data-analysis/049-pandas-pivot/pivot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas `pivot()` - reshape from long to wide\n",
    "\n",
    "This is a Notebook for the medium article [Reshaping a DataFrame from long to wide format using pivot()](https://bindichen.medium.com/reshaping-a-dataframe-from-long-to-wide-format-using-pivot-b099930b30ae)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Version of packages used in this Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy:  1.18.1\n",
      "pandas:  1.1.4\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Make sure your package version >= them\n",
    "print('numpy: ', np.__version__)\n",
    "print('pandas: ', pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Simplest pivot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UK</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UK</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>France</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>US</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UK</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>US</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UK</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>France</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>US</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UK</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country        Date  Cases\n",
       "0   France  22/01/2020      1\n",
       "1       US  22/01/2020      2\n",
       "2       UK  22/01/2020      3\n",
       "3   France  23/01/2020      4\n",
       "4       US  23/01/2020      5\n",
       "5       UK  23/01/2020      6\n",
       "6   France  24/01/2020      7\n",
       "7       US  24/01/2020      8\n",
       "8       UK  24/01/2020      9\n",
       "9   France  25/01/2020     10\n",
       "10      US  25/01/2020     11\n",
       "11      UK  25/01/2020     12\n",
       "12  France  26/01/2020     13\n",
       "13      US  26/01/2020     14\n",
       "14      UK  26/01/2020     15"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('simple.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Country</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>22/01/2020</th>\n",
       "      <th>23/01/2020</th>\n",
       "      <th>24/01/2020</th>\n",
       "      <th>25/01/2020</th>\n",
       "      <th>26/01/2020</th>\n",
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       "      <th>24/01/2020</th>\n",
       "      <th>25/01/2020</th>\n",
       "      <th>26/01/2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>UK</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>UK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country                                                  Cases  \\\n",
       "Date 22/01/2020 23/01/2020 24/01/2020 25/01/2020 26/01/2020 22/01/2020   \n",
       "0        France        NaN        NaN        NaN        NaN        1.0   \n",
       "1            US        NaN        NaN        NaN        NaN        2.0   \n",
       "2            UK        NaN        NaN        NaN        NaN        3.0   \n",
       "3           NaN     France        NaN        NaN        NaN        NaN   \n",
       "4           NaN         US        NaN        NaN        NaN        NaN   \n",
       "5           NaN         UK        NaN        NaN        NaN        NaN   \n",
       "6           NaN        NaN     France        NaN        NaN        NaN   \n",
       "7           NaN        NaN         US        NaN        NaN        NaN   \n",
       "8           NaN        NaN         UK        NaN        NaN        NaN   \n",
       "9           NaN        NaN        NaN     France        NaN        NaN   \n",
       "10          NaN        NaN        NaN         US        NaN        NaN   \n",
       "11          NaN        NaN        NaN         UK        NaN        NaN   \n",
       "12          NaN        NaN        NaN        NaN     France        NaN   \n",
       "13          NaN        NaN        NaN        NaN         US        NaN   \n",
       "14          NaN        NaN        NaN        NaN         UK        NaN   \n",
       "\n",
       "                                                  \n",
       "Date 23/01/2020 24/01/2020 25/01/2020 26/01/2020  \n",
       "0           NaN        NaN        NaN        NaN  \n",
       "1           NaN        NaN        NaN        NaN  \n",
       "2           NaN        NaN        NaN        NaN  \n",
       "3           4.0        NaN        NaN        NaN  \n",
       "4           5.0        NaN        NaN        NaN  \n",
       "5           6.0        NaN        NaN        NaN  \n",
       "6           NaN        7.0        NaN        NaN  \n",
       "7           NaN        8.0        NaN        NaN  \n",
       "8           NaN        9.0        NaN        NaN  \n",
       "9           NaN        NaN       10.0        NaN  \n",
       "10          NaN        NaN       11.0        NaN  \n",
       "11          NaN        NaN       12.0        NaN  \n",
       "12          NaN        NaN        NaN       13.0  \n",
       "13          NaN        NaN        NaN       14.0  \n",
       "14          NaN        NaN        NaN       15.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(columns='Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Specifying the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>22/01/2020</th>\n",
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       "      <th>25/01/2020</th>\n",
       "      <th>26/01/2020</th>\n",
