696-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PlayerKey    GameID     PlayKey BodyPart    Surface  DM_M1  DM_M7  \\\n",
       "0        39873   39873-4  39873-4-32     Knee  Synthetic      1      1   \n",
       "1        46074   46074-7  46074-7-26     Knee    Natural      1      1   \n",
       "2        36557   36557-1  36557-1-70    Ankle  Synthetic      1      1   \n",
       "3        46646   46646-3  46646-3-30    Ankle    Natural      1      0   \n",
       "4        43532   43532-5  43532-5-69    Ankle  Synthetic      1      1   \n",
       "..         ...       ...         ...      ...        ...    ...    ...   \n",
       "100      44423  44423-13         NaN     Knee  Synthetic      1      0   \n",
       "101      31933  31933-20         NaN     Knee  Synthetic      1      0   \n",
       "102      47285   47285-4         NaN     Knee    Natural      1      1   \n",
       "103      37068  37068-19         NaN     Knee    Natural      1      1   \n",
       "104      36696  36696-24         NaN     Knee  Synthetic      1      1   \n",
       "\n",
       "     DM_M28  DM_M42  \n",
       "0         1       1  \n",
       "1         0       0  \n",
       "2         1       1  \n",
       "3         0       0  \n",
       "4         1       1  \n",
       "..      ...     ...  \n",
       "100       0       0  \n",
       "101       0       0  \n",
       "102       0       0  \n",
       "103       0       0  \n",
       "104       0       0  \n",
       "\n",
       "[105 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_injury"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>87.44</td>\n",
       "      <td>28.92</td>\n",
       "      <td>280.40</td>\n",
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       "      <td>261.17</td>\n",
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       "      <th>3</th>\n",
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       "      <td>28.92</td>\n",
       "      <td>278.79</td>\n",
       "      <td>0.01</td>\n",
       "      <td>260.66</td>\n",
       "      <td>0.10</td>\n",
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       "      <td>11.50</td>\n",
       "      <td>195.95</td>\n",
       "      <td>0.13</td>\n",
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       "      <td>47888-13-55</td>\n",
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       "      <td>74.94</td>\n",
       "      <td>11.37</td>\n",
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       "      <td>36.1</td>\n",
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       "      <td>74.87</td>\n",
       "      <td>11.11</td>\n",
       "      <td>194.47</td>\n",
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       "      <td>144.87</td>\n",
       "      <td>1.33</td>\n",
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       "      <td>47888-13-55</td>\n",
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       "      <td>74.84</td>\n",
       "      <td>10.99</td>\n",
       "      <td>193.74</td>\n",
       "      <td>0.13</td>\n",
       "      <td>144.87</td>\n",
       "      <td>1.31</td>\n",
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       "<p>76366748 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "              PlayKey  time                 event      x      y     dir   dis  \\\n",
       "0           26624-1-1   0.0  huddle_start_offense  87.46  28.93  288.24  0.01   \n",
       "1           26624-1-1   0.1                   NaN  87.45  28.92  283.91  0.01   \n",
       "2           26624-1-1   0.2                   NaN  87.44  28.92  280.40  0.01   \n",
       "3           26624-1-1   0.3                   NaN  87.44  28.92  278.79  0.01   \n",
       "4           26624-1-1   0.4                   NaN  87.44  28.92  275.44  0.01   \n",
       "...               ...   ...                   ...    ...    ...     ...   ...   \n",
       "76366743  47888-13-55  35.8                   NaN  74.97  11.50  195.95  0.13   \n",
       "76366744  47888-13-55  35.9                   NaN  74.94  11.37  195.01  0.13   \n",
       "76366745  47888-13-55  36.0                   NaN  74.90  11.24  193.99  0.13   \n",
       "76366746  47888-13-55  36.1                   NaN  74.87  11.11  194.47  0.13   \n",
       "76366747  47888-13-55  36.2                   NaN  74.84  10.99  193.74  0.13   \n",
       "\n",
       "               o     s  \n",
       "0         262.33  0.13  \n",
       "1         261.69  0.12  \n",
       "2         261.17  0.12  \n",
       "3         260.66  0.10  \n",
       "4         260.27  0.09  \n",
       "...          ...   ...  \n",
       "76366743  142.95  1.34  \n",
       "76366744  142.01  1.33  \n",
       "76366745  143.36  1.33  \n",
       "76366746  144.87  1.33  \n",
       "76366747  144.87  1.31  \n",
       "\n",
       "[76366748 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlayerKey</th>\n",
       "      <th>GameID</th>\n",
       "      <th>PlayKey</th>\n",
       "      <th>RosterPosition</th>\n",
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       "      <th>PlayerGame</th>\n",
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       "      <th>0</th>\n",
       "      <td>26624</td>\n",
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       "      <td>Quarterback</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
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       "      <td>QB</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26624</td>\n",
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       "      <td>26624-1-3</td>\n",
       "      <td>Quarterback</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>63</td>\n",
       "      <td>Clear and warm</td>\n",
       "      <td>Rush</td>\n",
       "      <td>3</td>\n",
