3.7_HW_AB_testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed72b8c1",
   "metadata": {},
   "source": [
    "# Домашнее задание по теме \"Дисперсионный анализ и A/B-тесты\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34529a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e450c5",
   "metadata": {},
   "source": [
    "### Блок 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19732481",
   "metadata": {},
   "source": [
    "##### Задание № 1\n",
    "Вам даны результаты проверки двух препаратов.\n",
    "    \n",
    "Требуется:\n",
    "- Оценить, есть ли различие в эффекте у этих пациентов\n",
    "- Визуально подтвердить результат\n",
    "    \n",
    "`A` - результаты для группы людей получающих препарат А.\n",
    "\n",
    "`B` - результаты для другой группы людей, получающих препарат B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4578b2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(11)\n",
    "A = stats.norm.rvs(scale=30, loc=11, size=100)\n",
    "B = A + stats.norm.rvs(scale=54, loc=11, size=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a83569",
   "metadata": {},
   "source": [
    "- Н0 - различия в препаратах нет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "938fac45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue = 0.085. Не отвергаем нулевую гипотезу, различия в препаратах нет\n"
     ]
    }
   ],
   "source": [
    "result = stats.ttest_ind(A, B)\n",
    "alpha = 0.05\n",
    "if (result.pvalue < alpha):\n",
    "    print( 'Отвергаем нулевую гипотезу')\n",
    "else:\n",
    "    print(f'pvalue = {round(result.pvalue,3)}. Не отвергаем нулевую гипотезу, различия в препаратах нет')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e067a0c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.734\n",
      "0.085\n"
     ]
    }
   ],
   "source": [
    "print(t.round(3))\n",
    "print(p_value.round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7fb1a217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(A, bins=20);\n",
    "plt.hist(B, bins=20, alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86b0276e",
   "metadata": {},
   "source": [
    "##### Задание № 2\n",
    "Допустим, вы решили устроить дачный эксперимент. Берем и поливаем одни огурцы водой, другие огурцы — водой с удобнением, третью группу огурцов будем поливать минералкой. \n",
    "    \n",
    " - Используя дисперсионный анализ, сделайте выводы о распредлениях результатов. Есть ли эффект от удобрения по сравнению с минералкой? \n",
    " - Помимо результата статистического теста, продемонстрируйте отличия визуально с помощью `boxplot` или `hist`\n",
    "    \n",
    "`water = [1,2,3,4,2,4,2,4,5,2,3,4,2,1,3,4,3,2,5,1]`\n",
    "\n",
    "`nutri = [1,2,4,6,5,6,7,5,4,5,6,7,4,3,5,5,6,5,4,3,5]`\n",
    "\n",
    "`mineral =[2,1,1,3,2,4,2,4,5,4,3,2,3,2,3,1,3,4,5,1,4]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "db9d7f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "water = [1,2,3,4,2,4,2,4,5,2,3,4,2,1,3,4,3,2,5,1]\n",
    "nutri = [1,2,4,6,5,6,7,5,4,5,6,7,4,3,5,5,6,5,4,3,5]\n",
    "mineral =[2,1,1,3,2,4,2,4,5,4,3,2,3,2,3,1,3,4,5,1,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff5dd87",
   "metadata": {},
   "source": [
    "Импортирем функцию для дисперсионного анализа из библиотеки scipy пакета stats - from scipy.stats import f_oneway "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2ec26205",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.525963044834192 2.9240047906326866e-05\n",
      "Не отклонять Н0\n"
     ]
    }
   ],
   "source": [
    "F, p_value = stats.f_oneway(water, nutri, mineral)\n",
    "a = 0.05 \n",
    "print(F, p_value)\n",
    "\n",
    "if p_value > a:\n",
    "    print('Отклонить Н0')\n",
    "else:\n",
    "    print('Не отклонять Н0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bf3c3381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(water, bins=20);\n",
    "plt.hist(nutri, bins=20);\n",
    "plt.hist(mineral, bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f5a12945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sizes = water + nutri + mineral\n",
    "methods = ['water']*len(water) + ['nutri']*len(nutri) + ['mineral']*len(mineral)\n",
    "df_mid = pd.DataFrame()\n",
    "df_mid['sizes'] = sizes\n",
    "df_mid['methods'] = methods\n",
    "\n",
    "ax = sns.boxplot(x='methods', y='sizes', data=df_mid)\n",
    "ax = sns.swarmplot(x=\"methods\", y=\"sizes\", data=df_mid)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7294c3a0",
   "metadata": {},
   "source": [
    "По гистограмме и боксплоту видно, что самый лучший эффект от воды с удобрением"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e21a35",
   "metadata": {},
   "source": [
    "### Блок 2\n",
    "#### Cookie Cats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e7d919",
   "metadata": {},
   "source": [
    "Cookie Cats — чрезвычайно популярная мобильная игра, разработанная Tactile Entertainment. Это классическая игра-головоломка в стиле «соедините три», в которой игрок должен соединять плитки одного цвета, чтобы очистить поле и выиграть уровень. Здесь также есть поющие кошки.\n",
