01. Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> \n",
    "        <h1>Python Data Visualization for Everyone</h1>\n",
    "        <h1>Pandas</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "        @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import watermark\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by printing out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.7\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 24.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: fa8b92758a62ab3cc6cc750b820ec3ea509705e6\n",
      "\n",
      "watermark : 2.4.3\n",
      "numpy     : 2.1.3\n",
      "matplotlib: 3.8.4\n",
      "pandas    : 2.2.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the default size of all figures to 11x8 inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (11, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrames and Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series and DataFrames can be thought of as dictionaries associating keys to lists of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {  \"id\": [23,    42,      12,     86], \n",
    "        \"Name\": [\"Bob\", \"Karen\", \"Kate\", \"Bill\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': [23, 42, 12, 86], 'Name': ['Bob', 'Karen', 'Kate', 'Bill']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Series corresponds to just a sigle list of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = pd.Series(data[\"id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1    42\n",
       "2    12\n",
       "3    86\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While a DataFrame can have multiple series/columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    " df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>id</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   Name\n",
       "0  23    Bob\n",
       "1  42  Karen\n",
       "2  12   Kate\n",
       "3  86   Bill"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way of looking at it, is that DataFrames are essentially groups of individual Series. Each Series can have it's own datatype **dtype**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id       int64\n",
       "Name    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get general information about how the DataFrame is being stored by calling __info()__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      4 non-null      int64 \n",
      " 1   Name    4 non-null      object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 196.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subsetting a DataFrame by column name we retrieve the underlying Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both columns and index values have types and possibly names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'Name'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can query the shape and number of dimensions of the DataFrame easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And relabel both index and column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = [\"row\" + str(i) for i in range(4)]\n",
    "df.columns = ['ID', 'First Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>First Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row0</th>\n",
       "      <td>23</td>\n",
       "      <td>Bob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>42</td>\n",
       "      <td>Karen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>12</td>\n",
       "      <td>Kate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>86</td>\n",
       "      <td>Bill</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID First Name\n",
       "row0  23        Bob\n",
       "row1  42      Karen\n",
       "row2  12       Kate\n",
       "row3  86       Bill"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID               42\n",
       "First Name    Karen\n",
       "Name: row1, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['row1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID               42\n",
       "First Name    Karen\n",
       "Name: row1, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and exporting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read csv files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File can be zipped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "green = pd.read_csv('data/green_tripdata_2014-04.csv.gz', \n",
    "        parse_dates=['lpep_pickup_datetime', 'Lpep_dropoff_datetime'], \n",
    "                    nrows=2000, index_col='VendorID', \n",
    "        dtype={'RateCodeID':'str', 'Trip_type': 'str'}\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read only 2,000 rows as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 21)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And used the right dtypes for each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2000 entries, 2 to 2\n",
      "Data columns (total 21 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   lpep_pickup_datetime   2000 non-null   datetime64[ns]\n",
      " 1   Lpep_dropoff_datetime  2000 non-null   datetime64[ns]\n",
      " 2   Store_and_fwd_flag     2000 non-null   object        \n",
      " 3   RateCodeID             2000 non-null   object        \n",
      " 4   Pickup_longitude       2000 non-null   float64       \n",
      " 5   Pickup_latitude        2000 non-null   float64       \n",
      " 6   Dropoff_longitude      2000 non-null   float64       \n",
      " 7   Dropoff_latitude       2000 non-null   float64       \n",
      " 8   Passenger_count        2000 non-null   int64         \n",
      " 9   Trip_distance          2000 non-null   float64       \n",
      " 10  Fare_amount            2000 non-null   float64       \n",
      " 11  Extra                  2000 non-null   float64       \n",
      " 12  MTA_tax                2000 non-null   float64       \n",
      " 13  Tip_amount             2000 non-null   float64       \n",
      " 14  Tolls_amount           2000 non-null   float64       \n",
      " 15  Ehail_fee              0 non-null      float64       \n",
      " 16  Total_amount           2000 non-null   float64       \n",
      " 17  Payment_type           2000 non-null   int64         \n",
      " 18  Trip_type              1590 non-null   object        \n",
      " 19  Unnamed: 20            0 non-null      float64       \n",
      " 20                         0 non-null      float64       \n",
      "dtypes: datetime64[ns](2), float64(14), int64(2), object(3)\n",
      "memory usage: 343.8+ KB\n"
     ]
    }
   ],
   "source": [
    "green.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Web pages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to use the Wikipedia page with the current numbers of cases for CoVID-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All we have to do is to provide the url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = pd.read_html(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which retrieves all the tables in the page, in the order they appear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the first one is the infobox on the top right hand corner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COVID-19 pandemic</th>\n",
       "      <th>COVID-19 pandemic.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Confirmed deaths per 100,000 population,[1][no...</td>\n",
       "      <td>Confirmed deaths per 100,000 population,[1][no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disease</td>\n",
       "      <td>COVID-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Virus strain</td>\n",
       "      <td>SARS-CoV-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Source</td>\n",
       "      <td>Probably bats, possibly via pangolins[2][3][4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Location</td>\n",
       "      <td>Worldwide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>First outbreak</td>\n",
       "      <td>China[5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Index case</td>\n",
       "      <td>Wuhan, Hubei, China 30°37′11″N 114°15′28″E﻿ / ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Date</td>\n",
       "      <td>First case of COVID-19: 17 November 2019; 5 ye...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Confirmed cases</td>\n",
       "      <td>777,384,606[6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Deaths</td>\n",
       "      <td>7,088,744[6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vaccinations</td>\n",
       "      <td>5,645,247,500[6] (total vaccinated)5,197,972,5...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    COVID-19 pandemic  \\\n",
       "0   Confirmed deaths per 100,000 population,[1][no...   \n",
       "1                                             Disease   \n",
       "2                                        Virus strain   \n",
       "3                                              Source   \n",
       "4                                            Location   \n",
       "5                                      First outbreak   \n",
       "6                                          Index case   \n",
       "7                                                Date   \n",
       "8                                     Confirmed cases   \n",
       "9                                              Deaths   \n",
       "10                                       Vaccinations   \n",
       "\n",
       "                                  COVID-19 pandemic.1  \n",
       "0   Confirmed deaths per 100,000 population,[1][no...  \n",
