pandasBonus-Answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas BONUS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you are using Google Colab, you must run the next line of code. *If you are NOT using Google Colab, do NOT run the next line.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://raw.githubusercontent.com/aGitHasNoName/dataScienceBasics/main/pigeonRacing.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>First, we will import pandas and load the pigeon racing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"pigeonRacing.txt\", delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>Let's take a look at our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Position</th>\n",
       "      <th>Avg Unirate</th>\n",
       "      <th>Name</th>\n",
       "      <th>Racing Pigeon</th>\n",
       "      <th>Color</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Qualifying Race Miles</th>\n",
       "      <th>Average Birdage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.26%</td>\n",
       "      <td>Dean Schultz</td>\n",
       "      <td>751 AU 18 PURP</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>469, 469</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.08%</td>\n",
       "      <td>Dick Fassio</td>\n",
       "      <td>9027 AU 19 SLI</td>\n",
       "      <td>BBAR</td>\n",
       "      <td>F</td>\n",
       "      <td>579, 500</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.42%</td>\n",
       "      <td>Gary Mosher</td>\n",
       "      <td>32826 AU 17 AA</td>\n",
       "      <td>BKC</td>\n",
       "      <td>F</td>\n",
       "      <td>494, 539</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.21%</td>\n",
       "      <td>Todd Bartholomew</td>\n",
       "      <td>35624 AU 17 JEDD</td>\n",
       "      <td>BC</td>\n",
       "      <td>F</td>\n",
       "      <td>547, 468</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.61%</td>\n",
       "      <td>Dustin Maxfield</td>\n",
       "      <td>3322 AU 17 OGN</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>462, 462</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Position Avg Unirate              Name     Racing Pigeon Color Sex  \\\n",
       "0         1       0.26%      Dean Schultz    751 AU 18 PURP    BB   F   \n",
       "1         2       1.08%       Dick Fassio    9027 AU 19 SLI  BBAR   F   \n",
       "2         3       1.42%       Gary Mosher    32826 AU 17 AA   BKC   F   \n",
       "3         4       2.21%  Todd Bartholomew  35624 AU 17 JEDD    BC   F   \n",
       "4         5       2.61%   Dustin Maxfield    3322 AU 17 OGN    BB   M   \n",
       "\n",
       "  Qualifying Race Miles  Average Birdage  \n",
       "0              469, 469            612.0  \n",
       "1              579, 500            139.0  \n",
       "2              494, 539            103.0  \n",
       "3              547, 468            226.0  \n",
       "4              462, 462            171.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br>Working with Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>Let's look at a larger sample of our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Avg Unirate</th>\n",
       "      <th>Name</th>\n",
       "      <th>Racing Pigeon</th>\n",
       "      <th>Color</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Qualifying Race Miles</th>\n",
       "      <th>Average Birdage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.61%</td>\n",
       "      <td>Dustin Maxfield</td>\n",
       "      <td>3322 AU 17 OGN</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>462, 462</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>9.67%</td>\n",
       "      <td>Todd Bartholomew</td>\n",
       "      <td>7005 AU 19 OGN</td>\n",
       "      <td>BC</td>\n",
       "      <td>F</td>\n",
       "      <td>468, 469</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.42%</td>\n",
       "      <td>Gary Mosher</td>\n",
       "      <td>32826 AU 17 AA</td>\n",
       "      <td>BKC</td>\n",
       "      <td>F</td>\n",
       "      <td>494, 539</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>9.53%</td>\n",
       "      <td>Rick Cline</td>\n",
       "      <td>9041 AU 19 MM</td>\n",
       "      <td>DCP</td>\n",
       "      <td>M</td>\n",
       "      <td>466, 541</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>7.13%</td>\n",
       "      <td>Terry Lynn</td>\n",
       "      <td>8 AU 16 GCM</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>513, 518</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>3.31%</td>\n",
       "      <td>Rodney Desrosiers</td>\n",
       "      <td>1608 AU 17 SC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>563, 459</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>8.89%</td>\n",
       "      <td>Larry Mitchell</td>\n",
       "      <td>1955 AU 19 HOTR</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>584, 505</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>9.86%</td>\n",
       "      <td>Dave Tracey</td>\n",
       "      <td>34 AU 18 PCO</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>537, 535</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>8.01%</td>\n",
       "      <td>Gary Mosher</td>\n",
       "      <td>32882 AU 17 AA</td>\n",
       "      <td>BLS</td>\n",
       "      <td>M</td>\n",
       "      <td>539, 494</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>8.07%</td>\n",
       "      <td>Alven Gines</td>\n",
       "      <td>9833 AU 19 SLI</td>\n",
       "      <td>BBAR</td>\n",
       "      <td>F</td>\n",
       "      <td>584, 505</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Position Avg Unirate               Name    Racing Pigeon Color Sex  \\\n",
       "4          5       2.61%    Dustin Maxfield   3322 AU 17 OGN    BB   M   \n",
       "31        32       9.67%   Todd Bartholomew   7005 AU 19 OGN    BC   F   \n",
       "2          3       1.42%        Gary Mosher   32826 AU 17 AA   BKC   F   \n",
       "29        30       9.53%         Rick Cline    9041 AU 19 MM   DCP   M   \n",
       "21        22       7.13%         Terry Lynn      8 AU 16 GCM    BB   F   \n",
       "8          9       3.31%  Rodney Desrosiers    1608 AU 17 SC   NaN   F   \n",
       "27        28       8.89%     Larry Mitchell  1955 AU 19 HOTR    BB   M   \n",
       "33        34       9.86%        Dave Tracey     34 AU 18 PCO    BB   M   \n",
       "22        23       8.01%        Gary Mosher   32882 AU 17 AA   BLS   M   \n",
       "23        24       8.07%        Alven Gines   9833 AU 19 SLI  BBAR   F   \n",
       "\n",
       "   Qualifying Race Miles  Average Birdage  \n",
       "4               462, 462            171.0  \n",
       "31              468, 469            139.0  \n",
       "2               494, 539            103.0  \n",
       "29              466, 541             42.0  \n",
       "21              513, 518             49.0  \n",
       "8               563, 459             46.0  \n",
       "27              584, 505            123.0  \n",
       "33              537, 535             72.0  \n",
       "22              539, 494            103.0  \n",
       "23              584, 505            123.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>Missing values are represented as a special pandas object: **`NaN`**. It stands for Not a Number. The NaN value will let you complete numerical calculations on your data - it will ignore the value, so it will not include the value in calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149.79310344827587"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Average Birdage\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Finding missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look for missing data in our DataFrame using `isnull()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Position</th>\n",
       "      <th>Avg Unirate</th>\n",
       "      <th>Name</th>\n",
       "      <th>Racing Pigeon</th>\n",
       "      <th>Color</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Qualifying Race Miles</th>\n",
       "      <th>Average Birdage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Position  Avg Unirate   Name  Racing Pigeon  Color    Sex  \\\n",
       "0      False        False  False          False  False  False   \n",
       "1      False        False  False          False  False  False   \n",
       "2      False        False  False          False  False  False   \n",
       "3      False        False  False          False  False  False   \n",
       "4      False        False  False          False  False  False   \n",
       "5      False        False  False          False   True  False   \n",
       "6      False        False  False          False  False  False   \n",
       "7      False        False  False          False  False  False   \n",
       "8      False        False  False          False   True  False   \n",
       "9      False        False  False          False  False  False   \n",
       "10     False        False  False          False  False  False   \n",
       "11     False        False  False          False  False  False   \n",
       "12     False        False  False          False   True  False   \n",
       "13     False        False  False          False  False  False   \n",
       "14     False        False  False          False  False  False   \n",
       "15     False        False  False          False  False  False   \n",
       "16     False        False  False          False  False  False   \n",
       "17     False        False  False          False  False  False   \n",
       "18     False        False  False          False  False  False   \n",
       "19     False        False  False          False   True  False   \n",
       "20     False        False  False          False   True  False   \n",
       "21     False        False  False          False  False  False   \n",
       "22     False        False  False          False  False  False   \n",
       "23     False        False  False          False  False  False   \n",
       "24     False        False  False          False  False  False   \n",
       "25     False        False  False          False  False  False   \n",
       "26     False        False  False          False   True  False   \n",
       "27     False        False  False          False  False  False   \n",
       "28     False        False  False          False   True  False   \n",
       "29     False        False  False          False  False  False   \n",
       "30     False        False  False          False  False  False   \n",
       "31     False        False  False          False  False  False   \n",
       "32     False        False  False          False  False  False   \n",
