practice_1-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "file_path = '../data/drinks.csv'\n",
    "drinks = pd.read_csv(file_path) # read_csv 함수로 데이터를 Dataframe 형태로 불러옵니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193 entries, 0 to 192\n",
      "Data columns (total 6 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   country                       193 non-null    object \n",
      " 1   beer_servings                 193 non-null    int64  \n",
      " 2   spirit_servings               193 non-null    int64  \n",
      " 3   wine_servings                 193 non-null    int64  \n",
      " 4   total_litres_of_pure_alcohol  193 non-null    float64\n",
      " 5   continent                     170 non-null    object \n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 9.2+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(drinks.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>261</td>\n",
       "      <td>72</td>\n",
       "      <td>212</td>\n",
       "      <td>10.4</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0        Afghanistan              0                0              0   \n",
       "1            Albania             89              132             54   \n",
       "2            Algeria             25                0             14   \n",
       "3            Andorra            245              138            312   \n",
       "4             Angola            217               57             45   \n",
       "5  Antigua & Barbuda            102              128             45   \n",
       "6          Argentina            193               25            221   \n",
       "7            Armenia             21              179             11   \n",
       "8          Australia            261               72            212   \n",
       "9            Austria            279               75            191   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0        AS  \n",
       "1                           4.9        EU  \n",
       "2                           0.7        AF  \n",
       "3                          12.4        EU  \n",
       "4                           5.9        AF  \n",
       "5                           4.9       NaN  \n",
       "6                           8.3        SA  \n",
       "7                           3.8        EU  \n",
       "8                          10.4        OC  \n",
       "9                           9.7        EU  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.160622</td>\n",
       "      <td>80.994819</td>\n",
       "      <td>49.450777</td>\n",
       "      <td>4.717098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.143103</td>\n",
       "      <td>88.284312</td>\n",
       "      <td>79.697598</td>\n",
       "      <td>3.773298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>188.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>376.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>14.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_servings  spirit_servings  wine_servings  \\\n",
       "count     193.000000       193.000000     193.000000   \n",
       "mean      106.160622        80.994819      49.450777   \n",
       "std       101.143103        88.284312      79.697598   \n",
       "min         0.000000         0.000000       0.000000   \n",
       "25%        20.000000         4.000000       1.000000   \n",
       "50%        76.000000        56.000000       8.000000   \n",
       "75%       188.000000       128.000000      59.000000   \n",
       "max       376.000000       438.000000     370.000000   \n",
       "\n",
       "       total_litres_of_pure_alcohol  \n",
       "count                    193.000000  \n",
       "mean                       4.717098  \n",
       "std                        3.773298  \n",
       "min                        0.000000  \n",
       "25%                        1.300000  \n",
       "50%                        4.200000  \n",
       "75%                        7.200000  \n",
       "max                       14.400000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               beer_servings  wine_servings\n",
      "beer_servings       1.000000       0.527172\n",
      "wine_servings       0.527172       1.000000\n"
     ]
    }
   ],
   "source": [
    "# 'beer_servings', 'wine_servings' 두 피처간의 상관계수를 계산합니다.\n",
    "# pearson은 상관계수를 구하는 계산 방법 중 하나를 의미하며, 가장 널리 쓰이는 방법입니다.\n",