       "1                                            COVID-19  \n",
       "2                                          SARS-CoV-2  \n",
       "3      Probably bats, possibly via pangolins[2][3][4]  \n",
       "4                                           Worldwide  \n",
       "5                                            China[5]  \n",
       "6   Wuhan, Hubei, China 30°37′11″N 114°15′28″E﻿ / ...  \n",
       "7   First case of COVID-19: 17 November 2019; 5 ye...  \n",
       "8                                      777,384,606[6]  \n",
       "9                                        7,088,744[6]  \n",
       "10  5,645,247,500[6] (total vaccinated)5,197,972,5...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the sixteenth one is the number of cases and deaths per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Region[30]</th>\n",
       "      <th>Total cases</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>Cases per million</th>\n",
       "      <th>Deaths per million</th>\n",
       "      <th>Current weekly cases</th>\n",
       "      <th>Current weekly deaths</th>\n",
       "      <th>Population millions</th>\n",
       "      <th>Vacci­nated %[31]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>European Union</td>\n",
       "      <td>179537758</td>\n",
       "      <td>1185108</td>\n",
       "      <td>401363</td>\n",
       "      <td>2649</td>\n",
       "      <td>886074</td>\n",
       "      <td>3985</td>\n",
       "      <td>447</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North America</td>\n",
       "      <td>103783777</td>\n",
       "      <td>1133607</td>\n",
       "      <td>281404</td>\n",
       "      <td>3074</td>\n",
       "      <td>476376</td>\n",
       "      <td>2975</td>\n",
       "      <td>369</td>\n",
       "      <td>76.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other Europe</td>\n",
       "      <td>57721948</td>\n",
       "      <td>498259</td>\n",
       "      <td>247054</td>\n",
       "      <td>2133</td>\n",
       "      <td>74354</td>\n",
       "      <td>248</td>\n",
       "      <td>234</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South America</td>\n",
       "      <td>65835789</td>\n",
       "      <td>1313061</td>\n",
       "      <td>153151</td>\n",
       "      <td>3055</td>\n",
       "      <td>378622</td>\n",
       "      <td>1252</td>\n",
       "      <td>430</td>\n",
       "      <td>81.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Russia and Central Asia</td>\n",
       "      <td>25646533</td>\n",
       "      <td>434988</td>\n",
       "      <td>108307</td>\n",
       "      <td>1837</td>\n",
       "      <td>49022</td>\n",
       "      <td>393</td>\n",
       "      <td>237</td>\n",
       "      <td>55.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Region[30]  Total cases  Total deaths  Cases per million  \\\n",
       "0           European Union    179537758       1185108             401363   \n",
       "1            North America    103783777       1133607             281404   \n",
       "2             Other Europe     57721948        498259             247054   \n",
       "3            South America     65835789       1313061             153151   \n",
       "4  Russia and Central Asia     25646533        434988             108307   \n",
       "\n",
       "   Deaths per million  Current weekly cases  Current weekly deaths  \\\n",
       "0                2649                886074                   3985   \n",
       "1                3074                476376                   2975   \n",
       "2                2133                 74354                    248   \n",
       "3                3055                378622                   1252   \n",
       "4                1837                 49022                    393   \n",
       "\n",
       "   Population millions  Vacci­nated %[31]  \n",
       "0                  447               75.1  \n",
       "1                  369               76.1  \n",
       "2                  234               61.2  \n",
       "3                  430               81.7  \n",
       "4                  237               55.9  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[15].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the formatting of the table, pandas interpreted the first two rows to be the headers. We can fix this by explicitly telling it to just use the first row for the column headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = pd.read_html(url, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Region[30]</th>\n",
       "      <th>Total cases</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>Cases per million</th>\n",
       "      <th>Deaths per million</th>\n",
       "      <th>Current weekly cases</th>\n",
       "      <th>Current weekly deaths</th>\n",
       "      <th>Population millions</th>\n",
       "      <th>Vacci­nated %[31]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>European Union</td>\n",
       "      <td>179537758</td>\n",
       "      <td>1185108</td>\n",
       "      <td>401363</td>\n",
       "      <td>2649</td>\n",
       "      <td>886074</td>\n",
       "      <td>3985</td>\n",
       "      <td>447</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North America</td>\n",
       "      <td>103783777</td>\n",
       "      <td>1133607</td>\n",
       "      <td>281404</td>\n",
       "      <td>3074</td>\n",
       "      <td>476376</td>\n",
       "      <td>2975</td>\n",
       "      <td>369</td>\n",
       "      <td>76.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other Europe</td>\n",
       "      <td>57721948</td>\n",
       "      <td>498259</td>\n",
       "      <td>247054</td>\n",
       "      <td>2133</td>\n",
       "      <td>74354</td>\n",
       "      <td>248</td>\n",
       "      <td>234</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South America</td>\n",
       "      <td>65835789</td>\n",
       "      <td>1313061</td>\n",
       "      <td>153151</td>\n",
       "      <td>3055</td>\n",
       "      <td>378622</td>\n",
       "      <td>1252</td>\n",
       "      <td>430</td>\n",
       "      <td>81.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Russia and Central Asia</td>\n",
       "      <td>25646533</td>\n",
       "      <td>434988</td>\n",
       "      <td>108307</td>\n",
       "      <td>1837</td>\n",
       "      <td>49022</td>\n",
       "      <td>393</td>\n",
       "      <td>237</td>\n",
       "      <td>55.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Central America</td>\n",
       "      <td>11338600</td>\n",
       "      <td>380660</td>\n",
       "      <td>63108</td>\n",
       "      <td>2119</td>\n",
       "      <td>60268</td>\n",
       "      <td>263</td>\n",
       "      <td>180</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Middle East</td>\n",
       "      <td>22549784</td>\n",
       "      <td>238106</td>\n",
       "      <td>86400</td>\n",
       "      <td>912</td>\n",
       "      <td>13457</td>\n",
       "      <td>65</td>\n",
       "      <td>261</td>\n",
       "      <td>51.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Oceania and islands in East Asia</td>\n",
       "      <td>60806544</td>\n",
       "      <td>318455</td>\n",
       "      <td>105317</td>\n",
       "      <td>552</td>\n",
       "      <td>1390401</td>\n",
       "      <td>2777</td>\n",
       "      <td>577</td>\n",
       "      <td>72.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Caribbean</td>\n",
       "      <td>2605473</td>\n",
       "      <td>26237</td>\n",
       "      <td>60179</td>\n",
       "      <td>606</td>\n",
       "      <td>737</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>South Asia</td>\n",
       "      <td>50347136</td>\n",
       "      <td>620218</td>\n",
       "      <td>27121</td>\n",
       "      <td>334</td>\n",
       "      <td>1911</td>\n",
       "      <td>24</td>\n",
       "      <td>1856</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>North Africa</td>\n",
       "      <td>3727346</td>\n",
       "      <td>83873</td>\n",
       "      <td>18382</td>\n",
       "      <td>414</td>\n",
       "      <td>668</td>\n",
       "      <td>8</td>\n",
       "      <td>203</td>\n",
       "      <td>41.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>East Asia</td>\n",
       "      <td>57867768</td>\n",
       "      <td>189910</td>\n",
       "      <td>32416</td>\n",
       "      <td>106</td>\n",
       "      <td>644079</td>\n",
       "      <td>818</td>\n",
       "      <td>1785</td>\n",
       "      <td>87.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>8636456</td>\n",
       "      <td>172907</td>\n",
       "      <td>7598</td>\n",
       "      <td>152</td>\n",
       "      <td>6960</td>\n",
       "      <td>27</td>\n",
       "      <td>1137</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Totals (or average)</td>\n",
       "      <td>650404912</td>\n",
       "      <td>6595389</td>\n",
       "      <td>83826</td>\n",
       "      <td>850</td>\n",
       "      <td>3982929</td>\n",
       "      <td>12860</td>\n",
       "      <td>7759</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Region[30]  Total cases  Total deaths  \\\n",
       "0                     European Union    179537758       1185108   \n",
       "1                      North America    103783777       1133607   \n",
       "2                       Other Europe     57721948        498259   \n",
