russia-alcohol.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1fa6fe63",
   "metadata": {},
   "source": [
    "M. Tosic, 2021\n",
    "\n",
    "This notebook was created as a project in my data science course. \n",
    "\n",
    "The macthing areticle to this notebook is available here:\n",
    "https://medium.com/p/4afb7d7ea0cc/edit\n",
    "\n",
    "A requirement for the project is also to show the implementation of the **CRISP-DM** process:\n",
    "* 1. Business Understanding\n",
    "* 2. Data Understanding\n",
    "* 3. Prepare Data\n",
    "* 4. Data Modeling(Optional)\n",
    "* 5. Evaluate the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ede51a",
   "metadata": {},
   "source": [
    "# 1. Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd889a23",
   "metadata": {},
   "source": [
    "The goal I set out to achieve is to understand better the alcohol comsumption in Russia, what kinds of beverages are popular and are there any regional differences.\n",
    "\n",
    "* What kinds of alcoholic beverages were consumed in Russia over the observed period?\n",
    "\n",
    "* What is the composition of the total average volume on a yearly level?\n",
    "\n",
    "* Focusing on the dominant alcoholic beverages, what is the total and relative volume of pure alcohol per year?\n",
    "\n",
    "* Are there any regional differences in total consumption?\n",
    "\n",
    "* What is the average yearly distribution of alcoholic beverages consumed in Krasnodar Krai?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f784660a",
   "metadata": {},
   "source": [
    "# 2. Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe82f807",
   "metadata": {},
   "source": [
    "The dataset I shall use is from https://www.kaggle.com/dwdkills/alcohol-consumption-in-russia\n",
    "\n",
    "It contains the values of the consumed alcohol volumes of beer, brandy champagne, vodka and wine for different regions in Russia during the period between 1998 and 2016.\n",
    "\n",
    "Dataset has 1615 rows and 7 columns. Keys for columns:\n",
    "\n",
    "* \"year\" - year (1998-2016)\n",
    "* \"region\" - name of a federal subject of Russia. It could be oblast, republic, krai, autonomous okrug, federal city and a single autonomous oblast\n",
    "* \"wine\" - sale of wine in litres by year per capita\n",
    "* \"beer\" - sale of beer in litres by year per capita\n",
    "* \"vodka\" - sale of vodka in litres by year per capita\n",
    "* \"champagne\" - sale of champagne in litres by year per capita\n",
    "* \"brandy\" - sale of brandy in litres by year per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "022e6bc8",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>Republic of Adygea</td>\n",
       "      <td>1.90</td>\n",
       "      <td>8.80</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>Altai Krai</td>\n",
       "      <td>3.30</td>\n",
       "      <td>19.20</td>\n",
       "      <td>11.30</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>Amur Oblast</td>\n",
       "      <td>2.10</td>\n",
       "      <td>21.20</td>\n",
       "      <td>17.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1998</td>\n",
       "      <td>Arkhangelsk Oblast</td>\n",
       "      <td>4.30</td>\n",
       "      <td>10.60</td>\n",
       "      <td>11.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>Astrakhan Oblast</td>\n",
       "      <td>2.90</td>\n",
       "      <td>18.00</td>\n",
       "      <td>9.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year              region  wine  beer  vodka  champagne  brandy\n",
       "0  1998  Republic of Adygea  1.90  8.80   3.40       0.30    0.10\n",
       "1  1998          Altai Krai  3.30 19.20  11.30       1.10    0.10\n",
       "2  1998         Amur Oblast  2.10 21.20  17.30       0.70    0.40\n",
       "3  1998  Arkhangelsk Oblast  4.30 10.60  11.70       0.40    0.30\n",
       "4  1998    Astrakhan Oblast  2.90 18.00   9.50       0.80    0.20"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "\n",
    "df = pd.read_csv('./russia_alcohol.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "efa71454",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1,615.00</td>\n",
       "      <td>1,552.00</td>\n",
       "      <td>1,557.00</td>\n",
       "      <td>1,554.00</td>\n",
       "      <td>1,552.00</td>\n",
       "      <td>1,549.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2,007.00</td>\n",
       "      <td>5.63</td>\n",
       "      <td>51.26</td>\n",
       "      <td>11.82</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.48</td>\n",
       "      <td>2.81</td>\n",
       "      <td>25.37</td>\n",
       "      <td>5.13</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1,998.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2,002.00</td>\n",
       "      <td>3.58</td>\n",
       "      <td>32.40</td>\n",
       "      <td>8.30</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2,007.00</td>\n",
       "      <td>5.40</td>\n",
       "      <td>49.97</td>\n",
       "      <td>11.50</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2,012.00</td>\n",
       "      <td>7.38</td>\n",
       "      <td>67.40</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2,016.00</td>\n",
       "      <td>18.10</td>\n",
       "      <td>207.30</td>\n",
       "      <td>40.60</td>\n",
       "      <td>5.56</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year     wine     beer    vodka  champagne   brandy\n",
       "count 1,615.00 1,552.00 1,557.00 1,554.00   1,552.00 1,549.00\n",
       "mean  2,007.00     5.63    51.26    11.82       1.31     0.53\n",