       "      <td>QB</td>\n",
       "      <td>QB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26624</td>\n",
       "      <td>26624-1</td>\n",
       "      <td>26624-1-4</td>\n",
       "      <td>Quarterback</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>63</td>\n",
       "      <td>Clear and warm</td>\n",
       "      <td>Rush</td>\n",
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       "      <td>QB</td>\n",
       "      <td>QB</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26624</td>\n",
       "      <td>26624-1</td>\n",
       "      <td>26624-1-5</td>\n",
       "      <td>Quarterback</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>63</td>\n",
       "      <td>Clear and warm</td>\n",
       "      <td>Pass</td>\n",
       "      <td>5</td>\n",
       "      <td>QB</td>\n",
       "      <td>QB</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>267000</th>\n",
       "      <td>47888</td>\n",
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       "      <td>99</td>\n",
       "      <td>13</td>\n",
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       "      <td>Synthetic</td>\n",
       "      <td>33</td>\n",
       "      <td>Sunny and cold</td>\n",
       "      <td>Pass</td>\n",
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       "      <td>DB</td>\n",
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       "    <tr>\n",
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       "      <td>47888</td>\n",
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       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>33</td>\n",
       "      <td>Sunny and cold</td>\n",
       "      <td>Pass</td>\n",
       "      <td>52</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
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       "    <tr>\n",
       "      <th>267002</th>\n",
       "      <td>47888</td>\n",
       "      <td>47888-13</td>\n",
       "      <td>47888-13-53</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>99</td>\n",
       "      <td>13</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>33</td>\n",
       "      <td>Sunny and cold</td>\n",
       "      <td>Pass</td>\n",
       "      <td>53</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
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       "    <tr>\n",
       "      <th>267003</th>\n",
       "      <td>47888</td>\n",
       "      <td>47888-13</td>\n",
       "      <td>47888-13-54</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>99</td>\n",
       "      <td>13</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>33</td>\n",
       "      <td>Sunny and cold</td>\n",
       "      <td>Pass</td>\n",
       "      <td>54</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
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       "    <tr>\n",
       "      <th>267004</th>\n",
       "      <td>47888</td>\n",
       "      <td>47888-13</td>\n",
       "      <td>47888-13-55</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>99</td>\n",
       "      <td>13</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>33</td>\n",
       "      <td>Sunny and cold</td>\n",
       "      <td>Rush</td>\n",
       "      <td>55</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267005 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PlayerKey    GameID      PlayKey RosterPosition  PlayerDay  \\\n",
       "0           26624   26624-1    26624-1-1    Quarterback          1   \n",
       "1           26624   26624-1    26624-1-2    Quarterback          1   \n",
       "2           26624   26624-1    26624-1-3    Quarterback          1   \n",
       "3           26624   26624-1    26624-1-4    Quarterback          1   \n",
       "4           26624   26624-1    26624-1-5    Quarterback          1   \n",
       "...           ...       ...          ...            ...        ...   \n",
       "267000      47888  47888-13  47888-13-51     Cornerback         99   \n",
       "267001      47888  47888-13  47888-13-52     Cornerback         99   \n",
       "267002      47888  47888-13  47888-13-53     Cornerback         99   \n",
       "267003      47888  47888-13  47888-13-54     Cornerback         99   \n",
       "267004      47888  47888-13  47888-13-55     Cornerback         99   \n",
       "\n",
       "        PlayerGame StadiumType  FieldType  Temperature         Weather  \\\n",
       "0                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "1                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "2                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "3                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "4                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "...            ...         ...        ...          ...             ...   \n",
       "267000          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "267001          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "267002          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "267003          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "267004          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "\n",
       "       PlayType  PlayerGamePlay Position PositionGroup  \n",
       "0          Pass               1       QB            QB  \n",
       "1          Pass               2       QB            QB  \n",
       "2          Rush               3       QB            QB  \n",
       "3          Rush               4       QB            QB  \n",
       "4          Pass               5       QB            QB  \n",
       "...         ...             ...      ...           ...  \n",
       "267000     Pass              51       DB            DB  \n",
       "267001     Pass              52       DB            DB  \n",