       "3                      South America     65835789       1313061   \n",
       "4            Russia and Central Asia     25646533        434988   \n",
       "5                    Central America     11338600        380660   \n",
       "6                        Middle East     22549784        238106   \n",
       "7   Oceania and islands in East Asia     60806544        318455   \n",
       "8                          Caribbean      2605473         26237   \n",
       "9                         South Asia     50347136        620218   \n",
       "10                      North Africa      3727346         83873   \n",
       "11                         East Asia     57867768        189910   \n",
       "12                Sub-Saharan Africa      8636456        172907   \n",
       "13               Totals (or average)    650404912       6595389   \n",
       "\n",
       "    Cases per million  Deaths per million  Current weekly cases  \\\n",
       "0              401363                2649                886074   \n",
       "1              281404                3074                476376   \n",
       "2              247054                2133                 74354   \n",
       "3              153151                3055                378622   \n",
       "4              108307                1837                 49022   \n",
       "5               63108                2119                 60268   \n",
       "6               86400                 912                 13457   \n",
       "7              105317                 552               1390401   \n",
       "8               60179                 606                   737   \n",
       "9               27121                 334                  1911   \n",
       "10              18382                 414                   668   \n",
       "11              32416                 106                644079   \n",
       "12               7598                 152                  6960   \n",
       "13              83826                 850               3982929   \n",
       "\n",
       "    Current weekly deaths  Population millions  Vacci­nated %[31]  \n",
       "0                    3985                  447               75.1  \n",
       "1                    2975                  369               76.1  \n",
       "2                     248                  234               61.2  \n",
       "3                    1252                  430               81.7  \n",
       "4                     393                  237               55.9  \n",
       "5                     263                  180               69.0  \n",
       "6                      65                  261               51.9  \n",
       "7                    2777                  577               72.1  \n",
       "8                      25                   43               46.4  \n",
       "9                      24                 1856               69.4  \n",
       "10                      8                  203               41.3  \n",
       "11                    818                 1785               87.2  \n",
       "12                     27                 1137               27.6  \n",
       "13                  12860                 7759               67.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top/bottom N number of values are easy to access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
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       "      <th>RateCodeID</th>\n",
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       "      <th>Pickup_latitude</th>\n",
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       "      <th>Dropoff_latitude</th>\n",
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       "    <tr>\n",
       "      <th>VendorID</th>\n",
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       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 14:24:20</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>7.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 17:21:33</td>\n",
       "      <td>N</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.987663</td>\n",
       "      <td>40.780872</td>\n",
       "      <td>1</td>\n",
       "      <td>8.95</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 15:06:18</td>\n",
       "      <td>N</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 08:09:27</td>\n",
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       "      <td>5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 16:15:13</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 16:31:57</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-73.950539</td>\n",
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       "      <td>1</td>\n",
       "      <td>5.20</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 10:59:14</td>\n",
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       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 05:23:51</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-73.885551</td>\n",
       "      <td>40.655041</td>\n",
       "      <td>5</td>\n",
       "      <td>4.73</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 16:58:33</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-73.891830</td>\n",
       "      <td>40.826359</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 08:10:50</td>\n",
       "      <td>N</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         lpep_pickup_datetime Lpep_dropoff_datetime Store_and_fwd_flag  \\\n",
       "VendorID                                                                 \n",
       "2                  2014-04-01   2014-04-01 14:24:20                  N   \n",
       "2                  2014-04-01   2014-04-01 17:21:33                  N   \n",
       "2                  2014-04-01   2014-04-01 15:06:18                  N   \n",
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       "2                  2014-04-01   2014-04-01 16:58:33                  N   \n",
       "2                  2014-04-01   2014-04-01 08:10:50                  N   \n",
       "\n",
       "         RateCodeID  Pickup_longitude  Pickup_latitude  Dropoff_longitude  \\\n",
       "VendorID                                                                    \n",
       "2                 1               0.0              0.0           0.000000   \n",
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       "2                 1               0.0              0.0         -73.947670   \n",
       "2                 1               0.0              0.0           0.000000   \n",
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       "2                 1               0.0              0.0           0.000000   \n",
       "2                 1               0.0              0.0         -73.885551   \n",
       "2                 1               0.0              0.0         -73.891830   \n",
       "2                 1               0.0              0.0         -73.852058   \n",
       "\n",
       "          Dropoff_latitude  Passenger_count  Trip_distance  ...  Extra  \\\n",
       "VendorID                                                    ...          \n",
       "2                 0.000000                1           7.45  ...    0.0   \n",
       "2                40.780872                1           8.95  ...    1.0   \n",
       "2                40.831764                1           1.32  ...    0.0   \n",
       "2                40.808651                5           0.10  ...    0.0   \n",
       "2                 0.000000                1           7.09  ...    0.0   \n",
       "2                40.786633                1           5.20  ...    0.0   \n",
       "2                 0.000000                1           8.96  ...    0.0   \n",
       "2                40.655041                5           4.73  ...    0.5   \n",
       "2                40.826359                1           0.19  ...    0.0   \n",
       "2                40.840832                1           0.90  ...    0.0   \n",
       "\n",
       "          MTA_tax  Tip_amount  Tolls_amount  Ehail_fee  Total_amount  \\\n",
       "VendorID                                                               \n",
       "2             0.5         0.0           0.0        NaN          23.5   \n",
       "2             0.5         0.0           0.0        NaN          32.5   \n",
       "2             0.5         0.0           0.0        NaN           7.0   \n",
       "2             0.5         0.0           0.0        NaN           3.5   \n",
       "2             0.5         4.7           0.0        NaN          28.7   \n",
       "2             0.5         0.0           0.0        NaN          17.5   \n",
       "2             0.5         4.0           0.0        NaN          43.0   \n",
       "2             0.5         0.0           0.0        NaN          17.5   \n",
       "2             0.5         0.0           0.0        NaN           3.5   \n",
       "2             0.5         0.0           0.0        NaN           5.5   \n",
       "\n",
       "          Payment_type  Trip_type Unnamed: 20      \n",
       "VendorID                                           \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    1          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    1          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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   ],
   "source": [
    "green.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
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   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "<p>2 rows × 21 columns</p>\n",