    "corr = drinks[['beer_servings', 'wine_servings']].corr(method = 'pearson')\n",
    "print(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              beer_servings  spirit_servings  wine_servings  \\\n",
      "beer_servings                      1.000000         0.458819       0.527172   \n",
      "spirit_servings                    0.458819         1.000000       0.194797   \n",
      "wine_servings                      0.527172         0.194797       1.000000   \n",
      "total_litres_of_pure_alcohol       0.835839         0.654968       0.667598   \n",
      "\n",
      "                              total_litres_of_pure_alcohol  \n",
      "beer_servings                                     0.835839  \n",
      "spirit_servings                                   0.654968  \n",
      "wine_servings                                     0.667598  \n",
      "total_litres_of_pure_alcohol                      1.000000  \n"
     ]
    }
   ],
   "source": [
    "# 피처간의 상관계수 행렬을 구합니다.\n",
    "cols = ['beer_servings', 'spirit_servings', 'wine_servings', 'total_litres_of_pure_alcohol']\n",
    "corr = drinks[cols].corr(method = 'pearson')\n",
    "print(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Using cached seaborn-0.11.1-py3-none-any.whl (285 kB)\n",
      "Requirement already satisfied: numpy>=1.15 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from seaborn) (1.20.3)\n",
      "Requirement already satisfied: pandas>=0.23 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from seaborn) (1.2.4)\n",
      "Collecting scipy>=1.0\n",
      "  Downloading scipy-1.6.3-cp38-cp38-macosx_10_9_x86_64.whl (30.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 30.8 MB 26.7 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: matplotlib>=2.2 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from seaborn) (3.4.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (8.2.0)\n",
      "Requirement already satisfied: six in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from pandas>=0.23->seaborn) (2021.1)\n",
      "Installing collected packages: scipy, seaborn\n",
      "Successfully installed scipy-1.6.3 seaborn-0.11.1\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# corr 행렬 히트맵을 시각화합니다.\n",
    "cols_view = ['beer', 'spirit', 'wine', 'alcohol'] # 그래프 출력을 위한 cols 이름을 축약합니다.\n",
    "sns.set(font_scale=1.5)\n",
    "hm = sns.heatmap(corr.values,\n",
    "            cbar=True,\n",
    "            annot=True, \n",
    "            square=True,\n",
    "            fmt='.2f',\n",
    "            annot_kws={'size': 15},\n",
    "            yticklabels=cols_view,\n",
    "            xticklabels=cols_view)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 시각화 라이브러리를 이용한 피처간의 scatter plot을 출력합니다.\n",
    "sns.set(style='whitegrid', context='notebook')\n",
    "sns.pairplot(drinks[['beer_servings', 'spirit_servings', \n",
    "                     'wine_servings', 'total_litres_of_pure_alcohol']], height=2.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country                          0\n",
      "beer_servings                    0\n",
      "spirit_servings                  0\n",
      "wine_servings                    0\n",
      "total_litres_of_pure_alcohol     0\n",
      "continent                       23\n",
      "dtype: int64\n",
      "------------------------------------\n",
      "country                          object\n",
      "beer_servings                     int64\n",
      "spirit_servings                   int64\n",
      "wine_servings                     int64\n",
      "total_litres_of_pure_alcohol    float64\n",
      "continent                        object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(drinks.isnull().sum())\n",
    "print(\"------------------------------------\")\n",
    "print(drinks.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측데이터를 처리합니다 : 기타 대륙으로 통합 -> 'OT'\n",
    "drinks['continent'] = drinks['continent'].fillna('OT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>OT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>261</td>\n",
       "      <td>72</td>\n",
       "      <td>212</td>\n",
       "      <td>10.4</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0        Afghanistan              0                0              0   \n",
       "1            Albania             89              132             54   \n",
