Практическая 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f2b20030",
   "metadata": {},
   "source": [
    "# <center> Практическая работа №2 </center>\n",
    "## <center> Маркетинговая кампания сети быстрого питания"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4e4800",
   "metadata": {},
   "source": [
    "Группа: ММР212            \n",
    "Выполнила: Шубина Анастасия"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef3edaf7",
   "metadata": {},
   "source": [
    "<a id='Content'></a>\n",
    "## Оглавление\n",
    "- [Импорт пакетов](#Packages)\n",
    "- [Описание данных](#Variables)\n",
    "- [Чтение набора данных](#Dataset)\n",
    "- [Предварительный анализ данных](#EDA)\n",
    "- [Тестирование](#Tests)\n",
    " - [Тест Тьюки](#TestTukey)\n",
    " - [U-тест Манна-Уитни](#UTest)\n",
    "- [Вывод](#Сonclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072f5412",
   "metadata": {},
   "source": [
    "<a id='Packages'></a>\n",
    "## Импорт пакетов\n",
    "[Наверх](#Content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2037363a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "from statsmodels.graphics.factorplots import interaction_plot\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420eba94",
   "metadata": {},
   "source": [
    "<a id='Variables'></a>\n",
    "    \n",
    "## Описание данных\n",
    "[Наверх](#Content)\n",
    "\n",
    "[Источник](https://www.kaggle.com/datasets/chebotinaa/fast-food-marketing-campaign-ab-test)\n",
    "\n",
    "| Имя переменной | Описание |\n",
    "|:-- | :-- |\n",
    "| MarketID |Уникальный индетификатор рынка |\n",
    "| MarketSize |Размер рынка по объёму продаж |\n",
    "| LocationID |Уникальный индетификатор для точки продаж |\n",
    "| AgeOfStore |Возраст точки в годах |\n",
    "| Promotion |Одна из трёх протестированных кампаний |\n",
    "| week |Одна из четырёх недель, когда проводилась кампания |\n",
    "| SalesInThousands |Объём продаж для определённых LocationID, Promotion и week |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1874ab2d",
   "metadata": {},
   "source": [
    "<a id='Dataset'></a>\n",
    "## Чтение набора данных\n",
    "[Наверх](#Content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acf33f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Чтение данных\n",
    "fastfood_df = pd.read_csv('WA_Marketing-Campaign.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "440397cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Установка глобальных стилей для графиков\n",
    "colors = ['#464776', '#746BB9', '#EFB5C3', '#EFF2CF', '#E4D7F0']\n",
    "sns.set_style('white')\n",
    "plt.rc('axes', titlesize=13)\n",
    "plt.rc('axes', labelsize=12)\n",
    "plt.rc('xtick', labelsize=11)\n",
    "plt.rc('ytick', labelsize=11)\n",
    "plt.rc('legend', fontsize=11)\n",
    "plt.rc('font', size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4005b6",
   "metadata": {},
   "source": [
    "<a id='EDA'></a>\n",
    "## Предварительный анализ данных\n",
    "[Наверх](#Content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b05e758f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarketID</th>\n",
       "      <th>MarketSize</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>AgeOfStore</th>\n",
       "      <th>Promotion</th>\n",
       "      <th>week</th>\n",
       "      <th>SalesInThousands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>33.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>35.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>29.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>39.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>27.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>44.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MarketID MarketSize  LocationID  AgeOfStore  Promotion  week  \\\n",
       "0         1     Medium           1           4          3     1   \n",
       "1         1     Medium           1           4          3     2   \n",
       "2         1     Medium           1           4          3     3   \n",
       "3         1     Medium           1           4          3     4   \n",
       "4         1     Medium           2           5          2     1   \n",
       "5         1     Medium           2           5          2     2   \n",
       "6         1     Medium           2           5          2     3   \n",
       "7         1     Medium           2           5          2     4   \n",
       "8         1     Medium           3          12          1     1   \n",
       "9         1     Medium           3          12          1     2   \n",
       "\n",
       "   SalesInThousands  \n",
       "0             33.73  \n",
       "1             35.67  \n",
       "2             29.03  \n",
       "3             39.25  \n",
       "4             27.81  \n",
       "5             34.67  \n",
       "6             27.98  \n",
       "7             27.72  \n",
       "8             44.54  \n",
       "9             37.94  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4a2142f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 548 entries, 0 to 547\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   MarketID          548 non-null    int64  \n",
      " 1   MarketSize        548 non-null    object \n",
      " 2   LocationID        548 non-null    int64  \n",
      " 3   AgeOfStore        548 non-null    int64  \n",
      " 4   Promotion         548 non-null    int64  \n",
      " 5   week              548 non-null    int64  \n",
      " 6   SalesInThousands  548 non-null    float64\n",
      "dtypes: float64(1), int64(5), object(1)\n",
      "memory usage: 30.1+ KB\n"
     ]
    }
   ],
   "source": [
    "fastfood_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e37fed0c",
   "metadata": {},
   "source": [
    "Исходя из информации выше в датасете: 7 столбцов и 548 строк.    \n",
    "1 переменная является строковым типом данных, 5 переменных - целочисленный тип и 1 переменная с плавающей точкой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56c3ecce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MarketID            0\n",
