data-analysis/045-pandas-crosstab/pandas-crosstab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas `crosstab`\n",
    "\n",
    "This is a notebook for the medium article [Summarizing data with Pandas crosstab](https://bindichen.medium.com/summarizing-data-with-pandas-crosstab-efc8b9abecf)\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": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    return pd.read_csv('coffee_sales.csv', parse_dates=['order_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>order_date</th>\n",
       "      <th>market</th>\n",
       "      <th>region</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product</th>\n",
       "      <th>cost</th>\n",
       "      <th>inventory</th>\n",
       "      <th>net_profit</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Central</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Amaretto</td>\n",
       "      <td>89</td>\n",
       "      <td>777</td>\n",
       "      <td>94</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Central</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Columbian</td>\n",
       "      <td>83</td>\n",
       "      <td>623</td>\n",
       "      <td>68</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Central</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Decaf Irish Cream</td>\n",
       "      <td>95</td>\n",
       "      <td>821</td>\n",
       "      <td>101</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Central</td>\n",
       "      <td>Tea</td>\n",
       "      <td>Green Tea</td>\n",
       "      <td>44</td>\n",
       "      <td>623</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Wholesale</td>\n",
       "      <td>Central</td>\n",
       "      <td>Espresso</td>\n",
       "      <td>Caffe Mocha</td>\n",
       "      <td>54</td>\n",
       "      <td>456</td>\n",
       "      <td>54</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date     market   region product_category            product  cost  \\\n",
       "0 2010-01-01  Wholesale  Central           Coffee           Amaretto    89   \n",
       "1 2010-01-01  Wholesale  Central           Coffee          Columbian    83   \n",
       "2 2010-01-01  Wholesale  Central           Coffee  Decaf Irish Cream    95   \n",
       "3 2010-01-01  Wholesale  Central              Tea          Green Tea    44   \n",
       "4 2010-01-01  Wholesale  Central         Espresso        Caffe Mocha    54   \n",
       "\n",
       "   inventory  net_profit  sales  \n",
       "0        777          94    219  \n",
       "1        623          68    190  \n",
       "2        821         101    234  \n",
       "3        623          30    100  \n",
       "4        456          54    134  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = load_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Creating a simple cross tabulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>product_category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>384</td>\n",
       "      <td>288</td>\n",
       "      <td>336</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>168</td>\n",
       "      <td>240</td>\n",
       "      <td>216</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>192</td>\n",
       "      <td>288</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>312</td>\n",
       "      <td>360</td>\n",
       "      <td>312</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category  Coffee  Espresso  Herbal tea  Tea\n",
       "region                                             \n",
       "Central              384       288         336  336\n",
       "East                 168       240         216  264\n",
       "South                192       288         192    0\n",
       "West                 312       360         312  360"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['region'], df['product_category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Product Category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>384</td>\n",
       "      <td>288</td>\n",
       "      <td>336</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>168</td>\n",
       "      <td>240</td>\n",
       "      <td>216</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>192</td>\n",
       "      <td>288</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>312</td>\n",
       "      <td>360</td>\n",
       "      <td>312</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Product Category  Coffee  Espresso  Herbal tea  Tea\n",
       "US Region                                          \n",
       "Central              384       288         336  336\n",
       "East                 168       240         216  264\n",
       "South                192       288         192    0\n",
       "West                 312       360         312  360"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    df['product_category'], \n",
    "    rownames=['US Region'], \n",
    "    colnames=['Product Category']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Display multiple rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "\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>product_category</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Coffee</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Espresso</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Herbal tea</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>market</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Wholesale</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Wholesale</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Wholesale</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Wholesale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</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>Central</th>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>192</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>72</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>144</td>\n",
