NBA Mvp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pywaffle import Waffle\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from highlight_text import htext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Player (Team)</th>\n",
       "      <th>1st Place Votes</th>\n",
       "      <th>2nd Place Votes</th>\n",
       "      <th>3rd Place Votes</th>\n",
       "      <th>4th Place Votes</th>\n",
       "      <th>5th Place Votes</th>\n",
       "      <th>Total Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Giannis, Antetokounmpo, (Milwaukee)]</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[LeBron, James, (Los, Angeles, Lakers)]</td>\n",
       "      <td>16</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[James, Harden, (Houston)]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Luka, Dončić, (Dallas)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Kawhi, Leonard, (LA, Clippers)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[Anthony, Davis, (Los, Angeles, Lakers)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[Chris, Paul, (Oklahoma, City)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[Damian, Lillard, (Portland)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[Nikola, Jokić, (Denver)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[Pascal, Siakam, (Toronto)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[Jimmy, Butler, (Miami)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[Jayson, Tatum, (Boston)]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Player (Team)  1st Place Votes  \\\n",
       "0      [Giannis, Antetokounmpo, (Milwaukee)]               85   \n",
       "1    [LeBron, James, (Los, Angeles, Lakers)]               16   \n",
       "2                 [James, Harden, (Houston)]                0   \n",
       "3                   [Luka, Dončić, (Dallas)]                0   \n",
       "4           [Kawhi, Leonard, (LA, Clippers)]                0   \n",
       "5   [Anthony, Davis, (Los, Angeles, Lakers)]                0   \n",
       "6            [Chris, Paul, (Oklahoma, City)]                0   \n",
       "7              [Damian, Lillard, (Portland)]                0   \n",
       "8                  [Nikola, Jokić, (Denver)]                0   \n",
       "9                [Pascal, Siakam, (Toronto)]                0   \n",
       "10                  [Jimmy, Butler, (Miami)]                0   \n",
       "11                 [Jayson, Tatum, (Boston)]                0   \n",
       "\n",
       "    2nd Place Votes  3rd Place Votes  4th Place Votes  5th Place Votes  \\\n",
       "0                16                0                0                0   \n",
       "1                84                1                0                0   \n",
       "2                 1               64               10               10   \n",
       "3                 0               14               36               22   \n",
       "4                 0                9               31               30   \n",
       "5                 0                5               14               15   \n",
       "6                 0                3                1                8   \n",
       "7                 0                1                4                6   \n",
       "8                 0                2                2                2   \n",
       "9                 0                2                1                4   \n",
       "10                0                0                2                3   \n",
       "11                0                0                0                1   \n",
       "\n",
       "    Total Points  \n",
       "0            962  \n",
       "1            753  \n",
       "2            367  \n",
       "3            200  \n",
       "4            168  \n",
       "5             82  \n",
       "6             26  \n",
       "7             23  \n",
       "8             18  \n",
       "9             17  \n",
       "10             9  \n",
       "11             1  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in data\n",
    "df = pd.read_csv('nbamvp2020.csv')\n",
    "\n",
    "#clean up player names\n",
    "df['Player (Team)'] = df['Player (Team)'].str.split(\" \",expand=False)[::-1]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Giannis Antetokounmpo (Milwaukee)</th>\n",
       "      <th>LeBron James (Los Angeles Lakers)</th>\n",
       "      <th>James Harden (Houston)</th>\n",
       "      <th>Luka Dončić (Dallas)</th>\n",
       "      <th>Kawhi Leonard (LA Clippers)</th>\n",
       "      <th>Anthony Davis (Los Angeles Lakers)</th>\n",
       "      <th>Chris Paul (Oklahoma City)</th>\n",
       "      <th>Damian Lillard (Portland)</th>\n",
       "      <th>Nikola Jokić (Denver)</th>\n",
       "      <th>Pascal Siakam (Toronto)</th>\n",
       "      <th>Jimmy Butler (Miami)</th>\n",
       "      <th>Jayson Tatum (Boston)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1st Place Votes</th>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd Place Votes</th>\n",
       "      <td>16</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3rd Place Votes</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4th Place Votes</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5th Place Votes</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Giannis Antetokounmpo (Milwaukee)  \\\n",
       "1st Place Votes                                85   \n",
       "2nd Place Votes                                16   \n",
       "3rd Place Votes                                 0   \n",
       "4th Place Votes                                 0   \n",
       "5th Place Votes                                 0   \n",
       "\n",
       "                LeBron James (Los Angeles Lakers) James Harden (Houston)  \\\n",
       "1st Place Votes                                16                      0   \n",
       "2nd Place Votes                                84                      1   \n",
       "3rd Place Votes                                 1                     64   \n",
       "4th Place Votes                                 0                     10   \n",
       "5th Place Votes                                 0                     10   \n",
       "\n",
       "                Luka Dončić (Dallas) Kawhi Leonard (LA Clippers)  \\\n",
       "1st Place Votes                    0                           0   \n",