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      "text/plain": [
       "         lpep_pickup_datetime Lpep_dropoff_datetime Store_and_fwd_flag  \\\n",
       "VendorID                                                                 \n",
       "2         2014-04-01 02:35:37   2014-04-01 02:49:16                  N   \n",
       "2         2014-04-01 02:36:00   2014-04-01 02:45:44                  N   \n",
       "\n",
       "         RateCodeID  Pickup_longitude  Pickup_latitude  Dropoff_longitude  \\\n",
       "VendorID                                                                    \n",
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       "\n",
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       "VendorID                                                    ...          \n",
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       "\n",
       "          MTA_tax  Tip_amount  Tolls_amount  Ehail_fee  Total_amount  \\\n",
       "VendorID                                                               \n",
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       "\n",
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       "VendorID                                           \n",
       "2                    1          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "\n",
       "[2 rows x 21 columns]"
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     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And individual rows, which can be indexed by Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 00:00:00</td>\n",
       "      <td>2014-04-01 17:21:33</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-73.987663</td>\n",
       "      <td>40.780872</td>\n",
       "      <td>1</td>\n",
       "      <td>8.95</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 00:00:00</td>\n",
       "      <td>2014-04-01 15:06:18</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-73.946922</td>\n",
       "      <td>40.831764</td>\n",
       "      <td>1</td>\n",
       "      <td>1.32</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 00:00:00</td>\n",
       "      <td>2014-04-01 08:09:27</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-73.947670</td>\n",
       "      <td>40.808651</td>\n",
       "      <td>5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 00:00:00</td>\n",
       "      <td>2014-04-01 16:15:13</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>7.09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 02:35:15</td>\n",
       "      <td>2014-04-01 02:40:47</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.927505</td>\n",
       "      <td>40.810944</td>\n",
       "      <td>-73.920647</td>\n",
       "      <td>40.830791</td>\n",
       "      <td>1</td>\n",
       "      <td>1.56</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 02:35:22</td>\n",
       "      <td>2014-04-01 02:47:14</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.963692</td>\n",
       "      <td>40.676941</td>\n",
       "      <td>-73.956619</td>\n",
       "      <td>40.637798</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 02:35:23</td>\n",
       "      <td>2014-04-01 02:43:53</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.925369</td>\n",
       "      <td>40.761814</td>\n",
       "      <td>-73.886261</td>\n",
       "      <td>40.749565</td>\n",
       "      <td>1</td>\n",
       "      <td>2.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 02:35:37</td>\n",
       "      <td>2014-04-01 02:49:16</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.937965</td>\n",
       "      <td>40.808022</td>\n",
       "      <td>-73.976608</td>\n",
       "      <td>40.748375</td>\n",
       "      <td>2</td>\n",
       "      <td>4.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01 02:36:00</td>\n",
       "      <td>2014-04-01 02:45:44</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.870270</td>\n",
       "      <td>40.749130</td>\n",
       "      <td>-73.942863</td>\n",
       "      <td>40.838142</td>\n",
       "      <td>1</td>\n",
       "      <td>2.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1590 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lpep_pickup_datetime Lpep_dropoff_datetime Store_and_fwd_flag  \\\n",
       "VendorID                                                                 \n",
       "2         2014-04-01 00:00:00   2014-04-01 14:24:20                  N   \n",
       "2         2014-04-01 00:00:00   2014-04-01 17:21:33                  N   \n",
       "2         2014-04-01 00:00:00   2014-04-01 15:06:18                  N   \n",
       "2         2014-04-01 00:00:00   2014-04-01 08:09:27                  N   \n",
       "2         2014-04-01 00:00:00   2014-04-01 16:15:13                  N   \n",
       "...                       ...                   ...                ...   \n",
       "2         2014-04-01 02:35:15   2014-04-01 02:40:47                  N   \n",
       "2         2014-04-01 02:35:22   2014-04-01 02:47:14                  N   \n",
       "2         2014-04-01 02:35:23   2014-04-01 02:43:53                  N   \n",
       "2         2014-04-01 02:35:37   2014-04-01 02:49:16                  N   \n",
       "2         2014-04-01 02:36:00   2014-04-01 02:45:44                  N   \n",
       "\n",
       "         RateCodeID  Pickup_longitude  Pickup_latitude  Dropoff_longitude  \\\n",
       "VendorID                                                                    \n",
       "2                 1          0.000000         0.000000           0.000000   \n",
       "2                 1          0.000000         0.000000         -73.987663   \n",
       "2                 1          0.000000         0.000000         -73.946922   \n",
       "2                 1          0.000000         0.000000         -73.947670   \n",
       "2                 1          0.000000         0.000000           0.000000   \n",
       "...             ...               ...              ...                ...   \n",
       "2                 1        -73.927505        40.810944         -73.920647   \n",
       "2                 1        -73.963692        40.676941         -73.956619   \n",
       "2                 1        -73.925369        40.761814         -73.886261   \n",
       "2                 1        -73.937965        40.808022         -73.976608   \n",
       "2                 1        -73.870270        40.749130         -73.942863   \n",
       "\n",
       "          Dropoff_latitude  Passenger_count  Trip_distance  ...  Extra  \\\n",
       "VendorID                                                    ...          \n",
       "2                 0.000000                1           7.45  ...    0.0   \n",
       "2                40.780872                1           8.95  ...    1.0   \n",
       "2                40.831764                1           1.32  ...    0.0   \n",
       "2                40.808651                5           0.10  ...    0.0   \n",
       "2                 0.000000                1           7.09  ...    0.0   \n",
       "...                    ...              ...            ...  ...    ...   \n",
       "2                40.830791                1           1.56  ...    0.5   \n",
       "2                40.637798                1           3.70  ...    0.5   \n",
       "2                40.749565                1           2.29  ...    0.5   \n",
       "2                40.748375                2           4.99  ...    0.5   \n",
       "2                40.838142                1           2.33  ...    0.5   \n",
       "\n",
       "          MTA_tax  Tip_amount  Tolls_amount  Ehail_fee  Total_amount  \\\n",
       "VendorID                                                               \n",
       "2             0.5         0.0           0.0        NaN          23.5   \n",
       "2             0.5         0.0           0.0        NaN          32.5   \n",
       "2             0.5         0.0           0.0        NaN           7.0   \n",
       "2             0.5         0.0           0.0        NaN           3.5   \n",
       "2             0.5         4.7           0.0        NaN          28.7   \n",
       "...           ...         ...           ...        ...           ...   \n",
       "2             0.5         0.0           0.0        NaN           8.0   \n",
       "2             0.5         3.5           0.0        NaN          18.0   \n",
       "2             0.5         0.0           0.0        NaN          11.0   \n",
       "2             0.5         2.5           0.0        NaN          19.0   \n",
       "2             0.5         0.0           0.0        NaN          11.0   \n",
       "\n",
       "          Payment_type  Trip_type Unnamed: 20      \n",
       "VendorID                                           \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    1          1         NaN NaN  \n",
       "...                ...        ...         ...  ..  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    1          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    1          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "\n",
       "[1590 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green.loc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lpep_pickup_datetime     2014-04-01 01:27:36\n",
       "Lpep_dropoff_datetime    2014-04-01 01:31:40\n",
       "Store_and_fwd_flag                         N\n",
       "RateCodeID                                 1\n",
