tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro to Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], columns=[\"A\", \"B\", \"C\"], index=[\"x\",\"y\",\"z\",'zz'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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",
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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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zz</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C\n",
       "x    1   2   3\n",
       "y    4   5   6\n",
       "z    7   8   9\n",
       "zz  10  11  12"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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",
       "    .dataframe tbody tr th {\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zz</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C\n",
       "z    7   8   9\n",
       "zz  10  11  12"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C'], dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x', 'y', 'z', 'zz']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4 entries, x to zz\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   A       4 non-null      int64\n",
      " 1   B       4 non-null      int64\n",
      " 2   C       4 non-null      int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 128.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.872983</td>\n",
       "      <td>3.872983</td>\n",
       "      <td>3.872983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.250000</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>5.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.750000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>9.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A          B          C\n",
       "count   4.000000   4.000000   4.000000\n",
       "mean    5.500000   6.500000   7.500000\n",
       "std     3.872983   3.872983   3.872983\n",
       "min     1.000000   2.000000   3.000000\n",
       "25%     3.250000   4.250000   5.250000\n",
       "50%     5.500000   6.500000   7.500000\n",
       "75%     7.750000   8.750000   9.750000\n",
       "max    10.000000  11.000000  12.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    4\n",
       "B    4\n",
       "C    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  7, 10])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
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       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zz</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     A   B   C\n",
       "x    1   2   3\n",
       "y    4   5   6\n",
       "z    7   8   9\n",
       "zz  10  11  12"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading in Dataframes from Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee = pd.read_csv('./warmup-data/coffee.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.read_parquet('./data/results.parquet')\n",
    "bios = pd.read_csv('./data/bios.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "## To read an excel spreadsheet\n",
    "olympics_data = pd.read_excel('./data/olympics-data.xlsx', sheet_name=\"results\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing Data with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Day Coffee Type  Units Sold\n",
      "0      Monday    Espresso          25\n",
      "1      Monday       Latte          15\n",
      "2     Tuesday    Espresso          30\n",
      "3     Tuesday       Latte          20\n",
      "4   Wednesday    Espresso          35\n",
      "5   Wednesday       Latte          25\n",
      "6    Thursday    Espresso          40\n",
      "7    Thursday       Latte          30\n",
      "8      Friday    Espresso          45\n",
      "9      Friday       Latte          35\n",
      "10   Saturday    Espresso          45\n",
      "11   Saturday       Latte          35\n",
      "12     Sunday    Espresso          45\n",
      "13     Sunday       Latte          35\n"
     ]
    }
   ],
   "source": [
    "print(coffee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Coffee Type</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Monday</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Espresso</td>\n",
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       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
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       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold\n",
       "0      Monday    Espresso          25\n",
       "1      Monday       Latte          15\n",
       "2     Tuesday    Espresso          30\n",
       "3     Tuesday       Latte          20\n",
       "4   Wednesday    Espresso          35\n",
       "5   Wednesday       Latte          25\n",
       "6    Thursday    Espresso          40\n",
       "7    Thursday       Latte          30\n",
       "8      Friday    Espresso          45\n",
       "9      Friday       Latte          35\n",
       "10   Saturday    Espresso          45\n",
       "11   Saturday       Latte          35\n",
       "12     Sunday    Espresso          45\n",
       "13     Sunday       Latte          35"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(coffee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day Coffee Type  Units Sold\n",
       "0     Monday    Espresso          25\n",
       "1     Monday       Latte          15\n",
       "2    Tuesday    Espresso          30\n",
       "3    Tuesday       Latte          20\n",
       "4  Wednesday    Espresso          35"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold\n",
       "4   Wednesday    Espresso          35\n",
       "5   Wednesday       Latte          25\n",
       "6    Thursday    Espresso          40\n",
       "7    Thursday       Latte          30\n",
       "8      Friday    Espresso          45\n",
       "9      Friday       Latte          35\n",
       "10   Saturday    Espresso          45\n",
       "11   Saturday       Latte          35\n",
       "12     Sunday    Espresso          45\n",
       "13     Sunday       Latte          35"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "    .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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day Coffee Type  Units Sold\n",
       "11  Saturday       Latte          35\n",
       "0     Monday    Espresso          25\n",
       "6   Thursday    Espresso          40\n",
       "7   Thursday       Latte          30\n",
       "13    Sunday       Latte          35"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.sample(5) # Pass in random_state to make deterministic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day              Monday\n",
       "Coffee Type    Espresso\n",
       "Units Sold           25\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc\n",
    "# coffee.loc[Rows, Columns]\n",
    "\n",
    "coffee.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day Coffee Type  Units Sold\n",
       "0     Monday    Espresso          25\n",
       "1     Monday       Latte          15\n",
       "5  Wednesday       Latte          25"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.loc[[0,1,5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Day</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day  Units Sold\n",
       "5  Wednesday          25\n",
       "6   Thursday          40\n",
       "7   Thursday          30\n",
       "8     Friday          45\n",
       "9     Friday          35"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.loc[5:9, [\"Day\", \"Units Sold\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Day</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day  Units Sold\n",
       "0      Monday          25\n",
       "1      Monday          15\n",
       "2     Tuesday          30\n",
       "3     Tuesday          20\n",
       "4   Wednesday          35\n",
       "5   Wednesday          25\n",
       "6    Thursday          40\n",
       "7    Thursday          30\n",
       "8      Friday          45\n",
       "9      Friday          35\n",
       "10   Saturday          45\n",
       "11   Saturday          35\n",
       "12     Sunday          45\n",
       "13     Sunday          35"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.iloc[:, [0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee.index = coffee[\"Day\"]"
   ]
  },
  {
   "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",
       "    .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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Day Coffee Type  Units Sold\n",
       "Day                                         \n",
       "Monday        Monday    Espresso          25\n",
       "Monday        Monday       Latte          15\n",
       "Tuesday      Tuesday    Espresso          30\n",
       "Tuesday      Tuesday       Latte          20\n",
       "Wednesday  Wednesday    Espresso          35\n",
       "Wednesday  Wednesday       Latte          25"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.loc[\"Monday\":\"Wednesday\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee = pd.read_csv('./warmup-data/coffee.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee.loc[1:3, \"Units Sold\"] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimized way to get single values (.at & .iat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.at[0,\"Units Sold\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Latte'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.iat[3,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Monday\n",
       "1        Monday\n",
       "2       Tuesday\n",
       "3       Tuesday\n",
       "4     Wednesday\n",
       "5     Wednesday\n",
       "6      Thursday\n",
       "7      Thursday\n",
       "8        Friday\n",
       "9        Friday\n",
       "10     Saturday\n",
       "11     Saturday\n",
       "12       Sunday\n",
       "13       Sunday\n",
       "Name: Day, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Monday\n",
       "1        Monday\n",
       "2       Tuesday\n",
       "3       Tuesday\n",
       "4     Wednesday\n",
       "5     Wednesday\n",
       "6      Thursday\n",
       "7      Thursday\n",
       "8        Friday\n",
       "9        Friday\n",
       "10     Saturday\n",
       "11     Saturday\n",
       "12       Sunday\n",
       "13       Sunday\n",
       "Name: Day, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee[\"Day\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sort Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold\n",
       "8      Friday    Espresso          45\n",
       "10   Saturday    Espresso          45\n",
       "12     Sunday    Espresso          45\n",
       "6    Thursday    Espresso          40\n",
       "4   Wednesday    Espresso          35\n",
       "9      Friday       Latte          35\n",
       "11   Saturday       Latte          35\n",
       "13     Sunday       Latte          35\n",
       "7    Thursday       Latte          30\n",
       "0      Monday    Espresso          25\n",
       "5   Wednesday       Latte          25\n",
       "1      Monday       Latte          10\n",
       "2     Tuesday    Espresso          10\n",
       "3     Tuesday       Latte          10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.sort_values([\"Units Sold\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold\n",
       "8      Friday    Espresso          45\n",
       "10   Saturday    Espresso          45\n",
       "12     Sunday    Espresso          45\n",
       "6    Thursday    Espresso          40\n",
       "4   Wednesday    Espresso          35\n",
       "9      Friday       Latte          35\n",
       "11   Saturday       Latte          35\n",
       "13     Sunday       Latte          35\n",
       "7    Thursday       Latte          30\n",
       "0      Monday    Espresso          25\n",
       "5   Wednesday       Latte          25\n",
       "2     Tuesday    Espresso          10\n",
       "1      Monday       Latte          10\n",
       "3     Tuesday       Latte          10"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.sort_values([\"Units Sold\", \"Coffee Type\"], ascending=[0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iterate over dataframe with for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Day              Monday\n",
      "Coffee Type    Espresso\n",
      "Units Sold           25\n",
      "Name: 0, dtype: object\n",
      "Coffee Type of Row: Espresso\n",
      "1\n",
      "Day            Monday\n",
      "Coffee Type     Latte\n",
      "Units Sold         10\n",
      "Name: 1, dtype: object\n",
      "Coffee Type of Row: Latte\n",
      "2\n",
      "Day             Tuesday\n",
      "Coffee Type    Espresso\n",
      "Units Sold           10\n",
      "Name: 2, dtype: object\n",
      "Coffee Type of Row: Espresso\n",
      "3\n",
      "Day            Tuesday\n",
      "Coffee Type      Latte\n",
      "Units Sold          10\n",
      "Name: 3, dtype: object\n",
      "Coffee Type of Row: Latte\n",
      "4\n",
      "Day            Wednesday\n",
      "Coffee Type     Espresso\n",
      "Units Sold            35\n",
      "Name: 4, dtype: object\n",
      "Coffee Type of Row: Espresso\n",
      "5\n",
      "Day            Wednesday\n",
      "Coffee Type        Latte\n",
      "Units Sold            25\n",
      "Name: 5, dtype: object\n",
      "Coffee Type of Row: Latte\n",
      "6\n",
      "Day            Thursday\n",
      "Coffee Type    Espresso\n",
      "Units Sold           40\n",
      "Name: 6, dtype: object\n",
      "Coffee Type of Row: Espresso\n",
      "7\n",
      "Day            Thursday\n",
      "Coffee Type       Latte\n",
      "Units Sold           30\n",
      "Name: 7, dtype: object\n",
      "Coffee Type of Row: Latte\n",
      "8\n",
      "Day              Friday\n",
      "Coffee Type    Espresso\n",
      "Units Sold           45\n",
      "Name: 8, dtype: object\n",
      "Coffee Type of Row: Espresso\n",
      "9\n",
      "Day            Friday\n",
      "Coffee Type     Latte\n",
      "Units Sold         35\n",
      "Name: 9, dtype: object\n",
      "Coffee Type of Row: Latte\n",
      "10\n",
      "Day            Saturday\n",
      "Coffee Type    Espresso\n",
      "Units Sold           45\n",
      "Name: 10, dtype: object\n",
      "Coffee Type of Row: Espresso\n",
      "11\n",
      "Day            Saturday\n",
      "Coffee Type       Latte\n",
      "Units Sold           35\n",
      "Name: 11, dtype: object\n",
      "Coffee Type of Row: Latte\n",
      "12\n",
      "Day              Sunday\n",
      "Coffee Type    Espresso\n",
      "Units Sold           45\n",
      "Name: 12, dtype: object\n",
      "Coffee Type of Row: Espresso\n",
      "13\n",
      "Day            Sunday\n",
      "Coffee Type     Latte\n",
      "Units Sold         35\n",
      "Name: 13, dtype: object\n",
      "Coffee Type of Row: Latte\n"
     ]
    }
   ],
   "source": [
    "for index, row in coffee.iterrows():\n",
    "    print(index)\n",
    "    print(row)\n",
    "    print(\"Coffee Type of Row:\", row[\"Coffee Type\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Arnaud Boetsch</td>\n",
       "      <td>1969-04-01</td>\n",
       "      <td>Meulan</td>\n",
       "      <td>Yvelines</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>183.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jean Borotra</td>\n",
       "      <td>1898-08-13</td>\n",
       "      <td>Biarritz</td>\n",
       "      <td>Pyrénées-Atlantiques</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>183.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1994-07-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Jacques Brugnon</td>\n",
       "      <td>1895-05-11</td>\n",
       "      <td>Paris VIIIe</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>168.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1978-03-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Albert Canet</td>\n",
       "      <td>1878-04-17</td>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930-07-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   athlete_id                   name   born_date    born_city  \\\n",
       "0           1  Jean-François Blanchy  1886-12-12     Bordeaux   \n",
       "1           2         Arnaud Boetsch  1969-04-01       Meulan   \n",
       "2           3           Jean Borotra  1898-08-13     Biarritz   \n",
       "3           4        Jacques Brugnon  1895-05-11  Paris VIIIe   \n",
       "4           5           Albert Canet  1878-04-17   Wandsworth   \n",
       "\n",
       "            born_region born_country     NOC  height_cm  weight_kg   died_date  \n",
       "0               Gironde          FRA  France        NaN        NaN  1960-10-02  \n",
       "1              Yvelines          FRA  France      183.0       76.0         NaN  \n",
       "2  Pyrénées-Atlantiques          FRA  France      183.0       76.0  1994-07-17  \n",
       "3                 Paris          FRA  France      168.0       64.0  1978-03-20  \n",
       "4               England          GBR  France        NaN        NaN  1930-07-25  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
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       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5089</th>\n",
       "      <td>5108</td>\n",
       "      <td>Viktor Pankrashkin</td>\n",
       "      <td>1957-06-19</td>\n",
       "      <td>Moskva (Moscow)</td>\n",
       "      <td>Moskva</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Soviet Union</td>\n",
       "      <td>220.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1993-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5583</th>\n",
       "      <td>5606</td>\n",
       "      <td>Paulinho Villas Boas</td>\n",
       "      <td>1963-01-26</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>217.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5673</th>\n",
       "      <td>5696</td>\n",
       "      <td>Gunther Behnke</td>\n",
       "      <td>1963-01-19</td>\n",
       "      <td>Leverkusen</td>\n",
       "      <td>Nordrhein-Westfalen</td>\n",
       "      <td>GER</td>\n",
       "      <td>Germany</td>\n",
       "      <td>221.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5716</th>\n",
       "      <td>5739</td>\n",
       "      <td>Uwe Blab</td>\n",
       "      <td>1962-03-26</td>\n",
       "      <td>München (Munich)</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>GER</td>\n",
