Who Should You Be Stacking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "pd.options.mode.chained_assignment = None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Tm</th>\n",
       "      <th>FantPos</th>\n",
       "      <th>FantasyPoints</th>\n",
       "      <th>FantasyPoints/GM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>CAR</td>\n",
       "      <td>RB</td>\n",
       "      <td>469.20</td>\n",
       "      <td>29.32500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>BAL</td>\n",
       "      <td>QB</td>\n",
       "      <td>415.68</td>\n",
       "      <td>27.71200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>TEN</td>\n",
       "      <td>RB</td>\n",
       "      <td>294.60</td>\n",
       "      <td>19.64000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>GNB</td>\n",
       "      <td>RB</td>\n",
       "      <td>314.80</td>\n",
       "      <td>19.67500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>DAL</td>\n",
       "      <td>RB</td>\n",
       "      <td>311.70</td>\n",
       "      <td>19.48125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tm FantPos  FantasyPoints  FantasyPoints/GM\n",
       "0  CAR      RB         469.20          29.32500\n",
       "1  BAL      QB         415.68          27.71200\n",
       "2  TEN      RB         294.60          19.64000\n",
       "3  GNB      RB         314.80          19.67500\n",
       "4  DAL      RB         311.70          19.48125"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import our CSV file\n",
    "df = pd.read_csv('2019.csv')\n",
    "\n",
    "#drop unneccessary columns\n",
    "df.drop(['Rk', '2PM', '2PP', 'FantPt', 'DKPt', 'FDPt', \n",
    "         'VBD', 'PosRank', 'OvRank', 'PPR', 'Fmb', \n",
    "         'GS', 'Age', 'Tgt', 'Y/A', 'Att', 'Att.1', 'Cmp', 'Y/R'], axis=1, inplace=True)\n",
    "\n",
    "#fix name formatting\n",
    "df['Player'] = df['Player'].apply(lambda x: x.split('*')[0]).apply(lambda x: x.split('\\\\')[0])\n",
    "\n",
    "#rename columns\n",
    "df.rename({\n",
    "    'TD': 'PassingTD',\n",
    "    'TD.1': 'RushingTD',\n",
    "    'TD.2': 'ReceivingTD',\n",
    "    'TD.3': 'TotalTD',\n",
    "    'Yds': 'PassingYDs',\n",
    "    'Yds.1': 'RushingYDs',\n",
    "    'Yds.2': 'ReceivingYDs',\n",
    "}, axis=1, inplace=True)\n",
    "\n",
    "\"\"\"Extra stuff before we parition DataFrames by position\"\"\"\n",
    "\n",
    "#Make sure to put paranthesis if you break lines.\n",
    "df['FantasyPoints'] = (df['PassingYDs']*0.04 +df['Rec'] + df['PassingTD']*4 - df['Int']*2 + df['RushingYDs']*.1 \n",