       "Pickup_longitude                  -73.903275\n",
       "Pickup_latitude                    40.745735\n",
       "Dropoff_longitude                 -73.897652\n",
       "Dropoff_latitude                   40.744522\n",
       "Passenger_count                            1\n",
       "Trip_distance                           0.76\n",
       "Fare_amount                              5.0\n",
       "Extra                                    0.5\n",
       "MTA_tax                                  0.5\n",
       "Tip_amount                               0.0\n",
       "Tolls_amount                             0.0\n",
       "Ehail_fee                                NaN\n",
       "Total_amount                             6.0\n",
       "Payment_type                               2\n",
       "Trip_type                                  1\n",
       "Unnamed: 20                              NaN\n",
       "                                         NaN\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green.iloc[1336]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows behave a named tuples, so you can access individual elements by position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(5.0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green.iloc[1336, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(5.0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green.iloc[1336].Fare_amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ranges can also be used with iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>lpep_pickup_datetime</th>\n",
       "      <th>Lpep_dropoff_datetime</th>\n",
       "      <th>Store_and_fwd_flag</th>\n",
       "      <th>RateCodeID</th>\n",
       "      <th>Pickup_longitude</th>\n",
       "      <th>Pickup_latitude</th>\n",
       "      <th>Dropoff_longitude</th>\n",
       "      <th>Dropoff_latitude</th>\n",
       "      <th>Passenger_count</th>\n",
       "      <th>Trip_distance</th>\n",
       "      <th>...</th>\n",
       "      <th>Extra</th>\n",
       "      <th>MTA_tax</th>\n",
       "      <th>Tip_amount</th>\n",
       "      <th>Tolls_amount</th>\n",
       "      <th>Ehail_fee</th>\n",
       "      <th>Total_amount</th>\n",
       "      <th>Payment_type</th>\n",
       "      <th>Trip_type</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VendorID</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>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 17:21:33</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.987663</td>\n",
       "      <td>40.780872</td>\n",
       "      <td>1</td>\n",
       "      <td>8.95</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 15:06:18</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.946922</td>\n",
       "      <td>40.831764</td>\n",
       "      <td>1</td>\n",
       "      <td>1.32</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-04-01</td>\n",
       "      <td>2014-04-01 08:09:27</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-73.947670</td>\n",
       "      <td>40.808651</td>\n",
       "      <td>5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lpep_pickup_datetime Lpep_dropoff_datetime Store_and_fwd_flag  \\\n",
       "VendorID                                                                 \n",
       "2                  2014-04-01   2014-04-01 17:21:33                  N   \n",
       "2                  2014-04-01   2014-04-01 15:06:18                  N   \n",
       "2                  2014-04-01   2014-04-01 08:09:27                  N   \n",
       "\n",
       "         RateCodeID  Pickup_longitude  Pickup_latitude  Dropoff_longitude  \\\n",
       "VendorID                                                                    \n",
       "2                 1               0.0              0.0         -73.987663   \n",
       "2                 1               0.0              0.0         -73.946922   \n",
       "2                 1               0.0              0.0         -73.947670   \n",
       "\n",
       "          Dropoff_latitude  Passenger_count  Trip_distance  ...  Extra  \\\n",
       "VendorID                                                    ...          \n",
       "2                40.780872                1           8.95  ...    1.0   \n",
       "2                40.831764                1           1.32  ...    0.0   \n",
       "2                40.808651                5           0.10  ...    0.0   \n",
       "\n",
       "          MTA_tax  Tip_amount  Tolls_amount  Ehail_fee  Total_amount  \\\n",
       "VendorID                                                               \n",
       "2             0.5         0.0           0.0        NaN          32.5   \n",
       "2             0.5         0.0           0.0        NaN           7.0   \n",
       "2             0.5         0.0           0.0        NaN           3.5   \n",
       "\n",
       "          Payment_type  Trip_type Unnamed: 20      \n",
       "VendorID                                           \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "2                    2          1         NaN NaN  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "green.iloc[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apple stock information from https://finance.yahoo.com/quote/AAPL/history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can automatically convert the Date column using __pd.read_csv__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/AAPL.csv', parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         datetime64[ns]\n",
       "Open                float64\n",
       "High                float64\n",
       "Low                 float64\n",
       "Close               float64\n",
       "Adj Close           float64\n",
       "Volume              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we now set the Date column to be the index, we effectively create our first Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that pandas automatically generated a \"DatetimeIndex\" object that allos us to take advantage of the fact that we are dealing with dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 9887 entries, 1980-12-12 to 2020-02-28\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       9886 non-null   float64\n",
      " 1   High       9886 non-null   float64\n",
      " 2   Low        9886 non-null   float64\n",
      " 3   Close      9886 non-null   float64\n",
      " 4   Adj Close  9886 non-null   float64\n",
      " 5   Volume     9886 non-null   float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 540.7 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily access parts of the date object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.406782</td>\n",
       "      <td>117258400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.486607</td>\n",
       "      <td>0.486607</td>\n",
       "      <td>0.385558</td>\n",
       "      <td>43971200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.450893</td>\n",
       "      <td>0.450893</td>\n",
       "      <td>0.357260</td>\n",
       "      <td>26432000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.366103</td>\n",
       "      <td>21610400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.477679</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.376715</td>\n",
       "      <td>18362400.0</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>2020-02-24</th>\n",
       "      <td>297.260010</td>\n",
       "      <td>304.179993</td>\n",
       "      <td>289.230011</td>\n",
       "      <td>298.179993</td>\n",
       "      <td>298.179993</td>\n",
       "      <td>55548800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>300.950012</td>\n",
       "      <td>302.529999</td>\n",
       "      <td>286.130005</td>\n",
       "      <td>288.079987</td>\n",
       "      <td>288.079987</td>\n",
       "      <td>57668400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>286.529999</td>\n",
       "      <td>297.880005</td>\n",
       "      <td>286.500000</td>\n",
       "      <td>292.649994</td>\n",
       "      <td>292.649994</td>\n",
       "      <td>49513700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>281.100006</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>272.959991</td>\n",
       "      <td>273.519989</td>\n",
       "      <td>273.519989</td>\n",
       "      <td>80151400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>257.260010</td>\n",
       "      <td>278.410004</td>\n",
       "      <td>256.369995</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>106627500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9887 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "1980-12-12    0.513393    0.515625    0.513393    0.513393    0.406782   \n",
       "1980-12-15    0.488839    0.488839    0.486607    0.486607    0.385558   \n",
       "1980-12-16    0.453125    0.453125    0.450893    0.450893    0.357260   \n",
       "1980-12-17    0.462054    0.464286    0.462054    0.462054    0.366103   \n",
       "1980-12-18    0.475446    0.477679    0.475446    0.475446    0.376715   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-02-24  297.260010  304.179993  289.230011  298.179993  298.179993   \n",
       "2020-02-25  300.950012  302.529999  286.130005  288.079987  288.079987   \n",
       "2020-02-26  286.529999  297.880005  286.500000  292.649994  292.649994   \n",
       "2020-02-27  281.100006  286.000000  272.959991  273.519989  273.519989   \n",
       "2020-02-28  257.260010  278.410004  256.369995  273.359985  273.359985   \n",
       "\n",