       "      <td>Germany West Germany</td>\n",
       "      <td>218.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>5804</td>\n",
       "      <td>Tommy Burleson</td>\n",
       "      <td>1952-02-24</td>\n",
       "      <td>Crossnore</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>223.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5796</th>\n",
       "      <td>5819</td>\n",
       "      <td>Andy Campbell</td>\n",
       "      <td>1956-07-21</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>218.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6223</th>\n",
       "      <td>6250</td>\n",
       "      <td>Lars Hansen</td>\n",
       "      <td>1954-09-27</td>\n",
       "      <td>København (Copenhagen)</td>\n",
       "      <td>Hovedstaden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>216.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6270</th>\n",
       "      <td>6298</td>\n",
       "      <td>Hu Zhangbao</td>\n",
       "      <td>1963-04-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>People's Republic of China</td>\n",
       "      <td>216.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6409</th>\n",
       "      <td>6440</td>\n",
       "      <td>Sergey Kovalenko</td>\n",
       "      <td>1947-08-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Soviet Union</td>\n",
       "      <td>216.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2004-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6420</th>\n",
       "      <td>6451</td>\n",
       "      <td>Jānis Krūmiņš</td>\n",
       "      <td>1930-01-30</td>\n",
       "      <td>Cēsis</td>\n",
       "      <td>Cēsu novads</td>\n",
       "      <td>LAT</td>\n",
       "      <td>Soviet Union</td>\n",
       "      <td>218.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1994-11-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6504</th>\n",
       "      <td>6537</td>\n",
       "      <td>Luc Longley</td>\n",
       "      <td>1969-01-19</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>220.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6722</th>\n",
       "      <td>6755</td>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>1972-03-06</td>\n",
       "      <td>Newark</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>216.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6937</th>\n",
       "      <td>6972</td>\n",
       "      <td>David Robinson</td>\n",
       "      <td>1965-08-06</td>\n",
       "      <td>Key West</td>\n",
       "      <td>Florida</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>216.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>7013</td>\n",
       "      <td>Arvydas Sabonis</td>\n",
       "      <td>1964-12-19</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>Kaunas</td>\n",
       "      <td>LTU</td>\n",
       "      <td>Lithuania Soviet Union</td>\n",
       "      <td>223.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7074</th>\n",
       "      <td>7111</td>\n",
       "      <td>Paulo da Silva</td>\n",
       "      <td>1963-07-21</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>217.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7188</th>\n",
       "      <td>7226</td>\n",
       "      <td>Vladimir Tkachenko</td>\n",
       "      <td>1957-09-20</td>\n",
       "      <td>Golovinka</td>\n",
       "      <td>Krasnodar Kray</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Soviet Union</td>\n",
       "      <td>220.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7281</th>\n",
       "      <td>7320</td>\n",
       "      <td>Stojko Vranković</td>\n",
       "      <td>1964-01-22</td>\n",
       "      <td>Drniš</td>\n",
       "      <td>Šibensko-kninska županija</td>\n",
       "      <td>CRO</td>\n",
       "      <td>Croatia Yugoslavia</td>\n",
       "      <td>217.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7376</th>\n",
       "      <td>7416</td>\n",
       "      <td>Eurelijus Žukauskas</td>\n",
       "      <td>1973-08-22</td>\n",
       "      <td>Klaipėda</td>\n",
       "      <td>Klaipėda</td>\n",
       "      <td>LTU</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>218.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52608</th>\n",
       "      <td>52983</td>\n",
       "      <td>Aleksey Kazakov</td>\n",
       "      <td>1976-03-18</td>\n",
       "      <td>Naberezhnye Chelny</td>\n",
       "      <td>Respublika Tatarstan</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>217.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82100</th>\n",
       "      <td>82753</td>\n",
       "      <td>Frédéric Weis</td>\n",
       "      <td>1977-06-22</td>\n",
       "      <td>Thionville</td>\n",
       "      <td>Moselle</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>218.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89070</th>\n",
       "      <td>89782</td>\n",
       "      <td>Yao Ming</td>\n",
       "      <td>1980-09-12</td>\n",
       "      <td>Xuhui District</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>CHN</td>\n",
       "      <td>People's Republic of China</td>\n",
       "      <td>226.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89075</th>\n",
       "      <td>89787</td>\n",
       "      <td>Roberto Dueñas</td>\n",
       "      <td>1975-11-01</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>ESP</td>\n",
       "      <td>Spain</td>\n",
       "      <td>221.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107408</th>\n",
       "      <td>108533</td>\n",
       "      <td>Peter John Ramos</td>\n",
       "      <td>1985-05-23</td>\n",
       "      <td>Fajardo</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>PUR</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>219.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112312</th>\n",
       "      <td>113568</td>\n",
       "      <td>Stanko Barać</td>\n",
       "      <td>1986-08-13</td>\n",
       "      <td>Mostar</td>\n",
       "      <td>Hercegovačko-neretvanski kanton</td>\n",
       "      <td>BIH</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>217.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112332</th>\n",
       "      <td>113588</td>\n",
       "      <td>Andreas Glyniadakis</td>\n",
       "      <td>1981-08-26</td>\n",
       "      <td>Chania</td>\n",
       "      <td>Kriti</td>\n",
       "      <td>GRE</td>\n",
       "      <td>Greece</td>\n",
       "      <td>216.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112337</th>\n",
       "      <td>113593</td>\n",
       "      <td>Hamed Haddadi</td>\n",
       "      <td>1985-05-19</td>\n",
       "      <td>Ahvaz</td>\n",
       "      <td>Khuzestan</td>\n",
       "      <td>IRI</td>\n",
       "      <td>Islamic Republic of Iran</td>\n",
       "      <td>218.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118663</th>\n",
       "      <td>120400</td>\n",
       "      <td>Timofey Mozgov</td>\n",
       "      <td>1986-07-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>216.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118676</th>\n",
       "      <td>120415</td>\n",
       "      <td>Dmitry Musersky</td>\n",
       "      <td>1988-10-29</td>\n",
       "      <td>Makiïvka</td>\n",
       "      <td>Donetsk</td>\n",
       "      <td>UKR</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>219.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120266</th>\n",
       "      <td>122147</td>\n",
       "      <td>Zhang Zhaoxu</td>\n",
       "      <td>1987-11-18</td>\n",
       "      <td>Binzhou</td>\n",
       "      <td>Shandong</td>\n",
       "      <td>CHN</td>\n",
       "      <td>People's Republic of China</td>\n",
       "      <td>221.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121694</th>\n",
       "      <td>123709</td>\n",
       "      <td>Salah Mejri</td>\n",
       "      <td>1986-06-15</td>\n",
       "      <td>Jendouba</td>\n",
       "      <td>Jendouba</td>\n",
       "      <td>TUN</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>216.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123850</th>\n",
       "      <td>126093</td>\n",
       "      <td>Tyson Chandler</td>\n",
       "      <td>1982-10-02</td>\n",
       "      <td>Hanford</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>216.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130460</th>\n",
       "      <td>133147</td>\n",
       "      <td>Li Muhao</td>\n",
       "      <td>1992-06-02</td>\n",
       "      <td>Guiyang</td>\n",
       "      <td>Guizhou</td>\n",
       "      <td>CHN</td>\n",
       "      <td>People's Republic of China</td>\n",
       "      <td>218.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130461</th>\n",
       "      <td>133148</td>\n",
       "      <td>Zhou Qi</td>\n",
       "      <td>1996-01-16</td>\n",
       "      <td>Xinxiang</td>\n",
       "      <td>Henan</td>\n",
       "      <td>CHN</td>\n",
       "      <td>People's Republic of China</td>\n",
       "      <td>217.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138671</th>\n",
       "      <td>142084</td>\n",
       "      <td>Ondřej Balvín</td>\n",
       "      <td>1992-09-20</td>\n",
       "      <td>Ústí nad Labem</td>\n",
       "      <td>Ústecký kraj</td>\n",
       "      <td>CZE</td>\n",
       "      <td>Czechia</td>\n",
       "      <td>216.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139365</th>\n",
       "      <td>142836</td>\n",
       "      <td>Moustapha Fall</td>\n",
       "      <td>1992-02-23</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>218.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete_id                   name   born_date               born_city  \\\n",
       "5089          5108     Viktor Pankrashkin  1957-06-19         Moskva (Moscow)   \n",
       "5583          5606   Paulinho Villas Boas  1963-01-26               São Paulo   \n",
       "5673          5696         Gunther Behnke  1963-01-19              Leverkusen   \n",
       "5716          5739               Uwe Blab  1962-03-26        München (Munich)   \n",
       "5781          5804         Tommy Burleson  1952-02-24               Crossnore   \n",
       "5796          5819          Andy Campbell  1956-07-21               Melbourne   \n",
       "6223          6250            Lars Hansen  1954-09-27  København (Copenhagen)   \n",
       "6270          6298            Hu Zhangbao  1963-04-05                     NaN   \n",
       "6409          6440       Sergey Kovalenko  1947-08-11                     NaN   \n",
       "6420          6451          Jānis Krūmiņš  1930-01-30                   Cēsis   \n",
       "6504          6537            Luc Longley  1969-01-19               Melbourne   \n",
       "6722          6755       Shaquille O'Neal  1972-03-06                  Newark   \n",
       "6937          6972         David Robinson  1965-08-06                Key West   \n",
       "6978          7013        Arvydas Sabonis  1964-12-19                  Kaunas   \n",
       "7074          7111         Paulo da Silva  1963-07-21               São Paulo   \n",
       "7188          7226     Vladimir Tkachenko  1957-09-20               Golovinka   \n",
       "7281          7320       Stojko Vranković  1964-01-22                   Drniš   \n",
       "7376          7416    Eurelijus Žukauskas  1973-08-22                Klaipėda   \n",
       "52608        52983        Aleksey Kazakov  1976-03-18      Naberezhnye Chelny   \n",
       "82100        82753          Frédéric Weis  1977-06-22              Thionville   \n",
       "89070        89782               Yao Ming  1980-09-12          Xuhui District   \n",
       "89075        89787         Roberto Dueñas  1975-11-01                  Madrid   \n",
       "107408      108533       Peter John Ramos  1985-05-23                 Fajardo   \n",
       "112312      113568           Stanko Barać  1986-08-13                  Mostar   \n",
       "112332      113588    Andreas Glyniadakis  1981-08-26                  Chania   \n",
       "112337      113593          Hamed Haddadi  1985-05-19                   Ahvaz   \n",
       "118663      120400         Timofey Mozgov  1986-07-16                     NaN   \n",
       "118676      120415        Dmitry Musersky  1988-10-29                Makiïvka   \n",
       "120266      122147           Zhang Zhaoxu  1987-11-18                 Binzhou   \n",
       "121694      123709            Salah Mejri  1986-06-15                Jendouba   \n",
       "123850      126093         Tyson Chandler  1982-10-02                 Hanford   \n",
       "130460      133147               Li Muhao  1992-06-02                 Guiyang   \n",
       "130461      133148                Zhou Qi  1996-01-16                Xinxiang   \n",
       "138671      142084          Ondřej Balvín  1992-09-20          Ústí nad Labem   \n",
       "139365      142836         Moustapha Fall  1992-02-23                   Paris   \n",
       "\n",
       "                            born_region born_country  \\\n",
       "5089                             Moskva          RUS   \n",
       "5583                          São Paulo          BRA   \n",
       "5673                Nordrhein-Westfalen          GER   \n",
       "5716                             Bayern          GER   \n",
       "5781                     North Carolina          USA   \n",
       "5796                           Victoria          AUS   \n",
       "6223                        Hovedstaden          DEN   \n",
       "6270                                NaN          NaN   \n",
       "6409                                NaN          NaN   \n",
       "6420                        Cēsu novads          LAT   \n",
       "6504                           Victoria          AUS   \n",
       "6722                         New Jersey          USA   \n",
       "6937                            Florida          USA   \n",
       "6978                             Kaunas          LTU   \n",
       "7074                          São Paulo          BRA   \n",
       "7188                     Krasnodar Kray          RUS   \n",
       "7281          Šibensko-kninska županija          CRO   \n",
       "7376                           Klaipėda          LTU   \n",
       "52608              Respublika Tatarstan          RUS   \n",
       "82100                           Moselle          FRA   \n",
       "89070                          Shanghai          CHN   \n",
       "89075                            Madrid          ESP   \n",
       "107408                      Puerto Rico          PUR   \n",
       "112312  Hercegovačko-neretvanski kanton          BIH   \n",
       "112332                            Kriti          GRE   \n",
       "112337                        Khuzestan          IRI   \n",
       "118663                              NaN          NaN   \n",
       "118676                          Donetsk          UKR   \n",
       "120266                         Shandong          CHN   \n",
       "121694                         Jendouba          TUN   \n",
       "123850                       California          USA   \n",
       "130460                          Guizhou          CHN   \n",
       "130461                            Henan          CHN   \n",
       "138671                     Ústecký kraj          CZE   \n",
       "139365                            Paris          FRA   \n",
       "\n",
       "                               NOC  height_cm  weight_kg   died_date  \n",
       "5089                  Soviet Union      220.0      112.0  1993-07-24  \n",
       "5583                        Brazil      217.0      106.0         NaN  \n",
       "5673                       Germany      221.0      114.0         NaN  \n",
       "5716          Germany West Germany      218.0      110.0         NaN  \n",
       "5781                 United States      223.0      102.0         NaN  \n",
       "5796                     Australia      218.0       93.0         NaN  \n",
       "6223                        Canada      216.0      105.0         NaN  \n",
       "6270    People's Republic of China      216.0      135.0         NaN  \n",
       "6409                  Soviet Union      216.0      111.0  2004-11-18  \n",
       "6420                  Soviet Union      218.0      141.0  1994-11-20  \n",
       "6504                     Australia      220.0      135.0         NaN  \n",
       "6722                 United States      216.0      137.0         NaN  \n",
       "6937                 United States      216.0      107.0         NaN  \n",
       "6978        Lithuania Soviet Union      223.0      122.0         NaN  \n",
       "7074                        Brazil      217.0      106.0         NaN  \n",
       "7188                  Soviet Union      220.0      110.0         NaN  \n",
       "7281            Croatia Yugoslavia      217.0      115.0         NaN  \n",
       "7376                     Lithuania      218.0      115.0         NaN  \n",
       "52608           Russian Federation      217.0      102.0         NaN  \n",
       "82100                       France      218.0      110.0         NaN  \n",
       "89070   People's Republic of China      226.0      141.0         NaN  \n",
       "89075                        Spain      221.0      137.0         NaN  \n",
       "107408                 Puerto Rico      219.0      113.0         NaN  \n",
       "112312                     Croatia      217.0      110.0         NaN  \n",
       "112332                      Greece      216.0      115.0         NaN  \n",
       "112337    Islamic Republic of Iran      218.0      110.0         NaN  \n",
       "118663          Russian Federation      216.0      113.0         NaN  \n",
       "118676          Russian Federation      219.0      104.0         NaN  \n",
       "120266  People's Republic of China      221.0      110.0         NaN  \n",
       "121694                     Tunisia      216.0      110.0         NaN  \n",
       "123850               United States      216.0      107.0         NaN  \n",
       "130460  People's Republic of China      218.0      115.0         NaN  \n",
       "130461  People's Republic of China      217.0       95.0         NaN  \n",
       "138671                     Czechia      216.0      107.0         NaN  \n",
       "139365                      France      218.0        NaN         NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios.loc[bios[\"height_cm\"] > 215]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>name</th>\n",
       "      <th>height_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5089</th>\n",