    "\n",
    "По мере прохождения уровней игроки время от времени сталкиваются с воротами, которые заставляют их ждать или совершать покупки в приложении, чтобы продолжить. Помимо стимулирования покупок в приложении, эти ворота дают игрокам вынужденный перерыв в игре, что, как мы надеемся, приведёт к увеличению времени, проведённому в игре, и продлению удовольствия от игры.\n",
    "\n",
    "<b>Но где ставить ворота? </b>\n",
    "Изначально первые ворота были размещены на уровне `30`. В этом проекте вам предлагается проанализировать A/B-тест, в котором создатели переместили первые ворота в Cookie Cats с `30` уровня на уровень `40`. В частности, вам надо рассмотреть влияние A/B-теста на удержание игроков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "635aba03",
   "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>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>165</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  version  sum_gamerounds  retention_1  retention_7\n",
       "0     116  gate_30               3        False        False\n",
       "1     337  gate_30              38         True        False\n",
       "2     377  gate_40             165         True        False\n",
       "3     483  gate_40               1        False        False\n",
       "4     488  gate_40             179         True         True"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cookie_cats = 'https://raw.githubusercontent.com/a-milenkin/datasets_for_t-tests/main/cookie_cats.csv'\n",
    "\n",
    "data = pd.read_csv(cookie_cats, on_bad_lines='skip')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00544d2c",
   "metadata": {},
   "source": [
    "**Данные получены от 90 189 игроков, которые установили игру во время проведения A/B-теста. Переменные:**\n",
    "\n",
    "* `userid` - уникальный номер, идентифицирующий каждого игрока.\n",
    "* `version` - был ли игрок помещён в контрольную группу (gate_30 - ворота на уровне 30) или в тестовую группу (gate_40 - ворота на уровне 40).\n",
    "* `sum_gamerounds` - количество игровых раундов, сыгранных игроком в течение первой недели после установки\n",
    "* `retention_1` - игрок вернулся и поиграл через 1 день после установки?\n",
    "* `retention_7` - игрок вернулся и играл через 7 дней после установки?\n",
    "\n",
    "Когда игрок устанавливал игру, ему случайным образом назначали `gate_30` или `gate_40`.\n",
    "\n",
    "#### Задание: провести анализ данных и сделать выводы о лучшем месте для установки ворот.\n",
    "    \n",
    "**Замечание:** приведённых вспомогательных фрагментов кода не хватает для ответа на вопрос. \n",
    "\n",
    "Делать выводы необходимо, опираясь на статистические тесты, разобранные в лекции. \n",
    "\n",
    "Большим плюсом будет построение визуализиции для подтверждения ваших выводов. Рекомендуем использовать `seaborn` c его `sns.boxplot`/`sns.carplot`, `sns.barplot`, но можно обойтись и `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "12631461",
   "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>userid</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gate_30</th>\n",
       "      <td>44700</td>\n",
       "      <td>44700</td>\n",
       "      <td>44700</td>\n",
       "      <td>44700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gate_40</th>\n",
       "      <td>45489</td>\n",
       "      <td>45489</td>\n",
       "      <td>45489</td>\n",
       "      <td>45489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         userid  sum_gamerounds  retention_1  retention_7\n",
       "version                                                  \n",
       "gate_30   44700           44700        44700        44700\n",
       "gate_40   45489           45489        45489        45489"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сгрупируем результаты теста по версиям игры.\n",
    "data.groupby('version').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "793c6122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_30 = data.loc[data.version == 'gate_30']\n",
    "v_40 = data.loc[data.version == 'gate_40']\n",
    "v_30 = v_30.drop(v_30.loc[v_30.sum_gamerounds > 200].index)\n",
    "v_40 = v_40.drop(v_40.loc[v_40.sum_gamerounds > 200].index)\n",
    "\n",
    "bins = 200\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.hist(v_30['sum_gamerounds'], bins=bins, alpha=1, edgecolor='black', label='gate 30')\n",
    "plt.hist(v_40['sum_gamerounds'], bins=bins, alpha=0.7, label='gate 40')\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(0.45, 0.35))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa74877",
   "metadata": {},
   "source": [
    "У gate_30 наблюдаются большие значения на уровне 30-50 пройденных раундов, а скорость их убывания снижается, и это означает, что игроки выходили из игры реже, пройдя такое же количество раундов, чем при выборке gate_40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0992c261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1040.36x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data.loc[data['sum_gamerounds'] < 100].copy();\n",