       "33     False        False  False          False  False  False   \n",
       "\n",
       "    Qualifying Race Miles  Average Birdage  \n",
       "0                   False            False  \n",
       "1                   False            False  \n",
       "2                   False            False  \n",
       "3                   False            False  \n",
       "4                   False            False  \n",
       "5                   False            False  \n",
       "6                   False             True  \n",
       "7                   False            False  \n",
       "8                   False            False  \n",
       "9                   False            False  \n",
       "10                  False            False  \n",
       "11                  False            False  \n",
       "12                  False            False  \n",
       "13                  False            False  \n",
       "14                  False            False  \n",
       "15                  False            False  \n",
       "16                  False            False  \n",
       "17                  False            False  \n",
       "18                  False             True  \n",
       "19                  False            False  \n",
       "20                  False             True  \n",
       "21                  False            False  \n",
       "22                  False            False  \n",
       "23                  False            False  \n",
       "24                  False            False  \n",
       "25                  False            False  \n",
       "26                  False            False  \n",
       "27                  False            False  \n",
       "28                  False             True  \n",
       "29                  False            False  \n",
       "30                  False            False  \n",
       "31                  False            False  \n",
       "32                  False             True  \n",
       "33                  False            False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>We can do the opposite - look for all values that aren't missing - using `notnull()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Avg Unirate</th>\n",
       "      <th>Name</th>\n",
       "      <th>Racing Pigeon</th>\n",
       "      <th>Color</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Qualifying Race Miles</th>\n",
       "      <th>Average Birdage</th>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>3</th>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>7</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
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       "      <th>11</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Position  Avg Unirate  Name  Racing Pigeon  Color   Sex  \\\n",
       "0       True         True  True           True   True  True   \n",
       "1       True         True  True           True   True  True   \n",
       "2       True         True  True           True   True  True   \n",
       "3       True         True  True           True   True  True   \n",
       "4       True         True  True           True   True  True   \n",
       "5       True         True  True           True  False  True   \n",
       "6       True         True  True           True   True  True   \n",
       "7       True         True  True           True   True  True   \n",
       "8       True         True  True           True  False  True   \n",
       "9       True         True  True           True   True  True   \n",
       "10      True         True  True           True   True  True   \n",
       "11      True         True  True           True   True  True   \n",
       "12      True         True  True           True  False  True   \n",
       "13      True         True  True           True   True  True   \n",
       "14      True         True  True           True   True  True   \n",
       "15      True         True  True           True   True  True   \n",
       "16      True         True  True           True   True  True   \n",
       "17      True         True  True           True   True  True   \n",
       "18      True         True  True           True   True  True   \n",
       "19      True         True  True           True  False  True   \n",
       "20      True         True  True           True  False  True   \n",
       "21      True         True  True           True   True  True   \n",
       "22      True         True  True           True   True  True   \n",
       "23      True         True  True           True   True  True   \n",
       "24      True         True  True           True   True  True   \n",
       "25      True         True  True           True   True  True   \n",
       "26      True         True  True           True  False  True   \n",
       "27      True         True  True           True   True  True   \n",
       "28      True         True  True           True  False  True   \n",
       "29      True         True  True           True   True  True   \n",
       "30      True         True  True           True   True  True   \n",
       "31      True         True  True           True   True  True   \n",
       "32      True         True  True           True   True  True   \n",
       "33      True         True  True           True   True  True   \n",
       "\n",
       "    Qualifying Race Miles  Average Birdage  \n",
       "0                    True             True  \n",
       "1                    True             True  \n",
       "2                    True             True  \n",
       "3                    True             True  \n",
       "4                    True             True  \n",
       "5                    True             True  \n",
       "6                    True            False  \n",
       "7                    True             True  \n",
       "8                    True             True  \n",
       "9                    True             True  \n",
       "10                   True             True  \n",
       "11                   True             True  \n",
       "12                   True             True  \n",
       "13                   True             True  \n",
       "14                   True             True  \n",
       "15                   True             True  \n",
       "16                   True             True  \n",
       "17                   True             True  \n",
       "18                   True            False  \n",
       "19                   True             True  \n",
       "20                   True            False  \n",
       "21                   True             True  \n",
       "22                   True             True  \n",
       "23                   True             True  \n",
       "24                   True             True  \n",
       "25                   True             True  \n",
       "26                   True             True  \n",
       "27                   True             True  \n",
       "28                   True            False  \n",
       "29                   True             True  \n",
       "30                   True             True  \n",
       "31                   True             True  \n",
       "32                   True            False  \n",
       "33                   True             True  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>This would be hard to sort through in a big DataFrame, so we can add the `nunique()` aggregation function to see which columns have missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Position                 1\n",
       "Avg Unirate              1\n",
       "Name                     1\n",
       "Racing Pigeon            1\n",
       "Color                    2\n",
       "Sex                      1\n",
       "Qualifying Race Miles    1\n",
       "Average Birdage          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>Or we could check a particular column of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Color\"].isnull().nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>Another way to look for missing data is to get a count of each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Position                 34\n",
       "Avg Unirate              34\n",
       "Name                     34\n",
       "Racing Pigeon            34\n",
       "Color                    27\n",
       "Sex                      34\n",
       "Qualifying Race Miles    34\n",
       "Average Birdage          29\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>We have two columns with missing data - one is numerical data (Average Birdage) and one is categorial (Color).\n",
    "<br><br>We're going to walk through a few common ways to deal with missing data. \n",
    "<br><br>First, let's check the number of rows and columns in our original DataFrame to compare later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Drop all rows with NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop every row with any NaN value using the `dropna()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_nan_rows = df.dropna()\n",
    "df_no_nan_rows.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Drop all columns with NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same function, but we add the keyword argument for columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34, 6)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_nan_columns = df.dropna(axis=1)\n",