       "      <td>Viktor Pankrashkin</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5583</th>\n",
       "      <td>Paulinho Villas Boas</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5673</th>\n",
       "      <td>Gunther Behnke</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5716</th>\n",
       "      <td>Uwe Blab</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>Tommy Burleson</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5796</th>\n",
       "      <td>Andy Campbell</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6223</th>\n",
       "      <td>Lars Hansen</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6270</th>\n",
       "      <td>Hu Zhangbao</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6409</th>\n",
       "      <td>Sergey Kovalenko</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6420</th>\n",
       "      <td>Jānis Krūmiņš</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6504</th>\n",
       "      <td>Luc Longley</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6722</th>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6937</th>\n",
       "      <td>David Robinson</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>Arvydas Sabonis</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7074</th>\n",
       "      <td>Paulo da Silva</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7188</th>\n",
       "      <td>Vladimir Tkachenko</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7281</th>\n",
       "      <td>Stojko Vranković</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7376</th>\n",
       "      <td>Eurelijus Žukauskas</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52608</th>\n",
       "      <td>Aleksey Kazakov</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82100</th>\n",
       "      <td>Frédéric Weis</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89070</th>\n",
       "      <td>Yao Ming</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89075</th>\n",
       "      <td>Roberto Dueñas</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107408</th>\n",
       "      <td>Peter John Ramos</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112312</th>\n",
       "      <td>Stanko Barać</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112332</th>\n",
       "      <td>Andreas Glyniadakis</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112337</th>\n",
       "      <td>Hamed Haddadi</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118663</th>\n",
       "      <td>Timofey Mozgov</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118676</th>\n",
       "      <td>Dmitry Musersky</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120266</th>\n",
       "      <td>Zhang Zhaoxu</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121694</th>\n",
       "      <td>Salah Mejri</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123850</th>\n",
       "      <td>Tyson Chandler</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130460</th>\n",
       "      <td>Li Muhao</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130461</th>\n",
       "      <td>Zhou Qi</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138671</th>\n",
       "      <td>Ondřej Balvín</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139365</th>\n",
       "      <td>Moustapha Fall</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name  height_cm\n",
       "5089       Viktor Pankrashkin      220.0\n",
       "5583     Paulinho Villas Boas      217.0\n",
       "5673           Gunther Behnke      221.0\n",
       "5716                 Uwe Blab      218.0\n",
       "5781           Tommy Burleson      223.0\n",
       "5796            Andy Campbell      218.0\n",
       "6223              Lars Hansen      216.0\n",
       "6270              Hu Zhangbao      216.0\n",
       "6409         Sergey Kovalenko      216.0\n",
       "6420            Jānis Krūmiņš      218.0\n",
       "6504              Luc Longley      220.0\n",
       "6722         Shaquille O'Neal      216.0\n",
       "6937           David Robinson      216.0\n",
       "6978          Arvydas Sabonis      223.0\n",
       "7074           Paulo da Silva      217.0\n",
       "7188       Vladimir Tkachenko      220.0\n",
       "7281         Stojko Vranković      217.0\n",
       "7376      Eurelijus Žukauskas      218.0\n",
       "52608         Aleksey Kazakov      217.0\n",
       "82100           Frédéric Weis      218.0\n",
       "89070                Yao Ming      226.0\n",
       "89075          Roberto Dueñas      221.0\n",
       "107408       Peter John Ramos      219.0\n",
       "112312           Stanko Barać      217.0\n",
       "112332    Andreas Glyniadakis      216.0\n",
       "112337          Hamed Haddadi      218.0\n",
       "118663         Timofey Mozgov      216.0\n",
       "118676        Dmitry Musersky      219.0\n",
       "120266           Zhang Zhaoxu      221.0\n",
       "121694            Salah Mejri      216.0\n",
       "123850         Tyson Chandler      216.0\n",
       "130460               Li Muhao      218.0\n",
       "130461                Zhou Qi      217.0\n",
       "138671          Ondřej Balvín      216.0\n",
       "139365         Moustapha Fall      218.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios.loc[bios[\"height_cm\"] > 215, [\"name\", \"height_cm\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Short-hand syntax (without .loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>height_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5089</th>\n",
       "      <td>Viktor Pankrashkin</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5583</th>\n",
       "      <td>Paulinho Villas Boas</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5673</th>\n",
       "      <td>Gunther Behnke</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5716</th>\n",
       "      <td>Uwe Blab</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>Tommy Burleson</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5796</th>\n",
       "      <td>Andy Campbell</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6223</th>\n",
       "      <td>Lars Hansen</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6270</th>\n",
       "      <td>Hu Zhangbao</td>\n",
       "      <td>216.0</td>\n",
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       "    <tr>\n",
       "      <th>6409</th>\n",
       "      <td>Sergey Kovalenko</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6420</th>\n",
       "      <td>Jānis Krūmiņš</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6504</th>\n",
       "      <td>Luc Longley</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6722</th>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6937</th>\n",
       "      <td>David Robinson</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>Arvydas Sabonis</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7074</th>\n",
       "      <td>Paulo da Silva</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7188</th>\n",
       "      <td>Vladimir Tkachenko</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7281</th>\n",
       "      <td>Stojko Vranković</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7376</th>\n",
       "      <td>Eurelijus Žukauskas</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52608</th>\n",
       "      <td>Aleksey Kazakov</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82100</th>\n",
       "      <td>Frédéric Weis</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89070</th>\n",
       "      <td>Yao Ming</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89075</th>\n",
       "      <td>Roberto Dueñas</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107408</th>\n",
       "      <td>Peter John Ramos</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112312</th>\n",
       "      <td>Stanko Barać</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112332</th>\n",
       "      <td>Andreas Glyniadakis</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112337</th>\n",
       "      <td>Hamed Haddadi</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118663</th>\n",
       "      <td>Timofey Mozgov</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118676</th>\n",
       "      <td>Dmitry Musersky</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120266</th>\n",
       "      <td>Zhang Zhaoxu</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121694</th>\n",
       "      <td>Salah Mejri</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123850</th>\n",
       "      <td>Tyson Chandler</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130460</th>\n",
       "      <td>Li Muhao</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130461</th>\n",
       "      <td>Zhou Qi</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138671</th>\n",
       "      <td>Ondřej Balvín</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139365</th>\n",
       "      <td>Moustapha Fall</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name  height_cm\n",
       "5089       Viktor Pankrashkin      220.0\n",
       "5583     Paulinho Villas Boas      217.0\n",
       "5673           Gunther Behnke      221.0\n",
       "5716                 Uwe Blab      218.0\n",
       "5781           Tommy Burleson      223.0\n",
       "5796            Andy Campbell      218.0\n",
       "6223              Lars Hansen      216.0\n",
       "6270              Hu Zhangbao      216.0\n",
       "6409         Sergey Kovalenko      216.0\n",
       "6420            Jānis Krūmiņš      218.0\n",
       "6504              Luc Longley      220.0\n",
       "6722         Shaquille O'Neal      216.0\n",
       "6937           David Robinson      216.0\n",
       "6978          Arvydas Sabonis      223.0\n",
       "7074           Paulo da Silva      217.0\n",
       "7188       Vladimir Tkachenko      220.0\n",
       "7281         Stojko Vranković      217.0\n",
       "7376      Eurelijus Žukauskas      218.0\n",
       "52608         Aleksey Kazakov      217.0\n",
       "82100           Frédéric Weis      218.0\n",
       "89070                Yao Ming      226.0\n",
       "89075          Roberto Dueñas      221.0\n",
       "107408       Peter John Ramos      219.0\n",
       "112312           Stanko Barać      217.0\n",
       "112332    Andreas Glyniadakis      216.0\n",
       "112337          Hamed Haddadi      218.0\n",
       "118663         Timofey Mozgov      216.0\n",
       "118676        Dmitry Musersky      219.0\n",
       "120266           Zhang Zhaoxu      221.0\n",
       "121694            Salah Mejri      216.0\n",
       "123850         Tyson Chandler      216.0\n",
       "130460               Li Muhao      218.0\n",
       "130461                Zhou Qi      217.0\n",
       "138671          Ondřej Balvín      216.0\n",
       "139365         Moustapha Fall      218.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios[bios['height_cm'] > 215][[\"name\",\"height_cm\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple filter conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>5804</td>\n",
       "      <td>Tommy Burleson</td>\n",
       "      <td>1952-02-24</td>\n",
       "      <td>Crossnore</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>223.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6722</th>\n",
       "      <td>6755</td>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>1972-03-06</td>\n",
       "      <td>Newark</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>216.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6937</th>\n",
       "      <td>6972</td>\n",
       "      <td>David Robinson</td>\n",
       "      <td>1965-08-06</td>\n",
       "      <td>Key West</td>\n",
       "      <td>Florida</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>216.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123850</th>\n",
       "      <td>126093</td>\n",
       "      <td>Tyson Chandler</td>\n",
       "      <td>1982-10-02</td>\n",
       "      <td>Hanford</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>216.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete_id              name   born_date  born_city     born_region  \\\n",
       "5781          5804    Tommy Burleson  1952-02-24  Crossnore  North Carolina   \n",
       "6722          6755  Shaquille O'Neal  1972-03-06     Newark      New Jersey   \n",
       "6937          6972    David Robinson  1965-08-06   Key West         Florida   \n",
       "123850      126093    Tyson Chandler  1982-10-02    Hanford      California   \n",
       "\n",
       "       born_country            NOC  height_cm  weight_kg died_date  \n",
       "5781            USA  United States      223.0      102.0       NaN  \n",
       "6722            USA  United States      216.0      137.0       NaN  \n",
       "6937            USA  United States      216.0      107.0       NaN  \n",
       "123850          USA  United States      216.0      107.0       NaN  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios[(bios['height_cm'] > 215) & (bios['born_country']=='USA')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filter by string conditions"
   ]
  },
  {
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>1907</td>\n",
       "      <td>Keith Hanlon</td>\n",
       "      <td>1966-09-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3505</th>\n",
       "      <td>3517</td>\n",
       "      <td>Keith Wallace</td>\n",
       "      <td>1961-03-29</td>\n",
       "      <td>Preston</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>165.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1999-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6228</th>\n",
       "      <td>6255</td>\n",
       "      <td>Keith Hartley</td>\n",
       "      <td>1940-10-15</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8898</th>\n",
       "      <td>8946</td>\n",
       "      <td>Keith Mwila</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>12118</td>\n",
       "      <td>Keith Hervey</td>\n",
       "      <td>1898-11-03</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1973-02-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>109900</th>\n",
       "      <td>111105</td>\n",
       "      <td>Keith Cumberpatch</td>\n",
       "      <td>1927-08-25</td>\n",
       "      <td>Christchurch</td>\n",
       "      <td>Canterbury</td>\n",
       "      <td>NZL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-11-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115973</th>\n",
       "      <td>117348</td>\n",
       "      <td>Keith Sanderson</td>\n",
       "      <td>1975-02-02</td>\n",
       "      <td>Plymouth</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>183.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117676</th>\n",
       "      <td>119195</td>\n",
       "      <td>Duncan Keith</td>\n",
       "      <td>1983-07-16</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Manitoba</td>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>185.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122121</th>\n",
       "      <td>124176</td>\n",
       "      <td>Keith Ferguson</td>\n",
       "      <td>1979-09-07</td>\n",
       "      <td>Sale</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>176.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127310</th>\n",
       "      <td>129749</td>\n",
       "      <td>Tracy Keith-Matchitt</td>\n",
       "      <td>1990-03-30</td>\n",
       "      <td>Palmerston North</td>\n",
       "      <td>Manawatu-Wanganui</td>\n",
       "      <td>NZL</td>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete_id                  name   born_date         born_city  \\\n",
       "1897          1907          Keith Hanlon  1966-09-01               NaN   \n",
       "3505          3517         Keith Wallace  1961-03-29           Preston   \n",
       "6228          6255         Keith Hartley  1940-10-15         Vancouver   \n",
       "8898          8946           Keith Mwila  1966-01-01               NaN   \n",
       "12053        12118          Keith Hervey  1898-11-03            Fulham   \n",
       "...            ...                   ...         ...               ...   \n",
       "109900      111105     Keith Cumberpatch  1927-08-25      Christchurch   \n",
       "115973      117348       Keith Sanderson  1975-02-02          Plymouth   \n",
       "117676      119195          Duncan Keith  1983-07-16          Winnipeg   \n",
       "122121      124176        Keith Ferguson  1979-09-07              Sale   \n",
       "127310      129749  Tracy Keith-Matchitt  1990-03-30  Palmerston North   \n",
       "\n",
       "              born_region born_country            NOC  height_cm  weight_kg  \\\n",
       "1897                  NaN          NaN        Ireland        NaN        NaN   \n",
       "3505              England          GBR  Great Britain      165.0       51.0   \n",
       "6228     British Columbia          CAN         Canada      200.0       85.0   \n",
       "8898                  NaN          NaN         Zambia        NaN        NaN   \n",
       "12053             England          GBR  Great Britain        NaN        NaN   \n",
       "...                   ...          ...            ...        ...        ...   \n",
       "109900         Canterbury          NZL    New Zealand        NaN        NaN   \n",
       "115973      Massachusetts          USA  United States      183.0       95.0   \n",
       "117676           Manitoba          CAN         Canada      185.0       88.0   \n",
       "122121           Victoria          AUS      Australia      176.0       78.0   \n",
       "127310  Manawatu-Wanganui          NZL   Cook Islands      167.0       60.0   \n",
       "\n",
       "         died_date  \n",
       "1897           NaN  \n",
       "3505    1999-12-31  \n",
       "6228           NaN  \n",
       "8898    1993-01-09  \n",
       "12053   1973-02-22  \n",
       "...            ...  \n",
       "109900  2013-11-15  \n",
       "115973         NaN  \n",
       "117676         NaN  \n",
       "122121         NaN  \n",
       "127310         NaN  \n",
       "\n",
       "[70 rows x 10 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios[bios['name'].str.contains(\"keith\", case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Patrick Chila</td>\n",
       "      <td>1969-11-27</td>\n",
       "      <td>Ris-Orangis</td>\n",