    "                       + df['RushingTD']*6 + df['ReceivingYDs']*.1 + df['ReceivingTD']*6 - df['FL']*2)\n",
    "\n",
    "df['FantasyPoints/GM'] = df['FantasyPoints']/df['G']\n",
    "\n",
    "#Unfortnately, our DataFrame in limited. \n",
    "df = df[df['Tm'] != '2TM']\n",
    "df = df[df['Tm'] != '3TM']\n",
    "\n",
    "rb_df = df[df['FantPos'] == 'RB']\n",
    "rb_df['Rec/G'] = rb_df['Rec']/rb_df['G']\n",
    "rb_df = rb_df[rb_df['Rec/G'] > 5]\n",
    "\n",
    "df = df[['Tm', 'FantPos', 'FantasyPoints', 'FantasyPoints/GM']]\n",
    "rb_df = df[['Tm', 'FantPos', 'FantasyPoints', 'FantasyPoints/GM']]\n",
    "\n",
    "rb_df = rb_df[rb_df['Tm'] != '2TM']\n",
    "rb_df = rb_df[rb_df['Tm'] != '3TM']\n",
    "\n",
    "\"\"\" End Extra stuff \"\"\"\n",
    "\n",
    "#seperate dataframes based off position\n",
    "qb_df = df[df['FantPos'] == 'QB']\n",
    "wr_df = df[df['FantPos'] == 'WR']\n",
    "te_df = df[df['FantPos'] == 'TE']\n",
    "\n",
    "rb_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>QB1</th>\n",
       "      <th>RB1</th>\n",
       "      <th>RB2</th>\n",
       "      <th>WR1</th>\n",
       "      <th>WR2</th>\n",
       "      <th>WR3</th>\n",
       "      <th>TE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.662427</td>\n",
       "      <td>-0.942848</td>\n",
       "      <td>0.193401</td>\n",
       "      <td>-0.321642</td>\n",
       "      <td>1.908750</td>\n",
       "      <td>-0.788397</td>\n",
       "      <td>-0.151962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.955497</td>\n",
       "      <td>0.422255</td>\n",
       "      <td>0.884451</td>\n",
       "      <td>0.574035</td>\n",
       "      <td>-2.049843</td>\n",
       "      <td>-1.426291</td>\n",
       "      <td>2.224263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.123029</td>\n",
       "      <td>0.143862</td>\n",
       "      <td>3.163930</td>\n",
       "      <td>0.148401</td>\n",
       "      <td>0.894236</td>\n",
       "      <td>1.249023</td>\n",
       "      <td>0.238273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.761624</td>\n",
       "      <td>-0.646450</td>\n",
       "      <td>-1.418682</td>\n",
       "      <td>0.044796</td>\n",
       "      <td>1.044976</td>\n",
       "      <td>0.704022</td>\n",
       "      <td>-2.492340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.064568</td>\n",
       "      <td>-0.275181</td>\n",
       "      <td>-1.917817</td>\n",
       "      <td>-0.538464</td>\n",