       "2            Algeria             25                0             14   \n",
       "3            Andorra            245              138            312   \n",
       "4             Angola            217               57             45   \n",
       "5  Antigua & Barbuda            102              128             45   \n",
       "6          Argentina            193               25            221   \n",
       "7            Armenia             21              179             11   \n",
       "8          Australia            261               72            212   \n",
       "9            Austria            279               75            191   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0        AS  \n",
       "1                           4.9        EU  \n",
       "2                           0.7        AF  \n",
       "3                          12.4        EU  \n",
       "4                           5.9        AF  \n",
       "5                           4.9        OT  \n",
       "6                           8.3        SA  \n",
       "7                           3.8        EU  \n",
       "8                          10.4        OC  \n",
       "9                           9.7        EU  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = drinks['continent'].value_counts().index.tolist()\n",
    "fracs1 = drinks['continent'].value_counts().values.tolist()\n",
    "explode = (0, 0, 0, 0.25, 0, 0)\n",
    "\n",
    "plt.pie(fracs1, explode=explode, labels=labels, autopct='%.0f%%', shadow=True)\n",
    "plt.title('null data to \\'OT\\'')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[53, 45, 44, 23, 16, 12]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks['continent'].value_counts().values.tolist()"
   ]
  },
  {
   "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></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>16.339623</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>60.840909</td>\n",
       "      <td>0</td>\n",
       "      <td>326</td>\n",
       "      <td>2677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>132.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "      <td>5965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>58.437500</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OT</th>\n",
       "      <td>165.739130</td>\n",
       "      <td>68</td>\n",
       "      <td>438</td>\n",
       "      <td>3812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean  min  max   sum\n",
       "continent                            \n",
       "AF          16.339623    0  152   866\n",
       "AS          60.840909    0  326  2677\n",
       "EU         132.555556    0  373  5965\n",
       "OC          58.437500    0  254   935\n",
       "OT         165.739130   68  438  3812"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 대륙별 spirit_servings의 평균, 최소, 최대, 합계를 계산합니다.\n",
    "result = drinks.groupby('continent').spirit_servings.agg(['mean', 'min', 'max', 'sum'])\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continent\n",
      "EU    8.617778\n",
      "OT    5.995652\n",
      "SA    6.308333\n",
      "Name: total_litres_of_pure_alcohol, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 전체 평균보다 많은 알코올을 섭취하는 대륙을 구합니다.\n",
    "total_mean = drinks.total_litres_of_pure_alcohol.mean()\n",
    "continent_mean = drinks.groupby('continent')['total_litres_of_pure_alcohol'].mean()\n",
    "continent_over_mean = continent_mean[continent_mean >= total_mean]\n",
    "print(continent_over_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EU\n"
     ]
    }
   ],
   "source": [
    "# 평균 beer_servings이 가장 높은 대륙을 구합니다.\n",
    "beer_continent = drinks.groupby('continent').beer_servings.mean().idxmax()\n",
    "print(beer_continent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 대륙별 spirit_servings의 평균, 최소, 최대, 합계를 시각화합니다.\n",
    "n_groups = len(result.index)\n",
    "means = result['mean'].tolist()\n",
    "mins = result['min'].tolist()\n",
    "maxs = result['max'].tolist()\n",
    "sums = result['sum'].tolist()\n",
    " \n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.1\n",
    " \n",
    "rects1 = plt.bar(index, means, bar_width,\n",
    "                 color='r',\n",
    "                 label='Mean')\n",
    " \n",
    "rects2 = plt.bar(index + bar_width, mins, bar_width,\n",
    "                 color='g',\n",
    "                 label='Min')\n",
    "\n",