       "std       5.48     2.81    25.37     5.13       0.80     0.40\n",
       "min   1,998.00     0.10     0.40     0.05       0.10     0.00\n",
       "25%   2,002.00     3.58    32.40     8.30       0.80     0.20\n",
       "50%   2,007.00     5.40    49.97    11.50       1.20     0.40\n",
       "75%   2,012.00     7.38    67.40    15.00       1.67     0.70\n",
       "max   2,016.00    18.10   207.30    40.60       5.56     2.30"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ab2d4b",
   "metadata": {},
   "source": [
    "# 3. Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "65e9dfa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year        0.00\n",
       "region      0.00\n",
       "wine        0.04\n",
       "beer        0.04\n",
       "vodka       0.04\n",
       "champagne   0.04\n",
       "brandy      0.04\n",
       "dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df.isnull())/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "53ccf1bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         False\n",
       "region       False\n",
       "wine         False\n",
       "beer         False\n",
       "vodka        False\n",
       "champagne    False\n",
       "brandy       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "55317099",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1998</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1998</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1998</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1998</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1999</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1999</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1999</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1999</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2000</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2000</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2000</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2001</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>2001</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2001</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>2002</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>2002</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>2002</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>2003</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>2003</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2003</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2004</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>2004</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>2004</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>2005</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>2005</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>2005</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>2006</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>2006</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2006</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>2006</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>2007</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>2007</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>2007</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>2007</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>2008</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>2008</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>2008</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>2008</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>2009</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>2009</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2009</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>2009</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>2010</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>2010</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>2010</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>2010</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>2011</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>2011</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>2011</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>2011</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>2012</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>2012</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>2012</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>2012</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>2013</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>2013</td>\n",
       "      <td>Republic of Crimea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>2013</td>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354</th>\n",
       "      <td>2013</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>2014</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>2014</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>2015</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>2015</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>2016</td>\n",
       "      <td>Republic of Ingushetia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>2016</td>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year                  region  wine  beer  vodka  champagne  brandy\n",
       "17    1998  Republic of Ingushetia   NaN   NaN    NaN        NaN     NaN\n",
       "31    1998      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "63    1998              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "79    1998        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "102   1999  Republic of Ingushetia   NaN   NaN    NaN        NaN     NaN\n",