       "      <td>Essonne</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>180.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>120</td>\n",
       "      <td>Patrick Wheatley</td>\n",
       "      <td>1899-01-20</td>\n",
       "      <td>Vryheid</td>\n",
       "      <td>KwaZulu-Natal</td>\n",
       "      <td>RSA</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1967-11-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>320</td>\n",
       "      <td>Patrick De Koning</td>\n",
       "      <td>1961-04-23</td>\n",
       "      <td>Dendermonde</td>\n",
       "      <td>Oost-Vlaanderen</td>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>178.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>1907</td>\n",
       "      <td>Keith Hanlon</td>\n",
       "      <td>1966-09-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>2125</td>\n",
       "      <td>Patrick Jopp</td>\n",
       "      <td>1962-01-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>176.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>143975</th>\n",
       "      <td>147633</td>\n",
       "      <td>Patrick Chinyemba</td>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144172</th>\n",
       "      <td>147850</td>\n",
       "      <td>Patrick Jakob</td>\n",
       "      <td>1996-10-17</td>\n",
       "      <td>Sankt Johann in Tirol</td>\n",
       "      <td>Tirol</td>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144547</th>\n",
       "      <td>148239</td>\n",
       "      <td>Patrick Galbraith</td>\n",
       "      <td>1986-03-11</td>\n",
       "      <td>Haderslev</td>\n",
       "      <td>Syddanmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144565</th>\n",
       "      <td>148257</td>\n",
       "      <td>Patrick Russell</td>\n",
       "      <td>1993-01-04</td>\n",
       "      <td>Gentofte</td>\n",
       "      <td>Hovedstaden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>186.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145435</th>\n",
       "      <td>149158</td>\n",
       "      <td>Patrick Gasienica</td>\n",
       "      <td>1998-11-28</td>\n",
       "      <td>McHenry</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-06-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete_id               name   born_date              born_city  \\\n",
       "6                7      Patrick Chila  1969-11-27            Ris-Orangis   \n",
       "119            120   Patrick Wheatley  1899-01-20                Vryheid   \n",
       "319            320  Patrick De Koning  1961-04-23            Dendermonde   \n",
       "1897          1907       Keith Hanlon  1966-09-01                    NaN   \n",
       "2115          2125       Patrick Jopp  1962-01-08                    NaN   \n",
       "...            ...                ...         ...                    ...   \n",
       "143975      147633  Patrick Chinyemba  2001-01-03                    NaN   \n",
       "144172      147850      Patrick Jakob  1996-10-17  Sankt Johann in Tirol   \n",
       "144547      148239  Patrick Galbraith  1986-03-11              Haderslev   \n",
       "144565      148257    Patrick Russell  1993-01-04               Gentofte   \n",
       "145435      149158  Patrick Gasienica  1998-11-28                McHenry   \n",
       "\n",
       "            born_region born_country            NOC  height_cm  weight_kg  \\\n",
       "6               Essonne          FRA         France      180.0       73.0   \n",
       "119       KwaZulu-Natal          RSA  Great Britain        NaN        NaN   \n",
       "319     Oost-Vlaanderen          BEL        Belgium      178.0       92.0   \n",
       "1897                NaN          NaN        Ireland        NaN        NaN   \n",
       "2115                NaN          NaN    Switzerland      176.0       67.0   \n",
       "...                 ...          ...            ...        ...        ...   \n",
       "143975              NaN          NaN         Zambia        NaN        NaN   \n",
       "144172            Tirol          AUT        Austria        NaN        NaN   \n",
       "144547       Syddanmark          DEN        Denmark        NaN        NaN   \n",
       "144565      Hovedstaden          DEN        Denmark      186.0       93.0   \n",
       "145435         Illinois          USA  United States        NaN        NaN   \n",
       "\n",
       "         died_date  \n",
       "6              NaN  \n",
       "119     1967-11-05  \n",
       "319            NaN  \n",
       "1897           NaN  \n",
       "2115           NaN  \n",
       "...            ...  \n",
       "143975         NaN  \n",
       "144172         NaN  \n",
       "144547         NaN  \n",
       "144565         NaN  \n",
       "145435  2023-06-12  \n",
       "\n",
       "[303 rows x 10 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Regex syntax\n",
    "bios[bios['name'].str.contains('keith|patrick', case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/rz/zcgcqm0x1bl9cj8slq9l2s1c0000gn/T/ipykernel_10015/351328603.py:10: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  repeated_letters = bios[bios['name'].str.contains(r'(.)\\1', na=False)]\n",
      "/var/folders/rz/zcgcqm0x1bl9cj8slq9l2s1c0000gn/T/ipykernel_10015/351328603.py:25: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  start_end_same = bios[bios['name'].str.contains(r'^(.).*\\1$', na=False)]\n",
      "/var/folders/rz/zcgcqm0x1bl9cj8slq9l2s1c0000gn/T/ipykernel_10015/351328603.py:31: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  three_or_more_vowels = bios[bios['name'].str.contains(r'([AEIOUaeiou].*){3,}', na=False)]\n"
     ]
    }
   ],
   "source": [
    "# Other cool regex filters\n",
    "\n",
    "# Find athletes born in cities that start with a vowel:\n",
    "vowel_cities = bios[bios['born_city'].str.contains(r'^[AEIOUaeiou]', na=False)]\n",
    "\n",
    "# Find athletes with names that contain exactly two vowels:\n",
    "two_vowels = bios[bios['name'].str.contains(r'^[^AEIOUaeiou]*[AEIOUaeiou][^AEIOUaeiou]*[AEIOUaeiou][^AEIOUaeiou]*$', na=False)]\n",
    "\n",
    "# Find athletes with names that have repeated consecutive letters (e.g., \"Aaron\", \"Emmett\"):\n",
    "repeated_letters = bios[bios['name'].str.contains(r'(.)\\1', na=False)]\n",
    "\n",
    "# Find athletes with names ending in 'son' or 'sen':\n",
    "son_sen_names = bios[bios['name'].str.contains(r'son$|sen$', case=False, na=False)]\n",
    "\n",
    "# Find athletes born in a year starting with '19':\n",
    "born_19xx = bios[bios['born_date'].str.contains(r'^19', na=False)]\n",
    "\n",
    "# Find athletes with names that do not contain any vowels:\n",
    "no_vowels = bios[bios['name'].str.contains(r'^[^AEIOUaeiou]*$', na=False)]\n",
    "\n",
    "# Find athletes whose names contain a hyphen or an apostrophe:\n",
    "hyphen_apostrophe = bios[bios['name'].str.contains(r\"[-']\", na=False)]\n",
    "\n",
    "# Find athletes with names that start and end with the same letter:\n",
    "start_end_same = bios[bios['name'].str.contains(r'^(.).*\\1$', na=False, case=False)]\n",
    "\n",
    "# Find athletes with a born_city that has exactly 7 characters:\n",
    "city_seven_chars = bios[bios['born_city'].str.contains(r'^.{7}$', na=False)]\n",
    "\n",
    "# Find athletes with names containing three or more vowels:\n",
    "three_or_more_vowels = bios[bios['name'].str.contains(r'([AEIOUaeiou].*){3,}', na=False)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [athlete_id, name, born_date, born_city, born_region, born_country, NOC, height_cm, weight_kg, died_date]\n",
       "Index: []"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Don't use regex search (exact match)\n",
    "bios[bios['name'].str.contains('keith|patrick', case=False, regex=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3505</th>\n",
       "      <td>3517</td>\n",
       "      <td>Keith Wallace</td>\n",
       "      <td>1961-03-29</td>\n",
       "      <td>Preston</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>165.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1999-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>12118</td>\n",
       "      <td>Keith Hervey</td>\n",
       "      <td>1898-11-03</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1973-02-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14577</th>\n",
       "      <td>14674</td>\n",
       "      <td>Keith Harrison</td>\n",
       "      <td>1933-03-28</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16166</th>\n",
       "      <td>16281</td>\n",
       "      <td>Keith Reynolds</td>\n",
       "      <td>1963-12-25</td>\n",
       "      <td>Solihull</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>173.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18734</th>\n",
       "      <td>18862</td>\n",
       "      <td>Keith Sinclair</td>\n",
       "      <td>1945-06-26</td>\n",
       "      <td>Sunderland</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>190.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29897</th>\n",
       "      <td>30123</td>\n",
       "      <td>Keith Langley</td>\n",
       "      <td>1961-06-03</td>\n",
       "      <td>Aldershot</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>173.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34011</th>\n",
       "      <td>34275</td>\n",
       "      <td>Keith Remfry</td>\n",
       "      <td>1947-11-17</td>\n",
       "      <td>Ealing</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>193.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2015-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46885</th>\n",
       "      <td>47234</td>\n",
       "      <td>Keith Collin</td>\n",
       "      <td>1937-01-18</td>\n",
       "      <td>Marylebone</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>168.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1991-03-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50929</th>\n",
       "      <td>51288</td>\n",
       "      <td>Keith Carter</td>\n",
       "      <td>1924-08-30</td>\n",
       "      <td>Akron</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-05-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51185</th>\n",
       "      <td>51544</td>\n",
       "      <td>Keith Russell</td>\n",
       "      <td>1948-01-15</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>188.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52913</th>\n",
       "      <td>53288</td>\n",
       "      <td>Keith Erickson</td>\n",
       "      <td>1944-04-19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>196.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55317</th>\n",
       "      <td>55712</td>\n",
       "      <td>Keith Boxell</td>\n",
       "      <td>1958-05-06</td>\n",
       "      <td>Clapham</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>170.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57818</th>\n",
       "      <td>58226</td>\n",
       "      <td>Keith Peache</td>\n",
       "      <td>1947-08-10</td>\n",
       "      <td>Lewisham</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>180.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61748</th>\n",
       "      <td>62202</td>\n",
       "      <td>Keith Grogono</td>\n",
       "      <td>1912-11-04</td>\n",
       "      <td>Stratford</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999-03-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62620</th>\n",
       "      <td>63086</td>\n",
       "      <td>Keith Musto</td>\n",
       "      <td>1936-01-12</td>\n",
       "      <td>Rochford</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>174.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62678</th>\n",
       "      <td>63144</td>\n",
       "      <td>Keith Notary</td>\n",
       "      <td>1960-01-22</td>\n",
       "      <td>Merritt Island</td>\n",
       "      <td>Florida</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>170.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68324</th>\n",
       "      <td>68841</td>\n",
       "      <td>Keith Angus</td>\n",
       "      <td>1943-04-05</td>\n",
       "      <td>Sheffield</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>170.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68472</th>\n",
       "      <td>68989</td>\n",
       "      <td>Keith Cullen</td>\n",
       "      <td>1972-06-13</td>\n",
       "      <td>Ilford</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>177.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68997</th>\n",
       "      <td>69514</td>\n",
       "      <td>Keith Stock</td>\n",
       "      <td>1957-03-18</td>\n",
       "      <td>Woolwich</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>176.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77550</th>\n",
       "      <td>78141</td>\n",
       "      <td>Keith Brantly</td>\n",
       "      <td>1962-05-23</td>\n",
       "      <td>Scott Air Force Base</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>180.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84097</th>\n",
       "      <td>84766</td>\n",
       "      <td>Keith Christiansen</td>\n",
       "      <td>1944-07-14</td>\n",
       "      <td>International Falls</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>165.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2018-11-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94646</th>\n",
       "      <td>95413</td>\n",
       "      <td>Keith Meyer</td>\n",
       "      <td>1938-06-20</td>\n",
       "      <td>Geneva</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95267</th>\n",
       "      <td>96037</td>\n",
       "      <td>Keith Oliver</td>\n",
       "      <td>1947-10-27</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>172.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96452</th>\n",
       "      <td>97229</td>\n",
       "      <td>Keith Schellenberg</td>\n",
       "      <td>1929-03-13</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-10-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97499</th>\n",
       "      <td>98286</td>\n",
       "      <td>Keith Tkachuk</td>\n",
       "      <td>1972-03-28</td>\n",
       "      <td>Melrose</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>188.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98068</th>\n",
       "      <td>98860</td>\n",
       "      <td>Keith Wegeman</td>\n",
       "      <td>1929-08-28</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1974-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99921</th>\n",
       "      <td>100722</td>\n",
       "      <td>Keith Carney</td>\n",
       "      <td>1970-02-03</td>\n",
       "      <td>Providence</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>188.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115973</th>\n",
       "      <td>117348</td>\n",
       "      <td>Keith Sanderson</td>\n",
       "      <td>1975-02-02</td>\n",
       "      <td>Plymouth</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>183.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete_id                name   born_date             born_city  \\\n",
       "3505          3517       Keith Wallace  1961-03-29               Preston   \n",
       "12053        12118        Keith Hervey  1898-11-03                Fulham   \n",
       "14577        14674      Keith Harrison  1933-03-28            Birmingham   \n",
       "16166        16281      Keith Reynolds  1963-12-25              Solihull   \n",
       "18734        18862      Keith Sinclair  1945-06-26            Sunderland   \n",
       "29897        30123       Keith Langley  1961-06-03             Aldershot   \n",
       "34011        34275        Keith Remfry  1947-11-17                Ealing   \n",
       "46885        47234        Keith Collin  1937-01-18            Marylebone   \n",
       "50929        51288        Keith Carter  1924-08-30                 Akron   \n",
       "51185        51544       Keith Russell  1948-01-15                  Mesa   \n",
       "52913        53288      Keith Erickson  1944-04-19         San Francisco   \n",
       "55317        55712        Keith Boxell  1958-05-06               Clapham   \n",
       "57818        58226        Keith Peache  1947-08-10              Lewisham   \n",
       "61748        62202       Keith Grogono  1912-11-04             Stratford   \n",
       "62620        63086         Keith Musto  1936-01-12              Rochford   \n",
       "62678        63144        Keith Notary  1960-01-22        Merritt Island   \n",
       "68324        68841         Keith Angus  1943-04-05             Sheffield   \n",
       "68472        68989        Keith Cullen  1972-06-13                Ilford   \n",
       "68997        69514         Keith Stock  1957-03-18              Woolwich   \n",
       "77550        78141       Keith Brantly  1962-05-23  Scott Air Force Base   \n",
       "84097        84766  Keith Christiansen  1944-07-14   International Falls   \n",
       "94646        95413         Keith Meyer  1938-06-20                Geneva   \n",
       "95267        96037        Keith Oliver  1947-10-27             Liverpool   \n",
       "96452        97229  Keith Schellenberg  1929-03-13         Middlesbrough   \n",
       "97499        98286       Keith Tkachuk  1972-03-28               Melrose   \n",
       "98068        98860       Keith Wegeman  1929-08-28                Denver   \n",
       "99921       100722        Keith Carney  1970-02-03            Providence   \n",
       "115973      117348     Keith Sanderson  1975-02-02              Plymouth   \n",
       "\n",
       "          born_region born_country            NOC  height_cm  weight_kg  \\\n",
       "3505          England          GBR  Great Britain      165.0       51.0   \n",
       "12053         England          GBR  Great Britain        NaN        NaN   \n",
       "14577         England          GBR  Great Britain        NaN        NaN   \n",
       "16166         England          GBR  Great Britain      173.0       68.0   \n",
       "18734         England          GBR  Great Britain      190.0       79.0   \n",
       "29897         England          GBR  Great Britain      173.0       70.0   \n",
       "34011         England          GBR  Great Britain      193.0      114.0   \n",
       "46885         England          GBR  Great Britain      168.0       63.0   \n",
       "50929            Ohio          USA  United States        NaN        NaN   \n",
       "51185         Arizona          USA  United States      188.0       73.0   \n",