    "\n",
    "sns.catplot(x=\"version\", y=\"sum_gamerounds\",\n",
    "            hue=\"retention_1\", col = 'retention_7',\n",
    "            data=df, kind=\"box\",\n",
    "            height=5, aspect=.95);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ba5453",
   "metadata": {},
   "source": [
    "gate_30 - больше возвратов после 1 и 7 дней (как впервые, так и повторно)\n",
    "\n",
    "gate_40 - меньше игроков, которые более не вернулись (ни на 1 день, ни на 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3bc17ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10));\n",
    "\n",
    "boot_1d = []\n",
    "for i in range(1000):\n",
    "    boot_mean = data.sample(frac = 1, replace = True).groupby('version')['retention_7'].mean()\n",
    "    boot_1d.append(boot_mean)\n",
    "    \n",
    "boot_1d = pd.DataFrame(boot_1d)\n",
    "boot_1d.plot(kind='density');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb10582",
   "metadata": {},
   "source": [
    "По графику можно сказать, что средние сгенерированных 1000 выборок из генеральной сопокупности показывают, что версия gate 30 имеет большее значение, соответственно, в нее играют чаще"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f1656dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7939569526814368, 0.3729086824696095)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import f_oneway \n",
    "#from scipy.stats import wilcoxon\n",
    "from scipy.stats import mannwhitneyu\n",
    "import scipy.stats as stats\n",
    "\n",
    "f, p = f_oneway(data[data['version'] == 'gate_30']['sum_gamerounds'], data[data['version'] == 'gate_40']['sum_gamerounds'])\n",
    "f, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "4d4d46fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=1024331250.500, p=0.050\n"
     ]
    }
   ],
   "source": [
    "stat, p = mannwhitneyu(data[data['version'] == 'gate_30']['sum_gamerounds'], data[data['version'] == 'gate_40']['sum_gamerounds'])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "0c9b548a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-value=10.014, p-value=0.002\n"
     ]
    }
   ],
   "source": [
    "#Проведем дисперсиннный анализ возврата игроков после 7 дней по группам gate_30 и gate_40\n",
    "fvalue, pvalue = f_oneway (data[data.version == 'gate_30']['retention_7'], data[data.version == 'gate_40']['retention_7'])\n",
    "print('F-value=%.3f, p-value=%.3f' % (fvalue, pvalue))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67e51bd",
   "metadata": {},
   "source": [
    "Эффект от переноса ворот есть, он не случайный."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25a97c7",
   "metadata": {},
   "source": [
    "### Блок 3\n",
    "#### Покемоны и A/B-тестирование"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c9a888",
   "metadata": {},
   "source": [
    "Покемоны — это маленькие существа, которые сражаются друг с другом на соревнованиях. Все покемоны имеют разные характеристики (сила атаки, защиты и т. д.) и относятся к одному или двум так называемым классам (вода, огонь и т. д.).\n",
    "\n",
    "Профессор Оук является изобретателем Pokedex, портативного устройства, которое хранит информацию обо всех существующих покемонах. Как его ведущий специалист по данным, Вы только что получили от него запрос с просьбой осуществить аналитику данных на всех устройствах Pokedex. \n",
    "\n",
    "**Описание набора данных**\n",
    "Профессор Оук скопировал всё содержимое в память одного устройства Pokedex, в результате чего получился набор данных, с которым Вы будете работать в этой задаче. В этом файле каждая строка представляет характеристики одного покемона:\n",
    "\n",
    "* `pid`: Numeric - ID покемона\n",
    "* `HP`: Numeric - Очки здоровья\n",
    "* `Attack`: Numeric - Сила обычной атаки\n",
    "* `Defense`: Numeric - Сила обычной защиты\n",
    "* `Sp. Atk`: Numeric - Сила специальной атаки\n",
    "* `Sp. Def`: Numeric - Сила специальной защиты\n",
    "* `Speed`: Numeric - Скорость движений\n",
    "* `Legendary`: Boolean - «True», если покемон редкий\n",
    "* `Class 1`: Categorical - Класс покемона\n",
    "* `Class 2`: Categorical - Класс покемона\n",
    "\n",
    "**Обратите внимание, что у покемона может быть один или два класса. Если у покемона два класса, считается, что они имеют одинаковую значимость.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5cec57b5",
   "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>pid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Class 1</th>\n",
       "      <th>Class 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Mega Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pid           Name Class 1 Class 2  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "0    1      Bulbasaur   Grass  Poison  45      49       49       65       65   \n",