       "                 Volume  \n",
       "Date                     \n",
       "1980-12-12  117258400.0  \n",
       "1980-12-15   43971200.0  \n",
       "1980-12-16   26432000.0  \n",
       "1980-12-17   21610400.0  \n",
       "1980-12-18   18362400.0  \n",
       "...                 ...  \n",
       "2020-02-24   55548800.0  \n",
       "2020-02-25   57668400.0  \n",
       "2020-02-26   49513700.0  \n",
       "2020-02-27   80151400.0  \n",
       "2020-02-28  106627500.0  \n",
       "\n",
       "[9887 rows x 6 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
       "       ...\n",
       "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2],\n",
       "      dtype='int32', name='Date', length=9887)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980,\n",
       "       ...\n",
       "       2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020],\n",
       "      dtype='int32', name='Date', length=9887)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([12, 15, 16, 17, 18, 19, 22, 23, 24, 26,\n",
       "       ...\n",
       "       14, 18, 19, 20, 21, 24, 25, 26, 27, 28],\n",
       "      dtype='int32', name='Date', length=9887)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And slice the DataFrame by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>Date</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>2010-01-04</th>\n",
       "      <td>30.49</td>\n",
       "      <td>30.64</td>\n",
       "      <td>30.34</td>\n",
       "      <td>30.57</td>\n",
       "      <td>26.54</td>\n",
       "      <td>123432400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>30.66</td>\n",
       "      <td>30.80</td>\n",
       "      <td>30.46</td>\n",
       "      <td>30.63</td>\n",
       "      <td>26.58</td>\n",
       "      <td>150476200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>30.63</td>\n",
       "      <td>30.75</td>\n",
       "      <td>30.11</td>\n",
       "      <td>30.14</td>\n",
       "      <td>26.16</td>\n",
       "      <td>138040000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>30.25</td>\n",
       "      <td>30.29</td>\n",
       "      <td>29.86</td>\n",
       "      <td>30.08</td>\n",
       "      <td>26.11</td>\n",
       "      <td>119282800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>30.04</td>\n",
       "      <td>30.29</td>\n",
       "      <td>29.87</td>\n",
       "      <td>30.28</td>\n",
       "      <td>26.29</td>\n",
       "      <td>111902700.0</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>2010-06-04</th>\n",
       "      <td>36.89</td>\n",
       "      <td>37.41</td>\n",
       "      <td>36.38</td>\n",
       "      <td>36.57</td>\n",
       "      <td>31.74</td>\n",
       "      <td>189576100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-07</th>\n",
       "      <td>36.90</td>\n",
       "      <td>37.02</td>\n",
       "      <td>35.79</td>\n",
       "      <td>35.85</td>\n",
       "      <td>31.12</td>\n",
       "      <td>221735500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-08</th>\n",
       "      <td>36.18</td>\n",
       "      <td>36.26</td>\n",
       "      <td>35.09</td>\n",
       "      <td>35.62</td>\n",
       "      <td>30.92</td>\n",
       "      <td>250192600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-09</th>\n",
       "      <td>35.92</td>\n",
       "      <td>35.99</td>\n",
       "      <td>34.64</td>\n",
       "      <td>34.74</td>\n",
       "      <td>30.16</td>\n",
       "      <td>213657500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-10</th>\n",
       "      <td>34.98</td>\n",
       "      <td>35.85</td>\n",
       "      <td>34.60</td>\n",
       "      <td>35.79</td>\n",
       "      <td>31.06</td>\n",
       "      <td>194089000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Adj Close       Volume\n",
       "Date                                                          \n",
       "2010-01-04  30.49  30.64  30.34  30.57      26.54  123432400.0\n",
       "2010-01-05  30.66  30.80  30.46  30.63      26.58  150476200.0\n",
       "2010-01-06  30.63  30.75  30.11  30.14      26.16  138040000.0\n",
       "2010-01-07  30.25  30.29  29.86  30.08      26.11  119282800.0\n",
       "2010-01-08  30.04  30.29  29.87  30.28      26.29  111902700.0\n",
       "...           ...    ...    ...    ...        ...          ...\n",
       "2010-06-04  36.89  37.41  36.38  36.57      31.74  189576100.0\n",
       "2010-06-07  36.90  37.02  35.79  35.85      31.12  221735500.0\n",
       "2010-06-08  36.18  36.26  35.09  35.62      30.92  250192600.0\n",
       "2010-06-09  35.92  35.99  34.64  34.74      30.16  213657500.0\n",
       "2010-06-10  34.98  35.85  34.60  35.79      31.06  194089000.0\n",
       "\n",
       "[110 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['2010':'2010-06-10'].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we note that the last value is also included"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge and Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define 2 toy DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.DataFrame({\"lkey\":  [\"foo\", \"bar\", \"baz\", \"foo\"], \n",
    "                  \"value\": [1,     2,     3,     4]})\n",
    "\n",
    "B = pd.DataFrame({\"rkey\":  [\"foo\", \"bar\", \"qux\", \"bar\"], \n",
    "                  \"value\": [5,     6,     7,     8]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge allows us to join them by specifying an arbitrary column on each of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lkey</th>\n",
       "      <th>value_x</th>\n",
       "      <th>rkey</th>\n",
       "      <th>value_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>bar</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>bar</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baz</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "      <td>foo</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lkey  value_x rkey  value_y\n",
       "0  foo        1  foo      5.0\n",
       "1  bar        2  bar      6.0\n",
       "2  bar        2  bar      8.0\n",
       "3  baz        3  NaN      NaN\n",
       "4  foo        4  foo      5.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.merge(B, left_on=\"lkey\", right_on=\"rkey\", how=\"left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, join performs the join using the respective Indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.set_index('lkey', inplace=True)\n",
    "B.set_index('rkey', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>value_A</th>\n",
       "      <th>value_B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lkey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value_A  value_B\n",
       "lkey                  \n",
       "foo         1        5\n",
       "bar         2        6\n",
       "bar         2        8\n",
       "foo         4        5"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.join(B, lsuffix=\"_A\", rsuffix=\"_B\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.406782</td>\n",
       "      <td>117258400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.486607</td>\n",
       "      <td>0.486607</td>\n",
       "      <td>0.385558</td>\n",
       "      <td>43971200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.450893</td>\n",
       "      <td>0.450893</td>\n",
       "      <td>0.357260</td>\n",
       "      <td>26432000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.366103</td>\n",
       "      <td>21610400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.477679</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.376715</td>\n",
       "      <td>18362400.0</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>2020-02-24</th>\n",
       "      <td>297.260010</td>\n",
       "      <td>304.179993</td>\n",
       "      <td>289.230011</td>\n",
       "      <td>298.179993</td>\n",
       "      <td>298.179993</td>\n",
       "      <td>55548800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>300.950012</td>\n",
       "      <td>302.529999</td>\n",
       "      <td>286.130005</td>\n",
       "      <td>288.079987</td>\n",
       "      <td>288.079987</td>\n",
       "      <td>57668400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>286.529999</td>\n",
       "      <td>297.880005</td>\n",
       "      <td>286.500000</td>\n",
       "      <td>292.649994</td>\n",
       "      <td>292.649994</td>\n",
       "      <td>49513700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>281.100006</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>272.959991</td>\n",
       "      <td>273.519989</td>\n",
       "      <td>273.519989</td>\n",
       "      <td>80151400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>257.260010</td>\n",
       "      <td>278.410004</td>\n",
       "      <td>256.369995</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>106627500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9887 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "1980-12-12    0.513393    0.515625    0.513393    0.513393    0.406782   \n",
       "1980-12-15    0.488839    0.488839    0.486607    0.486607    0.385558   \n",
       "1980-12-16    0.453125    0.453125    0.450893    0.450893    0.357260   \n",
       "1980-12-17    0.462054    0.464286    0.462054    0.462054    0.366103   \n",
       "1980-12-18    0.475446    0.477679    0.475446    0.475446    0.376715   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-02-24  297.260010  304.179993  289.230011  298.179993  298.179993   \n",