       "      <td>-1.120904</td>\n",
       "      <td>0.387909</td>\n",
       "      <td>-1.053393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.245629</td>\n",
       "      <td>-0.369765</td>\n",
       "      <td>-0.292815</td>\n",
       "      <td>-1.286994</td>\n",
       "      <td>1.492433</td>\n",
       "      <td>1.740076</td>\n",
       "      <td>-1.198034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-0.029742</td>\n",
       "      <td>0.165455</td>\n",
       "      <td>-0.321102</td>\n",
       "      <td>-0.343957</td>\n",
       "      <td>-0.793967</td>\n",
       "      <td>0.980073</td>\n",
       "      <td>0.612537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.172555</td>\n",
       "      <td>0.332189</td>\n",
       "      <td>-0.525807</td>\n",
       "      <td>0.722858</td>\n",
       "      <td>2.903896</td>\n",
       "      <td>-0.492734</td>\n",
       "      <td>-0.788610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.546073</td>\n",
       "      <td>0.149860</td>\n",
       "      <td>-0.239813</td>\n",
       "      <td>1.768029</td>\n",
       "      <td>1.080489</td>\n",
       "      <td>1.276057</td>\n",
       "      <td>-1.095745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.525823</td>\n",
       "      <td>-1.278865</td>\n",
       "      <td>0.715868</td>\n",
       "      <td>0.147350</td>\n",
       "      <td>0.292447</td>\n",
       "      <td>1.028476</td>\n",
       "      <td>1.553367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        QB1       RB1       RB2       WR1       WR2       WR3        TE\n",
       "0  0.662427 -0.942848  0.193401 -0.321642  1.908750 -0.788397 -0.151962\n",
       "1 -1.955497  0.422255  0.884451  0.574035 -2.049843 -1.426291  2.224263\n",
       "2 -0.123029  0.143862  3.163930  0.148401  0.894236  1.249023  0.238273\n",
       "3  0.761624 -0.646450 -1.418682  0.044796  1.044976  0.704022 -2.492340\n",
       "4  0.064568 -0.275181 -1.917817 -0.538464 -1.120904  0.387909 -1.053393\n",
       "5  0.245629 -0.369765 -0.292815 -1.286994  1.492433  1.740076 -1.198034\n",
       "6 -0.029742  0.165455 -0.321102 -0.343957 -0.793967  0.980073  0.612537\n",
       "7  0.172555  0.332189 -0.525807  0.722858  2.903896 -0.492734 -0.788610\n",
       "8  0.546073  0.149860 -0.239813  1.768029  1.080489  1.276057 -1.095745\n",
       "9  0.525823 -1.278865  0.715868  0.147350  0.292447  1.028476  1.553367"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "examp_column_names = ['QB1', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE']\n",