       "116   1999      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "148   1999              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "164   1999        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "187   2000  Republic of Ingushetia   NaN   NaN    NaN        NaN     NaN\n",
       "201   2000      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "233   2000              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "249   2000        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "286   2001      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "318   2001              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "334   2001        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "371   2002      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "403   2002              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "419   2002        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "456   2003      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "488   2003              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "504   2003        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "541   2004      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "573   2004              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "589   2004        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "612   2005  Republic of Ingushetia  0.20  0.80   0.80       0.10     NaN\n",
       "626   2005      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "658   2005              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "674   2005        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "697   2006  Republic of Ingushetia  0.30  0.90   1.00       0.10     NaN\n",
       "711   2006      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "743   2006              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "759   2006        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "782   2007  Republic of Ingushetia  0.30  1.00   1.00       0.10     NaN\n",
       "796   2007      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "828   2007              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "844   2007        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "867   2008  Republic of Ingushetia   NaN   NaN    NaN        NaN     NaN\n",
       "881   2008      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "913   2008              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "929   2008        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "952   2009  Republic of Ingushetia   NaN   NaN   0.10        NaN     NaN\n",
       "966   2009      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "998   2009              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "1014  2009        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "1037  2010  Republic of Ingushetia   NaN   NaN   0.05        NaN     NaN\n",
       "1051  2010      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "1083  2010              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "1099  2010        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "1122  2011  Republic of Ingushetia   NaN   NaN    NaN        NaN     NaN\n",
       "1136  2011      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "1168  2011              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "1184  2011        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "1207  2012  Republic of Ingushetia   NaN   NaN    NaN        NaN     NaN\n",
       "1221  2012      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "1253  2012              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "1269  2012        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "1292  2013  Republic of Ingushetia   NaN   NaN    NaN        NaN     NaN\n",
       "1306  2013      Republic of Crimea   NaN   NaN    NaN        NaN     NaN\n",
       "1338  2013              Sevastopol   NaN   NaN    NaN        NaN     NaN\n",
       "1354  2013        Chechen Republic   NaN   NaN    NaN        NaN     NaN\n",
       "1377  2014  Republic of Ingushetia   NaN  0.40    NaN        NaN     NaN\n",
       "1439  2014        Chechen Republic   NaN  4.90    NaN        NaN     NaN\n",
       "1462  2015  Republic of Ingushetia   NaN  0.50    NaN        NaN     NaN\n",
       "1524  2015        Chechen Republic   NaN  2.20    NaN        NaN     NaN\n",
       "1547  2016  Republic of Ingushetia   NaN   NaN    NaN        NaN     NaN\n",
       "1609  2016        Chechen Republic   NaN  1.20    NaN        NaN     NaN"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 100)\n",
    "row_has_NaN = df.isnull().any(axis=1)\n",
    "rows_with_NaN = df[row_has_NaN]\n",
    "rows_with_NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6dcc869",
   "metadata": {},
   "source": [
    "There seems to be no data for the Republic of Crimea and Sevastopol before 2014 which is when the Ukraine crisis happened and Russia took over souverinity of the region. It is also noticable that some regoins with a major muslim population such as the Chechen Republic or Republic of Ingushetia are missing data. However this could also be due to general political instability or other iregluarities in the collection of the statistical data.\n",
    "\n",
