create_table_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b2d6509",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "from plottable import ColumnDefinition, Table\n",
    "from plottable.cmap import normed_cmap\n",
    "from plottable.plots import image, circled_image\n",
    "from plottable.formatters import decimal_to_percent\n",
    "import psycopg2 as pg\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "import random\n",
    "random.seed(73)\n",
    "\n",
    "load_dotenv()\n",
    "current_directory = os.environ[\"current_directory\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52e2280d",
   "metadata": {},
   "outputs": [],
   "source": [
    "connection_string = os.environ[\"connection_string\"]\n",
    "conn = pg.connect(connection_string)\n",
    "cur = conn.cursor()\n",
    "cur.execute(\"\"\"SELECT DISTINCT p.id, p.name, t.team, p.position\n",
    "                FROM players AS p\n",
    "                JOIN tracking AS t ON p.id = t.player_id\n",
    "                WHERE p.position IN (\n",
    "                    'OLB', 'CB', 'SS', 'ILB', 'DT', 'FS',\n",
    "                    'MLB', 'NT', 'DB', 'DE'\n",
    "                    )\n",
    "                \"\"\")\n",
    "defensive_players = cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0730e698",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\n",
    "        \"id\",\n",
    "        \"Player\",\n",
    "        \"Team\",\n",
    "        \"Position\"\n",
    "    ]\n",
    "    \n",
    "df = pd.DataFrame(defensive_players, columns = cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3159c37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['logo'] = df['Team'].apply(\n",
    "        lambda x: \n",
    "    f\"{current_directory}/team_logos/{x}_logo.png\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1a50780",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"profile_picture\"] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50887894",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['profile_picture', 'Player', 'logo', 'Team', 'Position']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58da020f",
   "metadata": {},
   "outputs": [],
   "source": [
    "bg_color = \"#FFFFFF\"\n",
    "text_color = \"#000000\"\n",
    "    \n",
    "row_colors = {\"#91C465\", \"D0F0C0\", \"F0FFF0\", \"F5FFFA\"}\n",
    "    \n",
    "plt.rcParams['text.color'] = text_color\n",
    "plt.rcParams['font.family'] = \"monospace\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7cdd9aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"tackles_above_expected\"] = [round(random.uniform(-20, 20), 2) for _ in range(len(df))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b3af1cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add and populate dummy tackle type place holders \n",
    "tackle_types = [\"five_star\", \"four_star\", \"three_star\", \"two_star\", \"one_star\", \"total\"]\n",
    "\n",
    "for tackle in tackle_types:\n",
    "    tackles = [random.choices([0, 1, 2, 3], weights=[0.60, 0.20, 0.15, 0.05])[0] for _ in range(len(df))]\n",
    "    df[f\"{tackle}_tackles\"] = tackles\n",
    "    df[f\"{tackle}_opportunities\"] = [random.randrange(4, 100) for _ in range(len(df))]\n",
    "    df[f\"{tackle}_percentage\"] = round(df[f\"{tackle}_tackles\"] / df[f\"{tackle}_opportunities\"], 2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f310021f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_table = df.sort_values(by = \"tackles_above_expected\", ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8ce33ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_profile_picture_file_path(player):\n",
    "    divided_name = player.split()\n",
    "    return f\"{current_directory}/profile_photos/{divided_name[0]}_{divided_name[1]}.png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca416523",
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_table['profile_picture'] = mock_table['Player'].apply(get_profile_picture_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5362500",
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_table = mock_table.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "183b8ca7",
   "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>profile_picture</th>\n",
       "      <th>Player</th>\n",
       "      <th>logo</th>\n",
       "      <th>Team</th>\n",
       "      <th>Position</th>\n",
       "      <th>tackles_above_expected</th>\n",
       "      <th>five_star_tackles</th>\n",
       "      <th>five_star_opportunities</th>\n",
       "      <th>five_star_percentage</th>\n",
       "      <th>four_star_tackles</th>\n",
       "      <th>...</th>\n",
       "      <th>three_star_percentage</th>\n",
       "      <th>two_star_tackles</th>\n",
       "      <th>two_star_opportunities</th>\n",
       "      <th>two_star_percentage</th>\n",
       "      <th>one_star_tackles</th>\n",
       "      <th>one_star_opportunities</th>\n",
       "      <th>one_star_percentage</th>\n",
       "      <th>total_tackles</th>\n",
       "      <th>total_opportunities</th>\n",