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       "    <tr>\n",
       "      <th>Country</th>\n",
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       "      <th>France</th>\n",
       "      <td>1</td>\n",
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       "      <td>10</td>\n",
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       "      <th>UK</th>\n",
       "      <td>3</td>\n",
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       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "             Cases                                            \n",
       "Date    22/01/2020 23/01/2020 24/01/2020 25/01/2020 26/01/2020\n",
       "Country                                                       \n",
       "France           1          4          7         10         13\n",
       "UK               3          6          9         12         15\n",
       "US               2          5          8         11         14"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(columns='Date', index='Country')\n",
    "\n",
    "# Note that the result has hierarchically indexed columns Date and Cases (Also known as MultiIndex columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>22/01/2020</th>\n",
       "      <th>23/01/2020</th>\n",
       "      <th>24/01/2020</th>\n",
       "      <th>25/01/2020</th>\n",
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       "      <th>Country</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date     22/01/2020  23/01/2020  24/01/2020  25/01/2020  26/01/2020\n",
       "Country                                                            \n",
       "France            1           4           7          10          13\n",
       "UK                3           6           9          12          15\n",
       "US                2           5           8          11          14"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spcify the values='Cases' to remove the hierarchically indexed 'Date'\n",
    "df.pivot(columns='Date', index='Country', values='Cases')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Country</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country      Lat      Long        Date  Cases\n",
       "0   France  31.8257  117.2264  22/01/2020      1\n",
       "1       US  40.0000 -100.0000  22/01/2020      2\n",
       "2       UK  55.3781   -3.4360  22/01/2020      3\n",
       "3   France  31.8257  117.2264  23/01/2020      4\n",
       "4       US  40.0000 -100.0000  23/01/2020      5\n",
       "5       UK  55.3781   -3.4360  23/01/2020      6\n",
       "6   France  31.8257  117.2264  24/01/2020      7\n",
       "7       US  40.0000 -100.0000  24/01/2020      8\n",
       "8       UK  55.3781   -3.4360  24/01/2020      9\n",
       "9   France  31.8257  117.2264  25/01/2020     10\n",
       "10      US  40.0000 -100.0000  25/01/2020     11\n",
       "11      UK  55.3781   -3.4360  25/01/2020     12\n",
       "12  France  31.8257  117.2264  26/01/2020     13\n",
       "13      US  40.0000 -100.0000  26/01/2020     14\n",
       "14      UK  55.3781   -3.4360  26/01/2020     15"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = pd.read_csv('data.csv')\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>22/01/2020</th>\n",
       "      <th>23/01/2020</th>\n",
       "      <th>24/01/2020</th>\n",
       "      <th>25/01/2020</th>\n",
       "      <th>26/01/2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</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>France</th>\n",
       "      <th>31.8257</th>\n",
       "      <th>117.2264</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <th>55.3781</th>\n",
       "      <th>-3.4360</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <th>40.0000</th>\n",
       "      <th>-100.0000</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date                       22/01/2020  23/01/2020  24/01/2020  25/01/2020  \\\n",
       "Country Lat     Long                                                        \n",
       "France  31.8257  117.2264           1           4           7          10   \n",
       "UK      55.3781 -3.4360             3           6           9          12   \n",
       "US      40.0000 -100.0000           2           5           8          11   \n",
       "\n",
       "Date                       26/01/2020  \n",
       "Country Lat     Long                   \n",
       "France  31.8257  117.2264          13  \n",
       "UK      55.3781 -3.4360            15  \n",
       "US      40.0000 -100.0000          14  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.pivot(columns='Date', index=['Country', 'Lat', 'Long'], values='Cases')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Multiple values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>22/01/2020</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>23/01/2020</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>80</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>24/01/2020</td>\n",
       "      <td>90</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>110</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>25/01/2020</td>\n",
       "      <td>120</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>France</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>130</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>US</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-100.0000</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>140</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UK</td>\n",
       "      <td>55.3781</td>\n",