    "rects3 = plt.bar(index + bar_width * 2, maxs, bar_width,\n",
    "                 color='b',\n",
    "                 label='Max')\n",
    " \n",
    "rects3 = plt.bar(index + bar_width * 3, sums, bar_width,\n",
    "                 color='y',\n",
    "                 label='Sum')\n",
    "\n",
    "plt.xticks(index, result.index.tolist())\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 대륙별 total_litres_of_pure_alcohol을 시각화합니다.\n",
    "continents = continent_mean.index.tolist()\n",
    "continents.append('mean')\n",
    "x_pos = np.arange(len(continents))\n",
    "alcohol = continent_mean.tolist()\n",
    "alcohol.append(total_mean)\n",
    " \n",
    "bar_list = plt.bar(x_pos, alcohol, align='center', alpha=0.5)\n",
    "bar_list[len(continents) - 1].set_color('r')\n",
    "plt.plot([0., 6], [total_mean, total_mean], \"k--\")\n",
    "plt.xticks(x_pos, continents)\n",
    "\n",
    "plt.ylabel('total_litres_of_pure_alcohol')\n",
    "plt.title('total_litres_of_pure_alcohol by Continent')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 대륙별 beer_servings을 시각화합니다.\n",
    "beer_group = drinks.groupby('continent')['beer_servings'].sum()\n",
    "continents = beer_group.index.tolist()\n",
    "y_pos = np.arange(len(continents))\n",
    "alcohol = beer_group.tolist()\n",
    " \n",
    "bar_list = plt.bar(y_pos, alcohol, align='center', alpha=0.5)\n",
    "bar_list[continents.index(\"EU\")].set_color('r')\n",
    "plt.xticks(y_pos, continents)\n",
    "plt.ylabel('beer_servings')\n",
    "plt.title('beer_servings by Continent')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.339622641509433, 60.84090909090909, 132.55555555555554, 58.4375, 165.7391304347826, 114.75]\n",
      "[0, 0, 0, 0, 68, 25]\n",
      "[152, 326, 373, 254, 438, 302]\n",
      "[866, 2677, 5965, 935, 3812, 1377]\n"
     ]
    }
   ],
   "source": [
    "means = result['mean'].tolist()\n",
    "print(means)\n",
    "mins = result['min'].tolist()\n",
    "print(mins)\n",
    "maxs = result['max'].tolist()\n",
    "print(maxs)\n",
    "sums = result['sum'].tolist()\n",
    "print(sums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 대륙별 total_litres_of_pure_alcohol 시각화 - 그래프 색상을 변경합니다.\n",
    "continents = continent_mean.index.tolist()\n",
    "continents.append('mean')\n",
    "x_pos = np.arange(len(continents))\n",
    "alcohol = continent_mean.tolist()\n",
    "alcohol.append(total_mean)\n",
    " \n",
    "bar_list = plt.bar(x_pos, alcohol, align='center', alpha=0.5)\n",
    "bar_list[len(continents) - 1].set_color('green')\n",
    "plt.plot([0., 6], [total_mean, total_mean], \"k--\")\n",
    "plt.xticks(x_pos, continents)\n",
    "\n",
    "plt.ylabel('total_litres_of_pure_alcohol')\n",
    "plt.title('total_litres_of_pure_alcohol by Continent')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 대륙별 total_litres_of_pure_alcohol 시각화 - 점선을 변경합니다.\n",
    "continents = continent_mean.index.tolist()\n",
    "continents.append('mean')\n",
    "x_pos = np.arange(len(continents))\n",
    "alcohol = continent_mean.tolist()\n",
    "alcohol.append(total_mean)\n",
    " \n",
    "bar_list = plt.bar(x_pos, alcohol, align='center', alpha=0.5)\n",
    "bar_list[len(continents) - 1].set_color('green')\n",
    "plt.plot([3., 5], [total_mean + 1.5, total_mean + 1.5], \"k--\")\n",
    "plt.xticks(x_pos, continents)\n",
    "\n",
    "plt.ylabel('total_litres_of_pure_alcohol')\n",
    "plt.title('total_litres_of_pure_alcohol by Continent')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (1.6.3)\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /Users/A202009066/anaconda3/envs/pda/lib/python3.8/site-packages (from scipy) (1.20.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The t-statistic and p-value assuming equal variances is -7.268 and 0.000.\n",
      "The t-statistic and p-value not assuming equal variances is -7.144 and 0.000\n"
     ]
    }
   ],
   "source": [
    "# 아프리카와 유럽간의 맥주 소비량 차이를 검정합니다.\n",
    "africa = drinks.loc[drinks['continent']=='AF']\n",
    "europe = drinks.loc[drinks['continent']=='EU']\n",
    "\n",
    "from scipy import stats\n",
    "tTestResult = stats.ttest_ind(africa['beer_servings'], europe['beer_servings'])\n",
    "tTestResultDiffVar = stats.ttest_ind(africa['beer_servings'], europe['beer_servings'], equal_var=False)\n",
    "\n",
    "print(\"The t-statistic and p-value assuming equal variances is %.3f and %.3f.\" % tTestResult)\n",