       "      <th>total_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Lamarcus Joyner</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>NYJ</td>\n",
       "      <td>SS</td>\n",
       "      <td>19.96</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Derion Kendrick</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>LA</td>\n",
       "      <td>CB</td>\n",
       "      <td>19.95</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Xavien Howard</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>MIA</td>\n",
       "      <td>CB</td>\n",
       "      <td>19.94</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Sheldon Rankins</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>NYJ</td>\n",
       "      <td>DT</td>\n",
       "      <td>19.92</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>B.J. Hill</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>CIN</td>\n",
       "      <td>DT</td>\n",
       "      <td>19.83</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Daniel Wise</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>WAS</td>\n",
       "      <td>DE</td>\n",
       "      <td>19.81</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Trey Flowers</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>MIA</td>\n",
       "      <td>OLB</td>\n",
       "      <td>19.78</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Johnathan Hankins</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>DAL</td>\n",
       "      <td>NT</td>\n",
       "      <td>19.77</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Danielle Hunter</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>MIN</td>\n",
       "      <td>OLB</td>\n",
       "      <td>19.76</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Blake Cashman</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>HOU</td>\n",
       "      <td>ILB</td>\n",
       "      <td>19.68</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     profile_picture             Player  \\\n",
       "0  /Users/winch/Data Science & Machine Learning P...    Lamarcus Joyner   \n",
       "1  /Users/winch/Data Science & Machine Learning P...    Derion Kendrick   \n",
       "2  /Users/winch/Data Science & Machine Learning P...      Xavien Howard   \n",
       "3  /Users/winch/Data Science & Machine Learning P...    Sheldon Rankins   \n",
       "4  /Users/winch/Data Science & Machine Learning P...          B.J. Hill   \n",
       "5  /Users/winch/Data Science & Machine Learning P...        Daniel Wise   \n",
       "6  /Users/winch/Data Science & Machine Learning P...       Trey Flowers   \n",
       "7  /Users/winch/Data Science & Machine Learning P...  Johnathan Hankins   \n",
       "8  /Users/winch/Data Science & Machine Learning P...    Danielle Hunter   \n",
       "9  /Users/winch/Data Science & Machine Learning P...      Blake Cashman   \n",
       "\n",
       "                                                logo Team Position  \\\n",
       "0  /Users/winch/Data Science & Machine Learning P...  NYJ       SS   \n",
       "1  /Users/winch/Data Science & Machine Learning P...   LA       CB   \n",
       "2  /Users/winch/Data Science & Machine Learning P...  MIA       CB   \n",
       "3  /Users/winch/Data Science & Machine Learning P...  NYJ       DT   \n",
       "4  /Users/winch/Data Science & Machine Learning P...  CIN       DT   \n",
       "5  /Users/winch/Data Science & Machine Learning P...  WAS       DE   \n",
       "6  /Users/winch/Data Science & Machine Learning P...  MIA      OLB   \n",
       "7  /Users/winch/Data Science & Machine Learning P...  DAL       NT   \n",
       "8  /Users/winch/Data Science & Machine Learning P...  MIN      OLB   \n",
       "9  /Users/winch/Data Science & Machine Learning P...  HOU      ILB   \n",
       "\n",
       "   tackles_above_expected  five_star_tackles  five_star_opportunities  \\\n",
       "0                   19.96                  0                       79   \n",
       "1                   19.95                  3                       12   \n",
       "2                   19.94                  0                       69   \n",
       "3                   19.92                  0                       27   \n",
       "4                   19.83                  2                       74   \n",
       "5                   19.81                  0                       64   \n",
       "6                   19.78                  2                       62   \n",
       "7                   19.77                  0                       45   \n",
       "8                   19.76                  1                       97   \n",
       "9                   19.68                  2                       85   \n",
       "\n",
       "   five_star_percentage  four_star_tackles  ...  three_star_percentage  \\\n",
       "0                  0.00                  0  ...                   0.00   \n",
       "1                  0.25                  2  ...                   0.00   \n",
       "2                  0.00                  0  ...                   0.00   \n",
       "3                  0.00                  0  ...                   0.00   \n",
       "4                  0.03                  0  ...                   0.04   \n",
