2022-08Aug-16.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f464ebd7",
   "metadata": {},
   "source": [
    "# Agenda: Real-world\n",
    "\n",
    "1. Recap and Q&A\n",
    "2. More about CSV\n",
    "3. Sorting with data\n",
    "4. Grouping\n",
    "5. Pivot tables\n",
    "6. Joining\n",
    "7. Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46f507c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "547a9dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = DataFrame([[10, 20, 30, 40],\n",
    "                [50, 60, 70, 80]],\n",
    "               index=list('ab'),\n",
    "               columns=list('wxyz'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ecc6166",
   "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>w</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    w   x   y   z\n",
       "a  10  20  30  40\n",
       "b  50  60  70  80"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1cfa65d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "w    50\n",
       "x    60\n",
       "y    70\n",
       "z    80\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve an entire row, use .loc\n",
    "\n",
    "df.loc['b']     # this retrieves a row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afd382e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    20\n",
       "b    60\n",
       "Name: x, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['x']    # this retrieves a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6462e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    30\n",
       "b    70\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['y']   # so does this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f479c54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row selectors and column selectors with .loc\n",
    "\n",
    "df.loc['b', 'y']  # row b and column y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea099e48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    60\n",
       "y    70\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I want all of the elements from row b, columns x and y\n",
    "\n",
    "df.loc['b', ['x', 'y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "004fbf4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    w   x   y   z\n",
       "a  10  20  30  40\n",
       "b  50  60  70  80"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27b867e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "Name: w, dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['w'] > 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c98b90ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "    x   z\n",
       "b  60  80"
      ]
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# wherever w is greater than 30,\n",
    "# we want to see columns x and z\n",
    "\n",
    "df.loc[\n",
    "    df['w'] > 30,   # row selector -- a boolean series, which will act as a mask index\n",
    "    ['x', 'z']       \n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5e4ede96",
   "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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>RateCodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-06-02 11:19:29</td>\n",
       "      <td>2015-06-02 11:47:52</td>\n",
       "      <td>1</td>\n",
       "      <td>1.63</td>\n",
       "      <td>-73.954430</td>\n",
       "      <td>40.764141</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.974754</td>\n",
       "      <td>40.754093</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-06-02 11:19:30</td>\n",
       "      <td>2015-06-02 11:27:56</td>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-73.971443</td>\n",
       "      <td>40.758942</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.978539</td>\n",
       "      <td>40.761909</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-06-02 11:19:31</td>\n",
       "      <td>2015-06-02 11:30:30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>-73.978111</td>\n",
       "      <td>40.738434</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.990273</td>\n",
       "      <td>40.745438</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-06-02 11:19:31</td>\n",
       "      <td>2015-06-02 11:39:02</td>\n",
       "      <td>1</td>\n",
       "      <td>2.13</td>\n",
       "      <td>-73.945892</td>\n",
       "      <td>40.773529</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.971527</td>\n",
       "      <td>40.760330</td>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-02 11:19:32</td>\n",
       "      <td>2015-06-02 11:32:49</td>\n",
       "      <td>1</td>\n",
       "      <td>1.40</td>\n",
       "      <td>-73.979088</td>\n",
       "      <td>40.776772</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.982162</td>\n",
       "      <td>40.758999</td>\n",
       "      <td>2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         2  2015-06-02 11:19:29   2015-06-02 11:47:52                1   \n",
       "1         2  2015-06-02 11:19:30   2015-06-02 11:27:56                1   \n",
       "2         2  2015-06-02 11:19:31   2015-06-02 11:30:30                1   \n",
       "3         2  2015-06-02 11:19:31   2015-06-02 11:39:02                1   \n",
       "4         1  2015-06-02 11:19:32   2015-06-02 11:32:49                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  RateCodeID  \\\n",
       "0           1.63        -73.954430        40.764141           1   \n",
       "1           0.46        -73.971443        40.758942           1   \n",
       "2           0.87        -73.978111        40.738434           1   \n",
       "3           2.13        -73.945892        40.773529           1   \n",
       "4           1.40        -73.979088        40.776772           1   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  dropoff_latitude  payment_type  \\\n",
       "0                  N         -73.974754         40.754093             2   \n",
       "1                  N         -73.978539         40.761909             1   \n",
       "2                  N         -73.990273         40.745438             1   \n",
       "3                  N         -73.971527         40.760330             1   \n",
       "4                  N         -73.982162         40.758999             2   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0         17.0    0.0      0.5        0.00           0.0   \n",
       "1          6.5    0.0      0.5        1.00           0.0   \n",
       "2          8.0    0.0      0.5        2.20           0.0   \n",
       "3         13.5    0.0      0.5        2.86           0.0   \n",
       "4          9.5    0.0      0.5        0.00           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  \n",
       "0                    0.3         17.80  \n",
       "1                    0.3          8.30  \n",
       "2                    0.3         11.00  \n",
       "3                    0.3         17.16  \n",
       "4                    0.3         10.30  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('taxi.csv')\n",
    "df.head()   # shows us the first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "84dbec13",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.63</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2.13</td>\n",
       "      <td>17.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.40</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  trip_distance  total_amount\n",
       "0                1           1.63         17.80\n",
       "1                1           0.46          8.30\n",
       "2                1           0.87         11.00\n",
       "3                1           2.13         17.16\n",
       "4                1           1.40         10.30"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('taxi.csv', usecols=['passenger_count', 'trip_distance', 'total_amount'])\n",
    "df.head()   # shows us the first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a41d1195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trip_distance     3.278653\n",
       "total_amount     17.679939\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show me how much, on average, people paid and how far they traveled when the number of passengers was\n",
    "# greater than 2\n",
    "\n",
    "df.loc[\n",
    "    df['passenger_count'] > 2,  # row selector \n",
    "    ['trip_distance', 'total_amount']  # column selector\n",
    "].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e3467fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('taxi.csv', header=None,\n",
    "                usecols=[3, 4])  # the first row is *not* considered to be the headers (column names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "81f8c70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>passenger_count</td>\n",
       "      <td>trip_distance</td>\n",
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       "      <td>1.63</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                 3              4\n",
       "0  passenger_count  trip_distance\n",
       "1                1           1.63\n",
       "2                1            .46\n",
       "3                1            .87\n",
       "4                1           2.13"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21dade5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('taxi.csv', header=None,\n",
    "                usecols=[3, 4],\n",
    "                names=['count', 'distance'])  # the first row is *not* considered to be the headers (column names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "373bc16c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>passenger_count</td>\n",
       "      <td>trip_distance</td>\n",
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       "      <td>2.13</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>1</td>\n",
       "      <td>4.50</td>\n",
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       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>1</td>\n",
       "      <td>5.59</td>\n",
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       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>6</td>\n",
       "      <td>1.54</td>\n",
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       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>1</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       distance\n",
       "0     passenger_count  trip_distance\n",
       "1                   1           1.63\n",
       "2                   1            .46\n",
       "3                   1            .87\n",
       "4                   1           2.13\n",
       "...               ...            ...\n",
       "9995                1           2.70\n",
       "9996                1           4.50\n",
       "9997                1           5.59\n",
       "9998                6           1.54\n",
       "9999                1           5.80\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0b083e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all values in df other than those in row 0\n",
    "df = df[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ae4d2c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>4.50</td>\n",
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       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>1</td>\n",
       "      <td>5.59</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     count distance\n",
       "1        1     1.63\n",
       "2        1      .46\n",
       "3        1      .87\n",
       "4        1     2.13\n",
       "5        1     1.40\n",
       "...    ...      ...\n",
       "9995     1     2.70\n",
       "9996     1     4.50\n",
       "9997     1     5.59\n",
       "9998     6     1.54\n",
       "9999     1     5.80\n",
       "\n",
       "[9999 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1b2a5e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d9/v8tsklln4477fll05wkgcpth0000gn/T/ipykernel_7275/2498805070.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    inf\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what happens if I now ask for the mean values of these two columns?\n",
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "97099608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       object\n",
       "distance    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes  # what dtypes do we have in these two columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9f935d04",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'count'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[0;32mIn [36]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcount\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mint64)\n\u001b[1;32m      2\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdistance\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdistance\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat64)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/core/indexing.py:967\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    964\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    966\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m--> 967\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/core/indexing.py:1202\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1200\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[1;32m   1201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m-> 1202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_label\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/core/indexing.py:1153\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m   1151\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m   1152\u001b[0m     \u001b[38;5;66;03m# GH#5667 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[0;32m-> 1153\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/core/generic.py:3864\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3862\u001b[0m             new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[1;32m   3863\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 3864\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3866\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m   3867\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/core/indexes/range.py:389\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    388\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[0;32m--> 389\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m    390\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mget_loc(key, method\u001b[38;5;241m=\u001b[39mmethod, tolerance\u001b[38;5;241m=\u001b[39mtolerance)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'count'"
     ]
    }
   ],
   "source": [
    "df['count'] = df.loc['count'].astype(np.int64)\n",
    "df['distance'] = df.loc['distance'].astype(np.float64)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0f26bb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  distance\n",
       "1      1      1.63\n",
       "2      1      0.46\n",
       "3      1      0.87\n",
       "4      1      2.13\n",
       "5      1      1.40"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45f7d916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         int64\n",
       "distance    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f744c610",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('AAPL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2e7fff01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-09</td>\n",
       "      <td>169.880005</td>\n",
       "      <td>173.089996</td>\n",
       "      <td>169.850006</td>\n",
       "      <td>170.050003</td>\n",
       "      <td>170.050003</td>\n",
       "      <td>29017700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-10</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>171.529999</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>28408600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>173.919998</td>\n",
       "      <td>171.699997</td>\n",
       "      <td>172.440002</td>\n",
       "      <td>172.440002</td>\n",
       "      <td>22431600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>173.410004</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>173.039993</td>\n",
       "      <td>174.139999</td>\n",
       "      <td>174.139999</td>\n",
       "      <td>22889300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>174.779999</td>\n",
       "      <td>175.839996</td>\n",
       "      <td>173.850006</td>\n",
       "      <td>174.729996</td>\n",
       "      <td>174.729996</td>\n",
       "      <td>25124300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2018-04-09  169.880005  173.089996  169.850006  170.050003  170.050003   \n",
       "1  2018-04-10  173.000000  174.000000  171.529999  173.250000  173.250000   \n",
       "2  2018-04-11  172.229996  173.919998  171.699997  172.440002  172.440002   \n",
       "3  2018-04-12  173.410004  175.000000  173.039993  174.139999  174.139999   \n",
       "4  2018-04-13  174.779999  175.839996  173.850006  174.729996  174.729996   \n",
       "\n",
       "     Volume  \n",
       "0  29017700  \n",
       "1  28408600  \n",
       "2  22431600  \n",
       "3  22889300  \n",
       "4  25124300  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7664d3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Adj Close    float64\n",
       "Volume         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cfa6b2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the index of the data frame to be the Date column from the input CSV\n",
    "df = pd.read_csv('AAPL.csv', index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "923ce720",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-09</th>\n",
       "      <td>169.880005</td>\n",
       "      <td>173.089996</td>\n",
       "      <td>169.850006</td>\n",
       "      <td>170.050003</td>\n",
       "      <td>170.050003</td>\n",
       "      <td>29017700</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-10</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>171.529999</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>28408600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-11</th>\n",
       "      <td>172.229996</td>\n",
       "      <td>173.919998</td>\n",
       "      <td>171.699997</td>\n",
       "      <td>172.440002</td>\n",
       "      <td>172.440002</td>\n",
       "      <td>22431600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-12</th>\n",
       "      <td>173.410004</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>173.039993</td>\n",
       "      <td>174.139999</td>\n",
       "      <td>174.139999</td>\n",
       "      <td>22889300</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-13</th>\n",
       "      <td>174.779999</td>\n",
       "      <td>175.839996</td>\n",
       "      <td>173.850006</td>\n",
       "      <td>174.729996</td>\n",
       "      <td>174.729996</td>\n",
       "      <td>25124300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-16</th>\n",
       "      <td>175.029999</td>\n",
       "      <td>176.190002</td>\n",
       "      <td>174.830002</td>\n",
       "      <td>175.820007</td>\n",
       "      <td>175.820007</td>\n",
       "      <td>21578400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-17</th>\n",
       "      <td>176.490005</td>\n",
       "      <td>178.940002</td>\n",
       "      <td>176.410004</td>\n",
       "      <td>178.240005</td>\n",
       "      <td>178.240005</td>\n",
       "      <td>26605400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-18</th>\n",
       "      <td>177.809998</td>\n",
       "      <td>178.820007</td>\n",
       "      <td>176.880005</td>\n",
       "      <td>177.839996</td>\n",
       "      <td>177.839996</td>\n",
       "      <td>20754500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-19</th>\n",
       "      <td>173.759995</td>\n",
       "      <td>175.389999</td>\n",
       "      <td>172.660004</td>\n",
       "      <td>172.800003</td>\n",
       "      <td>172.800003</td>\n",
       "      <td>34808800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-20</th>\n",
       "      <td>170.600006</td>\n",
       "      <td>171.220001</td>\n",
       "      <td>165.429993</td>\n",
       "      <td>165.720001</td>\n",
       "      <td>165.720001</td>\n",
       "      <td>65491100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23</th>\n",
       "      <td>166.830002</td>\n",
       "      <td>166.919998</td>\n",
       "      <td>164.089996</td>\n",
       "      <td>165.240005</td>\n",
       "      <td>165.240005</td>\n",
       "      <td>36515500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24</th>\n",
       "      <td>165.669998</td>\n",
       "      <td>166.330002</td>\n",
       "      <td>161.220001</td>\n",
       "      <td>162.940002</td>\n",
       "      <td>162.940002</td>\n",
       "      <td>33692000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25</th>\n",
       "      <td>162.619995</td>\n",
       "      <td>165.419998</td>\n",
       "      <td>162.410004</td>\n",
       "      <td>163.649994</td>\n",
       "      <td>163.649994</td>\n",
       "      <td>28382100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26</th>\n",
       "      <td>164.119995</td>\n",
       "      <td>165.729996</td>\n",
       "      <td>163.369995</td>\n",
       "      <td>164.220001</td>\n",
       "      <td>164.220001</td>\n",
       "      <td>27963000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-27</th>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.330002</td>\n",
       "      <td>160.630005</td>\n",
       "      <td>162.320007</td>\n",
       "      <td>162.320007</td>\n",
       "      <td>35655800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>162.130005</td>\n",
       "      <td>167.259995</td>\n",
       "      <td>161.839996</td>\n",
       "      <td>165.259995</td>\n",
       "      <td>165.259995</td>\n",
       "      <td>42427400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>166.410004</td>\n",
       "      <td>169.199997</td>\n",
       "      <td>165.270004</td>\n",
       "      <td>169.100006</td>\n",
       "      <td>169.100006</td>\n",
       "      <td>53569400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-02</th>\n",
       "      <td>175.229996</td>\n",
       "      <td>177.750000</td>\n",
       "      <td>173.800003</td>\n",
       "      <td>176.570007</td>\n",
       "      <td>176.570007</td>\n",
       "      <td>66539400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-03</th>\n",
       "      <td>175.880005</td>\n",
       "      <td>177.500000</td>\n",
       "      <td>174.440002</td>\n",
       "      <td>176.889999</td>\n",
       "      <td>176.889999</td>\n",
       "      <td>34068200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-04</th>\n",
       "      <td>178.250000</td>\n",
       "      <td>184.250000</td>\n",
       "      <td>178.169998</td>\n",
       "      <td>183.830002</td>\n",
       "      <td>183.830002</td>\n",
       "      <td>56201300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-07</th>\n",
       "      <td>185.179993</td>\n",
       "      <td>187.669998</td>\n",
       "      <td>184.750000</td>\n",
       "      <td>185.160004</td>\n",
       "      <td>185.160004</td>\n",
       "      <td>42451400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-08</th>\n",
       "      <td>184.990005</td>\n",
       "      <td>186.220001</td>\n",
       "      <td>183.669998</td>\n",
       "      <td>186.050003</td>\n",
       "      <td>186.050003</td>\n",
       "      <td>28030400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2018-04-09  169.880005  173.089996  169.850006  170.050003  170.050003   \n",
       "2018-04-10  173.000000  174.000000  171.529999  173.250000  173.250000   \n",
       "2018-04-11  172.229996  173.919998  171.699997  172.440002  172.440002   \n",
       "2018-04-12  173.410004  175.000000  173.039993  174.139999  174.139999   \n",
       "2018-04-13  174.779999  175.839996  173.850006  174.729996  174.729996   \n",
       "2018-04-16  175.029999  176.190002  174.830002  175.820007  175.820007   \n",
       "2018-04-17  176.490005  178.940002  176.410004  178.240005  178.240005   \n",
       "2018-04-18  177.809998  178.820007  176.880005  177.839996  177.839996   \n",
       "2018-04-19  173.759995  175.389999  172.660004  172.800003  172.800003   \n",
       "2018-04-20  170.600006  171.220001  165.429993  165.720001  165.720001   \n",
       "2018-04-23  166.830002  166.919998  164.089996  165.240005  165.240005   \n",
       "2018-04-24  165.669998  166.330002  161.220001  162.940002  162.940002   \n",
       "2018-04-25  162.619995  165.419998  162.410004  163.649994  163.649994   \n",
       "2018-04-26  164.119995  165.729996  163.369995  164.220001  164.220001   \n",
       "2018-04-27  164.000000  164.330002  160.630005  162.320007  162.320007   \n",
       "2018-04-30  162.130005  167.259995  161.839996  165.259995  165.259995   \n",
       "2018-05-01  166.410004  169.199997  165.270004  169.100006  169.100006   \n",
       "2018-05-02  175.229996  177.750000  173.800003  176.570007  176.570007   \n",
       "2018-05-03  175.880005  177.500000  174.440002  176.889999  176.889999   \n",
       "2018-05-04  178.250000  184.250000  178.169998  183.830002  183.830002   \n",
       "2018-05-07  185.179993  187.669998  184.750000  185.160004  185.160004   \n",
       "2018-05-08  184.990005  186.220001  183.669998  186.050003  186.050003   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2018-04-09  29017700  \n",
       "2018-04-10  28408600  \n",
       "2018-04-11  22431600  \n",
       "2018-04-12  22889300  \n",
       "2018-04-13  25124300  \n",
       "2018-04-16  21578400  \n",
       "2018-04-17  26605400  \n",
       "2018-04-18  20754500  \n",
       "2018-04-19  34808800  \n",
       "2018-04-20  65491100  \n",
       "2018-04-23  36515500  \n",
       "2018-04-24  33692000  \n",
       "2018-04-25  28382100  \n",
       "2018-04-26  27963000  \n",
       "2018-04-27  35655800  \n",
       "2018-04-30  42427400  \n",
       "2018-05-01  53569400  \n",
       "2018-05-02  66539400  \n",
       "2018-05-03  34068200  \n",
       "2018-05-04  56201300  \n",
       "2018-05-07  42451400  \n",
       "2018-05-08  28030400  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6fdbaef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open         1.851800e+02\n",
       "High         1.876700e+02\n",
       "Low          1.847500e+02\n",
       "Close        1.851600e+02\n",
       "Adj Close    1.851600e+02\n",
       "Volume       4.245140e+07\n",
       "Name: 2018-05-07, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2018-05-07']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a52802a4",
   "metadata": {},
   "outputs": [
    {
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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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-09</td>\n",
       "      <td>169.880005</td>\n",
       "      <td>173.089996</td>\n",
       "      <td>169.850006</td>\n",
       "      <td>170.050003</td>\n",
       "      <td>170.050003</td>\n",
       "      <td>29017700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-10</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>171.529999</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>28408600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-11</td>\n",
       "      <td>172.229996</td>\n",
       "      <td>173.919998</td>\n",
       "      <td>171.699997</td>\n",
       "      <td>172.440002</td>\n",
       "      <td>172.440002</td>\n",
       "      <td>22431600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>173.410004</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>173.039993</td>\n",
       "      <td>174.139999</td>\n",
       "      <td>174.139999</td>\n",
       "      <td>22889300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>174.779999</td>\n",
       "      <td>175.839996</td>\n",
       "      <td>173.850006</td>\n",
       "      <td>174.729996</td>\n",
       "      <td>174.729996</td>\n",
       "      <td>25124300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-04-16</td>\n",
       "      <td>175.029999</td>\n",
       "      <td>176.190002</td>\n",
       "      <td>174.830002</td>\n",
       "      <td>175.820007</td>\n",
       "      <td>175.820007</td>\n",
       "      <td>21578400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>176.490005</td>\n",
       "      <td>178.940002</td>\n",
       "      <td>176.410004</td>\n",
       "      <td>178.240005</td>\n",
       "      <td>178.240005</td>\n",
       "      <td>26605400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>177.809998</td>\n",
       "      <td>178.820007</td>\n",
       "      <td>176.880005</td>\n",
       "      <td>177.839996</td>\n",
       "      <td>177.839996</td>\n",
       "      <td>20754500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-04-19</td>\n",
       "      <td>173.759995</td>\n",
       "      <td>175.389999</td>\n",
       "      <td>172.660004</td>\n",
       "      <td>172.800003</td>\n",
       "      <td>172.800003</td>\n",
       "      <td>34808800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>170.600006</td>\n",
       "      <td>171.220001</td>\n",
       "      <td>165.429993</td>\n",
       "      <td>165.720001</td>\n",
       "      <td>165.720001</td>\n",
       "      <td>65491100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>166.830002</td>\n",
       "      <td>166.919998</td>\n",
       "      <td>164.089996</td>\n",
       "      <td>165.240005</td>\n",
       "      <td>165.240005</td>\n",
       "      <td>36515500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-04-24</td>\n",
