Pandas - Data Analysis with Pandas and Python - BP/07_MultiIndex.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to MultiIndex Module\n",
    "- Multiple layers, Multiple levels, Multiple tiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, prices of Bigmac kinda represents country's economy indicator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Britain</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/2016</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Date    Country  Price in US Dollars\n",
       "0  1/2016  Argentina                 2.39\n",
       "1  1/2016  Australia                 3.74\n",
       "2  1/2016     Brazil                 3.35\n",
       "3  1/2016    Britain                 4.22\n",
       "4  1/2016     Canada                 4.14"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv')\n",
    "bigmac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 652 entries, 0 to 651\n",
      "Data columns (total 3 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Date                 652 non-null    object \n",
      " 1   Country              652 non-null    object \n",
      " 2   Price in US Dollars  652 non-null    float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 15.4+ KB\n"
     ]
    }
   ],
   "source": [
    "bigmac.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                   0\n",
       "Country                0\n",
       "Price in US Dollars    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization Points\n",
    "+ Date are in object datatype. If necessary, we need to change to Datetime.\n",
    "+ Prices are in object (String), so we need to change them to float to allow us make calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Britain</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Price in US Dollars\n",
       "0 2016-01-01  Argentina                 2.39\n",
       "1 2016-01-01  Australia                 3.74\n",
       "2 2016-01-01     Brazil                 3.35\n",
       "3 2016-01-01    Britain                 4.22\n",
       "4 2016-01-01     Canada                 4.14"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv', parse_dates=['Date'])\n",
    "bigmac.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Create a MultiIndex with the `set_index` Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Price in US Dollars\n",
       "0 2016-01-01  Argentina                 2.39\n",
       "1 2016-01-01  Australia                 3.74\n",
       "2 2016-01-01     Brazil                 3.35"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv', parse_dates=['Date'])\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can have multiIndex with 2 layers, 3 layers, many more, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>4.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Country       Date                           \n",
       "Argentina     2016-01-01                 2.39\n",
       "Australia     2016-01-01                 3.74\n",
       "Brazil        2016-01-01                 3.35\n",
       "Britain       2016-01-01                 4.22\n",
       "Canada        2016-01-01                 4.14\n",
       "...                                       ...\n",
       "Turkey        2010-01-01                 3.83\n",
       "UAE           2010-01-01                 2.99\n",
       "Ukraine       2010-01-01                 1.83\n",
       "United States 2010-01-01                 3.58\n",
       "Uruguay       2010-01-01                 3.32\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.set_index(keys=['Date', 'Country'])\n",
    "bigmac.set_index(keys=['Country', 'Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TIPS: Column with less values should be the most Outer Layer \n",
    "In our case => Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                    12\n",
       "Country                 58\n",
       "Price in US Dollars    330\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2016-01-01 Argentina                 2.39\n",
       "           Australia                 3.74\n",
       "           Brazil                    3.35"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = bigmac.set_index(keys=['Date', 'Country'])\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `sort_index()` after multi indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = bigmac.sort_index() # sort by date, then by country \n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index and Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList(['Date', 'Country'])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index\n",
    "\n",
    "bigmac.index.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.multi.MultiIndex"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bigmac.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### calling `.index` returns tuple for multi Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('2010-01-01',      'Argentina'),\n",
       "            ('2010-01-01',      'Australia'),\n",
       "            ('2010-01-01',         'Brazil'),\n",
       "            ('2010-01-01',        'Britain'),\n",
       "            ('2010-01-01',         'Canada'),\n",
       "            ('2010-01-01',          'Chile'),\n",
       "            ('2010-01-01',          'China'),\n",
       "            ('2010-01-01',       'Colombia'),\n",
       "            ('2010-01-01',     'Costa Rica'),\n",
       "            ('2010-01-01', 'Czech Republic'),\n",
       "            ...\n",
       "            ('2016-01-01',    'Switzerland'),\n",
       "            ('2016-01-01',         'Taiwan'),\n",
       "            ('2016-01-01',       'Thailand'),\n",
       "            ('2016-01-01',         'Turkey'),\n",
       "            ('2016-01-01',            'UAE'),\n",
       "            ('2016-01-01',        'Ukraine'),\n",
       "            ('2016-01-01',  'United States'),\n",
       "            ('2016-01-01',        'Uruguay'),\n",
       "            ('2016-01-01',      'Venezuela'),\n",
       "            ('2016-01-01',        'Vietnam')],\n",
       "           names=['Date', 'Country'], length=652)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2010-01-01 00:00:00'), 'Argentina')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date:  2010-01-01 00:00:00\n",
      "Name:  Argentina\n"
     ]
    }
   ],
   "source": [
    "date, name = bigmac.index[0]\n",
    "print('date: ', date)\n",
    "print('Name: ', name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Extract Index Level Values with the `get_level_values` Method"
   ]
  },
  {
   "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></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv', parse_dates=['Date'], index_col=['Date', 'Country'])\n",
    "bigmac = bigmac.sort_index()\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-01', '2010-01-01', '2010-01-01', '2010-01-01',\n",
       "               '2010-01-01', '2010-01-01', '2010-01-01', '2010-01-01',\n",
       "               '2010-01-01', '2010-01-01',\n",
       "               ...\n",
       "               '2016-01-01', '2016-01-01', '2016-01-01', '2016-01-01',\n",
       "               '2016-01-01', '2016-01-01', '2016-01-01', '2016-01-01',\n",
       "               '2016-01-01', '2016-01-01'],\n",
       "              dtype='datetime64[ns]', name='Date', length=652, freq=None)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index.get_level_values('Date')\n",
    "bigmac.index.get_level_values(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Argentina', 'Australia', 'Brazil', 'Britain', 'Canada', 'Chile',\n",
       "       'China', 'Colombia', 'Costa Rica', 'Czech Republic',\n",
       "       ...\n",
       "       'Switzerland', 'Taiwan', 'Thailand', 'Turkey', 'UAE', 'Ukraine',\n",
       "       'United States', 'Uruguay', 'Venezuela', 'Vietnam'],\n",
       "      dtype='object', name='Country', length=652)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index.get_level_values(1)\n",
    "bigmac.index.get_level_values('Country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4) Change Index Level Name with the `set_names` Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76\n",
       "           Britain                   3.67\n",
       "           Canada                    3.97"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv', parse_dates=['Date'], index_col=['Date', 'Country'])\n",
    "bigmac = bigmac.sort_index()\n",
    "bigmac.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('2010-01-01',      'Argentina'),\n",
       "            ('2010-01-01',      'Australia'),\n",
       "            ('2010-01-01',         'Brazil'),\n",
       "            ('2010-01-01',        'Britain'),\n",
       "            ('2010-01-01',         'Canada'),\n",
       "            ('2010-01-01',          'Chile'),\n",
       "            ('2010-01-01',          'China'),\n",
       "            ('2010-01-01',       'Colombia'),\n",
       "            ('2010-01-01',     'Costa Rica'),\n",
       "            ('2010-01-01', 'Czech Republic'),\n",
       "            ...\n",
       "            ('2016-01-01',    'Switzerland'),\n",
       "            ('2016-01-01',         'Taiwan'),\n",
       "            ('2016-01-01',       'Thailand'),\n",
       "            ('2016-01-01',         'Turkey'),\n",
       "            ('2016-01-01',            'UAE'),\n",
       "            ('2016-01-01',        'Ukraine'),\n",
       "            ('2016-01-01',  'United States'),\n",
       "            ('2016-01-01',        'Uruguay'),\n",
       "            ('2016-01-01',      'Venezuela'),\n",
       "            ('2016-01-01',        'Vietnam')],\n",
       "           names=['Date', 'Country'], length=652)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the names of Our Index\n",
    "- need to be in the same order when passing `names = []` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('2010-01-01',      'Argentina'),\n",
       "            ('2010-01-01',      'Australia'),\n",
       "            ('2010-01-01',         'Brazil'),\n",
       "            ('2010-01-01',        'Britain'),\n",
       "            ('2010-01-01',         'Canada'),\n",
       "            ('2010-01-01',          'Chile'),\n",
       "            ('2010-01-01',          'China'),\n",
       "            ('2010-01-01',       'Colombia'),\n",
       "            ('2010-01-01',     'Costa Rica'),\n",
       "            ('2010-01-01', 'Czech Republic'),\n",
       "            ...\n",
       "            ('2016-01-01',    'Switzerland'),\n",
       "            ('2016-01-01',         'Taiwan'),\n",
       "            ('2016-01-01',       'Thailand'),\n",
       "            ('2016-01-01',         'Turkey'),\n",
       "            ('2016-01-01',            'UAE'),\n",
       "            ('2016-01-01',        'Ukraine'),\n",
       "            ('2016-01-01',  'United States'),\n",
       "            ('2016-01-01',        'Uruguay'),\n",
       "            ('2016-01-01',      'Venezuela'),\n",
       "            ('2016-01-01',        'Vietnam')],\n",
       "           names=['Day', 'Location'], length=652)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index.set_names(names = ['Day', 'Location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmac.index.set_names(names = ['Day', 'Location'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Day        Location                      \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If we want to change only one index of multiindex, we can pass using `level` parameter\n",
    "+ `level = level_number`\n",
    "+ or `level = 'current index name'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('2010-01-01',      'Argentina'),\n",
       "            ('2010-01-01',      'Australia'),\n",
       "            ('2010-01-01',         'Brazil'),\n",
       "            ('2010-01-01',        'Britain'),\n",
       "            ('2010-01-01',         'Canada'),\n",
       "            ('2010-01-01',          'Chile'),\n",
       "            ('2010-01-01',          'China'),\n",
       "            ('2010-01-01',       'Colombia'),\n",
       "            ('2010-01-01',     'Costa Rica'),\n",
       "            ('2010-01-01', 'Czech Republic'),\n",
       "            ...\n",
       "            ('2016-01-01',    'Switzerland'),\n",
       "            ('2016-01-01',         'Taiwan'),\n",
       "            ('2016-01-01',       'Thailand'),\n",
       "            ('2016-01-01',         'Turkey'),\n",
       "            ('2016-01-01',            'UAE'),\n",
       "            ('2016-01-01',        'Ukraine'),\n",
       "            ('2016-01-01',  'United States'),\n",
       "            ('2016-01-01',        'Uruguay'),\n",
       "            ('2016-01-01',      'Venezuela'),\n",
       "            ('2016-01-01',        'Vietnam')],\n",
       "           names=['Date', 'Location'], length=652)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # only changing from Day to Date\n",
    "bigmac.index.set_names(names='Date', level=0)\n",
    "bigmac.index.set_names(names='Date', level='Day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('2010-01-01',      'Argentina'),\n",
       "            ('2010-01-01',      'Australia'),\n",
       "            ('2010-01-01',         'Brazil'),\n",
       "            ('2010-01-01',        'Britain'),\n",
       "            ('2010-01-01',         'Canada'),\n",
       "            ('2010-01-01',          'Chile'),\n",
       "            ('2010-01-01',          'China'),\n",
       "            ('2010-01-01',       'Colombia'),\n",
       "            ('2010-01-01',     'Costa Rica'),\n",
       "            ('2010-01-01', 'Czech Republic'),\n",
       "            ...\n",
       "            ('2016-01-01',    'Switzerland'),\n",
       "            ('2016-01-01',         'Taiwan'),\n",
       "            ('2016-01-01',       'Thailand'),\n",
       "            ('2016-01-01',         'Turkey'),\n",
       "            ('2016-01-01',            'UAE'),\n",
       "            ('2016-01-01',        'Ukraine'),\n",
       "            ('2016-01-01',  'United States'),\n",
       "            ('2016-01-01',        'Uruguay'),\n",
       "            ('2016-01-01',      'Venezuela'),\n",
       "            ('2016-01-01',        'Vietnam')],\n",
       "           names=['Day', 'Country'], length=652)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only changing from Location to Country\n",
    "bigmac.index.set_names(names='Country', level=1) \n",
    "bigmac.index.set_names(names='Country', level='Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.index.set_names(names=['Date', 'Country'], inplace=True)\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5) The `sort_index` Method on a `MultiIndex DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2016-01-01 Argentina                 2.39\n",
       "           Australia                 3.74\n",
       "           Brazil                    3.35"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv', parse_dates=['Date'], index_col=['Date', 'Country'])\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Canada</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Date       Country                           \n",
       "2016-01-01 Vietnam                       2.67\n",
       "           Venezuela                     0.66\n",
       "           Uruguay                       3.74\n",
       "           United States                 4.93\n",
       "           Ukraine                       1.54\n",
       "...                                       ...\n",
       "2010-01-01 Canada                        3.97\n",
       "           Britain                       3.67\n",
       "           Brazil                        4.76\n",
       "           Australia                     3.98\n",
       "           Argentina                     1.84\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.sort_index() # sort_index works in the sequence of index (Date => Country)\n",
    "bigmac.sort_index(ascending=True)\n",
    "\n",
    "bigmac.sort_index(ascending=False)  # all levels are sorting descending (Date => Country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What if we want to customize Sorting order for index?\n",
    "+ we can pass boolean as a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Date       Country                           \n",
       "2010-01-01 Uruguay                       3.32\n",
       "           United States                 3.58\n",
       "           Ukraine                       1.83\n",
       "           UAE                           2.99\n",
       "           Turkey                        3.83\n",
       "...                                       ...\n",
       "2016-01-01 Brazil                        3.35\n",
       "           Belgium                       4.25\n",
       "           Austria                       3.76\n",
       "           Australia                     3.74\n",
       "           Argentina                     2.39\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.sort_index(ascending=[True, False])\n",
    "\n",
    "# ascending for Date, descending for Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Date       Country                           \n",
       "2016-01-01 Argentina                     2.39\n",
       "           Australia                     3.74\n",
       "           Austria                       3.76\n",
