{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reshaping Data with Pandas\n",
    "\n",
    "When working with data, it is common to encounter situations where the format of your `DataFrame` is not ideal for analysis or visualization. Reshaping data can help you transform the structure of your data to better suit your needs. This recipe illustrates two powerful tools available in Pandas for reshaping data, namely\n",
    "\n",
    "\n",
    "- `pivot`: This method allows you to \"pivot\" a DataFrame, turning unique values from one column into new columns and filling them with corresponding values from another column. It is especially useful for converting long-form data (also known as tidy data) into a wide format.\n",
    "  \n",
    "- `melt`: This method is the inverse of pivot. It is used to \"melt\" a wide-format DataFrame into a long-format, where multiple columns are unpivoted into a single column, making it easier to work with or visualize when you have a large number of variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting ready\n",
    "Before starting, we are going to create two `DataFrame` objects to work with.\n",
    "\n",
    "- `df_vertical` this data is in a vertical/long format where each row represents a record of a single observation or instance, and each column represents a single variable. \n",
    "  \n",
    "- `df_horizontal` this data is in horizontal/wide format where multiple columns/rows represent levels of a categorical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vertical = pd.DataFrame({\n",
    "    'Month': ['Jan', 'Jan', 'Feb', 'Feb', 'Mar', 'Mar', 'Apr', 'Apr'],\n",
    "    'Region': ['North', 'South', 'North', 'South', 'North', 'South', 'North', 'South'],\n",
    "    'Product': ['A', 'A', 'A', 'A', 'A', 'A', 'B', 'B'],\n",
    "    'Sales': [250, 200, 260, 210, 270, 220, 180, 210],\n",
    "    'Costs': [130, 100, 130, 100, 120, 120, 100, 100]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_horizontal = pd.DataFrame({\n",
    "    'Month': ['Jan', 'Feb', 'Mar', 'Apr'],\n",
    "    'North': [250, 260, 270, 180],\n",
    "    'South': [200, 210, 220, 210]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to do it \n",
    "\n",
    "\n",
    "### Use `pivot` to go from vertical to horizontal\n",
    "\n",
    "1. Take a look at the `df_vertical` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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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>Month</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>North</td>\n",
       "      <td>A</td>\n",
       "      <td>250</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jan</td>\n",
       "      <td>South</td>\n",
       "      <td>A</td>\n",
       "      <td>200</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feb</td>\n",
       "      <td>North</td>\n",
       "      <td>A</td>\n",
       "      <td>260</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feb</td>\n",
       "      <td>South</td>\n",
       "      <td>A</td>\n",
       "      <td>210</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mar</td>\n",
       "      <td>North</td>\n",
       "      <td>A</td>\n",
       "      <td>270</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mar</td>\n",
       "      <td>South</td>\n",
       "      <td>A</td>\n",
       "      <td>220</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Apr</td>\n",
       "      <td>North</td>\n",
       "      <td>B</td>\n",
       "      <td>180</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apr</td>\n",
       "      <td>South</td>\n",
       "      <td>B</td>\n",
       "      <td>210</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month Region Product  Sales  Costs\n",
       "0   Jan  North       A    250    130\n",
       "1   Jan  South       A    200    100\n",
       "2   Feb  North       A    260    130\n",
       "3   Feb  South       A    210    100\n",
       "4   Mar  North       A    270    120\n",
       "5   Mar  South       A    220    120\n",
       "6   Apr  North       B    180    100\n",
       "7   Apr  South       B    210    100"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vertical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vertical.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Convert the `df_vertical` into horizontal/wide format by using the Pandas method `pivot`. This method requires three inputs:\n",
    "- `index`\n",
    "- `columns`\n",
    "- `values`\n",
    "which will determine the structure of the new `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Region</th>\n",
       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>180</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>260</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>250</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>270</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Region  North  South\n",
       "Month               \n",
       "Apr       180    210\n",
       "Feb       260    210\n",
       "Jan       250    200\n",
       "Mar       270    220"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vertical.pivot(index='Month', columns='Region', values='Sales')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this format each row represents a month, each column represents a region, and the sales values are filled in accordingly. Here, the `Month` column becomes the index, the `Region` column values become the column names, and the `Sales` values are filled into the corresponding cells. \n",
    "\n",
    "Note that the  information in the columns `Costs` and `Product` is lost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Pass a list into the input `values`  to obtain a pivot with several values on it. In this case, we are going to pass `Sales` and `Costs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sales</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Costs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th>North</th>\n",
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       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>180</td>\n",
       "      <td>210</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>260</td>\n",