    "print(\"The t-statistic and p-value not assuming equal variances is %.3f and %.3f\" % tTestResultDiffVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>alcohol_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>0.223333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>0.185714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>0.153704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>0.151111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  alcohol_rate\n",
       "63         Gambia      0.266667\n",
       "153  Sierra Leone      0.223333\n",
       "124       Nigeria      0.185714\n",
       "179        Uganda      0.153704\n",
       "142        Rwanda      0.151111"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total_servings 피처를 생성합니다.\n",
    "drinks['total_servings'] = drinks['beer_servings'] + drinks['wine_servings'] + drinks['spirit_servings']\n",
    "\n",
    "# 술 소비량 대비 알콜 비율 피처를 생성합니다.\n",
    "drinks['alcohol_rate'] = drinks['total_litres_of_pure_alcohol'] / drinks['total_servings']\n",
    "drinks['alcohol_rate'] = drinks['alcohol_rate'].fillna(0)\n",
    "\n",
    "# 순위 정보를 생성합니다.\n",
    "country_with_rank = drinks[['country', 'alcohol_rate']]\n",
    "country_with_rank = country_with_rank.sort_values(by=['alcohol_rate'], ascending=0)\n",
    "country_with_rank.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 국가별 순위 정보를 그래프로 시각화합니다.\n",
    "country_list = country_with_rank.country.tolist()\n",
    "x_pos = np.arange(len(country_list))\n",
    "rank = country_with_rank.alcohol_rate.tolist()\n",
    " \n",
    "bar_list = plt.bar(x_pos, rank)\n",
    "bar_list[country_list.index(\"South Korea\")].set_color('r')\n",
    "plt.ylabel('alcohol rate')\n",
    "plt.title('liquor drink rank by contry')\n",
    "plt.axis([0, 200, 0, 0.3])\n",
    "\n",
    "korea_rank = country_list.index(\"South Korea\")\n",
    "korea_alc_rate = country_with_rank[country_with_rank['country'] == 'South Korea']['alcohol_rate'].values[0]\n",
    "plt.annotate('South Korea : ' + str(korea_rank + 1), \n",
    "             xy=(korea_rank, korea_alc_rate), \n",
    "             xytext=(korea_rank + 10, korea_alc_rate + 0.05),\n",
    "             arrowprops=dict(facecolor='red', shrink=0.05))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "file_path = '../data/drinks.csv'\n",
    "drinks = pd.read_csv(file_path) # read_csv 함수로 데이터를 Dataframe 형태로 불러옵니다.\n",
    "drinks['continent'] = drinks['continent'].fillna('OT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 대륙별 평균 wine_servings 피처를 만들어서 병합합니다.\n",
    "result = drinks.groupby('continent').mean()['wine_servings']\n",
    "df = result.to_frame().reset_index()\n",
    "df = df.rename(columns={'wine_servings': 'wine_servings_cont_avg'})\n",
    "drinks = pd.merge(drinks, df, on='continent', how='outer')\n",
    "\n",
    "# 위와 같은 방법의 코드입니다.\n",
    "drinks['wine_servings_cont_avg'] = drinks.groupby('continent')['wine_servings'].transform(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>wine_servings_cont_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>AF</td>\n",
       "      <td>16.264151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>EU</td>\n",
       "      <td>142.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>EU</td>\n",
       "      <td>142.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>OC</td>\n",
       "      <td>35.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>AS</td>\n",
       "      <td>9.068182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  wine_servings_cont_avg\n",
       "131  Sierra Leone        AF               16.264151\n",
       "84         Sweden        EU              142.222222\n",
       "53        Croatia        EU              142.222222\n",
       "179          Fiji        OC               35.625000\n",
       "14         Jordan        AS                9.068182"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결과를 출력합니다.\n",
    "drinks[['country', 'continent', 'wine_servings_cont_avg']].sample(5).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 국가별 total_servings 피처를 만들어서 병합합니다.\n",
    "drinks['total_servings'] = drinks['beer_servings'] + drinks['wine_servings'] + drinks['spirit_servings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>total_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>343</td>\n",
       "      <td>56</td>\n",