    "df_no_nan_columns.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Drop rows with NaN in a particular column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first, you might try to use the regular `drop()` function with a boolean, but this won't work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'[False, False, False, False, False, True, False, False, True, False, False, False, True, False, False, False, False, False, False, True, True, False, False, False, False, False, True, False, True, False, False, False, False, False] not found in axis'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_drop_rows_nan_color \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColor\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39misnull())\n\u001b[1;32m      2\u001b[0m df_drop_rows_nan_color\u001b[38;5;241m.\u001b[39mshape\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:5581\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5433\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[1;32m   5434\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   5435\u001b[0m     labels: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5442\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   5443\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5444\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   5445\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m   5446\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5579\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[1;32m   5580\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5581\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdrop(\n\u001b[1;32m   5582\u001b[0m         labels\u001b[38;5;241m=\u001b[39mlabels,\n\u001b[1;32m   5583\u001b[0m         axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[1;32m   5584\u001b[0m         index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[1;32m   5585\u001b[0m         columns\u001b[38;5;241m=\u001b[39mcolumns,\n\u001b[1;32m   5586\u001b[0m         level\u001b[38;5;241m=\u001b[39mlevel,\n\u001b[1;32m   5587\u001b[0m         inplace\u001b[38;5;241m=\u001b[39minplace,\n\u001b[1;32m   5588\u001b[0m         errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[1;32m   5589\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/generic.py:4788\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4786\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m   4787\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4788\u001b[0m         obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_drop_axis(labels, axis, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m   4790\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[1;32m   4791\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/generic.py:4830\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4828\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m   4829\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4830\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m   4831\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m   4833\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m   4834\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:7070\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   7068\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m   7069\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 7070\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask]\u001b[38;5;241m.\u001b[39mtolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   7071\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[1;32m   7072\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[0;31mKeyError\u001b[0m: '[False, False, False, False, False, True, False, False, True, False, False, False, True, False, False, False, False, False, False, True, True, False, False, False, False, False, True, False, True, False, False, False, False, False] not found in axis'"
     ]
    }
   ],
   "source": [
    "df_drop_rows_nan_color = df.drop(df[\"Color\"].isnull())\n",
    "df_drop_rows_nan_color.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>Instead, we can create a subset of the DataFrame that includes only rows that are not null in the Color column. We are using the pandas function `notnull()` to return any non-NaN values. Instead of adding the function to the end of the DataFrame or the end of the column, we use the function on its own with the `pd.notnull()` syntax. We pass the function the piece of the DataFrame that we want it to search through. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 8)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop_rows_nan_color = df[pd.notnull(df[\"Color\"])]\n",
    "df_drop_rows_nan_color.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Replace all NaNs with zero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has a `fillna()` function that will work over the entire DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34, 8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fill_zeros = df.fillna(0)\n",
    "df_fill_zeros.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Avg Unirate</th>\n",
       "      <th>Name</th>\n",
       "      <th>Racing Pigeon</th>\n",
       "      <th>Color</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Qualifying Race Miles</th>\n",
       "      <th>Average Birdage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>5.88%</td>\n",
       "      <td>Kevin Coey</td>\n",
       "      <td>349 AU 17 BUCK</td>\n",
       "      <td>BLCH</td>\n",
       "      <td>F</td>\n",
       "      <td>536, 540</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.21%</td>\n",
       "      <td>Todd Bartholomew</td>\n",
       "      <td>35624 AU 17 JEDD</td>\n",
       "      <td>BC</td>\n",
       "      <td>F</td>\n",
       "      <td>547, 468</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>4.68%</td>\n",
       "      <td>Dennis Heidenreich</td>\n",
       "      <td>1127 AU 18 UVI</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>594, 515</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.26%</td>\n",
       "      <td>Dean Schultz</td>\n",
       "      <td>751 AU 18 PURP</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>469, 469</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>8.11%</td>\n",
       "      <td>Lek Dhiraprasiddhi</td>\n",
       "      <td>161 AU 15 BUCK</td>\n",
       "      <td>DCWF</td>\n",
       "      <td>F</td>\n",
       "      <td>520, 506</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>4.70%</td>\n",
       "      <td>Butch Smith</td>\n",
       "      <td>313 AU 18 ER</td>\n",
       "      <td>BLK</td>\n",
       "      <td>F</td>\n",
       "      <td>505, 593</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>6.47%</td>\n",
       "      <td>Thomas Erdner</td>\n",
       "      <td>12011 AU 19 FOYS</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>553, 470</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>4.76%</td>\n",
       "      <td>Gordon Smith</td>\n",
       "      <td>4592 AU 19 SMIT</td>\n",
       "      <td>BCHK</td>\n",
       "      <td>F</td>\n",
       "      <td>516, 595</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>7.13%</td>\n",
       "      <td>Terry Lynn</td>\n",
       "      <td>8 AU 16 GCM</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>513, 518</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>6.07%</td>\n",
       "      <td>Joe Bevington</td>\n",
       "      <td>15377 AU 19 AA</td>\n",
       "      <td>DC</td>\n",
       "      <td>F</td>\n",
       "      <td>488, 572</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Position Avg Unirate                Name     Racing Pigeon Color Sex  \\\n",
       "16        17       5.88%          Kevin Coey    349 AU 17 BUCK  BLCH   F   \n",
       "3          4       2.21%    Todd Bartholomew  35624 AU 17 JEDD    BC   F   \n",
       "12        13       4.68%  Dennis Heidenreich    1127 AU 18 UVI     0   F   \n",
       "0          1       0.26%        Dean Schultz    751 AU 18 PURP    BB   F   \n",
       "24        25       8.11%  Lek Dhiraprasiddhi    161 AU 15 BUCK  DCWF   F   \n",
       "13        14       4.70%         Butch Smith      313 AU 18 ER   BLK   F   \n",
       "18        19       6.47%       Thomas Erdner  12011 AU 19 FOYS    BB   F   \n",
       "14        15       4.76%        Gordon Smith   4592 AU 19 SMIT  BCHK   F   \n",
       "21        22       7.13%          Terry Lynn       8 AU 16 GCM    BB   F   \n",
       "17        18       6.07%       Joe Bevington    15377 AU 19 AA    DC   F   \n",
       "\n",
       "   Qualifying Race Miles  Average Birdage  \n",
       "16              536, 540             72.0  \n",
       "3               547, 468            226.0  \n",
       "12              594, 515             85.0  \n",
       "0               469, 469            612.0  \n",
       "24              520, 506             49.0  \n",
       "13              505, 593             32.0  \n",
       "18              553, 470              0.0  \n",
       "14              516, 595             91.0  \n",
       "21              513, 518             49.0  \n",
       "17              488, 572            353.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fill_zeros.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>This replaced NaNs in both the Colors and Average Birdage columns with 0. That might not be what you want to do."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Replace NaNs in only one column with the mean value of that column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one is tricky. I don't expect you to remember how to do this. As with many things in Pandas, it isn't always intuitive how to solve every unique situation. Pandas users rely on looking things up online, and that is what I did to solve this one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>First, we need to make a copy of the original DataFrame because we are going to reference the new version on the left side of our assignment operator. If we don't do this, we would have to change the original df.\n",
    "<br><br>Then I am going to reassign the Average Birdage column to the same column, but with the `fillna()` function applied. I am going to fill the NaNs with the mean value of that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Position</th>\n",
       "      <th>Avg Unirate</th>\n",
       "      <th>Name</th>\n",
       "      <th>Racing Pigeon</th>\n",
       "      <th>Color</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Qualifying Race Miles</th>\n",
       "      <th>Average Birdage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>8.89%</td>\n",
       "      <td>Larry Mitchell</td>\n",
       "      <td>1955 AU 19 HOTR</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>584, 505</td>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>6.81%</td>\n",
       "      <td>Dustin Maxfield</td>\n",
       "      <td>7800 AU 19 OGN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>541, 462</td>\n",
       "      <td>149.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>6.47%</td>\n",
       "      <td>Thomas Erdner</td>\n",
       "      <td>12011 AU 19 FOYS</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>553, 470</td>\n",
       "      <td>149.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>6.78%</td>\n",
       "      <td>Keith Delpozzo</td>\n",
       "      <td>913 AU 16 SRI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>466, 567</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.26%</td>\n",
       "      <td>Dean Schultz</td>\n",
       "      <td>751 AU 18 PURP</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>469, 469</td>\n",
       "      <td>612.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>8.07%</td>\n",
       "      <td>Alven Gines</td>\n",
       "      <td>9833 AU 19 SLI</td>\n",
       "      <td>BBAR</td>\n",