       "5                  0.00                  2  ...                   0.05   \n",
       "6                  0.03                  0  ...                   0.06   \n",
       "7                  0.00                  1  ...                   0.00   \n",
       "8                  0.01                  0  ...                   0.00   \n",
       "9                  0.02                  2  ...                   0.00   \n",
       "\n",
       "   two_star_tackles  two_star_opportunities  two_star_percentage  \\\n",
       "0                 0                      36                 0.00   \n",
       "1                 0                      12                 0.00   \n",
       "2                 0                      10                 0.00   \n",
       "3                 1                      25                 0.04   \n",
       "4                 0                      91                 0.00   \n",
       "5                 1                      17                 0.06   \n",
       "6                 0                      60                 0.00   \n",
       "7                 0                       8                 0.00   \n",
       "8                 0                      87                 0.00   \n",
       "9                 0                      15                 0.00   \n",
       "\n",
       "   one_star_tackles  one_star_opportunities  one_star_percentage  \\\n",
       "0                 0                      14                 0.00   \n",
       "1                 0                      13                 0.00   \n",
       "2                 0                      33                 0.00   \n",
       "3                 0                      42                 0.00   \n",
       "4                 0                      36                 0.00   \n",
       "5                 1                      30                 0.03   \n",
       "6                 0                      17                 0.00   \n",
       "7                 0                      58                 0.00   \n",
       "8                 0                      28                 0.00   \n",
       "9                 0                      45                 0.00   \n",
       "\n",
       "   total_tackles  total_opportunities  total_percentage  \n",
       "0              0                   32              0.00  \n",
       "1              0                   75              0.00  \n",
       "2              1                   88              0.01  \n",
       "3              0                   17              0.00  \n",
       "4              3                   30              0.10  \n",
       "5              0                   97              0.00  \n",
       "6              1                   74              0.01  \n",
       "7              0                   92              0.00  \n",
       "8              0                   95              0.00  \n",
       "9              2                   25              0.08  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mock_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2347df31",
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_table['Rank'] = mock_table[\"tackles_above_expected\"].rank(ascending=False).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5997966e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_table = mock_table[['Rank','profile_picture', 'Player', 'logo', 'Team', 'Position',\n",
    "       'tackles_above_expected', 'five_star_tackles', 'five_star_opportunities',\n",
    "       'five_star_percentage', 'four_star_tackles', 'four_star_opportunities',\n",
    "       'four_star_percentage', 'three_star_tackles', 'three_star_opportunities',\n",
    "       'three_star_percentage', 'two_star_tackles', 'two_star_opportunities',\n",
    "       'two_star_percentage', 'one_star_tackles', 'one_star_opportunities',\n",
    "       'one_star_percentage', 'total_tackles', 'total_opportunities',\n",
    "       'total_percentage']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ad799f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>profile_picture</th>\n",
       "      <th>Player</th>\n",
       "      <th>logo</th>\n",
       "      <th>Team</th>\n",
       "      <th>Position</th>\n",
       "      <th>tackles_above_expected</th>\n",
       "      <th>five_star_tackles</th>\n",
       "      <th>five_star_opportunities</th>\n",
       "      <th>five_star_percentage</th>\n",
       "      <th>...</th>\n",
       "      <th>three_star_percentage</th>\n",
       "      <th>two_star_tackles</th>\n",
       "      <th>two_star_opportunities</th>\n",
       "      <th>two_star_percentage</th>\n",
       "      <th>one_star_tackles</th>\n",
       "      <th>one_star_opportunities</th>\n",
       "      <th>one_star_percentage</th>\n",
       "      <th>total_tackles</th>\n",
       "      <th>total_opportunities</th>\n",
       "      <th>total_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Lamarcus Joyner</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>NYJ</td>\n",
       "      <td>SS</td>\n",