    "\n",
    "random_numbers = np.random.randn(10, 7)\n",
    "\n",
    "example_df = pd.DataFrame(random_numbers, columns=examp_column_names)\n",
    "\n",
    "example_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>QB1</th>\n",
       "      <th>TE1</th>\n",
       "      <th>RB1</th>\n",
       "      <th>RB2</th>\n",
       "      <th>WR1</th>\n",
       "      <th>WR2</th>\n",
       "      <th>WR3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ARI</td>\n",
       "      <td>17.455000</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>17.455000</td>\n",
       "      <td>11.087500</td>\n",
       "      <td>11.087500</td>\n",
       "      <td>11.087500</td>\n",
       "      <td>6.536364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ATL</td>\n",
       "      <td>17.689333</td>\n",
       "      <td>14.592308</td>\n",
       "      <td>18.273333</td>\n",
       "      <td>17.689333</td>\n",
       "      <td>18.273333</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BAL</td>\n",
       "      <td>27.712000</td>\n",
       "      <td>13.813333</td>\n",
       "      <td>27.712000</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>10.457143</td>\n",
       "      <td>5.943750</td>\n",
       "      <td>3.756250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BUF</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>5.313333</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>14.654667</td>\n",
       "      <td>14.654667</td>\n",
       "      <td>12.053333</td>\n",
       "      <td>4.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CAR</td>\n",
       "      <td>13.652308</td>\n",
       "      <td>8.835714</td>\n",
       "      <td>29.325000</td>\n",
       "      <td>15.366667</td>\n",
       "      <td>15.366667</td>\n",
       "      <td>10.731250</td>\n",
       "      <td>3.556250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           QB1        TE1        RB1        RB2        WR1        WR2  \\\n",
       "Tm                                                                      \n",
       "ARI  17.455000   3.180000  17.455000  11.087500  11.087500  11.087500   \n",
       "ATL  17.689333  14.592308  18.273333  17.689333  18.273333  15.000000   \n",
       "BAL  27.712000  13.813333  27.712000  15.900000  10.457143   5.943750   \n",
       "BUF  17.660000   5.313333  17.660000  14.654667  14.654667  12.053333   \n",
       "CAR  13.652308   8.835714  29.325000  15.366667  15.366667  10.731250   \n",