       "      <td>F</td>\n",
       "      <td>584, 505</td>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>9.69%</td>\n",
       "      <td>Curt Templeton</td>\n",
       "      <td>784 AU 17 POLK</td>\n",
       "      <td>BBAR</td>\n",
       "      <td>F</td>\n",
       "      <td>462, 462</td>\n",
       "      <td>149.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.61%</td>\n",
       "      <td>Dustin Maxfield</td>\n",
       "      <td>3322 AU 17 OGN</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>462, 462</td>\n",
       "      <td>171.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>9.67%</td>\n",
       "      <td>Todd Bartholomew</td>\n",
       "      <td>7005 AU 19 OGN</td>\n",
       "      <td>BC</td>\n",
       "      <td>F</td>\n",
       "      <td>468, 469</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.42%</td>\n",
       "      <td>Gary Mosher</td>\n",
       "      <td>32826 AU 17 AA</td>\n",
       "      <td>BKC</td>\n",
       "      <td>F</td>\n",
       "      <td>494, 539</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Position Avg Unirate              Name     Racing Pigeon Color Sex  \\\n",
       "27        28       8.89%    Larry Mitchell   1955 AU 19 HOTR    BB   M   \n",
       "20        21       6.81%   Dustin Maxfield    7800 AU 19 OGN   NaN   F   \n",
       "18        19       6.47%     Thomas Erdner  12011 AU 19 FOYS    BB   F   \n",
       "19        20       6.78%    Keith Delpozzo     913 AU 16 SRI   NaN   M   \n",
       "0          1       0.26%      Dean Schultz    751 AU 18 PURP    BB   F   \n",
       "23        24       8.07%       Alven Gines    9833 AU 19 SLI  BBAR   F   \n",
       "32        33       9.69%    Curt Templeton    784 AU 17 POLK  BBAR   F   \n",
       "4          5       2.61%   Dustin Maxfield    3322 AU 17 OGN    BB   M   \n",
       "31        32       9.67%  Todd Bartholomew    7005 AU 19 OGN    BC   F   \n",
       "2          3       1.42%       Gary Mosher    32826 AU 17 AA   BKC   F   \n",
       "\n",
       "   Qualifying Race Miles  Average Birdage  \n",
       "27              584, 505       123.000000  \n",
       "20              541, 462       149.793103  \n",
       "18              553, 470       149.793103  \n",
       "19              466, 567        31.000000  \n",
       "0               469, 469       612.000000  \n",
       "23              584, 505       123.000000  \n",
       "32              462, 462       149.793103  \n",
       "4               462, 462       171.000000  \n",
       "31              468, 469       139.000000  \n",
       "2               494, 539       103.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_birdage = df.copy()\n",
    "df_mean_birdage[\"Average Birdage\"] = df_mean_birdage[\"Average Birdage\"].fillna(df[\"Average Birdage\"].mean())\n",
    "df_mean_birdage.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write code to replace any missing data in the `Color` column with the string \"BB\". I've started for you by making a copy of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_replace_colors = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_replace_colors[\"Color\"] = df_replace_colors[\"Color\"].fillna(\"BB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Avg Unirate</th>\n",
       "      <th>Name</th>\n",
       "      <th>Racing Pigeon</th>\n",
       "      <th>Color</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Qualifying Race Miles</th>\n",
       "      <th>Average Birdage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>3.44%</td>\n",
       "      <td>Dave Tracey</td>\n",
       "      <td>324 AU 17 GCM</td>\n",
       "      <td>BECA</td>\n",
       "      <td>F</td>\n",
       "      <td>535, 537</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>3.69%</td>\n",
       "      <td>Gary Mosher</td>\n",
       "      <td>47663 AU 17 AA</td>\n",
       "      <td>BLS</td>\n",
       "      <td>M</td>\n",
       "      <td>539, 494</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>6.47%</td>\n",
       "      <td>Thomas Erdner</td>\n",
       "      <td>12011 AU 19 FOYS</td>\n",
       "      <td>BB</td>\n",
       "      <td>F</td>\n",
       "      <td>553, 470</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3.27%</td>\n",
       "      <td>Dean Schultz</td>\n",
       "      <td>1707 AU 17 SCHU</td>\n",
       "      <td>BC</td>\n",
       "      <td>F</td>\n",
       "      <td>469, 469</td>\n",
       "      <td>526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>6.07%</td>\n",
       "      <td>Joe Bevington</td>\n",
       "      <td>15377 AU 19 AA</td>\n",
       "      <td>DC</td>\n",
       "      <td>F</td>\n",
       "      <td>488, 572</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.08%</td>\n",
       "      <td>Dick Fassio</td>\n",
       "      <td>9027 AU 19 SLI</td>\n",
       "      <td>BBAR</td>\n",
       "      <td>F</td>\n",
       "      <td>579, 500</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>4.70%</td>\n",
       "      <td>Butch Smith</td>\n",
       "      <td>313 AU 18 ER</td>\n",
       "      <td>BLK</td>\n",
       "      <td>F</td>\n",
       "      <td>505, 593</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>8.81%</td>\n",
       "      <td>Edward King</td>\n",
       "      <td>77692 AU 16 ARPU</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>632, 538</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>9.65%</td>\n",
       "      <td>Joe Bevington</td>\n",
       "      <td>25221 AU 18 ARPU</td>\n",
       "      <td>BC</td>\n",
       "      <td>F</td>\n",
       "      <td>488, 572</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.61%</td>\n",
       "      <td>Dustin Maxfield</td>\n",
       "      <td>3322 AU 17 OGN</td>\n",
       "      <td>BB</td>\n",
       "      <td>M</td>\n",
       "      <td>462, 462</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Position Avg Unirate             Name     Racing Pigeon Color Sex  \\\n",
       "9         10       3.44%      Dave Tracey     324 AU 17 GCM  BECA   F   \n",
       "10        11       3.69%      Gary Mosher    47663 AU 17 AA   BLS   M   \n",
       "18        19       6.47%    Thomas Erdner  12011 AU 19 FOYS    BB   F   \n",
       "7          8       3.27%     Dean Schultz   1707 AU 17 SCHU    BC   F   \n",
       "17        18       6.07%    Joe Bevington    15377 AU 19 AA    DC   F   \n",
       "1          2       1.08%      Dick Fassio    9027 AU 19 SLI  BBAR   F   \n",
       "13        14       4.70%      Butch Smith      313 AU 18 ER   BLK   F   \n",
       "26        27       8.81%      Edward King  77692 AU 16 ARPU    BB   M   \n",
       "30        31       9.65%    Joe Bevington  25221 AU 18 ARPU    BC   F   \n",
       "4          5       2.61%  Dustin Maxfield    3322 AU 17 OGN    BB   M   \n",
       "\n",
       "   Qualifying Race Miles  Average Birdage  \n",
       "9               535, 537             72.0  \n",
       "10              539, 494            103.0  \n",
       "18              553, 470              NaN  \n",
       "7               469, 469            526.0  \n",
       "17              488, 572            353.0  \n",
       "1               579, 500            139.0  \n",
       "13              505, 593             32.0  \n",
       "26              632, 538             76.0  \n",
       "30              488, 572            353.0  \n",
       "4               462, 462            171.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_replace_colors.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br>Data types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's reload the WNBA game data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you are using Google Colab, you must run the next line of code. *If you are NOT using Google Colab, do NOT run the next line.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://raw.githubusercontent.com/aGitHasNoName/pandasBasics/main/wnba-team-elo-ratings.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>Our sample dataset was taken from FiveThirtyEight. It contains game data for WNBA games since 1997."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"wnba-team-elo-ratings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/10/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/8/2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>10/6/2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season        date team1 team2               name1               name2  \\\n",
       "0    2019  10/10/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1    2019  10/10/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2    2019   10/8/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3    2019   10/8/2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "4    2019   10/6/2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "0        0        1      89      78      1684      1634       1692       1627   \n",
       "1        0        1      78      89      1634      1684       1627       1692   \n",
       "2        0        1      86      90      1693      1626       1684       1634   \n",
       "3        0        1      90      86      1626      1693       1634       1684   \n",
       "4        0        1      94      81      1671      1648       1693       1626   \n",
       "\n",
       "   prob1  is_home1  \n",
       "0  0.718         1  \n",
       "1  0.282         0  \n",
       "2  0.476         0  \n",
       "3  0.524         1  \n",
       "4  0.399         0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>We can check the data type of all columns using the `dtypes` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season         int64\n",
       "date          object\n",
       "team1         object\n",
       "team2         object\n",
       "name1         object\n",
       "name2         object\n",
       "neutral        int64\n",
       "playoff        int64\n",
       "score1         int64\n",
       "score2         int64\n",
       "elo1_pre       int64\n",
       "elo2_pre       int64\n",
       "elo1_post      int64\n",
       "elo2_post      int64\n",
       "prob1        float64\n",
       "is_home1       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>An `object` is a string or a column with mixed datatypes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the data type of a column using the method function `astype()`. Let's change the elo ratings from integers to floats. We can only change one column at a time. Since we only want the change applied to one column, we have to only include that column on the left side of our assignment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season         int64\n",
       "date          object\n",