       "           Belgium                       4.25\n",
       "           Brazil                        3.35\n",
       "...                                       ...\n",
       "2010-01-01 Turkey                        3.83\n",
       "           UAE                           2.99\n",
       "           Ukraine                       1.83\n",
       "           United States                 3.58\n",
       "           Uruguay                       3.32\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.sort_index(ascending=[False, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmac.sort_index()\n",
    "\n",
    "bigmac.sort_index(ascending=True)\n",
    "bigmac.sort_index(ascending=False)\n",
    "\n",
    "bigmac.sort_index(ascending=[True, False])\n",
    "bigmac.sort_index(ascending=[False, True], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2016-01-01 Argentina                 2.39\n",
       "           Australia                 3.74\n",
       "           Austria                   3.76"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting by Single Level using `level` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Date       Country                           \n",
       "2010-01-01 Argentina                     1.84\n",
       "           Australia                     3.98\n",
       "           Brazil                        4.76\n",
       "           Britain                       3.67\n",
       "           Canada                        3.97\n",
       "...                                       ...\n",
       "2016-01-01 Ukraine                       1.54\n",
       "           United States                 4.93\n",
       "           Uruguay                       3.74\n",
       "           Venezuela                     0.66\n",
       "           Vietnam                       2.67\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.sort_index(level=0)\n",
    "bigmac.sort_index(level='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2016-01-01 Vietnam                   2.67\n",
       "2015-07-01 Vietnam                   2.75\n",
       "2015-01-01 Vietnam                   2.81\n",
       "2014-07-01 Vietnam                   2.83\n",
       "2014-01-01 Vietnam                   2.84\n",
       "...                                   ...\n",
       "2012-07-01 Argentina                 4.16\n",
       "2012-01-01 Argentina                 4.64\n",
       "2011-07-01 Argentina                 4.84\n",
       "2010-07-01 Argentina                 3.56\n",
       "2010-01-01 Argentina                 1.84\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# when we sort on level of 1 or Country level, Date values are brought along (not necessarily sorted though)\n",
    "bigmac.sort_index(level=1, ascending=False)\n",
    "bigmac.sort_index(level='Country', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6) Extract Rows from a `MultiIndex DataFrame` using `.loc` and `.iloc` Accessors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv', index_col=['Date', 'Country'], parse_dates=['Date'])\n",
    "bigmac = bigmac.sort_index()\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following syntax is a bit ambiguous because 2nd value can be either\n",
    "+ Index name\n",
    "+ or Column name.\n",
    "This will lead to confusion. That's why we shouldn't use like that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        Country       \n",
       "2010-01-01  Argentina         1.84\n",
       "            Australia         3.98\n",
       "            Brazil            4.76\n",
       "            Britain           3.67\n",
       "            Canada            3.97\n",
       "            Chile             3.18\n",
       "            China             1.83\n",
       "            Colombia          3.91\n",
       "            Costa Rica        3.52\n",
       "            Czech Republic    3.71\n",
       "            Denmark           5.99\n",
       "            Egypt             2.38\n",
       "            Euro area         4.84\n",
       "            Hong Kong         1.91\n",
       "            Hungary           3.86\n",
       "            Indonesia         2.24\n",
       "            Israel            3.99\n",
       "            Japan             3.50\n",
       "            Latvia            3.09\n",
       "            Lithuania         2.87\n",
       "            Malaysia          2.08\n",
       "            Mexico            2.50\n",
       "            New Zealand       3.61\n",
       "            Norway            7.02\n",
       "            Pakistan          2.42\n",
       "            Peru              2.81\n",
       "            Philippines       2.21\n",
       "            Poland            2.86\n",
       "            Russia            2.34\n",
       "            Saudi Arabia      2.67\n",
       "            Singapore         3.19\n",
       "            South Africa      2.46\n",
       "            South Korea       2.98\n",
       "            Sri Lanka         1.83\n",
       "            Sweden            5.51\n",
       "            Switzerland       6.30\n",
       "            Taiwan            2.36\n",
       "            Thailand          2.11\n",
       "            Turkey            3.83\n",
       "            UAE               2.99\n",
       "            Ukraine           1.83\n",
       "            United States     3.58\n",
       "            Uruguay           3.32\n",
       "Name: Price in US Dollars, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.loc['2010-01-01']\n",
    "\n",
    "bigmac.loc['2010-01-01', 'Brazil']\n",
    "bigmac.loc['2010-01-01', 'Price in US Dollars']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correct Approach (Panda Convention) for `.loc` Accessor for MultiIndex\n",
    "+ **1st Argument as tuple for ROW Index** (it can include index or multi indexes)\n",
    "+ **2nd Arugment as tuple for COLUMN names**\n",
    "+ **NOTE: if there is one value to pass in 1st Argument, make sure to use comma , to indicate that it is tuple.**\n",
    "\n",
    "This is consistent and less ambiguous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Costa Rica</th>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Euro area</th>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>3.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>3.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peru</th>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>5.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAE</th>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price in US Dollars\n",
       "Country                            \n",
       "Argentina                      1.84\n",
       "Australia                      3.98\n",
       "Brazil                         4.76\n",
       "Britain                        3.67\n",
       "Canada                         3.97\n",
       "Chile                          3.18\n",
       "China                          1.83\n",
       "Colombia                       3.91\n",
       "Costa Rica                     3.52\n",
       "Czech Republic                 3.71\n",
       "Denmark                        5.99\n",
       "Egypt                          2.38\n",
       "Euro area                      4.84\n",
       "Hong Kong                      1.91\n",
       "Hungary                        3.86\n",
       "Indonesia                      2.24\n",
       "Israel                         3.99\n",
       "Japan                          3.50\n",
       "Latvia                         3.09\n",
       "Lithuania                      2.87\n",
       "Malaysia                       2.08\n",
       "Mexico                         2.50\n",
       "New Zealand                    3.61\n",
       "Norway                         7.02\n",
       "Pakistan                       2.42\n",
       "Peru                           2.81\n",
       "Philippines                    2.21\n",
       "Poland                         2.86\n",
       "Russia                         2.34\n",
       "Saudi Arabia                   2.67\n",
       "Singapore                      3.19\n",
       "South Africa                   2.46\n",
       "South Korea                    2.98\n",
       "Sri Lanka                      1.83\n",
       "Sweden                         5.51\n",
       "Switzerland                    6.30\n",
       "Taiwan                         2.36\n",
       "Thailand                       2.11\n",
       "Turkey                         3.83\n",
       "UAE                            2.99\n",
       "Ukraine                        1.83\n",
       "United States                  3.58\n",
       "Uruguay                        3.32"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.loc[('2010-01-01', 'Brazil')]\n",
    "bigmac.loc[('2010-01-01', 'Brazil'), 'Price in US Dollars']\n",
    "bigmac.loc[('2010-01-01', 'Brazil'), ['Price in US Dollars', 'Price in US Dollars']]\n",
    "\n",
    "# NOTE: if there is one value to pass in 1st Argument, make sure to use comma , to indicate that it is tuple.\n",
    "bigmac.loc[('2010-01-01', )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## using `.iloc[]` for multiindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Denmark</th>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <th>Turkey</th>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Denmark                   5.99\n",
       "           Malaysia                  2.08\n",
       "           Singapore                 3.19\n",
       "2012-07-01 Turkey                    4.52"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.iloc[0]\n",
    "bigmac.iloc[10]\n",
    "bigmac.iloc[[10, 20, 30]]\n",
    "bigmac.iloc[[10, 20, 30, 250]]|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7) The `transpose` Method\n",
    "- swap the indexes of the row labels and column labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv', index_col=['Date', 'Country'], parse_dates=['Date'])\n",
    "bigmac = bigmac.sort_index()\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th colspan=\"10\" halign=\"left\">2010-01-01</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">2016-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Britain</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Chile</th>\n",
       "      <th>China</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Costa Rica</th>\n",
       "      <th>Czech Republic</th>\n",
       "      <th>...</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Turkey</th>\n",
       "      <th>UAE</th>\n",
       "      <th>Ukraine</th>\n",
       "      <th>United States</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>Vietnam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price in US Dollars</th>\n",
       "      <td>1.84</td>\n",
       "      <td>3.98</td>\n",
       "      <td>4.76</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.71</td>\n",
       "      <td>...</td>\n",
       "      <td>6.44</td>\n",
       "      <td>2.08</td>\n",
       "      <td>3.09</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.54</td>\n",
       "      <td>4.93</td>\n",
       "      <td>3.74</td>\n",
       "      <td>0.66</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 652 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Date                2010-01-01                                              \\\n",
       "Country              Argentina Australia Brazil Britain Canada Chile China   \n",
       "Price in US Dollars       1.84      3.98   4.76    3.67   3.97  3.18  1.83   \n",
       "\n",
       "Date                                                    ...  2016-01-01  \\\n",
       "Country             Colombia Costa Rica Czech Republic  ... Switzerland   \n",
       "Price in US Dollars     3.91       3.52           3.71  ...        6.44   \n",
       "\n",
       "Date                                                                    \\\n",
       "Country             Taiwan Thailand Turkey   UAE Ukraine United States   \n",
       "Price in US Dollars   2.08     3.09   3.41  3.54    1.54          4.93   \n",
       "\n",
       "Date                                           \n",
       "Country             Uruguay Venezuela Vietnam  \n",
       "Price in US Dollars    3.74      0.66    2.67  \n",
       "\n",
       "[1 rows x 652 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = bigmac.transpose()\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## using the format of Best Practice tuple() for both row index and column labels\n",
    "\n",
    "NOTE: Always remember:\n",
    "+ **1st Argument as tuple for ROW Index** (it can include index or multi indexes)\n",
    "+ **2nd Arugment as tuple for COLUMN names** (it can include index or multi indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th colspan=\"21\" halign=\"left\">2010-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Chile</th>\n",
       "      <th>China</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Costa Rica</th>\n",
       "      <th>Czech Republic</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Egypt</th>\n",
       "      <th>Euro area</th>\n",
       "      <th>Hong Kong</th>\n",
       "      <th>...</th>\n",
       "      <th>Poland</th>\n",
       "      <th>Russia</th>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>South Africa</th>\n",
       "      <th>South Korea</th>\n",
       "      <th>Sri Lanka</th>\n",
       "      <th>Sweden</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>Taiwan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price in US Dollars</th>\n",
       "      <td>3.97</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.71</td>\n",
       "      <td>5.99</td>\n",
       "      <td>2.38</td>\n",
       "      <td>4.84</td>\n",
       "      <td>1.91</td>\n",
       "      <td>...</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.34</td>\n",
       "      <td>2.67</td>\n",
       "      <td>3.19</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.98</td>\n",
       "      <td>1.83</td>\n",
       "      <td>5.51</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Date                2010-01-01                                                 \\\n",
       "Country                 Canada Chile China Colombia Costa Rica Czech Republic   \n",
       "Price in US Dollars       3.97  3.18  1.83     3.91       3.52           3.71   \n",
       "\n",
       "Date                                                   ...                \\\n",
       "Country             Denmark Egypt Euro area Hong Kong  ... Poland Russia   \n",
       "Price in US Dollars    5.99  2.38      4.84      1.91  ...   2.86   2.34   \n",
       "\n",
       "Date                                                                           \\\n",
       "Country             Saudi Arabia Singapore South Africa South Korea Sri Lanka   \n",
       "Price in US Dollars         2.67      3.19         2.46        2.98      1.83   \n",
       "\n",
       "Date                                           \n",
       "Country             Sweden Switzerland Taiwan  \n",
       "Price in US Dollars   5.51         6.3   2.36  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "bigmac.loc[('Price in US Dollars',)]\n",
    "\n",
    "bigmac.loc[('Price in US Dollars', ), ('2010-01-01', )]\n",
    "\n",
    "bigmac.loc[('Price in US Dollars', ), ('2010-01-01', 'Canada')]\n",
    "\n",
    "bigmac.loc[('Price in US Dollars', ), ('2010-01-01', 'Canada'): ('2010-01-01', 'Taiwan')] \n",
    "# to extract values, we need to tell pandas both multi indexes (Date and Country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8) The `swaplevel()` Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-01-01</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Date       Country                       \n",
       "2010-01-01 Argentina                 1.84\n",
       "           Australia                 3.98\n",
       "           Brazil                    4.76"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = pd.read_csv('Data/bigmac.csv', index_col=['Date', 'Country'], parse_dates=['Date'])\n",
    "bigmac = bigmac.sort_index()\n",
    "bigmac.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our case, there are only two level of indexes. So pandas know to swap them accordingly.\n",
    "\n",
    "**If we have more than two level of indexes, we need to tell pandas which one (`i` )to swap with which (`j`).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Price in US Dollars\n",
       "Country       Date                           \n",
       "Argentina     2010-01-01                 1.84\n",
       "Australia     2010-01-01                 3.98\n",
       "Brazil        2010-01-01                 4.76\n",
       "Britain       2010-01-01                 3.67\n",
       "Canada        2010-01-01                 3.97\n",
       "...                                       ...\n",
       "Ukraine       2016-01-01                 1.54\n",
       "United States 2016-01-01                 4.93\n",
       "Uruguay       2016-01-01                 3.74\n",
       "Venezuela     2016-01-01                 0.66\n",
       "Vietnam       2016-01-01                 2.67\n",
       "\n",
       "[652 rows x 1 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac.swaplevel()\n",
    "\n",
    "bigmac.swaplevel('Date', 'Country')\n",
    "bigmac.swaplevel('Country', 'Date')\n",
    "\n",
    "bigmac.swaplevel(i=1, j=0)\n",