       "      <td>210</td>\n",
       "      <td>130</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>250</td>\n",
       "      <td>200</td>\n",
       "      <td>130</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>270</td>\n",
       "      <td>220</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sales       Costs      \n",
       "Region North South North South\n",
       "Month                         \n",
       "Apr      180   210   100   100\n",
       "Feb      260   210   130   100\n",
       "Jan      250   200   130   100\n",
       "Mar      270   220   120   120"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vertical.pivot(index='Month', columns='Region', values=['Sales', 'Costs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Pass a list into the input `columns` to include the information about the product type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th colspan=\"4\" halign=\"left\">Sales</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Costs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th colspan=\"2\" halign=\"left\">A</th>\n",
       "      <th colspan=\"2\" halign=\"left\">B</th>\n",
       "      <th colspan=\"2\" halign=\"left\">A</th>\n",
       "      <th colspan=\"2\" halign=\"left\">B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>Apr</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>260.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>250.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>270.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sales                       Costs                     \n",
       "Product      A             B             A             B       \n",
       "Region   North  South  North  South  North  South  North  South\n",
       "Month                                                          \n",
       "Apr        NaN    NaN  180.0  210.0    NaN    NaN  100.0  100.0\n",
       "Feb      260.0  210.0    NaN    NaN  130.0  100.0    NaN    NaN\n",
       "Jan      250.0  200.0    NaN    NaN  130.0  100.0    NaN    NaN\n",
       "Mar      270.0  220.0    NaN    NaN  120.0  120.0    NaN    NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vertical.pivot(index='Month', columns=['Product', 'Region'], values=['Sales', 'Costs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Similarly, you can pass a list into the input `index` to include the information about the product type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sales</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Costs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "      <th>North</th>\n",
       "      <th>South</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <th>B</th>\n",
       "      <td>180</td>\n",
       "      <td>210</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <th>A</th>\n",
       "      <td>260</td>\n",
       "      <td>210</td>\n",
       "      <td>130</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <th>A</th>\n",
       "      <td>250</td>\n",
       "      <td>200</td>\n",
       "      <td>130</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <th>A</th>\n",
       "      <td>270</td>\n",
       "      <td>220</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales       Costs      \n",
       "Region        North South North South\n",
       "Month Product                        \n",
       "Apr   B         180   210   100   100\n",
       "Feb   A         260   210   130   100\n",
       "Jan   A         250   200   130   100\n",
       "Mar   A         270   220   120   120"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vertical.pivot(index=['Month','Product'], columns=['Region'], values=['Sales', 'Costs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Use `melt` to go from horizontal to vertical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Take a look at the `df_horizontal` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  Month  North  South\n",
       "0   Jan    250    200\n",
       "1   Feb    260    210\n",
       "2   Mar    270    220\n",
       "3   Apr    180    210"
      ]
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     "execution_count": 10,
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   "source": [
    "df_horizontal"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Convert the `df_horizontal` into vertical/long format by using the Pandas function `melt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>South</td>\n",
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      "text/plain": [
       "   variable value\n",
       "0     Month   Jan\n",
       "1     Month   Feb\n",
       "2     Month   Mar\n",
       "3     Month   Apr\n",
       "4     North   250\n",
       "5     North   260\n",
       "6     North   270\n",
       "7     North   180\n",
       "8     South   200\n",
       "9     South   210\n",
       "10    South   220\n",
       "11    South   210"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(df_horizontal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. To get a better result we need to pass the inputs\n",
    "   \n",
    "- `id_vars`\n",
    "- `value_vars`\n",
    "- `var_name`\n",
    "- `value_name`\n",
    "\n",
    "which will determine the structure of the new `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Region</th>\n",
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       "      <th>6</th>\n",
       "      <td>Mar</td>\n",
       "      <td>South</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apr</td>\n",
       "      <td>South</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  Month Region  Sales\n",
       "0   Jan  North    250\n",
       "1   Feb  North    260\n",
       "2   Mar  North    270\n",
       "3   Apr  North    180\n",
       "4   Jan  South    200\n",
       "5   Feb  South    210\n",
       "6   Mar  South    220\n",
       "7   Apr  South    210"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melted_df = pd.melt(df_horizontal, id_vars=['Month'], value_vars=['North', 'South'], var_name='Region', value_name='Sales')\n",
    "melted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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