       "267002     Pass              53       DB            DB  \n",
       "267003     Pass              54       DB            DB  \n",
       "267004     Rush              55       DB            DB  \n",
       "\n",
       "[267005 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PlayerKey</th>\n",
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       "      <td>Synthetic</td>\n",
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       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267005 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PlayerKey    GameID      PlayKey RosterPosition  PlayerDay  \\\n",
       "0           26624   26624-1    26624-1-1    Quarterback          1   \n",
       "1           26624   26624-1    26624-1-2    Quarterback          1   \n",
       "2           26624   26624-1    26624-1-3    Quarterback          1   \n",
       "3           26624   26624-1    26624-1-4    Quarterback          1   \n",
       "4           26624   26624-1    26624-1-5    Quarterback          1   \n",
       "...           ...       ...          ...            ...        ...   \n",
       "267000      47888  47888-13  47888-13-51     Cornerback         99   \n",
       "267001      47888  47888-13  47888-13-52     Cornerback         99   \n",
       "267002      47888  47888-13  47888-13-53     Cornerback         99   \n",
       "267003      47888  47888-13  47888-13-54     Cornerback         99   \n",
       "267004      47888  47888-13  47888-13-55     Cornerback         99   \n",
       "\n",
       "        PlayerGame StadiumType  FieldType  Temperature         Weather  \\\n",
       "0                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "1                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "2                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "3                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "4                1     Outdoor  Synthetic           63  Clear and warm   \n",
       "...            ...         ...        ...          ...             ...   \n",
       "267000          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "267001          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "267002          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "267003          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "267004          13     Outdoor  Synthetic           33  Sunny and cold   \n",
       "\n",
       "       PlayType  PlayerGamePlay Position PositionGroup  \n",
       "0          Pass               1       QB            QB  \n",
       "1          Pass               2       QB            QB  \n",
       "2          Rush               3       QB            QB  \n",
       "3          Rush               4       QB            QB  \n",
       "4          Pass               5       QB            QB  \n",
       "...         ...             ...      ...           ...  \n",
       "267000     Pass              51       DB            DB  \n",
       "267001     Pass              52       DB            DB  \n",
       "267002     Pass              53       DB            DB  \n",
       "267003     Pass              54       DB            DB  \n",
       "267004     Rush              55       DB            DB  \n",
       "\n",
       "[267005 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>26624-12</td>\n",
       "      <td>Quarterback</td>\n",
       "      <td>88</td>\n",
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       "      <td>Outdoors</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>48</td>\n",
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       "      <td>Pass</td>\n",
       "      <td>61</td>\n",
       "      <td>QB</td>\n",
       "      <td>QB</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5707</th>\n",
       "      <td>47888-5</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>Outdoors</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>46</td>\n",
       "      <td>Partly cloudy</td>\n",
       "      <td>Pass</td>\n",
       "      <td>24</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5708</th>\n",
       "      <td>47888-6</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>43</td>\n",
       "      <td>6</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>40</td>\n",
       "      <td>Clear and Cool</td>\n",
       "      <td>Pass</td>\n",
       "      <td>29</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5709</th>\n",
       "      <td>47888-7</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>Outdoors</td>\n",
       "      <td>Natural</td>\n",
       "      <td>46</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Punt Not Returned</td>\n",
       "      <td>16</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5710</th>\n",
       "      <td>47888-8</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>54</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Pass</td>\n",
       "      <td>21</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5711</th>\n",
       "      <td>47888-9</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>71</td>\n",
       "      <td>9</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>53</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Pass</td>\n",
       "      <td>54</td>\n",
       "      <td>DB</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5712 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        GameID RosterPosition  PlayerDay  PlayerGame StadiumType  FieldType  \\\n",
       "0      26624-1    Quarterback          1           1     Outdoor  Synthetic   \n",
       "1     26624-10    Quarterback         74          10     Outdoor    Natural   \n",
       "2     26624-11    Quarterback         81          11     Outdoor  Synthetic   \n",