       "52913      California          USA  United States      196.0       86.0   \n",
       "55317         England          GBR  Great Britain      170.0       87.0   \n",
       "57818         England          GBR  Great Britain      180.0       98.0   \n",
       "61748         England          GBR  Great Britain        NaN        NaN   \n",
       "62620         England          GBR  Great Britain      174.0       72.0   \n",
       "62678         Florida          USA  United States      170.0       66.0   \n",
       "68324         England          GBR  Great Britain      170.0       59.0   \n",
       "68472         England          GBR  Great Britain      177.0       61.0   \n",
       "68997         England          GBR  Great Britain      176.0       73.0   \n",
       "77550        Illinois          USA  United States      180.0       64.0   \n",
       "84097       Minnesota          USA  United States      165.0       69.0   \n",
       "94646        Illinois          USA  United States        NaN        NaN   \n",
       "95267         England          GBR  Great Britain      172.0       68.0   \n",
       "96452         England          GBR  Great Britain        NaN        NaN   \n",
       "97499   Massachusetts          USA  United States      188.0      102.0   \n",
       "98068        Colorado          USA  United States        NaN        NaN   \n",
       "99921    Rhode Island          USA  United States      188.0       93.0   \n",
       "115973  Massachusetts          USA  United States      183.0       95.0   \n",
       "\n",
       "         died_date  \n",
       "3505    1999-12-31  \n",
       "12053   1973-02-22  \n",
       "14577          NaN  \n",
       "16166          NaN  \n",
       "18734          NaN  \n",
       "29897          NaN  \n",
       "34011   2015-09-16  \n",
       "46885   1991-03-06  \n",
       "50929   2013-05-03  \n",
       "51185          NaN  \n",
       "52913          NaN  \n",
       "55317          NaN  \n",
       "57818          NaN  \n",
       "61748   1999-03-22  \n",
       "62620          NaN  \n",
       "62678          NaN  \n",
       "68324          NaN  \n",
       "68472          NaN  \n",
       "68997          NaN  \n",
       "77550          NaN  \n",
       "84097   2018-11-05  \n",
       "94646   2010-07-25  \n",
       "95267          NaN  \n",
       "96452   2019-10-28  \n",
       "97499          NaN  \n",
       "98068   1974-08-22  \n",
       "99921          NaN  \n",
       "115973         NaN  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## isin method & startswith\n",
    "bios[bios['born_country'].isin([\"USA\", \"FRA\", \"GBR\"]) & (bios['name'].str.startswith(\"Keith\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make sure to smash that like button & subscribe tehehehe\n"
     ]
    }
   ],
   "source": [
    "print(\"Make sure to smash that like button & subscribe tehehehe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Query functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11030</th>\n",
       "      <td>11088</td>\n",
       "      <td>David Halpern</td>\n",
       "      <td>1955-08-18</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>178.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12800</th>\n",
       "      <td>12870</td>\n",
       "      <td>Todd Trewin</td>\n",
       "      <td>1958-04-20</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>180.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15476</th>\n",
       "      <td>15583</td>\n",
       "      <td>Scott McKinley</td>\n",
       "      <td>1968-10-15</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>183.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29079</th>\n",
       "      <td>29293</td>\n",
       "      <td>Joyce Tanac</td>\n",
       "      <td>1950-09-27</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>156.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31135</th>\n",
       "      <td>31371</td>\n",
       "      <td>Bill Kuhlemeier</td>\n",
       "      <td>1908-01-14</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001-07-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>133392</th>\n",
       "      <td>136331</td>\n",
       "      <td>Hans Struzyna</td>\n",
       "      <td>1989-03-31</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>188.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135448</th>\n",
       "      <td>138662</td>\n",
       "      <td>Maude Davis Crossland</td>\n",
       "      <td>2003-03-19</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136993</th>\n",
       "      <td>140229</td>\n",
       "      <td>Jenell Berhorst</td>\n",
       "      <td>2003-12-13</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143507</th>\n",
       "      <td>147159</td>\n",
       "      <td>Nevin Harrison</td>\n",
       "      <td>2002-06-02</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>175.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145446</th>\n",
       "      <td>149169</td>\n",
       "      <td>Corinne Stoddard</td>\n",
       "      <td>2001-08-15</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete_id                   name   born_date born_city born_region  \\\n",
       "11030        11088          David Halpern  1955-08-18   Seattle  Washington   \n",
       "12800        12870            Todd Trewin  1958-04-20   Seattle  Washington   \n",
       "15476        15583         Scott McKinley  1968-10-15   Seattle  Washington   \n",
       "29079        29293            Joyce Tanac  1950-09-27   Seattle  Washington   \n",
       "31135        31371        Bill Kuhlemeier  1908-01-14   Seattle  Washington   \n",
       "...            ...                    ...         ...       ...         ...   \n",
       "133392      136331          Hans Struzyna  1989-03-31   Seattle  Washington   \n",
       "135448      138662  Maude Davis Crossland  2003-03-19   Seattle  Washington   \n",
       "136993      140229        Jenell Berhorst  2003-12-13   Seattle  Washington   \n",
       "143507      147159         Nevin Harrison  2002-06-02   Seattle  Washington   \n",
       "145446      149169       Corinne Stoddard  2001-08-15   Seattle  Washington   \n",
       "\n",
       "       born_country            NOC  height_cm  weight_kg   died_date  \n",
       "11030           USA  United States      178.0       79.0         NaN  \n",
       "12800           USA  United States      180.0       75.0         NaN  \n",
       "15476           USA  United States      183.0       75.0         NaN  \n",
       "29079           USA  United States      156.0       49.0         NaN  \n",
       "31135           USA  United States        NaN        NaN  2001-07-08  \n",
       "...             ...            ...        ...        ...         ...  \n",
       "133392          USA  United States      188.0       91.0         NaN  \n",
       "135448          USA       Colombia        NaN        NaN         NaN  \n",
       "136993          USA  United States        NaN        NaN         NaN  \n",
       "143507          USA  United States      175.0       73.0         NaN  \n",
       "145446          USA  United States        NaN        NaN         NaN  \n",
       "\n",
       "[102 rows x 10 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios.query('born_country == \"USA\" and born_city == \"Seattle\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding / Removing 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",
       "        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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day Coffee Type  Units Sold\n",
       "0     Monday    Espresso          25\n",
       "1     Monday       Latte          10\n",
       "2    Tuesday    Espresso          10\n",
       "3    Tuesday       Latte          10\n",
       "4  Wednesday    Espresso          35"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee['price'] = 4.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee['new_price'] = np.where(coffee['Coffee Type']=='Espresso', 3.99, 5.99) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>price</th>\n",
       "      <th>new_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold  price  new_price\n",
       "0      Monday    Espresso        25.0   3.99       3.99\n",
       "1      Monday       Latte        10.0   5.99       5.99\n",
       "2     Tuesday    Espresso         NaN   3.99       3.99\n",
       "3     Tuesday       Latte         NaN   5.99       5.99\n",
       "4   Wednesday    Espresso        35.0   3.99       3.99\n",
       "5   Wednesday       Latte        25.0   5.99       5.99\n",
       "6    Thursday    Espresso        40.0   3.99       3.99\n",
       "7    Thursday       Latte        30.0   5.99       5.99\n",
       "8      Friday    Espresso        45.0   3.99       3.99\n",
       "9      Friday       Latte        35.0   5.99       5.99\n",
       "10   Saturday    Espresso        45.0   3.99       3.99\n",
       "11   Saturday       Latte        35.0   5.99       5.99\n",
       "12     Sunday    Espresso        45.0   3.99       3.99\n",
       "13     Sunday       Latte        35.0   5.99       5.99"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee.drop(columns=['price'], inplace=True)\n",
    "\n",
    "# the below would also have worked\n",
    "# coffee = coffee.drop(columns=['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee = coffee[['Day', 'Coffee Type', 'Units Sold', 'new_price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee['revenue'] = coffee['Units Sold'] * coffee['new_price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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",
       "        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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>new_price</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>99.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>59.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.99</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.99</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>139.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>149.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>159.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>179.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold  new_price  revenue\n",
       "0      Monday    Espresso        25.0       3.99    99.75\n",
       "1      Monday       Latte        10.0       5.99    59.90\n",
       "2     Tuesday    Espresso         NaN       3.99      NaN\n",
       "3     Tuesday       Latte         NaN       5.99      NaN\n",
       "4   Wednesday    Espresso        35.0       3.99   139.65\n",
       "5   Wednesday       Latte        25.0       5.99   149.75\n",
       "6    Thursday    Espresso        40.0       3.99   159.60\n",
       "7    Thursday       Latte        30.0       5.99   179.70\n",
       "8      Friday    Espresso        45.0       3.99   179.55\n",
       "9      Friday       Latte        35.0       5.99   209.65\n",
       "10   Saturday    Espresso        45.0       3.99   179.55\n",
       "11   Saturday       Latte        35.0       5.99   209.65\n",
       "12     Sunday    Espresso        45.0       3.99   179.55\n",
       "13     Sunday       Latte        35.0       5.99   209.65"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee.rename(columns={'new_price': 'price'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_new = bios.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_new['first_name'] = bios_new['name'].str.split(' ').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
       "    .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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "      <th>first_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>1907</td>\n",
       "      <td>Keith Hanlon</td>\n",
       "      <td>1966-09-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3505</th>\n",
       "      <td>3517</td>\n",
       "      <td>Keith Wallace</td>\n",
       "      <td>1961-03-29</td>\n",
       "      <td>Preston</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>165.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1999-12-31</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6228</th>\n",
       "      <td>6255</td>\n",
       "      <td>Keith Hartley</td>\n",
       "      <td>1940-10-15</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8898</th>\n",
       "      <td>8946</td>\n",
       "      <td>Keith Mwila</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993-01-09</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>12118</td>\n",
       "      <td>Keith Hervey</td>\n",
       "      <td>1898-11-03</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1973-02-22</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99921</th>\n",
       "      <td>100722</td>\n",
       "      <td>Keith Carney</td>\n",
       "      <td>1970-02-03</td>\n",
       "      <td>Providence</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>188.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102227</th>\n",
       "      <td>103168</td>\n",
       "      <td>Keith Beavers</td>\n",
       "      <td>1983-02-09</td>\n",
       "      <td>London</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>185.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109900</th>\n",
       "      <td>111105</td>\n",
       "      <td>Keith Cumberpatch</td>\n",
       "      <td>1927-08-25</td>\n",
       "      <td>Christchurch</td>\n",
       "      <td>Canterbury</td>\n",
       "      <td>NZL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-11-15</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115973</th>\n",
       "      <td>117348</td>\n",
       "      <td>Keith Sanderson</td>\n",
       "      <td>1975-02-02</td>\n",
       "      <td>Plymouth</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>183.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122121</th>\n",
       "      <td>124176</td>\n",
       "      <td>Keith Ferguson</td>\n",
       "      <td>1979-09-07</td>\n",
       "      <td>Sale</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>176.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Keith</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete_id               name   born_date     born_city  \\\n",
       "1897          1907       Keith Hanlon  1966-09-01           NaN   \n",
       "3505          3517      Keith Wallace  1961-03-29       Preston   \n",
       "6228          6255      Keith Hartley  1940-10-15     Vancouver   \n",
       "8898          8946        Keith Mwila  1966-01-01           NaN   \n",
       "12053        12118       Keith Hervey  1898-11-03        Fulham   \n",
       "...            ...                ...         ...           ...   \n",
       "99921       100722       Keith Carney  1970-02-03    Providence   \n",
       "102227      103168      Keith Beavers  1983-02-09        London   \n",
       "109900      111105  Keith Cumberpatch  1927-08-25  Christchurch   \n",
       "115973      117348    Keith Sanderson  1975-02-02      Plymouth   \n",
       "122121      124176     Keith Ferguson  1979-09-07          Sale   \n",
       "\n",
       "             born_region born_country            NOC  height_cm  weight_kg  \\\n",
       "1897                 NaN          NaN        Ireland        NaN        NaN   \n",
       "3505             England          GBR  Great Britain      165.0       51.0   \n",
       "6228    British Columbia          CAN         Canada      200.0       85.0   \n",
       "8898                 NaN          NaN         Zambia        NaN        NaN   \n",
       "12053            England          GBR  Great Britain        NaN        NaN   \n",
       "...                  ...          ...            ...        ...        ...   \n",
       "99921       Rhode Island          USA  United States      188.0       93.0   \n",
       "102227           Ontario          CAN         Canada      185.0       75.0   \n",
       "109900        Canterbury          NZL    New Zealand        NaN        NaN   \n",
       "115973     Massachusetts          USA  United States      183.0       95.0   \n",
       "122121          Victoria          AUS      Australia      176.0       78.0   \n",
       "\n",
       "         died_date first_name  \n",
       "1897           NaN      Keith  \n",
       "3505    1999-12-31      Keith  \n",
       "6228           NaN      Keith  \n",
       "8898    1993-01-09      Keith  \n",
       "12053   1973-02-22      Keith  \n",
       "...            ...        ...  \n",
       "99921          NaN      Keith  \n",
       "102227         NaN      Keith  \n",
       "109900  2013-11-15      Keith  \n",
       "115973         NaN      Keith  \n",
       "122121         NaN      Keith  \n",
       "\n",
       "[64 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios_new.query('first_name == \"Keith\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_new['born_datetime'] = pd.to_datetime(bios_new['born_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_new['born_year'] = bios_new['born_datetime'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arnaud Boetsch</td>\n",
       "      <td>1969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jean Borotra</td>\n",
       "      <td>1898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jacques Brugnon</td>\n",
       "      <td>1895.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albert Canet</td>\n",
       "      <td>1878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145495</th>\n",
       "      <td>Polina Luchnikova</td>\n",
       "      <td>2002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145496</th>\n",
       "      <td>Valeriya Merkusheva</td>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145497</th>\n",
       "      <td>Yuliya Smirnova</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145498</th>\n",
       "      <td>André Foussard</td>\n",
       "      <td>1899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145499</th>\n",
       "      <td>Bill Phillips</td>\n",
       "      <td>1913.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name  born_year\n",
       "0       Jean-François Blanchy     1886.0\n",
       "1              Arnaud Boetsch     1969.0\n",
       "2                Jean Borotra     1898.0\n",
       "3             Jacques Brugnon     1895.0\n",
       "4                Albert Canet     1878.0\n",
       "...                       ...        ...\n",
       "145495      Polina Luchnikova     2002.0\n",