       "      <td>19.96</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Derion Kendrick</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>LA</td>\n",
       "      <td>CB</td>\n",
       "      <td>19.95</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Xavien Howard</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>MIA</td>\n",
       "      <td>CB</td>\n",
       "      <td>19.94</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Sheldon Rankins</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>NYJ</td>\n",
       "      <td>DT</td>\n",
       "      <td>19.92</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>B.J. Hill</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>CIN</td>\n",
       "      <td>DT</td>\n",
       "      <td>19.83</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>0.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Daniel Wise</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>WAS</td>\n",
       "      <td>DE</td>\n",
       "      <td>19.81</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Trey Flowers</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>MIA</td>\n",
       "      <td>OLB</td>\n",
       "      <td>19.78</td>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "      <td>0.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Johnathan Hankins</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>DAL</td>\n",
       "      <td>NT</td>\n",
       "      <td>19.77</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Danielle Hunter</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>MIN</td>\n",
       "      <td>OLB</td>\n",
       "      <td>19.76</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>Blake Cashman</td>\n",
       "      <td>/Users/winch/Data Science &amp; Machine Learning P...</td>\n",
       "      <td>HOU</td>\n",
       "      <td>ILB</td>\n",
       "      <td>19.68</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                                    profile_picture             Player  \\\n",
       "0     1  /Users/winch/Data Science & Machine Learning P...    Lamarcus Joyner   \n",
       "1     2  /Users/winch/Data Science & Machine Learning P...    Derion Kendrick   \n",
       "2     3  /Users/winch/Data Science & Machine Learning P...      Xavien Howard   \n",
       "3     4  /Users/winch/Data Science & Machine Learning P...    Sheldon Rankins   \n",
       "4     5  /Users/winch/Data Science & Machine Learning P...          B.J. Hill   \n",
       "5     6  /Users/winch/Data Science & Machine Learning P...        Daniel Wise   \n",
       "6     7  /Users/winch/Data Science & Machine Learning P...       Trey Flowers   \n",
       "7     8  /Users/winch/Data Science & Machine Learning P...  Johnathan Hankins   \n",
       "8     9  /Users/winch/Data Science & Machine Learning P...    Danielle Hunter   \n",
       "9    10  /Users/winch/Data Science & Machine Learning P...      Blake Cashman   \n",
       "\n",
       "                                                logo Team Position  \\\n",
       "0  /Users/winch/Data Science & Machine Learning P...  NYJ       SS   \n",
       "1  /Users/winch/Data Science & Machine Learning P...   LA       CB   \n",
       "2  /Users/winch/Data Science & Machine Learning P...  MIA       CB   \n",
       "3  /Users/winch/Data Science & Machine Learning P...  NYJ       DT   \n",
       "4  /Users/winch/Data Science & Machine Learning P...  CIN       DT   \n",
       "5  /Users/winch/Data Science & Machine Learning P...  WAS       DE   \n",
       "6  /Users/winch/Data Science & Machine Learning P...  MIA      OLB   \n",
       "7  /Users/winch/Data Science & Machine Learning P...  DAL       NT   \n",
       "8  /Users/winch/Data Science & Machine Learning P...  MIN      OLB   \n",
       "9  /Users/winch/Data Science & Machine Learning P...  HOU      ILB   \n",
       "\n",
       "   tackles_above_expected  five_star_tackles  five_star_opportunities  \\\n",
       "0                   19.96                  0                       79   \n",
       "1                   19.95                  3                       12   \n",
       "2                   19.94                  0                       69   \n",
       "3                   19.92                  0                       27   \n",
       "4                   19.83                  2                       74   \n",
       "5                   19.81                  0                       64   \n",
       "6                   19.78                  2                       62   \n",
       "7                   19.77                  0                       45   \n",
       "8                   19.76                  1                       97   \n",
       "9                   19.68                  2                       85   \n",
       "\n",
       "   five_star_percentage  ...  three_star_percentage  two_star_tackles  \\\n",
       "0                  0.00  ...                   0.00                 0   \n",
       "1                  0.25  ...                   0.00                 0   \n",
       "2                  0.00  ...                   0.00                 0   \n",
       "3                  0.00  ...                   0.00                 1   \n",