       "3     26624-12    Quarterback         88          12    Outdoors  Synthetic   \n",
       "4     26624-13    Quarterback         96          13     Outdoor    Natural   \n",
       "...        ...            ...        ...         ...         ...        ...   \n",
       "5707   47888-5     Cornerback         37           5    Outdoors  Synthetic   \n",
       "5708   47888-6     Cornerback         43           6     Outdoor  Synthetic   \n",
       "5709   47888-7     Cornerback         50           7    Outdoors    Natural   \n",
       "5710   47888-8     Cornerback         64           8     Outdoor  Synthetic   \n",
       "5711   47888-9     Cornerback         71           9     Outdoor  Synthetic   \n",
       "\n",
       "      Temperature         Weather           PlayType  PlayerGamePlay Position  \\\n",
       "0              63  Clear and warm               Pass              81       QB   \n",
       "1              66           Sunny               Pass              57       QB   \n",
       "2              47   Partly Cloudy               Rush              70       QB   \n",
       "3              48   Mostly cloudy               Rush              68       QB   \n",
       "4              55           Clear               Pass              61       QB   \n",
       "...           ...             ...                ...             ...      ...   \n",
       "5707           46   Partly cloudy               Pass              24       DB   \n",
       "5708           40  Clear and Cool               Pass              29       DB   \n",
       "5709           46           Sunny  Punt Not Returned              16       DB   \n",
       "5710           54   Partly Cloudy               Pass              21       DB   \n",
       "5711           53          Cloudy               Pass              54       DB   \n",
       "\n",
       "     PositionGroup  \n",
       "0               QB  \n",
       "1               QB  \n",
       "2               QB  \n",
       "3               QB  \n",
       "4               QB  \n",
       "...            ...  \n",
       "5707            DB  \n",
       "5708            DB  \n",
       "5709            DB  \n",
       "5710            DB  \n",
       "5711            DB  \n",
       "\n",
       "[5712 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_playerinfo = df_plays.groupby(['GameID']).agg({'RosterPosition':'last',\n",
    "                                                  'PlayerDay':'last',\n",
    "                                                  'PlayerGame':'last',\n",
    "                                                  'StadiumType':'last',\n",
    "                                                  'FieldType':'last',\n",
    "                                                  'Temperature':'last',\n",
    "                                                  'Weather':'last',\n",
    "                                                  'PlayType':'last',\n",
    "                                                  'PlayerGamePlay':'last',\n",
    "                                                  'Position':'last',\n",
    "                                                  'PositionGroup':'last'}).reset_index()\n",
    "df_playerinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>PlayerKey</th>\n",
       "      <th>GameID</th>\n",
       "      <th>PlayKey</th>\n",
       "      <th>BodyPart</th>\n",
       "      <th>Surface</th>\n",
       "      <th>DM_M1</th>\n",
       "      <th>DM_M7</th>\n",
       "      <th>DM_M28</th>\n",
       "      <th>DM_M42</th>\n",
       "      <th>RosterPosition</th>\n",
       "      <th>PlayerDay</th>\n",
       "      <th>PlayerGame</th>\n",
       "      <th>StadiumType</th>\n",
       "      <th>FieldType</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Weather</th>\n",
       "      <th>PlayType</th>\n",
       "      <th>PlayerGamePlay</th>\n",
       "      <th>Position</th>\n",
       "      <th>PositionGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39873</td>\n",
       "      <td>39873-4</td>\n",
       "      <td>39873-4-32</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Linebacker</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>Indoors</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>84</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Punt</td>\n",
       "      <td>32</td>\n",
       "      <td>OLB</td>\n",
       "      <td>LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46074</td>\n",
       "      <td>46074-7</td>\n",
       "      <td>46074-7-26</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Natural</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Linebacker</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>Open</td>\n",
       "      <td>Natural</td>\n",
       "      <td>76</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Punt</td>\n",
       "      <td>26</td>\n",
       "      <td>OLB</td>\n",
       "      <td>LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36557</td>\n",
       "      <td>36557-1</td>\n",
       "      <td>36557-1-70</td>\n",
       "      <td>Ankle</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Safety</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>63</td>\n",
       "      <td>Clear and warm</td>\n",
       "      <td>Pass</td>\n",
       "      <td>70</td>\n",
       "      <td>SS</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46646</td>\n",
       "      <td>46646-3</td>\n",
       "      <td>46646-3-30</td>\n",
       "      <td>Ankle</td>\n",
       "      <td>Natural</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Linebacker</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Natural</td>\n",
       "      <td>80</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Punt</td>\n",