       "1    2        Ivysaur   Grass  Poison  60      62       63       80       80   \n",
       "2    3       Venusaur   Grass  Poison  80      82       83      100      100   \n",
       "3    4  Mega Venusaur   Grass  Poison  80     100      123      122      120   \n",
       "4    5     Charmander    Fire     NaN  39      52       43       60       50   \n",
       "\n",
       "   Speed  Legendary  \n",
       "0     45      False  \n",
       "1     60      False  \n",
       "2     80      False  \n",
       "3     80      False  \n",
       "4     65      False  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_path = 'https://raw.githubusercontent.com/a-milenkin/datasets_for_t-tests/main/pokemon.csv'\n",
    "pokemon = pd.read_csv(pokemon_path, on_bad_lines='skip')\n",
    "pokemon.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6406f3e",
   "metadata": {},
   "source": [
    "**Задание № 1**\n",
    "    \n",
    "Профессор Оук подозревает, что покемоны в классе `grass` имеют более сильную обычную атаку, чем покемоны в классе `rock`. Проверьте, прав ли он, и убедите его в своём выводе статистически.\n",
    "    \n",
    "Примечание: \n",
    "- покемоны, которые относятся к обоим классам, не должны входить в выборку;\n",
    "- Вы можете предположить, что распределение обычных атак является нормальным для всех классов покемонов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b921320d",
   "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>pid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Class 1</th>\n",
       "      <th>Class 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>123</td>\n",
       "      <td>Tangela</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>115</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>167</td>\n",
       "      <td>Chikorita</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>49</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>168</td>\n",
       "      <td>Bayleef</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>80</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>169</td>\n",
       "      <td>Meganium</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Bellossom</td>\n",
       "      <td>Grass</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pid       Name Class 1 Class 2  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
       "122  123    Tangela   Grass     NaN  65      55      115      100       40   \n",
       "166  167  Chikorita   Grass     NaN  45      49       65       49       65   \n",
       "167  168    Bayleef   Grass     NaN  60      62       80       63       80   \n",
       "168  169   Meganium   Grass     NaN  80      82      100       83      100   \n",
       "197  198  Bellossom   Grass     NaN  75      80       95       90      100   \n",
       "\n",
       "     Speed  Legendary  \n",
       "122     60      False  \n",
       "166     45      False  \n",
       "167     60      False  \n",
       "168     80      False  \n",
       "197     50      False  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grass_rock = pokemon.loc[((pokemon['Class 1'] == 'Grass') | (pokemon['Class 1'] == 'Rock')) & (pokemon['Class 2'].isna())]\n",
    "grass_rock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "9dd4bb76",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Class 1', y='Attack', data=grass_rock);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf67735",
   "metadata": {},
   "source": [
    "На графике видно, что средняя сила атаки у покемонов класса rock выше."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96f2ad81",
   "metadata": {},
   "source": [
    "H0 - покемоны класса Rock и Grass имеют равную силу атаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "047a3569",
   "metadata": {},
   "outputs": [],
   "source": [
    "Grass = grass_rock[grass_rock['Class 1'] == 'Grass']\n",
    "Rock = grass_rock[grass_rock['Class 1'] == 'Rock'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "81c201f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Отклонить Н0, сила атаки у классов разная\n"
     ]
    }
   ],
   "source": [
    "stats.ttest_ind(Grass['Attack'], Rock['Attack'])\n",
    "a = 0.05\n",
    "\n",
    "if p_value < a:\n",
    "    print('Отклонить Н0, сила атаки у классов разная')\n",
    "else:\n",
    "    print('Не отклонять Н0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "29edb3ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68\n",
      "103\n"
     ]
    }
   ],
   "source": [
    "a = int(Grass['Attack'].mean())\n",
    "b = int(Rock['Attack'].mean())\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35639ec0",
   "metadata": {},
   "source": [
    "Средняя сила атаки у класса Rock выше"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44965692",
   "metadata": {},
   "source": [
    "**Задание № 2**\n",
    "    \n",
    "Профессор Оук уже долго не может спать по ночам, ведь его волнует вопрос: а правда ли, что покемоны в классе `Water` в среднем быстрее покемонов в классе `Normal`. \n",