       "4                  0.03  ...                   0.04                 0   \n",
       "5                  0.00  ...                   0.05                 1   \n",
       "6                  0.03  ...                   0.06                 0   \n",
       "7                  0.00  ...                   0.00                 0   \n",
       "8                  0.01  ...                   0.00                 0   \n",
       "9                  0.02  ...                   0.00                 0   \n",
       "\n",
       "   two_star_opportunities  two_star_percentage  one_star_tackles  \\\n",
       "0                      36                 0.00                 0   \n",
       "1                      12                 0.00                 0   \n",
       "2                      10                 0.00                 0   \n",
       "3                      25                 0.04                 0   \n",
       "4                      91                 0.00                 0   \n",
       "5                      17                 0.06                 1   \n",
       "6                      60                 0.00                 0   \n",
       "7                       8                 0.00                 0   \n",
       "8                      87                 0.00                 0   \n",
       "9                      15                 0.00                 0   \n",
       "\n",
       "   one_star_opportunities  one_star_percentage  total_tackles  \\\n",
       "0                      14                 0.00              0   \n",
       "1                      13                 0.00              0   \n",
       "2                      33                 0.00              1   \n",
       "3                      42                 0.00              0   \n",
       "4                      36                 0.00              3   \n",
       "5                      30                 0.03              0   \n",
       "6                      17                 0.00              1   \n",
       "7                      58                 0.00              0   \n",
       "8                      28                 0.00              0   \n",
       "9                      45                 0.00              2   \n",
       "\n",
       "   total_opportunities  total_percentage  \n",
       "0                   32              0.00  \n",
       "1                   75              0.00  \n",
       "2                   88              0.01  \n",
       "3                   17              0.00  \n",
       "4                   30              0.10  \n",
       "5                   97              0.00  \n",
       "6                   74              0.01  \n",
       "7                   92              0.00  \n",
       "8                   95              0.00  \n",
       "9                   25              0.08  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mock_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95a7dc92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rank', 'profile_picture', 'Player', 'logo', 'Team', 'Position',\n",
       "       'tackles_above_expected', 'five_star_tackles',\n",
       "       'five_star_opportunities', 'five_star_percentage', 'four_star_tackles',\n",
       "       'four_star_opportunities', 'four_star_percentage', 'three_star_tackles',\n",
       "       'three_star_opportunities', 'three_star_percentage', 'two_star_tackles',\n",
       "       'two_star_opportunities', 'two_star_percentage', 'one_star_tackles',\n",
       "       'one_star_opportunities', 'one_star_percentage', 'total_tackles',\n",
       "       'total_opportunities', 'total_percentage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mock_table.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "228a0ce0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d46cfd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_defs = [\n",
    "    ColumnDefinition(\n",
    "        name = \"Rank\",\n",
    "        textprops = {\"ha\": \"center\", \"weight\":\"bold\"},\n",
    "        width = 0.4\n",
    "    ),\n",
    "    ColumnDefinition(\n",
    "        name = \"profile_picture\",\n",
    "        textprops = {\"ha\": \"center\", \"va\":\"center\", \"color\": bg_color},\n",
    "        width = 0.5,\n",
    "        plot_fn = circled_image\n",
    "    ),\n",
    "    ColumnDefinition(\n",
    "        name = \"Player\",\n",
    "        title = \"Player\",\n",
    "        textprops = {\"ha\": \"left\", \"weight\":\"bold\"},\n",
    "        width = 1.15,\n",
    "    ),\n",
    "      ColumnDefinition(\n",
    "        name = \"logo\",\n",
    "        title = \"Team\",\n",
    "        textprops = {\"ha\": \"center\", \"va\":\"center\", \"color\": text_color},\n",
    "        width = 0.5,\n",
    "        plot_fn = image\n",
    "    ),\n",
    "    ColumnDefinition(\n",
    "        name = \"Position\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5,\n",
    "    ),\n",
    "    ColumnDefinition(\n",
    "        name = \"tackles_above_expected\",\n",
    "        title = \"TAE\",\n",
    "        textprops = {\"ha\": \"center\", \"color\": text_color, \"weight\":\"bold\", \"bbox\": {\"boxstyle\": \"circle\", \"pad\": .35}},\n",