       "\n",
       "          WR3  \n",
       "Tm             \n",
       "ARI  6.536364  \n",
       "ATL  6.175000  \n",
       "BAL  3.756250  \n",
       "BUF  4.220000  \n",
       "CAR  3.556250  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_top_players(df, n):\n",
    "    return df.groupby('Tm').apply(lambda x: x.nlargest(n, ['FantasyPoints']).min()).reset_index(drop=True)\n",
    "\n",
    "qb_df = get_top_players(qb_df, 1)\n",
    "te_df = get_top_players(te_df, 1)\n",
    "rb1_df = get_top_players(rb_df, 1)\n",
    "rb2_df = get_top_players(rb_df, 2)\n",
    "wr1_df = get_top_players(wr_df, 1)\n",
    "wr2_df = get_top_players(wr_df, 2)\n",
    "wr3_df = get_top_players(wr_df, 3)\n",
    "\n",
    "new_names = {\n",
    "    'QB1': qb_df,\n",
    "    'TE1': te_df,\n",
    "    'RB1': rb1_df,\n",
    "    'RB2': rb2_df,\n",
    "    'WR1': wr1_df,\n",
    "    'WR2': wr2_df,\n",
    "    'WR3': wr3_df\n",
    "}\n",
    "\n",
    "for name, new_df in new_names.items():\n",
    "    new_df.rename({'FantasyPoints/GM': name}, axis=1, inplace=True)\n",
    "    new_df.drop(['FantPos', 'FantasyPoints'], axis=1, inplace=True)\n",
    "    new_df.set_index('Tm', inplace=True)\n",
    "    \n",
    "df = pd.concat([qb_df, te_df, rb1_df, rb2_df, wr1_df, wr2_df, wr3_df], axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = df.corr()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(15, 10)\n",
    "\n",
    "cmap = sns.diverging_palette(0, 250, as_cmap=True)\n",
    "\n",
    "vizCorrMatrix = sns.heatmap(corrMatrix,cmap=cmap, center=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAhjGDAQAAE6o1H8LQwQAAAIYRMAAAgGGcIgUAABPaWO8zpHQwAACAcQQMAABgGAEDAAAYxgwGAABMyDK1AAAAOyRgAAAAwwgYAADAMGYwAABgQms+gqGDAQAAjCNgAAAAwwgYAADAMGYwAABgQhtrPoShgwEAAAyjgwEAABNa8waGDgYAADCOgAEAAAwjYAAAAMMIGAAAwDCGvAEAYEK15lPeOhgAAMAwAgYAADCMgAEAAAxjBgMAACa0sd4jGDoYAADAODoYAAAwoQ2rSAEAAOyMgAEAAAwjYAAAAMOYwQAAgAm5kjcAAMAO7ShgVNU3Hua5+40vBwAA2MsWBoyq+oGqOpDklqp6Z1U9ZMvmd66yMAAAYO9Z1sF4ZZK/191/O8n+JO+qqu+Zb9v25LGq2ldVV1XVVe97928PKhUAAPa+quluu2FZwDi5u29Iku5+W5J/kOTNVfUjSXq7F3X3/u4+r7vPe+yTnzmuWgAA4Li2bBWpu6rqgd39qSTp7huq6klJfi/JWSuvDgAA2FOWdTAuSvKArU9094EkT0zyihXVBAAA7FELOxjd/e5tnv9skn+zkooAAIA9a9kqUmdX1Zuq6tVVdXpV/X5VfaGqrqmq86YqEgAA1sVG1WS3Xfl8S7a/KckVSW5J8t4kFye5b5IXJXn9aksDAABWqarOr6oPVtVNVXXRgv2eUVW9kybDsoBxr/mKUK9K8qXu/u3uvrO735XkHkdYPwAAnPCOl2Vqq+qkzJoGT03y8CTPrqqHH2a/eyd5fmYNh6WWBYyDW+5/bsE2AABgb3lskpu6++bu/kqStya54DD7/W+ZXR/vzp286bJlas+pqmszu6jeWfP7mT9+6I7KBgAAjkenJfn4lscHkjxu6w5V9egkZ3T371XVi3bypssCxnceUYkAAMBxo6r2Jdm35