       "      <td>72</td>\n",
       "      <td>144</td>\n",
       "      <td>96</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>144</td>\n",
       "      <td>48</td>\n",
       "      <td>216</td>\n",
       "      <td>72</td>\n",
       "      <td>144</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>240</td>\n",
       "      <td>72</td>\n",
       "      <td>288</td>\n",
       "      <td>72</td>\n",
       "      <td>240</td>\n",
       "      <td>72</td>\n",
       "      <td>288</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category Coffee           Espresso           Herbal tea            \\\n",
       "market           Retail Wholesale   Retail Wholesale     Retail Wholesale   \n",
       "region                                                                      \n",
       "Central             192       192      144       144        144       192   \n",
       "East                 72        96       96       144         72       144   \n",
       "South               144        48      216        72        144        48   \n",
       "West                240        72      288        72        240        72   \n",
       "\n",
       "product_category    Tea            \n",
       "market           Retail Wholesale  \n",
       "region                             \n",
       "Central             168       168  \n",
       "East                 96       168  \n",
       "South                 0         0  \n",
       "West                288        72  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiple columns\n",
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    [df['product_category'], df['market']]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "    .dataframe thead tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Product Category</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Coffee</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Espresso</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Herbal tea</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Market</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Wholesale</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Wholesale</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Wholesale</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Wholesale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US Region</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>Central</th>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>192</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>72</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>144</td>\n",
       "      <td>72</td>\n",
       "      <td>144</td>\n",
       "      <td>96</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>144</td>\n",
       "      <td>48</td>\n",
       "      <td>216</td>\n",
       "      <td>72</td>\n",
       "      <td>144</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>240</td>\n",
       "      <td>72</td>\n",
       "      <td>288</td>\n",
       "      <td>72</td>\n",
       "      <td>240</td>\n",
       "      <td>72</td>\n",
       "      <td>288</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Product Category Coffee           Espresso           Herbal tea            \\\n",
       "Market           Retail Wholesale   Retail Wholesale     Retail Wholesale   \n",
       "US Region                                                                   \n",
       "Central             192       192      144       144        144       192   \n",
       "East                 72        96       96       144         72       144   \n",
       "South               144        48      216        72        144        48   \n",
       "West                240        72      288        72        240        72   \n",
       "\n",
       "Product Category    Tea            \n",
       "Market           Retail Wholesale  \n",
       "US Region                          \n",
       "Central             168       168  \n",
       "East                 96       168  \n",
       "South                 0         0  \n",
       "West                288        72  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiple columns with custom labels\n",
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    [df['product_category'], df['market']],\n",
    "    rownames=['US Region'], \n",
    "    colnames=['Product Category', 'Market']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th>market</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\">Central</th>\n",
       "      <th>Retail</th>\n",
       "      <td>192</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesale</th>\n",
       "      <td>192</td>\n",
       "      <td>144</td>\n",
       "      <td>192</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">East</th>\n",
       "      <th>Retail</th>\n",
       "      <td>72</td>\n",
       "      <td>96</td>\n",
       "      <td>72</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesale</th>\n",
       "      <td>96</td>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">South</th>\n",
       "      <th>Retail</th>\n",
       "      <td>144</td>\n",
       "      <td>216</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesale</th>\n",
       "      <td>48</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">West</th>\n",
       "      <th>Retail</th>\n",
       "      <td>240</td>\n",
       "      <td>288</td>\n",
       "      <td>240</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wholesale</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category   Coffee  Espresso  Herbal tea  Tea\n",
       "region  market                                      \n",
       "Central Retail        192       144         144  168\n",
       "        Wholesale     192       144         192  168\n",
