fumblevsint.ipynb

{
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   "source": [
    "\n",
    "# Dependencies\n",
    "import nfl_data_py as nfl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import urllib.request\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.offsetbox import OffsetImage, AnnotationBbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2b1fedc",
   "metadata": {
    "execution": {
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    "id": "a2b1fedc",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "logos = nfl.import_team_desc()\n",
    "logos.head()\n",
    "logos = logos[['team_abbr', 'team_logo_espn']]\n",
    "logo_paths = []\n",
    "team_abbr = []\n",
    "\n",
    "if not os.path.exists(\"logos\"):\n",
    "    os.makedirs(\"logos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e8980cf",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "for team in range(len(logos)):\n",
    "    urllib.request.urlretrieve(logos['team_logo_espn'][team], f\"logos/{logos['team_abbr'][team]}.tif\")\n",
    "    logo_paths.append(f\"logos/{logos['team_abbr'][team]}.tif\")\n",
    "    team_abbr.append(logos['team_abbr'][team])\n",
    "  \n",
    "data = {'Team' : team_abbr, 'Logo Path' : logo_paths}\n",
    "logo_df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aad5eb88",
   "metadata": {
    "colab": {
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    },
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023 done.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downcasting floats.\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "    }\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>Team</th>\n",
       "      <th>fumble</th>\n",
       "      <th>interception</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARI</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATL</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BAL</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUF</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAR</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHI</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CIN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CLE</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DAL</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DEN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DET</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GB</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HOU</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IND</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>JAX</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KC</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LAC</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LV</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MIA</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MIN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NE</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NO</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NYG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NYJ</td>\n",
       "      <td>35.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PHI</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PIT</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SEA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SF</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>TB</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TEN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>WAS</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Team  fumble  interception\n",
       "0   ARI    21.0          12.0\n",
       "1   ATL    24.0          17.0\n",
       "2   BAL    30.0           8.0\n",
       "3   BUF    19.0          18.0\n",
       "4   CAR    23.0          10.0\n",
       "5   CHI    24.0          15.0\n",
       "6   CIN    11.0          14.0\n",
       "7   CLE    26.0          25.0\n",
       "8   DAL    12.0          12.0\n",
       "9   DEN    27.0           9.0\n",
       "10  DET    21.0          12.0\n",
       "11   GB    20.0          13.0\n",
       "12  HOU    18.0           8.0\n",
       "13  IND    17.0          10.0\n",
       "14  JAX    26.0          14.0\n",
       "15   KC    29.0          18.0\n",
       "16   LA     8.0          13.0\n",
       "17  LAC    26.0           8.0\n",
       "18   LV    14.0          18.0\n",
       "19  MIA    23.0          16.0\n",
       "20  MIN    25.0          19.0\n",
       "21   NE    17.0          21.0\n",
       "22   NO    15.0          11.0\n",
       "23  NYG    22.0          12.0\n",
       "24  NYJ    35.0          15.0\n",
       "25  PHI    26.0          16.0\n",
       "26  PIT    23.0          10.0\n",
       "27  SEA     8.0          12.0\n",
       "28   SF    17.0          13.0\n",
       "29   TB    21.0          12.0\n",
       "30  TEN    18.0          11.0\n",
       "31  WAS    19.0          21.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = nfl.import_pbp_data([2023])\n",
    "fumble_int = df[['posteam', 'fumble', 'interception']]\n",
    "fumble_int = fumble_int.groupby('posteam').sum()\n",
    "fumble_int = fumble_int.reset_index()\n",
    "fumble_int = fumble_int.rename(columns={'posteam' : 'Team'})\n",
    "fumble_int"
   ]
  },
  {
   "cell_type": "code",
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   "id": "bfxveA12W4YD",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>fumble</th>\n",
       "      <th>interception</th>\n",
       "      <th>Logo Path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARI</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>logos/ARI.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATL</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>logos/ATL.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BAL</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>logos/BAL.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUF</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>logos/BUF.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAR</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>logos/CAR.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHI</td>\n",