    "        cmap = normed_cmap(mock_table[\"tackles_above_expected\"], cmap = plt.cm.PiYG, num_stds=2),\n",
    "        width = 0.75,\n",
    "    ),\n",
    "    ColumnDefinition(\n",
    "        name = \"five_star_tackles\",\n",
    "        title = \"Tk\",\n",
    "        group = \"5 Star (0-25%)\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5,\n",
    "        border=\"left\"\n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"five_star_opportunities\",\n",
    "        title = \"Opp\",\n",
    "        group = \"5 Star (0-25%)\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"five_star_percentage\",\n",
    "        title = \"%\",\n",
    "        group = \"5 Star (0-25%)\",\n",
    "        formatter=decimal_to_percent, \n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "        ColumnDefinition(\n",
    "        name = \"four_star_tackles\",\n",
    "        title = \"Tk\",\n",
    "        group = \"4 Star (26-50%)\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5,\n",
    "        border=\"left\"\n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"four_star_opportunities\",\n",
    "        group = \"4 Star (26-50%)\",\n",
    "        title = \"Opp\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5,\n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"four_star_percentage\",\n",
    "        group = \"4 Star (26-50%)\",\n",
    "        title = \"%\",\n",
    "        formatter=decimal_to_percent, \n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "        ColumnDefinition(\n",
    "        name = \"three_star_tackles\",\n",
    "        group = \"3 Star (51-75%)\",\n",
    "        title = \"Tk\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5,\n",
    "        border=\"left\"\n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"three_star_opportunities\",\n",
    "        group = \"3 Star (51-75%)\",\n",
    "        title = \"Opp\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"three_star_percentage\",\n",
    "        group = \"3 Star (51-75%)\",\n",
    "        title = \"%\",\n",
    "        formatter=decimal_to_percent, \n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "        ColumnDefinition(\n",
    "        name = \"two_star_tackles\",\n",
    "        group = \"2 Star (76-90%)\",\n",
    "        title = \"Tk\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5,\n",
    "        border=\"left\"\n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"two_star_opportunities\",\n",
    "        group = \"2 Star (76-90%)\",\n",
    "        title = \"Opp\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"two_star_percentage\",\n",
    "        group = \"2 Star (76-90%)\",\n",
    "        title = \"%\",\n",
    "        formatter=decimal_to_percent, \n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "        ColumnDefinition(\n",
    "        name = \"one_star_tackles\",\n",
    "        group = \"1 Star (91-95%)\",\n",
    "        title = \"Tk\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5,\n",
    "        border=\"left\"    \n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"one_star_opportunities\",\n",
    "        group = \"1 Star (91-95%)\",\n",
    "        title = \"Opp\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"one_star_percentage\",\n",
    "        group = \"1 Star (91-95%)\",\n",
    "        title = \"%\",\n",
    "        formatter=decimal_to_percent,\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "        ColumnDefinition(\n",
    "        name = \"total_tackles\",\n",
    "        group = \"Total\",\n",
    "        title = \"Tk\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5,\n",
    "        border=\"left\"\n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"total_opportunities\",\n",
    "        group = \"Total\",\n",
    "        title = \"Op\",\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    ),\n",
    "     ColumnDefinition(\n",
    "        name = \"total_percentage\",\n",
    "        group = \"Total\",\n",
    "        title = \"%\",\n",
    "        formatter=decimal_to_percent,\n",
    "        textprops = {\"ha\": \"center\"},\n",
    "        width = 0.5 \n",
    "    )\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "520749a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x2160 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(40,30))\n",
    "fig.set_facecolor(bg_color)\n",
    "ax.set_facecolor(bg_color)\n",
    "\n",
    "table = Table(\n",
    "    mock_table,\n",
    "    column_definitions = col_defs,\n",
    "    index_col = \"Rank\",\n",
    "    row_dividers = True,\n",
    "    row_divider_kw = {\"linewidth\":1, \"linestyle\": (0, (1,5))},\n",
    "    footer_divider = True, \n",
    "    textprops = {\"fontsize\":14},\n",
    "    ax = ax\n",
    ").autoset_fontcolors(colnames=[\"tackles_above_expected\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e19d7b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = pd.read_csv(\"data/teams.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "716a114c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}