    "\n",
    "Проверьте, прав ли он, и убедите его в своем выводе статистически.\n",
    "    \n",
    "Примечание: \n",
    "- покемоны, которые относятся к обоим классам, не должны входить в выборку;\n",
    "- Вы можете предположить, что распределение обычных атак является нормальным для всех классов покемонов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "560a54f4",
   "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>pid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Class 1</th>\n",
       "      <th>Class 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Squirtle</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>43</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Wartortle</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>58</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Blastoise</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "      <td>105</td>\n",
       "      <td>78</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Mega Blastoise</td>\n",
       "      <td>Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "      <td>120</td>\n",
       "      <td>135</td>\n",
       "      <td>115</td>\n",
       "      <td>78</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Rattata</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pid            Name Class 1 Class 2  HP  Attack  Defense  Sp. Atk  \\\n",
       "9    10        Squirtle   Water     NaN  44      48       65       50   \n",
       "10   11       Wartortle   Water     NaN  59      63       80       65   \n",
       "11   12       Blastoise   Water     NaN  79      83      100       85   \n",
       "12   13  Mega Blastoise   Water     NaN  79     103      120      135   \n",
       "24   25         Rattata  Normal     NaN  30      56       35       25   \n",
       "\n",
       "    Sp. Def  Speed  Legendary  \n",
       "9        64     43      False  \n",
       "10       80     58      False  \n",
       "11      105     78      False  \n",
       "12      115     78      False  \n",
       "24       35     72      False  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water_normal = pokemon.loc[((pokemon['Class 1'] == 'Water') | (pokemon['Class 1'] == 'Normal')) & (pokemon['Class 2'].isna())]\n",
    "water_normal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "47c061e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Class 1', y='Speed', data=water_normal);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457d68b9",
   "metadata": {},
   "source": [
    "H0 - покемоны в классе Water в среднем быстрее покемонов в классе Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "a0529065",
   "metadata": {},
   "outputs": [],
   "source": [
    "Water = water_normal[water_normal['Class 1'] == 'Water']\n",
    "Normal = water_normal[water_normal['Class 1'] == 'Normal'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "5e3f3e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Не отклонять Н0, средняя скорость классов разная\n"
     ]
    }
   ],
   "source": [
    "result = stats.ttest_ind(Water['Speed'], Normal['Speed'], equal_var=False)\n",
    "a = 0.05\n",
    "\n",
    "if (result.pvalue < a):\n",
    "    print('Отклонить Н0, средняя скорость классов одинаковая')\n",
    "else:\n",
    "    print('Не отклонять Н0, средняя скорость классов разная')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "d3ba159f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Не отклонять Н0, средняя скорость классов разная\n"
     ]
    }
   ],
   "source": [
    "result = stats.mannwhitneyu(Water['Speed'], Normal['Speed'])\n",
    "a = 0.05\n",
    "\n",
    "if (result.pvalue < a):\n",
    "    print('Отклонить Н0, средняя скорость классов одинаковая')\n",
    "else:\n",
    "    print('Не отклонять Н0, средняя скорость классов разная')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6daf14cb",
   "metadata": {},
   "source": [
    "Покемоны в классе Water в среднем быстрее покемонов в классе Normal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e6aca85",
   "metadata": {},
   "source": [
    "**Задание № 3:**\n",
    "    \n",
    "Профессор Оук тот еще безумец. Он изобрел сыворотку, способную ускорить покемона. \n",
    "Однако мы усомнились в эффективности его вакцины. Професоор дал эту сыворотку некоторым покемонам (массив `treathed_pokemon`). \n",
    "Проверьте, работает ли вообще его сыворотка, и убедите всех в своем выводе статистически.\n",
    "    \n",
    "    \n",
    "*Вы можете предположить, что распределение скорости движения является нормальным для всех классов покемонов.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "cad7a185",
   "metadata": {},
   "outputs": [],
   "source": [
    "treathed_pokemon = ['Mega Beedrill','Mega Alakazam', 'Deoxys Normal Forme', 'Mega Lopunny']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "50477f8e",
   "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>Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Speed\n",