       "East    Retail         72        96          72   96\n",
       "        Wholesale      96       144         144  168\n",
       "South   Retail        144       216         144    0\n",
       "        Wholesale      48        72          48    0\n",
       "West    Retail        240       288         240  288\n",
       "        Wholesale      72        72          72   72"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiple rows\n",
    "pd.crosstab(\n",
    "    [df['region'], df['market']], \n",
    "    df['product_category']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Adding rows/columns subtotals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>product_category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</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>Central</th>\n",
       "      <td>384</td>\n",
       "      <td>288</td>\n",
       "      <td>336</td>\n",
       "      <td>336</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>168</td>\n",
       "      <td>240</td>\n",
       "      <td>216</td>\n",
       "      <td>264</td>\n",
       "      <td>888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>192</td>\n",
       "      <td>288</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>312</td>\n",
       "      <td>360</td>\n",
       "      <td>312</td>\n",
       "      <td>360</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1056</td>\n",
       "      <td>1176</td>\n",
       "      <td>1056</td>\n",
       "      <td>960</td>\n",
       "      <td>4248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category  Coffee  Espresso  Herbal tea  Tea   All\n",
       "region                                                   \n",
       "Central              384       288         336  336  1344\n",
       "East                 168       240         216  264   888\n",
       "South                192       288         192    0   672\n",
       "West                 312       360         312  360  1344\n",
       "All                 1056      1176        1056  960  4248"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['region'], df['product_category'], margins = True)"
   ]
  },
  {
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>product_category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "      <th>Subtotals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</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>Central</th>\n",
       "      <td>384</td>\n",
       "      <td>288</td>\n",
       "      <td>336</td>\n",
       "      <td>336</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>168</td>\n",
       "      <td>240</td>\n",
       "      <td>216</td>\n",
       "      <td>264</td>\n",
       "      <td>888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>192</td>\n",
       "      <td>288</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>312</td>\n",
       "      <td>360</td>\n",
       "      <td>312</td>\n",
       "      <td>360</td>\n",
       "      <td>1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subtotals</th>\n",
       "      <td>1056</td>\n",
       "      <td>1176</td>\n",
       "      <td>1056</td>\n",
       "      <td>960</td>\n",
       "      <td>4248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category  Coffee  Espresso  Herbal tea  Tea  Subtotals\n",
       "region                                                        \n",
       "Central              384       288         336  336       1344\n",
       "East                 168       240         216  264        888\n",
       "South                192       288         192    0        672\n",
       "West                 312       360         312  360       1344\n",
       "Subtotals           1056      1176        1056  960       4248"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# custom name\n",
    "pd.crosstab(\n",
    "    df['region'], df['product_category'], \n",
    "    margins = True, \n",
    "    margins_name = 'Subtotals'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Getting the percentage of occurs with `normalize`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>product_category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>0.090395</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.079096</td>\n",
       "      <td>0.079096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>0.039548</td>\n",
       "      <td>0.056497</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.062147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>0.045198</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.045198</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>0.073446</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.073446</td>\n",
       "      <td>0.084746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category    Coffee  Espresso  Herbal tea       Tea\n",
       "region                                                    \n",
       "Central           0.090395  0.067797    0.079096  0.079096\n",
       "East              0.039548  0.056497    0.050847  0.062147\n",
       "South             0.045198  0.067797    0.045198  0.000000\n",