       "      <td>165.669998</td>\n",
       "      <td>166.330002</td>\n",
       "      <td>161.220001</td>\n",
       "      <td>162.940002</td>\n",
       "      <td>162.940002</td>\n",
       "      <td>33692000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-04-25</td>\n",
       "      <td>162.619995</td>\n",
       "      <td>165.419998</td>\n",
       "      <td>162.410004</td>\n",
       "      <td>163.649994</td>\n",
       "      <td>163.649994</td>\n",
       "      <td>28382100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>164.119995</td>\n",
       "      <td>165.729996</td>\n",
       "      <td>163.369995</td>\n",
       "      <td>164.220001</td>\n",
       "      <td>164.220001</td>\n",
       "      <td>27963000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>164.330002</td>\n",
       "      <td>160.630005</td>\n",
       "      <td>162.320007</td>\n",
       "      <td>162.320007</td>\n",
       "      <td>35655800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>162.130005</td>\n",
       "      <td>167.259995</td>\n",
       "      <td>161.839996</td>\n",
       "      <td>165.259995</td>\n",
       "      <td>165.259995</td>\n",
       "      <td>42427400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>166.410004</td>\n",
       "      <td>169.199997</td>\n",
       "      <td>165.270004</td>\n",
       "      <td>169.100006</td>\n",
       "      <td>169.100006</td>\n",
       "      <td>53569400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-05-02</td>\n",
       "      <td>175.229996</td>\n",
       "      <td>177.750000</td>\n",
       "      <td>173.800003</td>\n",
       "      <td>176.570007</td>\n",
       "      <td>176.570007</td>\n",
       "      <td>66539400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-05-03</td>\n",
       "      <td>175.880005</td>\n",
       "      <td>177.500000</td>\n",
       "      <td>174.440002</td>\n",
       "      <td>176.889999</td>\n",
       "      <td>176.889999</td>\n",
       "      <td>34068200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-05-04</td>\n",
       "      <td>178.250000</td>\n",
       "      <td>184.250000</td>\n",
       "      <td>178.169998</td>\n",
       "      <td>183.830002</td>\n",
       "      <td>183.830002</td>\n",
       "      <td>56201300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-05-07</td>\n",
       "      <td>185.179993</td>\n",
       "      <td>187.669998</td>\n",
       "      <td>184.750000</td>\n",
       "      <td>185.160004</td>\n",
       "      <td>185.160004</td>\n",
       "      <td>42451400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-05-08</td>\n",
       "      <td>184.990005</td>\n",
       "      <td>186.220001</td>\n",
       "      <td>183.669998</td>\n",
       "      <td>186.050003</td>\n",
       "      <td>186.050003</td>\n",
       "      <td>28030400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date        Open        High         Low       Close   Adj Close  \\\n",
       "0   2018-04-09  169.880005  173.089996  169.850006  170.050003  170.050003   \n",
       "1   2018-04-10  173.000000  174.000000  171.529999  173.250000  173.250000   \n",
       "2   2018-04-11  172.229996  173.919998  171.699997  172.440002  172.440002   \n",
       "3   2018-04-12  173.410004  175.000000  173.039993  174.139999  174.139999   \n",
       "4   2018-04-13  174.779999  175.839996  173.850006  174.729996  174.729996   \n",
       "5   2018-04-16  175.029999  176.190002  174.830002  175.820007  175.820007   \n",
       "6   2018-04-17  176.490005  178.940002  176.410004  178.240005  178.240005   \n",
       "7   2018-04-18  177.809998  178.820007  176.880005  177.839996  177.839996   \n",
       "8   2018-04-19  173.759995  175.389999  172.660004  172.800003  172.800003   \n",
       "9   2018-04-20  170.600006  171.220001  165.429993  165.720001  165.720001   \n",
       "10  2018-04-23  166.830002  166.919998  164.089996  165.240005  165.240005   \n",
       "11  2018-04-24  165.669998  166.330002  161.220001  162.940002  162.940002   \n",
       "12  2018-04-25  162.619995  165.419998  162.410004  163.649994  163.649994   \n",
       "13  2018-04-26  164.119995  165.729996  163.369995  164.220001  164.220001   \n",
       "14  2018-04-27  164.000000  164.330002  160.630005  162.320007  162.320007   \n",
       "15  2018-04-30  162.130005  167.259995  161.839996  165.259995  165.259995   \n",
       "16  2018-05-01  166.410004  169.199997  165.270004  169.100006  169.100006   \n",
       "17  2018-05-02  175.229996  177.750000  173.800003  176.570007  176.570007   \n",
       "18  2018-05-03  175.880005  177.500000  174.440002  176.889999  176.889999   \n",
       "19  2018-05-04  178.250000  184.250000  178.169998  183.830002  183.830002   \n",
       "20  2018-05-07  185.179993  187.669998  184.750000  185.160004  185.160004   \n",
       "21  2018-05-08  184.990005  186.220001  183.669998  186.050003  186.050003   \n",
       "\n",
       "      Volume  \n",
       "0   29017700  \n",
       "1   28408600  \n",
       "2   22431600  \n",
       "3   22889300  \n",
       "4   25124300  \n",
       "5   21578400  \n",
       "6   26605400  \n",
       "7   20754500  \n",
       "8   34808800  \n",
       "9   65491100  \n",
       "10  36515500  \n",
       "11  33692000  \n",
       "12  28382100  \n",
       "13  27963000  \n",
       "14  35655800  \n",
       "15  42427400  \n",
       "16  53569400  \n",
       "17  66539400  \n",
       "18  34068200  \n",
       "19  56201300  \n",
       "20  42451400  \n",
       "21  28030400  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I can give read_csv  a URL!\n",
    "\n",
    "pd.read_csv('https://gist.githubusercontent.com/reuven/361d2c2b12dab426f4ed4efb396c89e5/raw/744dc0e9b193b53e3f76712cdfa32fa443440594/AAPL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "82b81174",
   "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>Open</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>169.880005</td>\n",
       "      <td>170.050003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>172.229996</td>\n",
       "      <td>172.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>173.410004</td>\n",
       "      <td>174.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.779999</td>\n",
       "      <td>174.729996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>175.029999</td>\n",
       "      <td>175.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>176.490005</td>\n",
       "      <td>178.240005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>177.809998</td>\n",
       "      <td>177.839996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>173.759995</td>\n",
       "      <td>172.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>170.600006</td>\n",
       "      <td>165.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>166.830002</td>\n",
       "      <td>165.240005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>165.669998</td>\n",
       "      <td>162.940002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>162.619995</td>\n",
       "      <td>163.649994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>164.119995</td>\n",
       "      <td>164.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>164.000000</td>\n",
       "      <td>162.320007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>162.130005</td>\n",
       "      <td>165.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>166.410004</td>\n",
       "      <td>169.100006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>175.229996</td>\n",
       "      <td>176.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>175.880005</td>\n",
       "      <td>176.889999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>178.250000</td>\n",
       "      <td>183.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>185.179993</td>\n",
       "      <td>185.160004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>184.990005</td>\n",
       "      <td>186.050003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Open       Close\n",
       "0   169.880005  170.050003\n",
       "1   173.000000  173.250000\n",
       "2   172.229996  172.440002\n",
       "3   173.410004  174.139999\n",
       "4   174.779999  174.729996\n",
       "5   175.029999  175.820007\n",
       "6   176.490005  178.240005\n",
       "7   177.809998  177.839996\n",
       "8   173.759995  172.800003\n",
       "9   170.600006  165.720001\n",
       "10  166.830002  165.240005\n",
       "11  165.669998  162.940002\n",
       "12  162.619995  163.649994\n",
       "13  164.119995  164.220001\n",
       "14  164.000000  162.320007\n",
       "15  162.130005  165.259995\n",
       "16  166.410004  169.100006\n",
       "17  175.229996  176.570007\n",
       "18  175.880005  176.889999\n",
       "19  178.250000  183.830002\n",
       "20  185.179993  185.160004\n",
       "21  184.990005  186.050003"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  we can choose columns, using usecols\n",
    "\n",
    "pd.read_csv('https://gist.githubusercontent.com/reuven/361d2c2b12dab426f4ed4efb396c89e5/raw/744dc0e9b193b53e3f76712cdfa32fa443440594/AAPL.csv',\n",
    "           usecols=['Open', 'Close'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6f1997",
   "metadata": {},
   "source": [
    "# Exercise: Cisco stock info (from 5 years ago)\n",
    "\n",
    "- Gist: https://gist.github.com/reuven/bb116ba2034bb10bb7e4e2caa5d8a000\n",
    "- URL of the gist's raw CSV, which you should use, is here: https://gist.githubusercontent.com/reuven/bb116ba2034bb10bb7e4e2caa5d8a000/raw/3660c4af808684dbf17af48b3d2f25b6a218535f/CSCO.csv\n",
    "\n",
    "1. Use `read_csv` to retrieve the Cisco stock information from 5 years ago, and put it into a data frame.  We're only interested in the `Date`, `Open`, `Close`, and `Volume` columns.\n",
    "2. Find the day on which the `Open` price was the highest.\n",
    "3. Find the day on which the `Close` price was the lowest.\n",
    "4. Find the `Date`, `Open`, and `Close` values on the day when the `Volume` was at its highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "241fb838",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ParserError",
     "evalue": "Error tokenizing data. C error: Expected 1 fields in line 8, saw 4\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mParserError\u001b[0m                               Traceback (most recent call last)",
      "Input \u001b[0;32mIn [48]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhttps://oreilly.com\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    306\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    307\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m    308\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    309\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m    310\u001b[0m     )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:680\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    665\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    666\u001b[0m     dialect,\n\u001b[1;32m    667\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    676\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    677\u001b[0m )\n\u001b[1;32m    678\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:581\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n\u001b[1;32m    580\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parser:\n\u001b[0;32m--> 581\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1255\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1253\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[1;32m   1254\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1255\u001b[0m     index, columns, col_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1256\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1257\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py:225\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    224\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_memory:\n\u001b[0;32m--> 225\u001b[0m         chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_low_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    226\u001b[0m         \u001b[38;5;66;03m# destructive to chunks\u001b[39;00m\n\u001b[1;32m    227\u001b[0m         data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/_libs/parsers.pyx:805\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/_libs/parsers.pyx:861\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/_libs/parsers.pyx:847\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/pandas/_libs/parsers.pyx:1960\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 1 fields in line 8, saw 4\n"
     ]
    }
   ],
   "source": [
    "pd.read_csv('https://oreilly.com')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9530b49f",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-22</td>\n",
       "      <td>38.520000</td>\n",
       "      <td>38.740002</td>\n",
       "      <td>38.470001</td>\n",
       "      <td>38.549999</td>\n",
       "      <td>38.264591</td>\n",
       "      <td>11441600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>38.549999</td>\n",
       "      <td>38.680000</td>\n",
       "      <td>38.360001</td>\n",
       "      <td>38.480000</td>\n",
       "      <td>38.195110</td>\n",
       "      <td>8186100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>38.540001</td>\n",
       "      <td>38.650002</td>\n",
       "      <td>38.450001</td>\n",
       "      <td>38.560001</td>\n",
       "      <td>38.274517</td>\n",
       "      <td>10543000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>38.730000</td>\n",
       "      <td>38.730000</td>\n",
       "      <td>38.450001</td>\n",
       "      <td>38.590000</td>\n",
       "      <td>38.304295</td>\n",
       "      <td>8807700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>38.410000</td>\n",
       "      <td>38.619999</td>\n",
       "      <td>38.299999</td>\n",
       "      <td>38.299999</td>\n",
       "      <td>38.016441</td>\n",
       "      <td>12583600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>38.669998</td>\n",
       "      <td>38.950001</td>\n",
       "      <td>38.430000</td>\n",
       "      <td>38.860001</td>\n",
       "      <td>38.572296</td>\n",
       "      <td>20135700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>38.720001</td>\n",
       "      <td>39.279999</td>\n",
       "      <td>38.529999</td>\n",
       "      <td>39.169998</td>\n",
       "      <td>38.879997</td>\n",
       "      <td>29536000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>39.049999</td>\n",
       "      <td>39.540001</td>\n",
       "      <td>38.930000</td>\n",
       "      <td>38.990002</td>\n",
       "      <td>38.990002</td>\n",
       "      <td>20731400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>39.549999</td>\n",
       "      <td>39.880001</td>\n",
       "      <td>39.369999</td>\n",
       "      <td>39.529999</td>\n",
       "      <td>39.529999</td>\n",
       "      <td>24588200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>39.520000</td>\n",
       "      <td>39.959999</td>\n",
       "      <td>39.349998</td>\n",
       "      <td>39.939999</td>\n",
       "      <td>39.939999</td>\n",
       "      <td>16582000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>39.790001</td>\n",
       "      <td>39.959999</td>\n",
       "      <td>39.540001</td>\n",
       "      <td>39.689999</td>\n",
       "      <td>39.689999</td>\n",
       "      <td>21449800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>39.650002</td>\n",
       "      <td>40.240002</td>\n",
       "      <td>39.630001</td>\n",
       "      <td>39.910000</td>\n",
       "      <td>39.910000</td>\n",
       "      <td>19473000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>40.139999</td>\n",
       "      <td>40.209999</td>\n",
       "      <td>39.750000</td>\n",
       "      <td>40.099998</td>\n",
       "      <td>40.099998</td>\n",
       "      <td>21685600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>40.220001</td>\n",
       "      <td>40.930000</td>\n",
       "      <td>40.049999</td>\n",
       "      <td>40.869999</td>\n",
       "      <td>40.869999</td>\n",
       "      <td>23770500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-01-16</td>\n",
       "      <td>40.900002</td>\n",
       "      <td>41.160000</td>\n",
       "      <td>40.320000</td>\n",
       "      <td>40.540001</td>\n",
       "      <td>40.540001</td>\n",
       "      <td>32845000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>40.840000</td>\n",
       "      <td>41.320000</td>\n",
       "      <td>40.709999</td>\n",
       "      <td>41.200001</td>\n",
       "      <td>41.200001</td>\n",
       "      <td>24427100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>41.240002</td>\n",
       "      <td>41.480000</td>\n",
       "      <td>41.029999</td>\n",
       "      <td>41.299999</td>\n",
       "      <td>41.299999</td>\n",
       "      <td>19610200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-01-19</td>\n",
       "      <td>41.279999</td>\n",
       "      <td>41.520000</td>\n",
       "      <td>41.099998</td>\n",
       "      <td>41.290001</td>\n",
       "      <td>41.290001</td>\n",
       "      <td>24207800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>41.200001</td>\n",
       "      <td>41.520000</td>\n",
       "      <td>41.020000</td>\n",
       "      <td>41.340000</td>\n",
       "      <td>41.340000</td>\n",
       "      <td>6792482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date       Open       High        Low      Close  Adj Close  \\\n",
       "0   2017-12-22  38.520000  38.740002  38.470001  38.549999  38.264591   \n",
       "1   2017-12-26  38.549999  38.680000  38.360001  38.480000  38.195110   \n",
       "2   2017-12-27  38.540001  38.650002  38.450001  38.560001  38.274517   \n",
       "3   2017-12-28  38.730000  38.730000  38.450001  38.590000  38.304295   \n",
       "4   2017-12-29  38.410000  38.619999  38.299999  38.299999  38.016441   \n",
       "5   2018-01-02  38.669998  38.950001  38.430000  38.860001  38.572296   \n",
       "6   2018-01-03  38.720001  39.279999  38.529999  39.169998  38.879997   \n",
       "7   2018-01-04  39.049999  39.540001  38.930000  38.990002  38.990002   \n",
       "8   2018-01-05  39.549999  39.880001  39.369999  39.529999  39.529999   \n",
       "9   2018-01-08  39.520000  39.959999  39.349998  39.939999  39.939999   \n",
       "10  2018-01-09  39.790001  39.959999  39.540001  39.689999  39.689999   \n",
       "11  2018-01-10  39.650002  40.240002  39.630001  39.910000  39.910000   \n",
       "12  2018-01-11  40.139999  40.209999  39.750000  40.099998  40.099998   \n",
       "13  2018-01-12  40.220001  40.930000  40.049999  40.869999  40.869999   \n",
       "14  2018-01-16  40.900002  41.160000  40.320000  40.540001  40.540001   \n",
       "15  2018-01-17  40.840000  41.320000  40.709999  41.200001  41.200001   \n",
       "16  2018-01-18  41.240002  41.480000  41.029999  41.299999  41.299999   \n",
       "17  2018-01-19  41.279999  41.520000  41.099998  41.290001  41.290001   \n",
       "18  2018-01-22  41.200001  41.520000  41.020000  41.340000  41.340000   \n",
       "\n",
       "      Volume  \n",
       "0   11441600  \n",
       "1    8186100  \n",
       "2   10543000  \n",
       "3    8807700  \n",
       "4   12583600  \n",
       "5   20135700  \n",
       "6   29536000  \n",
       "7   20731400  \n",
       "8   24588200  \n",
       "9   16582000  \n",
       "10  21449800  \n",
       "11  19473000  \n",
       "12  21685600  \n",
       "13  23770500  \n",
       "14  32845000  \n",
       "15  24427100  \n",
       "16  19610200  \n",
       "17  24207800  \n",
       "18   6792482  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cisco_csv_url = 'https://gist.githubusercontent.com/reuven/bb116ba2034bb10bb7e4e2caa5d8a000/raw/3660c4af808684dbf17af48b3d2f25b6a218535f/CSCO.csv'\n",
    "\n",
    "pd.read_csv(cisco_csv_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "18f6f906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Adj Close    float64\n",
       "Volume         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "745adc78",
   "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>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-22</td>\n",
       "      <td>38.520000</td>\n",
       "      <td>38.549999</td>\n",
       "      <td>11441600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>38.549999</td>\n",
       "      <td>38.480000</td>\n",
       "      <td>8186100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-27</td>\n",
       "      <td>38.540001</td>\n",
       "      <td>38.560001</td>\n",
       "      <td>10543000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>38.730000</td>\n",
       "      <td>38.590000</td>\n",
       "      <td>8807700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>38.410000</td>\n",
       "      <td>38.299999</td>\n",
       "      <td>12583600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>38.669998</td>\n",
       "      <td>38.860001</td>\n",
       "      <td>20135700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>38.720001</td>\n",
       "      <td>39.169998</td>\n",
       "      <td>29536000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>39.049999</td>\n",
       "      <td>38.990002</td>\n",
       "      <td>20731400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>39.549999</td>\n",
       "      <td>39.529999</td>\n",
       "      <td>24588200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>39.520000</td>\n",
       "      <td>39.939999</td>\n",
       "      <td>16582000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-01-09</td>\n",
       "      <td>39.790001</td>\n",
       "      <td>39.689999</td>\n",
       "      <td>21449800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>39.650002</td>\n",
       "      <td>39.910000</td>\n",
       "      <td>19473000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>40.139999</td>\n",
       "      <td>40.099998</td>\n",
       "      <td>21685600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>40.220001</td>\n",
       "      <td>40.869999</td>\n",
       "      <td>23770500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-01-16</td>\n",
       "      <td>40.900002</td>\n",
       "      <td>40.540001</td>\n",
       "      <td>32845000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>40.840000</td>\n",
       "      <td>41.200001</td>\n",
       "      <td>24427100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>41.240002</td>\n",
       "      <td>41.299999</td>\n",
       "      <td>19610200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-01-19</td>\n",
       "      <td>41.279999</td>\n",
       "      <td>41.290001</td>\n",
       "      <td>24207800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>41.200001</td>\n",
       "      <td>41.340000</td>\n",
       "      <td>6792482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date       Open      Close    Volume\n",
       "0   2017-12-22  38.520000  38.549999  11441600\n",
       "1   2017-12-26  38.549999  38.480000   8186100\n",
       "2   2017-12-27  38.540001  38.560001  10543000\n",
       "3   2017-12-28  38.730000  38.590000   8807700\n",
       "4   2017-12-29  38.410000  38.299999  12583600\n",
       "5   2018-01-02  38.669998  38.860001  20135700\n",
       "6   2018-01-03  38.720001  39.169998  29536000\n",
       "7   2018-01-04  39.049999  38.990002  20731400\n",
       "8   2018-01-05  39.549999  39.529999  24588200\n",
       "9   2018-01-08  39.520000  39.939999  16582000\n",
       "10  2018-01-09  39.790001  39.689999  21449800\n",
       "11  2018-01-10  39.650002  39.910000  19473000\n",
       "12  2018-01-11  40.139999  40.099998  21685600\n",
       "13  2018-01-12  40.220001  40.869999  23770500\n",
       "14  2018-01-16  40.900002  40.540001  32845000\n",
       "15  2018-01-17  40.840000  41.200001  24427100\n",
       "16  2018-01-18  41.240002  41.299999  19610200\n",
       "17  2018-01-19  41.279999  41.290001  24207800\n",
       "18  2018-01-22  41.200001  41.340000   6792482"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cisco_csv_url = 'https://gist.githubusercontent.com/reuven/bb116ba2034bb10bb7e4e2caa5d8a000/raw/3660c4af808684dbf17af48b3d2f25b6a218535f/CSCO.csv'\n",
    "\n",
    "df = pd.read_csv(cisco_csv_url, \n",
    "            usecols=['Date', 'Open', 'Close', 'Volume'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "50337d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    2018-01-19\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the day on which the open price was the highest\n",
    "\n",
    "df.loc[\n",
    "    # row selector: All days on which 'Open' was the same as df['Open'].max()\n",
    "    df['Open'] == df['Open'].max(),\n",
    "\n",
    "    # column selector is 'Date'\n",
    "    'Date'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0fa0321c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    2017-12-29\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the day on which the closing price was the lowest\n",
    "\n",
    "df.loc[\n",
    "    # row selector: All days on which 'Close' was the same as df['Close'].min()\n",
    "    df['Close'] == df['Close'].min(),\n",
    "\n",
    "    # column selector is 'Date'\n",
    "    'Date'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "38e4177b",
   "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>Open</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-01-16</td>\n",
       "      <td>40.900002</td>\n",
       "      <td>40.540001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date       Open      Close\n",
       "14  2018-01-16  40.900002  40.540001"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the Date, Open, and Close values on the day when the Volume was at its highest.\n",
    "\n",
    "df.loc[\n",
    "    \n",
    "    df['Volume'] == df['Volume'].max(),   # row selector\n",
    "    ['Date', 'Open', 'Close']\n",
    "\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc222bce",
   "metadata": {},
   "source": [
    "# `read_html`\n",
    "\n",
    "The method `pd.read_html` goes to a Web page, and finds all of the HTML tables that are on that page. Each one is downloaded as a separate data frame, into a list of data frames.  You can then retrieve the table that you want from that list, and perform analyses on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "016cbc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dfs = pd.read_html('https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4effdfab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "223eeab1",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.mw-parser-output .legend{page-break-inside:av...</td>\n",
       "      <td>$20,000 - $30,000 $10,000 - $20,000 $5,000 - $...</td>\n",
       "      <td>$1,000 - $2,500 $500 - $1,000 &lt;$500 No data</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0  \\\n",
       "0  .mw-parser-output .legend{page-break-inside:av...   \n",
       "\n",
       "                                                   1  \\\n",
       "0  $20,000 - $30,000 $10,000 - $20,000 $5,000 - $...   \n",
       "\n",
       "                                             2  \n",
       "0  $1,000 - $2,500 $500 - $1,000 <$500 No data  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_dfs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4f58cac9",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Country/Territory</th>\n",
       "      <th>UN Region</th>\n",
       "      <th colspan=\"2\" halign=\"left\">IMF[4][5]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">United Nations[6]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">World Bank[7]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Country/Territory</th>\n",
       "      <th>UN Region</th>\n",
       "      <th>Estimate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Estimate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Estimate</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Liechtenstein *</td>\n",
       "      <td>Europe</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>180227</td>\n",
       "      <td>2020</td>\n",
       "      <td>169049</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Monaco *</td>\n",
       "      <td>Europe</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>173696</td>\n",
       "      <td>2020</td>\n",
       "      <td>173688</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Luxembourg *</td>\n",
       "      <td>Europe</td>\n",
       "      <td>135046</td>\n",
       "      <td>2022</td>\n",
       "      <td>117182</td>\n",
       "      <td>2020</td>\n",
       "      <td>135683</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bermuda *</td>\n",
       "      <td>Americas</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>123945</td>\n",
       "      <td>2020</td>\n",
       "      <td>110870</td>\n",
       "      <td>2021</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Central AfricanRepublic *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>527</td>\n",
       "      <td>2022</td>\n",
       "      <td>481</td>\n",
       "      <td>2020</td>\n",
       "      <td>477</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Sierra Leone *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>513</td>\n",
       "      <td>2022</td>\n",
       "      <td>475</td>\n",
       "      <td>2020</td>\n",
       "      <td>485</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Madagascar *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>504</td>\n",
       "      <td>2022</td>\n",
       "      <td>470</td>\n",