    "bigmac.swaplevel(1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price in US Dollars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Britain</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Price in US Dollars\n",
       "Country   Date                           \n",
       "Argentina 2010-01-01                 1.84\n",
       "Australia 2010-01-01                 3.98\n",
       "Brazil    2010-01-01                 4.76\n",
       "Britain   2010-01-01                 3.67\n",
       "Canada    2010-01-01                 3.97"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmac = bigmac.swaplevel(1,0)\n",
    "bigmac.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9) The `.stack()` Method\n",
    "**it moves Column based indexes to Row based indexes.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Arab World</th>\n",
       "      <th>2015</th>\n",
       "      <td>392022276.0</td>\n",
       "      <td>2.530102e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>384222592.0</td>\n",
       "      <td>2.873600e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>376504253.0</td>\n",
       "      <td>2.846994e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Population           GDP\n",
       "country    year                           \n",
       "Arab World 2015  392022276.0  2.530102e+12\n",
       "           2014  384222592.0  2.873600e+12\n",
       "           2013  376504253.0  2.846994e+12"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world = pd.read_csv('Data/worldstats.csv', index_col=['country', 'year'])\n",
    "world.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 11211 entries, ('Arab World', 2015) to ('Zimbabwe', 1960)\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Population  11211 non-null  float64\n",
      " 1   GDP         11211 non-null  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 223.0+ KB\n"
     ]
    }
   ],
   "source": [
    "world.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After stacking, Population and GDP column are moved to Row based indexes.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country     year            \n",
       "Arab World  2015  Population    3.920223e+08\n",
       "                  GDP           2.530102e+12\n",
       "            2014  Population    3.842226e+08\n",
       "                  GDP           2.873600e+12\n",
       "            2013  Population    3.765043e+08\n",
       "                                    ...     \n",
       "Zimbabwe    1962  GDP           1.117602e+09\n",
       "            1961  Population    3.876638e+06\n",
       "                  GDP           1.096647e+09\n",
       "            1960  Population    3.752390e+06\n",
       "                  GDP           1.052990e+09\n",
       "Length: 22422, dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(world.stack())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can convert series to Dataframe using `.to_frame()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Arab World</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.920223e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>2.530102e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.842226e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>2.873600e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.765043e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zimbabwe</th>\n",
       "      <th>1962</th>\n",
       "      <th>GDP</th>\n",
       "      <td>1.117602e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1961</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.876638e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.096647e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1960</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.752390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.052990e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22422 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0\n",
       "country    year                         \n",
       "Arab World 2015 Population  3.920223e+08\n",
       "                GDP         2.530102e+12\n",
       "           2014 Population  3.842226e+08\n",
       "                GDP         2.873600e+12\n",
       "           2013 Population  3.765043e+08\n",
       "...                                  ...\n",
       "Zimbabwe   1962 GDP         1.117602e+09\n",
       "           1961 Population  3.876638e+06\n",
       "                GDP         1.096647e+09\n",
       "           1960 Population  3.752390e+06\n",
       "                GDP         1.052990e+09\n",
       "\n",
       "[22422 rows x 1 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.stack().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10) The `.unstack()` Method - Part 1\n",
    "**move Row Based Indexes to Column Based Indexes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Arab World</th>\n",
       "      <th>2015</th>\n",
       "      <td>392022276.0</td>\n",
       "      <td>2.530102e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>384222592.0</td>\n",
       "      <td>2.873600e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>376504253.0</td>\n",
       "      <td>2.846994e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Population           GDP\n",
       "country    year                           \n",
       "Arab World 2015  392022276.0  2.530102e+12\n",
       "           2014  384222592.0  2.873600e+12\n",
       "           2013  376504253.0  2.846994e+12"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world = pd.read_csv('Data/worldstats.csv', index_col=['country', 'year'])\n",
    "world.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country     year            \n",
       "Arab World  2015  Population    3.920223e+08\n",
       "                  GDP           2.530102e+12\n",
       "            2014  Population    3.842226e+08\n",
       "dtype: float64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = world.stack()\n",
    "s.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Population</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1960</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>8.994793e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>1.112489e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GDP</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2015</th>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <td>1.267740e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>7.343364e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen, Rep.</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>2.120156e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>1.389294e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28224 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               0\n",
       "           year country                         \n",
       "Population 1960 Afghanistan         8.994793e+06\n",
       "                Albania                      NaN\n",
       "                Algeria             1.112489e+07\n",
       "                Andorra                      NaN\n",
       "                Angola                       NaN\n",
       "...                                          ...\n",
       "GDP        2015 West Bank and Gaza  1.267740e+10\n",
       "                World               7.343364e+13\n",
       "                Yemen, Rep.                  NaN\n",
       "                Zambia              2.120156e+10\n",
       "                Zimbabwe            1.389294e+10\n",
       "\n",
       "[28224 rows x 1 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unstack()\n",
    "s.unstack().unstack()\n",
    "s.unstack().unstack().unstack().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11) The `.unstack()` Method - Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Arab World</th>\n",
       "      <th>2015</th>\n",
       "      <td>392022276.0</td>\n",
       "      <td>2.530102e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>384222592.0</td>\n",
       "      <td>2.873600e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>376504253.0</td>\n",
       "      <td>2.846994e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Population           GDP\n",
       "country    year                           \n",
       "Arab World 2015  392022276.0  2.530102e+12\n",
       "           2014  384222592.0  2.873600e+12\n",
       "           2013  376504253.0  2.846994e+12"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world = pd.read_csv('Data/worldstats.csv', index_col=['country', 'year'])\n",
    "world.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country     year            \n",
       "Arab World  2015  Population    3.920223e+08\n",
       "                  GDP           2.530102e+12\n",
       "            2014  Population    3.842226e+08\n",
       "dtype: float64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = world.stack()\n",
    "s.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Arab World</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.920223e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>2.530102e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.842226e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0\n",
       "country    year                         \n",
       "Arab World 2015 Population  3.920223e+08\n",
       "                GDP         2.530102e+12\n",
       "           2014 Population  3.842226e+08"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.to_frame().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can unstack on different level based on requirements using `level` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>country</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>Aruba</th>\n",
       "      <th>...</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela, RB</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>Virgin Islands (U.S.)</th>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <th>World</th>\n",
       "      <th>Yemen, Rep.</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1960</th>\n",
       "      <th>Population</th>\n",
       "      <td>8.994793e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.112489e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.146845e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.035056e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.049586e+06</td>\n",
       "      <td>3.752390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>5.377778e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.723638e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.607600e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24200000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.364643e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.987397e+08</td>\n",
       "      <td>1.052990e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1961</th>\n",
       "      <th>Population</th>\n",
       "      <td>9.164945e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.140486e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.461684e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.076121e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.142848e+06</td>\n",
       "      <td>3.876638e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>5.488889e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.434767e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.923367e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25700000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.420440e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.823597e+08</td>\n",
       "      <td>1.096647e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <th>Population</th>\n",
       "      <td>9.343772e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.169015e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.128768e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.790590e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.129064e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.240664e+06</td>\n",
       "      <td>4.006262e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>2013</th>\n",
       "      <th>GDP</th>\n",
       "      <td>2.004633e+10</td>\n",
       "      <td>1.278103e+10</td>\n",
       "      <td>2.097035e+11</td>\n",
       "      <td>3.249101e+09</td>\n",
       "      <td>1.249121e+11</td>\n",
       "      <td>1.200588e+09</td>\n",
       "      <td>2.846994e+12</td>\n",
       "      <td>6.239320e+11</td>\n",
       "      <td>1.112147e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.679566e+10</td>\n",
       "      <td>8.017876e+08</td>\n",
       "      <td>3.713366e+11</td>\n",
       "      <td>1.712220e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.247600e+10</td>\n",
       "      <td>7.643132e+13</td>\n",
       "      <td>3.595450e+10</td>\n",
       "      <td>2.804552e+10</td>\n",
       "      <td>1.349023e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.162751e+07</td>\n",
       "      <td>2.893654e+06</td>\n",
       "      <td>3.893433e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.422752e+07</td>\n",
       "      <td>9.090000e+04</td>\n",
       "      <td>3.842226e+08</td>\n",
       "      <td>4.298003e+07</td>\n",
       "      <td>3.006154e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.075770e+07</td>\n",
       "      <td>2.588830e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.072890e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.294682e+06</td>\n",
       "      <td>7.260780e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.572134e+07</td>\n",
       "      <td>1.524586e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>2.005019e+10</td>\n",
       "      <td>1.327796e+10</td>\n",
       "      <td>2.135185e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.267751e+11</td>\n",
       "      <td>1.220976e+09</td>\n",
       "      <td>2.873600e+12</td>\n",
       "      <td>5.480549e+11</td>\n",
       "      <td>1.164444e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.313285e+10</td>\n",
       "      <td>8.149546e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.862047e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.271560e+10</td>\n",
       "      <td>7.810634e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.713464e+10</td>\n",
       "      <td>1.419691e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.252656e+07</td>\n",
       "      <td>2.889167e+06</td>\n",
       "      <td>3.966652e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.502197e+07</td>\n",
       "      <td>9.181800e+04</td>\n",
       "      <td>3.920223e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.017712e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.129950e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.170380e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.422143e+06</td>\n",
       "      <td>7.346633e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.621177e+07</td>\n",
       "      <td>1.560275e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.919944e+10</td>\n",
       "      <td>1.145560e+10</td>\n",
       "      <td>1.668386e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.026431e+11</td>\n",
       "      <td>1.297285e+09</td>\n",
       "      <td>2.530102e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.056140e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.673280e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.935994e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.267740e+10</td>\n",
       "      <td>7.343364e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.120156e+10</td>\n",
       "      <td>1.389294e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 252 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "country           Afghanistan       Albania       Algeria       Andorra  \\\n",
       "year                                                                      \n",
       "1960 Population  8.994793e+06           NaN  1.112489e+07           NaN   \n",
       "     GDP         5.377778e+08           NaN  2.723638e+09           NaN   \n",
       "1961 Population  9.164945e+06           NaN  1.140486e+07           NaN   \n",
       "     GDP         5.488889e+08           NaN  2.434767e+09           NaN   \n",
       "1962 Population  9.343772e+06           NaN  1.169015e+07           NaN   \n",
       "...                       ...           ...           ...           ...   \n",
       "2013 GDP         2.004633e+10  1.278103e+10  2.097035e+11  3.249101e+09   \n",
       "2014 Population  3.162751e+07  2.893654e+06  3.893433e+07           NaN   \n",
       "     GDP         2.005019e+10  1.327796e+10  2.135185e+11           NaN   \n",
       "2015 Population  3.252656e+07  2.889167e+06  3.966652e+07           NaN   \n",
       "     GDP         1.919944e+10  1.145560e+10  1.668386e+11           NaN   \n",
       "\n",
       "country                Angola  Antigua and Barbuda    Arab World  \\\n",
       "year                                                               \n",
       "1960 Population           NaN                  NaN           NaN   \n",
       "     GDP                  NaN                  NaN           NaN   \n",
       "1961 Population           NaN                  NaN           NaN   \n",
       "     GDP                  NaN                  NaN           NaN   \n",