an93b1/c/NhXvK1i2lX1UaSX0ny40fyM5ctU/vRbQo9KcmFSQ67HQAAOLwpF3eah4n922w+kOSMLY9Pz2xxp033TvKIJH9cs6IfmOSSqnp6d1+13c9ctkztqVX14qp6XVX9UM38TJKbkzxr6ScCAACOV1cmObuqzqyqkzNrIFyyubG77+ju+3X3Q7r7IUnek2RhuEiWnyL1liS3Z7ZU7U8n+bkkJye5oLs/cNQfBQAA2FXd/dWqel6SdyQ5KcnF3X1DVb08yVXdfcnidzi8ZQHjod39yCSpqjcmuTXJg7r780fzwwAAgONHd1+a5NJDnnvpNvs+cSfvuSxg3LXlDe+uqr8QLgAA4Ojt1hW2p7IsYHxXVW1e/6KSnDJ/XEm6u09daXUAAMCesmwVqZOmKgQAANj7ll3JGwAAYMcEDAAAYJhlMxgAAMBAaz7jrYMBAACMo4MBAAATqjVvYehgAAAAwwgYAADAMAIGAAAwjBkMAACY0JqPYOhgAAAA4wgYAADAMAIGAAAwjBkMAACY0MaaD2HoYAAAAMPoYAAAwIR0MAAAAHZIwAAAAIYRMAAAgGEEDAAAYBhD3gAAMKE1n/HWwQAAAMYRMAAAgGEEDAAAYBgzGAAAMKFa8yEMHQwAAGAYAQMAABhGwAAAAIZZ+QzGkx/9bav+EWzjOx548m6XcEK766sHd7uEE9rVH74tr/it63e7jBPWRc96xG6XAHDc2ljvEQwdDAAAYByrSAEAwISsIgUAALBDAgYAADCMgAEAAAxjBgMAACa05iMYOhgAAMA4AgYAADCMgAEAAAwjYAAAAMMY8gYAgAltrPmUtw4GAAAwjA4GAABMaM0bGDoYAADAOAIGAAAwjIABAAAMYwYDAAAmZAYDAABghwQMAABgGAEDAAAYxgwGAABMyJW8AQAAdkjAAAAAhhEwAACAYQQMAABgGEPeAAAwIUPeAAAAO6SDAQAAE1rzBoYOBgAAMI6AAQAADCNgAAAAw5jBAACACdWaD2HoYAAAAMMIGAAAwDACBgAAMIwZDAAAmNDGeo9g6GAAAADj6GAAAMCErCIFAACwQwIGAAAwjIABAAAMI2AAAADDGPIGAIAJrfmM99F3MKrqnJGFAAAAe9+xnCL1zmFVAAAAa2HhKVJV9drtNiW5z4LX7UuyL0le/PJfzj+88MeOukAAAGDvWDaD8RNJXpjky4fZ9uztXtTd+5PsT5Kr/vwzfdTVAQDAmtlY8yGMZQHjyiTXd/flh26oql9YSUUAAMCetSxgPCPJnYfb0N1nji8HAADYyxYOeXf3bd39xcNtq6onrKYkAABgr1o25H1SkmclOS3JH3T39VX1tCQvSXJKkkevvkQAAFgfaz6CsfQUqV9LckaS9yV5bVV9NMn3Jrmou9++6uIAAIC9ZVnAOC/Jo7r7YFXdM8mtSR7W3Z9afWkAALB+as1bGMsutPeV7j6YJN19Z5IPCRcAAMB2lgWMc6rq2vntui2Pr6uqa6coEAAAWI2qOr+qPlhVN1XVRYfZ/oKqunGeAf6wqh687D2XnSL180n+JMntSe46urIBAIDjzXxBp9cneUqSA0murKpLuvvGLbtdneS87v5iVf2LJK9M8o8Xve+ygHFaktckOSfJtUkuT3JZkiu6+7aj+iQAAHAC2zh+RjAem+Sm7r45SarqrUkuSPK1gNHd/23L/u9J8qPL3nRhwOjuF81/2MmZDXw/PslPJvmPVfXZ7n74EX4IAADg+HBako9veXwgyeMW7P9TSX5/2Zsu62BsOiXJqUm+ZX67Jcl1O3wtAACwC6pqX5J9W57a3937Nzcf5iW9zfv8aGYNh7+77Gcuu9De/iTnJvl8kvdmdorUq7v79mVvDAAA7K55mNi/zeYDmV3zbtPpmTUS/pqqenJms9l/t7u/vOxnLltF6kFJ7pHkU0k+MS/is8veFAAAOO5dmeTsqjpzPhJxYZJLtu5QVY9O8oYkT+/uT+/kTZfNYJxfsyuBnJvZ/MULkzyiqm7LbND7ZUf+OQAA4MS1cZxcaK+7v1pVz0vyjiQnJbm4u2+oqpcnuaq7L0nyS0nuleS35xcI/Fh3P33R+y6dwejuTnJ9VX02yR3z29MymzoXMAAAYI/q7kuTXHrIcy/dcv/JR/qey2Ywnp9Z5+IJmV0H47IkVyS5OIa8AQCAQyzrYDwkyduS/Gx3f3L15QAAAHvZshmMF0xVCAAAnAiOkxGMlVm2ihQAAMCO7fRCewAAwAC15i0MHQwAAGAYAQMAABhGwAAAAIYxgwEAABPaWO8RDB0MAABgHAEDAAAYRsAAAACGETAAAIBhDHkDAMCEXGgPAABgh3QwAABgQmvewNDBAAAAxhEwAACAYQQMAABgGDMYAAAwIatIAQAA7JCAAQAADCNgAAAAw5jBAACACW2s9wiGDgYAADCOgAEAAAyz8lOkTlr3HhBs47vP+tbdLuGEdtfdB3e7hBPWA+5zSq780Gd2u4wT2mO+/b67XQJwAjODAQAAE3IdDAAAgB0SMAAAgGGcIgUAABNa9xFlHQwAAGAYAQMAABhGwAAAAIYxgwEAABOyTC0AAMAOCRgAAMAwAgYAADCMGQwAAJiQ62AAAADskA4GAABMyCpSAAAAOyRgAAAAwwgYAADAMGYwAABgQms+gqGDAQAAjCNgAAAAwwgYAADAMAIGAAAwjCFvAACY0MaaT3nrYAAAAMMIGAAAwDACBgAAMIwZDAAAmNCaj2DoYAAAAOPoYAAAwIRqzVsYOhgAAMAwAgYAADCMgAEAAAxjBgMAACa0sd4jGDoYAADAOAIGAAAwjIABAAAMYwYDAAAm5DoYAAAAOyRgAAAAwwgYAADAMAIGAAAwjCFvAACY0JrPeOtgAAAA4+yog1FV39jddx3y3P26+9bVlAUAAOvp5Hxlwp92jwl/1szCDkZV/UBVHUhyS1W9s6oesmXzO1dZGAAAsPcsO0XqlUn+Xnf/7ST7k7yrqr5nvm3bs8eqal9VXVVVV/3OW988qFQAAOB4t+wUqZO7+4Yk6e63VdWfJfndqrooSW/3ou7en1kgydUfvm3b/QAAgPWyLGDcVVUP7O5PJUl331BVT0rye0nOWnl1AADAnrLsFKmLkjxg6xPdfSDJE5O8YkU1AQAAe9TCDkZ3v3ub5z9bVX+8kooAAIA9a2HAqKqTkjwryWlJ/qC7r6+qpyV5SZJTkjx69SUCAAB7xbIZjF9LckaS9yV5bVV9NMn3Jrmou9++6uIAAIC9ZVnAOC/Jo7r7YFXdM8mtSR62OfQNAACw1bIh769098Ek6e47k3xIuAAAALazrINxTlVdO79fSc6aP64k3d2PWml1AADAnrIsYPx8kj9JcnuSu1ZfDgAAsJctCxinJXlNknOSXJvk8iSXJbmiu29bcW0AAMAes+w6GC9Kkqo6ObOB78cn+ckk/7GqPtvdD199iQAAwF6xbMh70ylJTk3yLfPbLUneu6qiAACA1auq86vqg1V1U1VddJjt96iq35xvf29VPWTZey670N7+JOcm+XxmgeLyJK/u7tuP6hMAAADHhflFtV+f5ClJDiS5sqou6e4bt+z2U0lu7+6HVdWFSf5dkn+86H2XdTAelOQeST6V5BPzH/zZo/sIAADAceSxSW7q7pu7+ytJ3prkgkP2uSDJm+f335bkSVVVi9502QzG+fM3ODez+YsXJnlEVd2W2aD3y478cwAAAMeB05J8fMvjA0ket90+3f3VqrojyX0zuwD3YS1bRSrd3Umur6rPJrljfntaZolHwAAAgONUVe1Lsm/LU/u7e//m5sO8pA99ix3s89csm8F4fmadiydkdh2My5JckeTiJNctei0AALC75mFi/zabDyQ5Y8vj0zNbzOlw+xyoqm/IbMGnhZerWNbBeEhm51r9bHd/csm+AADA3nFlkrOr6szM5q0vTPJPDtnnkiQ/llmT4RlJ/mh+htO2ls1gvOCoywUAAI5b85mK5yV5R5KTklzc3TdU1cuTXNXdlyT5tSRvqaqbMutcXLjsfZfOYAAAAOupuy9Ncukhz710y/07kzzzSN5zpxfaAwAAWErAAAAAhhEwAACAYQQMAABgGAEDAAAYRsAAAACGETAAAIBhBAwAAGAYAQMAABhGwAAAAIYRMAAAgGEEDAAAYJhv2O0CAADgRHLPO++e7ofde7oftUkHAwAAGEbAAAAAhhEwAACAYcxgAADAlLp3u4KV0sEAAACGETAAAIBhBAwAAGAYMxgAADClg2YwAAAAdkTAAAAAhln5KVLffE9nYXFiOvWWT+12CSe07zj9vrtdwgnrgffe7QpObO+85vZ87NNf2O0yTmj/6PsetNslwK7SwQAAAIbRXgAAgCm50B4AAMDO6GAAAMCEWgcDAABgZwQMAABgGAEDAAAYxgwGAABM6aAZDAAAgB0RMAAAgGEEDAAAYBgzGAAAMCXXwQAAANgZAQMAABhGwAAAAIYRMAAAgGEMeQMAwITakDcAAMDO6GAAAMCUDupgAAAA7IiAAQAADCNgAAAAw5jBAACAKfXB3a5gpXQwAACAYQQMAABgGAEDAAAYxgwGAABMqF0HAwAAYGd0MAAAYEo6GAAAADsjYAAAAMMIGAAAwDACBgAAMIwhbwAAmFIb8gYAANgRAQMAABhGwAAAAIYxgwEAABNqMxgAAAA7I2AAAADDCBgAAMAwZjAAAGBKB81gAAAA7MjSDkZVbSRJdx+sqpOTPCLJR7r7tlUXBwAAa+dEXkWqqv5Bkk8m+URVXZDkvyd5VZJrq+rvT1AfAACwh9SidXir6uokT01ySpJrkjymuz9YVQ9O8jvdfd42r9uXZN/84f7u3j+27OlU1b69XP9e5/jvHsd+dzn+u8vx3z2O/e5y/BlhacDo7kfP71/f3Y/Ysu1Pu/vvTFDjrqqqq7YLUqye4797HPvd5fjvLsd/9zj2u8vxZ4SlQ96bMxhJfnLLcyclOXlVRQEAAHvTsoCxL/Mg0d3v2/L8GUlesaqiAACAvWnhKlLdfeU2z3+kqk5bTUnHHech7i7Hf/c49rvL8d9djv/ucex3l+PPMVs2g3FSkmclOS3JH3T39VX1tCQvSXLK5nwGAABAsjxg/Hpmp0