       "      <td>2020</td>\n",
       "      <td>496</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>South Sudan *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>393</td>\n",
       "      <td>2022</td>\n",
       "      <td>1421</td>\n",
       "      <td>2020</td>\n",
       "      <td>1120</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Burundi *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>272</td>\n",
       "      <td>2022</td>\n",
       "      <td>286</td>\n",
       "      <td>2020</td>\n",
       "      <td>274</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>218 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country/Territory UN Region IMF[4][5]       United Nations[6]  \\\n",
       "             Country/Territory UN Region  Estimate  Year          Estimate   \n",
       "0                          NaN       NaN       NaN   NaN               NaN   \n",
       "1              Liechtenstein *    Europe         —     —            180227   \n",
       "2                     Monaco *    Europe         —     —            173696   \n",
       "3                 Luxembourg *    Europe    135046  2022            117182   \n",
       "4                    Bermuda *  Americas         —     —            123945   \n",
       "..                         ...       ...       ...   ...               ...   \n",
       "213  Central AfricanRepublic *    Africa       527  2022               481   \n",
       "214             Sierra Leone *    Africa       513  2022               475   \n",
       "215               Madagascar *    Africa       504  2022               470   \n",
       "216              South Sudan *    Africa       393  2022              1421   \n",
       "217                  Burundi *    Africa       272  2022               286   \n",
       "\n",
       "          World Bank[7]        \n",
       "     Year      Estimate  Year  \n",
       "0     NaN           NaN   NaN  \n",
       "1    2020        169049  2019  \n",
       "2    2020        173688  2020  \n",
       "3    2020        135683  2021  \n",
       "4    2020        110870  2021  \n",
       "..    ...           ...   ...  \n",
       "213  2020           477  2020  \n",
       "214  2020           485  2020  \n",
       "215  2020           496  2020  \n",
       "216  2020          1120  2015  \n",
       "217  2020           274  2020  \n",
       "\n",
       "[218 rows x 8 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_dfs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "26194dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = all_dfs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6e8b67e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory  Country/Territory    object\n",
       "UN Region          UN Region            object\n",
       "IMF[4][5]          Estimate             object\n",
       "                   Year                 object\n",
       "United Nations[6]  Estimate             object\n",
       "                   Year                 object\n",
       "World Bank[7]      Estimate             object\n",
       "                   Year                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2ecf8fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's reassign the index on df\n",
    "\n",
    "df.columns = ['country', 'un region', 'IMF estimate', 'IMF year', 'UN estimate', 'UN year', 'WB estimate', 'WB year']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3c2b4dac",
   "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>country</th>\n",
       "      <th>un region</th>\n",
       "      <th>IMF estimate</th>\n",
       "      <th>IMF year</th>\n",
       "      <th>UN estimate</th>\n",
       "      <th>UN year</th>\n",
       "      <th>WB estimate</th>\n",
       "      <th>WB year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Liechtenstein *</td>\n",
       "      <td>Europe</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>180227</td>\n",
       "      <td>2020</td>\n",
       "      <td>169049</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Monaco *</td>\n",
       "      <td>Europe</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>173696</td>\n",
       "      <td>2020</td>\n",
       "      <td>173688</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Luxembourg *</td>\n",
       "      <td>Europe</td>\n",
       "      <td>135046</td>\n",
       "      <td>2022</td>\n",
       "      <td>117182</td>\n",
       "      <td>2020</td>\n",
       "      <td>135683</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bermuda *</td>\n",
       "      <td>Americas</td>\n",
       "      <td>—</td>\n",
       "      <td>—</td>\n",
       "      <td>123945</td>\n",
       "      <td>2020</td>\n",
       "      <td>110870</td>\n",
       "      <td>2021</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Central AfricanRepublic *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>527</td>\n",
       "      <td>2022</td>\n",
       "      <td>481</td>\n",
       "      <td>2020</td>\n",
       "      <td>477</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Sierra Leone *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>513</td>\n",
       "      <td>2022</td>\n",
       "      <td>475</td>\n",
       "      <td>2020</td>\n",
       "      <td>485</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Madagascar *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>504</td>\n",
       "      <td>2022</td>\n",
       "      <td>470</td>\n",
       "      <td>2020</td>\n",
       "      <td>496</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>South Sudan *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>393</td>\n",
       "      <td>2022</td>\n",
       "      <td>1421</td>\n",
       "      <td>2020</td>\n",
       "      <td>1120</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Burundi *</td>\n",
       "      <td>Africa</td>\n",
       "      <td>272</td>\n",
       "      <td>2022</td>\n",
       "      <td>286</td>\n",
       "      <td>2020</td>\n",
       "      <td>274</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>218 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       country un region IMF estimate IMF year UN estimate  \\\n",
       "0                          NaN       NaN          NaN      NaN         NaN   \n",
       "1              Liechtenstein *    Europe            —        —      180227   \n",
       "2                     Monaco *    Europe            —        —      173696   \n",
       "3                 Luxembourg *    Europe       135046     2022      117182   \n",
       "4                    Bermuda *  Americas            —        —      123945   \n",
       "..                         ...       ...          ...      ...         ...   \n",
       "213  Central AfricanRepublic *    Africa          527     2022         481   \n",
       "214             Sierra Leone *    Africa          513     2022         475   \n",
       "215               Madagascar *    Africa          504     2022         470   \n",
       "216              South Sudan *    Africa          393     2022        1421   \n",
       "217                  Burundi *    Africa          272     2022         286   \n",
       "\n",
       "    UN year WB estimate WB year  \n",
       "0       NaN         NaN     NaN  \n",
       "1      2020      169049    2019  \n",
       "2      2020      173688    2020  \n",
       "3      2020      135683    2021  \n",
       "4      2020      110870    2021  \n",
       "..      ...         ...     ...  \n",
       "213    2020         477    2020  \n",
       "214    2020         485    2020  \n",
       "215    2020         496    2020  \n",
       "216    2020        1120    2015  \n",
       "217    2020         274    2020  \n",
       "\n",
       "[218 rows x 8 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "38437af9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                NaN\n",
       "1    Liechtenstein *\n",
       "2           Monaco *\n",
       "3       Luxembourg *\n",
       "4          Bermuda *\n",
       "5          Ireland *\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row selector: slice, until (not including) row 5\n",
    "# column selector: 'country'\n",
    "df.loc[:5, 'country']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa329b9",
   "metadata": {},
   "source": [
    "# Exercise: Gasoline prices\n",
    "\n",
    "1. Create a data frame of gasoline prices around the world, based on the URL https://tradingeconomics.com/country-list/gasoline-prices.\n",
    "2. In which countries is gasoline (USD/liter) more expensive than the US?\n",
    "3. In which countries is gasoline (USD/liter) more than 1.5x as expensive as in the US?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1e5884ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# all_dfs == list of data frames we got back from pd.read_html\n",
    "# read_html needs to get a URL as its argument\n",
    "# the URL can be either a string literal (with quotes) or a variable containing that string literal\n",
    "\n",
    "all_dfs = pd.read_html('https://tradingeconomics.com/country-list/gasoline-prices')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "02324c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e298c939",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the data frame, and assign it to df\n",
    "df = all_dfs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cc128be5",
   "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>Country</th>\n",
       "      <th>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russia</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Last  Previous Reference       Unit\n",
       "0     Indonesia  0.51      0.52    Jun/22  USD/Liter\n",
       "1  Saudi Arabia  0.62      0.62    Jul/22  USD/Liter\n",
       "2        Russia  0.86      0.96    Jul/22  USD/Liter\n",
       "3     Argentina  0.91      0.95    Jul/22  USD/Liter\n",
       "4         China  1.03      1.06    Jul/22  USD/Liter"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "169a964e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country       object\n",
       "Last         float64\n",
       "Previous     float64\n",
       "Reference     object\n",
       "Unit          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8b21b29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    1.2\n",
       "Name: Last, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\n",
    "    df['Country'] == 'United States',  # row selector -- looking for US row(s)\n",
    "    'Last'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d27ec1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8              India\n",
       "9             Turkey\n",
       "10         Australia\n",
       "11             Japan\n",
       "12            Canada\n",
       "13       South Korea\n",
       "14      South Africa\n",
       "15           Germany\n",
       "16            France\n",
       "17             Italy\n",
       "18         Singapore\n",
       "19             Spain\n",
       "20    United Kingdom\n",
       "21       Switzerland\n",
       "22       Netherlands\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in which countries is gasoline more expensive than the United States?\n",
    "\n",
    "df.loc[df['Last'] > df.loc[\n",
    "            df['Country'] == 'United States', \n",
    "            'Last'\n",
    "        ].mean(),\n",
    "       'Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7de9e0c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15           Germany\n",
       "16            France\n",
       "17             Italy\n",
       "18         Singapore\n",
       "19             Spain\n",
       "20    United Kingdom\n",
       "21       Switzerland\n",
       "22       Netherlands\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Last'] > (1.5 * df.loc[\n",
    "            df['Country'] == 'United States', \n",
    "            'Last'\n",
    "        ].mean()),\n",
    "       'Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1697048f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Country, dtype: object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Last'] > (2 * df.loc[\n",
    "            df['Country'] == 'United States', \n",
    "            'Last'\n",
    "        ].mean()),\n",
    "       'Country']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6919c802",
   "metadata": {},
   "source": [
    "# Next up:\n",
    "\n",
    "1. Sorting (by value, and by index)\n",
    "2. Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dc58f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# why might you get an error from read_html?\n",
    "\n",
    "# because read_html depends on the lxml package, which you can download and install from PyPI:\n",
    "\n",
    "# pip install lxml   # note: this is not a Python command, but rather something you should write at the command line\n",
    "\n",
    "\n",
    "all_dfs = pd.read_html('https://tradingeconomics.com/country-list/gasoline-prices')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8cd5f123",
   "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>Country</th>\n",
       "      <th>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russia</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>United States</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>India</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>France</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country  Last  Previous Reference       Unit\n",
       "0        Indonesia  0.51      0.52    Jun/22  USD/Liter\n",
       "1     Saudi Arabia  0.62      0.62    Jul/22  USD/Liter\n",
       "2           Russia  0.86      0.96    Jul/22  USD/Liter\n",
       "3        Argentina  0.91      0.95    Jul/22  USD/Liter\n",
       "4            China  1.03      1.06    Jul/22  USD/Liter\n",
       "5           Mexico  1.06      1.07    Jul/22  USD/Liter\n",
       "6           Brazil  1.09      1.41    Jul/22  USD/Liter\n",
       "7    United States  1.20      1.30    Jul/22  USD/Liter\n",
       "8            India  1.21      1.23    Jul/22  USD/Liter\n",
       "9           Turkey  1.25      1.49    Jul/22  USD/Liter\n",
       "10       Australia  1.26      1.46    Jul/22  USD/Liter\n",
       "11           Japan  1.28      1.29    Jul/22  USD/Liter\n",
       "12          Canada  1.42      1.59    Jul/22  USD/Liter\n",
       "13     South Korea  1.46      1.64    Jul/22  USD/Liter\n",
       "14    South Africa  1.60      1.48    Jul/22  USD/Liter\n",
       "15         Germany  1.82      1.99    Jul/22  USD/Liter\n",
       "16          France  1.98      2.27    Jul/22  USD/Liter\n",
       "17           Italy  2.04      2.26    Jul/22  USD/Liter\n",
       "18       Singapore  2.10      2.36    Jul/22  USD/Liter\n",
       "19           Spain  2.18      2.29    Jul/22  USD/Liter\n",
       "20  United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "21     Switzerland  2.24      2.14    May/22  USD/Liter\n",
       "22     Netherlands  2.29      2.54    Jul/22  USD/Liter"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "40d70b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this returns a series with one element, not a single floating-point value\n",
    "us_price = df.loc[df['Country'] == 'United States', 'Last']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "42493a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    1.2\n",
       "Name: Last, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "6e1de1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# since you know that there's only 1 value, you can use min, max, mean, etc.\n",
    "us_price.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f01bf94c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8      True\n",
       "9      True\n",
       "10     True\n",
       "11     True\n",
       "12     True\n",
       "13     True\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17     True\n",
       "18     True\n",
       "19     True\n",
       "20     True\n",
       "21     True\n",
       "22     True\n",
       "Name: Last, dtype: bool"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Last'] > us_price.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d46f28a6",
   "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",
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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>Country</th>\n",
       "      <th>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>France</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>India</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russia</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>United States</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country  Last  Previous Reference       Unit\n",
       "3        Argentina  0.91      0.95    Jul/22  USD/Liter\n",
       "10       Australia  1.26      1.46    Jul/22  USD/Liter\n",
       "6           Brazil  1.09      1.41    Jul/22  USD/Liter\n",
       "12          Canada  1.42      1.59    Jul/22  USD/Liter\n",
       "4            China  1.03      1.06    Jul/22  USD/Liter\n",
       "16          France  1.98      2.27    Jul/22  USD/Liter\n",
       "15         Germany  1.82      1.99    Jul/22  USD/Liter\n",
       "8            India  1.21      1.23    Jul/22  USD/Liter\n",
       "0        Indonesia  0.51      0.52    Jun/22  USD/Liter\n",
       "17           Italy  2.04      2.26    Jul/22  USD/Liter\n",
       "11           Japan  1.28      1.29    Jul/22  USD/Liter\n",
       "5           Mexico  1.06      1.07    Jul/22  USD/Liter\n",
       "22     Netherlands  2.29      2.54    Jul/22  USD/Liter\n",
       "2           Russia  0.86      0.96    Jul/22  USD/Liter\n",
       "1     Saudi Arabia  0.62      0.62    Jul/22  USD/Liter\n",
       "18       Singapore  2.10      2.36    Jul/22  USD/Liter\n",
       "14    South Africa  1.60      1.48    Jul/22  USD/Liter\n",
       "13     South Korea  1.46      1.64    Jul/22  USD/Liter\n",
       "19           Spain  2.18      2.29    Jul/22  USD/Liter\n",
       "21     Switzerland  2.24      2.14    May/22  USD/Liter\n",
       "9           Turkey  1.25      1.49    Jul/22  USD/Liter\n",
       "20  United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "7    United States  1.20      1.30    Jul/22  USD/Liter"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I want to sort gas prices by country name\n",
    "\n",
    "df.sort_values(by='Country')   # give me back a new data frame, same as df, but sorted (ascending) by country"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d5c9ce",
   "metadata": {},
   "source": [
    "# Where/why sort?\n",
    "\n",
    "1. For more aesthetic presentations\n",
    "2. Grab the top/bottom n values from our data set\n",
    "3. Sometimes, you can sort the data in a bunch of different ways -- sort_values lets us choose which way to sort"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28911d8",
   "metadata": {},
   "source": [
    "To sort our data by a column, just name the column in the `by` keyword argument\n",
    "\n",
    "Like most other methods that we can run on a data frame, `sort_values` does *not* modify anything, but rather returns a new data frame.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ddeb8de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to keep the changes, assign back to df\n",
    "\n",
    "df = df.sort_values(by='Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9d797910",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Last  Previous Reference       Unit\n",
       "3   Argentina  0.91      0.95    Jul/22  USD/Liter\n",
       "10  Australia  1.26      1.46    Jul/22  USD/Liter\n",
       "6      Brazil  1.09      1.41    Jul/22  USD/Liter\n",
       "12     Canada  1.42      1.59    Jul/22  USD/Liter\n",
       "4       China  1.03      1.06    Jul/22  USD/Liter"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293d89a8",
   "metadata": {},
   "source": [
    "# Sorting by the index\n",
    "\n",
    "Often, I want to sort values by the index.  This is especially true when the index contains useful information (e.g., country names, usernames, prices).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "9ab03364",
   "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>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Last  Previous Reference       Unit\n",
       "Country                                            \n",
       "Argentina       0.91      0.95    Jul/22  USD/Liter\n",
       "Australia       1.26      1.46    Jul/22  USD/Liter\n",
       "Brazil          1.09      1.41    Jul/22  USD/Liter\n",
       "Canada          1.42      1.59    Jul/22  USD/Liter\n",
       "China           1.03      1.06    Jul/22  USD/Liter\n",
       "France          1.98      2.27    Jul/22  USD/Liter\n",
       "Germany         1.82      1.99    Jul/22  USD/Liter\n",
       "India           1.21      1.23    Jul/22  USD/Liter\n",
       "Indonesia       0.51      0.52    Jun/22  USD/Liter\n",
       "Italy           2.04      2.26    Jul/22  USD/Liter\n",
       "Japan           1.28      1.29    Jul/22  USD/Liter\n",
       "Mexico          1.06      1.07    Jul/22  USD/Liter\n",
       "Netherlands     2.29      2.54    Jul/22  USD/Liter\n",
       "Russia          0.86      0.96    Jul/22  USD/Liter\n",
       "Saudi Arabia    0.62      0.62    Jul/22  USD/Liter\n",
       "Singapore       2.10      2.36    Jul/22  USD/Liter\n",
       "South Africa    1.60      1.48    Jul/22  USD/Liter\n",
       "South Korea     1.46      1.64    Jul/22  USD/Liter\n",
       "Spain           2.18      2.29    Jul/22  USD/Liter\n",
       "Switzerland     2.24      2.14    May/22  USD/Liter\n",
       "Turkey          1.25      1.49    Jul/22  USD/Liter\n",
       "United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "United States   1.20      1.30    Jul/22  USD/Liter"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Country')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f13967d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Last  Previous Reference       Unit\n",
       "Country                                            \n",
       "Indonesia       0.51      0.52    Jun/22  USD/Liter\n",
       "Saudi Arabia    0.62      0.62    Jul/22  USD/Liter\n",
       "Argentina       0.91      0.95    Jul/22  USD/Liter\n",
       "Russia          0.86      0.96    Jul/22  USD/Liter\n",
       "China           1.03      1.06    Jul/22  USD/Liter\n",
       "Mexico          1.06      1.07    Jul/22  USD/Liter\n",
       "India           1.21      1.23    Jul/22  USD/Liter\n",
       "Japan           1.28      1.29    Jul/22  USD/Liter\n",
       "United States   1.20      1.30    Jul/22  USD/Liter\n",
       "Brazil          1.09      1.41    Jul/22  USD/Liter\n",
       "Australia       1.26      1.46    Jul/22  USD/Liter\n",
       "South Africa    1.60      1.48    Jul/22  USD/Liter\n",
       "Turkey          1.25      1.49    Jul/22  USD/Liter\n",
       "Canada          1.42      1.59    Jul/22  USD/Liter\n",
       "South Korea     1.46      1.64    Jul/22  USD/Liter\n",
       "Germany         1.82      1.99    Jul/22  USD/Liter\n",
       "Switzerland     2.24      2.14    May/22  USD/Liter\n",
       "Italy           2.04      2.26    Jul/22  USD/Liter\n",
       "France          1.98      2.27    Jul/22  USD/Liter\n",
       "Spain           2.18      2.29    Jul/22  USD/Liter\n",
       "United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "Singapore       2.10      2.36    Jul/22  USD/Liter\n",
       "Netherlands     2.29      2.54    Jul/22  USD/Liter"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now if I sort by something else, say 'Previous', the index will be unsorted\n",
    "\n",
    "df = df.sort_values(by='Previous')  # we get a new data frame back, based on df\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a22a2542",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Last  Previous Reference       Unit\n",
       "Country                                            \n",
       "Argentina       0.91      0.95    Jul/22  USD/Liter\n",
       "Australia       1.26      1.46    Jul/22  USD/Liter\n",
       "Brazil          1.09      1.41    Jul/22  USD/Liter\n",
       "Canada          1.42      1.59    Jul/22  USD/Liter\n",
       "China           1.03      1.06    Jul/22  USD/Liter\n",
       "France          1.98      2.27    Jul/22  USD/Liter\n",
       "Germany         1.82      1.99    Jul/22  USD/Liter\n",
       "India           1.21      1.23    Jul/22  USD/Liter\n",
       "Indonesia       0.51      0.52    Jun/22  USD/Liter\n",
       "Italy           2.04      2.26    Jul/22  USD/Liter\n",
       "Japan           1.28      1.29    Jul/22  USD/Liter\n",
       "Mexico          1.06      1.07    Jul/22  USD/Liter\n",
       "Netherlands     2.29      2.54    Jul/22  USD/Liter\n",
       "Russia          0.86      0.96    Jul/22  USD/Liter\n",
       "Saudi Arabia    0.62      0.62    Jul/22  USD/Liter\n",
       "Singapore       2.10      2.36    Jul/22  USD/Liter\n",
       "South Africa    1.60      1.48    Jul/22  USD/Liter\n",
       "South Korea     1.46      1.64    Jul/22  USD/Liter\n",
       "Spain           2.18      2.29    Jul/22  USD/Liter\n",
       "Switzerland     2.24      2.14    May/22  USD/Liter\n",
       "Turkey          1.25      1.49    Jul/22  USD/Liter\n",
       "United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "United States   1.20      1.30    Jul/22  USD/Liter"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what if the country names are now my index\n",
    "# and I want to sort by country name again?\n",
    "\n",
    "# I can use sort_index\n",
    "\n",
    "df = df.sort_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7f4f7ce7",
   "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>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Last  Previous Reference       Unit\n",
       "Country                                            \n",
       "Indonesia       0.51      0.52    Jun/22  USD/Liter\n",
       "Saudi Arabia    0.62      0.62    Jul/22  USD/Liter\n",
       "Russia          0.86      0.96    Jul/22  USD/Liter\n",
       "Argentina       0.91      0.95    Jul/22  USD/Liter\n",
       "China           1.03      1.06    Jul/22  USD/Liter\n",
       "Mexico          1.06      1.07    Jul/22  USD/Liter\n",
       "Brazil          1.09      1.41    Jul/22  USD/Liter\n",
       "United States   1.20      1.30    Jul/22  USD/Liter\n",
       "India           1.21      1.23    Jul/22  USD/Liter\n",
       "Turkey          1.25      1.49    Jul/22  USD/Liter\n",
       "Australia       1.26      1.46    Jul/22  USD/Liter\n",
       "Japan           1.28      1.29    Jul/22  USD/Liter\n",
       "Canada          1.42      1.59    Jul/22  USD/Liter\n",
       "South Korea     1.46      1.64    Jul/22  USD/Liter\n",
       "South Africa    1.60      1.48    Jul/22  USD/Liter\n",
       "Germany         1.82      1.99    Jul/22  USD/Liter\n",
       "France          1.98      2.27    Jul/22  USD/Liter\n",
       "Italy           2.04      2.26    Jul/22  USD/Liter\n",
       "Singapore       2.10      2.36    Jul/22  USD/Liter\n",
       "Spain           2.18      2.29    Jul/22  USD/Liter\n",
       "United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "Switzerland     2.24      2.14    May/22  USD/Liter\n",
       "Netherlands     2.29      2.54    Jul/22  USD/Liter"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's sort by previous price again\n",
    "\n",
    "df.sort_values('Last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6ad1f41b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Country</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
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       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Last  Previous Reference       Unit\n",
       "Country                                            \n",
       "Argentina       0.91      0.95    Jul/22  USD/Liter\n",
       "Turkey          1.25      1.49    Jul/22  USD/Liter\n",
       "Spain           2.18      2.29    Jul/22  USD/Liter\n",
       "South Korea     1.46      1.64    Jul/22  USD/Liter\n",
       "South Africa    1.60      1.48    Jul/22  USD/Liter\n",
       "Singapore       2.10      2.36    Jul/22  USD/Liter\n",
       "Saudi Arabia    0.62      0.62    Jul/22  USD/Liter\n",
       "Russia          0.86      0.96    Jul/22  USD/Liter\n",
       "Netherlands     2.29      2.54    Jul/22  USD/Liter\n",
       "United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "Mexico          1.06      1.07    Jul/22  USD/Liter\n",
       "Italy           2.04      2.26    Jul/22  USD/Liter\n",
       "India           1.21      1.23    Jul/22  USD/Liter\n",
       "Germany         1.82      1.99    Jul/22  USD/Liter\n",
       "France          1.98      2.27    Jul/22  USD/Liter\n",
       "China           1.03      1.06    Jul/22  USD/Liter\n",
       "Canada          1.42      1.59    Jul/22  USD/Liter\n",
       "Brazil          1.09      1.41    Jul/22  USD/Liter\n",
       "Australia       1.26      1.46    Jul/22  USD/Liter\n",
       "Japan           1.28      1.29    Jul/22  USD/Liter\n",
       "United States   1.20      1.30    Jul/22  USD/Liter\n",
       "Indonesia       0.51      0.52    Jun/22  USD/Liter\n",
       "Switzerland     2.24      2.14    May/22  USD/Liter"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Reference')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b65dc059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Last  Previous Reference       Unit\n",
       "Country                                            \n",
       "Saudi Arabia    0.62      0.62    Jul/22  USD/Liter\n",