       "team1         object\n",
       "team2         object\n",
       "name1         object\n",
       "name2         object\n",
       "neutral        int64\n",
       "playoff        int64\n",
       "score1         int64\n",
       "score2         int64\n",
       "elo1_pre     float64\n",
       "elo2_pre       int64\n",
       "elo1_post      int64\n",
       "elo2_post      int64\n",
       "prob1        float64\n",
       "is_home1       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"elo1_pre\"] = df[\"elo1_pre\"].astype(\"float\")\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>To change all the elo columns, we could write a loop:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season         int64\n",
       "date          object\n",
       "team1         object\n",
       "team2         object\n",
       "name1         object\n",
       "name2         object\n",
       "neutral        int64\n",
       "playoff        int64\n",
       "score1         int64\n",
       "score2         int64\n",
       "elo1_pre     float64\n",
       "elo2_pre     float64\n",
       "elo1_post    float64\n",
       "elo2_post    float64\n",
       "prob1        float64\n",
       "is_home1       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in [\"elo2_pre\", \"elo1_post\", \"elo2_post\"]:\n",
    "    df[i] = df[i].astype(\"float\")\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write code to change the `season` column from an integer to an `object`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"season\"] = df[\"season\"].astype(\"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season        object\n",
       "date          object\n",
       "team1         object\n",
       "team2         object\n",
       "name1         object\n",
       "name2         object\n",
       "neutral        int64\n",
       "playoff        int64\n",
       "score1         int64\n",
       "score2         int64\n",
       "elo1_pre     float64\n",
       "elo2_pre     float64\n",
       "elo1_post    float64\n",
       "elo2_post    float64\n",
       "prob1        float64\n",
       "is_home1       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write code to change the playoff column from an integer to a boolean (try `bool`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"playoff\"] = df[\"playoff\"].astype(\"bool\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2594</th>\n",
       "      <td>2014</td>\n",
       "      <td>5/30/2014</td>\n",
       "      <td>MIN</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>88</td>\n",
       "      <td>72</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>1471.0</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>0.810</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7487</th>\n",
       "      <td>2003</td>\n",
       "      <td>8/16/2003</td>\n",
       "      <td>PHO</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>61</td>\n",
       "      <td>65</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>1558.0</td>\n",
       "      <td>1372.0</td>\n",
       "      <td>1564.0</td>\n",
       "      <td>0.359</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>2016</td>\n",
       "      <td>9/18/2016</td>\n",
       "      <td>LVA</td>\n",
       "      <td>PHO</td>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>65</td>\n",
       "      <td>81</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>1527.0</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>1538.0</td>\n",
       "      <td>0.320</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10145</th>\n",
       "      <td>1998</td>\n",
       "      <td>7/8/1998</td>\n",
       "      <td>DAL</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Detroit Shock</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1568.0</td>\n",
       "      <td>0.382</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2447</th>\n",
       "      <td>2014</td>\n",
       "      <td>7/5/2014</td>\n",
       "      <td>WAS</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>73</td>\n",
       "      <td>86</td>\n",
       "      <td>1446.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>1438.0</td>\n",
       "      <td>1558.0</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      season       date team1 team2                     name1  \\\n",
       "2594    2014  5/30/2014   MIN   LVA            Minnesota Lynx   \n",
       "7487    2003  8/16/2003   PHO   SAC           Phoenix Mercury   \n",
       "1359    2016  9/18/2016   LVA   PHO  San Antonio Silver Stars   \n",
       "10145   1998   7/8/1998   DAL   PHO             Detroit Shock   \n",
       "2447    2014   7/5/2014   WAS   ATL        Washington Mystics   \n",
       "\n",
       "                          name2  neutral  playoff  score1  score2  elo1_pre  \\\n",
       "2594   San Antonio Silver Stars        0    False      88      72    1643.0   \n",
       "7487        Sacramento Monarchs        0    False      61      65    1377.0   \n",
       "1359            Phoenix Mercury        0    False      65      81    1316.0   \n",
       "10145           Phoenix Mercury        0    False      76      78    1400.0   \n",
       "2447              Atlanta Dream        0    False      73      86    1446.0   \n",
       "\n",
       "       elo2_pre  elo1_post  elo2_post  prob1  is_home1  \n",
       "2594     1471.0     1650.0     1465.0  0.810         1  \n",
       "7487     1558.0     1372.0     1564.0  0.359         1  \n",
       "1359     1527.0     1304.0     1538.0  0.320         1  \n",
       "10145    1563.0     1395.0     1568.0  0.382         1  \n",
       "2447     1551.0     1438.0     1558.0  0.257         0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>A `datetime` is a special Python data type. To convert a column to a datetime object, you use a different function, `to_datetime`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>2017</td>\n",
       "      <td>2017-06-09</td>\n",
       "      <td>LAS</td>\n",
       "      <td>DAL</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Dallas Wings</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>90</td>\n",
       "      <td>96</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2018</td>\n",
       "      <td>2018-07-22</td>\n",
       "      <td>LVA</td>\n",
       "      <td>IND</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>88</td>\n",
       "      <td>74</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>0.810</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014-07-17</td>\n",
       "      <td>CON</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>101</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>2015</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>MIN</td>\n",
       "      <td>IND</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>69</td>\n",
       "      <td>75</td>\n",
       "      <td>1576.0</td>\n",
       "      <td>1597.0</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>1603.0</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5423</th>\n",
       "      <td>2008</td>\n",
       "      <td>2008-05-17</td>\n",
       "      <td>SEA</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Seattle Storm</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>67</td>\n",
       "      <td>61</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>1502.0</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>0.649</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     season       date team1 team2               name1            name2  \\\n",
       "1225   2017 2017-06-09   LAS   DAL  Los Angeles Sparks     Dallas Wings   \n",
       "591    2018 2018-07-22   LVA   IND      Las Vegas Aces    Indiana Fever   \n",
       "2389   2014 2014-07-17   CON   PHO     Connecticut Sun  Phoenix Mercury   \n",
       "1763   2015 2015-10-11   MIN   IND      Minnesota Lynx    Indiana Fever   \n",
       "5423   2008 2008-05-17   SEA   CHI       Seattle Storm      Chicago Sky   \n",
       "\n",
       "      neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "1225        0    False      90      96    1598.0    1436.0     1583.0   \n",
       "591         0    False      88      74    1498.0    1326.0     1503.0   \n",
       "2389        0    False      85     101    1447.0    1660.0     1442.0   \n",
       "1763        0     True      69      75    1576.0    1597.0     1570.0   \n",
       "5423        0    False      67      61    1495.0    1468.0     1502.0   \n",
       "\n",
       "      elo2_post  prob1  is_home1  \n",
       "1225     1450.0  0.615         0  \n",
       "591      1320.0  0.810         1  \n",
       "2389     1665.0  0.157         0  \n",
       "1763     1603.0  0.326         0  \n",
       "5423     1461.0  0.649         1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season               object\n",
       "date         datetime64[ns]\n",
       "team1                object\n",
       "team2                object\n",
       "name1                object\n",
       "name2                object\n",
       "neutral               int64\n",
       "playoff                bool\n",
       "score1                int64\n",
       "score2                int64\n",
       "elo1_pre            float64\n",
       "elo2_pre            float64\n",
       "elo1_post           float64\n",
       "elo2_post           float64\n",
       "prob1               float64\n",
       "is_home1              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>The format shown is YYYY-MM-DD. If a time is provided, it will also be converted.\n",
    "<br><br>Once a column is converted to a datetime object, you can sample that column based on several date formats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
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       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-06</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
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       "      <td>81</td>\n",
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       "      <td>1648.0</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-06</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1671.0</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  season       date team1 team2               name1               name2  \\\n",
       "4   2019 2019-10-06   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "5   2019 2019-10-06   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "4        0     True      94      81    1671.0    1648.0     1693.0     1626.0   \n",
       "5        0     True      81      94    1648.0    1671.0     1626.0     1693.0   \n",
       "\n",
       "   prob1  is_home1  \n",