       "145496    Valeriya Merkusheva     1999.0\n",
       "145497        Yuliya Smirnova     1998.0\n",
       "145498         André Foussard     1899.0\n",
       "145499          Bill Phillips     1913.0\n",
       "\n",
       "[145500 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios_new[['name','born_year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_new.to_csv('./data/bios_new.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios['height_category'] = bios['height_cm'].apply(lambda x: 'Short' if x < 165 else ('Average' if x < 185 else 'Tall'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_athlete(row):\n",
    "    if row['height_cm'] < 175 and row['weight_kg'] < 70:\n",
    "        return 'Lightweight'\n",
    "    elif row['height_cm'] < 185 or row['weight_kg'] <= 80:\n",
    "        return 'Middleweight'\n",
    "    \n",
    "    else:\n",
    "        return 'Heavyweight'\n",
    "    \n",
    "bios['Category'] = bios.apply(categorize_athlete, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "      <th>height_category</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
       "      <td>Tall</td>\n",
       "      <td>Heavyweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Arnaud Boetsch</td>\n",
       "      <td>1969-04-01</td>\n",
       "      <td>Meulan</td>\n",
       "      <td>Yvelines</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>183.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Average</td>\n",
       "      <td>Middleweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jean Borotra</td>\n",
       "      <td>1898-08-13</td>\n",
       "      <td>Biarritz</td>\n",
       "      <td>Pyrénées-Atlantiques</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>183.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1994-07-17</td>\n",
       "      <td>Average</td>\n",
       "      <td>Middleweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Jacques Brugnon</td>\n",
       "      <td>1895-05-11</td>\n",
       "      <td>Paris VIIIe</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>168.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1978-03-20</td>\n",
       "      <td>Average</td>\n",
       "      <td>Lightweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Albert Canet</td>\n",
       "      <td>1878-04-17</td>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930-07-25</td>\n",
       "      <td>Tall</td>\n",
       "      <td>Heavyweight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   athlete_id                   name   born_date    born_city  \\\n",
       "0           1  Jean-François Blanchy  1886-12-12     Bordeaux   \n",
       "1           2         Arnaud Boetsch  1969-04-01       Meulan   \n",
       "2           3           Jean Borotra  1898-08-13     Biarritz   \n",
       "3           4        Jacques Brugnon  1895-05-11  Paris VIIIe   \n",
       "4           5           Albert Canet  1878-04-17   Wandsworth   \n",
       "\n",
       "            born_region born_country     NOC  height_cm  weight_kg  \\\n",
       "0               Gironde          FRA  France        NaN        NaN   \n",
       "1              Yvelines          FRA  France      183.0       76.0   \n",
       "2  Pyrénées-Atlantiques          FRA  France      183.0       76.0   \n",
       "3                 Paris          FRA  France      168.0       64.0   \n",
       "4               England          GBR  France        NaN        NaN   \n",
       "\n",
       "    died_date height_category      Category  \n",
       "0  1960-10-02            Tall   Heavyweight  \n",
       "1         NaN         Average  Middleweight  \n",
       "2  1994-07-17         Average  Middleweight  \n",
       "3  1978-03-20         Average   Lightweight  \n",
       "4  1930-07-25            Tall   Heavyweight  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging & Concatenating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "nocs = pd.read_csv('./data/noc_regions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_new = pd.merge(bios, nocs, left_on='born_country', right_on='NOC', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "bios_new.rename(columns={'region': 'born_country_full'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "usa = bios[bios['born_country']=='USA'].copy()\n",
    "gbr = bios[bios['born_country']=='GBR'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.concat([usa,gbr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144811</th>\n",
       "      <td>148512</td>\n",
       "      <td>Benjamin Alexander</td>\n",
       "      <td>1983-05-08</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144815</th>\n",
       "      <td>148517</td>\n",
       "      <td>Ashley Watson</td>\n",
       "      <td>1993-10-28</td>\n",
       "      <td>Peterborough</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145005</th>\n",
       "      <td>148716</td>\n",
       "      <td>Peder Kongshaug</td>\n",
       "      <td>2001-08-13</td>\n",
       "      <td>Wimbledon</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>184.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145319</th>\n",
       "      <td>149041</td>\n",
       "      <td>Axel Brown</td>\n",
       "      <td>1992-04-02</td>\n",
       "      <td>Harrogate</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145388</th>\n",
       "      <td>149111</td>\n",
       "      <td>Jean-Luc Baker</td>\n",
       "      <td>1993-10-07</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        athlete_id                name   born_date     born_city born_region  \\\n",
       "144811      148512  Benjamin Alexander  1983-05-08        London     England   \n",
       "144815      148517       Ashley Watson  1993-10-28  Peterborough     England   \n",
       "145005      148716     Peder Kongshaug  2001-08-13     Wimbledon     England   \n",
       "145319      149041          Axel Brown  1992-04-02     Harrogate     England   \n",
       "145388      149111      Jean-Luc Baker  1993-10-07       Burnley     England   \n",
       "\n",
       "       born_country                  NOC  height_cm  weight_kg died_date  \n",
       "144811          GBR              Jamaica        NaN        NaN       NaN  \n",
       "144815          GBR              Jamaica        NaN        NaN       NaN  \n",
       "145005          GBR               Norway      184.0       86.0       NaN  \n",
       "145319          GBR  Trinidad and Tobago        NaN        NaN       NaN  \n",
       "145388          GBR        United States        NaN        NaN       NaN  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df = pd.merge(results, bios, on='athlete_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>year</th>\n",
       "      <th>type</th>\n",
       "      <th>discipline</th>\n",
       "      <th>event</th>\n",
       "      <th>as</th>\n",
       "      <th>athlete_id</th>\n",
       "      <th>noc</th>\n",
       "      <th>team</th>\n",
       "      <th>place</th>\n",
       "      <th>tied</th>\n",
       "      <th>medal</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1912.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>Singles, Men (Olympic)</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1</td>\n",
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       "      <td>17.0</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1912.0</td>\n",
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       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1</td>\n",
       "      <td>FRA</td>\n",
       "      <td>Jean Montariol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Singles, Men (Olympic)</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
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       "      <td>FRA</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1920.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>Doubles, Mixed (Olympic)</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1</td>\n",
       "      <td>FRA</td>\n",
       "      <td>Jeanne Vaussard</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>None</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1920.0</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>Doubles, Men (Olympic)</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1</td>\n",
       "      <td>FRA</td>\n",
       "      <td>Jacques Brugnon</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year    type discipline                     event                     as  \\\n",
       "0  1912.0  Summer     Tennis    Singles, Men (Olympic)  Jean-François Blanchy   \n",
       "1  1912.0  Summer     Tennis    Doubles, Men (Olympic)  Jean-François Blanchy   \n",
       "2  1920.0  Summer     Tennis    Singles, Men (Olympic)  Jean-François Blanchy   \n",
       "3  1920.0  Summer     Tennis  Doubles, Mixed (Olympic)  Jean-François Blanchy   \n",
       "4  1920.0  Summer     Tennis    Doubles, Men (Olympic)  Jean-François Blanchy   \n",
       "\n",
       "   athlete_id  noc             team  place   tied medal  \\\n",
       "0           1  FRA             None   17.0   True  None   \n",
       "1           1  FRA   Jean Montariol    NaN  False  None   \n",
       "2           1  FRA             None   32.0   True  None   \n",
       "3           1  FRA  Jeanne Vaussard    8.0   True  None   \n",
       "4           1  FRA  Jacques Brugnon    4.0  False  None   \n",
       "\n",
       "                    name   born_date born_city born_region born_country  \\\n",
       "0  Jean-François Blanchy  1886-12-12  Bordeaux     Gironde          FRA   \n",
       "1  Jean-François Blanchy  1886-12-12  Bordeaux     Gironde          FRA   \n",
       "2  Jean-François Blanchy  1886-12-12  Bordeaux     Gironde          FRA   \n",
       "3  Jean-François Blanchy  1886-12-12  Bordeaux     Gironde          FRA   \n",
       "4  Jean-François Blanchy  1886-12-12  Bordeaux     Gironde          FRA   \n",
       "\n",
       "      NOC  height_cm  weight_kg   died_date  \n",
       "0  France        NaN        NaN  1960-10-02  \n",
       "1  France        NaN        NaN  1960-10-02  \n",
       "2  France        NaN        NaN  1960-10-02  \n",
       "3  France        NaN        NaN  1960-10-02  \n",
       "4  France        NaN        NaN  1960-10-02  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "coffee.loc[[2,3], 'Units Sold'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     25.00\n",
       "1     10.00\n",
       "2     33.75\n",
       "3     33.75\n",
       "4     35.00\n",
       "5     25.00\n",
       "6     40.00\n",
       "7     30.00\n",
       "8     45.00\n",
       "9     35.00\n",
       "10    45.00\n",
       "11    35.00\n",
       "12    45.00\n",
       "13    35.00\n",
       "Name: Units Sold, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure to set this to your Units Sold column if you want these changes to stick\n",
    "coffee['Units Sold'].fillna(coffee['Units Sold'].mean()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     25.000000\n",
       "1     10.000000\n",
       "2     18.333333\n",
       "3     26.666667\n",
       "4     35.000000\n",
       "5     25.000000\n",
       "6     40.000000\n",
       "7     30.000000\n",
       "8     45.000000\n",
       "9     35.000000\n",
       "10    45.000000\n",
       "11    35.000000\n",
       "12    45.000000\n",
       "13    35.000000\n",
       "Name: Units Sold, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coffee['Units Sold'] = coffee['Units Sold'].interpolate()\n",
    "coffee['Units Sold'].interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold\n",
       "0      Monday    Espresso        25.0\n",
       "1      Monday       Latte        10.0\n",
       "4   Wednesday    Espresso        35.0\n",
       "5   Wednesday       Latte        25.0\n",
       "6    Thursday    Espresso        40.0\n",
       "7    Thursday       Latte        30.0\n",
       "8      Friday    Espresso        45.0\n",
       "9      Friday       Latte        35.0\n",
       "10   Saturday    Espresso        45.0\n",
       "11   Saturday       Latte        35.0\n",
       "12     Sunday    Espresso        45.0\n",
       "13     Sunday       Latte        35.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.dropna(subset=['Units Sold']) # Use inplace=True if you want to update the coffee df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>price</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>99.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>89.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>139.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>149.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>159.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>179.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold  price  revenue\n",
       "0      Monday    Espresso        15.0   3.99    99.75\n",
       "1      Monday       Latte        15.0   5.99    89.85\n",
       "4   Wednesday    Espresso        35.0   3.99   139.65\n",
       "5   Wednesday       Latte        25.0   5.99   149.75\n",
       "6    Thursday    Espresso        40.0   3.99   159.60\n",
       "7    Thursday       Latte        30.0   5.99   179.70\n",
       "8      Friday    Espresso        45.0   3.99   179.55\n",
       "9      Friday       Latte        35.0   5.99   209.65\n",
       "10   Saturday    Espresso        45.0   3.99   179.55\n",
       "11   Saturday       Latte        35.0   5.99   209.65\n",
       "12     Sunday    Espresso        45.0   3.99   179.55\n",
       "13     Sunday       Latte        35.0   5.99   209.65"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee[coffee['Units Sold'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>price</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.99</td>\n",
       "      <td>99.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>89.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>3.99</td>\n",
       "      <td>119.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>5.99</td>\n",
       "      <td>119.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>3.99</td>\n",
       "      <td>139.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>149.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.99</td>\n",
       "      <td>159.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>179.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>3.99</td>\n",
       "      <td>179.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold  price  revenue\n",
       "0      Monday    Espresso   15.000000   3.99    99.75\n",
       "1      Monday       Latte   15.000000   5.99    89.85\n",
       "2     Tuesday    Espresso   21.666667   3.99   119.70\n",
       "3     Tuesday       Latte   28.333333   5.99   119.80\n",
       "4   Wednesday    Espresso   35.000000   3.99   139.65\n",
       "5   Wednesday       Latte   25.000000   5.99   149.75\n",
       "6    Thursday    Espresso   40.000000   3.99   159.60\n",
       "7    Thursday       Latte   30.000000   5.99   179.70\n",
       "8      Friday    Espresso   45.000000   3.99   179.55\n",
       "9      Friday       Latte   35.000000   5.99   209.65\n",
       "10   Saturday    Espresso   45.000000   3.99   179.55\n",
       "11   Saturday       Latte   35.000000   5.99   209.65\n",
       "12     Sunday    Espresso   45.000000   3.99   179.55\n",
       "13     Sunday       Latte   35.000000   5.99   209.65"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Arnaud Boetsch</td>\n",
       "      <td>1969-04-01</td>\n",
       "      <td>Meulan</td>\n",
       "      <td>Yvelines</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>183.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jean Borotra</td>\n",
       "      <td>1898-08-13</td>\n",
       "      <td>Biarritz</td>\n",
       "      <td>Pyrénées-Atlantiques</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>183.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1994-07-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Jacques Brugnon</td>\n",
       "      <td>1895-05-11</td>\n",
       "      <td>Paris VIIIe</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>168.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1978-03-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Albert Canet</td>\n",
       "      <td>1878-04-17</td>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930-07-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   athlete_id                   name   born_date    born_city  \\\n",
       "0           1  Jean-François Blanchy  1886-12-12     Bordeaux   \n",
       "1           2         Arnaud Boetsch  1969-04-01       Meulan   \n",
       "2           3           Jean Borotra  1898-08-13     Biarritz   \n",
       "3           4        Jacques Brugnon  1895-05-11  Paris VIIIe   \n",
       "4           5           Albert Canet  1878-04-17   Wandsworth   \n",
       "\n",
       "            born_region born_country     NOC  height_cm  weight_kg   died_date  \n",
       "0               Gironde          FRA  France        NaN        NaN  1960-10-02  \n",
       "1              Yvelines          FRA  France      183.0       76.0         NaN  \n",
       "2  Pyrénées-Atlantiques          FRA  France      183.0       76.0  1994-07-17  \n",
       "3                 Paris          FRA  France      168.0       64.0  1978-03-20  \n",
       "4               England          GBR  France        NaN        NaN  1930-07-25  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "born_city\n",
       "Budapest           1378\n",
       "Moskva (Moscow)     883\n",
       "Oslo                708\n",
       "Stockholm           629\n",
       "Praha (Prague)      600\n",
       "                   ... \n",
       "Bodrogkisfalud        1\n",
       "Ternberg              1\n",
       "Klaus                 1\n",
       "Plaški                1\n",
       "Dulwich Hill          1\n",
       "Name: count, Length: 22368, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios['born_city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "born_region\n",
       "California       1634\n",
       "New York          990\n",
       "Illinois          585\n",
       "Pennsylvania      530\n",
       "Massachusetts     530\n",
       "New Jersey        381\n",
       "Texas             368\n",
       "Minnesota         365\n",
       "Ohio              328\n",
       "Michigan          319\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios[bios['born_country']=='USA']['born_region'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "born_region\n",