       "19     145\n",
       "71     150\n",
       "428    150\n",
       "476    135"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treathed_pokemons = pokemon[(pokemon['Name'].isin(treathed_pokemon))].filter(['Speed'])\n",
    "threated_speed = treathed_pokemons.mean()[0]\n",
    "treathed_pokemons.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "37ebcf83",
   "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>Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Speed\n",
       "0     45\n",
       "1     60\n",
       "2     80\n",
       "3     80\n",
       "4     65"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_pokemons = pokemon[(pokemon['Name'].isin(treathed_pokemon)==False)].filter(['Speed'])\n",
    "other_speed = other_pokemons.mean()[0]\n",
    "other_pokemons.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cecb8e5",
   "metadata": {},
   "source": [
    "Н0 - скорость покемонов с сывороткой и без одинаковая"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "91ed5615",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Отклонить Н0, скорость покемонов с сывороткой выше\n"
     ]
    }
   ],
   "source": [
    "result = stats.ttest_ind(treathed_pokemons, other_pokemons, equal_var=False)\n",
    "a = 0.05\n",
    "\n",
    "if (result.pvalue < a):\n",
    "    print('Отклонить Н0, скорость покемонов с сывороткой выше')\n",
    "else:\n",
    "    print('Не отклонять Н0, скорость покемонов с сывороткой и без одинаковая')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "741a0fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Отклонить Н0, скорость покемонов с сывороткой выше\n"
     ]
    }
   ],
   "source": [
    "result = stats.mannwhitneyu(treathed_pokemons, other_pokemons)\n",
    "a = 0.05\n",
    "\n",
    "if (result.pvalue < a):\n",
    "    print('Отклонить Н0, скорость покемонов с сывороткой выше')\n",
    "else:\n",
    "    print('Не отклонять Н0, скорость покемонов с сывороткой и без одинаковая')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "0a5523ed",
   "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>pid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Class 1</th>\n",
       "      <th>Class 2</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Mega Beedrill</td>\n",
       "      <td>Bug</td>\n",
       "      <td>Poison</td>\n",
       "      <td>65</td>\n",
       "      <td>150</td>\n",
       "      <td>40</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>145</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>Mega Alakazam</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>175</td>\n",
       "      <td>95</td>\n",
       "      <td>150</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>429</td>\n",
       "      <td>Deoxys Normal Forme</td>\n",
       "      <td>Psychic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>150</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>477</td>\n",
       "      <td>Mega Lopunny</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Fighting</td>\n",
       "      <td>65</td>\n",
       "      <td>136</td>\n",
       "      <td>94</td>\n",
       "      <td>54</td>\n",
       "      <td>96</td>\n",
       "      <td>135</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pid                 Name  Class 1   Class 2  HP  Attack  Defense  \\\n",
       "19    20        Mega Beedrill      Bug    Poison  65     150       40   \n",
       "71    72        Mega Alakazam  Psychic       NaN  55      50       65   \n",
       "428  429  Deoxys Normal Forme  Psychic       NaN  50     150       50   \n",
       "476  477         Mega Lopunny   Normal  Fighting  65     136       94   \n",
       "\n",
       "     Sp. Atk  Sp. Def  Speed  Legendary  \n",
       "19        15       80    145      False  \n",
       "71       175       95    150      False  \n",
       "428      150       50    150       True  \n",
       "476       54       96    135      False  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_treathed_pokemon = pokemon[pokemon['Name'].isin(treathed_pokemon)]\n",
    "df_treathed_pokemon "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "408e197e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Name', y='Speed', data=df_treathed_pokemon);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "68fdf3eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_df_treathed_pokemon = pd.concat([pokemon, df_treathed_pokemon]).drop_duplicates(keep=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "89725354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Speed', data=drop_df_treathed_pokemon);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293b019b",
   "metadata": {},
   "source": [
    "Показатели скорости покемонов из списка treated_pokemons выше средних значений из списка и находятся между 3-им квартилем и максимумом."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ac7a46",
   "metadata": {},
   "source": [
    "**Задание № 4**\n",
    "    \n",