       "      <td>244</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>225</td>\n",
       "      <td>81</td>\n",
       "      <td>76</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>162</td>\n",
       "      <td>3</td>\n",
       "      <td>74</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Germany</td>\n",
       "      <td>346</td>\n",
       "      <td>175</td>\n",
       "      <td>117</td>\n",
       "      <td>638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  beer_servings  wine_servings  spirit_servings  \\\n",
       "68      Lithuania            343             56              244   \n",
       "107      Ethiopia             20              0                3   \n",
       "133  South Africa            225             81               76   \n",
       "170       Ecuador            162              3               74   \n",
       "61        Germany            346            175              117   \n",
       "\n",
       "     total_servings  \n",
       "68              643  \n",
       "107              23  \n",
       "133             382  \n",
       "170             239  \n",
       "61              638  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결과를 출력합니다.\n",
    "drinks[['country', 'beer_servings', 'wine_servings', 'spirit_servings', 'total_servings']].sample(5).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전체 평균보다 적은 알코올을 섭취하는 대륙 중에서, spirit을 가장 많이 마시는 국가를 구합니다.\n",
    "total_mean = drinks.total_litres_of_pure_alcohol.mean()\n",
    "continent_mean = drinks.groupby('continent').total_litres_of_pure_alcohol.mean()\n",
    "continent_under_mean = continent_mean[continent_mean <= total_mean].index.tolist()\n",
    "df_continent_under_mean = drinks.loc[drinks.continent.isin(continent_under_mean)]\n",
    "\n",
    "most_spirit_under_mean = df_continent_under_mean.loc[df_continent_under_mean['spirit_servings'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Russian Federation'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결과를 출력합니다.\n",
    "most_spirit_under_mean['country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29    15.0\n",
       "Name: alcohol_rate_rank, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 술 소비량 대비 알콜 비율에 대한 칼럼을 만들어서 병합합니다.\n",
    "drinks['alcohol_rate'] = drinks['total_litres_of_pure_alcohol'] / drinks['total_servings']\n",
    "drinks['alcohol_rate'] = drinks['alcohol_rate'].fillna(0)\n",
    "\n",
    "# 술 소비량 대비 알콜 비율 : 전체 순위 중 한국의 순위를 구합니다.\n",
    "drinks['alcohol_rate_rank'] = drinks['alcohol_rate'].rank(ascending=False)\n",
    "drinks['alcohol_rate_rank'] = drinks['alcohol_rate_rank'].apply(np.floor)\n",
    "drinks.loc[drinks['country'] == 'South Korea'].alcohol_rate_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 대륙별 술 소비량 대비 알콜 비율을 구합니다.\n",
    "continent_sum = drinks.groupby('continent').sum()\n",
    "continent_sum['alcohol_rate_continent'] = continent_sum['total_litres_of_pure_alcohol'] / \\\n",
    "                                          continent_sum['total_servings']\n",
    "continent_sum = continent_sum.reset_index()\n",
    "continent_sum = continent_sum[['continent', 'alcohol_rate_continent']]\n",
    "\n",
    "drinks = pd.merge(drinks, continent_sum, on='continent', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>alcohol_rate_continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>SA</td>\n",
       "      <td>0.017909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>EU</td>\n",
       "      <td>0.018392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>EU</td>\n",
       "      <td>0.018392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Oman</td>\n",
       "      <td>AS</td>\n",
       "      <td>0.020293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Benin</td>\n",
       "      <td>AF</td>\n",
       "      <td>0.031970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country continent  alcohol_rate_continent\n",
       "167      Brazil        SA                0.017909\n",
       "48   Azerbaijan        EU                0.018392\n",
       "84       Sweden        EU                0.018392\n",
       "25         Oman        AS                0.020293\n",
       "91        Benin        AF                0.031970"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결과를 출력합니다.\n",
    "drinks[['country', 'continent', 'alcohol_rate_continent']].sample(5).head()"
   ]
  }
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