       "MarketSize          0\n",
       "LocationID          0\n",
       "AgeOfStore          0\n",
       "Promotion           0\n",
       "week                0\n",
       "SalesInThousands    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d7e326",
   "metadata": {},
   "source": [
    "Кол-во пустых значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c987393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Кол-во дублированных записей в датасете: 0\n"
     ]
    }
   ],
   "source": [
    "#5. Работа с пропущенными значениями\n",
    "dupl = sum(fastfood_df.duplicated())\n",
    "print(f\"Кол-во дублированных записей в датасете: {dupl}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80b3e843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>172.0</td>\n",
       "      <td>58.099012</td>\n",
       "      <td>16.553782</td>\n",
       "      <td>30.81</td>\n",
       "      <td>46.3525</td>\n",
       "      <td>55.385</td>\n",
       "      <td>63.6175</td>\n",
       "      <td>99.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>188.0</td>\n",
       "      <td>47.329415</td>\n",
       "      <td>15.108955</td>\n",
       "      <td>17.34</td>\n",
       "      <td>38.1700</td>\n",
       "      <td>45.385</td>\n",
       "      <td>51.7450</td>\n",
       "      <td>88.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>188.0</td>\n",
       "      <td>55.364468</td>\n",
       "      <td>16.766231</td>\n",
       "      <td>22.18</td>\n",
       "      <td>44.1975</td>\n",
       "      <td>51.165</td>\n",
       "      <td>61.7325</td>\n",
       "      <td>96.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count       mean        std    min      25%     50%      75%    max\n",
       "Promotion                                                                     \n",
       "1          172.0  58.099012  16.553782  30.81  46.3525  55.385  63.6175  99.65\n",
       "2          188.0  47.329415  15.108955  17.34  38.1700  45.385  51.7450  88.64\n",
       "3          188.0  55.364468  16.766231  22.18  44.1975  51.165  61.7325  96.48"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_df.groupby('Promotion')['SalesInThousands'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a79ff5d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MarketID             10\n",
       "MarketSize            3\n",
       "LocationID          137\n",
       "AgeOfStore           25\n",
       "Promotion             3\n",
       "week                  4\n",
       "SalesInThousands    517\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a66ec17",
   "metadata": {},
   "source": [
    "Кол-во уникальных значений в каждом столбце"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66391da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Promotion  MarketSize\n",
       "1          Large          56\n",
       "           Medium         96\n",
       "           Small          20\n",
       "2          Large          64\n",
       "           Medium        108\n",
       "           Small          16\n",
       "3          Large          48\n",
       "           Medium        116\n",
       "           Small          24\n",
       "Name: MarketID, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_df.groupby([\"Promotion\", \"MarketSize\"]).count()[\"MarketID\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60b11556",
   "metadata": {},
   "source": [
    "Данные о продажах в зависимости от метода продвижения и размера рынка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "61ca688e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Promotion'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fastfood_df.groupby(['Promotion', 'MarketSize']).size().unstack().plot(kind='bar', figsize=(9,9), \n",
    "                                                                       stacked=True, color=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e6e4296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='SalesInThousands'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fastfood_df.groupby('Promotion').sum().plot(kind='pie', y='SalesInThousands', startangle=45,\n",
    "                                                  figsize=(9,9), autopct='%1.1f%%', colors=colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd209a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fastfood_df['AgeOfStore'].hist(color=colors[1], figsize=(9,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0964f6bc",
   "metadata": {},
   "source": [
    "Уровень продвижения в зависимости от возраста точки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18fb20e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1)\n",
    "sns.barplot(x='MarketSize', y='SalesInThousands', data=fastfood_df, hue='week', color=colors[1])\n",
    "plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
    "plt.title(\"Кол-во продаж в зависимости от размера точки и недели проведения рекламной акции\", fontdict={'fontsize': 16})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30903260",
   "metadata": {},
   "source": [
    "Исходя из данных выше можно понять, что в каждом размеры рынка нет сильной разницы в зависимости от недели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "583f69bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.198785\n",
       "1      0.082351\n",
       "2     -0.879890\n",
       "3      0.601150\n",
       "4     -1.056687\n",
       "         ...   \n",
       "543    1.492358\n",
       "544   -0.505223\n",
       "545   -1.133882\n",
       "546   -1.340138\n",
       "547   -0.616827\n",
       "Name: SalesInThousands, Length: 548, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastfood_df.groupby('MarketID')['SalesInThousands'].apply(stats.zscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d487092a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe2bcb3ce50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(fastfood_df['SalesInThousands'], fastfood_df['MarketSize'], c=colors[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "844be10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SalesInThousands', ylabel='Count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(fastfood_df[fastfood_df['Promotion'] == 1]['SalesInThousands'], kde=True, color=colors[0])\n",