       "West              0.073446  0.084746    0.073446  0.084746"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['region'], df['product_category'], normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3\" ><thead>    <tr>        <th class=\"index_name level0\" >product_category</th>        <th class=\"col_heading level0 col0\" >Coffee</th>        <th class=\"col_heading level0 col1\" >Espresso</th>        <th class=\"col_heading level0 col2\" >Herbal tea</th>        <th class=\"col_heading level0 col3\" >Tea</th>    </tr>    <tr>        <th class=\"index_name level0\" >region</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3level0_row0\" class=\"row_heading level0 row0\" >Central</th>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row0_col0\" class=\"data row0 col0\" >9.04%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row0_col1\" class=\"data row0 col1\" >6.78%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row0_col2\" class=\"data row0 col2\" >7.91%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row0_col3\" class=\"data row0 col3\" >7.91%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3level0_row1\" class=\"row_heading level0 row1\" >East</th>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row1_col0\" class=\"data row1 col0\" >3.95%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row1_col1\" class=\"data row1 col1\" >5.65%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row1_col2\" class=\"data row1 col2\" >5.08%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row1_col3\" class=\"data row1 col3\" >6.21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3level0_row2\" class=\"row_heading level0 row2\" >South</th>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row2_col0\" class=\"data row2 col0\" >4.52%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row2_col1\" class=\"data row2 col1\" >6.78%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row2_col2\" class=\"data row2 col2\" >4.52%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row2_col3\" class=\"data row2 col3\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3level0_row3\" class=\"row_heading level0 row3\" >West</th>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row3_col0\" class=\"data row3 col0\" >7.34%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row3_col1\" class=\"data row3 col1\" >8.47%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row3_col2\" class=\"data row3 col2\" >7.34%</td>\n",
       "                        <td id=\"T_dd49e3f6_61ae_11ec_9e4e_f218983b86b3row3_col3\" class=\"data row3 col3\" >8.47%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f84b8d19d10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# formatted with a percent sign (%)\n",
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    df['product_category'], \n",
    "    normalize = True\n",
    ").style.format('{:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_9745e48c_5618_11ec_a71a_acde48001122\" ><thead>    <tr>        <th class=\"index_name level0\" >product_category</th>        <th class=\"col_heading level0 col0\" >Coffee</th>        <th class=\"col_heading level0 col1\" >Espresso</th>        <th class=\"col_heading level0 col2\" >Herbal tea</th>        <th class=\"col_heading level0 col3\" >Tea</th>        <th class=\"col_heading level0 col4\" >All</th>    </tr>    <tr>        <th class=\"index_name level0\" >region</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_9745e48c_5618_11ec_a71a_acde48001122level0_row0\" class=\"row_heading level0 row0\" >Central</th>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row0_col0\" class=\"data row0 col0\" >9.04%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row0_col1\" class=\"data row0 col1\" >6.78%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row0_col2\" class=\"data row0 col2\" >7.91%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row0_col3\" class=\"data row0 col3\" >7.91%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row0_col4\" class=\"data row0 col4\" >31.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9745e48c_5618_11ec_a71a_acde48001122level0_row1\" class=\"row_heading level0 row1\" >East</th>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row1_col0\" class=\"data row1 col0\" >3.95%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row1_col1\" class=\"data row1 col1\" >5.65%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row1_col2\" class=\"data row1 col2\" >5.08%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row1_col3\" class=\"data row1 col3\" >6.21%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row1_col4\" class=\"data row1 col4\" >20.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9745e48c_5618_11ec_a71a_acde48001122level0_row2\" class=\"row_heading level0 row2\" >South</th>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row2_col0\" class=\"data row2 col0\" >4.52%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row2_col1\" class=\"data row2 col1\" >6.78%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row2_col2\" class=\"data row2 col2\" >4.52%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row2_col3\" class=\"data row2 col3\" >0.00%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row2_col4\" class=\"data row2 col4\" >15.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9745e48c_5618_11ec_a71a_acde48001122level0_row3\" class=\"row_heading level0 row3\" >West</th>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row3_col0\" class=\"data row3 col0\" >7.34%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row3_col1\" class=\"data row3 col1\" >8.47%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row3_col2\" class=\"data row3 col2\" >7.34%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row3_col3\" class=\"data row3 col3\" >8.47%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row3_col4\" class=\"data row3 col4\" >31.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_9745e48c_5618_11ec_a71a_acde48001122level0_row4\" class=\"row_heading level0 row4\" >All</th>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row4_col0\" class=\"data row4 col0\" >24.86%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row4_col1\" class=\"data row4 col1\" >27.68%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row4_col2\" class=\"data row4 col2\" >24.86%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row4_col3\" class=\"data row4 col3\" >22.60%</td>\n",
       "                        <td id=\"T_9745e48c_5618_11ec_a71a_acde48001122row4_col4\" class=\"data row4 col4\" >100.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb31a558d10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# margins = True, and formatted with a percent sign (%)\n",