       "Argentina       0.91      0.95    Jul/22  USD/Liter\n",
       "Russia          0.86      0.96    Jul/22  USD/Liter\n",
       "China           1.03      1.06    Jul/22  USD/Liter\n",
       "Mexico          1.06      1.07    Jul/22  USD/Liter\n",
       "India           1.21      1.23    Jul/22  USD/Liter\n",
       "Japan           1.28      1.29    Jul/22  USD/Liter\n",
       "United States   1.20      1.30    Jul/22  USD/Liter\n",
       "Brazil          1.09      1.41    Jul/22  USD/Liter\n",
       "Australia       1.26      1.46    Jul/22  USD/Liter\n",
       "South Africa    1.60      1.48    Jul/22  USD/Liter\n",
       "Turkey          1.25      1.49    Jul/22  USD/Liter\n",
       "Canada          1.42      1.59    Jul/22  USD/Liter\n",
       "South Korea     1.46      1.64    Jul/22  USD/Liter\n",
       "Germany         1.82      1.99    Jul/22  USD/Liter\n",
       "Italy           2.04      2.26    Jul/22  USD/Liter\n",
       "France          1.98      2.27    Jul/22  USD/Liter\n",
       "Spain           2.18      2.29    Jul/22  USD/Liter\n",
       "United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "Singapore       2.10      2.36    Jul/22  USD/Liter\n",
       "Netherlands     2.29      2.54    Jul/22  USD/Liter\n",
       "Indonesia       0.51      0.52    Jun/22  USD/Liter\n",
       "Switzerland     2.24      2.14    May/22  USD/Liter"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can I sort first by date (alphabetically, in this case, which is weird!)\n",
    "# and then, within the dates, sort by Previous?\n",
    "\n",
    "# use a list, instead of a string, to name a column\n",
    "\n",
    "# in this case, we're telling Pandas:\n",
    "# first sort the rows by Reference\n",
    "# if the Reference value is the same for two rows, then we sort by Previous value\n",
    "\n",
    "df.sort_values(by=['Reference', 'Previous'])  # Reference in ascending order, then (if needed) Previous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c80d6218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Last  Previous Reference       Unit\n",
       "Country                                            \n",
       "Netherlands     2.29      2.54    Jul/22  USD/Liter\n",
       "Singapore       2.10      2.36    Jul/22  USD/Liter\n",
       "United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "Spain           2.18      2.29    Jul/22  USD/Liter\n",
       "France          1.98      2.27    Jul/22  USD/Liter\n",
       "Italy           2.04      2.26    Jul/22  USD/Liter\n",
       "Switzerland     2.24      2.14    May/22  USD/Liter\n",
       "Germany         1.82      1.99    Jul/22  USD/Liter\n",
       "South Korea     1.46      1.64    Jul/22  USD/Liter\n",
       "Canada          1.42      1.59    Jul/22  USD/Liter\n",
       "Turkey          1.25      1.49    Jul/22  USD/Liter\n",
       "South Africa    1.60      1.48    Jul/22  USD/Liter\n",
       "Australia       1.26      1.46    Jul/22  USD/Liter\n",
       "Brazil          1.09      1.41    Jul/22  USD/Liter\n",
       "United States   1.20      1.30    Jul/22  USD/Liter\n",
       "Japan           1.28      1.29    Jul/22  USD/Liter\n",
       "India           1.21      1.23    Jul/22  USD/Liter\n",
       "Mexico          1.06      1.07    Jul/22  USD/Liter\n",
       "China           1.03      1.06    Jul/22  USD/Liter\n",
       "Russia          0.86      0.96    Jul/22  USD/Liter\n",
       "Argentina       0.91      0.95    Jul/22  USD/Liter\n",
       "Saudi Arabia    0.62      0.62    Jul/22  USD/Liter\n",
       "Indonesia       0.51      0.52    Jun/22  USD/Liter"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can I sort first by date (alphabetically, in this case, which is weird!)\n",
    "# and then, within the dates, sort by Previous, in descending order?\n",
    "\n",
    "# use a list, instead of a string, to name a column\n",
    "\n",
    "# in this case, we're telling Pandas:\n",
    "# first sort the rows by Reference\n",
    "# if the Reference value is the same for two rows, then we sort by Previous value DESCENDING\n",
    "\n",
    "df.sort_values(by='Previous', ascending=False)  # this is how you sort in descending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1a23c026",
   "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>Last</th>\n",
       "      <th>Previous</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2.29</td>\n",
       "      <td>2.54</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2.10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2.22</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2.18</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.27</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2.04</td>\n",
       "      <td>2.26</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.99</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.46</td>\n",
       "      <td>1.64</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.59</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.49</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.41</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>1.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.06</td>\n",
       "      <td>1.07</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.06</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.96</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>Jul/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>Jun/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2.24</td>\n",
       "      <td>2.14</td>\n",
       "      <td>May/22</td>\n",
       "      <td>USD/Liter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Last  Previous Reference       Unit\n",
       "Country                                            \n",
       "Netherlands     2.29      2.54    Jul/22  USD/Liter\n",
       "Singapore       2.10      2.36    Jul/22  USD/Liter\n",
       "United Kingdom  2.22      2.32    Jul/22  USD/Liter\n",
       "Spain           2.18      2.29    Jul/22  USD/Liter\n",
       "France          1.98      2.27    Jul/22  USD/Liter\n",
       "Italy           2.04      2.26    Jul/22  USD/Liter\n",
       "Germany         1.82      1.99    Jul/22  USD/Liter\n",
       "South Korea     1.46      1.64    Jul/22  USD/Liter\n",
       "Canada          1.42      1.59    Jul/22  USD/Liter\n",
       "Turkey          1.25      1.49    Jul/22  USD/Liter\n",
       "South Africa    1.60      1.48    Jul/22  USD/Liter\n",
       "Australia       1.26      1.46    Jul/22  USD/Liter\n",
       "Brazil          1.09      1.41    Jul/22  USD/Liter\n",
       "United States   1.20      1.30    Jul/22  USD/Liter\n",
       "Japan           1.28      1.29    Jul/22  USD/Liter\n",
       "India           1.21      1.23    Jul/22  USD/Liter\n",
       "Mexico          1.06      1.07    Jul/22  USD/Liter\n",
       "China           1.03      1.06    Jul/22  USD/Liter\n",
       "Russia          0.86      0.96    Jul/22  USD/Liter\n",
       "Argentina       0.91      0.95    Jul/22  USD/Liter\n",
       "Saudi Arabia    0.62      0.62    Jul/22  USD/Liter\n",
       "Indonesia       0.51      0.52    Jun/22  USD/Liter\n",
       "Switzerland     2.24      2.14    May/22  USD/Liter"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reference in ascending order\n",
    "# previous in descending order\n",
    "df.sort_values(by=['Reference','Previous'], ascending=[True, False])  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e602910",
   "metadata": {},
   "source": [
    "# Exercise: Sorting taxi data\n",
    "\n",
    "1. Read the taxi data (`taxi.csv` or `nyc_taxi_2019-01.csv`) in, looking at columns `passenger_count`, `trip_distance`, and `total_amount`.\n",
    "2. What were the 10 longest trips that people took?\n",
    "3. What were the 10 cheapest trips that people took, where the amount was > 0?\n",
    "4. Set `trip_distance` to be the index.  Now sort by the index, and find the 10 shortest trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "3cea1f05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "taxi.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls taxi.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfeb91d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use a raw string when you're trying to open a path with \\ in it!  That\n",
    "# doubles the backslashes.\n",
    "\n",
    "df = pd.read_csv(r'E:\\Orielly\\Python for DA 5 weeks 2nd aug 930 pm\\taxi.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "139b7bc8",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method sort_values in module pandas.core.frame:\n",
      "\n",
      "sort_values(by, axis: 'Axis' = 0, ascending=True, inplace: 'bool' = False, kind: 'str' = 'quicksort', na_position: 'str' = 'last', ignore_index: 'bool' = False, key: 'ValueKeyFunc' = None) method of pandas.core.frame.DataFrame instance\n",
      "    Sort by the values along either axis.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "            by : str or list of str\n",
      "                Name or list of names to sort by.\n",
      "    \n",
      "                - if `axis` is 0 or `'index'` then `by` may contain index\n",
      "                  levels and/or column labels.\n",
      "                - if `axis` is 1 or `'columns'` then `by` may contain column\n",
      "                  levels and/or index labels.\n",
      "    axis : {0 or 'index', 1 or 'columns'}, default 0\n",
      "         Axis to be sorted.\n",
      "    ascending : bool or list of bool, default True\n",
      "         Sort ascending vs. descending. Specify list for multiple sort\n",
      "         orders.  If this is a list of bools, must match the length of\n",
      "         the by.\n",
      "    inplace : bool, default False\n",
      "         If True, perform operation in-place.\n",
      "    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'\n",
      "         Choice of sorting algorithm. See also :func:`numpy.sort` for more\n",
      "         information. `mergesort` and `stable` are the only stable algorithms. For\n",
      "         DataFrames, this option is only applied when sorting on a single\n",
      "         column or label.\n",
      "    na_position : {'first', 'last'}, default 'last'\n",
      "         Puts NaNs at the beginning if `first`; `last` puts NaNs at the\n",
      "         end.\n",
      "    ignore_index : bool, default False\n",
      "         If True, the resulting axis will be labeled 0, 1, …, n - 1.\n",
      "    \n",
      "         .. versionadded:: 1.0.0\n",
      "    \n",
      "    key : callable, optional\n",
      "        Apply the key function to the values\n",
      "        before sorting. This is similar to the `key` argument in the\n",
      "        builtin :meth:`sorted` function, with the notable difference that\n",
      "        this `key` function should be *vectorized*. It should expect a\n",
      "        ``Series`` and return a Series with the same shape as the input.\n",
      "        It will be applied to each column in `by` independently.\n",
      "    \n",
      "        .. versionadded:: 1.1.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    DataFrame or None\n",
      "        DataFrame with sorted values or None if ``inplace=True``.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    DataFrame.sort_index : Sort a DataFrame by the index.\n",
      "    Series.sort_values : Similar method for a Series.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> df = pd.DataFrame({\n",
      "    ...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],\n",
      "    ...     'col2': [2, 1, 9, 8, 7, 4],\n",
      "    ...     'col3': [0, 1, 9, 4, 2, 3],\n",
      "    ...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']\n",
      "    ... })\n",
      "    >>> df\n",
      "      col1  col2  col3 col4\n",
      "    0    A     2     0    a\n",
      "    1    A     1     1    B\n",
      "    2    B     9     9    c\n",
      "    3  NaN     8     4    D\n",
      "    4    D     7     2    e\n",
      "    5    C     4     3    F\n",
      "    \n",
      "    Sort by col1\n",
      "    \n",
      "    >>> df.sort_values(by=['col1'])\n",
      "      col1  col2  col3 col4\n",
      "    0    A     2     0    a\n",
      "    1    A     1     1    B\n",
      "    2    B     9     9    c\n",
      "    5    C     4     3    F\n",
      "    4    D     7     2    e\n",
      "    3  NaN     8     4    D\n",
      "    \n",
      "    Sort by multiple columns\n",
      "    \n",
      "    >>> df.sort_values(by=['col1', 'col2'])\n",
      "      col1  col2  col3 col4\n",
      "    1    A     1     1    B\n",
      "    0    A     2     0    a\n",
      "    2    B     9     9    c\n",
      "    5    C     4     3    F\n",
      "    4    D     7     2    e\n",
      "    3  NaN     8     4    D\n",
      "    \n",
      "    Sort Descending\n",
      "    \n",
      "    >>> df.sort_values(by='col1', ascending=False)\n",
      "      col1  col2  col3 col4\n",
      "    4    D     7     2    e\n",
      "    5    C     4     3    F\n",
      "    2    B     9     9    c\n",
      "    0    A     2     0    a\n",
      "    1    A     1     1    B\n",
      "    3  NaN     8     4    D\n",
      "    \n",
      "    Putting NAs first\n",
      "    \n",
      "    >>> df.sort_values(by='col1', ascending=False, na_position='first')\n",
      "      col1  col2  col3 col4\n",
      "    3  NaN     8     4    D\n",
      "    4    D     7     2    e\n",
      "    5    C     4     3    F\n",
      "    2    B     9     9    c\n",
      "    0    A     2     0    a\n",
      "    1    A     1     1    B\n",
      "    \n",
      "    Sorting with a key function\n",
      "    \n",
      "    >>> df.sort_values(by='col4', key=lambda col: col.str.lower())\n",
      "       col1  col2  col3 col4\n",
      "    0    A     2     0    a\n",
      "    1    A     1     1    B\n",
      "    2    B     9     9    c\n",
      "    3  NaN     8     4    D\n",
      "    4    D     7     2    e\n",
      "    5    C     4     3    F\n",
      "    \n",
      "    Natural sort with the key argument,\n",
      "    using the `natsort <https://github.com/SethMMorton/natsort>` package.\n",
      "    \n",
      "    >>> df = pd.DataFrame({\n",
      "    ...    \"time\": ['0hr', '128hr', '72hr', '48hr', '96hr'],\n",
      "    ...    \"value\": [10, 20, 30, 40, 50]\n",
      "    ... })\n",
      "    >>> df\n",
      "        time  value\n",
      "    0    0hr     10\n",
      "    1  128hr     20\n",
      "    2   72hr     30\n",
      "    3   48hr     40\n",
      "    4   96hr     50\n",
      "    >>> from natsort import index_natsorted\n",
      "    >>> df.sort_values(\n",
      "    ...    by=\"time\",\n",
      "    ...    key=lambda x: np.argsort(index_natsorted(df[\"time\"]))\n",
      "    ... )\n",
      "        time  value\n",
      "    0    0hr     10\n",
      "    3   48hr     40\n",
      "    2   72hr     30\n",
      "    4   96hr     50\n",
      "    1  128hr     20\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(df.sort_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e706bf42",
   "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>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.63</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2.13</td>\n",
       "      <td>17.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.40</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_count  trip_distance  total_amount\n",
       "0                1           1.63         17.80\n",
       "1                1           0.46          8.30\n",
       "2                1           0.87         11.00\n",
       "3                1           2.13         17.16\n",
       "4                1           1.40         10.30"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# short file -- taxi.csv (in the Jupyter directory)\n",
    "\n",
    "df = pd.read_csv('taxi.csv', usecols=['trip_distance', 'total_amount', 'passenger_count'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c949d957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4221    29.78\n",
       "9231    31.50\n",
       "4224    31.90\n",
       "3323    32.10\n",
       "4291    32.40\n",
       "5470    34.84\n",
       "809     35.51\n",
       "4583    37.20\n",
       "8513    60.30\n",
       "4270    64.60\n",
       "Name: trip_distance, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one option: sort in ascending order, and grab the final 10 rows\n",
    "df.sort_values('trip_distance').tail(10)['trip_distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "6a7b5c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4270</th>\n",
       "      <td>1</td>\n",
       "      <td>64.60</td>\n",
       "      <td>79.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8513</th>\n",
       "      <td>1</td>\n",
       "      <td>60.30</td>\n",
       "      <td>160.05</td>\n",
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       "    <tr>\n",
       "      <th>4583</th>\n",
       "      <td>1</td>\n",
       "      <td>37.20</td>\n",
       "      <td>210.14</td>\n",
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       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>1</td>\n",
       "      <td>35.51</td>\n",
       "      <td>135.13</td>\n",
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       "    <tr>\n",
       "      <th>5470</th>\n",
       "      <td>1</td>\n",
       "      <td>34.84</td>\n",
       "      <td>137.59</td>\n",
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       "    <tr>\n",
       "      <th>4291</th>\n",
       "      <td>1</td>\n",
       "      <td>32.40</td>\n",
       "      <td>63.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323</th>\n",
       "      <td>1</td>\n",
       "      <td>32.10</td>\n",
       "      <td>162.39</td>\n",
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       "    <tr>\n",
       "      <th>4224</th>\n",
       "      <td>1</td>\n",
       "      <td>31.90</td>\n",
       "      <td>252.35</td>\n",
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       "      <th>9231</th>\n",
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       "      <td>31.50</td>\n",
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       "    <tr>\n",
       "      <th>4221</th>\n",
       "      <td>2</td>\n",
       "      <td>29.78</td>\n",
       "      <td>75.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      passenger_count  trip_distance  total_amount\n",
       "4270                1          64.60         79.96\n",
       "8513                1          60.30        160.05\n",
       "4583                1          37.20        210.14\n",
       "809                 1          35.51        135.13\n",
       "5470                1          34.84        137.59\n",
       "4291                1          32.40         63.36\n",
       "3323                1          32.10        162.39\n",
       "4224                1          31.90        252.35\n",
       "9231                1          31.50        150.05\n",
       "4221                2          29.78         75.84"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another option: sort in DESCENDING order, and grab the first 10 rows\n",
    "df.sort_values('trip_distance', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "90136c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now, let's do the long taxi file, from January 2019\n",
    "\n",
    "df = pd.read_csv('../data/nyc_taxi_2019-01.csv', usecols=['passenger_count', 'trip_distance', 'total_amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7683c9b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7667792, 3)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4b6fa96f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4911293    132.80\n",
       "1144878    142.88\n",
       "4876401    143.63\n",
       "2567394    144.20\n",
       "4813319    160.52\n",
       "4881766    201.27\n",
       "4707513    211.36\n",
       "6770897    214.01\n",
       "4286612    700.70\n",
       "6074021    831.80\n",
       "Name: trip_distance, dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# option 1: sort in ascending order and grab the final 10\n",
    "\n",
    "df.sort_values('trip_distance').tail(10)['trip_distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "85f1174c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>4911293</th>\n",
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       "      <td>238.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         passenger_count  trip_distance  total_amount\n",
       "6074021                1         831.80         11.76\n",
       "4286612                1         700.70          9.00\n",
       "6770897                5         214.01        761.80\n",
       "4707513                2         211.36         56.56\n",
       "4881766                1         201.27        152.46\n",
       "4813319                4         160.52        143.56\n",
       "2567394                2         144.20         18.80\n",
       "4876401                1         143.63        456.56\n",
       "1144878                3         142.88        327.38\n",
       "4911293                1         132.80        238.70"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('trip_distance', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "90f2f935",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.30</td>\n",
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       "      <td>1</td>\n",
       "      <td>13.4</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         passenger_count  trip_distance  total_amount\n",
       "1636920                1            0.0          0.11\n",
       "393524                 1            1.1          0.30\n",
       "3308021                1            0.0          0.30\n",
       "5569448                1           46.5          0.30\n",
       "3308022                1            0.0          0.30\n",
       "7390163                1            0.6          0.30\n",
       "4220243                1           10.9          0.30\n",
       "3039165                2            1.1          0.30\n",
       "4934141                1            0.0          0.30\n",
       "5700266                1           13.4          0.30"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What were the 10 cheapest trips that people took, where the amount was > 0?\n",
    "\n",
    "\n",
    "df.loc[df['total_amount'] > 0].sort_values('total_amount').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "fb1ed799",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>7667783</th>\n",
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       "      <td>15.80</td>\n",
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       "      <th>7667784</th>\n",
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       "      <td>21.96</td>\n",
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       "    <tr>\n",
       "      <th>7667787</th>\n",
       "      <td>1</td>\n",
       "      <td>4.79</td>\n",
       "      <td>23.16</td>\n",
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       "</table>\n",
       "<p>7659247 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "         passenger_count  trip_distance  total_amount\n",
       "0                      1           1.50          9.95\n",
       "1                      1           2.60         16.30\n",
       "2                      3           0.00          5.80\n",
       "3                      5           0.00          7.55\n",
       "4                      5           0.00         55.55\n",
       "...                  ...            ...           ...\n",
       "7667783                2           4.15         15.80\n",
       "7667784                1           1.34          9.30\n",
       "7667785                1           1.45         14.16\n",
       "7667786                2           4.28         21.96\n",
       "7667787                1           4.79         23.16\n",
       "\n",
       "[7659247 rows x 3 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['total_amount'] > 0].sort_values('total_amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "99e1c91a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1.5</th>\n",
       "      <td>1</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.6</th>\n",
       "      <td>1</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>3</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>5</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>5</td>\n",
       "      <td>55.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               passenger_count  total_amount\n",
       "trip_distance                               \n",
       "1.5                          1          9.95\n",
       "2.6                          1         16.30\n",
       "0.0                          3          5.80\n",
       "0.0                          5          7.55\n",
       "0.0                          5         55.55"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set trip_distance to be the index. Now sort by the index, and find the 10 shortest trips.\n",
    "\n",
    "df = df.set_index('trip_distance')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b505bd2c",
   "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>passenger_count</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_distance</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>2</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>2</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>1</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>1</td>\n",
       "      <td>52.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201.27</th>\n",
       "      <td>1</td>\n",
       "      <td>152.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211.36</th>\n",
       "      <td>2</td>\n",
       "      <td>56.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214.01</th>\n",
       "      <td>5</td>\n",
       "      <td>761.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700.70</th>\n",
       "      <td>1</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831.80</th>\n",
       "      <td>1</td>\n",
       "      <td>11.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613022 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               passenger_count  total_amount\n",
       "trip_distance                               \n",
       "0.01                         1         -3.80\n",
       "0.01                         2          3.30\n",
       "0.01                         2          4.80\n",
       "0.01                         1          4.30\n",
       "0.01                         1         52.80\n",
       "...                        ...           ...\n",
       "201.27                       1        152.46\n",
       "211.36                       2         56.56\n",
       "214.01                       5        761.80\n",
       "700.70                       1          9.00\n",
       "831.80                       1         11.76\n",
       "\n",
       "[7613022 rows x 2 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index().loc[0.00001:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "e1feb74f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                   128\n",
       "passenger_count    61342336\n",
       "trip_distance      61342336\n",
       "total_amount       61342336\n",
       "dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "6c45809c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184027136"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.memory_usage().sum()   # 184 MB!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d691a66",
   "metadata": {},
   "source": [
    "# Grouping\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3340258d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7790883193389573"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I want to know the mean taxi distance when passenger_count is 1\n",
    "\n",
    "df.loc[\n",
    "    df['passenger_count'] == 1,\n",
    "    'trip_distance'\n",
    "].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e776328d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8805724948972533"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I also want to know the mean trip_distance when passenger_count is 2\n",
    "\n",
    "df.loc[\n",
    "    df['passenger_count'] == 2,\n",
    "    'trip_distance'\n",
    "].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "b39ec274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8406983328090596"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I also want to know the mean trip_distance when passenger_count is 3\n",
    "\n",
    "df.loc[\n",
    "    df['passenger_count'] == 3,\n",
    "    'trip_distance'\n",
    "].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "900311bb",
   "metadata": {},
   "source": [
    "# What are we doing here?\n",
    "\n",
    "We're trying to:\n",
    "\n",
    "- For every different value of `passenger_count`\n",
    "- Apply the `mean` method\n",
    "- To the `trip_distance` column\n",
    "\n",
    "This is known as grouping, and we wil get a series back in which the index contains all of the different (unique) values of `passenger_count` and the values will be the mean of `trip_distance` for each of these values of `passenger_count`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fdf91087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "passenger_count\n",
       "0    2.651561\n",
       "1    2.779088\n",
       "2    2.880572\n",
       "3    2.840698\n",
       "4    2.853084\n",
       "5    2.865741\n",
       "6    2.842335\n",
       "7    2.561579\n",