       "1962 Population           NaN                  NaN           NaN   \n",
       "...                       ...                  ...           ...   \n",
       "2013 GDP         1.249121e+11         1.200588e+09  2.846994e+12   \n",
       "2014 Population  2.422752e+07         9.090000e+04  3.842226e+08   \n",
       "     GDP         1.267751e+11         1.220976e+09  2.873600e+12   \n",
       "2015 Population  2.502197e+07         9.181800e+04  3.920223e+08   \n",
       "     GDP         1.026431e+11         1.297285e+09  2.530102e+12   \n",
       "\n",
       "country             Argentina       Armenia  Aruba  ...    Uzbekistan  \\\n",
       "year                                                ...                 \n",
       "1960 Population           NaN           NaN    NaN  ...           NaN   \n",
       "     GDP                  NaN           NaN    NaN  ...           NaN   \n",
       "1961 Population           NaN           NaN    NaN  ...           NaN   \n",
       "     GDP                  NaN           NaN    NaN  ...           NaN   \n",
       "1962 Population  2.128768e+07           NaN    NaN  ...           NaN   \n",
       "...                       ...           ...    ...  ...           ...   \n",
       "2013 GDP         6.239320e+11  1.112147e+10    NaN  ...  5.679566e+10   \n",
       "2014 Population  4.298003e+07  3.006154e+06    NaN  ...  3.075770e+07   \n",
       "     GDP         5.480549e+11  1.164444e+10    NaN  ...  6.313285e+10   \n",
       "2015 Population           NaN  3.017712e+06    NaN  ...  3.129950e+07   \n",
       "     GDP                  NaN  1.056140e+10    NaN  ...  6.673280e+10   \n",
       "\n",
       "country               Vanuatu  Venezuela, RB       Vietnam  \\\n",
       "year                                                         \n",
       "1960 Population           NaN   8.146845e+06           NaN   \n",
       "     GDP                  NaN   8.607600e+09           NaN   \n",
       "1961 Population           NaN   8.461684e+06           NaN   \n",
       "     GDP                  NaN   8.923367e+09           NaN   \n",
       "1962 Population           NaN   8.790590e+06           NaN   \n",
       "...                       ...            ...           ...   \n",
       "2013 GDP         8.017876e+08   3.713366e+11  1.712220e+11   \n",
       "2014 Population  2.588830e+05            NaN  9.072890e+07   \n",
       "     GDP         8.149546e+08            NaN  1.862047e+11   \n",
       "2015 Population           NaN            NaN  9.170380e+07   \n",
       "     GDP                  NaN            NaN  1.935994e+11   \n",
       "\n",
       "country          Virgin Islands (U.S.)  West Bank and Gaza         World  \\\n",
       "year                                                                       \n",
       "1960 Population                32000.0                 NaN  3.035056e+09   \n",
       "     GDP                    24200000.0                 NaN  1.364643e+12   \n",
       "1961 Population                34100.0                 NaN  3.076121e+09   \n",
       "     GDP                    25700000.0                 NaN  1.420440e+12   \n",
       "1962 Population                36300.0                 NaN  3.129064e+09   \n",
       "...                                ...                 ...           ...   \n",
       "2013 GDP                           NaN        1.247600e+10  7.643132e+13   \n",
       "2014 Population                    NaN        4.294682e+06  7.260780e+09   \n",
       "     GDP                           NaN        1.271560e+10  7.810634e+13   \n",
       "2015 Population                    NaN        4.422143e+06  7.346633e+09   \n",
       "     GDP                           NaN        1.267740e+10  7.343364e+13   \n",
       "\n",
       "country           Yemen, Rep.        Zambia      Zimbabwe  \n",
       "year                                                       \n",
       "1960 Population           NaN  3.049586e+06  3.752390e+06  \n",
       "     GDP                  NaN  6.987397e+08  1.052990e+09  \n",
       "1961 Population           NaN  3.142848e+06  3.876638e+06  \n",
       "     GDP                  NaN  6.823597e+08  1.096647e+09  \n",
       "1962 Population           NaN  3.240664e+06  4.006262e+06  \n",
       "...                       ...           ...           ...  \n",
       "2013 GDP         3.595450e+10  2.804552e+10  1.349023e+10  \n",
       "2014 Population           NaN  1.572134e+07  1.524586e+07  \n",
       "     GDP                  NaN  2.713464e+10  1.419691e+10  \n",
       "2015 Population           NaN  1.621177e+07  1.560275e+07  \n",
       "     GDP                  NaN  2.120156e+10  1.389294e+10  \n",
       "\n",
       "[112 rows x 252 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unstack(level=0)\n",
    "s.unstack(0) # country\n",
    "\n",
    "s.unstack(1) # year\n",
    "s.unstack(2) # Popultion and GDP\n",
    "\n",
    "# we can also use negative indexes (moving backwards)\n",
    "s.unstack(-1) #Population and GDP\n",
    "s.unstack(-2) # Year\n",
    "s.unstack(-3) # country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can unstack on different level based on requirements using `level` parameter using Name of actual index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>Aruba</th>\n",
       "      <th>...</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela, RB</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>Virgin Islands (U.S.)</th>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <th>World</th>\n",
       "      <th>Yemen, Rep.</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1960</th>\n",
       "      <th>Population</th>\n",
       "      <td>8.994793e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.112489e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.146845e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.035056e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.049586e+06</td>\n",
       "      <td>3.752390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>5.377778e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.723638e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.607600e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24200000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.364643e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.987397e+08</td>\n",
       "      <td>1.052990e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1961</th>\n",
       "      <th>Population</th>\n",
       "      <td>9.164945e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.140486e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.461684e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.076121e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.142848e+06</td>\n",
       "      <td>3.876638e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>5.488889e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.434767e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.923367e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25700000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.420440e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.823597e+08</td>\n",
       "      <td>1.096647e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <th>Population</th>\n",
       "      <td>9.343772e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.169015e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.128768e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.790590e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.129064e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.240664e+06</td>\n",
       "      <td>4.006262e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>2013</th>\n",
       "      <th>GDP</th>\n",
       "      <td>2.004633e+10</td>\n",
       "      <td>1.278103e+10</td>\n",
       "      <td>2.097035e+11</td>\n",
       "      <td>3.249101e+09</td>\n",
       "      <td>1.249121e+11</td>\n",
       "      <td>1.200588e+09</td>\n",
       "      <td>2.846994e+12</td>\n",
       "      <td>6.239320e+11</td>\n",
       "      <td>1.112147e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.679566e+10</td>\n",
       "      <td>8.017876e+08</td>\n",
       "      <td>3.713366e+11</td>\n",
       "      <td>1.712220e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.247600e+10</td>\n",
       "      <td>7.643132e+13</td>\n",
       "      <td>3.595450e+10</td>\n",
       "      <td>2.804552e+10</td>\n",
       "      <td>1.349023e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.162751e+07</td>\n",
       "      <td>2.893654e+06</td>\n",
       "      <td>3.893433e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.422752e+07</td>\n",
       "      <td>9.090000e+04</td>\n",
       "      <td>3.842226e+08</td>\n",
       "      <td>4.298003e+07</td>\n",
       "      <td>3.006154e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.075770e+07</td>\n",
       "      <td>2.588830e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.072890e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.294682e+06</td>\n",
       "      <td>7.260780e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.572134e+07</td>\n",
       "      <td>1.524586e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>2.005019e+10</td>\n",
       "      <td>1.327796e+10</td>\n",
       "      <td>2.135185e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.267751e+11</td>\n",
       "      <td>1.220976e+09</td>\n",
       "      <td>2.873600e+12</td>\n",
       "      <td>5.480549e+11</td>\n",
       "      <td>1.164444e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.313285e+10</td>\n",
       "      <td>8.149546e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.862047e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.271560e+10</td>\n",
       "      <td>7.810634e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.713464e+10</td>\n",
       "      <td>1.419691e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.252656e+07</td>\n",
       "      <td>2.889167e+06</td>\n",
       "      <td>3.966652e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.502197e+07</td>\n",
       "      <td>9.181800e+04</td>\n",
       "      <td>3.920223e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.017712e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.129950e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.170380e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.422143e+06</td>\n",
       "      <td>7.346633e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.621177e+07</td>\n",
       "      <td>1.560275e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.919944e+10</td>\n",
       "      <td>1.145560e+10</td>\n",
       "      <td>1.668386e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.026431e+11</td>\n",
       "      <td>1.297285e+09</td>\n",
       "      <td>2.530102e+12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.056140e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.673280e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.935994e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.267740e+10</td>\n",
       "      <td>7.343364e+13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.120156e+10</td>\n",
       "      <td>1.389294e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 252 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "country           Afghanistan       Albania       Algeria       Andorra  \\\n",
       "year                                                                      \n",
       "1960 Population  8.994793e+06           NaN  1.112489e+07           NaN   \n",
       "     GDP         5.377778e+08           NaN  2.723638e+09           NaN   \n",
       "1961 Population  9.164945e+06           NaN  1.140486e+07           NaN   \n",
       "     GDP         5.488889e+08           NaN  2.434767e+09           NaN   \n",
       "1962 Population  9.343772e+06           NaN  1.169015e+07           NaN   \n",
       "...                       ...           ...           ...           ...   \n",
       "2013 GDP         2.004633e+10  1.278103e+10  2.097035e+11  3.249101e+09   \n",
       "2014 Population  3.162751e+07  2.893654e+06  3.893433e+07           NaN   \n",
       "     GDP         2.005019e+10  1.327796e+10  2.135185e+11           NaN   \n",
       "2015 Population  3.252656e+07  2.889167e+06  3.966652e+07           NaN   \n",
       "     GDP         1.919944e+10  1.145560e+10  1.668386e+11           NaN   \n",
       "\n",
       "country                Angola  Antigua and Barbuda    Arab World  \\\n",
       "year                                                               \n",
       "1960 Population           NaN                  NaN           NaN   \n",
       "     GDP                  NaN                  NaN           NaN   \n",
       "1961 Population           NaN                  NaN           NaN   \n",
       "     GDP                  NaN                  NaN           NaN   \n",
       "1962 Population           NaN                  NaN           NaN   \n",
       "...                       ...                  ...           ...   \n",
       "2013 GDP         1.249121e+11         1.200588e+09  2.846994e+12   \n",
       "2014 Population  2.422752e+07         9.090000e+04  3.842226e+08   \n",
       "     GDP         1.267751e+11         1.220976e+09  2.873600e+12   \n",
       "2015 Population  2.502197e+07         9.181800e+04  3.920223e+08   \n",
       "     GDP         1.026431e+11         1.297285e+09  2.530102e+12   \n",
       "\n",
       "country             Argentina       Armenia  Aruba  ...    Uzbekistan  \\\n",
       "year                                                ...                 \n",
       "1960 Population           NaN           NaN    NaN  ...           NaN   \n",
       "     GDP                  NaN           NaN    NaN  ...           NaN   \n",
       "1961 Population           NaN           NaN    NaN  ...           NaN   \n",
       "     GDP                  NaN           NaN    NaN  ...           NaN   \n",
       "1962 Population  2.128768e+07           NaN    NaN  ...           NaN   \n",
       "...                       ...           ...    ...  ...           ...   \n",
       "2013 GDP         6.239320e+11  1.112147e+10    NaN  ...  5.679566e+10   \n",
       "2014 Population  4.298003e+07  3.006154e+06    NaN  ...  3.075770e+07   \n",
       "     GDP         5.480549e+11  1.164444e+10    NaN  ...  6.313285e+10   \n",
       "2015 Population           NaN  3.017712e+06    NaN  ...  3.129950e+07   \n",
       "     GDP                  NaN  1.056140e+10    NaN  ...  6.673280e+10   \n",
       "\n",
       "country               Vanuatu  Venezuela, RB       Vietnam  \\\n",
       "year                                                         \n",
       "1960 Population           NaN   8.146845e+06           NaN   \n",
       "     GDP                  NaN   8.607600e+09           NaN   \n",
       "1961 Population           NaN   8.461684e+06           NaN   \n",
       "     GDP                  NaN   8.923367e+09           NaN   \n",
       "1962 Population           NaN   8.790590e+06           NaN   \n",
       "...                       ...            ...           ...   \n",
       "2013 GDP         8.017876e+08   3.713366e+11  1.712220e+11   \n",
       "2014 Population  2.588830e+05            NaN  9.072890e+07   \n",
       "     GDP         8.149546e+08            NaN  1.862047e+11   \n",
       "2015 Population           NaN            NaN  9.170380e+07   \n",
       "     GDP                  NaN            NaN  1.935994e+11   \n",
       "\n",
       "country          Virgin Islands (U.S.)  West Bank and Gaza         World  \\\n",
       "year                                                                       \n",
       "1960 Population                32000.0                 NaN  3.035056e+09   \n",
       "     GDP                    24200000.0                 NaN  1.364643e+12   \n",
       "1961 Population                34100.0                 NaN  3.076121e+09   \n",
       "     GDP                    25700000.0                 NaN  1.420440e+12   \n",
       "1962 Population                36300.0                 NaN  3.129064e+09   \n",
       "...                                ...                 ...           ...   \n",
       "2013 GDP                           NaN        1.247600e+10  7.643132e+13   \n",
       "2014 Population                    NaN        4.294682e+06  7.260780e+09   \n",
       "     GDP                           NaN        1.271560e+10  7.810634e+13   \n",
       "2015 Population                    NaN        4.422143e+06  7.346633e+09   \n",
       "     GDP                           NaN        1.267740e+10  7.343364e+13   \n",
       "\n",
       "country           Yemen, Rep.        Zambia      Zimbabwe  \n",
       "year                                                       \n",
       "1960 Population           NaN  3.049586e+06  3.752390e+06  \n",
       "     GDP                  NaN  6.987397e+08  1.052990e+09  \n",
       "1961 Population           NaN  3.142848e+06  3.876638e+06  \n",
       "     GDP                  NaN  6.823597e+08  1.096647e+09  \n",
       "1962 Population           NaN  3.240664e+06  4.006262e+06  \n",
       "...                       ...           ...           ...  \n",
       "2013 GDP         3.595450e+10  2.804552e+10  1.349023e+10  \n",