       "4  0.399         0  \n",
       "5  0.601         1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"date\"] == \"10-6-2019\", :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>team2</th>\n",
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       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-06</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
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       "      <td>94</td>\n",
       "      <td>81</td>\n",
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       "      <td>1648.0</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-06</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1671.0</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  season       date team1 team2               name1               name2  \\\n",
       "4   2019 2019-10-06   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "5   2019 2019-10-06   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "4        0     True      94      81    1671.0    1648.0     1693.0     1626.0   \n",
       "5        0     True      81      94    1648.0    1671.0     1626.0     1693.0   \n",
       "\n",
       "   prob1  is_home1  \n",
       "4  0.399         0  \n",
       "5  0.601         1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"date\"] == \"10/6/19\", :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
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       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
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       "      <th>name2</th>\n",
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       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-06</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-06</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1671.0</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>0.601</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  season       date team1 team2               name1               name2  \\\n",
       "4   2019 2019-10-06   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "5   2019 2019-10-06   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "4        0     True      94      81    1671.0    1648.0     1693.0     1626.0   \n",
       "5        0     True      81      94    1648.0    1671.0     1626.0     1693.0   \n",
       "\n",
       "   prob1  is_home1  \n",
       "4  0.399         0  \n",
       "5  0.601         1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"date\"] == \"Oct 6 2019\", :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>You can also use other operators with datetime objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-10</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-10</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-08</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-08</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>1693.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  season       date team1 team2               name1               name2  \\\n",
       "0   2019 2019-10-10   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "1   2019 2019-10-10   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2   2019 2019-10-08   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "3   2019 2019-10-08   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "0        0     True      89      78    1684.0    1634.0     1692.0     1627.0   \n",
       "1        0     True      78      89    1634.0    1684.0     1627.0     1692.0   \n",
       "2        0     True      86      90    1693.0    1626.0     1684.0     1634.0   \n",
       "3        0     True      90      86    1626.0    1693.0     1634.0     1684.0   \n",
       "\n",
       "   prob1  is_home1  \n",
       "0  0.718         1  \n",
       "1  0.282         0  \n",
       "2  0.476         0  \n",
       "3  0.524         1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"date\"] > \"Oct 6 2019\", :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br>Exercise 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pick a random day between 2012 and 2019 and see if any games were played that day. Games are usually played between late May and mid October. If you can't find a game on the day you picked, try a different day until you find a game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>1671.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>0.763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>99</td>\n",
       "      <td>87</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1671.0</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  season       date team1 team2               name1               name2  \\\n",
       "6   2019 2019-10-01   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "7   2019 2019-10-01   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "\n",
       "   neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  elo2_post  \\\n",
       "6        0     True      87      99    1700.0    1618.0     1671.0     1648.0   \n",
       "7        0     True      99      87    1618.0    1700.0     1648.0     1671.0   \n",
       "\n",
       "   prob1  is_home1  \n",
       "6  0.763         1  \n",
       "7  0.237         0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"date\"] == \"10/01/2019\", :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>Write a multiple conditional to find games played between July 7, 2012 and September 7, 2012."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
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       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3212</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-09-07</td>\n",
       "      <td>MIN</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>97</td>\n",
       "      <td>93</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>0.792</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3213</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-09-07</td>\n",
       "      <td>IND</td>\n",
       "      <td>LVA</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "      <td>1588.0</td>\n",
       "      <td>1591.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3214</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-09-07</td>\n",
       "      <td>LVA</td>\n",
       "      <td>IND</td>\n",
       "      <td>San Antonio Silver Stars</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>78</td>\n",
       "      <td>82</td>\n",
       "      <td>1591.0</td>\n",
       "      <td>1588.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.617</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3215</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-09-07</td>\n",
       "      <td>LAS</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>96</td>\n",
       "      <td>68</td>\n",
       "      <td>1559.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3216</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-09-07</td>\n",
       "      <td>CON</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>82</td>\n",
       "      <td>91</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>0.871</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-07-07</td>\n",
       "      <td>ATL</td>\n",
       "      <td>PHO</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-07-07</td>\n",
       "      <td>LAS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>Seattle Storm</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>83</td>\n",
       "      <td>59</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>1512.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-07-07</td>\n",
       "      <td>SEA</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Seattle Storm</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>59</td>\n",
       "      <td>83</td>\n",
       "      <td>1512.0</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3360</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-07-07</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>86</td>\n",
       "      <td>88</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>1557.0</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3361</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-07-07</td>\n",
       "      <td>PHO</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>0.412</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     season       date team1 team2                     name1  \\\n",
       "3212   2012 2012-09-07   MIN   ATL            Minnesota Lynx   \n",
       "3213   2012 2012-09-07   IND   LVA             Indiana Fever   \n",
       "3214   2012 2012-09-07   LVA   IND  San Antonio Silver Stars   \n",
       "3215   2012 2012-09-07   LAS   WAS        Los Angeles Sparks   \n",
       "3216   2012 2012-09-07   CON   PHO           Connecticut Sun   \n",
       "...     ...        ...   ...   ...                       ...   \n",
       "3357   2012 2012-07-07   ATL   PHO             Atlanta Dream   \n",
       "3358   2012 2012-07-07   LAS   SEA        Los Angeles Sparks   \n",
       "3359   2012 2012-07-07   SEA   LAS             Seattle Storm   \n",
       "3360   2012 2012-07-07   CHI   IND               Chicago Sky   \n",
       "3361   2012 2012-07-07   PHO   ATL           Phoenix Mercury   \n",
       "\n",
       "                         name2  neutral  playoff  score1  score2  elo1_pre  \\\n",
       "3212             Atlanta Dream        0    False      97      93    1706.0   \n",
       "3213  San Antonio Silver Stars        0    False      82      78    1588.0   \n",
       "3214             Indiana Fever        0    False      78      82    1591.0   \n",
       "3215        Washington Mystics        0    False      96      68    1559.0   \n",
       "3216           Phoenix Mercury        0    False      82      91    1584.0   \n",
       "...                        ...      ...      ...     ...     ...       ...   \n",
       "3357           Phoenix Mercury        0    False     100      93    1542.0   \n",
       "3358             Seattle Storm        0    False      83      59    1483.0   \n",
       "3359        Los Angeles Sparks        0    False      59      83    1512.0   \n",
       "3360             Indiana Fever        0    False      86      88    1490.0   \n",
       "3361             Atlanta Dream        0    False      93     100    1401.0   \n",
       "\n",
       "      elo2_pre  elo1_post  elo2_post  prob1  is_home1  \n",
       "3212    1554.0     1709.0     1551.0  0.792         1  \n",
       "3213    1591.0     1600.0     1579.0  0.383         0  \n",
       "3214    1588.0     1579.0     1600.0  0.617         1  \n",
       "3215    1332.0     1574.0     1316.0  0.699         0  \n",