       "Utah              91\n",
       "Missouri          91\n",
       "North Carolina    86\n",
       "Arizona           83\n",
       "New Hampshire     83\n",
       "Vermont           68\n",
       "Mississippi       66\n",
       "Alabama           64\n",
       "Kentucky          62\n",
       "Tennessee         62\n",
       "Nebraska          60\n",
       "Rhode Island      56\n",
       "Montana           55\n",
       "South Carolina    50\n",
       "Maine             50\n",
       "Alaska            45\n",
       "Arkansas          42\n",
       "Idaho             41\n",
       "New Mexico        38\n",
       "Nevada            36\n",
       "South Dakota      27\n",
       "West Virginia     24\n",
       "Delaware          22\n",
       "North Dakota      16\n",
       "Wyoming           14\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios[bios['born_country']=='USA']['born_region'].value_counts().tail(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Groupby function in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coffee Type\n",
       "Espresso    235.0\n",
       "Latte       170.0\n",
       "Name: Units Sold, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.groupby(['Coffee Type'])['Units Sold'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coffee Type\n",
       "Espresso    39.166667\n",
       "Latte       28.333333\n",
       "Name: Units Sold, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.groupby(['Coffee Type'])['Units Sold'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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></th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Espresso</th>\n",
       "      <th>Friday</th>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>25.0</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>45.0</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>40.0</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Latte</th>\n",
       "      <th>Friday</th>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>35.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>30.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>25.0</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Units Sold  price\n",
       "Coffee Type Day                         \n",
       "Espresso    Friday           45.0   3.99\n",
       "            Monday           25.0   3.99\n",
       "            Saturday         45.0   3.99\n",
       "            Sunday           45.0   3.99\n",
       "            Thursday         40.0   3.99\n",
       "            Tuesday           0.0   3.99\n",
       "            Wednesday        35.0   3.99\n",
       "Latte       Friday           35.0   5.99\n",
       "            Monday           10.0   5.99\n",
       "            Saturday         35.0   5.99\n",
       "            Sunday           35.0   5.99\n",
       "            Thursday         30.0   5.99\n",
       "            Tuesday           0.0   5.99\n",
       "            Wednesday        25.0   5.99"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee.groupby(['Coffee Type', 'Day']).agg({'Units Sold': 'sum', 'price': 'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot = coffee.pivot(columns='Coffee Type', index='Day', values='revenue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coffee Type\n",
       "Espresso     937.65\n",
       "Latte       1018.30\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day\n",
       "Friday       389.20\n",
       "Monday       159.65\n",
       "Saturday     389.20\n",
       "Sunday       389.20\n",
       "Thursday     339.30\n",
       "Tuesday        0.00\n",
       "Wednesday    289.40\n",
       "dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using datetime with Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>year_born</th>\n",
       "      <th>month_born</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>1970.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>1972.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>1986.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>1975.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1857.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1857.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1857.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1857.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1885 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year_born  month_born  name\n",
       "1437     1970.0         1.0   239\n",
       "1461     1972.0         1.0   229\n",
       "1629     1986.0         1.0   227\n",
       "1497     1975.0         1.0   227\n",
       "1617     1985.0         1.0   225\n",
       "...         ...         ...   ...\n",
       "95       1857.0         5.0     1\n",
       "96       1857.0         7.0     1\n",
       "97       1857.0         8.0     1\n",
       "98       1857.0         9.0     1\n",
       "1884     2009.0         1.0     1\n",
       "\n",
       "[1885 rows x 3 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bios['born_date'] = pd.to_datetime(bios['born_date'])\n",
    "bios['month_born'] = bios['born_date'].dt.month\n",
    "bios['year_born'] = bios['born_date'].dt.year\n",
    "bios.groupby([bios['year_born'],bios['month_born']])['name'].count().reset_index().sort_values('name', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift() rank() cumsum() rolling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "latte = coffee[coffee['Coffee Type']==\"Latte\"].copy()\n",
    "latte['3day'] = latte['Units Sold'].rolling(3).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Day</th>\n",
       "      <th>Coffee Type</th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>price</th>\n",
       "      <th>revenue</th>\n",
       "      <th>yesterday_revenue</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>cumulative_revenue</th>\n",
       "      <th>3day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>89.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>5.99</td>\n",
       "      <td>119.80</td>\n",
       "      <td>89.85</td>\n",
       "      <td>133.333333</td>\n",
       "      <td>429.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>149.75</td>\n",
       "      <td>119.80</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>718.5</td>\n",
       "      <td>68.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>179.70</td>\n",
       "      <td>149.75</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>1057.8</td>\n",
       "      <td>83.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "      <td>179.70</td>\n",
       "      <td>116.666667</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "      <td>209.65</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1836.2</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>5.99</td>\n",
       "      <td>209.65</td>\n",
       "      <td>209.65</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2225.4</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day Coffee Type  Units Sold  price  revenue  yesterday_revenue  \\\n",
       "1      Monday       Latte   15.000000   5.99    89.85                NaN   \n",
       "3     Tuesday       Latte   28.333333   5.99   119.80              89.85   \n",
       "5   Wednesday       Latte   25.000000   5.99   149.75             119.80   \n",
       "7    Thursday       Latte   30.000000   5.99   179.70             149.75   \n",
       "9      Friday       Latte   35.000000   5.99   209.65             179.70   \n",
       "11   Saturday       Latte   35.000000   5.99   209.65             209.65   \n",
       "13     Sunday       Latte   35.000000   5.99   209.65             209.65   \n",
       "\n",
       "    pct_change  cumulative_revenue        3day  \n",
       "1          NaN               189.6         NaN  \n",
       "3   133.333333               429.1         NaN  \n",
       "5   125.000000               718.5   68.333333  \n",
       "7   120.000000              1057.8   83.333333  \n",
       "9   116.666667              1447.0   90.000000  \n",
       "11  100.000000              1836.2  100.000000  \n",
       "13  100.000000              2225.4  105.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Functionality (cont.)\n",
    "These two libraries didn't actually make it into final video\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pyjanitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>day</th>\n",
       "      <th>coffee_type</th>\n",
       "      <th>units_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Friday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>Latte</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day coffee_type  units_sold\n",
       "0      Monday    Espresso          25\n",
       "1      Monday       Latte          15\n",
       "2     Tuesday    Espresso          30\n",
       "3     Tuesday       Latte          20\n",
       "4   Wednesday    Espresso          35\n",
       "5   Wednesday       Latte          25\n",
       "6    Thursday    Espresso          40\n",
       "7    Thursday       Latte          30\n",
       "8      Friday    Espresso          45\n",
       "9      Friday       Latte          35\n",
       "10   Saturday    Espresso          45\n",
       "11   Saturday       Latte          35\n",
       "12     Sunday    Espresso          45\n",
       "13     Sunday       Latte          35"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import janitor\n",
    "\n",
    "coffee.clean_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install skimpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────────────────────── skimpy summary ─────────────────────────────────────────────────╮\n",
       "│ <span style=\"font-style: italic\">         Data Summary         </span> <span style=\"font-style: italic\">      Data Types       </span>                                                          │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓ ┏━━━━━━━━━━━━━┳━━━━━━━┓                                                          │\n",
       "│ ┃<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\"> dataframe         </span>┃<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\"> Values </span>┃ ┃<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\"> Column Type </span>┃<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\"> Count </span>┃                                                          │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩ ┡━━━━━━━━━━━━━╇━━━━━━━┩                                                          │\n",
       "│ │ Number of rows    │ 308408 │ │ string      │ 7     │                                                          │\n",
       "│ │ Number of columns │ 11     │ │ float64     │ 2     │                                                          │\n",
       "│ └───────────────────┴────────┘ │ int64       │ 1     │                                                          │\n",
       "│                                │ bool        │ 1     │                                                          │\n",
       "│                                └─────────────┴───────┘                                                          │\n",
       "│ <span style=\"font-style: italic\">                                                    number                                                    </span>  │\n",
       "│ ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓  │\n",
       "│ ┃<span style=\"font-weight: bold\"> column_name    </span>┃<span style=\"font-weight: bold\"> NA      </span>┃<span style=\"font-weight: bold\"> NA %  </span>┃<span style=\"font-weight: bold\"> mean   </span>┃<span style=\"font-weight: bold\"> sd     </span>┃<span style=\"font-weight: bold\"> p0    </span>┃<span style=\"font-weight: bold\"> p25    </span>┃<span style=\"font-weight: bold\"> p50    </span>┃<span style=\"font-weight: bold\"> p75     </span>┃<span style=\"font-weight: bold\"> p100    </span>┃<span style=\"font-weight: bold\"> hist    </span>┃  │\n",
       "│ ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">year          </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">   2601</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 0.84</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  2000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    31</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 1900</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  2000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  2000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">   2000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">   2000</span> │ <span style=\"color: #008000; text-decoration-color: #008000\">▁▂▂▅▇▇ </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">athlete_id    </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">      0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 73000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 41000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    1</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 34000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 74000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 110000</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 150000</span> │ <span style=\"color: #008000; text-decoration-color: #008000\">▆▇▆▇▇▅ </span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">place         </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">  25215</span> │ <span style=\"color: #008080; text-decoration-color: #008080\"> 8.18</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    16</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    19</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    1</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     5</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     9</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">     20</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">    180</span> │ <span style=\"color: #008000; text-decoration-color: #008000\">  ▇▁   </span> │  │\n",
       "│ └────────────────┴─────────┴───────┴────────┴────────┴───────┴────────┴────────┴─────────┴─────────┴─────────┘  │\n",
       "│ <span style=\"font-style: italic\">                                                     bool                                                     </span>  │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓  │\n",
       "│ ┃<span style=\"font-weight: bold\"> column_name                       </span>┃<span style=\"font-weight: bold\"> true              </span>┃<span style=\"font-weight: bold\"> true rate                    </span>┃<span style=\"font-weight: bold\"> hist                </span>┃  │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">tied                             </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">            45940</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                        0.15</span> │ <span style=\"color: #008000; text-decoration-color: #008000\">      ▇    ▁       </span> │  │\n",
       "│ └───────────────────────────────────┴───────────────────┴──────────────────────────────┴─────────────────────┘  │\n",
       "│ <span style=\"font-style: italic\">                                                    string                                                    </span>  │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓  │\n",
       "│ ┃<span style=\"font-weight: bold\"> column_name             </span>┃<span style=\"font-weight: bold\"> NA            </span>┃<span style=\"font-weight: bold\"> NA %        </span>┃<span style=\"font-weight: bold\"> words per row              </span>┃<span style=\"font-weight: bold\"> total words           </span>┃  │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">type                   </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">         2601</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">       0.84</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                      0.99</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">               305807</span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">discipline             </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">            1</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">          0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                         2</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">               610211</span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">event                  </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">            0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">          0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                       4.2</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">              1303323</span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">as                     </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">            0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">          0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                       2.1</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">               634574</span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">noc                    </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">            1</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">          0</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                         1</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">               308407</span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">team                   </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">       186694</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">      60.53</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                      0.62</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">               190405</span> │  │\n",
       "│ │ <span style=\"color: #af87ff; text-decoration-color: #af87ff\">medal                  </span> │ <span style=\"color: #008080; text-decoration-color: #008080\">       264269</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">      85.69</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                      0.14</span> │ <span style=\"color: #008080; text-decoration-color: #008080\">                44139</span> │  │\n",
       "│ └─────────────────────────┴───────────────┴─────────────┴────────────────────────────┴───────────────────────┘  │\n",