       "      <td>30</td>\n",
       "      <td>LB</td>\n",
       "      <td>LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43532</td>\n",
       "      <td>43532-5</td>\n",
       "      <td>43532-5-69</td>\n",
       "      <td>Ankle</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wide Receiver</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>Retractable Roof</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>89</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Kickoff</td>\n",
       "      <td>69</td>\n",
       "      <td>WR</td>\n",
       "      <td>WR</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>44423</td>\n",
       "      <td>44423-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Wide Receiver</td>\n",
       "      <td>365</td>\n",
       "      <td>13</td>\n",
       "      <td>Retr. Roof - Open</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>73</td>\n",
       "      <td>Mostly sunny</td>\n",
       "      <td>Pass</td>\n",
       "      <td>27</td>\n",
       "      <td>WR</td>\n",
       "      <td>WR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>31933</td>\n",
       "      <td>31933-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Tight End</td>\n",
       "      <td>394</td>\n",
       "      <td>20</td>\n",
       "      <td>Indoors</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>84</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>Pass</td>\n",
       "      <td>26</td>\n",
       "      <td>TE</td>\n",
       "      <td>TE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>47285</td>\n",
       "      <td>47285-4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Natural</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Wide Receiver</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>Natural</td>\n",
       "      <td>68</td>\n",
       "      <td>None</td>\n",
       "      <td>Rush</td>\n",
       "      <td>16</td>\n",
       "      <td>WR</td>\n",
       "      <td>WR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>37068</td>\n",
       "      <td>37068-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Natural</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Linebacker</td>\n",
       "      <td>417</td>\n",
       "      <td>19</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Natural</td>\n",
       "      <td>56</td>\n",
       "      <td>Clear and Sunny</td>\n",
       "      <td>Punt Not Returned</td>\n",
       "      <td>20</td>\n",
       "      <td>OLB</td>\n",
       "      <td>LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>36696</td>\n",
       "      <td>36696-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knee</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cornerback</td>\n",
       "      <td>439</td>\n",
       "      <td>24</td>\n",
       "      <td>Indoors</td>\n",
       "      <td>Synthetic</td>\n",
       "      <td>68</td>\n",
       "      <td>None</td>\n",
       "      <td>Kickoff Not Returned</td>\n",
       "      <td>22</td>\n",
       "      <td>CB</td>\n",
       "      <td>DB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PlayerKey    GameID     PlayKey BodyPart    Surface  DM_M1  DM_M7  \\\n",
       "0        39873   39873-4  39873-4-32     Knee  Synthetic      1      1   \n",
       "1        46074   46074-7  46074-7-26     Knee    Natural      1      1   \n",
       "2        36557   36557-1  36557-1-70    Ankle  Synthetic      1      1   \n",
       "3        46646   46646-3  46646-3-30    Ankle    Natural      1      0   \n",
       "4        43532   43532-5  43532-5-69    Ankle  Synthetic      1      1   \n",
       "..         ...       ...         ...      ...        ...    ...    ...   \n",
       "100      44423  44423-13         NaN     Knee  Synthetic      1      0   \n",
       "101      31933  31933-20         NaN     Knee  Synthetic      1      0   \n",
       "102      47285   47285-4         NaN     Knee    Natural      1      1   \n",
       "103      37068  37068-19         NaN     Knee    Natural      1      1   \n",
       "104      36696  36696-24         NaN     Knee  Synthetic      1      1   \n",
       "\n",
       "     DM_M28  DM_M42 RosterPosition  PlayerDay  PlayerGame        StadiumType  \\\n",
       "0         1       1     Linebacker         29           4            Indoors   \n",
       "1         0       0     Linebacker         50           7               Open   \n",
       "2         1       1         Safety          1           1            Outdoor   \n",
       "3         0       0     Linebacker         15           3            Outdoor   \n",
       "4         1       1  Wide Receiver         32           5   Retractable Roof   \n",
       "..      ...     ...            ...        ...         ...                ...   \n",
       "100       0       0  Wide Receiver        365          13  Retr. Roof - Open   \n",
       "101       0       0      Tight End        394          20            Indoors   \n",
       "102       0       0  Wide Receiver         22           4               None   \n",
       "103       0       0     Linebacker        417          19            Outdoor   \n",
       "104       0       0     Cornerback        439          24            Indoors   \n",
       "\n",
       "     FieldType  Temperature          Weather              PlayType  \\\n",
       "0    Synthetic           84           Cloudy                  Punt   \n",
       "1      Natural           76    Partly Cloudy                  Punt   \n",
       "2    Synthetic           63   Clear and warm                  Pass   \n",
       "3      Natural           80           Cloudy                  Punt   \n",
       "4    Synthetic           89    Partly Cloudy               Kickoff   \n",
       "..         ...          ...              ...                   ...   \n",