       "2014 Population           NaN  1.572134e+07  1.524586e+07  \n",
       "     GDP                  NaN  2.713464e+10  1.419691e+10  \n",
       "2015 Population           NaN  1.621177e+07  1.560275e+07  \n",
       "     GDP                  NaN  2.120156e+10  1.389294e+10  \n",
       "\n",
       "[112 rows x 252 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unstack('year')\n",
    "s.unstack('country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12) The `.unstack()` Method - Part 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country     year            \n",
       "Arab World  2015  Population    3.920223e+08\n",
       "                  GDP           2.530102e+12\n",
       "            2014  Population    3.842226e+08\n",
       "dtype: float64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world = pd.read_csv('Data/worldstats.csv', index_col=['country', 'year'])\n",
    "s = world.stack()\n",
    "s.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Arab World</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.920223e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>2.530102e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.842226e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0\n",
       "country    year                         \n",
       "Arab World 2015 Population  3.920223e+08\n",
       "                GDP         2.530102e+12\n",
       "           2014 Population  3.842226e+08"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.to_frame().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unstack using list of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>2015</th>\n",
       "      <th>2014</th>\n",
       "      <th>2013</th>\n",
       "      <th>2012</th>\n",
       "      <th>2011</th>\n",
       "      <th>2010</th>\n",
       "      <th>2009</th>\n",
       "      <th>2008</th>\n",
       "      <th>2007</th>\n",
       "      <th>2006</th>\n",
       "      <th>...</th>\n",
       "      <th>1969</th>\n",
       "      <th>1968</th>\n",
       "      <th>1967</th>\n",
       "      <th>1966</th>\n",
       "      <th>1965</th>\n",
       "      <th>1964</th>\n",
       "      <th>1963</th>\n",
       "      <th>1962</th>\n",
       "      <th>1961</th>\n",
       "      <th>1960</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>...</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>3.920223e+08</td>\n",
       "      <td>3.842226e+08</td>\n",
       "      <td>3.765043e+08</td>\n",
       "      <td>3.688026e+08</td>\n",
       "      <td>3.610318e+08</td>\n",
       "      <td>3.531122e+08</td>\n",
       "      <td>3.450542e+08</td>\n",
       "      <td>3.368865e+08</td>\n",
       "      <td>3.287666e+08</td>\n",
       "      <td>3.209067e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>5.036321e+06</td>\n",
       "      <td>4.874113e+06</td>\n",
       "      <td>4.718612e+06</td>\n",
       "      <td>4.568320e+06</td>\n",
       "      <td>4.422132e+06</td>\n",
       "      <td>4.279561e+06</td>\n",
       "      <td>4.140804e+06</td>\n",
       "      <td>4.006262e+06</td>\n",
       "      <td>3.876638e+06</td>\n",
       "      <td>3.752390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>2.530102e+12</td>\n",
       "      <td>2.873600e+12</td>\n",
       "      <td>2.846994e+12</td>\n",
       "      <td>2.773270e+12</td>\n",
       "      <td>2.497945e+12</td>\n",
       "      <td>2.103825e+12</td>\n",
       "      <td>1.798878e+12</td>\n",
       "      <td>2.081343e+12</td>\n",
       "      <td>1.641666e+12</td>\n",
       "      <td>1.404190e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.747999e+09</td>\n",
       "      <td>1.479600e+09</td>\n",
       "      <td>1.397002e+09</td>\n",
       "      <td>1.281750e+09</td>\n",
       "      <td>1.311436e+09</td>\n",
       "      <td>1.217138e+09</td>\n",
       "      <td>1.159512e+09</td>\n",
       "      <td>1.117602e+09</td>\n",
       "      <td>1.096647e+09</td>\n",
       "      <td>1.052990e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 11211 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year                2015          2014          2013          2012  \\\n",
       "country       Arab World    Arab World    Arab World    Arab World   \n",
       "Population  3.920223e+08  3.842226e+08  3.765043e+08  3.688026e+08   \n",
       "GDP         2.530102e+12  2.873600e+12  2.846994e+12  2.773270e+12   \n",
       "\n",
       "year                2011          2010          2009          2008  \\\n",
       "country       Arab World    Arab World    Arab World    Arab World   \n",
       "Population  3.610318e+08  3.531122e+08  3.450542e+08  3.368865e+08   \n",
       "GDP         2.497945e+12  2.103825e+12  1.798878e+12  2.081343e+12   \n",
       "\n",
       "year                2007          2006  ...          1969          1968  \\\n",
       "country       Arab World    Arab World  ...      Zimbabwe      Zimbabwe   \n",
       "Population  3.287666e+08  3.209067e+08  ...  5.036321e+06  4.874113e+06   \n",
       "GDP         1.641666e+12  1.404190e+12  ...  1.747999e+09  1.479600e+09   \n",
       "\n",
       "year                1967          1966          1965          1964  \\\n",
       "country         Zimbabwe      Zimbabwe      Zimbabwe      Zimbabwe   \n",
       "Population  4.718612e+06  4.568320e+06  4.422132e+06  4.279561e+06   \n",
       "GDP         1.397002e+09  1.281750e+09  1.311436e+09  1.217138e+09   \n",
       "\n",
       "year                1963          1962          1961          1960  \n",
       "country         Zimbabwe      Zimbabwe      Zimbabwe      Zimbabwe  \n",
       "Population  4.140804e+06  4.006262e+06  3.876638e+06  3.752390e+06  \n",
       "GDP         1.159512e+09  1.117602e+09  1.096647e+09  1.052990e+09  \n",
       "\n",
       "[2 rows x 11211 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unstack(level=[1, 0])\n",
    "s.unstack(level=['year', 'country'])\n",
    "# we can see year became 1st level and country became 2nd level. Pandas follow order to unstack based on what we provided in the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Arab World</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Zimbabwe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>2015</th>\n",
       "      <th>2014</th>\n",
       "      <th>2013</th>\n",
       "      <th>2012</th>\n",
       "      <th>2011</th>\n",
       "      <th>2010</th>\n",
       "      <th>2009</th>\n",
       "      <th>2008</th>\n",
       "      <th>2007</th>\n",
       "      <th>2006</th>\n",
       "      <th>...</th>\n",
       "      <th>1969</th>\n",
       "      <th>1968</th>\n",
       "      <th>1967</th>\n",
       "      <th>1966</th>\n",
       "      <th>1965</th>\n",
       "      <th>1964</th>\n",
       "      <th>1963</th>\n",
       "      <th>1962</th>\n",
       "      <th>1961</th>\n",
       "      <th>1960</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>3.920223e+08</td>\n",
       "      <td>3.842226e+08</td>\n",
       "      <td>3.765043e+08</td>\n",
       "      <td>3.688026e+08</td>\n",
       "      <td>3.610318e+08</td>\n",
       "      <td>3.531122e+08</td>\n",
       "      <td>3.450542e+08</td>\n",
       "      <td>3.368865e+08</td>\n",
       "      <td>3.287666e+08</td>\n",
       "      <td>3.209067e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>5.036321e+06</td>\n",
       "      <td>4.874113e+06</td>\n",
       "      <td>4.718612e+06</td>\n",
       "      <td>4.568320e+06</td>\n",
       "      <td>4.422132e+06</td>\n",
       "      <td>4.279561e+06</td>\n",
       "      <td>4.140804e+06</td>\n",
       "      <td>4.006262e+06</td>\n",
       "      <td>3.876638e+06</td>\n",
       "      <td>3.752390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>2.530102e+12</td>\n",
       "      <td>2.873600e+12</td>\n",
       "      <td>2.846994e+12</td>\n",
       "      <td>2.773270e+12</td>\n",
       "      <td>2.497945e+12</td>\n",
       "      <td>2.103825e+12</td>\n",
       "      <td>1.798878e+12</td>\n",
       "      <td>2.081343e+12</td>\n",
       "      <td>1.641666e+12</td>\n",
       "      <td>1.404190e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.747999e+09</td>\n",
       "      <td>1.479600e+09</td>\n",
       "      <td>1.397002e+09</td>\n",
       "      <td>1.281750e+09</td>\n",
       "      <td>1.311436e+09</td>\n",
       "      <td>1.217138e+09</td>\n",
       "      <td>1.159512e+09</td>\n",
       "      <td>1.117602e+09</td>\n",
       "      <td>1.096647e+09</td>\n",
       "      <td>1.052990e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 11211 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "country       Arab World                                            \\\n",
       "year                2015          2014          2013          2012   \n",
       "Population  3.920223e+08  3.842226e+08  3.765043e+08  3.688026e+08   \n",
       "GDP         2.530102e+12  2.873600e+12  2.846994e+12  2.773270e+12   \n",
       "\n",
       "country                                                             \\\n",
       "year                2011          2010          2009          2008   \n",
       "Population  3.610318e+08  3.531122e+08  3.450542e+08  3.368865e+08   \n",
       "GDP         2.497945e+12  2.103825e+12  1.798878e+12  2.081343e+12   \n",
       "\n",
       "country                                 ...      Zimbabwe                \\\n",
       "year                2007          2006  ...          1969          1968   \n",
       "Population  3.287666e+08  3.209067e+08  ...  5.036321e+06  4.874113e+06   \n",
       "GDP         1.641666e+12  1.404190e+12  ...  1.747999e+09  1.479600e+09   \n",
       "\n",
       "country                                                             \\\n",
       "year                1967          1966          1965          1964   \n",
       "Population  4.718612e+06  4.568320e+06  4.422132e+06  4.279561e+06   \n",
       "GDP         1.397002e+09  1.281750e+09  1.311436e+09  1.217138e+09   \n",
       "\n",
       "country                                                             \n",
       "year                1963          1962          1961          1960  \n",
       "Population  4.140804e+06  4.006262e+06  3.876638e+06  3.752390e+06  \n",
       "GDP         1.159512e+09  1.117602e+09  1.096647e+09  1.052990e+09  \n",
       "\n",
       "[2 rows x 11211 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unstack(level=[0, 1])\n",
    "s.unstack(level=['country', 'year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If we look at the following data, we can see for some country(such as Albania) there are no data for 1960, 1961,etc.\n",
    "This is because for those year there is no data(GDP and Population) for that country. So when we unstack year, those empty values became NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>year</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>...</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Afghanistan</th>\n",
       "      <th>Population</th>\n",
       "      <td>8.994793e+06</td>\n",
       "      <td>9.164945e+06</td>\n",
       "      <td>9.343772e+06</td>\n",
       "      <td>9.531555e+06</td>\n",
       "      <td>9.728645e+06</td>\n",
       "      <td>9.935358e+06</td>\n",
       "      <td>1.014884e+07</td>\n",
       "      <td>1.036860e+07</td>\n",
       "      <td>1.059979e+07</td>\n",
       "      <td>1.084951e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>2.518362e+07</td>\n",
       "      <td>2.587754e+07</td>\n",
       "      <td>2.652874e+07</td>\n",
       "      <td>2.720729e+07</td>\n",
       "      <td>2.796221e+07</td>\n",
       "      <td>2.880917e+07</td>\n",
       "      <td>2.972680e+07</td>\n",
       "      <td>3.068250e+07</td>\n",
       "      <td>3.162751e+07</td>\n",
       "      <td>3.252656e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>5.377778e+08</td>\n",
       "      <td>5.488889e+08</td>\n",
       "      <td>5.466667e+08</td>\n",
       "      <td>7.511112e+08</td>\n",
       "      <td>8.000000e+08</td>\n",
       "      <td>1.006667e+09</td>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>1.673333e+09</td>\n",
       "      <td>1.373333e+09</td>\n",
       "      <td>1.408889e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>7.057598e+09</td>\n",
       "      <td>9.843842e+09</td>\n",
       "      <td>1.019053e+10</td>\n",
       "      <td>1.248694e+10</td>\n",
       "      <td>1.593680e+10</td>\n",
       "      <td>1.793024e+10</td>\n",
       "      <td>2.053654e+10</td>\n",
       "      <td>2.004633e+10</td>\n",
       "      <td>2.005019e+10</td>\n",
       "      <td>1.919944e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Albania</th>\n",
       "      <th>Population</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.992547e+06</td>\n",
       "      <td>2.970017e+06</td>\n",
       "      <td>2.947314e+06</td>\n",
       "      <td>2.927519e+06</td>\n",
       "      <td>2.913021e+06</td>\n",
       "      <td>2.904780e+06</td>\n",
       "      <td>2.900247e+06</td>\n",
       "      <td>2.896652e+06</td>\n",
       "      <td>2.893654e+06</td>\n",
       "      <td>2.889167e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>8.992642e+09</td>\n",
       "      <td>1.070101e+10</td>\n",
       "      <td>1.288135e+10</td>\n",
       "      <td>1.204421e+10</td>\n",
       "      <td>1.192695e+10</td>\n",
       "      <td>1.289087e+10</td>\n",
       "      <td>1.231978e+10</td>\n",
       "      <td>1.278103e+10</td>\n",
       "      <td>1.327796e+10</td>\n",
       "      <td>1.145560e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <th>Population</th>\n",
       "      <td>1.112489e+07</td>\n",
       "      <td>1.140486e+07</td>\n",
       "      <td>1.169015e+07</td>\n",
       "      <td>1.198513e+07</td>\n",
       "      <td>1.229597e+07</td>\n",
       "      <td>1.262695e+07</td>\n",
       "      <td>1.298027e+07</td>\n",
       "      <td>1.335420e+07</td>\n",
       "      <td>1.374438e+07</td>\n",
       "      <td>1.414444e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>3.374933e+07</td>\n",
       "      <td>3.426197e+07</td>\n",
       "      <td>3.481106e+07</td>\n",
       "      <td>3.540179e+07</td>\n",
       "      <td>3.603616e+07</td>\n",
       "      <td>3.671713e+07</td>\n",
       "      <td>3.743943e+07</td>\n",
       "      <td>3.818614e+07</td>\n",
       "      <td>3.893433e+07</td>\n",
       "      <td>3.966652e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>Yemen, Rep.</th>\n",
       "      <th>GDP</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.908173e+10</td>\n",
       "      <td>2.563367e+10</td>\n",
       "      <td>3.039720e+10</td>\n",
       "      <td>2.845950e+10</td>\n",
       "      <td>3.090675e+10</td>\n",
       "      <td>3.107886e+10</td>\n",
       "      <td>3.207477e+10</td>\n",
       "      <td>3.595450e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Zambia</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.049586e+06</td>\n",
       "      <td>3.142848e+06</td>\n",
       "      <td>3.240664e+06</td>\n",
       "      <td>3.342894e+06</td>\n",
       "      <td>3.449266e+06</td>\n",
       "      <td>3.559687e+06</td>\n",
       "      <td>3.674088e+06</td>\n",
       "      <td>3.792864e+06</td>\n",
       "      <td>3.916928e+06</td>\n",
       "      <td>4.047479e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.238151e+07</td>\n",
       "      <td>1.273868e+07</td>\n",
       "      <td>1.311458e+07</td>\n",
       "      <td>1.350785e+07</td>\n",
       "      <td>1.391744e+07</td>\n",
       "      <td>1.434353e+07</td>\n",
       "      <td>1.478658e+07</td>\n",
       "      <td>1.524609e+07</td>\n",
       "      <td>1.572134e+07</td>\n",
       "      <td>1.621177e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>6.987397e+08</td>\n",
       "      <td>6.823597e+08</td>\n",
       "      <td>6.792797e+08</td>\n",
       "      <td>7.043397e+08</td>\n",
       "      <td>8.226397e+08</td>\n",
       "      <td>1.061200e+09</td>\n",
       "      <td>1.239000e+09</td>\n",
       "      <td>1.340639e+09</td>\n",
       "      <td>1.573739e+09</td>\n",
       "      <td>1.926399e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.275686e+10</td>\n",
       "      <td>1.405696e+10</td>\n",
       "      <td>1.791086e+10</td>\n",
       "      <td>1.532834e+10</td>\n",
       "      <td>2.026555e+10</td>\n",
       "      <td>2.345952e+10</td>\n",
       "      <td>2.550306e+10</td>\n",
       "      <td>2.804552e+10</td>\n",
       "      <td>2.713464e+10</td>\n",
       "      <td>2.120156e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Zimbabwe</th>\n",
       "      <th>Population</th>\n",
       "      <td>3.752390e+06</td>\n",
       "      <td>3.876638e+06</td>\n",
       "      <td>4.006262e+06</td>\n",
       "      <td>4.140804e+06</td>\n",
       "      <td>4.279561e+06</td>\n",
       "      <td>4.422132e+06</td>\n",
       "      <td>4.568320e+06</td>\n",
       "      <td>4.718612e+06</td>\n",
       "      <td>4.874113e+06</td>\n",
       "      <td>5.036321e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.312794e+07</td>\n",
       "      <td>1.329780e+07</td>\n",
       "      <td>1.349546e+07</td>\n",
       "      <td>1.372100e+07</td>\n",
       "      <td>1.397390e+07</td>\n",