       "8    3.142759\n",
       "9    1.486667\n",
       "Name: trip_distance, dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for all unique values of passenger_count, calculate the mean of trip_distance\n",
    "df.groupby('passenger_count')['trip_distance'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "1538dc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "passenger_count\n",
       "0    18.663658\n",
       "1    15.609601\n",
       "2    15.831294\n",
       "3    15.604015\n",
       "4    15.650307\n",
       "5    15.546940\n",
       "6    15.437892\n",
       "7    48.278421\n",
       "8    64.105517\n",
       "9    31.094444\n",
       "Name: total_amount, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('passenger_count')['total_amount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d487c153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_amount\n",
       "-362.80       1.0\n",
       "-322.30       1.0\n",
       "-320.30       2.0\n",
       "-300.30       1.0\n",
       "-284.80       1.0\n",
       "             ... \n",
       " 33023.53     0.0\n",
       " 34674.65     0.0\n",
       " 36090.30     0.0\n",
       " 356214.78    1.0\n",
       " 623261.66    1.0\n",
       "Name: passenger_count, Length: 12191, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# don't group by a column with floating-point values, or with very open-ended, distinct values\n",
    "\n",
    "# for each amount that the taxi passenger(s) paid,\n",
    "# find out the mean number of passengers\n",
    "\n",
    "df.groupby('total_amount')['passenger_count'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e1558d6",
   "metadata": {},
   "source": [
    "# Exercise: Average amount per payment type\n",
    "\n",
    "1. Create a data frame from NYC taxi data in January, 2019.  We want `trip_distance`, `total_amount`, and `payment_type`.  (The value of `payment_type` is 1 for cash, and 2 for credit.)\n",
    "2. What was the mean `trip_distance` for each type of payment?\n",
    "3. What was the mean `total_amount` for each type of payment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "789eccb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/nyc_taxi_2019-01.csv',\n",
    "                 usecols=['trip_distance', 'total_amount', 'payment_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "191a8b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payment_type\n",
       "1    2.920842\n",
       "2    2.500341\n",
       "3    2.423218\n",
       "4    2.653776\n",
       "Name: trip_distance, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is the mean trip_distance for each type of payment?\n",
    "\n",
    "# unique values: payment_type\n",
    "# column to calculate: trip_distance\n",
    "# aggregation method: mean\n",
    "\n",
    "df.groupby('payment_type')['trip_distance'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "35f5a2a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payment_type\n",
       "1    16.747057\n",
       "2    12.749036\n",
       "3    30.907704\n",
       "4     8.735856\n",
       "Name: total_amount, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('payment_type')['total_amount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "28c46f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payment_type\n",
       "1     36090.30\n",
       "2    356214.78\n",
       "3    623261.66\n",
       "4       791.56\n",
       "Name: total_amount, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('payment_type')['total_amount'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abebb214",
   "metadata": {},
   "source": [
    "# Next up:\n",
    "\n",
    "1. More advanced grouping\n",
    "    - Multiple columns\n",
    "    - Multiple aggregatrions\n",
    "2. Pivot tables\n",
    "\n",
    "Taking 10 minutes for a break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d0a127",
   "metadata": {},
   "source": [
    "# Grouping with multiple columns\n",
    "\n",
    "Returning to our taxi example: I want to know the mean for `total_amount` and also `trip_distance` for taxi rides in January 2019, grouped by `payment_type`\n",
    "\n",
    "- Group by: `payment_type`\n",
    "- Aggregation: `mean`\n",
    "- Calculate the mean on both `total_amount` and `trip_distance`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "588f15c5",
   "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>total_amount</th>\n",
       "      <th>trip_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.747057</td>\n",
       "      <td>2.920842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.749036</td>\n",
       "      <td>2.500341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.907704</td>\n",
       "      <td>2.423218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.735856</td>\n",
       "      <td>2.653776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              total_amount  trip_distance\n",
       "payment_type                             \n",
       "1                16.747057       2.920842\n",
       "2                12.749036       2.500341\n",
       "3                30.907704       2.423218\n",
       "4                 8.735856       2.653776"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each value of payment type,\n",
    "# I want the mean of total_amount and trip_distance\n",
    "\n",
    "df.groupby('payment_type')[['total_amount', 'trip_distance']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c17ecc5",
   "metadata": {},
   "source": [
    "# Grouping with multiple aggregations\n",
    "\n",
    "What if I only want to run my `groupby` on a single column, but I want to run multiple aggregation methods?\n",
    "\n",
    "- Group by: `payment_type`\n",
    "- Aggregation: `mean` and `std`\n",
    "- Calculate on: `total_amount`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "f3fb332e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payment_type\n",
       "1    16.747057\n",
       "2    12.749036\n",
       "3    30.907704\n",
       "4     8.735856\n",
       "Name: total_amount, dtype: float64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('payment_type')['total_amount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "31f82d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payment_type\n",
       "1      44.619759\n",
       "2     243.962684\n",
       "3    3425.306706\n",
       "4      24.367473\n",
       "Name: total_amount, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('payment_type')['total_amount'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "5c883963",
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.747057</td>\n",
       "      <td>44.619759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.749036</td>\n",
       "      <td>243.962684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.907704</td>\n",
       "      <td>3425.306706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.735856</td>\n",
       "      <td>24.367473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean          std\n",
       "payment_type                        \n",
       "1             16.747057    44.619759\n",
       "2             12.749036   243.962684\n",
       "3             30.907704  3425.306706\n",
       "4              8.735856    24.367473"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is how I can specify multiple aggregation methods\n",
    "\n",
    "# I get a data frame in which each column represents a different aggregation method\n",
    "# the index shows me on which column I'm grouping\n",
    "# we have to know on which column we're calculating\n",
    "\n",
    "df.groupby('payment_type')['total_amount'].agg(['mean', 'std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "af200de1",
   "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>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>831.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>214.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>107.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              min     max\n",
       "payment_type             \n",
       "1             0.0  831.80\n",
       "2             0.0  214.01\n",
       "3             0.0  107.80\n",
       "4             0.0   44.70"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what were, for each payment type, the minimum and maximum distances?\n",
    "\n",
    "df.groupby('payment_type')['trip_distance'].agg(['min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b718936f",
   "metadata": {},
   "source": [
    "# What about multiple columns *and* multiple aggregation methods?\n",
    "\n",
    "What if I want to know the minimum and maximum values, for each payment type, for each of the columns `trip_distance` and `total_amount`?\n",
    "\n",
    "- Group on: `payment_type`\n",
    "- Aggregation methods: `min` and `max`\n",
    "- Calculate on columns: `trip_distance` and `total_amount`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "4818dec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">trip_distance</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>831.80</td>\n",
       "      <td>-12.30</td>\n",
       "      <td>36090.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>214.01</td>\n",
       "      <td>-109.56</td>\n",
       "      <td>356214.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>107.80</td>\n",
       "      <td>-322.30</td>\n",
       "      <td>623261.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.70</td>\n",
       "      <td>-362.80</td>\n",
       "      <td>791.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             trip_distance         total_amount           \n",
       "                       min     max          min        max\n",
       "payment_type                                              \n",
       "1                      0.0  831.80       -12.30   36090.30\n",
       "2                      0.0  214.01      -109.56  356214.78\n",
       "3                      0.0  107.80      -322.30  623261.66\n",
       "4                      0.0   44.70      -362.80     791.56"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = df.groupby('payment_type')[['trip_distance', 'total_amount']].agg(['min', 'max'])\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6289daf1",
   "metadata": {},
   "source": [
    "The above is a data frame. But it's a data frame with a \"multi-index,\" here on the columns:\n",
    "\n",
    "- The row index is integers, 1, 2, 3, and 4 -- the unique values for `payment_type`\n",
    "- The columns contain a two-level index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "bab5d010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>831.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>214.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>107.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              min     max\n",
       "payment_type             \n",
       "1             0.0  831.80\n",
       "2             0.0  214.01\n",
       "3             0.0  107.80\n",
       "4             0.0   44.70"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['trip_distance']  # by specifying only the top level of the multi-index..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "0fa38852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payment_type\n",
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "Name: (trip_distance, min), dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['trip_distance', 'min']    # specify both levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "fe8cee31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"ID\",\"Name\",\"Sex\",\"Age\",\"Height\",\"Weight\",\"Team\",\"NOC\",\"Games\",\"Year\",\"Season\",\"City\",\"Sport\",\"Event\",\"Medal\"\n",
      "\"1\",\"A Dijiang\",\"M\",24,180,80,\"China\",\"CHN\",\"1992 Summer\",1992,\"Summer\",\"Barcelona\",\"Basketball\",\"Basketball Men's Basketball\",NA\n",
      "\"2\",\"A Lamusi\",\"M\",23,170,60,\"China\",\"CHN\",\"2012 Summer\",2012,\"Summer\",\"London\",\"Judo\",\"Judo Men's Extra-Lightweight\",NA\n",
      "\"3\",\"Gunnar Nielsen Aaby\",\"M\",24,NA,NA,\"Denmark\",\"DEN\",\"1920 Summer\",1920,\"Summer\",\"Antwerpen\",\"Football\",\"Football Men's Football\",NA\n",
      "\"4\",\"Edgar Lindenau Aabye\",\"M\",34,NA,NA,\"Denmark/Sweden\",\"DEN\",\"1900 Summer\",1900,\"Summer\",\"Paris\",\"Tug-Of-War\",\"Tug-Of-War Men's Tug-Of-War\",\"Gold\"\n",
      "\"5\",\"Christine Jacoba Aaftink\",\"F\",21,185,82,\"Netherlands\",\"NED\",\"1988 Winter\",1988,\"Winter\",\"Calgary\",\"Speed Skating\",\"Speed Skating Women's 500 metres\",NA\n",
      "\"5\",\"Christine Jacoba Aaftink\",\"F\",21,185,82,\"Netherlands\",\"NED\",\"1988 Winter\",1988,\"Winter\",\"Calgary\",\"Speed Skating\",\"Speed Skating Women's 1,000 metres\",NA\n",
      "\"5\",\"Christine Jacoba Aaftink\",\"F\",25,185,82,\"Netherlands\",\"NED\",\"1992 Winter\",1992,\"Winter\",\"Albertville\",\"Speed Skating\",\"Speed Skating Women's 500 metres\",NA\n",
      "\"5\",\"Christine Jacoba Aaftink\",\"F\",25,185,82,\"Netherlands\",\"NED\",\"1992 Winter\",1992,\"Winter\",\"Albertville\",\"Speed Skating\",\"Speed Skating Women's 1,000 metres\",NA\n",
      "\"5\",\"Christine Jacoba Aaftink\",\"F\",27,185,82,\"Netherlands\",\"NED\",\"1994 Winter\",1994,\"Winter\",\"Lillehammer\",\"Speed Skating\",\"Speed Skating Women's 500 metres\",NA\n"
     ]
    }
   ],
   "source": [
    "!head ../data/olympic_athlete_events.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf8bb35",
   "metadata": {},
   "source": [
    "# Exercise: Olympic stats\n",
    "\n",
    "1. Create a data frame from the `olympic_athlete_events.csv` file, in the Pandas Workout zipfile. Only load the following columns: `Sex`, `Age`, `Height`, `Weight`, `Team`, `Year`.\n",
    "2. What was the average height of an athlete, for each year in which the Olympic games took places?\n",
    "3. What were the minimum and maximum ages of athletes for each team in the games through their history?\n",
    "4. What were the min and max of height and weight for each year in which the games took place?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "881b026b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/olympic_athlete_events.csv',\n",
    "                usecols=['Sex', 'Age', 'Height', 'Weight', 'Team', 'Year'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "0bb518be",
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex   Age  Height  Weight            Team  Year\n",
       "0   M  24.0   180.0    80.0           China  1992\n",
       "1   M  23.0   170.0    60.0           China  2012\n",
       "2   M  24.0     NaN     NaN         Denmark  1920\n",
       "3   M  34.0     NaN     NaN  Denmark/Sweden  1900\n",
       "4   F  21.0   185.0    82.0     Netherlands  1988"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c1e32758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(271116, 6)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "e612efae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1896    172.739130\n",
       "1900    176.637931\n",
       "1904    175.788732\n",
       "1906    178.206226\n",
       "1908    177.543158\n",
       "1912    177.447989\n",
       "1920    175.752282\n",
       "1924    174.963039\n",
       "1928    175.162051\n",
       "1932    174.220115\n",
       "1936    175.723993\n",
       "1948    176.172797\n",
       "1952    174.138940\n",
       "1956    173.900968\n",
       "1960    173.141286\n",
       "1964    173.448574\n",
       "1968    173.945865\n",
       "1972    174.565363\n",
       "1976    174.920528\n",
       "1980    175.527488\n",
       "1984    175.540855\n",
       "1988    175.745252\n",
       "1992    176.174649\n",
       "1994    175.169862\n",
       "1996    175.895121\n",
       "1998    174.581369\n",
       "2000    176.089721\n",
       "2002    174.702451\n",
       "2004    175.972850\n",
       "2006    174.623172\n",
       "2008    176.211062\n",
       "2010    174.918182\n",
       "2012    176.262469\n",
       "2014    174.816670\n",
       "2016    176.034266\n",
       "Name: Height, dtype: float64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what was the average height of an athlete, for each year in which the Olympic games took place?\n",
    "\n",
    "# - group by: year\n",
    "# - aggregation: mean\n",
    "# - calculate on: Height\n",
    "\n",
    "df.groupby('Year')['Height'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "85281e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1896         NaN\n",
       "1900    0.022570\n",
       "1904   -0.004808\n",
       "1906    0.013752\n",
       "1908   -0.003721\n",
       "1912   -0.000536\n",
       "1920   -0.009556\n",
       "1924   -0.004491\n",
       "1928    0.001137\n",
       "1932   -0.005378\n",
       "1936    0.008632\n",
       "1948    0.002554\n",
       "1952   -0.011545\n",
       "1956   -0.001367\n",
       "1960   -0.004368\n",
       "1964    0.001775\n",
       "1968    0.002867\n",
       "1972    0.003561\n",
       "1976    0.002035\n",
       "1980    0.003470\n",
       "1984    0.000076\n",
       "1988    0.001164\n",
       "1992    0.002443\n",
       "1994   -0.005703\n",
       "1996    0.004140\n",
       "1998   -0.007469\n",
       "2000    0.008640\n",
       "2002   -0.007878\n",
       "2004    0.007272\n",
       "2006   -0.007670\n",
       "2008    0.009093\n",
       "2010   -0.007337\n",
       "2012    0.007685\n",
       "2014   -0.008203\n",
       "2016    0.006965\n",
       "Name: Height, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Year')['Height'].mean().pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "df91edbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30. Februar</th>\n",
       "      <td>23.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A North American Team</th>\n",
       "      <td>38.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acipactli</th>\n",
       "      <td>44.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acturus</th>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>17.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akatonbo</th>\n",
       "      <td>36.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alain IV</th>\n",
       "      <td>40.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcaid</th>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcyon-6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcyon-7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aldebaran</th>\n",
       "      <td>25.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aldebaran II</th>\n",
       "      <td>36.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aletta</th>\n",
       "      <td>26.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>14.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ali-Baba II</th>\n",
       "      <td>26.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ali-Baba IV</th>\n",
       "      <td>35.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ali-Baba IX</th>\n",
       "      <td>38.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ali-Baba VI</th>\n",
       "      <td>33.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allegro</th>\n",
       "      <td>30.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almaz</th>\n",
       "      <td>24.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aloha II</th>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amateur Athletic Association</th>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amolgavar</th>\n",
       "      <td>22.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amstel Amsterdam</th>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amulet-3</th>\n",
       "      <td>31.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amulet-7</th>\n",
       "      <td>31.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ancora</th>\n",
       "      <td>30.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorinha</th>\n",
       "      <td>24.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               min   max\n",
       "Team                                    \n",
       "30. Februar                   23.0  44.0\n",
       "A North American Team         38.0  44.0\n",
       "Acipactli                     44.0  52.0\n",
       "Acturus                       27.0  27.0\n",
       "Afghanistan                   17.0  35.0\n",
       "Akatonbo                      36.0  44.0\n",
       "Alain IV                      40.0  54.0\n",
       "Albania                       16.0  46.0\n",
       "Alcaid                        28.0  33.0\n",
       "Alcyon-6                       NaN   NaN\n",
       "Alcyon-7                       NaN   NaN\n",
       "Aldebaran                     25.0  40.0\n",
       "Aldebaran II                  36.0  39.0\n",
       "Aletta                        26.0  40.0\n",
       "Algeria                       14.0  38.0\n",
       "Ali-Baba II                   26.0  51.0\n",
       "Ali-Baba IV                   35.0  41.0\n",
       "Ali-Baba IX                   38.0  53.0\n",
       "Ali-Baba VI                   33.0  49.0\n",
       "Allegro                       30.0  38.0\n",
       "Almaz                         24.0  27.0\n",
       "Aloha II                      16.0  43.0\n",
       "Amateur Athletic Association  22.0  29.0\n",
       "American Samoa                16.0  43.0\n",
       "Amolgavar                     22.0  36.0\n",
       "Amstel Amsterdam              20.0  24.0\n",
       "Amulet-3                      31.0  34.0\n",
       "Amulet-7                      31.0  34.0\n",
       "Ancora                        30.0  36.0\n",
       "Andorinha                     24.0  40.0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What were the minimum and maximum ages of athletes for each team in the games through their history?\n",
    "\n",
    "# Group by: Team\n",
    "# Aggregate method: min, max\n",
    "# Calculate on: Age\n",
    "\n",
    "df.groupby('Team')['Age'].agg(['min', 'max']).head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "2b983285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ethnikos Gymnastikos Syllogos</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>11.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>11.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto Rico</th>\n",
       "      <td>11.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>11.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great Britain</th>\n",
       "      <td>11.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>11.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>11.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>11.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>11.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>12.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>12.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soviet Union</th>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>12.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Germany</th>\n",
       "      <td>12.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>12.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>12.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cote d'Ivoire</th>\n",
       "      <td>12.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>12.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>12.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>12.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroon</th>\n",
       "      <td>12.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Korea</th>\n",
       "      <td>12.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great Britain-2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>12.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechoslovakia</th>\n",
       "      <td>13.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>El Salvador</th>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>13.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                min   max\n",
       "Team                                     \n",
       "Ethnikos Gymnastikos Syllogos  10.0  10.0\n",
       "China                          11.0  45.0\n",
       "Italy                          11.0  71.0\n",
       "Puerto Rico                    11.0  65.0\n",
       "Romania                        11.0  57.0\n",
       "Great Britain                  11.0  84.0\n",
       "Japan                          11.0  71.0\n",
       "Norway                         11.0  62.0\n",
       "South Africa                   11.0  52.0\n",
       "Spain                          11.0  66.0\n",
       "Germany                        12.0  80.0\n",
       "United States                  12.0  97.0\n",
       "Hungary                        12.0  58.0\n",
       "Soviet Union                   12.0  49.0\n",
       "Colombia                       12.0  52.0\n",
       "East Germany                   12.0  40.0\n",
       "Austria                        12.0  76.0\n",
       "Belgium                        12.0  74.0\n",
       "Cote d'Ivoire                  12.0  45.0\n",
       "Mexico                         12.0  57.0\n",
       "Cambodia                       12.0  44.0\n",
       "Denmark                        12.0  73.0\n",
       "Malaysia                       12.0  49.0\n",
       "Cameroon                       12.0  35.0\n",
       "North Korea                    12.0  45.0\n",
       "Great Britain-2                12.0  57.0\n",
       "France                         12.0  81.0\n",
       "Czechoslovakia                 13.0  65.0\n",
       "El Salvador                    13.0  68.0\n",
       "Lebanon                        13.0  53.0"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Team')['Age'].agg(['min', 'max']).sort_values('min').head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "21c4e362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Height</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>154.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>153.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>155.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>165.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>157.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>157.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>142.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>142.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>147.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>147.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>147.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>140.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>150.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>137.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>137.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>137.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>127.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>130.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>136.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>131.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>132.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>127.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>136.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>176.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>148.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>136.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>176.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>142.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>136.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>149.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>139.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>147.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>137.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>149.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>140.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>146.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>133.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Height        Weight       \n",
       "        min    max    min    max\n",
       "Year                            \n",
       "1896  154.0  188.0   45.0  106.0\n",
       "1900  153.0  191.0   51.0  102.0\n",
       "1904  155.0  195.0   43.0  115.0\n",
       "1906  165.0  196.0   52.0  114.0\n",
       "1908  157.0  201.0   51.0  115.0\n",
       "1912  157.0  200.0   49.0  125.0\n",
       "1920  142.0  197.0   48.0  146.0\n",
       "1924  142.0  200.0   44.0  146.0\n",
       "1928  147.0  211.0   41.0  125.0\n",
       "1932  147.0  200.0   41.0  110.0\n",
       "1936  147.0  205.0   37.0  138.0\n",
       "1948  140.0  213.0   47.0  125.0\n",
       "1952  150.0  213.0   42.0  145.0\n",
       "1956  137.0  218.0   28.0  141.0\n",
       "1960  137.0  218.0   36.0  141.0\n",
       "1964  137.0  218.0   38.0  163.0\n",
       "1968  127.0  216.0   34.0  163.0\n",
       "1972  130.0  223.0   38.0  182.0\n",
       "1976  136.0  220.0   30.0  163.0\n",
       "1980  131.0  220.0   25.0  190.0\n",
       "1984  132.0  218.0   31.0  150.0\n",
       "1988  127.0  223.0   32.0  161.0\n",
       "1992  136.0  223.0   30.0  176.5\n",
       "1994  148.0  200.0   40.0  113.0\n",
       "1996  136.0  223.0   30.0  176.5\n",
       "1998  142.0  200.0   32.0  123.0\n",
       "2000  136.0  226.0   28.0  180.0\n",
       "2002  149.0  201.0   42.0  123.0\n",
       "2004  139.0  226.0   30.0  198.0\n",
       "2006  147.0  206.0   38.0  127.0\n",
       "2008  137.0  226.0   28.0  214.0\n",
       "2010  149.0  206.0   38.0  116.0\n",
       "2012  140.0  221.0   33.0  214.0\n",
       "2014  146.0  206.0   41.0  116.0\n",