       "100  Synthetic           73     Mostly sunny                  Pass   \n",
       "101  Synthetic           84    Partly Cloudy                  Pass   \n",
       "102    Natural           68             None                  Rush   \n",
       "103    Natural           56  Clear and Sunny     Punt Not Returned   \n",
       "104  Synthetic           68             None  Kickoff Not Returned   \n",
       "\n",
       "     PlayerGamePlay Position PositionGroup  \n",
       "0                32      OLB            LB  \n",
       "1                26      OLB            LB  \n",
       "2                70       SS            DB  \n",
       "3                30       LB            LB  \n",
       "4                69       WR            WR  \n",
       "..              ...      ...           ...  \n",
       "100              27       WR            WR  \n",
       "101              26       TE            TE  \n",
       "102              16       WR            WR  \n",
       "103              20      OLB            LB  \n",
       "104              22       CB            DB  \n",
       "\n",
       "[105 rows x 20 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged = pd.merge(df_injury,df_playerinfo,on='GameID',how='left')\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged['PositionGroup'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('PositionGroup')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of PositionGroup')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged['FieldType'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('FieldType (Plays w/ Injuries)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of FieldType (Plays w/ Injuries)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plays['FieldType'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('FieldType (All Plays)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of FieldType (All Plays)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged['BodyPart'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('BodyPart')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of BodyPart')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weather\n",
       "Sunny                                                                               18\n",
       "Partly Cloudy                                                                       16\n",
       "Cloudy                                                                              14\n",
       "Clear                                                                                9\n",
       "Indoor                                                                               5\n",
       "Indoors                                                                              4\n",
       "Rain                                                                                 4\n",
       "Cold                                                                                 3\n",
       "Mostly cloudy                                                                        2\n",
       "Cloudy, 50% change of rain                                                           2\n",
       "Controlled Climate                                                                   2\n",
       "Clear skies                                                                          2\n",
       "Mostly sunny                                                                         2\n",
       "Sunny and clear                                                                      1\n",
       "Cloudy and Cool                                                                      1\n",
       "Clear Skies                                                                          1\n",
       "N/A (Indoors)                                                                        1\n",
       "Clear and cold                                                                       1\n",
       "Rain shower                                                                          1\n",
       "Party Cloudy                                                                         1\n",
       "Mostly Sunny                                                                         1\n",
       "Light Rain                                                                           1\n",
       "Fair                                                                                 1\n",
       "Coudy                                                                                1\n",
       "Sun & clouds                                                                         1\n",
       "Cloudy with periods of rain, thunder possible. Winds shifting to WNW, 10-20 mph.     1\n",
       "Clear and warm                                                                       1\n",
       "Clear and Sunny                                                                      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged['Weather'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([df_merged.PlayerGame], bins=15, label=['PlayerGame'])\n",
    "plt.xlabel('Game#')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of PlayerGame')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged[df_merged['BodyPart'] == 'Knee']['FieldType'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('FieldType (Knee Injuries)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of FieldType (Knee Injuries)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged[df_merged['BodyPart'] == 'Ankle']['FieldType'].value_counts().plot(kind='bar')\n",
    "plt.xlabel('FieldType (Ankle Injuries)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of FieldType (Ankle Injuries)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}