       "      <td>-3.4360</td>\n",
       "      <td>26/01/2020</td>\n",
       "      <td>150</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country      Lat      Long        Date  Cases  Recovered\n",
       "0   France  31.8257  117.2264  22/01/2020     10          1\n",
       "1       US  40.0000 -100.0000  22/01/2020     20          2\n",
       "2       UK  55.3781   -3.4360  22/01/2020     30          3\n",
       "3   France  31.8257  117.2264  23/01/2020     40          4\n",
       "4       US  40.0000 -100.0000  23/01/2020     50          5\n",
       "5       UK  55.3781   -3.4360  23/01/2020     60          6\n",
       "6   France  31.8257  117.2264  24/01/2020     70          7\n",
       "7       US  40.0000 -100.0000  24/01/2020     80          8\n",
       "8       UK  55.3781   -3.4360  24/01/2020     90          9\n",
       "9   France  31.8257  117.2264  25/01/2020    100         10\n",
       "10      US  40.0000 -100.0000  25/01/2020    110         11\n",
       "11      UK  55.3781   -3.4360  25/01/2020    120         12\n",
       "12  France  31.8257  117.2264  26/01/2020    130         13\n",
       "13      US  40.0000 -100.0000  26/01/2020    140         14\n",
       "14      UK  55.3781   -3.4360  26/01/2020    150         15"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3 = pd.read_csv('data-2.csv')\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>22/01/2020</th>\n",
       "      <th>23/01/2020</th>\n",
       "      <th>24/01/2020</th>\n",
       "      <th>25/01/2020</th>\n",
       "      <th>26/01/2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</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>France</th>\n",
       "      <th>31.8257</th>\n",
       "      <th>117.2264</th>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>100</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <th>55.3781</th>\n",
       "      <th>-3.4360</th>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>120</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <th>40.0000</th>\n",
       "      <th>-100.0000</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date                       22/01/2020  23/01/2020  24/01/2020  25/01/2020  \\\n",
       "Country Lat     Long                                                        \n",
       "France  31.8257  117.2264          10          40          70         100   \n",
       "UK      55.3781 -3.4360            30          60          90         120   \n",
       "US      40.0000 -100.0000          20          50          80         110   \n",
       "\n",
       "Date                       26/01/2020  \n",
       "Country Lat     Long                   \n",
       "France  31.8257  117.2264         130  \n",
       "UK      55.3781 -3.4360           150  \n",
       "US      40.0000 -100.0000         140  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# single values\n",
    "df_3.pivot(\n",
    "    columns='Date', \n",
    "    index=['Country', 'Lat', 'Long'], \n",
    "    values='Cases'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Cases</th>\n",
       "      <th colspan=\"5\" halign=\"left\">Recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>22/01/2020</th>\n",
       "      <th>23/01/2020</th>\n",
       "      <th>24/01/2020</th>\n",
       "      <th>25/01/2020</th>\n",
       "      <th>26/01/2020</th>\n",
       "      <th>22/01/2020</th>\n",
       "      <th>23/01/2020</th>\n",
       "      <th>24/01/2020</th>\n",
       "      <th>25/01/2020</th>\n",
       "      <th>26/01/2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</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>France</th>\n",
       "      <th>31.8257</th>\n",
       "      <th>117.2264</th>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>100</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <th>55.3781</th>\n",
       "      <th>-3.4360</th>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>120</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <th>40.0000</th>\n",
       "      <th>-100.0000</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>80</td>\n",
       "      <td>110</td>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Cases                                   \\\n",
       "Date                      22/01/2020 23/01/2020 24/01/2020 25/01/2020   \n",
       "Country Lat     Long                                                    \n",
       "France  31.8257  117.2264         10         40         70        100   \n",
       "UK      55.3781 -3.4360           30         60         90        120   \n",
       "US      40.0000 -100.0000         20         50         80        110   \n",
       "\n",
       "                                      Recovered                        \\\n",
       "Date                      26/01/2020 22/01/2020 23/01/2020 24/01/2020   \n",
       "Country Lat     Long                                                    \n",
       "France  31.8257  117.2264        130          1          4          7   \n",
       "UK      55.3781 -3.4360          150          3          6          9   \n",
       "US      40.0000 -100.0000        140          2          5          8   \n",
       "\n",
       "                                                 \n",
       "Date                      25/01/2020 26/01/2020  \n",
       "Country Lat     Long                             \n",
       "France  31.8257  117.2264         10         13  \n",
       "UK      55.3781 -3.4360           12         15  \n",