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    df['product_category'], \n",
    "    margins = True, \n",
    "    normalize = True\n",
    ").style.format('{:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3\" ><thead>    <tr>        <th class=\"index_name level0\" >product_category</th>        <th class=\"col_heading level0 col0\" >Coffee</th>        <th class=\"col_heading level0 col1\" >Espresso</th>        <th class=\"col_heading level0 col2\" >Herbal tea</th>        <th class=\"col_heading level0 col3\" >Tea</th>    </tr>    <tr>        <th class=\"index_name level0\" >region</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3level0_row0\" class=\"row_heading level0 row0\" >Central</th>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row0_col0\" class=\"data row0 col0\" >28.57%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row0_col1\" class=\"data row0 col1\" >21.43%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row0_col2\" class=\"data row0 col2\" >25.00%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row0_col3\" class=\"data row0 col3\" >25.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3level0_row1\" class=\"row_heading level0 row1\" >East</th>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row1_col0\" class=\"data row1 col0\" >18.92%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row1_col1\" class=\"data row1 col1\" >27.03%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row1_col2\" class=\"data row1 col2\" >24.32%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row1_col3\" class=\"data row1 col3\" >29.73%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3level0_row2\" class=\"row_heading level0 row2\" >South</th>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row2_col0\" class=\"data row2 col0\" >28.57%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row2_col1\" class=\"data row2 col1\" >42.86%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row2_col2\" class=\"data row2 col2\" >28.57%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row2_col3\" class=\"data row2 col3\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3level0_row3\" class=\"row_heading level0 row3\" >West</th>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row3_col0\" class=\"data row3 col0\" >23.21%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row3_col1\" class=\"data row3 col1\" >26.79%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row3_col2\" class=\"data row3 col2\" >23.21%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row3_col3\" class=\"data row3 col3\" >26.79%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3level0_row4\" class=\"row_heading level0 row4\" >All</th>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row4_col0\" class=\"data row4 col0\" >24.86%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row4_col1\" class=\"data row4 col1\" >27.68%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row4_col2\" class=\"data row4 col2\" >24.86%</td>\n",
       "                        <td id=\"T_d8c7b9a6_61af_11ec_9e4e_f218983b86b3row4_col3\" class=\"data row4 col3\" >22.60%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f84bfbef050>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# margins = index, and formatted with a percent sign (%)\n",
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    df['product_category'], \n",
    "    margins = True, \n",
    "    normalize = 'index'\n",
    ").style.format('{:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3\" ><thead>    <tr>        <th class=\"index_name level0\" >product_category</th>        <th class=\"col_heading level0 col0\" >Coffee</th>        <th class=\"col_heading level0 col1\" >Espresso</th>        <th class=\"col_heading level0 col2\" >Herbal tea</th>        <th class=\"col_heading level0 col3\" >Tea</th>        <th class=\"col_heading level0 col4\" >All</th>    </tr>    <tr>        <th class=\"index_name level0\" >region</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3level0_row0\" class=\"row_heading level0 row0\" >Central</th>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row0_col0\" class=\"data row0 col0\" >36.36%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row0_col1\" class=\"data row0 col1\" >24.49%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row0_col2\" class=\"data row0 col2\" >31.82%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row0_col3\" class=\"data row0 col3\" >35.00%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row0_col4\" class=\"data row0 col4\" >31.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3level0_row1\" class=\"row_heading level0 row1\" >East</th>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row1_col0\" class=\"data row1 col0\" >15.91%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row1_col1\" class=\"data row1 col1\" >20.41%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row1_col2\" class=\"data row1 col2\" >20.45%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row1_col3\" class=\"data row1 col3\" >27.50%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row1_col4\" class=\"data row1 col4\" >20.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3level0_row2\" class=\"row_heading level0 row2\" >South</th>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row2_col0\" class=\"data row2 col0\" >18.18%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row2_col1\" class=\"data row2 col1\" >24.49%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row2_col2\" class=\"data row2 col2\" >18.18%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row2_col3\" class=\"data row2 col3\" >0.00%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row2_col4\" class=\"data row2 col4\" >15.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3level0_row3\" class=\"row_heading level0 row3\" >West</th>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row3_col0\" class=\"data row3 col0\" >29.55%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row3_col1\" class=\"data row3 col1\" >30.61%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row3_col2\" class=\"data row3 col2\" >29.55%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row3_col3\" class=\"data row3 col3\" >37.50%</td>\n",