    "sns.histplot(fastfood_df[fastfood_df['Promotion'] == 2]['SalesInThousands'], kde=True, color=colors[1])\n",
    "sns.histplot(fastfood_df[fastfood_df['Promotion'] == 3]['SalesInThousands'], kde=True, color=colors[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96900636",
   "metadata": {},
   "source": [
    "Исходя из выше указанной гистограммы мы видим, что данные распределены не равномерно."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36bf0e28",
   "metadata": {},
   "source": [
    "<a id='Tests'></a>\n",
    "## Тестирование гипотез\n",
    "[Наверх](#Content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab6e4c6",
   "metadata": {},
   "source": [
    "<a id='TestTukey'></a>\n",
    "### Тест Тьюки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "82c324e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tukey_test(table, var):\n",
    "    tukey = pairwise_tukeyhsd(endog=fastfood_df['SalesInThousands'],\n",
    "                              groups=fastfood_df['Promotion'],\n",
    "                              alpha=0.05)\n",
    "    print(tukey)\n",
    "    tukey.plot_simultaneous() \n",
    "    title=\"Сравнение между рекламными акциями в \" + var\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Продажи в тысячах\")\n",
    "    plt.ylabel(\"Рекламные акции\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32e8f93b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1 group2 meandiff p-adj   lower    upper  reject\n",
      "-----------------------------------------------------\n",
      "     1      2 -10.7696    0.0 -14.7738 -6.7654   True\n",
      "     1      3  -2.7345 0.2444  -6.7388  1.2697  False\n",
      "     2      3   8.0351    0.0   4.1208 11.9493   True\n",
      "-----------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_test(fastfood_df,\"общем датасете\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "49e5edd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "MS_Small=fastfood_df[fastfood_df['MarketSize'] =='Small']\n",
    "MS_Medium=fastfood_df[fastfood_df['MarketSize'] =='Medium']\n",
    "MS_Large=fastfood_df[fastfood_df['MarketSize'] =='Large']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a149938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1 group2 meandiff p-adj   lower    upper  reject\n",
      "-----------------------------------------------------\n",
      "     1      2 -10.7696    0.0 -14.7738 -6.7654   True\n",
      "     1      3  -2.7345 0.2444  -6.7388  1.2697  False\n",
      "     2      3   8.0351    0.0   4.1208 11.9493   True\n",
      "-----------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_test(MS_Small,\"малом размере рынка\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8f320b41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1 group2 meandiff p-adj   lower    upper  reject\n",
      "-----------------------------------------------------\n",
      "     1      2 -10.7696    0.0 -14.7738 -6.7654   True\n",
      "     1      3  -2.7345 0.2444  -6.7388  1.2697  False\n",
      "     2      3   8.0351    0.0   4.1208 11.9493   True\n",
      "-----------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_test(MS_Medium,\"среднем размере рынка\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6645a2e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1 group2 meandiff p-adj   lower    upper  reject\n",
      "-----------------------------------------------------\n",
      "     1      2 -10.7696    0.0 -14.7738 -6.7654   True\n",
      "     1      3  -2.7345 0.2444  -6.7388  1.2697  False\n",
      "     2      3   8.0351    0.0   4.1208 11.9493   True\n",
      "-----------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tukey_test(MS_Large,\"большом размере рынка\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4de2b0",
   "metadata": {},
   "source": [
    "<a id='UTest'></a>\n",
    "### U-тест Манна-Уитни\n",
    "[Наверх](#Content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "261255be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=22957.5, pvalue=5.845935246838518e-12)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(fastfood_df[fastfood_df['Promotion'] == 1]['SalesInThousands'], \n",
    "                   fastfood_df[fastfood_df['Promotion'] == 2]['SalesInThousands'], \n",
    "                   alternative='two-sided')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b91df42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=18247.0, pvalue=0.035084095693231204)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(fastfood_df[fastfood_df['Promotion'] == 1]['SalesInThousands'], \n",
    "                   fastfood_df[fastfood_df['Promotion'] == 3]['SalesInThousands'], \n",
    "                   alternative='two-sided')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "83bbd648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=12093.0, pvalue=1.1970084441651803e-07)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(fastfood_df[fastfood_df['Promotion'] == 2]['SalesInThousands'], \n",
    "                   fastfood_df[fastfood_df['Promotion'] == 3]['SalesInThousands'], \n",
    "                   alternative='two-sided')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a18bf1",
   "metadata": {},
   "source": [
    "<a id='Сonclusion'></a>\n",
    "## Вывод\n",
    "[Наверх](#Content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72fd30c",
   "metadata": {},
   "source": [
    "Вывод, полученный из расчета с использованием t-критерия Стьюдента, заключается в том, что рекламные материалы  1 и 3 существенно лучше оказывают влияние на уровень продаж новых позиций меню компании быстрого питания чем рекламный материал 2. Следует испольовать именно их."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d351c78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}