       "2016  133.0  218.0   30.0  170.0"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What were the min and max of height and weight for each year in which the games took place?\n",
    "\n",
    "# grouping by Year\n",
    "# aggregating on min + max\n",
    "# calculating on height + weight\n",
    "\n",
    "df.groupby('Year')[['Height', 'Weight']].agg(['min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b80c7952",
   "metadata": {},
   "source": [
    "# Pivot tables\n",
    "\n",
    "Pivot tables are basically 2D groupby queries.  Normally, we've said that we want to group on a particular column (with unique values), and then calculate our aggregate method on a particular column.\n",
    "\n",
    "- Group on:\n",
    "- Aggregation method:\n",
    "- Calculate on:\n",
    "\n",
    "In a pivot table, we're going to group on *two* separate columns, each of which has unique values (i.e., categorical data). We'll end up with a data frame in which the index (rows) represents all of the unique values from one column, the columns represent all of the unique values from a second column, and the cells contain the result of running our aggregation method for all values at that intersection.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "6df007fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's look at taxi data again!\n",
    "\n",
    "df = pd.read_csv('../data/nyc_taxi_2019-01.csv',\n",
    "                usecols=['passenger_count', 'trip_distance', 'total_amount', 'payment_type'])\n",
    "\n",
    "# I want to know: What was, for each combination of passenger_count and payment_type, the \n",
    "# mean total_amount?\n",
    "\n",
    "# This is where pivot tables come in\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "86e0883e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>payment_type</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.764643</td>\n",
       "      <td>12.306161</td>\n",
       "      <td>38.630381</td>\n",
       "      <td>13.339928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.628241</td>\n",
       "      <td>12.618859</td>\n",
       "      <td>34.609163</td>\n",
       "      <td>8.538202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.985211</td>\n",
       "      <td>13.132331</td>\n",
       "      <td>13.240993</td>\n",
       "      <td>9.805937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.672447</td>\n",
       "      <td>13.206434</td>\n",
       "      <td>12.371247</td>\n",
       "      <td>7.335938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.759958</td>\n",
       "      <td>13.531921</td>\n",
       "      <td>15.262684</td>\n",
       "      <td>13.673812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.671059</td>\n",
       "      <td>12.820309</td>\n",
       "      <td>-5.558167</td>\n",
       "      <td>-5.830643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.592230</td>\n",
       "      <td>12.649407</td>\n",
       "      <td>-3.495205</td>\n",
       "      <td>-4.445522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75.430000</td>\n",
       "      <td>23.337143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-75.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62.898519</td>\n",
       "      <td>80.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33.756250</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "payment_type             1          2          3          4\n",
       "passenger_count                                            \n",
       "0                20.764643  12.306161  38.630381  13.339928\n",
       "1                16.628241  12.618859  34.609163   8.538202\n",
       "2                16.985211  13.132331  13.240993   9.805937\n",
       "3                16.672447  13.206434  12.371247   7.335938\n",
       "4                16.759958  13.531921  15.262684  13.673812\n",
       "5                16.671059  12.820309  -5.558167  -5.830643\n",
       "6                16.592230  12.649407  -3.495205  -4.445522\n",
       "7                75.430000  23.337143        NaN -75.800000\n",
       "8                62.898519  80.400000        NaN        NaN\n",
       "9                33.756250   9.800000        NaN        NaN"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for every combination of passenger_count (index) and payment_type (columns),\n",
    "# show me the mean total_amount\n",
    "\n",
    "df.pivot_table(index='passenger_count', columns='payment_type', values='total_amount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "ae3e1317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>payment_type</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36090.30</td>\n",
       "      <td>204.80</td>\n",
       "      <td>24249.43</td>\n",
       "      <td>115.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>845.15</td>\n",
       "      <td>356214.78</td>\n",
       "      <td>623261.66</td>\n",
       "      <td>791.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>449.32</td>\n",
       "      <td>515.82</td>\n",
       "      <td>589.96</td>\n",
       "      <td>157.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>463.30</td>\n",
       "      <td>341.97</td>\n",
       "      <td>130.30</td>\n",
       "      <td>142.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>447.36</td>\n",
       "      <td>500.30</td>\n",
       "      <td>110.56</td>\n",
       "      <td>185.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>468.60</td>\n",
       "      <td>761.80</td>\n",
       "      <td>60.80</td>\n",
       "      <td>64.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>490.80</td>\n",
       "      <td>282.56</td>\n",
       "      <td>177.80</td>\n",
       "      <td>58.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>136.95</td>\n",
       "      <td>75.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-75.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>114.96</td>\n",
       "      <td>80.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>110.76</td>\n",
       "      <td>9.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "payment_type            1          2          3       4\n",
       "passenger_count                                        \n",
       "0                36090.30     204.80   24249.43  115.56\n",
       "1                  845.15  356214.78  623261.66  791.56\n",
       "2                  449.32     515.82     589.96  157.90\n",
       "3                  463.30     341.97     130.30  142.80\n",
       "4                  447.36     500.30     110.56  185.56\n",
       "5                  468.60     761.80      60.80   64.32\n",
       "6                  490.80     282.56     177.80   58.56\n",
       "7                  136.95      75.80        NaN  -75.80\n",
       "8                  114.96      80.80        NaN     NaN\n",
       "9                  110.76       9.80        NaN     NaN"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change (set) the aggregation method\n",
    "\n",
    "# show us the max value of total_amount\n",
    "# for each combination of passenger_count and payment_type\n",
    "\n",
    "df.pivot_table(index='passenger_count',   # categorical data, one row per unique value\n",
    "               columns='payment_type',    # categorical data, one column per unique value\n",
    "               values='total_amount',     # what column are we calculating on?\n",
    "               aggfunc='max')             # what aggregation method (default: mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5745833a",
   "metadata": {},
   "source": [
    "# Exercise: Pivoting with the Olympics\n",
    "\n",
    "1. We're going to use our data frame from the previous exercise with Olympic data.\n",
    "2. For each year, and each team, show the mean height.\n",
    "3. For each year, and each value of `Sex`, show the max age.\n",
    "4. For each year, and each value of `Sex`, show the min and max ages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "515efd55",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/olympic_athlete_events.csv',\n",
    "                usecols=['Sex', 'Age', 'Height', 'Weight', 'Team', 'Year'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "933681cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Team</th>\n",
       "      <th>30. Februar</th>\n",
       "      <th>Acipactli</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Akatonbo</th>\n",
       "      <th>Alain IV</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Aldebaran</th>\n",
       "      <th>Aldebaran II</th>\n",
       "      <th>Aletta</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>...</th>\n",
       "      <th>Ylliam II</th>\n",
       "      <th>Ylliam VII</th>\n",
       "      <th>Ylliam VIII</th>\n",
       "      <th>Yugoslavia</th>\n",
       "      <th>Yugoslavia-1</th>\n",
       "      <th>Yugoslavia-2</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zefyros</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>rn-2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.428571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>171.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>172.950000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178.352941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.687500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.5</td>\n",
       "      <td>176.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.032787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>NaN</td>\n",
       "      <td>174.666667</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>182.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.724138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.733333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.161290</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169.777778</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.965909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.260684</td>\n",
       "      <td>179.5</td>\n",
       "      <td>175.00</td>\n",
       "      <td>168.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.254545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.450000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.543590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.214286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.942308</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.809524</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.256000</td>\n",
       "      <td>186.2</td>\n",
       "      <td>183.25</td>\n",
       "      <td>173.714286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.950000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.046083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.781250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.489362</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>181.375000</td>\n",
       "      <td>183.0</td>\n",
       "      <td>183.00</td>\n",
       "      <td>169.714286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.142857</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.705882</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.313725</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178.960000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.285714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.623188</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.785714</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.982143</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.714286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.928571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.263158</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.444444</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.027027</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.838710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 699 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Team  30. Februar   Acipactli  Afghanistan  Akatonbo  Alain IV     Albania  \\\n",
       "Year                                                                         \n",
       "1896          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1900          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1904          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1906          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1908          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1912          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1920          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1924          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1928          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1932          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1936          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1948          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1952        171.5         NaN          NaN       NaN       NaN         NaN   \n",
       "1956          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1960          NaN         NaN   170.687500       NaN       NaN         NaN   \n",
       "1964          NaN  174.666667   161.000000     182.0     176.0         NaN   \n",
       "1968          NaN         NaN   170.200000       NaN       NaN         NaN   \n",
       "1972          NaN         NaN   168.333333       NaN       NaN         NaN   \n",
       "1976          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1980          NaN         NaN   168.363636       NaN       NaN         NaN   \n",
       "1984          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1988          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1992          NaN         NaN          NaN       NaN       NaN  163.000000   \n",
       "1994          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1996          NaN         NaN          NaN       NaN       NaN  169.000000   \n",
       "1998          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "2000          NaN         NaN          NaN       NaN       NaN  167.800000   \n",
       "2002          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "2004          NaN         NaN   170.750000       NaN       NaN  172.285714   \n",
       "2006          NaN         NaN          NaN       NaN       NaN  180.000000   \n",
       "2008          NaN         NaN   179.250000       NaN       NaN  173.416667   \n",
       "2010          NaN         NaN          NaN       NaN       NaN  180.000000   \n",
       "2012          NaN         NaN   171.666667       NaN       NaN  176.800000   \n",
       "2014          NaN         NaN          NaN       NaN       NaN  163.000000   \n",
       "2016          NaN         NaN   173.666667       NaN       NaN  176.166667   \n",
       "\n",
       "Team  Aldebaran  Aldebaran II      Aletta     Algeria  ...  Ylliam II  \\\n",
       "Year                                                   ...              \n",
       "1896        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1900        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1904        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1906        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1908        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1912        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1920        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1924        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1928        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1932        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1936        NaN           NaN         NaN         NaN  ...      179.0   \n",
       "1948        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1952        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1956        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1960        NaN         177.5  176.333333         NaN  ...        NaN   \n",
       "1964      174.5           NaN         NaN  175.000000  ...        NaN   \n",
       "1968        NaN           NaN         NaN  169.777778  ...        NaN   \n",
       "1972        NaN           NaN         NaN  173.166667  ...        NaN   \n",
       "1976        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1980        NaN           NaN         NaN  175.450000  ...        NaN   \n",
       "1984        NaN           NaN         NaN  176.809524  ...        NaN   \n",
       "1988        NaN           NaN         NaN  176.166667  ...        NaN   \n",
       "1992        NaN           NaN         NaN  170.428571  ...        NaN   \n",
       "1994        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1996        NaN           NaN         NaN  176.000000  ...        NaN   \n",
       "1998        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "2000        NaN           NaN         NaN  174.313725  ...        NaN   \n",
       "2002        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "2004        NaN           NaN         NaN  173.623188  ...        NaN   \n",
       "2006        NaN           NaN         NaN  171.333333  ...        NaN   \n",
       "2008        NaN           NaN         NaN  176.982143  ...        NaN   \n",
       "2010        NaN           NaN         NaN  170.000000  ...        NaN   \n",
       "2012        NaN           NaN         NaN  174.263158  ...        NaN   \n",
       "2014        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "2016        NaN           NaN         NaN  174.027027  ...        NaN   \n",
       "\n",
       "Team  Ylliam VII  Ylliam VIII  Yugoslavia  Yugoslavia-1  Yugoslavia-2  \\\n",
       "Year                                                                    \n",
       "1896         NaN          NaN         NaN           NaN           NaN   \n",
       "1900         NaN          NaN         NaN           NaN           NaN   \n",
       "1904         NaN          NaN         NaN           NaN           NaN   \n",
       "1906         NaN          NaN         NaN           NaN           NaN   \n",
       "1908         NaN          NaN         NaN           NaN           NaN   \n",
       "1912         NaN          NaN         NaN           NaN           NaN   \n",
       "1920         NaN          NaN         NaN           NaN           NaN   \n",
       "1924         NaN          NaN  190.000000           NaN           NaN   \n",
       "1928         NaN          NaN  173.428571           NaN           NaN   \n",
       "1932         NaN          NaN  192.000000           NaN           NaN   \n",
       "1936         NaN          NaN  179.900000           NaN           NaN   \n",
       "1948  179.333333          NaN  176.666667           NaN           NaN   \n",
       "1952         NaN        180.0  172.950000           NaN           NaN   \n",
       "1956         NaN          NaN  178.352941           NaN           NaN   \n",
       "1960         NaN          NaN  175.032787           NaN           NaN   \n",
       "1964         NaN          NaN  176.724138           NaN           NaN   \n",
       "1968         NaN          NaN  174.965909           NaN           NaN   \n",
       "1972         NaN          NaN  175.260684         179.5        175.00   \n",
       "1976         NaN          NaN  184.254545           NaN           NaN   \n",
       "1980         NaN          NaN  182.543590           NaN           NaN   \n",
       "1984         NaN          NaN  180.256000         186.2        183.25   \n",
       "1988         NaN          NaN  182.046083           NaN           NaN   \n",
       "1992         NaN          NaN  181.375000         183.0        183.00   \n",
       "1994         NaN          NaN         NaN           NaN           NaN   \n",
       "1996         NaN          NaN         NaN           NaN           NaN   \n",
       "1998         NaN          NaN         NaN           NaN           NaN   \n",
       "2000         NaN          NaN         NaN           NaN           NaN   \n",
       "2002         NaN          NaN         NaN           NaN           NaN   \n",
       "2004         NaN          NaN         NaN           NaN           NaN   \n",
       "2006         NaN          NaN         NaN           NaN           NaN   \n",
       "2008         NaN          NaN         NaN           NaN           NaN   \n",
       "2010         NaN          NaN         NaN           NaN           NaN   \n",
       "2012         NaN          NaN         NaN           NaN           NaN   \n",
       "2014         NaN          NaN         NaN           NaN           NaN   \n",
       "2016         NaN          NaN         NaN           NaN           NaN   \n",
       "\n",
       "Team      Zambia  Zefyros    Zimbabwe   rn-2  \n",
       "Year                                          \n",
       "1896         NaN      NaN         NaN    NaN  \n",
       "1900         NaN      NaN         NaN    NaN  \n",
       "1904         NaN      NaN         NaN    NaN  \n",
       "1906         NaN      NaN         NaN    NaN  \n",
       "1908         NaN      NaN         NaN    NaN  \n",
       "1912         NaN      NaN         NaN  176.0  \n",
       "1920         NaN      NaN         NaN    NaN  \n",
       "1924         NaN      NaN         NaN    NaN  \n",
       "1928         NaN      NaN         NaN    NaN  \n",
       "1932         NaN      NaN         NaN    NaN  \n",
       "1936         NaN      NaN         NaN    NaN  \n",
       "1948         NaN      NaN         NaN    NaN  \n",
       "1952         NaN      NaN         NaN    NaN  \n",
       "1956         NaN      NaN         NaN    NaN  \n",
       "1960         NaN    180.0  155.000000    NaN  \n",
       "1964  175.733333      NaN  174.161290    NaN  \n",
       "1968  175.250000      NaN         NaN    NaN  \n",
       "1972  168.500000      NaN         NaN    NaN  \n",
       "1976         NaN      NaN         NaN    NaN  \n",
       "1980  174.214286      NaN  171.942308    NaN  \n",
       "1984  173.714286      NaN  173.950000    NaN  \n",
       "1988  172.781250      NaN  176.489362    NaN  \n",
       "1992  169.714286      NaN  171.142857    NaN  \n",
       "1994         NaN      NaN         NaN    NaN  \n",
       "1996  170.166667      NaN  176.705882    NaN  \n",
       "1998         NaN      NaN         NaN    NaN  \n",
       "2000  173.166667      NaN  178.960000    NaN  \n",
       "2002         NaN      NaN         NaN    NaN  \n",
       "2004  173.666667      NaN  177.785714    NaN  \n",
       "2006         NaN      NaN         NaN    NaN  \n",
       "2008  168.714286      NaN  171.928571    NaN  \n",
       "2010         NaN      NaN         NaN    NaN  \n",
       "2012  176.166667      NaN  174.444444    NaN  \n",
       "2014         NaN      NaN  182.000000    NaN  \n",
       "2016  175.166667      NaN  167.838710    NaN  \n",
       "\n",
       "[35 rows x 699 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each year, and each team, show the mean height.\n",
    "\n",
    "# Index (rows): Years\n",
    "# Columns: Teams\n",
    "# Calculate on: Height\n",
    "# Aggregation method: mean\n",
    "\n",
    "df.pivot_table(index='Year', columns='Team', values='Height', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "529d1fcf",
   "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>Sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>46.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>63.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>26.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>54.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>45.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>42.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>74.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>69.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>72.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>45.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>61.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>65.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>66.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>69.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>49.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>52.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>63.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>53.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>40.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>58.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>53.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>44.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>58.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>46.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>57.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>62.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex      F     M\n",
       "Year            \n",
       "1896   NaN  40.0\n",
       "1900  46.0  71.0\n",
       "1904  63.0  71.0\n",
       "1906  26.0  54.0\n",
       "1908  54.0  61.0\n",
       "1912  45.0  67.0\n",
       "1920  42.0  72.0\n",
       "1924  74.0  81.0\n",
       "1928  67.0  97.0\n",
       "1932  69.0  96.0\n",
       "1936  73.0  74.0\n",
       "1948  72.0  84.0\n",
       "1952  45.0  65.0\n",
       "1956  61.0  67.0\n",
       "1960  65.0  64.0\n",
       "1964  43.0  60.0\n",
       "1968  66.0  68.0\n",
       "1972  69.0  66.0\n",
       "1976  48.0  70.0\n",
       "1980  49.0  70.0\n",
       "1984  52.0  60.0\n",
       "1988  63.0  70.0\n",
       "1992  53.0  62.0\n",
       "1994  40.0  46.0\n",
       "1996  58.0  63.0\n",
       "1998  44.0  50.0\n",
       "2000  53.0  63.0\n",
       "2002  48.0  48.0\n",
       "2004  56.0  57.0\n",
       "2006  44.0  52.0\n",
       "2008  58.0  67.0\n",
       "2010  46.0  51.0\n",
       "2012  57.0  71.0\n",
       "2014  45.0  55.0\n",
       "2016  62.0  60.0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each year, and each value of Sex, show the max age.\n",
    "\n",
    "# index: Year\n",
    "# columns: Sex\n",
    "# values: Age\n",
    "# aggregation function: max\n",
    "\n",
    "df.pivot_table(index='Year', columns='Sex', values='Age', aggfunc='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "de8bf641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">min</th>\n",
       "      <th colspan=\"2\" halign=\"left\">max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       min         max      \n",
       "Sex      F     M     F     M\n",
       "Year                        \n",
       "1896   NaN  10.0   NaN  40.0\n",
       "1900  13.0  15.0  46.0  71.0\n",
       "1904  24.0  14.0  63.0  71.0\n",
       "1906  21.0  13.0  26.0  54.0\n",
       "1908  21.0  14.0  54.0  61.0\n",
       "1912  13.0  15.0  45.0  67.0\n",
       "1920  13.0  13.0  42.0  72.0\n",
       "1924  11.0  14.0  74.0  81.0\n",
       "1928  11.0  13.0  67.0  97.0\n",
       "1932  11.0  13.0  69.0  96.0\n",
       "1936  11.0  12.0  73.0  74.0\n",
       "1948  12.0  13.0  72.0  84.0\n",
       "1952  13.0  12.0  45.0  65.0\n",
       "1956  12.0  13.0  61.0  67.0\n",
       "1960  11.0  13.0  65.0  64.0\n",
       "1964  12.0  13.0  43.0  60.0\n",
       "1968  11.0  12.0  66.0  68.0\n",
       "1972  13.0  12.0  69.0  66.0\n",
       "1976  12.0  14.0  48.0  70.0\n",
       "1980  13.0  13.0  49.0  70.0\n",
       "1984  13.0  12.0  52.0  60.0\n",
       "1988  11.0  12.0  63.0  70.0\n",
       "1992  12.0  11.0  53.0  62.0\n",
       "1994  13.0  15.0  40.0  46.0\n",
       "1996  12.0  14.0  58.0  63.0\n",
       "1998  15.0  14.0  44.0  50.0\n",
       "2000  13.0  14.0  53.0  63.0\n",
       "2002  15.0  15.0  48.0  48.0\n",
       "2004  13.0  14.0  56.0  57.0\n",
       "2006  14.0  15.0  44.0  52.0\n",
       "2008  12.0  13.0  58.0  67.0\n",
       "2010  15.0  15.0  46.0  51.0\n",
       "2012  13.0  15.0  57.0  71.0\n",
       "2014  15.0  15.0  45.0  55.0\n",
       "2016  13.0  14.0  62.0  60.0"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each year, and each value of Sex, show the min and max ages.\n",
    "\n",
    "# index: Year\n",
    "# columns: Sex\n",
    "# values: Age\n",
    "# aggregation function: max\n",
    "\n",
    "df.pivot_table(index='Year', columns='Sex', values='Age', aggfunc=['min','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166aa8b9",
   "metadata": {},
   "source": [
    "# Next up\n",
    "\n",
    "1. Joining\n",
    "2. Cleaning data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "62d980b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">max</th>\n",
       "      <th colspan=\"2\" halign=\"left\">min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>46.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>63.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>26.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>54.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>45.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>42.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>74.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>67.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>69.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>72.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>45.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>61.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>65.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>66.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>69.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>49.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>52.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>63.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>53.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>40.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>58.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>53.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>44.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>58.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>46.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>57.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>62.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       max         min      \n",