O9L8njknw0yfcmuai73z5FgQAAwN6xbAbjvCRP6e4XJ/nhJM9M8sR1CRdVdXpV/Srnm5gAAAYPSURBVJeq+vOqurmqXldV96iqJ1bVHVX1gaq6tqreXVX3n7/mnKq6oqq+XFUv2u3PsFdV1X3nx/cDVfWpqvrElse95f4Hquqi+WueV1U3zbffb7c/w15WVXfPj+31VfV/V9V95s8/pKq+NN92TVVdXlXfMd9236r6b1X1/1XV63b3E+xtR3n8n1JV76+q6+b//MHd/RR7S1X9SlX9yy2P31FVb9zy+Jer6gVbjv+NVfWfquob59t9/4/BgOPv+38MBhz/x275b/I1VfUju/E52DuWBYyvdPfBJOnuO5N8qLs/tfqyVq+qKsnvJnl7d5+d5OzMrvfxyvku/727v7u7H5XkyiTPnT9/W5LnZ3bBQY5Sd39mfny/O8l/SPIrWx5/YfP+/La5oMBlSZ6cWSeNY/Ol+bF9RGbf6edu2fbh+bbvSvLmzE6JTJI7k/zrJIL1sTua439rkr/f3Y9M8mNJ3jJpxXvf5Uken3xtdcT7JTl3y/bHZ/bvmA/P/z30yCSnZ3aacOL7f6yO9fj7/h+bYz3+1yc5b77t/CRvqKqFc7yc2JYFjHPmf8G/tqqu2/L4uqq6dooCV+gHk9zZ3W9Kku6+O8nPJnlOkntt7jQPIvdOcvt8v0/Ph9/vmrziE1x3X93dH9ntOtbQFZnNWR3Oqfn6d/8L3f0nmf2ixTg7Pf5Xd/ct8+dvSHLPqrrHBPWti8sy/wUrs1+srk/y+ar6W/Pj+J2ZH+vka/9NeF/m/9v4/h+zYz3+vv/H5liP/xe7+6vzzfdMsv359ZAlq0gl+fkkf5LZl27dfqE+N8n7tz7R3Z+rqo8keViS76+qDyS5b5Iv5Ot/RWT1Tpkf+03/trt/c9eqWWM1W8jhSUl+bcvTZ82P/72TfFNm81eswDEc/3+U5Oru/vLqq1wP3X1LVX21qh6U2S9am8Hue5PckeTaJF/Z3L+q7pnZsf9fdqHctTP4+Pv+H6ERx7+qHpfk4iQPTvJPtwQO+BuWdTBOS/KazFLsm5P88ySPSPL57t7rp6lUDp/Aa/7PzVOkzkjypnz91ClWb/P0kc2bcDHeZoj7TJJvTfKuLds2T9E5K8m/jCULV+Goj39VnZvk32X272OOzOZfcTd/wbpiy+PL5/ucteV/m491917v1h9Pjvn4+/4fk2M6/t393u4+N8ljkrx4HkLgsBYGjO5+UXc/PskDM/sL/m2ZXdH7+qq6cYL6VumGzIbYv6aqTk3ygCQfPGTfS5L8jxPVBVP40vxc2gdndjHN526zn+/+ahzV8a+q05P85yTP6e4Pr7zK9bN5HvojMztF5D2Z/QV38/zz5OvnoD8syfdU1dN3o9A1dUzH3/f/mA35/nf3n2V2ZscjpiiavWlZB2PTKZmdC/wt89stSd67qqIm8odJvqmqnpN87VSFX07yuiRfOmTf70viX2asne6+I7NFC160uVrIIXz3V+hIjv98pan/muTF3X3ZYfZlucuSPC3Jbd19d3ffluQ+mf2SdcXWHbv7k0kuSvLiyatcX0d9/H3/hziW43/m5lB3VT04yXck+ch0pbPXLAwYVbW/qi5L8puZfQEvT/LM7j6vu39iigJXpWcXAPmRJM+oqj/PrB14sLv/zXyX799cji3JP03ywiSpqgdW1YEkL0jyr6rqwLzzwTin1F9fpvYVSVJVz58f+9OTXLt1iT2OXndfneSaJBfOnzpry3f/F5P89Oa+8xmlVyf58fl3/+FT17tujuD4Py+zvyr+6y3/37j/9BXvaddltnrOew557o7uvvUw+789sz9EfX/i+z/AsRx/3/9jdyzH//uSXDM/feo/J/mft3kNJFl+ob0/yOzLeH1m4eKKJNf3ohftUVX1+CS/keQfdvf7l+0PAAD8TQsDRvK1ZVrPzdcHgTbXbb+iu1+28goBAIA9Y2nA+NqOs+GqJ2QWMp6W5L7dfZ8V1gYAAOwxy06Ren5mgeIJmV0H47LMTpO6LMl1m1f5BgAASJZfaO8hSd6W5GfnKwoAAABsa8enSAEAACyz0+tgAAAALCVgAAAAwwgYAADAMAIGAAAwjIABAAAM8/8DVlbfATdH8cQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This is for the Part 3 of Python for Fantasy Football Analysis\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(15, 10)\n",
    "\n",
    "mask = np.zeros_like(corrMatrix, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "vizCorrMatrix = sns.heatmap(corrMatrix, mask=mask,cmap=cmap, center=0)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}