       "3216    1332.0     1552.0     1364.0  0.871         1  \n",
       "...        ...        ...        ...    ...       ...  \n",
       "3357    1401.0     1552.0     1392.0  0.588         0  \n",
       "3358    1512.0     1505.0     1491.0  0.573         1  \n",
       "3359    1483.0     1491.0     1505.0  0.427         0  \n",
       "3360    1553.0     1486.0     1557.0  0.305         0  \n",
       "3361    1542.0     1392.0     1552.0  0.412         1  \n",
       "\n",
       "[150 rows x 16 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df[\"date\"] >= \"07/07/2012\") & (df[\"date\"] <= \"09/07/2012\"), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3212</th>\n",
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       "      <td>1574.0</td>\n",
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       "      <td>0.699</td>\n",
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       "      <th>3216</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-09-07</td>\n",
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       "    <tr>\n",
       "      <th>3360</th>\n",
       "      <td>2012</td>\n",
       "      <td>2012-07-07</td>\n",
       "      <td>CHI</td>\n",
       "      <td>IND</td>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>0</td>\n",
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       "      <td>86</td>\n",
       "      <td>88</td>\n",
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       "      <td>1553.0</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>1557.0</td>\n",
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       "    <tr>\n",
       "      <th>3361</th>\n",
       "      <td>2012</td>\n",
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       "      <td>ATL</td>\n",
       "      <td>Phoenix Mercury</td>\n",
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       "<p>150 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     season       date team1 team2                     name1  \\\n",
       "3212   2012 2012-09-07   MIN   ATL            Minnesota Lynx   \n",
       "3213   2012 2012-09-07   IND   LVA             Indiana Fever   \n",
       "3214   2012 2012-09-07   LVA   IND  San Antonio Silver Stars   \n",
       "3215   2012 2012-09-07   LAS   WAS        Los Angeles Sparks   \n",
       "3216   2012 2012-09-07   CON   PHO           Connecticut Sun   \n",
       "...     ...        ...   ...   ...                       ...   \n",
       "3357   2012 2012-07-07   ATL   PHO             Atlanta Dream   \n",
       "3358   2012 2012-07-07   LAS   SEA        Los Angeles Sparks   \n",
       "3359   2012 2012-07-07   SEA   LAS             Seattle Storm   \n",
       "3360   2012 2012-07-07   CHI   IND               Chicago Sky   \n",
       "3361   2012 2012-07-07   PHO   ATL           Phoenix Mercury   \n",
       "\n",
       "                         name2  neutral  playoff  score1  score2  elo1_pre  \\\n",
       "3212             Atlanta Dream        0    False      97      93    1706.0   \n",
       "3213  San Antonio Silver Stars        0    False      82      78    1588.0   \n",
       "3214             Indiana Fever        0    False      78      82    1591.0   \n",
       "3215        Washington Mystics        0    False      96      68    1559.0   \n",
       "3216           Phoenix Mercury        0    False      82      91    1584.0   \n",
       "...                        ...      ...      ...     ...     ...       ...   \n",
       "3357           Phoenix Mercury        0    False     100      93    1542.0   \n",
       "3358             Seattle Storm        0    False      83      59    1483.0   \n",
       "3359        Los Angeles Sparks        0    False      59      83    1512.0   \n",
       "3360             Indiana Fever        0    False      86      88    1490.0   \n",
       "3361             Atlanta Dream        0    False      93     100    1401.0   \n",
       "\n",
       "      elo2_pre  elo1_post  elo2_post  prob1  is_home1  \n",
       "3212    1554.0     1709.0     1551.0  0.792         1  \n",
       "3213    1591.0     1600.0     1579.0  0.383         0  \n",
       "3214    1588.0     1579.0     1600.0  0.617         1  \n",
       "3215    1332.0     1574.0     1316.0  0.699         0  \n",
       "3216    1332.0     1552.0     1364.0  0.871         1  \n",
       "...        ...        ...        ...    ...       ...  \n",
       "3357    1401.0     1552.0     1392.0  0.588         0  \n",
       "3358    1512.0     1505.0     1491.0  0.573         1  \n",
       "3359    1483.0     1491.0     1505.0  0.427         0  \n",
       "3360    1553.0     1486.0     1557.0  0.305         0  \n",
       "3361    1542.0     1392.0     1552.0  0.412         1  \n",
       "\n",
       "[150 rows x 16 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"date\"].between(\"07/07/2012\", \"09/07/2012\"), :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>We can set a column from our DataFrame as the index using set_index(), but if there are duplicates in the column, it won't be that useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
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       "      <td>1997</td>\n",
       "      <td>1997-06-21</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLE</th>\n",
       "      <td>1997</td>\n",
       "      <td>1997-06-21</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Cleveland Rockers</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>56</td>\n",
       "      <td>76</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10488 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      season       date team2                name1                name2  \\\n",
       "team1                                                                     \n",
       "WAS     2019 2019-10-10   CON   Washington Mystics      Connecticut Sun   \n",
       "CON     2019 2019-10-10   WAS      Connecticut Sun   Washington Mystics   \n",
       "WAS     2019 2019-10-08   CON   Washington Mystics      Connecticut Sun   \n",
       "CON     2019 2019-10-08   WAS      Connecticut Sun   Washington Mystics   \n",
       "WAS     2019 2019-10-06   CON   Washington Mystics      Connecticut Sun   \n",
       "...      ...        ...   ...                  ...                  ...   \n",
       "SAC     1997 1997-06-21   LVA  Sacramento Monarchs          Utah Starzz   \n",
       "NYL     1997 1997-06-21   LAS     New York Liberty   Los Angeles Sparks   \n",
       "LAS     1997 1997-06-21   NYL   Los Angeles Sparks     New York Liberty   \n",
       "LVA     1997 1997-06-21   SAC          Utah Starzz  Sacramento Monarchs   \n",
       "CLE     1997 1997-06-21   HOU    Cleveland Rockers       Houston Comets   \n",
       "\n",
       "       neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "team1                                                                    \n",
       "WAS          0     True      89      78    1684.0    1634.0     1692.0   \n",
       "CON          0     True      78      89    1634.0    1684.0     1627.0   \n",
       "WAS          0     True      86      90    1693.0    1626.0     1684.0   \n",
       "CON          0     True      90      86    1626.0    1693.0     1634.0   \n",
       "WAS          0     True      94      81    1671.0    1648.0     1693.0   \n",
       "...        ...      ...     ...     ...       ...       ...        ...   \n",
       "SAC          0    False      73      61    1500.0    1500.0     1521.0   \n",
       "NYL          0    False      67      57    1500.0    1500.0     1519.0   \n",
       "LAS          0    False      57      67    1500.0    1500.0     1481.0   \n",
       "LVA          0    False      61      73    1500.0    1500.0     1479.0   \n",
       "CLE          0    False      56      76    1500.0    1500.0     1470.0   \n",
       "\n",
       "       elo2_post  prob1  is_home1  \n",
       "team1                              \n",
       "WAS       1627.0  0.718         1  \n",
       "CON       1692.0  0.282         0  \n",
       "WAS       1634.0  0.476         0  \n",
       "CON       1684.0  0.524         1  \n",
       "WAS       1626.0  0.399         0  \n",
       "...          ...    ...       ...  \n",
       "SAC       1479.0  0.387         0  \n",
       "NYL       1481.0  0.387         0  \n",
       "LAS       1519.0  0.613         1  \n",
       "LVA       1521.0  0.613         1  \n",
       "CLE       1530.0  0.613         1  \n",
       "\n",
       "[10488 rows x 15 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"team1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2019-10-06</th>\n",
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       "      <td>81</td>\n",
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       "      <td>0.399</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1997-06-21</th>\n",
       "      <td>1997</td>\n",
       "      <td>SAC</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
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       "      <td>Los Angeles Sparks</td>\n",
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       "      <td>False</td>\n",
       "      <td>67</td>\n",
       "      <td>57</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1519.0</td>\n",
       "      <td>1481.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1997-06-21</th>\n",
       "      <td>1997</td>\n",
       "      <td>LVA</td>\n",
       "      <td>SAC</td>\n",
       "      <td>Utah Starzz</td>\n",
       "      <td>Sacramento Monarchs</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-06-21</th>\n",
       "      <td>1997</td>\n",
       "      <td>CLE</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Cleveland Rockers</td>\n",
       "      <td>Houston Comets</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>56</td>\n",
       "      <td>76</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1470.0</td>\n",
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      ],
      "text/plain": [
       "           season team1 team2                name1                name2  \\\n",
       "date                                                                      \n",
       "2019-10-10   2019   WAS   CON   Washington Mystics      Connecticut Sun   \n",
       "2019-10-10   2019   CON   WAS      Connecticut Sun   Washington Mystics   \n",
       "2019-10-08   2019   WAS   CON   Washington Mystics      Connecticut Sun   \n",
       "2019-10-08   2019   CON   WAS      Connecticut Sun   Washington Mystics   \n",
       "2019-10-06   2019   WAS   CON   Washington Mystics      Connecticut Sun   \n",
       "...           ...   ...   ...                  ...                  ...   \n",