       "      <td>24.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>logos/CHI.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CIN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>logos/CIN.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CLE</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>logos/CLE.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DAL</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>logos/DAL.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DEN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>logos/DEN.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DET</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>logos/DET.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GB</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>logos/GB.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HOU</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>logos/HOU.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IND</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>logos/IND.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>JAX</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>logos/JAX.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KC</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>logos/KC.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>logos/LA.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LAC</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>logos/LAC.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LV</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>logos/LV.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MIA</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>logos/MIA.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MIN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>logos/MIN.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NE</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>logos/NE.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NO</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>logos/NO.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NYG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>logos/NYG.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NYJ</td>\n",
       "      <td>35.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>logos/NYJ.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PHI</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>logos/PHI.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PIT</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>logos/PIT.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SEA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>logos/SEA.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SF</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>logos/SF.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>TB</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>logos/TB.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TEN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>logos/TEN.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>WAS</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>logos/WAS.tif</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Team  fumble  interception      Logo Path\n",
       "0   ARI    21.0          12.0  logos/ARI.tif\n",
       "1   ATL    24.0          17.0  logos/ATL.tif\n",
       "2   BAL    30.0           8.0  logos/BAL.tif\n",
       "3   BUF    19.0          18.0  logos/BUF.tif\n",
       "4   CAR    23.0          10.0  logos/CAR.tif\n",
       "5   CHI    24.0          15.0  logos/CHI.tif\n",
       "6   CIN    11.0          14.0  logos/CIN.tif\n",
       "7   CLE    26.0          25.0  logos/CLE.tif\n",
       "8   DAL    12.0          12.0  logos/DAL.tif\n",
       "9   DEN    27.0           9.0  logos/DEN.tif\n",
       "10  DET    21.0          12.0  logos/DET.tif\n",
       "11   GB    20.0          13.0   logos/GB.tif\n",
       "12  HOU    18.0           8.0  logos/HOU.tif\n",
       "13  IND    17.0          10.0  logos/IND.tif\n",
       "14  JAX    26.0          14.0  logos/JAX.tif\n",
       "15   KC    29.0          18.0   logos/KC.tif\n",
       "16   LA     8.0          13.0   logos/LA.tif\n",
       "17  LAC    26.0           8.0  logos/LAC.tif\n",
       "18   LV    14.0          18.0   logos/LV.tif\n",
       "19  MIA    23.0          16.0  logos/MIA.tif\n",
       "20  MIN    25.0          19.0  logos/MIN.tif\n",
       "21   NE    17.0          21.0   logos/NE.tif\n",
       "22   NO    15.0          11.0   logos/NO.tif\n",
       "23  NYG    22.0          12.0  logos/NYG.tif\n",
       "24  NYJ    35.0          15.0  logos/NYJ.tif\n",
       "25  PHI    26.0          16.0  logos/PHI.tif\n",
       "26  PIT    23.0          10.0  logos/PIT.tif\n",
       "27  SEA     8.0          12.0  logos/SEA.tif\n",
       "28   SF    17.0          13.0   logos/SF.tif\n",
       "29   TB    21.0          12.0   logos/TB.tif\n",
       "30  TEN    18.0          11.0  logos/TEN.tif\n",
       "31  WAS    19.0          21.0  logos/WAS.tif"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.merge(fumble_int, logo_df)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "lS1DT5JIXApW",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-16T12:02:06.706551Z",
     "iopub.status.busy": "2024-06-16T12:02:06.706182Z",
     "iopub.status.idle": "2024-06-16T12:02:06.709405Z",
     "shell.execute_reply": "2024-06-16T12:02:06.708819Z"
    },
    "id": "lS1DT5JIXApW"
   },
   "outputs": [],
   "source": [
    "def getImage(path):\n",
    "    return OffsetImage(plt.imread(path, format=\"tif\"), zoom=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "Dx9xdqf9XDct",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "execution": {
     "iopub.execute_input": "2024-06-16T12:02:06.711809Z",
     "iopub.status.busy": "2024-06-16T12:02:06.711451Z",
     "iopub.status.idle": "2024-06-16T12:02:08.022434Z",
     "shell.execute_reply": "2024-06-16T12:02:08.021751Z"
    },
    "id": "Dx9xdqf9XDct",
    "outputId": "f2866d28-d248-4fd8-e637-e82f691b1920"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [20, 14]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "x = df2['interception']\n",
    "y = df2['fumble']\n",
    "\n",
    "paths = df2['Logo Path']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for x0, y0, path in zip(x, y, paths):\n",
    "   ab = AnnotationBbox(getImage(path), (x0, y0), frameon=False)\n",
    "   ax.add_artist(ab)\n",
    "\n",
    "plt.xlim(5, 30)\n",
    "plt.ylim(5, 40)\n",
    "plt.title(\"2023 NFL Fumbles Vs Interceptions\", fontdict={'fontsize':35});\n",
    "plt.xlabel(\"Interceptions\", fontdict={'fontsize':21});\n",
    "plt.ylabel(\"Fumbles\", fontdict={'fontsize':21});\n",
    "plt.savefig(\"FumblesInt\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3.10.6 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}