       "      <td>1.425559e+07</td>\n",
       "      <td>1.456548e+07</td>\n",
       "      <td>1.489809e+07</td>\n",
       "      <td>1.524586e+07</td>\n",
       "      <td>1.560275e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.052990e+09</td>\n",
       "      <td>1.096647e+09</td>\n",
       "      <td>1.117602e+09</td>\n",
       "      <td>1.159512e+09</td>\n",
       "      <td>1.217138e+09</td>\n",
       "      <td>1.311436e+09</td>\n",
       "      <td>1.281750e+09</td>\n",
       "      <td>1.397002e+09</td>\n",
       "      <td>1.479600e+09</td>\n",
       "      <td>1.747999e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>5.443896e+09</td>\n",
       "      <td>5.291950e+09</td>\n",
       "      <td>4.415703e+09</td>\n",
       "      <td>8.157077e+09</td>\n",
       "      <td>9.422161e+09</td>\n",
       "      <td>1.095623e+10</td>\n",
       "      <td>1.239272e+10</td>\n",
       "      <td>1.349023e+10</td>\n",
       "      <td>1.419691e+10</td>\n",
       "      <td>1.389294e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>504 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year                            1960          1961          1962  \\\n",
       "country                                                            \n",
       "Afghanistan Population  8.994793e+06  9.164945e+06  9.343772e+06   \n",
       "            GDP         5.377778e+08  5.488889e+08  5.466667e+08   \n",
       "Albania     Population           NaN           NaN           NaN   \n",
       "            GDP                  NaN           NaN           NaN   \n",
       "Algeria     Population  1.112489e+07  1.140486e+07  1.169015e+07   \n",
       "...                              ...           ...           ...   \n",
       "Yemen, Rep. GDP                  NaN           NaN           NaN   \n",
       "Zambia      Population  3.049586e+06  3.142848e+06  3.240664e+06   \n",
       "            GDP         6.987397e+08  6.823597e+08  6.792797e+08   \n",
       "Zimbabwe    Population  3.752390e+06  3.876638e+06  4.006262e+06   \n",
       "            GDP         1.052990e+09  1.096647e+09  1.117602e+09   \n",
       "\n",
       "year                            1963          1964          1965  \\\n",
       "country                                                            \n",
       "Afghanistan Population  9.531555e+06  9.728645e+06  9.935358e+06   \n",
       "            GDP         7.511112e+08  8.000000e+08  1.006667e+09   \n",
       "Albania     Population           NaN           NaN           NaN   \n",
       "            GDP                  NaN           NaN           NaN   \n",
       "Algeria     Population  1.198513e+07  1.229597e+07  1.262695e+07   \n",
       "...                              ...           ...           ...   \n",
       "Yemen, Rep. GDP                  NaN           NaN           NaN   \n",
       "Zambia      Population  3.342894e+06  3.449266e+06  3.559687e+06   \n",
       "            GDP         7.043397e+08  8.226397e+08  1.061200e+09   \n",
       "Zimbabwe    Population  4.140804e+06  4.279561e+06  4.422132e+06   \n",
       "            GDP         1.159512e+09  1.217138e+09  1.311436e+09   \n",
       "\n",
       "year                            1966          1967          1968  \\\n",
       "country                                                            \n",
       "Afghanistan Population  1.014884e+07  1.036860e+07  1.059979e+07   \n",
       "            GDP         1.400000e+09  1.673333e+09  1.373333e+09   \n",
       "Albania     Population           NaN           NaN           NaN   \n",
       "            GDP                  NaN           NaN           NaN   \n",
       "Algeria     Population  1.298027e+07  1.335420e+07  1.374438e+07   \n",
       "...                              ...           ...           ...   \n",
       "Yemen, Rep. GDP                  NaN           NaN           NaN   \n",
       "Zambia      Population  3.674088e+06  3.792864e+06  3.916928e+06   \n",
       "            GDP         1.239000e+09  1.340639e+09  1.573739e+09   \n",
       "Zimbabwe    Population  4.568320e+06  4.718612e+06  4.874113e+06   \n",
       "            GDP         1.281750e+09  1.397002e+09  1.479600e+09   \n",
       "\n",
       "year                            1969  ...          2006          2007  \\\n",
       "country                               ...                               \n",
       "Afghanistan Population  1.084951e+07  ...  2.518362e+07  2.587754e+07   \n",
       "            GDP         1.408889e+09  ...  7.057598e+09  9.843842e+09   \n",
       "Albania     Population           NaN  ...  2.992547e+06  2.970017e+06   \n",
       "            GDP                  NaN  ...  8.992642e+09  1.070101e+10   \n",
       "Algeria     Population  1.414444e+07  ...  3.374933e+07  3.426197e+07   \n",
       "...                              ...  ...           ...           ...   \n",
       "Yemen, Rep. GDP                  NaN  ...  1.908173e+10  2.563367e+10   \n",
       "Zambia      Population  4.047479e+06  ...  1.238151e+07  1.273868e+07   \n",
       "            GDP         1.926399e+09  ...  1.275686e+10  1.405696e+10   \n",
       "Zimbabwe    Population  5.036321e+06  ...  1.312794e+07  1.329780e+07   \n",
       "            GDP         1.747999e+09  ...  5.443896e+09  5.291950e+09   \n",
       "\n",
       "year                            2008          2009          2010  \\\n",
       "country                                                            \n",
       "Afghanistan Population  2.652874e+07  2.720729e+07  2.796221e+07   \n",
       "            GDP         1.019053e+10  1.248694e+10  1.593680e+10   \n",
       "Albania     Population  2.947314e+06  2.927519e+06  2.913021e+06   \n",
       "            GDP         1.288135e+10  1.204421e+10  1.192695e+10   \n",
       "Algeria     Population  3.481106e+07  3.540179e+07  3.603616e+07   \n",
       "...                              ...           ...           ...   \n",
       "Yemen, Rep. GDP         3.039720e+10  2.845950e+10  3.090675e+10   \n",
       "Zambia      Population  1.311458e+07  1.350785e+07  1.391744e+07   \n",
       "            GDP         1.791086e+10  1.532834e+10  2.026555e+10   \n",
       "Zimbabwe    Population  1.349546e+07  1.372100e+07  1.397390e+07   \n",
       "            GDP         4.415703e+09  8.157077e+09  9.422161e+09   \n",
       "\n",
       "year                            2011          2012          2013  \\\n",
       "country                                                            \n",
       "Afghanistan Population  2.880917e+07  2.972680e+07  3.068250e+07   \n",
       "            GDP         1.793024e+10  2.053654e+10  2.004633e+10   \n",
       "Albania     Population  2.904780e+06  2.900247e+06  2.896652e+06   \n",
       "            GDP         1.289087e+10  1.231978e+10  1.278103e+10   \n",
       "Algeria     Population  3.671713e+07  3.743943e+07  3.818614e+07   \n",
       "...                              ...           ...           ...   \n",
       "Yemen, Rep. GDP         3.107886e+10  3.207477e+10  3.595450e+10   \n",
       "Zambia      Population  1.434353e+07  1.478658e+07  1.524609e+07   \n",
       "            GDP         2.345952e+10  2.550306e+10  2.804552e+10   \n",
       "Zimbabwe    Population  1.425559e+07  1.456548e+07  1.489809e+07   \n",
       "            GDP         1.095623e+10  1.239272e+10  1.349023e+10   \n",
       "\n",
       "year                            2014          2015  \n",
       "country                                             \n",
       "Afghanistan Population  3.162751e+07  3.252656e+07  \n",
       "            GDP         2.005019e+10  1.919944e+10  \n",
       "Albania     Population  2.893654e+06  2.889167e+06  \n",
       "            GDP         1.327796e+10  1.145560e+10  \n",
       "Algeria     Population  3.893433e+07  3.966652e+07  \n",
       "...                              ...           ...  \n",
       "Yemen, Rep. GDP                  NaN           NaN  \n",
       "Zambia      Population  1.572134e+07  1.621177e+07  \n",
       "            GDP         2.713464e+10  2.120156e+10  \n",
       "Zimbabwe    Population  1.524586e+07  1.560275e+07  \n",
       "            GDP         1.419691e+10  1.389294e+10  \n",
       "\n",
       "[504 rows x 56 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.unstack('year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can use `fill_value` to fill in those empty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>...</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Afghanistan</th>\n",
       "      <th>Population</th>\n",
       "      <td>8.994793e+06</td>\n",
       "      <td>9.164945e+06</td>\n",
       "      <td>9.343772e+06</td>\n",
       "      <td>9.531555e+06</td>\n",
       "      <td>9.728645e+06</td>\n",
       "      <td>9.935358e+06</td>\n",
       "      <td>1.014884e+07</td>\n",
       "      <td>1.036860e+07</td>\n",
       "      <td>1.059979e+07</td>\n",
       "      <td>1.084951e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>2.518362e+07</td>\n",
       "      <td>2.587754e+07</td>\n",
       "      <td>2.652874e+07</td>\n",
       "      <td>2.720729e+07</td>\n",
       "      <td>2.796221e+07</td>\n",
       "      <td>2.880917e+07</td>\n",
       "      <td>2.972680e+07</td>\n",
       "      <td>3.068250e+07</td>\n",
       "      <td>3.162751e+07</td>\n",
       "      <td>3.252656e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>5.377778e+08</td>\n",
       "      <td>5.488889e+08</td>\n",
       "      <td>5.466667e+08</td>\n",
       "      <td>7.511112e+08</td>\n",
       "      <td>8.000000e+08</td>\n",
       "      <td>1.006667e+09</td>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>1.673333e+09</td>\n",
       "      <td>1.373333e+09</td>\n",
       "      <td>1.408889e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>7.057598e+09</td>\n",
       "      <td>9.843842e+09</td>\n",
       "      <td>1.019053e+10</td>\n",
       "      <td>1.248694e+10</td>\n",
       "      <td>1.593680e+10</td>\n",
       "      <td>1.793024e+10</td>\n",
       "      <td>2.053654e+10</td>\n",
       "      <td>2.004633e+10</td>\n",
       "      <td>2.005019e+10</td>\n",
       "      <td>1.919944e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <th>Population</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.992547e+06</td>\n",
       "      <td>2.970017e+06</td>\n",
       "      <td>2.947314e+06</td>\n",
       "      <td>2.927519e+06</td>\n",
       "      <td>2.913021e+06</td>\n",
       "      <td>2.904780e+06</td>\n",
       "      <td>2.900247e+06</td>\n",
       "      <td>2.896652e+06</td>\n",
       "      <td>2.893654e+06</td>\n",
       "      <td>2.889167e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year                            1960          1961          1962  \\\n",
       "country                                                            \n",
       "Afghanistan Population  8.994793e+06  9.164945e+06  9.343772e+06   \n",
       "            GDP         5.377778e+08  5.488889e+08  5.466667e+08   \n",
       "Albania     Population  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "year                            1963          1964          1965  \\\n",
       "country                                                            \n",
       "Afghanistan Population  9.531555e+06  9.728645e+06  9.935358e+06   \n",
       "            GDP         7.511112e+08  8.000000e+08  1.006667e+09   \n",
       "Albania     Population  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "year                            1966          1967          1968  \\\n",
       "country                                                            \n",
       "Afghanistan Population  1.014884e+07  1.036860e+07  1.059979e+07   \n",
       "            GDP         1.400000e+09  1.673333e+09  1.373333e+09   \n",
       "Albania     Population  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "year                            1969  ...          2006          2007  \\\n",
       "country                               ...                               \n",
       "Afghanistan Population  1.084951e+07  ...  2.518362e+07  2.587754e+07   \n",
       "            GDP         1.408889e+09  ...  7.057598e+09  9.843842e+09   \n",
       "Albania     Population  0.000000e+00  ...  2.992547e+06  2.970017e+06   \n",
       "\n",
       "year                            2008          2009          2010  \\\n",
       "country                                                            \n",
       "Afghanistan Population  2.652874e+07  2.720729e+07  2.796221e+07   \n",
       "            GDP         1.019053e+10  1.248694e+10  1.593680e+10   \n",
       "Albania     Population  2.947314e+06  2.927519e+06  2.913021e+06   \n",
       "\n",
       "year                            2011          2012          2013  \\\n",
       "country                                                            \n",
       "Afghanistan Population  2.880917e+07  2.972680e+07  3.068250e+07   \n",
       "            GDP         1.793024e+10  2.053654e+10  2.004633e+10   \n",
       "Albania     Population  2.904780e+06  2.900247e+06  2.896652e+06   \n",
       "\n",
       "year                            2014          2015  \n",
       "country                                             \n",
       "Afghanistan Population  3.162751e+07  3.252656e+07  \n",
       "            GDP         2.005019e+10  1.919944e+10  \n",
       "Albania     Population  2.893654e+06  2.889167e+06  \n",
       "\n",
       "[3 rows x 56 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = s.unstack('year', fill_value=0)\n",
    "s.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13) The `.pivot()` Method\n",
    "+ **re-orient the data set**\n",
    "+ basically take values currently in column to column headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Salesman</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Bob</td>\n",
       "      <td>7172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>Bob</td>\n",
       "      <td>6362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>Bob</td>\n",
       "      <td>5982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Salesman  Revenue\n",
       "0 2016-01-01      Bob     7172\n",
       "1 2016-01-02      Bob     6362\n",
       "2 2016-01-03      Bob     5982"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = pd.read_csv('Data/salesmen.csv', parse_dates=['Date'])\n",
    "sales['Salesman'] = sales['Salesman'].astype('category')\n",
    "sales.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1830"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ronald    366\n",
       "Dave      366\n",
       "Jeb       366\n",
       "Bob       366\n",
       "Oscar     366\n",
       "Name: Salesman, dtype: int64"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Salesman'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for our case, we have only 5 Salesman. Instead of storing data in current format which gonna take a lot of memory usage, we can re-orient the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we can condense the data as follow\n",
    "+ we can see original dataframe of 1830 is reduced down to 366."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "366"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sales.pivot(index='Date', columns='Salesman', values='Revenue'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "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>Salesman</th>\n",
       "      <th>Bob</th>\n",
       "      <th>Dave</th>\n",
       "      <th>Jeb</th>\n",
       "      <th>Oscar</th>\n",
       "      <th>Ronald</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>7172</td>\n",
       "      <td>1864</td>\n",
       "      <td>4430</td>\n",
       "      <td>5250</td>\n",
       "      <td>2639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>6362</td>\n",
       "      <td>8278</td>\n",
       "      <td>8026</td>\n",
       "      <td>8661</td>\n",
       "      <td>4951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>5982</td>\n",
       "      <td>4226</td>\n",
       "      <td>5188</td>\n",
       "      <td>7075</td>\n",
       "      <td>2703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>7917</td>\n",
       "      <td>3868</td>\n",
       "      <td>3144</td>\n",
       "      <td>2524</td>\n",
       "      <td>4258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>7837</td>\n",
       "      <td>2287</td>\n",
       "      <td>938</td>\n",
       "      <td>2793</td>\n",
       "      <td>7771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>2045</td>\n",
       "      <td>2843</td>\n",
       "      <td>6666</td>\n",
       "      <td>835</td>\n",
       "      <td>2981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>100</td>\n",
       "      <td>8888</td>\n",
       "      <td>1243</td>\n",
       "      <td>3073</td>\n",
       "      <td>6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>4115</td>\n",
       "      <td>9490</td>\n",
       "      <td>3498</td>\n",
       "      <td>6424</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>2577</td>\n",
       "      <td>3594</td>\n",
       "      <td>8858</td>\n",
       "      <td>7088</td>\n",
       "      <td>2570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>3845</td>\n",