       "╰────────────────────────────────────────────────────── End ──────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────────────────────── skimpy summary ─────────────────────────────────────────────────╮\n",
       "│ \u001b[3m         Data Summary         \u001b[0m \u001b[3m      Data Types       \u001b[0m                                                          │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓ ┏━━━━━━━━━━━━━┳━━━━━━━┓                                                          │\n",
       "│ ┃\u001b[1;36m \u001b[0m\u001b[1;36mdataframe        \u001b[0m\u001b[1;36m \u001b[0m┃\u001b[1;36m \u001b[0m\u001b[1;36mValues\u001b[0m\u001b[1;36m \u001b[0m┃ ┃\u001b[1;36m \u001b[0m\u001b[1;36mColumn Type\u001b[0m\u001b[1;36m \u001b[0m┃\u001b[1;36m \u001b[0m\u001b[1;36mCount\u001b[0m\u001b[1;36m \u001b[0m┃                                                          │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩ ┡━━━━━━━━━━━━━╇━━━━━━━┩                                                          │\n",
       "│ │ Number of rows    │ 308408 │ │ string      │ 7     │                                                          │\n",
       "│ │ Number of columns │ 11     │ │ float64     │ 2     │                                                          │\n",
       "│ └───────────────────┴────────┘ │ int64       │ 1     │                                                          │\n",
       "│                                │ bool        │ 1     │                                                          │\n",
       "│                                └─────────────┴───────┘                                                          │\n",
       "│ \u001b[3m                                                    number                                                    \u001b[0m  │\n",
       "│ ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓  │\n",
       "│ ┃\u001b[1m \u001b[0m\u001b[1mcolumn_name   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mNA     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mNA % \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mmean  \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msd    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp0   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp25   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp50   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp75    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mp100   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mhist   \u001b[0m\u001b[1m \u001b[0m┃  │\n",
       "│ ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩  │\n",
       "│ │ \u001b[38;5;141myear          \u001b[0m │ \u001b[36m   2601\u001b[0m │ \u001b[36m 0.84\u001b[0m │ \u001b[36m  2000\u001b[0m │ \u001b[36m    31\u001b[0m │ \u001b[36m 1900\u001b[0m │ \u001b[36m  2000\u001b[0m │ \u001b[36m  2000\u001b[0m │ \u001b[36m   2000\u001b[0m │ \u001b[36m   2000\u001b[0m │ \u001b[32m▁▂▂▅▇▇ \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mathlete_id    \u001b[0m │ \u001b[36m      0\u001b[0m │ \u001b[36m    0\u001b[0m │ \u001b[36m 73000\u001b[0m │ \u001b[36m 41000\u001b[0m │ \u001b[36m    1\u001b[0m │ \u001b[36m 34000\u001b[0m │ \u001b[36m 74000\u001b[0m │ \u001b[36m 110000\u001b[0m │ \u001b[36m 150000\u001b[0m │ \u001b[32m▆▇▆▇▇▅ \u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mplace         \u001b[0m │ \u001b[36m  25215\u001b[0m │ \u001b[36m 8.18\u001b[0m │ \u001b[36m    16\u001b[0m │ \u001b[36m    19\u001b[0m │ \u001b[36m    1\u001b[0m │ \u001b[36m     5\u001b[0m │ \u001b[36m     9\u001b[0m │ \u001b[36m     20\u001b[0m │ \u001b[36m    180\u001b[0m │ \u001b[32m  ▇▁   \u001b[0m │  │\n",
       "│ └────────────────┴─────────┴───────┴────────┴────────┴───────┴────────┴────────┴─────────┴─────────┴─────────┘  │\n",
       "│ \u001b[3m                                                     bool                                                     \u001b[0m  │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓  │\n",
       "│ ┃\u001b[1m \u001b[0m\u001b[1mcolumn_name                      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mtrue             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mtrue rate                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mhist               \u001b[0m\u001b[1m \u001b[0m┃  │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩  │\n",
       "│ │ \u001b[38;5;141mtied                             \u001b[0m │ \u001b[36m            45940\u001b[0m │ \u001b[36m                        0.15\u001b[0m │ \u001b[32m      ▇    ▁       \u001b[0m │  │\n",
       "│ └───────────────────────────────────┴───────────────────┴──────────────────────────────┴─────────────────────┘  │\n",
       "│ \u001b[3m                                                    string                                                    \u001b[0m  │\n",
       "│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓  │\n",
       "│ ┃\u001b[1m \u001b[0m\u001b[1mcolumn_name            \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mNA           \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mNA %       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mwords per row             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mtotal words          \u001b[0m\u001b[1m \u001b[0m┃  │\n",
       "│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩  │\n",
       "│ │ \u001b[38;5;141mtype                   \u001b[0m │ \u001b[36m         2601\u001b[0m │ \u001b[36m       0.84\u001b[0m │ \u001b[36m                      0.99\u001b[0m │ \u001b[36m               305807\u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mdiscipline             \u001b[0m │ \u001b[36m            1\u001b[0m │ \u001b[36m          0\u001b[0m │ \u001b[36m                         2\u001b[0m │ \u001b[36m               610211\u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mevent                  \u001b[0m │ \u001b[36m            0\u001b[0m │ \u001b[36m          0\u001b[0m │ \u001b[36m                       4.2\u001b[0m │ \u001b[36m              1303323\u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mas                     \u001b[0m │ \u001b[36m            0\u001b[0m │ \u001b[36m          0\u001b[0m │ \u001b[36m                       2.1\u001b[0m │ \u001b[36m               634574\u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mnoc                    \u001b[0m │ \u001b[36m            1\u001b[0m │ \u001b[36m          0\u001b[0m │ \u001b[36m                         1\u001b[0m │ \u001b[36m               308407\u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mteam                   \u001b[0m │ \u001b[36m       186694\u001b[0m │ \u001b[36m      60.53\u001b[0m │ \u001b[36m                      0.62\u001b[0m │ \u001b[36m               190405\u001b[0m │  │\n",
       "│ │ \u001b[38;5;141mmedal                  \u001b[0m │ \u001b[36m       264269\u001b[0m │ \u001b[36m      85.69\u001b[0m │ \u001b[36m                      0.14\u001b[0m │ \u001b[36m                44139\u001b[0m │  │\n",
       "│ └─────────────────────────┴───────────────┴─────────────┴────────────────────────────┴───────────────────────┘  │\n",
       "╰────────────────────────────────────────────────────── End ──────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimpy import skim\n",
    "\n",
    "skim(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14 entries, 0 to 13\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Day                14 non-null     object \n",
      " 1   Coffee Type        14 non-null     object \n",
      " 2   Units Sold         14 non-null     float64\n",
      " 3   price              14 non-null     float64\n",
      " 4   revenue            14 non-null     float64\n",
      " 5   yesterday_revenue  12 non-null     float64\n",
      " 6   pct_change         12 non-null     float64\n",
      "dtypes: float64(5), object(2)\n",
      "memory usage: 916.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "coffee.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_numpy = pd.read_csv('./data/results.csv')\n",
    "results_arrow = pd.read_csv('./data/results.csv', engine='pyarrow', dtype_backend='pyarrow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 308408 entries, 0 to 308407\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   year        305807 non-null  float64\n",
      " 1   type        305807 non-null  object \n",
      " 2   discipline  308407 non-null  object \n",
      " 3   event       308408 non-null  object \n",
      " 4   as          308408 non-null  object \n",
      " 5   athlete_id  308408 non-null  int64  \n",
      " 6   noc         308407 non-null  object \n",
      " 7   team        121714 non-null  object \n",
      " 8   place       283193 non-null  float64\n",
      " 9   tied        308408 non-null  bool   \n",
      " 10  medal       44139 non-null   object \n",
      "dtypes: bool(1), float64(2), int64(1), object(7)\n",
      "memory usage: 23.8+ MB\n"
     ]
    }
   ],
   "source": [
    "results_numpy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 308408 entries, 0 to 308407\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count   Dtype          \n",
      "---  ------      --------------   -----          \n",
      " 0   year        305807 non-null  double[pyarrow]\n",
      " 1   type        305807 non-null  string[pyarrow]\n",
      " 2   discipline  308407 non-null  string[pyarrow]\n",
      " 3   event       308408 non-null  string[pyarrow]\n",
      " 4   as          308408 non-null  string[pyarrow]\n",
      " 5   athlete_id  308408 non-null  int64[pyarrow] \n",
      " 6   noc         308407 non-null  string[pyarrow]\n",
      " 7   team        121714 non-null  string[pyarrow]\n",
      " 8   place       283193 non-null  double[pyarrow]\n",
      " 9   tied        308408 non-null  bool[pyarrow]  \n",
      " 10  medal       44139 non-null   string[pyarrow]\n",
      "dtypes: bool[pyarrow](1), double[pyarrow](2), int64[pyarrow](1), string[pyarrow](7)\n",
      "memory usage: 37.5 MB\n"
     ]
    }
   ],
   "source": [
    "results_arrow.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "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>athlete_id</th>\n",
       "      <th>name</th>\n",
       "      <th>born_date</th>\n",
       "      <th>born_city</th>\n",
       "      <th>born_region</th>\n",
       "      <th>born_country</th>\n",
       "      <th>NOC</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>died_date</th>\n",
       "      <th>month_born</th>\n",
       "      <th>year_born</th>\n",
       "      <th>height_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Jean-François Blanchy</td>\n",
       "      <td>1886-12-12</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Gironde</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960-10-02</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Arnaud Boetsch</td>\n",
       "      <td>1969-04-01</td>\n",
       "      <td>Meulan</td>\n",
       "      <td>Yvelines</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>183.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>27597.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jean Borotra</td>\n",
       "      <td>1898-08-13</td>\n",
       "      <td>Biarritz</td>\n",
       "      <td>Pyrénées-Atlantiques</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>183.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1994-07-17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>27597.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Jacques Brugnon</td>\n",
       "      <td>1895-05-11</td>\n",
       "      <td>Paris VIIIe</td>\n",
       "      <td>Paris</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>168.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1978-03-20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>83975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Albert Canet</td>\n",
       "      <td>1878-04-17</td>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>England</td>\n",
       "      <td>GBR</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930-07-25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1878.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   athlete_id                   name  born_date    born_city  \\\n",
       "0           1  Jean-François Blanchy 1886-12-12     Bordeaux   \n",
       "1           2         Arnaud Boetsch 1969-04-01       Meulan   \n",
       "2           3           Jean Borotra 1898-08-13     Biarritz   \n",
       "3           4        Jacques Brugnon 1895-05-11  Paris VIIIe   \n",
       "4           5           Albert Canet 1878-04-17   Wandsworth   \n",
       "\n",
       "            born_region born_country     NOC  height_cm  weight_kg  \\\n",
       "0               Gironde          FRA  France        NaN        NaN   \n",
       "1              Yvelines          FRA  France      183.0       76.0   \n",
       "2  Pyrénées-Atlantiques          FRA  France      183.0       76.0   \n",
       "3                 Paris          FRA  France      168.0       64.0   \n",
       "4               England          GBR  France        NaN        NaN   \n",
       "\n",
       "    died_date  month_born  year_born  height_rank  \n",
       "0  1960-10-02        12.0     1886.0          NaN  \n",
       "1         NaN         4.0     1969.0      27597.5  \n",
       "2  1994-07-17         8.0     1898.0      27597.5  \n",
       "3  1978-03-20         5.0     1895.0      83975.0  \n",
       "4  1930-07-25         4.0     1878.0          NaN  "
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_bios = bios[(bios['born_region'] == 'New Hampshire') | (bios['born_city'] == 'San Francisco')]\n",
    "\n",
    "bios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "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>Date</th>\n",
       "      <th>Item</th>\n",
       "      <th>Units Sold</th>\n",
       "      <th>Price Per Unit</th>\n",
       "      <th>Salesperson</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>Apple</td>\n",
       "      <td>30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>Banana</td>\n",
       "      <td>21</td>\n",
       "      <td>0.50</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>Orange</td>\n",
       "      <td>15</td>\n",
       "      <td>0.75</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-05-02</td>\n",
       "      <td>Apple</td>\n",
       "      <td>40</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Alice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-05-02</td>\n",
       "      <td>Banana</td>\n",
       "      <td>34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>Alice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-05-03</td>\n",
       "      <td>Orange</td>\n",
       "      <td>20</td>\n",
       "      <td>0.75</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024-05-03</td>\n",
       "      <td>Apple</td>\n",
       "      <td>45</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Alice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-05-03</td>\n",
       "      <td>Orange</td>\n",
       "      <td>25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Item  Units Sold  Price Per Unit Salesperson\n",
       "0  2024-05-01   Apple          30            1.00        John\n",
       "1  2024-05-01  Banana          21            0.50        John\n",
       "2  2024-05-01  Orange          15            0.75        John\n",
       "3  2024-05-02   Apple          40            1.00       Alice\n",
       "4  2024-05-02  Banana          34            0.50       Alice\n",
       "5  2024-05-03  Orange          20            0.75        John\n",
       "6  2024-05-03   Apple          45            1.00       Alice\n",
       "7  2024-05-03  Orange          25            0.75        John"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Creating a DataFrame\n",
    "data = {\n",
    "    'Date': ['2024-05-01', '2024-05-01', '2024-05-01', '2024-05-02', '2024-05-02', '2024-05-03', '2024-05-03', '2024-05-03'],\n",
    "    'Item': ['Apple', 'Banana', 'Orange', 'Apple', 'Banana', 'Orange', 'Apple', 'Orange'],\n",
    "    'Units Sold': [30, 21, 15, 40, 34, 20, 45, 25],\n",
    "    'Price Per Unit': [1.0, 0.5, 0.75, 1.0, 0.5, 0.75, 1.0, 0.75],\n",
    "    'Salesperson': ['John', 'John', 'John', 'Alice', 'Alice', 'John', 'Alice', 'John']\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Display the DataFrame\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "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>Salesperson</th>\n",
       "      <th>Alice</th>\n",
       "      <th>John</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-02</th>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-05-03</th>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Salesperson  Alice  John\n",
       "Date                    \n",
       "2024-05-01     NaN  66.0\n",
       "2024-05-02    74.0   NaN\n",
       "2024-05-03    45.0  45.0"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table = pd.pivot_table(df, values='Units Sold', index='Date', columns='Item', aggfunc='sum')\n",
    "pivot_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming your DataFrame is named 'bios' and already loaded\n",
    "# First, filter out rows where the height_cm data is missing\n",
    "bios_filtered = bios.dropna(subset=['height_cm'])\n",
    "\n",
    "# Plotting the histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(bios_filtered['height_cm'], bins=20, color='blue', edgecolor='black')\n",
    "\n",
    "plt.title('Distribution of Athlete Heights in Olympics')\n",
    "plt.xlabel('Height in cm')\n",
    "plt.ylabel('Number of Athletes')\n",
    "plt.grid(True)\n",
    "\n",
    "# Using a logarithmic scale for the y-axis if the data spread is wide\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What Next???"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out some of my other tutorials:\n",
    "- [Cleaning Data w/ Pandas](https://www.youtube.com/live/oad9tVEsfI0?si=qnDOg9BSRFxcP5gZ)\n",
    "- [Solving 100 Python Pandas Problems](https://youtu.be/i7v2m-ebXB4?si=VSJHnZryqMv8GW54)\n",
    "- [Real-world Data Analsys Problems w/ Python Pandas](https://youtu.be/eMOA1pPVUc4)\n",
    "\n",
    "Platforms to Try\n",
    "- [Stratascratch](https://stratascratch.com/?via=keith)\n",
    "- [Analyst Builder](https://www.analystbuilder.com/?via=keith)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tutorial",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}