    "Профессор Оук всегда любил истории про легендарных покемонов. Однако профессор не очень уверен, что они лучше остальных покемонов. \n",
    "Оук предложил разобраться в этом Вам. \n",
    "\n",
    "Действительно ли сумма характеристик `HP`, `Attack`, `Defense` у легендарных покемонов выше, чем у других покемонов? А произведение этих же параметров? \n",
    "\n",
    "Найдите ответы на эти вопросы и убедите всех в своем выводе статистически.\n",
    "\n",
    "*Вы можете предположить, что распределение сум и произведений этих параметров является нормальным для всех классов покемонов.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "502736dd",
   "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>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sum</th>\n",
       "      <th>Prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>275</td>\n",
       "      <td>765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>265</td>\n",
       "      <td>688500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>280</td>\n",
       "      <td>810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>106</td>\n",
       "      <td>110</td>\n",
       "      <td>90</td>\n",
       "      <td>306</td>\n",
       "      <td>1049400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>106</td>\n",
       "      <td>190</td>\n",
       "      <td>100</td>\n",
       "      <td>396</td>\n",
       "      <td>2014000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      HP  Attack  Defense  Sum     Prod\n",
       "156   90      85      100  275   765000\n",
       "157   90      90       85  265   688500\n",
       "158   90     100       90  280   810000\n",
       "162  106     110       90  306  1049400\n",
       "163  106     190      100  396  2014000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pokemon = pokemon[(pokemon['Legendary'] == True)][['HP','Attack','Defense']]\n",
    "df_pokemon['Sum'] = df_pokemon['HP'] + df_pokemon['Attack'] + df_pokemon['Defense']\n",
    "df_pokemon['Prod'] = df_pokemon['HP'] * df_pokemon['Attack'] * df_pokemon['Defense']\n",
    "df_pokemon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "33011725",
   "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>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sum</th>\n",
       "      <th>Prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>143</td>\n",
       "      <td>108045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>185</td>\n",
       "      <td>234360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>245</td>\n",
       "      <td>544480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>123</td>\n",
       "      <td>303</td>\n",
       "      <td>984000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>134</td>\n",
       "      <td>87204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HP  Attack  Defense  Sum    Prod\n",
       "0  45      49       49  143  108045\n",
       "1  60      62       63  185  234360\n",
       "2  80      82       83  245  544480\n",
       "3  80     100      123  303  984000\n",
       "4  39      52       43  134   87204"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_pokemon = pokemon[(pokemon['Legendary'] == False)][['HP','Attack','Defense']]\n",
    "df2_pokemon['Sum'] = df2_pokemon['HP'] + df2_pokemon['Attack'] + df2_pokemon['Defense']\n",
    "df2_pokemon['Prod'] = df2_pokemon['HP'] * df2_pokemon['Attack'] * df2_pokemon['Defense']\n",
    "df2_pokemon.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "087a09f2",
   "metadata": {},
   "source": [
    "H0 - средняя сумма показателей у поколений покемонов одинаковая"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "52703102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Отклонить Н0, средняя сумма показателей у поколений покемонов разная\n"
     ]
    }
   ],
   "source": [
    "result = stats.ttest_ind(df2_pokemon['Sum'], df_pokemon['Sum'], equal_var=False)\n",
    "a = 0.05\n",
    "\n",
    "if (result.pvalue < a):\n",
    "    print('Отклонить Н0, средняя сумма показателей у поколений покемонов разная')\n",
    "else:\n",
    "    print('Не отклонять Н0, средняя сумма показателей у поколений покемонов одинаковая')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb332d1",
   "metadata": {},
   "source": [
    "Средняя сумма показателей у легендарных покемонов больше, чем у обычных."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b832f049",
   "metadata": {},
   "source": [
    "H0 - среднее произведение у поколений покемонов одинаковое"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "64ce8379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Отклонить Н0, средняя сумма показателей у поколений покемонов разная\n"
     ]
    }
   ],
   "source": [
    "result = stats.ttest_ind(df2_pokemon['Prod'], df_pokemon['Prod'], equal_var=False)\n",
    "a = 0.05\n",
    "\n",
    "if (result.pvalue < a):\n",
    "    print('Отклонить Н0, средняя сумма показателей у поколений покемонов разная')\n",