       "1997-06-21   1997   SAC   LVA  Sacramento Monarchs          Utah Starzz   \n",
       "1997-06-21   1997   NYL   LAS     New York Liberty   Los Angeles Sparks   \n",
       "1997-06-21   1997   LAS   NYL   Los Angeles Sparks     New York Liberty   \n",
       "1997-06-21   1997   LVA   SAC          Utah Starzz  Sacramento Monarchs   \n",
       "1997-06-21   1997   CLE   HOU    Cleveland Rockers       Houston Comets   \n",
       "\n",
       "            neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "date                                                                          \n",
       "2019-10-10        0     True      89      78    1684.0    1634.0     1692.0   \n",
       "2019-10-10        0     True      78      89    1634.0    1684.0     1627.0   \n",
       "2019-10-08        0     True      86      90    1693.0    1626.0     1684.0   \n",
       "2019-10-08        0     True      90      86    1626.0    1693.0     1634.0   \n",
       "2019-10-06        0     True      94      81    1671.0    1648.0     1693.0   \n",
       "...             ...      ...     ...     ...       ...       ...        ...   \n",
       "1997-06-21        0    False      73      61    1500.0    1500.0     1521.0   \n",
       "1997-06-21        0    False      67      57    1500.0    1500.0     1519.0   \n",
       "1997-06-21        0    False      57      67    1500.0    1500.0     1481.0   \n",
       "1997-06-21        0    False      61      73    1500.0    1500.0     1479.0   \n",
       "1997-06-21        0    False      56      76    1500.0    1500.0     1470.0   \n",
       "\n",
       "            elo2_post  prob1  is_home1  \n",
       "date                                    \n",
       "2019-10-10     1627.0  0.718         1  \n",
       "2019-10-10     1692.0  0.282         0  \n",
       "2019-10-08     1634.0  0.476         0  \n",
       "2019-10-08     1684.0  0.524         1  \n",
       "2019-10-06     1626.0  0.399         0  \n",
       "...               ...    ...       ...  \n",
       "1997-06-21     1479.0  0.387         0  \n",
       "1997-06-21     1481.0  0.387         0  \n",
       "1997-06-21     1519.0  0.613         1  \n",
       "1997-06-21     1521.0  0.613         1  \n",
       "1997-06-21     1530.0  0.613         1  \n",
       "\n",
       "[10488 rows x 15 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <br><br><br>Cleaning data while loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several of the data cleaning tasks we've learned can be done when loading the file in as a pandas DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Telling pandas to make a particular column the index while loading:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/8/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/6/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            season team1 team2               name1               name2  \\\n",
       "date                                                                     \n",
       "10/10/2019    2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "10/10/2019    2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "10/8/2019     2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "10/8/2019     2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "10/6/2019     2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "\n",
       "            neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "date                                                                          \n",
       "10/10/2019        0        1      89      78      1684      1634       1692   \n",
       "10/10/2019        0        1      78      89      1634      1684       1627   \n",
       "10/8/2019         0        1      86      90      1693      1626       1684   \n",
       "10/8/2019         0        1      90      86      1626      1693       1634   \n",
       "10/6/2019         0        1      94      81      1671      1648       1693   \n",
       "\n",
       "            elo2_post  prob1  is_home1  \n",
       "date                                    \n",
       "10/10/2019       1627  0.718         1  \n",
       "10/10/2019       1692  0.282         0  \n",
       "10/8/2019        1634  0.476         0  \n",
       "10/8/2019        1684  0.524         1  \n",
       "10/6/2019        1626  0.399         0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"wnba-team-elo-ratings.csv\", index_col=\"date\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<br>Telling pandas to make your date column the index and convert it to a datetime object when loading:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <th>2019-10-10</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-08</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-08</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-06</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            season team1 team2               name1               name2  \\\n",
       "date                                                                     \n",
       "2019-10-10    2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "2019-10-10    2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2019-10-08    2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "2019-10-08    2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "2019-10-06    2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "\n",
       "            neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "date                                                                          \n",
       "2019-10-10        0        1      89      78      1684      1634       1692   \n",
       "2019-10-10        0        1      78      89      1634      1684       1627   \n",
       "2019-10-08        0        1      86      90      1693      1626       1684   \n",
       "2019-10-08        0        1      90      86      1626      1693       1634   \n",
       "2019-10-06        0        1      94      81      1671      1648       1693   \n",
       "\n",
       "            elo2_post  prob1  is_home1  \n",
       "date                                    \n",
       "2019-10-10       1627  0.718         1  \n",
       "2019-10-10       1692  0.282         0  \n",
       "2019-10-08       1634  0.476         0  \n",
       "2019-10-08       1684  0.524         1  \n",
       "2019-10-06       1626  0.399         0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"wnba-team-elo-ratings.csv\", index_col=\"date\", parse_dates=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<br>Telling pandas to only load some columns:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>season</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season team1 team2  score1  score2\n",
       "0    2019   WAS   CON      89      78\n",
       "1    2019   CON   WAS      78      89\n",
       "2    2019   WAS   CON      86      90\n",
       "3    2019   CON   WAS      90      86\n",
       "4    2019   WAS   CON      94      81"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"wnba-team-elo-ratings.csv\", usecols=[\"season\", \"team1\", \"score1\", \"team2\", \"score2\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<br>Telling pandas to use YYYY-DD-MM instead of YYYY-MM-DD:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>season</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>name1</th>\n",
       "      <th>name2</th>\n",
       "      <th>neutral</th>\n",
       "      <th>playoff</th>\n",
       "      <th>score1</th>\n",
       "      <th>score2</th>\n",
       "      <th>elo1_pre</th>\n",
       "      <th>elo2_pre</th>\n",
       "      <th>elo1_post</th>\n",
       "      <th>elo2_post</th>\n",
       "      <th>prob1</th>\n",
       "      <th>is_home1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <th>10/10/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>78</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>1692</td>\n",
       "      <td>1627</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/10/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>1627</td>\n",
       "      <td>1692</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/8/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>1684</td>\n",
       "      <td>1634</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/8/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>CON</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>1626</td>\n",
       "      <td>1693</td>\n",
       "      <td>1634</td>\n",
       "      <td>1684</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10/6/2019</th>\n",
       "      <td>2019</td>\n",
       "      <td>WAS</td>\n",
       "      <td>CON</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>1671</td>\n",
       "      <td>1648</td>\n",
       "      <td>1693</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            season team1 team2               name1               name2  \\\n",
       "date                                                                     \n",
       "10/10/2019    2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "10/10/2019    2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "10/8/2019     2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "10/8/2019     2019   CON   WAS     Connecticut Sun  Washington Mystics   \n",
       "10/6/2019     2019   WAS   CON  Washington Mystics     Connecticut Sun   \n",
       "\n",
       "            neutral  playoff  score1  score2  elo1_pre  elo2_pre  elo1_post  \\\n",
       "date                                                                          \n",
       "10/10/2019        0        1      89      78      1684      1634       1692   \n",
       "10/10/2019        0        1      78      89      1634      1684       1627   \n",
       "10/8/2019         0        1      86      90      1693      1626       1684   \n",
       "10/8/2019         0        1      90      86      1626      1693       1634   \n",
       "10/6/2019         0        1      94      81      1671      1648       1693   \n",
       "\n",
       "            elo2_post  prob1  is_home1  \n",
       "date                                    \n",
       "10/10/2019       1627  0.718         1  \n",
       "10/10/2019       1692  0.282         0  \n",
       "10/8/2019        1634  0.476         0  \n",
       "10/8/2019        1684  0.524         1  \n",
       "10/6/2019        1626  0.399         0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"wnba-team-elo-ratings.csv\", index_col=\"date\", parse_dates=True, dayfirst=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>And many more tricks: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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