       "2nd Place Votes                    0                           0   \n",
       "3rd Place Votes                   14                           9   \n",
       "4th Place Votes                   36                          31   \n",
       "5th Place Votes                   22                          30   \n",
       "\n",
       "                Anthony Davis (Los Angeles Lakers) Chris Paul (Oklahoma City)  \\\n",
       "1st Place Votes                                  0                          0   \n",
       "2nd Place Votes                                  0                          0   \n",
       "3rd Place Votes                                  5                          3   \n",
       "4th Place Votes                                 14                          1   \n",
       "5th Place Votes                                 15                          8   \n",
       "\n",
       "                Damian Lillard (Portland) Nikola Jokić (Denver)  \\\n",
       "1st Place Votes                         0                     0   \n",
       "2nd Place Votes                         0                     0   \n",
       "3rd Place Votes                         1                     2   \n",
       "4th Place Votes                         4                     2   \n",
       "5th Place Votes                         6                     2   \n",
       "\n",
       "                Pascal Siakam (Toronto) Jimmy Butler (Miami)  \\\n",
       "1st Place Votes                       0                    0   \n",
       "2nd Place Votes                       0                    0   \n",
       "3rd Place Votes                       2                    0   \n",
       "4th Place Votes                       1                    2   \n",
       "5th Place Votes                       4                    3   \n",
       "\n",
       "                Jayson Tatum (Boston)  \n",
       "1st Place Votes                     0  \n",
       "2nd Place Votes                     0  \n",
       "3rd Place Votes                     0  \n",
       "4th Place Votes                     0  \n",
       "5th Place Votes                     1  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in data\n",
    "df = pd.read_csv('nbamvp2020.csv')\n",
    "\n",
    "#clean up player names\n",
    "#df['Player (Team)'] = df['Player (Team)'].str.rsplit(\" \",expand=True)[::-1]\n",
    "\n",
    "#transpose the data - This is done so we can plot everything correctly\n",
    "df = df.T\n",
    "\n",
    "#Change column names to be the player names\n",
    "df.columns = df.iloc[0]\n",
    "\n",
    "#We want to drop the player names row as well as the total points row\n",
    "df = df.drop('Player (Team)')\n",
    "df = df.drop('Total Points')\n",
    "\n",
    "#Create a new list of columns to recreate the dataframe to be exactly how we want it\n",
    "my_list = df.columns.values.tolist()\n",
    "df = pd.DataFrame(df,columns=my_list)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Waffle size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(\n",
    "    FigureClass = Waffle,\n",
    "    rows = 10,\n",
    "    labels = list(df.index),\n",
    "    values = df.iloc[:,0],\n",
    "    legend = {\n",
    "        'loc': 'lower left'\n",
    "    },\n",
    "    title={\n",
    "        'label': 'Giannis',\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(\n",
    "    FigureClass = Waffle,\n",
    "    rows = 10,\n",
    "    figsize = (10,10),\n",
    "    plots = {\n",
    "        '431': {\n",
    "            'values': df.iloc[:,0],\n",
    "            'title' : {\n",
    "                'label': my_list[0],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        '432': {\n",
    "            'values': df.iloc[:,1],\n",
    "            'title' : {\n",
    "                'label': my_list[1],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        '433': {\n",
    "            'values': df.iloc[:,2],\n",
    "            'title' : {\n",
    "                'label': my_list[2],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        '434': {\n",
    "            'values': df.iloc[:,3],\n",
    "            'title' : {\n",
    "                'label': my_list[3],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        '435': {\n",
    "            'values': df.iloc[:,4],\n",
    "            'title' : {\n",
    "                'label': my_list[4],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        '436': {\n",
    "            'values': df.iloc[:,5],\n",
    "            'title' : {\n",
    "                'label': my_list[5],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        '437': {\n",
    "            'values': df.iloc[:,6],\n",
    "            'title' : {\n",
    "                'label': my_list[6],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        '438': {\n",
    "            'values': df.iloc[:,7],\n",
    "            'title' : {\n",
    "                'label': my_list[7],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        '439': {\n",
    "            'values': df.iloc[:,8],\n",
    "            'title' : {\n",
    "                'label': my_list[8],\n",
    "                'color': 'white',\n",
    "                'loc':'left'\n",
    "            }\n",
    "        },\n",
    "        \n",
    "        \n",
    "        \n",
    "    },\n",
    "    rounding_rule='floor',\n",
    "    colors = ('#2a9d8f','#e9c46a','#f4a261','#e76f51','#eae2b7'),\n",
    "    facecolor='#003049'\n",
    "    \n",
    ")\n",
    "\n",
    "s = \"NBA MVP Voting \\n<1st>, <2nd>, <3rd>, <4th> + <5th> place votes\"\n",
    "htext.fig_htext(s,\n",
    "                x=.01,y=.99,\n",
    "                fontfamily='Andale Mono',\n",
    "                highlight_weights=['bold'],\n",
    "                string_weight='bold',\n",
    "                fontsize=18,\n",
    "                color='white',\n",
    "                highlight_colors=['#2a9d8f','#e9c46a','#f4a261','#e76f51','#eae2b7'])\n",
    "\n",
    "fig.patch.set_facecolor('#003049')\n",
    "fig.text(.01,0.2,\"@mckayjohns / data via nba.com\",fontstyle='italic',fontsize=11,fontfamily='Andale Mono',color='white')\n",
    "\n",
    "plt.savefig('nbamvp.png',dpi=300,bbox_inches = 'tight',facecolor='#003049')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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