       "2020-02-25  300.950012  302.529999  286.130005  288.079987  288.079987   \n",
       "2020-02-26  286.529999  297.880005  286.500000  292.649994  292.649994   \n",
       "2020-02-27  281.100006  286.000000  272.959991  273.519989  273.519989   \n",
       "2020-02-28  257.260010  278.410004  256.369995  273.359985  273.359985   \n",
       "\n",
       "                 Volume  \n",
       "Date                     \n",
       "1980-12-12  117258400.0  \n",
       "1980-12-15   43971200.0  \n",
       "1980-12-16   26432000.0  \n",
       "1980-12-17   21610400.0  \n",
       "1980-12-18   18362400.0  \n",
       "...                 ...  \n",
       "2020-02-24   55548800.0  \n",
       "2020-02-25   57668400.0  \n",
       "2020-02-26   49513700.0  \n",
       "2020-02-27   80151400.0  \n",
       "2020-02-28  106627500.0  \n",
       "\n",
       "[9887 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "apple['year'] = data.index.year\n",
    "apple['month'] = data.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>0.543613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>0.434125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>0.341835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>0.669096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>0.478605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>0.360934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>0.579651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1.391004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>1.483537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1.487812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1.341491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>1.874806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1.957238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1.465239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1.217162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1.447864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>0.889981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>0.641670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1.091602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2.063222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>3.265133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1.444245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1.367103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1.324626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2.537639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>6.668005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>10.115805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>18.324843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>20.282716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>20.973447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>37.120351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>52.000618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>82.292817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>67.519269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>92.264654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>120.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>104.604008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>150.551075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>189.053427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>208.255952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>311.609499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Close\n",
       "year            \n",
       "1980    0.543613\n",
       "1981    0.434125\n",
       "1982    0.341835\n",
       "1983    0.669096\n",
       "1984    0.478605\n",
       "1985    0.360934\n",
       "1986    0.579651\n",
       "1987    1.391004\n",
       "1988    1.483537\n",
       "1989    1.487812\n",
       "1990    1.341491\n",
       "1991    1.874806\n",
       "1992    1.957238\n",
       "1993    1.465239\n",
       "1994    1.217162\n",
       "1995    1.447864\n",
       "1996    0.889981\n",
       "1997    0.641670\n",
       "1998    1.091602\n",
       "1999    2.063222\n",
       "2000    3.265133\n",
       "2001    1.444245\n",
       "2002    1.367103\n",
       "2003    1.324626\n",
       "2004    2.537639\n",
       "2005    6.668005\n",
       "2006   10.115805\n",
       "2007   18.324843\n",
       "2008   20.282716\n",
       "2009   20.973447\n",
       "2010   37.120351\n",
       "2011   52.000618\n",
       "2012   82.292817\n",
       "2013   67.519269\n",
       "2014   92.264654\n",
       "2015  120.040000\n",
       "2016  104.604008\n",
       "2017  150.551075\n",
       "2018  189.053427\n",
       "2019  208.255952\n",
       "2020  311.609499"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple[['Close', 'year']].groupby('year').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>year</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>1</th>\n",
       "      <td>29.673609</td>\n",
       "      <td>48.340000</td>\n",
       "      <td>61.225428</td>\n",
       "      <td>71.117483</td>\n",
       "      <td>76.777959</td>\n",
       "      <td>110.641501</td>\n",
       "      <td>98.428946</td>\n",
       "      <td>119.570000</td>\n",
       "      <td>174.005237</td>\n",
       "      <td>154.166190</td>\n",
       "      <td>311.916191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.399398</td>\n",
       "      <td>50.168948</td>\n",
       "      <td>71.081572</td>\n",
       "      <td>65.258422</td>\n",
       "      <td>75.225714</td>\n",
       "      <td>125.432105</td>\n",
       "      <td>95.746500</td>\n",
       "      <td>133.714209</td>\n",
       "      <td>167.638947</td>\n",
       "      <td>171.727893</td>\n",
       "      <td>311.270522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.915838</td>\n",
       "      <td>49.641553</td>\n",
       "      <td>82.501104</td>\n",
       "      <td>63.120143</td>\n",
       "      <td>76.173468</td>\n",
       "      <td>125.970908</td>\n",
       "      <td>104.267273</td>\n",
       "      <td>140.617826</td>\n",
       "      <td>174.496192</td>\n",
       "      <td>183.293810</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.878368</td>\n",
       "      <td>48.630785</td>\n",
       "      <td>86.571857</td>\n",
       "      <td>59.966428</td>\n",
       "      <td>77.296327</td>\n",
       "      <td>127.291428</td>\n",
       "      <td>106.739048</td>\n",
       "      <td>142.886842</td>\n",
       "      <td>169.834287</td>\n",
       "      <td>200.516191</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.923071</td>\n",
       "      <td>48.826394</td>\n",
       "      <td>80.667597</td>\n",
       "      <td>63.778961</td>\n",
       "      <td>86.170817</td>\n",
       "      <td>128.761500</td>\n",
       "      <td>94.974762</td>\n",
       "      <td>152.227726</td>\n",
       "      <td>185.536818</td>\n",
       "      <td>191.273637</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37.306494</td>\n",
       "      <td>47.297337</td>\n",
       "      <td>82.080340</td>\n",
       "      <td>60.791143</td>\n",
       "      <td>91.885443</td>\n",
       "      <td>127.806817</td>\n",
       "      <td>96.622273</td>\n",
       "      <td>147.831362</td>\n",
       "      <td>188.621429</td>\n",
       "      <td>192.969000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36.421429</td>\n",
       "      <td>53.176857</td>\n",
       "      <td>85.866871</td>\n",
       "      <td>61.308182</td>\n",
       "      <td>95.625909</td>\n",
       "      <td>125.335455</td>\n",
       "      <td>98.556500</td>\n",
       "      <td>148.299499</td>\n",
       "      <td>190.311429</td>\n",
       "      <td>205.216364</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>35.903312</td>\n",
       "      <td>53.823230</td>\n",
       "      <td>91.813727</td>\n",
       "      <td>69.263376</td>\n",
       "      <td>98.436667</td>\n",
       "      <td>113.394762</td>\n",
       "      <td>107.665218</td>\n",
       "      <td>159.021306</td>\n",
       "      <td>213.346089</td>\n",
       "      <td>204.957728</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>39.121564</td>\n",
       "      <td>56.070476</td>\n",
       "      <td>97.366918</td>\n",
       "      <td>68.597786</td>\n",
       "      <td>100.548572</td>\n",
       "      <td>112.797619</td>\n",
       "      <td>110.857142</td>\n",
       "      <td>157.608500</td>\n",
       "      <td>222.073685</td>\n",
       "      <td>217.994999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>42.990612</td>\n",
       "      <td>56.747143</td>\n",
       "      <td>90.673469</td>\n",
       "      <td>72.106398</td>\n",
       "      <td>101.699566</td>\n",
       "      <td>113.360000</td>\n",
       "      <td>115.707143</td>\n",
       "      <td>157.817273</td>\n",
       "      <td>220.845652</td>\n",
       "      <td>235.286521</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>44.512585</td>\n",
       "      <td>54.973606</td>\n",
       "      <td>80.620817</td>\n",
       "      <td>74.945214</td>\n",
       "      <td>113.285263</td>\n",
       "      <td>118.162500</td>\n",
       "      <td>110.154285</td>\n",
       "      <td>172.406191</td>\n",
       "      <td>191.235714</td>\n",
       "      <td>262.520503</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>45.926559</td>\n",
       "      <td>56.132925</td>\n",
       "      <td>76.007857</td>\n",