       "                        <td id=\"T_e0ff0b92_61af_11ec_9e4e_f218983b86b3row3_col4\" class=\"data row3 col4\" >31.64%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f84b8d3ab50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# margins = columns, and formatted with a percent sign (%)\n",
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    df['product_category'], \n",
    "    margins = True, \n",
    "    normalize = 'columns'\n",
    ").style.format('{:.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Performing aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>product_category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>74.390625</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>87.065476</td>\n",
       "      <td>87.690476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>110.976190</td>\n",
       "      <td>95.600000</td>\n",
       "      <td>101.240741</td>\n",
       "      <td>46.962121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>78.916667</td>\n",
       "      <td>62.305556</td>\n",
       "      <td>56.270833</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>90.814103</td>\n",
       "      <td>84.133333</td>\n",
       "      <td>98.987179</td>\n",
       "      <td>100.844444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category      Coffee   Espresso  Herbal tea         Tea\n",
       "region                                                         \n",
       "Central            74.390625  89.666667   87.065476   87.690476\n",
       "East              110.976190  95.600000  101.240741   46.962121\n",
       "South              78.916667  62.305556   56.270833         NaN\n",
       "West               90.814103  84.133333   98.987179  100.844444"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    df['product_category'], \n",
    "    values = df['cost'],\n",
    "    aggfunc = 'mean'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>product_category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>74.39</td>\n",
       "      <td>89.67</td>\n",
       "      <td>87.07</td>\n",
       "      <td>87.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>110.98</td>\n",
       "      <td>95.60</td>\n",
       "      <td>101.24</td>\n",
       "      <td>46.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>78.92</td>\n",
       "      <td>62.31</td>\n",
       "      <td>56.27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>90.81</td>\n",
       "      <td>84.13</td>\n",
       "      <td>98.99</td>\n",
       "      <td>100.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category  Coffee  Espresso  Herbal tea     Tea\n",
       "region                                                \n",
       "Central            74.39     89.67       87.07   87.69\n",
       "East              110.98     95.60      101.24   46.96\n",
       "South              78.92     62.31       56.27     NaN\n",
       "West               90.81     84.13       98.99  100.84"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To round the result, we can call round():\n",
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    df['product_category'], \n",
    "    values = df['cost'],\n",
    "    aggfunc = 'mean'\n",
    ").round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>product_category</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Espresso</th>\n",
       "      <th>Herbal tea</th>\n",
       "      <th>Tea</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>74.390625</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>87.065476</td>\n",
       "      <td>87.690476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>110.976190</td>\n",
       "      <td>95.600000</td>\n",
       "      <td>101.240741</td>\n",
       "      <td>46.962121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>78.916667</td>\n",
       "      <td>62.305556</td>\n",
       "      <td>56.270833</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>90.814103</td>\n",
       "      <td>84.133333</td>\n",
       "      <td>98.987179</td>\n",
       "      <td>100.844444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "product_category      Coffee   Espresso  Herbal tea         Tea\n",
       "region                                                         \n",
       "Central            74.390625  89.666667   87.065476   87.690476\n",
       "East              110.976190  95.600000  101.240741   46.962121\n",
       "South              78.916667  62.305556   56.270833    0.000000\n",
       "West               90.814103  84.133333   98.987179  100.844444"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(\n",
    "    df['region'], \n",
    "    df['product_category'], \n",
    "    values = df['cost'],\n",
    "    aggfunc = 'mean',\n",
    ").fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(\n",
    "    pd.crosstab(\n",
    "        df['region'], \n",
    "        [df['product_category'], df['market']],\n",
    "        rownames=['US Region'], \n",
    "        colnames=['Product Category', 'Market'],\n",
    "        values = df['cost'],\n",
    "        aggfunc = 'mean'\n",
    "    ), \n",
    "    annot = True\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Thanks for reading\n",
    "\n",
    "This is a notebook for the medium article [Summarizing data with Pandas crosstab](https://bindichen.medium.com/summarizing-data-with-pandas-crosstab-efc8b9abecf)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf-tutorial",
   "language": "python",
   "name": "tf-tutorial"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}