       "US      40.0000 -100.0000         11         14  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiple values to view different categories side by side:\n",
    "df_3.pivot(\n",
    "    columns='Date', \n",
    "    index=['Country', 'Lat', 'Long'], \n",
    "    values=['Cases', 'Recovered']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. A ValueError for any duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Store_Manager</th>\n",
       "      <th>Location</th>\n",
       "      <th>Num_employee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tom</td>\n",
       "      <td>London</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tom</td>\n",
       "      <td>London</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Oxford</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Store_Manager   Location  Num_employee\n",
       "0           Tom     London             1\n",
       "1           Tom     London             2\n",
       "2         Chris     Oxford             3\n",
       "3         Chris  Cambridge             4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    \"Store_Manager\": ['Tom', 'Tom', 'Chris', 'Chris'],\n",
    "    \"Location\": ['London', 'London', 'Oxford', 'Cambridge'],\n",
    "    \"Num_employee\": [1, 2, 3, 4]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Index contains duplicate entries, cannot reshape",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-ce165d87e299>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Store_Manager'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Location'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Num_employee'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m )\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mpivot\u001b[0;34m(self, index, columns, values)\u001b[0m\n\u001b[1;32m   6676\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6678\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6680\u001b[0m     _shared_docs[\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/pivot.py\u001b[0m in \u001b[0;36mpivot\u001b[0;34m(data, index, columns, values)\u001b[0m\n\u001b[1;32m    475\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mindexed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mindexed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36munstack\u001b[0;34m(self, level, fill_value)\u001b[0m\n\u001b[1;32m   3900\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0munstack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3902\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0munstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3904\u001b[0m     \u001b[0;31m# ----------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/reshape.py\u001b[0m in \u001b[0;36munstack\u001b[0;34m(obj, level, fill_value)\u001b[0m\n\u001b[1;32m    423\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0m_unstack_extension_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m         unstacker = _Unstacker(\n\u001b[0;32m--> 425\u001b[0;31m             \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_expanddim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    426\u001b[0m         )\n\u001b[1;32m    427\u001b[0m         return unstacker.get_result(\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/reshape.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, index, level, constructor)\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unstacked DataFrame is too big, causing int32 overflow\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_selectors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tf-tutorial/lib/python3.7/site-packages/pandas/core/reshape/reshape.py\u001b[0m in \u001b[0;36m_make_selectors\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index contains duplicate entries, cannot reshape\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroup_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomp_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Index contains duplicate entries, cannot reshape"
     ]
    }
   ],
   "source": [
    "# A ValueError is raised if there are any duplicates.\n",
    "# Notice that the first two rows are the same for our index and columns arguments.\n",
    "\n",
    "df.pivot(\n",
    "    index='Store_Manager', \n",
    "    columns='Location', \n",
    "    values='Num_employee'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use `pivot_talbe` instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Location</th>\n",
       "      <th>Cambridge</th>\n",
       "      <th>London</th>\n",
       "      <th>Oxford</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store_Manager</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Location       Cambridge  London  Oxford\n",
       "Store_Manager                           \n",
       "Chris                4.0     NaN     3.0\n",
       "Tom                  NaN     1.5     NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use pivot_table\n",
    "# But bear in mind that pivot_table() will perform the mean aggregation by default. \n",
    "df.pivot_table(\n",
    "    index='Store_Manager', \n",
    "    columns='Location', \n",
    "    values='Num_employee'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thanks for reading\n",
    "\n",
    "This is a Notebook for the medium article [Reshaping a DataFrame from long to wide format using pivot()](https://bindichen.medium.com/reshaping-a-dataframe-from-long-to-wide-format-using-pivot-b099930b30ae)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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