       "      <td>79.951088</td>\n",
       "      <td>112.411364</td>\n",
       "      <td>111.726818</td>\n",
       "      <td>114.335714</td>\n",
       "      <td>171.891500</td>\n",
       "      <td>164.266317</td>\n",
       "      <td>276.525710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year        2010       2011       2012       2013        2014        2015  \\\n",
       "month                                                                       \n",
       "1      29.673609  48.340000  61.225428  71.117483   76.777959  110.641501   \n",
       "2      28.399398  50.168948  71.081572  65.258422   75.225714  125.432105   \n",
       "3      31.915838  49.641553  82.501104  63.120143   76.173468  125.970908   \n",
       "4      35.878368  48.630785  86.571857  59.966428   77.296327  127.291428   \n",
       "5      35.923071  48.826394  80.667597  63.778961   86.170817  128.761500   \n",
       "6      37.306494  47.297337  82.080340  60.791143   91.885443  127.806817   \n",
       "7      36.421429  53.176857  85.866871  61.308182   95.625909  125.335455   \n",
       "8      35.903312  53.823230  91.813727  69.263376   98.436667  113.394762   \n",
       "9      39.121564  56.070476  97.366918  68.597786  100.548572  112.797619   \n",
       "10     42.990612  56.747143  90.673469  72.106398  101.699566  113.360000   \n",
       "11     44.512585  54.973606  80.620817  74.945214  113.285263  118.162500   \n",
       "12     45.926559  56.132925  76.007857  79.951088  112.411364  111.726818   \n",
       "\n",
       "year         2016        2017        2018        2019        2020  \n",
       "month                                                              \n",
       "1       98.428946  119.570000  174.005237  154.166190  311.916191  \n",
       "2       95.746500  133.714209  167.638947  171.727893  311.270522  \n",
       "3      104.267273  140.617826  174.496192  183.293810         NaN  \n",
       "4      106.739048  142.886842  169.834287  200.516191         NaN  \n",
       "5       94.974762  152.227726  185.536818  191.273637         NaN  \n",
       "6       96.622273  147.831362  188.621429  192.969000         NaN  \n",
       "7       98.556500  148.299499  190.311429  205.216364         NaN  \n",
       "8      107.665218  159.021306  213.346089  204.957728         NaN  \n",
       "9      110.857142  157.608500  222.073685  217.994999         NaN  \n",
       "10     115.707143  157.817273  220.845652  235.286521         NaN  \n",
       "11     110.154285  172.406191  191.235714  262.520503         NaN  \n",
       "12     114.335714  171.891500  164.266317  276.525710         NaN  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(apple['2010':], index='month', columns='year', values='Close', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.515625</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.513393</td>\n",
       "      <td>0.406782</td>\n",
       "      <td>117258400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.488839</td>\n",
       "      <td>0.486607</td>\n",
       "      <td>0.486607</td>\n",
       "      <td>0.385558</td>\n",
       "      <td>43971200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.450893</td>\n",
       "      <td>0.450893</td>\n",
       "      <td>0.357260</td>\n",
       "      <td>26432000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.462054</td>\n",
       "      <td>0.366103</td>\n",
       "      <td>21610400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.477679</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.475446</td>\n",
       "      <td>0.376715</td>\n",
       "      <td>18362400.0</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>2020-02-24</th>\n",
       "      <td>297.260010</td>\n",
       "      <td>304.179993</td>\n",
       "      <td>289.230011</td>\n",
       "      <td>298.179993</td>\n",
       "      <td>298.179993</td>\n",
       "      <td>55548800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>300.950012</td>\n",
       "      <td>302.529999</td>\n",
       "      <td>286.130005</td>\n",
       "      <td>288.079987</td>\n",
       "      <td>288.079987</td>\n",
       "      <td>57668400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>286.529999</td>\n",
       "      <td>297.880005</td>\n",
       "      <td>286.500000</td>\n",
       "      <td>292.649994</td>\n",
       "      <td>292.649994</td>\n",
       "      <td>49513700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>281.100006</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>272.959991</td>\n",
       "      <td>273.519989</td>\n",
       "      <td>273.519989</td>\n",
       "      <td>80151400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>257.260010</td>\n",
       "      <td>278.410004</td>\n",
       "      <td>256.369995</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>273.359985</td>\n",
       "      <td>106627500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9887 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "1980-12-12    0.513393    0.515625    0.513393    0.513393    0.406782   \n",
       "1980-12-15    0.488839    0.488839    0.486607    0.486607    0.385558   \n",
       "1980-12-16    0.453125    0.453125    0.450893    0.450893    0.357260   \n",
       "1980-12-17    0.462054    0.464286    0.462054    0.462054    0.366103   \n",
       "1980-12-18    0.475446    0.477679    0.475446    0.475446    0.376715   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2020-02-24  297.260010  304.179993  289.230011  298.179993  298.179993   \n",
       "2020-02-25  300.950012  302.529999  286.130005  288.079987  288.079987   \n",
       "2020-02-26  286.529999  297.880005  286.500000  292.649994  292.649994   \n",
       "2020-02-27  281.100006  286.000000  272.959991  273.519989  273.519989   \n",
       "2020-02-28  257.260010  278.410004  256.369995  273.359985  273.359985   \n",
       "\n",
       "                 Volume  \n",
       "Date                     \n",
       "1980-12-12  117258400.0  \n",
       "1980-12-15   43971200.0  \n",
       "1980-12-16   26432000.0  \n",
       "1980-12-17   21610400.0  \n",
       "1980-12-18   18362400.0  \n",
       "...                 ...  \n",
       "2020-02-24   55548800.0  \n",
       "2020-02-25   57668400.0  \n",
       "2020-02-26   49513700.0  \n",
       "2020-02-27   80151400.0  \n",
       "2020-02-28  106627500.0  \n",
       "\n",
       "[9887 rows x 6 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For quick and dirty plots, you can just use **plot**. It won't be pretty, but it will get the job done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(y='Close')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naturally, we can also choose the color of the line to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(y='Close', color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Easily add a legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Date')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data.plot(y='Close', color='red', label='APPL Stock')\n",
    "ax.set_ylabel('Closing Price')\n",
    "ax.set_xlabel('Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do something slightly more serious. We start by loading some data from a csv file using **pandas**.\n",
    "\n",
    "Marriage age data from: https://www.statcrunch.com/5.0/viewreport.php?reportid=41662"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = pd.read_csv('data/ages.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick look at the data to make sure it is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Wife</th>\n",
       "      <th>Husband</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wife  Husband\n",
       "0    43       49\n",
       "1    28       25\n",
       "2    30       40\n",
       "3    57       52\n",
       "4    52       58"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 2)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naturally, we could also have used the pandas internal plotting functionality. Indeed, pandas, like many other plotting libraries just use matplotlib in the background so you can do things like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Wife', ylabel='Husband'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ages.plot(x='Wife', y='Husband', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pandas** supports several basic types of plots out of the box. To illustrate the bar plot we will use some made up data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataLA = [20, 14, 23]\n",
    "dataSF = [11, 18, 28]\n",
    "\n",
    "bars = pd.DataFrame({'LA': dataLA, 'SF': dataSF})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>LA</th>\n",
       "      <th>SF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LA  SF\n",
       "0  20  11\n",
       "1  14  18\n",
       "2  23  28"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have two different datasets with 3 data points (classes) each. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pie plots are **EVIL**! But just in case you are too... Here we use the **figsize** parameter to **plt.figure** to set the figure size in inches. Notice that this is slightly different than what we did above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame({'y': [17, 18, 20, 22, 24], 'z': [17, 18, 20, 22, 24]})\n",
    "\n",
    "ax_lst = data.plot(kind='pie', subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" border=\"0\" width=300px> \n",
    "</center>"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}