    "Since rows with no data are pointless I am deleting them. The row with some data such as multiple examples of Chechen Republic reporting only beer are left in for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a40f78f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1615, 7)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bbc6c862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1559, 7)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drink_cat = [\"beer\",\"vodka\",\"wine\",\"champagne\",\"brandy\"]\n",
    "df.dropna(how = \"all\", subset = drink_cat, inplace = True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "61ca0533",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>year</th>\n",
       "      <th>region</th>\n",
       "      <th>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "      <th>total_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>Republic of Adygea</td>\n",
       "      <td>1.90</td>\n",
       "      <td>8.80</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.10</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>Altai Krai</td>\n",
       "      <td>3.30</td>\n",
       "      <td>19.20</td>\n",
       "      <td>11.30</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>Amur Oblast</td>\n",
       "      <td>2.10</td>\n",
       "      <td>21.20</td>\n",
       "      <td>17.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>41.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1998</td>\n",
       "      <td>Arkhangelsk Oblast</td>\n",
       "      <td>4.30</td>\n",
       "      <td>10.60</td>\n",
       "      <td>11.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>27.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>Astrakhan Oblast</td>\n",
       "      <td>2.90</td>\n",
       "      <td>18.00</td>\n",
       "      <td>9.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.20</td>\n",
       "      <td>31.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year              region  wine  beer  vodka  champagne  brandy  \\\n",
       "0  1998  Republic of Adygea  1.90  8.80   3.40       0.30    0.10   \n",
       "1  1998          Altai Krai  3.30 19.20  11.30       1.10    0.10   \n",
       "2  1998         Amur Oblast  2.10 21.20  17.30       0.70    0.40   \n",
       "3  1998  Arkhangelsk Oblast  4.30 10.60  11.70       0.40    0.30   \n",
       "4  1998    Astrakhan Oblast  2.90 18.00   9.50       0.80    0.20   \n",
       "\n",
       "   total_volume  \n",
       "0         14.50  \n",
       "1         35.00  \n",
       "2         41.70  \n",
       "3         27.30  \n",
       "4         31.40  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"total_volume\"] = df[drink_cat].sum(axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32784be",
   "metadata": {},
   "source": [
    "Rows wheres some of the drink categories were Nan were dropped to avoid having an unexpected spread in the values of total_volume. This should be ok since pandas handles Nan values with the use of the mean() method automatically."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c000cb9",
   "metadata": {},
   "source": [
    "# 4. Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ebeca3c",
   "metadata": {},
   "source": [
    "## 4.1 What kinds of alcoholic beverages were consumed in Russia over the observed time period?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d0d59acf",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "      <th>total_volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>3.03</td>\n",
       "      <td>20.42</td>\n",
       "      <td>12.67</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.23</td>\n",
       "      <td>37.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>3.12</td>\n",
       "      <td>25.40</td>\n",
       "      <td>13.47</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.21</td>\n",
       "      <td>43.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>3.34</td>\n",
       "      <td>30.13</td>\n",
       "      <td>13.57</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.23</td>\n",
       "      <td>48.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>3.94</td>\n",
       "      <td>36.34</td>\n",
       "      <td>14.24</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.28</td>\n",
       "      <td>55.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>4.40</td>\n",
       "      <td>42.38</td>\n",
       "      <td>14.79</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.32</td>\n",
       "      <td>62.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      wine  beer  vodka  champagne  brandy  total_volume\n",
       "year                                                    \n",
       "1998  3.03 20.42  12.67       0.91    0.23         37.26\n",
       "1999  3.12 25.40  13.47       0.85    0.21         43.04\n",
       "2000  3.34 30.13  13.57       0.89    0.23         48.17\n",
       "2001  3.94 36.34  14.24       0.93    0.28         55.73\n",
       "2002  4.40 42.38  14.79       0.97    0.32         62.85"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data = df.groupby(['year'], as_index=True).mean()\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "2e74ab1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_bar_chart(df,x_val,y_val,title_string,labels_dict):\n",
    "    '''Function uses plotly.express to create a bar chart and show it in the notebook right-away.\n",
    "    INPUT\n",
    "    df: dataframe to be visualizes in bar chart\n",
    "    x_val: column to be represented on x-axis\n",
    "    y_val: column to be represented on y-axis\n",
    "    title_string: the title as a string\n",
    "    labels_dict: labels can be renamed by entering a dictionary.\n",
    "    OUTPUT\n",
    "    None\n",
    "    '''\n",
    "    fig = px.bar(df, x=x_val, y=y_val, \n",
    "            title=title_string, \n",