       "Sex      F     M     F     M\n",
       "Year                        \n",
       "1896   NaN  40.0   NaN  10.0\n",
       "1900  46.0  71.0  13.0  15.0\n",
       "1904  63.0  71.0  24.0  14.0\n",
       "1906  26.0  54.0  21.0  13.0\n",
       "1908  54.0  61.0  21.0  14.0\n",
       "1912  45.0  67.0  13.0  15.0\n",
       "1920  42.0  72.0  13.0  13.0\n",
       "1924  74.0  81.0  11.0  14.0\n",
       "1928  67.0  97.0  11.0  13.0\n",
       "1932  69.0  96.0  11.0  13.0\n",
       "1936  73.0  74.0  11.0  12.0\n",
       "1948  72.0  84.0  12.0  13.0\n",
       "1952  45.0  65.0  13.0  12.0\n",
       "1956  61.0  67.0  12.0  13.0\n",
       "1960  65.0  64.0  11.0  13.0\n",
       "1964  43.0  60.0  12.0  13.0\n",
       "1968  66.0  68.0  11.0  12.0\n",
       "1972  69.0  66.0  13.0  12.0\n",
       "1976  48.0  70.0  12.0  14.0\n",
       "1980  49.0  70.0  13.0  13.0\n",
       "1984  52.0  60.0  13.0  12.0\n",
       "1988  63.0  70.0  11.0  12.0\n",
       "1992  53.0  62.0  12.0  11.0\n",
       "1994  40.0  46.0  13.0  15.0\n",
       "1996  58.0  63.0  12.0  14.0\n",
       "1998  44.0  50.0  15.0  14.0\n",
       "2000  53.0  63.0  13.0  14.0\n",
       "2002  48.0  48.0  15.0  15.0\n",
       "2004  56.0  57.0  13.0  14.0\n",
       "2006  44.0  52.0  14.0  15.0\n",
       "2008  58.0  67.0  12.0  13.0\n",
       "2010  46.0  51.0  15.0  15.0\n",
       "2012  57.0  71.0  13.0  15.0\n",
       "2014  45.0  55.0  15.0  15.0\n",
       "2016  62.0  60.0  13.0  14.0"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can I pass a tuple of strings to aggfunc, or must it be a list?\n",
    "# answer: It doesn't matter\n",
    "\n",
    "df.pivot_table(index='Year', columns='Sex', values='Age', aggfunc={'min','max'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "115d0391",
   "metadata": {},
   "source": [
    "# Joining \n",
    "\n",
    "If I have two data frames, and they share an index, then I can \"join\" them together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "3738e3ba",
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex   Age  Height  Weight            Team  Year\n",
       "0   M  24.0   180.0    80.0           China  1992\n",
       "1   M  23.0   170.0    60.0           China  2012\n",
       "2   M  24.0     NaN     NaN         Denmark  1920\n",
       "3   M  34.0     NaN     NaN  Denmark/Sweden  1900\n",
       "4   F  21.0   185.0    82.0     Netherlands  1988"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "ef33d42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I can load data about (a subset of) the OECD\n",
    "# \"club of mostly-rich countries\"\n",
    "\n",
    "# let's find information about the Olympics only for OECD countries\n",
    "# (note: only the subset in a file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "080ce0b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../data/oecd_locations.csv  ../data/oecd_tourism.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../data/oecd*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "f124d877",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "﻿AUS,Australia\r\n",
      "AUT,Austria\r\n",
      "BEL,Belgium\r\n",
      "CAN,Canada\r\n",
      "DNK,Denmark\r\n",
      "FIN,Finland\r\n",
      "FRA,France\r\n",
      "DEU,Germany\r\n",
      "HUN,Hungary\r\n",
      "ITA,Italy\r\n",
      "JPN,Japan\r\n",
      "KOR,Korea\r\n",
      "GBR,United Kingdom\r\n",
      "USA,United States\r\n",
      "BRA,Brazil\r\n",
      "ISR,Israel\r\n"
     ]
    }
   ],
   "source": [
    "!cat ../data/oecd_locations.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "6c95d091",
   "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>abbreviation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>AUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>DNK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>FIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>DEU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>HUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>ITA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>JPN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>KOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>ISR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               abbreviation\n",
       "name                       \n",
       "Australia               AUS\n",
       "Austria                 AUT\n",
       "Belgium                 BEL\n",
       "Canada                  CAN\n",
       "Denmark                 DNK\n",
       "Finland                 FIN\n",
       "France                  FRA\n",
       "Germany                 DEU\n",
       "Hungary                 HUN\n",
       "Italy                   ITA\n",
       "Japan                   JPN\n",
       "Korea                   KOR\n",
       "United Kingdom          GBR\n",
       "United States           USA\n",
       "Brazil                  BRA\n",
       "Israel                  ISR"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's turn that into a data frame\n",
    "\n",
    "locations_df = pd.read_csv('../data/oecd_locations.csv',\n",
    "                          header=None,\n",
    "                          names=['abbreviation', 'name'],\n",
    "                          index_col='name')\n",
    "locations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "43003533",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I want to create a new data frame containing Olympic data *plus* the OECD abbreviation for\n",
    "# each country.  I'm willing to throw away all data for countries *not* in locations_df.\n",
    "\n",
    "# in order for this to work, I have to make sure that the indexes\n",
    "# contain overlapping values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "dcbd0a04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure that the country names are in the index\n",
    "df = df.set_index('Team')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "19422868",
   "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>abbreviation</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>30.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>19.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>AUS</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2004.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>USA</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1972.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102836 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              abbreviation Sex   Age  Height  Weight    Year\n",
       "Australia              AUS   M  21.0   184.0    87.0  2008.0\n",
       "Australia              AUS   M  30.0   178.0    66.0  2008.0\n",
       "Australia              AUS   M  34.0   178.0    66.0  2012.0\n",
       "Australia              AUS   M  19.0   169.0    74.0  2000.0\n",
       "Australia              AUS   M  23.0   169.0    74.0  2004.0\n",
       "...                    ...  ..   ...     ...     ...     ...\n",
       "United States          USA   M  27.0   175.0    77.0  2004.0\n",
       "United States          USA   M  31.0   175.0    77.0  2008.0\n",
       "United States          USA   M  31.0   175.0    77.0  2008.0\n",
       "United States          USA   M  25.0   175.0    64.0  1964.0\n",
       "United States          USA   M  21.0   175.0    75.0  1972.0\n",
       "\n",
       "[102836 rows x 6 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now I have two data frames, each of which has country names in its index\n",
    "# now I can join them -- and wherever the index has the same value, we'll get\n",
    "# a new, wider data frame back with all values from both data frames\n",
    "\n",
    "# for each row in locations_df,\n",
    "# find the row in df with the same index\n",
    "# and join them together\n",
    "# (and yes, we might have more than one row in df that matches\n",
    "# a row in locations_df)\n",
    "\n",
    "locations_df.join(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f7bac9",
   "metadata": {},
   "source": [
    "# Using joins\n",
    "\n",
    "Imagine that you have two data frames:\n",
    "\n",
    "1. Contains sales information for each country in which your company operates\n",
    "2. Contains general reports about the economy for each country in which your company operates\n",
    "\n",
    "You might want to join these together into a single data frame, so that you can analyze them together.  Did your company do better in countries with rosy economies?\n",
    "\n",
    "The key thing here is that the two data frames *must* have indexes that overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "4861a363",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "﻿\"LOCATION\",\"INDICATOR\",\"SUBJECT\",\"MEASURE\",\"FREQUENCY\",\"TIME\",\"Value\",\"Flag Codes\"\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2008\",31159.8,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2009\",29980.7,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2010\",35165.5,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2011\",38710.1,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2012\",38003.7,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2013\",36965,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2014\",38047.9,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2015\",36226,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2016\",39082.3,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2017\",43959.1,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2018\",47259.8,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2019\",47930.9,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2008\",27620,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2009\",25629.6,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2010\",31916.5,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2011\",39381.5,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2012\",41632.8,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2013\",42428.3,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2014\",38774.5,\r\n",
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      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2018\",42478,\r\n",
      "\"AUS\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2019\",41452,\r\n",
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      "\"AUT\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2010\",18577.7,\r\n",
      "\"AUT\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2011\",19833,\r\n",
      "\"AUT\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2012\",21390.4,\r\n",
      "\"AUT\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2013\",22734.9,\r\n",
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      "\"CAN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2019\",43681.5,\r\n",
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      "\"CZE\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2014\",7619.6,\r\n",
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      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2008\",4749.628,\r\n",
      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2009\",4128.136,\r\n",
      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2010\",4352.2,\r\n",
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      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2013\",4697.9,\r\n",
      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2014\",5182,\r\n",
      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2015\",4818.4,\r\n",
      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2016\",4961.6,\r\n",
      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2017\",5509,\r\n",
      "\"CZE\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2018\",6069.1,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2008\",8751.64,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2009\",7501.394,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2010\",7778.2,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2011\",8916.4,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2012\",8763.5,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2013\",9957.3,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2014\",10416.7,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2015\",8936.3,\r\n",
      "\"DNK\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2016\",9857.4,\r\n",
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      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2008\",36157,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2009\",43701.7,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2010\",54880,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2011\",72585.1,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2012\",101977,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2013\",128576,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2014\",227343.733,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2015\",249830.544,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2016\",261129.14,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2017\",254788.887,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2018\",276899.97,\r\n",
      "\"CHN\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2019\",250739.779,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2008\",1254.222,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2009\",1184.781,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2010\",1235.482,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2011\",1523.682,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2012\",2154.429,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2013\",2365.365,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2014\",2239.708,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2015\",1833.334,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2016\",2038.191,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2017\",2355.968,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2018\",2650.685,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2019\",2922.729,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2008\",1361.035,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2009\",1319.462,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2010\",1488.549,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2011\",1830.507,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2012\",3609.205,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2013\",3772.142,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2014\",3771.043,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2015\",3046.157,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2016\",2579.918,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2017\",2694.666,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2018\",2852.235,\r\n",
      "\"KAZ\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2019\",2955.29,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2008\",1113.474,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2009\",986.527,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2010\",949.958,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2011\",1149.423,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2012\",1079.057,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2013\",1221.487,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2014\",1352.74,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2015\",1321.638,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2016\",1460.953,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2017\",1706.146,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2018\",1941.074,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT_REC\",\"USD\",\"A\",\"2019\",2000.579,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2008\",1448.21,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2009\",1105.117,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2010\",1102.353,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2011\",1263.55,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2012\",1187.555,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2013\",1289.398,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2014\",1359.859,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2015\",1253.644,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2016\",1351.098,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2017\",1549.183,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2018\",1837.317,\r\n",
      "\"SRB\",\"TOUR_REC_EXP\",\"INT-EXP\",\"USD\",\"A\",\"2019\",1999.313,\r\n"
     ]
    }
   ],
   "source": [
    "!cat ../data/oecd_tourism.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b018a7",
   "metadata": {},
   "source": [
    "# Exercise: OECD tourism\n",
    "\n",
    "The file `oecd_tourism.csv` contains information for each country in the OECD, for many different years, showing how much they received in tourism dollars (`INT_REC`) and how much they spent in tourism dollars (`INT_EXP`).\n",
    "\n",
    "Join the contents of this file together with `oecd_locations` -- which maps from the three-letter country abbreviations to country names.\n",
    "\n",
    "I'd like to see a single data frame in which we have: (Long) country name, subject (int-rec or int-exp), year, and value.\n",
    "\n",
    "Finally, show a pivot table, per country name and subject value, the average amount they used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "09c33b29",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DNK</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FIN</td>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HUN</td>\n",
       "      <td>Hungary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KOR</td>\n",
       "      <td>Korea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ISR</td>\n",
       "      <td>Israel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0               1\n",
       "0   AUS       Australia\n",
       "1   AUT         Austria\n",
       "2   BEL         Belgium\n",
       "3   CAN          Canada\n",
       "4   DNK         Denmark\n",
       "5   FIN         Finland\n",
       "6   FRA          France\n",
       "7   DEU         Germany\n",
       "8   HUN         Hungary\n",
       "9   ITA           Italy\n",
       "10  JPN           Japan\n",
       "11  KOR           Korea\n",
       "12  GBR  United Kingdom\n",
       "13  USA   United States\n",
       "14  BRA          Brazil\n",
       "15  ISR          Israel"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first, let's set up locations_df\n",
    "\n",
    "locations_df = pd.read_csv('../data/oecd_locations.csv', header=None)\n",
    "locations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "0a23905d",
   "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>abbrevation</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DNK</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FIN</td>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HUN</td>\n",
       "      <td>Hungary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KOR</td>\n",
       "      <td>Korea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ISR</td>\n",
       "      <td>Israel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   abbrevation            name\n",
       "0          AUS       Australia\n",
       "1          AUT         Austria\n",
       "2          BEL         Belgium\n",
       "3          CAN          Canada\n",
       "4          DNK         Denmark\n",
       "5          FIN         Finland\n",
       "6          FRA          France\n",
       "7          DEU         Germany\n",
       "8          HUN         Hungary\n",
       "9          ITA           Italy\n",
       "10         JPN           Japan\n",
       "11         KOR           Korea\n",
       "12         GBR  United Kingdom\n",
       "13         USA   United States\n",
       "14         BRA          Brazil\n",
       "15         ISR          Israel"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# option 1 for giving our columns names: assign after-the-fact\n",
    "\n",
    "locations_df.columns = ['abbrevation', 'name']\n",
    "locations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "ec7863f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abbreviation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUT</th>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEL</th>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAN</th>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNK</th>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIN</th>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRA</th>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEU</th>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HUN</th>\n",
       "      <td>Hungary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITA</th>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPN</th>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KOR</th>\n",
       "      <td>Korea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR</th>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRA</th>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISR</th>\n",
       "      <td>Israel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name\n",
       "abbreviation                \n",
       "AUS                Australia\n",
       "AUT                  Austria\n",
       "BEL                  Belgium\n",
       "CAN                   Canada\n",
       "DNK                  Denmark\n",
       "FIN                  Finland\n",
       "FRA                   France\n",
       "DEU                  Germany\n",
       "HUN                  Hungary\n",
       "ITA                    Italy\n",
       "JPN                    Japan\n",
       "KOR                    Korea\n",
       "GBR           United Kingdom\n",
       "USA            United States\n",
       "BRA                   Brazil\n",
       "ISR                   Israel"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# option 2: use the \"names\" parameter when we load the CSV\n",
    "locations_df = pd.read_csv('../data/oecd_locations.csv', \n",
    "                           header=None,\n",
    "                          names=['abbreviation', 'name'],\n",
    "                          index_col='abbreviation')\n",
    "locations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "62b292fb",
   "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>SUBJECT</th>\n",
       "      <th>TIME</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOCATION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2008</td>\n",
       "      <td>31159.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2009</td>\n",
       "      <td>29980.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2010</td>\n",
       "      <td>35165.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2011</td>\n",
       "      <td>38710.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2012</td>\n",
       "      <td>38003.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRB</th>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2015</td>\n",
       "      <td>1253.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRB</th>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2016</td>\n",
       "      <td>1351.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRB</th>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2017</td>\n",
       "      <td>1549.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRB</th>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2018</td>\n",
       "      <td>1837.317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRB</th>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2019</td>\n",
       "      <td>1999.313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1234 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SUBJECT  TIME      Value\n",
       "LOCATION                          \n",
       "AUS       INT_REC  2008  31159.800\n",
       "AUS       INT_REC  2009  29980.700\n",
       "AUS       INT_REC  2010  35165.500\n",
       "AUS       INT_REC  2011  38710.100\n",
       "AUS       INT_REC  2012  38003.700\n",
       "...           ...   ...        ...\n",
       "SRB       INT-EXP  2015   1253.644\n",
       "SRB       INT-EXP  2016   1351.098\n",
       "SRB       INT-EXP  2017   1549.183\n",
       "SRB       INT-EXP  2018   1837.317\n",
       "SRB       INT-EXP  2019   1999.313\n",
       "\n",
       "[1234 rows x 3 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tourism_df = pd.read_csv('../data/oecd_tourism.csv',\n",
    "                        usecols=['LOCATION', 'SUBJECT', 'TIME', 'Value'],\n",
    "                        index_col='LOCATION')\n",
    "tourism_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "4b8ecbce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>SUBJECT</th>\n",
       "      <th>TIME</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>Australia</td>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2008</td>\n",
       "      <td>31159.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>Australia</td>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2009</td>\n",
       "      <td>29980.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>Australia</td>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2010</td>\n",
       "      <td>35165.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>Australia</td>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2011</td>\n",
       "      <td>38710.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUS</th>\n",
       "      <td>Australia</td>\n",
       "      <td>INT_REC</td>\n",
       "      <td>2012</td>\n",
       "      <td>38003.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>United States</td>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2015</td>\n",
       "      <td>144667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>United States</td>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2016</td>\n",
       "      <td>147639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>United States</td>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2017</td>\n",
       "      <td>158331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>United States</td>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2018</td>\n",
       "      <td>172548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>United States</td>\n",
       "      <td>INT-EXP</td>\n",
       "      <td>2019</td>\n",
       "      <td>182365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name  SUBJECT  TIME     Value\n",
       "AUS      Australia  INT_REC  2008   31159.8\n",
       "AUS      Australia  INT_REC  2009   29980.7\n",
       "AUS      Australia  INT_REC  2010   35165.5\n",
       "AUS      Australia  INT_REC  2011   38710.1\n",
       "AUS      Australia  INT_REC  2012   38003.7\n",
       "..             ...      ...   ...       ...\n",
       "USA  United States  INT-EXP  2015  144667.0\n",
       "USA  United States  INT-EXP  2016  147639.0\n",
       "USA  United States  INT-EXP  2017  158331.0\n",
       "USA  United States  INT-EXP  2018  172548.0\n",
       "USA  United States  INT-EXP  2019  182365.0\n",
       "\n",
       "[364 rows x 4 columns]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = locations_df.join(tourism_df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "8e96ffb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>SUBJECT</th>\n",
       "      <th>INT-EXP</th>\n",
       "      <th>INT_REC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>36727.966667</td>\n",
       "      <td>38540.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>11934.563636</td>\n",
       "      <td>21413.209091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>20859.883455</td>\n",
       "      <td>12190.591636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>21564.351833</td>\n",
       "      <td>6321.476083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>40984.633333</td>\n",
       "      <td>24202.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>11326.169636</td>\n",
       "      <td>9398.957636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>5877.080909</td>\n",
       "      <td>4700.236273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>51394.272273</td>\n",
       "      <td>65063.335727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>96615.075545</td>\n",
       "      <td>53408.570636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>2918.390182</td>\n",
       "      <td>7299.353000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>6726.524833</td>\n",
       "      <td>6542.383250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>34148.908455</td>\n",
       "      <td>44930.211545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>32197.925000</td>\n",
       "      <td>25015.858333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>25573.509091</td>\n",
       "      <td>17780.754545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>75262.227273</td>\n",
       "      <td>51752.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>142080.666667</td>\n",
       "      <td>201613.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SUBJECT               INT-EXP        INT_REC\n",
       "name                                        \n",