       "      <td>6830</td>\n",
       "      <td>9717</td>\n",
       "      <td>8408</td>\n",
       "      <td>2619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Salesman     Bob  Dave   Jeb  Oscar  Ronald\n",
       "Date                                       \n",
       "2016-01-01  7172  1864  4430   5250    2639\n",
       "2016-01-02  6362  8278  8026   8661    4951\n",
       "2016-01-03  5982  4226  5188   7075    2703\n",
       "2016-01-04  7917  3868  3144   2524    4258\n",
       "2016-01-05  7837  2287   938   2793    7771\n",
       "...          ...   ...   ...    ...     ...\n",
       "2016-12-27  2045  2843  6666    835    2981\n",
       "2016-12-28   100  8888  1243   3073    6129\n",
       "2016-12-29  4115  9490  3498   6424    7662\n",
       "2016-12-30  2577  3594  8858   7088    2570\n",
       "2016-12-31  3845  6830  9717   8408    2619\n",
       "\n",
       "[366 rows x 5 columns]"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.pivot(index='Date', columns='Salesman', values='Revenue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14) The `.pivot_table()` Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "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>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Item</th>\n",
       "      <th>Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wanda</td>\n",
       "      <td>Female</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>Burger</td>\n",
       "      <td>15.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eric</td>\n",
       "      <td>Male</td>\n",
       "      <td>Stamford</td>\n",
       "      <td>Daily</td>\n",
       "      <td>Chalupa</td>\n",
       "      <td>10.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charles</td>\n",
       "      <td>Male</td>\n",
       "      <td>New York</td>\n",
       "      <td>Never</td>\n",
       "      <td>Sushi</td>\n",
       "      <td>42.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender      City Frequency     Item  Spend\n",
       "0      Wanda  Female  Stamford    Weekly   Burger  15.66\n",
       "1       Eric    Male  Stamford     Daily  Chalupa  10.56\n",
       "2    Charles    Male  New York     Never    Sushi  42.14"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods = pd.read_csv('Data/foods.csv')\n",
    "foods.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the average spending and Total Spending per Gender?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "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>Spend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>50.709629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>49.397623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Spend\n",
       "Gender           \n",
       "Female  50.709629\n",
       "Male    49.397623"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.pivot_table(index='Gender', values='Spend', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "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>Spend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>25963.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>24106.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Spend\n",
       "Gender          \n",
       "Female  25963.33\n",
       "Male    24106.04"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.pivot_table(index='Gender', values='Spend', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What about other sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "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>Spend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burger</th>\n",
       "      <td>7765.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>8270.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>7644.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>8758.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>8886.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>8742.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Spend\n",
       "Item              \n",
       "Burger     7765.73\n",
       "Burrito    8270.44\n",
       "Chalupa    7644.52\n",
       "Donut      8758.76\n",
       "Ice Cream  8886.99\n",
       "Sushi      8742.93"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.pivot_table(index='Item', values='Spend', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can also make multi Index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Spend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>4094.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>4257.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>4152.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>4743.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>4032.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>4683.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>3671.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>4012.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>3492.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>4015.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>4854.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>4059.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Spend\n",
       "Gender Item              \n",
       "Female Burger     4094.30\n",
       "       Burrito    4257.82\n",
       "       Chalupa    4152.26\n",
       "       Donut      4743.00\n",
       "       Ice Cream  4032.87\n",
       "       Sushi      4683.08\n",
       "Male   Burger     3671.43\n",
       "       Burrito    4012.62\n",
       "       Chalupa    3492.26\n",
       "       Donut      4015.76\n",
       "       Ice Cream  4854.12\n",
       "       Sushi      4059.85"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.pivot_table(index=['Gender', 'Item'], values='Spend', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>City</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>1239.04</td>\n",
       "      <td>1639.24</td>\n",
       "      <td>1216.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>978.95</td>\n",
       "      <td>1458.76</td>\n",
       "      <td>1820.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>876.58</td>\n",
       "      <td>1673.33</td>\n",
       "      <td>1602.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>1446.78</td>\n",
       "      <td>1639.26</td>\n",
       "      <td>1656.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>1521.62</td>\n",
       "      <td>1479.22</td>\n",
       "      <td>1032.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>1480.29</td>\n",
       "      <td>1742.88</td>\n",
       "      <td>1459.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>1294.09</td>\n",
       "      <td>938.18</td>\n",
       "      <td>1439.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>1399.40</td>\n",
       "      <td>1312.93</td>\n",
       "      <td>1300.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>1227.77</td>\n",
       "      <td>1114.23</td>\n",
       "      <td>1150.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>1345.27</td>\n",
       "      <td>1249.36</td>\n",
       "      <td>1421.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>1603.63</td>\n",
       "      <td>2191.27</td>\n",
       "      <td>1059.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>1396.15</td>\n",
       "      <td>1395.88</td>\n",
       "      <td>1267.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "City              New York  Philadelphia  Stamford\n",
       "Gender Item                                       \n",
       "Female Burger      1239.04       1639.24   1216.02\n",
       "       Burrito      978.95       1458.76   1820.11\n",
       "       Chalupa      876.58       1673.33   1602.35\n",
       "       Donut       1446.78       1639.26   1656.96\n",
       "       Ice Cream   1521.62       1479.22   1032.03\n",
       "       Sushi       1480.29       1742.88   1459.91\n",
       "Male   Burger      1294.09        938.18   1439.16\n",
       "       Burrito     1399.40       1312.93   1300.29\n",
       "       Chalupa     1227.77       1114.23   1150.26\n",
       "       Donut       1345.27       1249.36   1421.13\n",
       "       Ice Cream   1603.63       2191.27   1059.22\n",
       "       Sushi       1396.15       1395.88   1267.82"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.pivot_table(index=['Gender', 'Item'], columns=['City'], values='Spend', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Daily</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Monthly</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Never</th>\n",
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       "      <th colspan=\"3\" halign=\"left\">Weekly</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Yearly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>...</th>\n",
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       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</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 rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>262.67</td>\n",
       "      <td>231.68</td>\n",
       "      <td>144.66</td>\n",
       "      <td>171.86</td>\n",
       "      <td>215.05</td>\n",
       "      <td>238.49</td>\n",
       "      <td>97.89</td>\n",
       "      <td>218.97</td>\n",
       "      <td>90.97</td>\n",
       "      <td>94.96</td>\n",
       "      <td>...</td>\n",
       "      <td>95.05</td>\n",
       "      <td>63.16</td>\n",
       "      <td>409.05</td>\n",
       "      <td>97.53</td>\n",
       "      <td>184.35</td>\n",
       "      <td>32.00</td>\n",
       "      <td>155.02</td>\n",
       "      <td>259.30</td>\n",
       "      <td>123.17</td>\n",
       "      <td>307.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>224.45</td>\n",
       "      <td>321.57</td>\n",
       "      <td>195.63</td>\n",
       "      <td>122.74</td>\n",
       "      <td>34.28</td>\n",
       "      <td>67.94</td>\n",
       "      <td>189.73</td>\n",
       "      <td>382.30</td>\n",
       "      <td>366.34</td>\n",
       "      <td>103.60</td>\n",
       "      <td>...</td>\n",
       "      <td>168.01</td>\n",
       "      <td>167.54</td>\n",
       "      <td>198.11</td>\n",
       "      <td>234.49</td>\n",
       "      <td>39.69</td>\n",
       "      <td>62.82</td>\n",
       "      <td>230.91</td>\n",
       "      <td>71.26</td>\n",
       "      <td>116.75</td>\n",
       "      <td>172.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>43.19</td>\n",
       "      <td>23.49</td>\n",
       "      <td>95.70</td>\n",
       "      <td>158.37</td>\n",
       "      <td>289.96</td>\n",
       "      <td>161.98</td>\n",
       "      <td>35.15</td>\n",
       "      <td>121.97</td>\n",
       "      <td>156.36</td>\n",
       "      <td>39.73</td>\n",
       "      <td>...</td>\n",
       "      <td>40.59</td>\n",
       "      <td>160.00</td>\n",
       "      <td>274.51</td>\n",
       "      <td>175.25</td>\n",
       "      <td>171.52</td>\n",
       "      <td>84.41</td>\n",
       "      <td>204.69</td>\n",
       "      <td>157.82</td>\n",
       "      <td>504.44</td>\n",
       "      <td>348.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>478.10</td>\n",
       "      <td>247.40</td>\n",
       "      <td>124.35</td>\n",
       "      <td>284.53</td>\n",
       "      <td>50.25</td>\n",
       "      <td>229.30</td>\n",
       "      <td>56.07</td>\n",
       "      <td>433.58</td>\n",
       "      <td>157.33</td>\n",
       "      <td>130.63</td>\n",
       "      <td>...</td>\n",
       "      <td>158.24</td>\n",
       "      <td>90.81</td>\n",
       "      <td>183.25</td>\n",
       "      <td>104.66</td>\n",
       "      <td>285.56</td>\n",
       "      <td>208.80</td>\n",
       "      <td>440.06</td>\n",
       "      <td>62.95</td>\n",
       "      <td>175.23</td>\n",
       "      <td>224.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>262.19</td>\n",
       "      <td>177.69</td>\n",
       "      <td>92.88</td>\n",
       "      <td>92.53</td>\n",
       "      <td>74.51</td>\n",
       "      <td>125.85</td>\n",
       "      <td>206.15</td>\n",
       "      <td>156.03</td>\n",
       "      <td>77.66</td>\n",
       "      <td>232.26</td>\n",
       "      <td>...</td>\n",
       "      <td>279.33</td>\n",
       "      <td>242.35</td>\n",
       "      <td>203.10</td>\n",
       "      <td>117.73</td>\n",
       "      <td>227.62</td>\n",
       "      <td>285.28</td>\n",
       "      <td>125.03</td>\n",
       "      <td>151.67</td>\n",
       "      <td>159.86</td>\n",
       "      <td>15.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>81.07</td>\n",
       "      <td>348.53</td>\n",
       "      <td>393.27</td>\n",
       "      <td>93.16</td>\n",
       "      <td>78.71</td>\n",
       "      <td>108.39</td>\n",
       "      <td>69.33</td>\n",
       "      <td>190.58</td>\n",
       "      <td>19.56</td>\n",
       "      <td>343.94</td>\n",
       "      <td>...</td>\n",
       "      <td>194.75</td>\n",
       "      <td>87.70</td>\n",
       "      <td>314.24</td>\n",
       "      <td>27.82</td>\n",
       "      <td>410.89</td>\n",
       "      <td>334.00</td>\n",
       "      <td>315.36</td>\n",
       "      <td>232.41</td>\n",
       "      <td>145.85</td>\n",
       "      <td>362.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>319.46</td>\n",
       "      <td>112.70</td>\n",
       "      <td>197.72</td>\n",
       "      <td>187.29</td>\n",
       "      <td>213.14</td>\n",
       "      <td>27.16</td>\n",
       "      <td>90.32</td>\n",
       "      <td>17.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.47</td>\n",
       "      <td>...</td>\n",
       "      <td>326.64</td>\n",
       "      <td>225.68</td>\n",
       "      <td>188.06</td>\n",
       "      <td>159.75</td>\n",
       "      <td>69.69</td>\n",
       "      <td>99.89</td>\n",
       "      <td>310.21</td>\n",
       "      <td>49.61</td>\n",
       "      <td>197.36</td>\n",
       "      <td>225.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>236.21</td>\n",
       "      <td>165.76</td>\n",
       "      <td>276.23</td>\n",
       "      <td>147.54</td>\n",
       "      <td>89.58</td>\n",
       "      <td>119.60</td>\n",
       "      <td>86.78</td>\n",
       "      <td>378.32</td>\n",
       "      <td>351.84</td>\n",
       "      <td>189.92</td>\n",
       "      <td>...</td>\n",
       "      <td>30.15</td>\n",
       "      <td>202.40</td>\n",
       "      <td>27.71</td>\n",
       "      <td>9.84</td>\n",
       "      <td>256.74</td>\n",
       "      <td>289.25</td>\n",
       "      <td>161.85</td>\n",
       "      <td>220.70</td>\n",
       "      <td>118.51</td>\n",
       "      <td>262.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>54.09</td>\n",
       "      <td>274.81</td>\n",
       "      <td>192.64</td>\n",
       "      <td>333.76</td>\n",
       "      <td>90.70</td>\n",
       "      <td>343.76</td>\n",
       "      <td>199.09</td>\n",
       "      <td>242.98</td>\n",
       "      <td>138.70</td>\n",
       "      <td>251.52</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.69</td>\n",
       "      <td>130.75</td>\n",
       "      <td>174.02</td>\n",
       "      <td>163.20</td>\n",
       "      <td>135.68</td>\n",
       "      <td>44.37</td>\n",
       "      <td>167.74</td>\n",
       "      <td>68.81</td>\n",
       "      <td>116.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>230.00</td>\n",
       "      <td>190.71</td>\n",
       "      <td>129.42</td>\n",
       "      <td>183.73</td>\n",
       "      <td>259.29</td>\n",
       "      <td>119.53</td>\n",
       "      <td>219.63</td>\n",
       "      <td>53.65</td>\n",
       "      <td>219.64</td>\n",
       "      <td>139.80</td>\n",
       "      <td>...</td>\n",
       "      <td>139.89</td>\n",
       "      <td>16.25</td>\n",
       "      <td>99.01</td>\n",
       "      <td>163.31</td>\n",
       "      <td>74.44</td>\n",
       "      <td>115.80</td>\n",
       "      <td>311.27</td>\n",
       "      <td>143.10</td>\n",
       "      <td>44.61</td>\n",
       "      <td>16.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>178.22</td>\n",
       "      <td>148.69</td>\n",
       "      <td>15.17</td>\n",
       "      <td>152.20</td>\n",
       "      <td>386.55</td>\n",
       "      <td>74.36</td>\n",
       "      <td>57.54</td>\n",
       "      <td>321.94</td>\n",
       "      <td>119.77</td>\n",
       "      <td>347.55</td>\n",
       "      <td>...</td>\n",
       "      <td>182.71</td>\n",
       "      <td>270.42</td>\n",
       "      <td>138.44</td>\n",
       "      <td>40.83</td>\n",
       "      <td>223.86</td>\n",
       "      <td>221.24</td>\n",
       "      <td>239.42</td>\n",
       "      <td>159.70</td>\n",
       "      <td>239.94</td>\n",
       "      <td>96.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>225.57</td>\n",
       "      <td>280.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>430.86</td>\n",
       "      <td>155.59</td>\n",
       "      <td>93.03</td>\n",
       "      <td>164.01</td>\n",
       "      <td>245.77</td>\n",
       "      <td>204.90</td>\n",
       "      <td>9.31</td>\n",
       "      <td>...</td>\n",
       "      <td>314.65</td>\n",
       "      <td>160.49</td>\n",
       "      <td>91.26</td>\n",
       "      <td>224.15</td>\n",