    "            labels=labels_dict)\n",
    "   # fig.show()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9d4dbbbf",
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_bar_chart(df_data,df_data.index,drink_cat,\n",
    "               \"Average sales volumes of alcoholic beverages per capita in Russia (1998-2016)\",\n",
    "               {\"value\":\"volume in liters\", \"variable\":\"alcoholic beverage\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65152b97",
   "metadata": {},
   "source": [
    "Beer is the dominant type of alcohol across the complete timeframe. Vodka takes seconds place with wine at its heels. Champagne and brandy however seem to be on average much less popular in comparison to other alcohol types.\n",
    "\n",
    "The total volume of alcohol per capita has doubled from 1998 to 2008 reaching a plateau around the turn of the decade. The 2010s show a decline in alcohol consumption.\n",
    "\n",
    "Even though the general trend of total alcohol volume consumed is decreasing, beer volumes in 2016 amount to more than double the volume at the start of the millennium. With vodka, it is a different story, representing in 2016 one about half of the value in 98'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae445f9",
   "metadata": {},
   "source": [
    "## 4.2 What is the composition of the total average volume on a yearly level?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "728c3542",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>wine</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>54.80</td>\n",
       "      <td>34.00</td>\n",
       "      <td>8.13</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>59.00</td>\n",
       "      <td>31.29</td>\n",
       "      <td>7.25</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>62.55</td>\n",
       "      <td>28.18</td>\n",
       "      <td>6.94</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>65.22</td>\n",
       "      <td>25.55</td>\n",
       "      <td>7.07</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>67.42</td>\n",
       "      <td>23.53</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      beer  vodka  wine  champagne  brandy\n",
       "year                                      \n",
       "1998 54.80  34.00  8.13       2.45    0.61\n",
       "1999 59.00  31.29  7.25       1.97    0.49\n",
       "2000 62.55  28.18  6.94       1.85    0.49\n",
       "2001 65.22  25.55  7.07       1.67    0.49\n",
       "2002 67.42  23.53  7.00       1.54    0.51"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_per = df_data[drink_cat].apply(lambda x: 100 * x / np.sum(df_data[drink_cat], axis = 1))\n",
    "df_data_per.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326adba5",
   "metadata": {},
   "source": [
    "Let's take a closer look at the relative mix of alcoholic beverages on a yearly level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "049e9a71",
   "metadata": {},
   "outputs": [
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   "source": [
    "make_bar_chart(df_data_per,df_data_per.index, drink_cat,\n",
    "               \"Normalized composition of total volume consumed (1998-2016)\",\n",
    "               {\"value\":\"percent of year total\", \"variable\":\"alcoholic beverage\"})"
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   "source": [
    "While the proportion of wine, champagne, and brandy in the total mix is not overly different over the years, beer and vodka are competing heavily and beer is winning!\n",
    "\n",
    "Since the total volumes were similar in 2002 and 2016 we can compare the proportions directly. While in 2002 more than a quarter of the mix was vodka, in 2016 it is less than half of that.\n",
    "\n",
    "One should also keep in mind that the alcohol content of these beverages is radically different! Let's assume beer is 5% alcohol while vodka is 40% and calculate the amount of pure alcohol consumed."
   ]
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   "id": "dd21bba7",
   "metadata": {},
   "source": [
    "## 4.3 Focusing on the dominant alcoholic beverages, what is the total and relative volume of pure alcohol per year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "e89c575d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data_alc_pure = df_data[[\"beer\", \"vodka\"]].copy()\n",
    "df_data_alc_pure[\"beer_alc\"] = df_data_alc_pure[\"beer\"].apply(lambda x: 0.05 * x)\n",
    "df_data_alc_pure[\"vodka_alc\"] = df_data_alc_pure[\"vodka\"].apply(lambda x: 0.40 * x)\n",
    "df_data_alc_pure[\"total_alc\"] = df_data_alc_pure[\"vodka_alc\"] + df_data_alc_pure[\"beer_alc\"] "
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   "cell_type": "code",
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   "id": "5a126fa9",
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   "source": [
    "make_bar_chart(df_data_alc_pure,df_data_alc_pure.index, [\"beer_alc\", \"vodka_alc\"],\n",
    "               \"Average yearly volumes of pure alcohol from beer and vodka\",\n",
    "               {\"value\":\"volume in liters\", \"variable\":\"alcoholic beverage\"})"
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    "So even-tough according to the first figure, the total volume of alcoholic beverages in 2016 is still greater than at the start of the observed time, the volume of pure alcohol consumed is ca. 20% lower! The larger proportion of beer and lesser that of vodka in the mix provides a good explanation."
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   "source": [
    "## 4.4 Are there any regional differences in reference to total consumption?"