       "Australia        36727.966667   38540.900000\n",
       "Austria          11934.563636   21413.209091\n",
       "Belgium          20859.883455   12190.591636\n",
       "Brazil           21564.351833    6321.476083\n",
       "Canada           40984.633333   24202.591667\n",
       "Denmark          11326.169636    9398.957636\n",
       "Finland           5877.080909    4700.236273\n",
       "France           51394.272273   65063.335727\n",
       "Germany          96615.075545   53408.570636\n",
       "Hungary           2918.390182    7299.353000\n",
       "Israel            6726.524833    6542.383250\n",
       "Italy            34148.908455   44930.211545\n",
       "Japan            32197.925000   25015.858333\n",
       "Korea            25573.509091   17780.754545\n",
       "United Kingdom   75262.227273   51752.090909\n",
       "United States   142080.666667  201613.500000"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table\n",
    "\n",
    "# index: name\n",
    "# columns: subject value\n",
    "# aggregation method: mean\n",
    "# value: Value\n",
    "\n",
    "df.pivot_table(index='name', columns='SUBJECT', values='Value', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "0370b80c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/olympic_athlete_events.csv',\n",
    "                usecols=['Sex', 'Age', 'Height', 'Weight', 'Team', 'Year'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "d9a17b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Team</th>\n",
       "      <th>30. Februar</th>\n",
       "      <th>Acipactli</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Akatonbo</th>\n",
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       "      <th>Algeria</th>\n",
       "      <th>...</th>\n",
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       "      <th>Yugoslavia-2</th>\n",
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       "  <tbody>\n",
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       "      <th>1896</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.428571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>171.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>172.950000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178.352941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.687500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.5</td>\n",
       "      <td>176.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.032787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>NaN</td>\n",
       "      <td>174.666667</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>182.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.724138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.733333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.161290</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169.777778</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.965909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.260684</td>\n",
       "      <td>179.5</td>\n",
       "      <td>175.00</td>\n",
       "      <td>168.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.254545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.363636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.450000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.543590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.214286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.942308</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.809524</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.256000</td>\n",
       "      <td>186.2</td>\n",
       "      <td>183.25</td>\n",
       "      <td>173.714286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.950000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.046083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.781250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.489362</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.428571</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>181.375000</td>\n",
       "      <td>183.0</td>\n",
       "      <td>183.00</td>\n",
       "      <td>169.714286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.142857</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.705882</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.313725</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178.960000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.285714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.623188</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.785714</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.982143</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168.714286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.928571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.263158</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.444444</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>176.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.027027</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.838710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 699 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Team  30. Februar   Acipactli  Afghanistan  Akatonbo  Alain IV     Albania  \\\n",
       "Year                                                                         \n",
       "1896          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1900          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1904          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1906          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1908          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1912          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1920          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1924          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1928          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1932          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1936          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1948          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1952        171.5         NaN          NaN       NaN       NaN         NaN   \n",
       "1956          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1960          NaN         NaN   170.687500       NaN       NaN         NaN   \n",
       "1964          NaN  174.666667   161.000000     182.0     176.0         NaN   \n",
       "1968          NaN         NaN   170.200000       NaN       NaN         NaN   \n",
       "1972          NaN         NaN   168.333333       NaN       NaN         NaN   \n",
       "1976          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1980          NaN         NaN   168.363636       NaN       NaN         NaN   \n",
       "1984          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1988          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1992          NaN         NaN          NaN       NaN       NaN  163.000000   \n",
       "1994          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "1996          NaN         NaN          NaN       NaN       NaN  169.000000   \n",
       "1998          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "2000          NaN         NaN          NaN       NaN       NaN  167.800000   \n",
       "2002          NaN         NaN          NaN       NaN       NaN         NaN   \n",
       "2004          NaN         NaN   170.750000       NaN       NaN  172.285714   \n",
       "2006          NaN         NaN          NaN       NaN       NaN  180.000000   \n",
       "2008          NaN         NaN   179.250000       NaN       NaN  173.416667   \n",
       "2010          NaN         NaN          NaN       NaN       NaN  180.000000   \n",
       "2012          NaN         NaN   171.666667       NaN       NaN  176.800000   \n",
       "2014          NaN         NaN          NaN       NaN       NaN  163.000000   \n",
       "2016          NaN         NaN   173.666667       NaN       NaN  176.166667   \n",
       "\n",
       "Team  Aldebaran  Aldebaran II      Aletta     Algeria  ...  Ylliam II  \\\n",
       "Year                                                   ...              \n",
       "1896        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1900        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1904        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1906        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1908        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1912        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1920        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1924        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1928        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1932        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1936        NaN           NaN         NaN         NaN  ...      179.0   \n",
       "1948        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1952        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1956        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1960        NaN         177.5  176.333333         NaN  ...        NaN   \n",
       "1964      174.5           NaN         NaN  175.000000  ...        NaN   \n",
       "1968        NaN           NaN         NaN  169.777778  ...        NaN   \n",
       "1972        NaN           NaN         NaN  173.166667  ...        NaN   \n",
       "1976        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1980        NaN           NaN         NaN  175.450000  ...        NaN   \n",
       "1984        NaN           NaN         NaN  176.809524  ...        NaN   \n",
       "1988        NaN           NaN         NaN  176.166667  ...        NaN   \n",
       "1992        NaN           NaN         NaN  170.428571  ...        NaN   \n",
       "1994        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "1996        NaN           NaN         NaN  176.000000  ...        NaN   \n",
       "1998        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "2000        NaN           NaN         NaN  174.313725  ...        NaN   \n",
       "2002        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "2004        NaN           NaN         NaN  173.623188  ...        NaN   \n",
       "2006        NaN           NaN         NaN  171.333333  ...        NaN   \n",
       "2008        NaN           NaN         NaN  176.982143  ...        NaN   \n",
       "2010        NaN           NaN         NaN  170.000000  ...        NaN   \n",
       "2012        NaN           NaN         NaN  174.263158  ...        NaN   \n",
       "2014        NaN           NaN         NaN         NaN  ...        NaN   \n",
       "2016        NaN           NaN         NaN  174.027027  ...        NaN   \n",
       "\n",
       "Team  Ylliam VII  Ylliam VIII  Yugoslavia  Yugoslavia-1  Yugoslavia-2  \\\n",
       "Year                                                                    \n",
       "1896         NaN          NaN         NaN           NaN           NaN   \n",
       "1900         NaN          NaN         NaN           NaN           NaN   \n",
       "1904         NaN          NaN         NaN           NaN           NaN   \n",
       "1906         NaN          NaN         NaN           NaN           NaN   \n",
       "1908         NaN          NaN         NaN           NaN           NaN   \n",
       "1912         NaN          NaN         NaN           NaN           NaN   \n",
       "1920         NaN          NaN         NaN           NaN           NaN   \n",
       "1924         NaN          NaN  190.000000           NaN           NaN   \n",
       "1928         NaN          NaN  173.428571           NaN           NaN   \n",
       "1932         NaN          NaN  192.000000           NaN           NaN   \n",
       "1936         NaN          NaN  179.900000           NaN           NaN   \n",
       "1948  179.333333          NaN  176.666667           NaN           NaN   \n",
       "1952         NaN        180.0  172.950000           NaN           NaN   \n",
       "1956         NaN          NaN  178.352941           NaN           NaN   \n",
       "1960         NaN          NaN  175.032787           NaN           NaN   \n",
       "1964         NaN          NaN  176.724138           NaN           NaN   \n",
       "1968         NaN          NaN  174.965909           NaN           NaN   \n",
       "1972         NaN          NaN  175.260684         179.5        175.00   \n",
       "1976         NaN          NaN  184.254545           NaN           NaN   \n",
       "1980         NaN          NaN  182.543590           NaN           NaN   \n",
       "1984         NaN          NaN  180.256000         186.2        183.25   \n",
       "1988         NaN          NaN  182.046083           NaN           NaN   \n",
       "1992         NaN          NaN  181.375000         183.0        183.00   \n",
       "1994         NaN          NaN         NaN           NaN           NaN   \n",
       "1996         NaN          NaN         NaN           NaN           NaN   \n",
       "1998         NaN          NaN         NaN           NaN           NaN   \n",
       "2000         NaN          NaN         NaN           NaN           NaN   \n",
       "2002         NaN          NaN         NaN           NaN           NaN   \n",
       "2004         NaN          NaN         NaN           NaN           NaN   \n",
       "2006         NaN          NaN         NaN           NaN           NaN   \n",
       "2008         NaN          NaN         NaN           NaN           NaN   \n",
       "2010         NaN          NaN         NaN           NaN           NaN   \n",
       "2012         NaN          NaN         NaN           NaN           NaN   \n",
       "2014         NaN          NaN         NaN           NaN           NaN   \n",
       "2016         NaN          NaN         NaN           NaN           NaN   \n",
       "\n",
       "Team      Zambia  Zefyros    Zimbabwe   rn-2  \n",
       "Year                                          \n",
       "1896         NaN      NaN         NaN    NaN  \n",
       "1900         NaN      NaN         NaN    NaN  \n",
       "1904         NaN      NaN         NaN    NaN  \n",
       "1906         NaN      NaN         NaN    NaN  \n",
       "1908         NaN      NaN         NaN    NaN  \n",
       "1912         NaN      NaN         NaN  176.0  \n",
       "1920         NaN      NaN         NaN    NaN  \n",
       "1924         NaN      NaN         NaN    NaN  \n",
       "1928         NaN      NaN         NaN    NaN  \n",
       "1932         NaN      NaN         NaN    NaN  \n",
       "1936         NaN      NaN         NaN    NaN  \n",
       "1948         NaN      NaN         NaN    NaN  \n",
       "1952         NaN      NaN         NaN    NaN  \n",
       "1956         NaN      NaN         NaN    NaN  \n",
       "1960         NaN    180.0  155.000000    NaN  \n",
       "1964  175.733333      NaN  174.161290    NaN  \n",
       "1968  175.250000      NaN         NaN    NaN  \n",
       "1972  168.500000      NaN         NaN    NaN  \n",
       "1976         NaN      NaN         NaN    NaN  \n",
       "1980  174.214286      NaN  171.942308    NaN  \n",
       "1984  173.714286      NaN  173.950000    NaN  \n",
       "1988  172.781250      NaN  176.489362    NaN  \n",
       "1992  169.714286      NaN  171.142857    NaN  \n",
       "1994         NaN      NaN         NaN    NaN  \n",
       "1996  170.166667      NaN  176.705882    NaN  \n",
       "1998         NaN      NaN         NaN    NaN  \n",
       "2000  173.166667      NaN  178.960000    NaN  \n",
       "2002         NaN      NaN         NaN    NaN  \n",
       "2004  173.666667      NaN  177.785714    NaN  \n",
       "2006         NaN      NaN         NaN    NaN  \n",
       "2008  168.714286      NaN  171.928571    NaN  \n",
       "2010         NaN      NaN         NaN    NaN  \n",
       "2012  176.166667      NaN  174.444444    NaN  \n",
       "2014         NaN      NaN  182.000000    NaN  \n",
       "2016  175.166667      NaN  167.838710    NaN  \n",
       "\n",
       "[35 rows x 699 columns]"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='Year', columns='Team', values='Height', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "445cb94f",
   "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>Team</th>\n",
       "      <th>30. Februar</th>\n",
       "      <th>Acipactli</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Akatonbo</th>\n",
       "      <th>Alain IV</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Aldebaran</th>\n",
       "      <th>Aldebaran II</th>\n",
       "      <th>Aletta</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>...</th>\n",
       "      <th>Ylliam II</th>\n",
       "      <th>Ylliam VII</th>\n",
       "      <th>Ylliam VIII</th>\n",
       "      <th>Yugoslavia</th>\n",
       "      <th>Yugoslavia-1</th>\n",
       "      <th>Yugoslavia-2</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zefyros</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>rn-2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 699 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [30. Februar, Acipactli, Afghanistan, Akatonbo, Alain IV, Albania, Aldebaran, Aldebaran II, Aletta, Algeria, Ali-Baba II, Ali-Baba IV, Ali-Baba IX, Ali-Baba VI, Almaz, Aloha II, Amateur Athletic Association, American Samoa, Andorra, Andromeda, Angelita, Angola, Antigua and Barbuda, Apache, Aphrodite, Ardilla, Aretusa, Argentina, Argentina-1, Argentina-2, Argeste, Argo II, Argonaut Rowing Club, Armenia, Aruba, Astrid III, Atlanta, Australasia, Australia, Australia-1, Australia-2, Australia-3, Austria, Austria-1, Austria-2, Azerbaijan, Babe, Baccara, Bahamas, Bahrain, Bajazzo, Balaton, Ballerina IV, Bangladesh, Barbados, Barco Deloro, Barrenjoey, Beaver, Belarus, Belarus-1, Belarus-2, Belgium, Belgium-1, Belgium-2, Belize, Bella, Bellatrix IX, Bellatrix XIII, Bem II, Benin, Bermuda, Bermudes, Bermudian, Bhutan, Bim, Bingo, Bluebottle, Bohemia, Bohemia-1, Bolivia, Bonaparte, Boreas, Bosnia and Herzegovina, Boston Archers, Botswana, Brazil, Brazil-1, Brazil-2, Bremen, Brentina, Briar, Brigantia, British Virgin Islands, Bronia, Brunei, Bu III, Bulgaria, Bulgaria-1, Bulgaria-2, Buraddoo, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 699 columns]"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we have a \"dropna\" method for series -- returning a new series, without the NaN values\n",
    "\n",
    "# a similar \"dropna\" method exists for data frames -- returning a new data frame, without any *row*\n",
    "#  containing a NaN value\n",
    "\n",
    "df.pivot_table(index='Year', columns='Team', values='Height', aggfunc='mean').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "d811d4e8",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F</td>\n",
       "      <td>25.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1992</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271111</th>\n",
       "      <td>M</td>\n",
       "      <td>29.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Poland-1</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271112</th>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271113</th>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271114</th>\n",
       "      <td>M</td>\n",
       "      <td>30.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271115</th>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206165 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sex   Age  Height  Weight         Team  Year\n",
       "0        M  24.0   180.0    80.0        China  1992\n",
       "1        M  23.0   170.0    60.0        China  2012\n",
       "4        F  21.0   185.0    82.0  Netherlands  1988\n",
       "5        F  21.0   185.0    82.0  Netherlands  1988\n",
       "6        F  25.0   185.0    82.0  Netherlands  1992\n",
       "...     ..   ...     ...     ...          ...   ...\n",
       "271111   M  29.0   179.0    89.0     Poland-1  1976\n",
       "271112   M  27.0   176.0    59.0       Poland  2014\n",
       "271113   M  27.0   176.0    59.0       Poland  2014\n",
       "271114   M  30.0   185.0    96.0       Poland  1998\n",
       "271115   M  34.0   185.0    96.0       Poland  2002\n",
       "\n",
       "[206165 rows x 6 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "e56ade17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(271116, 6)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "697597f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64951"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "271116 - 206165"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "e10cd193",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# I only want teams that have had at least 1,000 athletes in the games\n",
    "\n",
    "# df.groupby('Team')['Year'].count() > 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "f1de3f12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Argentina', 'Australia', 'Austria', 'Belarus', 'Belgium', 'Brazil',\n",
       "       'Bulgaria', 'Canada', 'China', 'Chinese Taipei', 'Colombia', 'Cuba',\n",
       "       'Czech Republic', 'Czechoslovakia', 'Denmark', 'East Germany', 'Egypt',\n",
       "       'Finland', 'France', 'Germany', 'Great Britain', 'Greece', 'Hungary',\n",
       "       'India', 'Ireland', 'Italy', 'Japan', 'Kazakhstan', 'Mexico',\n",
       "       'Netherlands', 'New Zealand', 'Norway', 'Poland', 'Portugal', 'Romania',\n",
       "       'Russia', 'Slovakia', 'Slovenia', 'South Africa', 'South Korea',\n",
       "       'Soviet Union', 'Spain', 'Sweden', 'Switzerland', 'Turkey', 'Ukraine',\n",
       "       'United States', 'West Germany', 'Yugoslavia'],\n",
       "      dtype='object', name='Team')"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Team')['Year'].count()[df.groupby('Team')['Year'].count() > 1000].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "5d665d5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>1992</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271102</th>\n",
       "      <td>F</td>\n",
       "      <td>19.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271103</th>\n",
       "      <td>F</td>\n",
       "      <td>23.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271107</th>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271108</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271109</th>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43156 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sex   Age  Height  Weight           Team  Year\n",
       "0        M  24.0   180.0    80.0          China  1992\n",
       "1        M  23.0   170.0    60.0          China  2012\n",
       "2        M  24.0     NaN     NaN        Denmark  1920\n",
       "10       M  31.0   188.0    75.0  United States  1992\n",
       "11       M  31.0   188.0    75.0  United States  1992\n",
       "...     ..   ...     ...     ...            ...   ...\n",
       "271102   F  19.0   171.0    64.0         Russia  2000\n",
       "271103   F  23.0   171.0    64.0         Russia  2004\n",
       "271107   M  21.0   175.0    75.0  United States  1972\n",
       "271108   M  24.0   183.0    72.0         Russia  2000\n",
       "271109   M  28.0   183.0    72.0         Russia  2004\n",
       "\n",
       "[43156 rows x 6 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Team'].isin(['United States', 'China', 'Russia', 'United Kingdom', 'France', 'Denmark'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "854c71ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Team</th>\n",
       "      <th>China</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>France</th>\n",
       "      <th>Russia</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>169.233333</td>\n",
       "      <td>183.111111</td>\n",
       "      <td>173.013072</td>\n",
       "      <td>174.342857</td>\n",
       "      <td>175.442857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>170.401442</td>\n",
       "      <td>179.628205</td>\n",
       "      <td>174.487640</td>\n",
       "      <td>176.404896</td>\n",
       "      <td>177.481333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>168.913580</td>\n",
       "      <td>175.750000</td>\n",
       "      <td>174.827586</td>\n",
       "      <td>175.248588</td>\n",
       "      <td>173.467181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>170.956633</td>\n",
       "      <td>180.035714</td>\n",
       "      <td>176.231760</td>\n",
       "      <td>177.142140</td>\n",
       "      <td>177.325269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>169.010000</td>\n",
       "      <td>173.181818</td>\n",
       "      <td>175.774390</td>\n",
       "      <td>174.434579</td>\n",
       "      <td>173.768421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>172.746939</td>\n",
       "      <td>178.805825</td>\n",
       "      <td>176.114790</td>\n",
       "      <td>176.194492</td>\n",
       "      <td>176.936080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>170.307143</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>174.038710</td>\n",
       "      <td>174.490196</td>\n",
       "      <td>173.867797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>176.022760</td>\n",
       "      <td>182.970297</td>\n",
       "      <td>176.963636</td>\n",
       "      <td>176.940678</td>\n",
       "      <td>177.471774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>169.561983</td>\n",
       "      <td>175.739130</td>\n",
       "      <td>174.765625</td>\n",
       "      <td>174.584362</td>\n",
       "      <td>174.488449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>175.451404</td>\n",
       "      <td>180.462069</td>\n",
       "      <td>177.335714</td>\n",
       "      <td>177.700555</td>\n",
       "      <td>178.091181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>169.809524</td>\n",
       "      <td>183.642857</td>\n",
       "      <td>173.772487</td>\n",
       "      <td>174.451104</td>\n",
       "      <td>174.714715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>174.985507</td>\n",
       "      <td>180.944056</td>\n",
       "      <td>177.748016</td>\n",
       "      <td>176.149746</td>\n",
       "      <td>177.789398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Team       China     Denmark      France      Russia  United States\n",
       "Year                                                               \n",
       "1994  169.233333  183.111111  173.013072  174.342857     175.442857\n",
       "1996  170.401442  179.628205  174.487640  176.404896     177.481333\n",
       "1998  168.913580  175.750000  174.827586  175.248588     173.467181\n",
       "2000  170.956633  180.035714  176.231760  177.142140     177.325269\n",
       "2002  169.010000  173.181818  175.774390  174.434579     173.768421\n",
       "2004  172.746939  178.805825  176.114790  176.194492     176.936080\n",
       "2006  170.307143  170.000000  174.038710  174.490196     173.867797\n",
       "2008  176.022760  182.970297  176.963636  176.940678     177.471774\n",
       "2010  169.561983  175.739130  174.765625  174.584362     174.488449\n",
       "2012  175.451404  180.462069  177.335714  177.700555     178.091181\n",
       "2014  169.809524  183.642857  173.772487  174.451104     174.714715\n",
       "2016  174.985507  180.944056  177.748016  176.149746     177.789398"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.loc[df['Team'].isin(['United States', 'China', 'Russia', 'United Kingdom', 'France', 'Denmark'])]\n",
    "new_df.pivot_table(index='Year', columns='Team', values='Height', aggfunc='mean').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "1b01ede5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get only the rows from df for countries that have had > 1,000 athletes in the games\n",
    "\n",
    "new_df = df.loc[df['Team'].isin(df.groupby('Team')['Year'].count()[df.groupby('Team')['Year'].count() > 1000].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "c9a4694c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>Year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271110</th>\n",
       "      <td>F</td>\n",
       "      <td>33.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271112</th>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271113</th>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271114</th>\n",
       "      <td>M</td>\n",
       "      <td>30.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271115</th>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>224007 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sex   Age  Height  Weight         Team  Year\n",
       "0        M  24.0   180.0    80.0        China  1992\n",
       "1        M  23.0   170.0    60.0        China  2012\n",
       "2        M  24.0     NaN     NaN      Denmark  1920\n",
       "4        F  21.0   185.0    82.0  Netherlands  1988\n",
       "5        F  21.0   185.0    82.0  Netherlands  1988\n",
       "...     ..   ...     ...     ...          ...   ...\n",
       "271110   F  33.0   171.0    69.0      Belarus  2016\n",
       "271112   M  27.0   176.0    59.0       Poland  2014\n",
       "271113   M  27.0   176.0    59.0       Poland  2014\n",
       "271114   M  30.0   185.0    96.0       Poland  1998\n",
       "271115   M  34.0   185.0    96.0       Poland  2002\n",
       "\n",
       "[224007 rows x 6 columns]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "869567e8",
   "metadata": {},
   "source": [
    "# Next week:\n",
    "\n",
    "1. Text data (strings) \n",
    "2. Date/time data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eab42c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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