       "      <td>150.15</td>\n",
       "      <td>260.33</td>\n",
       "      <td>338.16</td>\n",
       "      <td>119.44</td>\n",
       "      <td>33.80</td>\n",
       "      <td>48.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Frequency           Daily                        Monthly               \\\n",
       "City             New York Philadelphia Stamford New York Philadelphia   \n",
       "Gender Item                                                             \n",
       "Female Burger      262.67       231.68   144.66   171.86       215.05   \n",
       "       Burrito     224.45       321.57   195.63   122.74        34.28   \n",
       "       Chalupa      43.19        23.49    95.70   158.37       289.96   \n",
       "       Donut       478.10       247.40   124.35   284.53        50.25   \n",
       "       Ice Cream   262.19       177.69    92.88    92.53        74.51   \n",
       "       Sushi        81.07       348.53   393.27    93.16        78.71   \n",
       "Male   Burger      319.46       112.70   197.72   187.29       213.14   \n",
       "       Burrito     236.21       165.76   276.23   147.54        89.58   \n",
       "       Chalupa      54.09       274.81   192.64   333.76        90.70   \n",
       "       Donut       230.00       190.71   129.42   183.73       259.29   \n",
       "       Ice Cream   178.22       148.69    15.17   152.20       386.55   \n",
       "       Sushi       225.57       280.93      NaN   430.86       155.59   \n",
       "\n",
       "Frequency                    Never                          Often  ...  \\\n",
       "City             Stamford New York Philadelphia Stamford New York  ...   \n",
       "Gender Item                                                        ...   \n",
       "Female Burger      238.49    97.89       218.97    90.97    94.96  ...   \n",
       "       Burrito      67.94   189.73       382.30   366.34   103.60  ...   \n",
       "       Chalupa     161.98    35.15       121.97   156.36    39.73  ...   \n",
       "       Donut       229.30    56.07       433.58   157.33   130.63  ...   \n",
       "       Ice Cream   125.85   206.15       156.03    77.66   232.26  ...   \n",
       "       Sushi       108.39    69.33       190.58    19.56   343.94  ...   \n",
       "Male   Burger       27.16    90.32        17.31      NaN    55.47  ...   \n",
       "       Burrito     119.60    86.78       378.32   351.84   189.92  ...   \n",
       "       Chalupa     343.76   199.09       242.98   138.70   251.52  ...   \n",
       "       Donut       119.53   219.63        53.65   219.64   139.80  ...   \n",
       "       Ice Cream    74.36    57.54       321.94   119.77   347.55  ...   \n",
       "       Sushi        93.03   164.01       245.77   204.90     9.31  ...   \n",
       "\n",
       "Frequency            Once   Seldom                         Weekly  \\\n",
       "City             Stamford New York Philadelphia Stamford New York   \n",
       "Gender Item                                                         \n",
       "Female Burger       95.05    63.16       409.05    97.53   184.35   \n",
       "       Burrito     168.01   167.54       198.11   234.49    39.69   \n",
       "       Chalupa      40.59   160.00       274.51   175.25   171.52   \n",
       "       Donut       158.24    90.81       183.25   104.66   285.56   \n",
       "       Ice Cream   279.33   242.35       203.10   117.73   227.62   \n",
       "       Sushi       194.75    87.70       314.24    27.82   410.89   \n",
       "Male   Burger      326.64   225.68       188.06   159.75    69.69   \n",
       "       Burrito      30.15   202.40        27.71     9.84   256.74   \n",
       "       Chalupa        NaN    11.69       130.75   174.02   163.20   \n",
       "       Donut       139.89    16.25        99.01   163.31    74.44   \n",
       "       Ice Cream   182.71   270.42       138.44    40.83   223.86   \n",
       "       Sushi       314.65   160.49        91.26   224.15   150.15   \n",
       "\n",
       "Frequency                                Yearly                        \n",
       "City             Philadelphia Stamford New York Philadelphia Stamford  \n",
       "Gender Item                                                            \n",
       "Female Burger           32.00   155.02   259.30       123.17   307.03  \n",
       "       Burrito          62.82   230.91    71.26       116.75   172.98  \n",
       "       Chalupa          84.41   204.69   157.82       504.44   348.16  \n",
       "       Donut           208.80   440.06    62.95       175.23   224.48  \n",
       "       Ice Cream       285.28   125.03   151.67       159.86    15.24  \n",
       "       Sushi           334.00   315.36   232.41       145.85   362.74  \n",
       "Male   Burger           99.89   310.21    49.61       197.36   225.07  \n",
       "       Burrito         289.25   161.85   220.70       118.51   262.64  \n",
       "       Chalupa         135.68    44.37   167.74        68.81   116.19  \n",
       "       Donut           115.80   311.27   143.10        44.61    16.52  \n",
       "       Ice Cream       221.24   239.42   159.70       239.94    96.32  \n",
       "       Sushi           260.33   338.16   119.44        33.80    48.71  \n",
       "\n",
       "[12 rows x 24 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.pivot_table(index=['Gender', 'Item'], columns=['Frequency', 'City'], values='Spend', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other avaliable `aggfunc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>2.25</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>1.02</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>1.96</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>3.15</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>13.39</td>\n",
       "      <td>7.61</td>\n",
       "      <td>8.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>2.52</td>\n",
       "      <td>11.68</td>\n",
       "      <td>8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>5.43</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>15.90</td>\n",
       "      <td>8.58</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>11.61</td>\n",
       "      <td>1.94</td>\n",
       "      <td>10.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>1.49</td>\n",
       "      <td>1.26</td>\n",
       "      <td>6.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>14.06</td>\n",
       "      <td>4.89</td>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>3.28</td>\n",
       "      <td>2.01</td>\n",
       "      <td>32.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "City              New York  Philadelphia  Stamford\n",
       "Gender Item                                       \n",
       "Female Burger         2.25          1.97      6.24\n",
       "       Burrito        1.02          1.04      1.18\n",
       "       Chalupa        1.96          9.35      9.09\n",
       "       Donut          3.15          2.13      1.68\n",
       "       Ice Cream     13.39          7.61      8.80\n",
       "       Sushi          2.52         11.68      8.20\n",
       "Male   Burger         5.43          1.71      2.83\n",
       "       Burrito       15.90          8.58      3.64\n",
       "       Chalupa       11.61          1.94     10.56\n",
       "       Donut          1.49          1.26      6.63\n",
       "       Ice Cream     14.06          4.89      3.43\n",
       "       Sushi          3.28          2.01     32.15"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods.pivot_table(index=['Gender', 'Item'], columns='City', values='Spend', aggfunc='count')\n",
    "foods.pivot_table(index=['Gender', 'Item'], columns='City', values='Spend', aggfunc='max')\n",
    "foods.pivot_table(index=['Gender', 'Item'], columns='City', values='Spend', aggfunc='min')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Directly performing with pandas, insteading of directly calling on DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "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>City</th>\n",
       "      <th>New York</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Stamford</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Female</th>\n",
       "      <th>Burger</th>\n",
       "      <td>24</td>\n",
       "      <td>31</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Male</th>\n",
       "      <th>Burger</th>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chalupa</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ice Cream</th>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sushi</th>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "City              New York  Philadelphia  Stamford\n",
       "Gender Item                                       \n",
       "Female Burger           24            31        27\n",
       "       Burrito          23            28        34\n",
       "       Chalupa          19            32        25\n",
       "       Donut            31            30        34\n",
       "       Ice Cream        27            32        22\n",
       "       Sushi            31            30        32\n",
       "Male   Burger           22            21        31\n",
       "       Burrito          25            30        28\n",
       "       Chalupa          25            23        23\n",
       "       Donut            30            33        29\n",
       "       Ice Cream        29            41        25\n",
       "       Sushi            27            28        18"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(data=foods, index=['Gender', 'Item'], columns='City', values='Spend', aggfunc='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 15) The `pd.melt()` Method\n",
    "**take the aggregated data set into Tabular format.**\n",
    "\n",
    "+ `id_vars`: columns which are going to be preserved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Salesman</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boris</td>\n",
       "      <td>602908</td>\n",
       "      <td>233879</td>\n",
       "      <td>354479</td>\n",
       "      <td>32704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>43790</td>\n",
       "      <td>514863</td>\n",
       "      <td>297151</td>\n",
       "      <td>544493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>392668</td>\n",
       "      <td>113579</td>\n",
       "      <td>430882</td>\n",
       "      <td>247231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Travis</td>\n",
       "      <td>834663</td>\n",
       "      <td>266785</td>\n",
       "      <td>749238</td>\n",
       "      <td>570524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald</td>\n",
       "      <td>580935</td>\n",
       "      <td>411379</td>\n",
       "      <td>110390</td>\n",
       "      <td>651572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ted</td>\n",
       "      <td>656644</td>\n",
       "      <td>70803</td>\n",
       "      <td>375948</td>\n",
       "      <td>321388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>486141</td>\n",
       "      <td>600753</td>\n",
       "      <td>742716</td>\n",
       "      <td>404995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>479662</td>\n",
       "      <td>742806</td>\n",
       "      <td>770712</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>992673</td>\n",
       "      <td>879183</td>\n",
       "      <td>37945</td>\n",
       "      <td>293710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Salesman      Q1      Q2      Q3      Q4\n",
       "0    Boris  602908  233879  354479   32704\n",
       "1      Bob   43790  514863  297151  544493\n",
       "2    Tommy  392668  113579  430882  247231\n",
       "3   Travis  834663  266785  749238  570524\n",
       "4   Donald  580935  411379  110390  651572\n",
       "5      Ted  656644   70803  375948  321388\n",
       "6      Jeb  486141  600753  742716  404995\n",
       "7    Stacy  479662  742806  770712    2501\n",
       "8   Morgan  992673  879183   37945  293710"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = pd.read_csv('Data/quarters.csv')\n",
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salesman</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q1</td>\n",
       "      <td>602908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q1</td>\n",
       "      <td>43790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q1</td>\n",
       "      <td>392668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q1</td>\n",
       "      <td>834663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q1</td>\n",
       "      <td>580935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q1</td>\n",
       "      <td>656644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q1</td>\n",
       "      <td>486141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q1</td>\n",
       "      <td>479662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q1</td>\n",
       "      <td>992673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q2</td>\n",
       "      <td>233879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q2</td>\n",
       "      <td>514863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q2</td>\n",
       "      <td>113579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q2</td>\n",
       "      <td>266785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q2</td>\n",
       "      <td>411379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q2</td>\n",
       "      <td>70803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q2</td>\n",
       "      <td>600753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q2</td>\n",
       "      <td>742806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q2</td>\n",
       "      <td>879183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q3</td>\n",
       "      <td>354479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q3</td>\n",
       "      <td>297151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q3</td>\n",
       "      <td>430882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q3</td>\n",
       "      <td>749238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q3</td>\n",
       "      <td>110390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q3</td>\n",
       "      <td>375948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q3</td>\n",
       "      <td>742716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q3</td>\n",
       "      <td>770712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q3</td>\n",
       "      <td>37945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Boris</td>\n",
       "      <td>Q4</td>\n",
       "      <td>32704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Bob</td>\n",
       "      <td>Q4</td>\n",
       "      <td>544493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>Q4</td>\n",
       "      <td>247231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Travis</td>\n",
       "      <td>Q4</td>\n",
       "      <td>570524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Donald</td>\n",
       "      <td>Q4</td>\n",
       "      <td>651572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ted</td>\n",
       "      <td>Q4</td>\n",
       "      <td>321388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Jeb</td>\n",
       "      <td>Q4</td>\n",
       "      <td>404995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Stacy</td>\n",
       "      <td>Q4</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Morgan</td>\n",
       "      <td>Q4</td>\n",
       "      <td>293710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Salesman Quarter  Revenue\n",
       "0     Boris      Q1   602908\n",
       "1       Bob      Q1    43790\n",
       "2     Tommy      Q1   392668\n",
       "3    Travis      Q1   834663\n",
       "4    Donald      Q1   580935\n",
       "5       Ted      Q1   656644\n",
       "6       Jeb      Q1   486141\n",
       "7     Stacy      Q1   479662\n",
       "8    Morgan      Q1   992673\n",
       "9     Boris      Q2   233879\n",
       "10      Bob      Q2   514863\n",
       "11    Tommy      Q2   113579\n",
       "12   Travis      Q2   266785\n",
       "13   Donald      Q2   411379\n",
       "14      Ted      Q2    70803\n",
       "15      Jeb      Q2   600753\n",
       "16    Stacy      Q2   742806\n",
       "17   Morgan      Q2   879183\n",
       "18    Boris      Q3   354479\n",
       "19      Bob      Q3   297151\n",
       "20    Tommy      Q3   430882\n",
       "21   Travis      Q3   749238\n",
       "22   Donald      Q3   110390\n",
       "23      Ted      Q3   375948\n",
       "24      Jeb      Q3   742716\n",
       "25    Stacy      Q3   770712\n",
       "26   Morgan      Q3    37945\n",
       "27    Boris      Q4    32704\n",
       "28      Bob      Q4   544493\n",
       "29    Tommy      Q4   247231\n",
       "30   Travis      Q4   570524\n",
       "31   Donald      Q4   651572\n",
       "32      Ted      Q4   321388\n",
       "33      Jeb      Q4   404995\n",
       "34    Stacy      Q4     2501\n",
       "35   Morgan      Q4   293710"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(sales, id_vars='Salesman', var_name='Quarter', value_name='Revenue') # we want to preserved Salesman"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  }
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