   ]
  },
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   "cell_type": "code",
   "execution_count": 156,
   "id": "7043061e",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>wine</th>\n",
       "      <th>beer</th>\n",
       "      <th>vodka</th>\n",
       "      <th>champagne</th>\n",
       "      <th>brandy</th>\n",
       "      <th>total_volume_mean</th>\n",
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       "      <th>0</th>\n",
       "      <td>Altai Krai</td>\n",
       "      <td>3.17</td>\n",
       "      <td>48.37</td>\n",
       "      <td>9.47</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.24</td>\n",
       "      <td>62.46</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Altai Republic</td>\n",
       "      <td>5.19</td>\n",
       "      <td>36.72</td>\n",
       "      <td>9.68</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.25</td>\n",
       "      <td>52.79</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amur Oblast</td>\n",
       "      <td>5.22</td>\n",
       "      <td>53.38</td>\n",
       "      <td>14.22</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.40</td>\n",
       "      <td>74.33</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkhangelsk Oblast</td>\n",
       "      <td>8.67</td>\n",
       "      <td>47.04</td>\n",
       "      <td>17.38</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.87</td>\n",
       "      <td>75.59</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Astrakhan Oblast</td>\n",
       "      <td>4.47</td>\n",
       "      <td>54.70</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.36</td>\n",
       "      <td>69.68</td>\n",
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       "               region  wine  beer  vodka  champagne  brandy  total_volume_mean\n",
       "0          Altai Krai  3.17 48.37   9.47       1.21    0.24              62.46\n",
       "1      Altai Republic  5.19 36.72   9.68       0.93    0.25              52.79\n",
       "2         Amur Oblast  5.22 53.38  14.22       1.12    0.40              74.33\n",
       "3  Arkhangelsk Oblast  8.67 47.04  17.38       1.63    0.87              75.59\n",
       "4    Astrakhan Oblast  4.47 54.70   9.30       0.86    0.36              69.68"
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    "df_region = df.groupby(['region'], as_index=False).mean()#regional average over the timeperiod\n",
    "df_region.drop([\"year\",\"total_volume\"], inplace = True, axis = 1)\n",
    "df_region[\"total_volume_mean\"] = df_region[drink_cat].sum(axis=1)\n",
    "df_region.head()"
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   "id": "f4cdae26",
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   "source": [
    "make_bar_chart(df_region.sort_values(by = \"total_volume_mean\", ascending = False), \n",
    "               \"region\", drink_cat, \n",
    "               \"Average volumes of alc. beverages per region (1998-2016)\",\n",
    "               {\"value\": \"volume in l\",\"variable\":\"alcohol beverage\"} )"
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  {
   "cell_type": "markdown",
   "id": "8146693c",
   "metadata": {},
   "source": [
    "Let's zoom in to to the top five regions in Russia in terms of the consumption of alcoholic beverages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ffd7b1e8",
   "metadata": {},
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    "make_bar_chart(df_region.sort_values(by = \"total_volume_mean\", ascending = False).head(5),\n",
    "               \"region\", drink_cat, \n",
    "               \"Top five regions in Russia by volume of alcoholic beverages\",\n",
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   "source": [
    "It isn't surprising that the two regions with the most alcohol sales are Moscow and Saint-Petersburg, but it is however interesting that the following few regions are very remote regions such as Yamalo-Nenets Autonomous Okrug or the Komi republic."
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  {
   "cell_type": "code",
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   "id": "47d17ad8",
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    "make_bar_chart(df_region.sort_values(by = \"total_volume_mean\").head(5), \"region\", drink_cat, \n",
    "               \"Five regions in Russia with the lowest volume of alcoholic beverages sales\",\n",
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    "The republic with the lowest alcohol sales per capita is the Chechen Republic. Not surprising since the population is predominantly Muslim (95% according to Wikipedia). Similar goes for the other five regions with the lowest alcohol sales. Interestingly the Chechen Republic only reports on average a few liters of beer per capita and no stronger alcohol at all.\n",
    "Ok, that's all fine and good, but I mentioned at the beginning that my girlfriend and her family prefer wine and champagne to other drinks, so is it a regional thing in Krasnodar?"
   ]
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   "source": [
    "## 4.5 What is the average yearly distribution of alcoholic beverages consumed in Krasnodar Krai?"
   ]
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       "      <th></th>\n",
       "      <th>year</th>\n",
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       "      <th>29</th>\n",
       "      <td>1998</td>\n",
       "      <td>Krasnodar Krai</td>\n",
       "      <td>8.50</td>\n",
       "      <td>18.30</td>\n",
       "      <td>8.70</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>36.80</td>\n",
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       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1999</td>\n",
       "      <td>Krasnodar Krai</td>\n",
       "      <td>6.80</td>\n",
       "      <td>39.60</td>\n",
       "      <td>8.20</td>\n",
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       "      <td>42.60</td>\n",
       "      <td>8.70</td>\n",
       "      <td>1.40</td>\n",
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       "      <th>284</th>\n",
       "      <td>2001</td>\n",
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       "      <td>6.60</td>\n",
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       "      <td>8.90</td>\n",
       "      <td>1.40</td>\n",
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       "      <th>369</th>\n",
       "      <td>2002</td>\n",
       "      <td>Krasnodar Krai</td>\n",
       "      <td>7.90</td>\n",
       "      <td>50.10</td>\n",
       "      <td>7.90</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.50</td>\n",
       "      <td>67.70</td>\n",
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       "     year          region  wine  beer  vodka  champagne  brandy  total_volume\n",
       "29   1998  Krasnodar Krai  8.50 18.30   8.70       1.00    0.30         36.80\n",
       "114  1999  Krasnodar Krai  6.80 39.60   8.20       1.00    0.30         55.90\n",
       "199  2000  Krasnodar Krai  7.00 42.60   8.70       1.40    0.30         60.00\n",
       "284  2001  Krasnodar Krai  6.60 48.00   8.90       1.40    0.30         65.20\n",
       "369  2002  Krasnodar Krai  7.90 50.10   7.90       1.30    0.50         67.70"
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   "source": [
    "df_krasnodar = df[df.region == \"Krasnodar Krai\"]\n",
    "df_krasnodar.head()"
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   "source": [
    "make_bar_chart(df_krasnodar, \"year\", drink_cat, \n",
    "               \"Average sales volumes of alcoholic beverages per capita in Kransnodarsky Krai (1998-2016)\",\n",
    "               {\"value\":\"volume in liters\", \"variable\":\"alcoholic beverage\"})"
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    "The total volume is greater than average in recent years, but the mix is different. Wine is generally preferred to vodka, especially in recent years with wine and champagne almost doubling the volume of the spirit. \n",
    "So that explains it. And also reminds me to buy a good wine next time I visit my girlfriends' parents. \n",
    "Oh, and if the popularity of vodka continuous to decline in the future as we saw in the stats, I can do with the occasional toast!"
   ]
  },
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   "cell_type": "markdown",
   "id": "4557de7f",
   "metadata": {},
   "source": [
    "# 5. Evaluation (Recap)"
   ]
  },
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   "id": "02fe7143",
   "metadata": {},
   "source": [
    "Analyzing the questions listed at the start we have found the following results:\n",
    "* Beer ist the dominant alcoholic beverage in russia in the observed period followed by vodka.\n",
    "* One average wine is the third most popular followed by champagne and brandy.\n",
    "* The popularity of beer continues to grow in comparisson to vodka on average.\n",
    "* This trend results on a drop of overal pure alcohol consumption in the 2010s.\n",
    "* There are strong regional differences in russia, e.g. Moscow and Saint Petersbung being the regions with the greatest volume at double the average.\n",
    "* Some regions have a very low consumption of alcohol e.g. the Chechen Republic where only a few liters of beer are reported on average.\n",
    "* The regions of particular concern (see article on medium) Krasnodar Krai shows a greater popularity of wine than vodka.\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4911ef",
   "metadata": {},
   "source": [
    "На здоро́вье ! (Nazdarovya!)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47da324",
   "metadata": {},
   "source": [
    "## The End."
   ]
  }
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