    "else:\n",
    "    print('Не отклонять Н0, средняя сумма показателей у поколений покемонов одинаковая')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e980042f",
   "metadata": {},
   "source": [
    "Среднее произведение показателей у легендарных покемонов больше, чем у обычных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "5a338527",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shali\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\shali\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df2_pokemon['Sum']);\n",
    "sns.boxplot(df_pokemon['Sum']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8f903b",
   "metadata": {},
   "source": [
    "Cумма и произведение характеристик HP, Attack, Defense у легендарных покемонов находится между 3 квартилем и максимумом, а значит их характеристики выше."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5feab86",
   "metadata": {},
   "source": [
    "**Задание № 5**\n",
    "    \n",
    "Профессор Оук частенько наблюдает за боями покемонов. После очередных таких боев Оук выделил три класса (массив `best_defence_class`), которые на его взгляд одинаковы по \"силе обычной защиты\" `Defense`. \n",
    "\n",
    "Проверьте, действительно ли эти классы покемонов не отличаются по уровню защиты статистически значимо. Все та же статистика Вам в помощь! \n",
    "\n",
    "*Вы можете предположить, что распределение сум и произведений этих параметров является нормальным для всех классов покемонов.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "88f9380c",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_defence_class = ['Rock', 'Ground','Steel','Ice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3ee5079c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class 1</th>\n",
       "      <th>Class 2</th>\n",
       "      <th>Defense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Ground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Poison</td>\n",
       "      <td>Ground</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Poison</td>\n",
       "      <td>Ground</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Ground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class 1 Class 2  Defense\n",
       "32  Ground     NaN       85\n",
       "33  Ground     NaN      110\n",
       "36  Poison  Ground       87\n",
       "39  Poison  Ground       77\n",
       "55  Ground     NaN       25"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_defence = pokemon[(pokemon['Class 2'].isin(best_defence_class)) | (pokemon['Class 1'].isin(best_defence_class))][['Class 1','Class 2','Defense']]\n",
    "best_defence.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ca564ec6",
   "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>Class 1</th>\n",
       "      <th>Class 2</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Ground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110</td>\n",
       "      <td>Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Poison</td>\n",
       "      <td>Ground</td>\n",
       "      <td>87</td>\n",
       "      <td>Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Poison</td>\n",
       "      <td>Ground</td>\n",
       "      <td>77</td>\n",
       "      <td>Ground</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Ground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>Ground</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class 1 Class 2  Defense   Class\n",
       "32  Ground     NaN       85  Ground\n",
       "33  Ground     NaN      110  Ground\n",
       "36  Poison  Ground       87  Ground\n",
       "39  Poison  Ground       77  Ground\n",
       "55  Ground     NaN       25  Ground"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_defence['Class'] = np.where(best_defence['Class 1'].isin(best_defence_class), best_defence['Class 1'], best_defence['Class 2'])\n",
    "best_defence.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0557be61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='Defense'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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33aTY2Fjt2LFDcXFxiomJCWSNAFD1wlV9w44kRdhdABA6KvStnJeXpyuuuELbt2+Xz+dTt27dFBsbqylTpsjr9erZZ58NdJ0AAAAVUqE5OyNGjFC7du20Z8+eUhPGrr76ai1atChgxQEAAFRWhXp2li1bpuXLl6t27dqlljdu3Fg//PBDQAoDAAAIhAr17BQXF6uoqOiE5fn5+YqNja10UQAAAIFSobDTrVs3PfHEE/73lmXpwIEDGj9+vK688spA1QYAAFBpFRrGmjZtmrp06aJzzz1XXq9XN910k7Zs2aKkpCS9/vrrga4RAACgwioUdlJTU7V27VrNnj1ba9asUXFxsQYMGKCsrCzucAkAAEJKhe8iERkZqdtuu0233XZbIOsBAAAIqArN2XnllVe0YMEC//sxY8aoTp066tixo/Ly8gJWHAAAQGVVKOxMnjzZP1z1ySef6Omnn9aUKVOUlJSku+66K6AFAgAAVEaFhrG+//57NWvWTJI0b948/eUvf9Edd9yhTp06qXPnzoGsDwAAoFIq1LMTExOj3bt3S5IWLlyorl27SpLcbrcOHToUuOoAAAAqqUI9O926ddPAgQN1wQUX6JtvvlHPnj0lSRs2bNCZZ54ZyPoAAAAqpUI9O//4xz/UoUMH/fTTT3rzzTeVmJgoSVqzZo1uvPHGgBYIAABQGRXq2alTp46efvrpE5ZPmDCh0gUBAAAEUoXvs7N371599tln2rVrl4qLi/3LLcvSLbfcEpDiAAAAKqtCYeedd95RVlaWDh48qNjYWFmW5V9H2IGTGGPk9XrtLqPSSrbBCe2Rjl0QUfJnDwCUpUJhZ/To0br99ts1efJkRUVFBbomIGR4vV5lZGTYXUZAZWZm2l1CQHg8Hh5PA+C0VGiC8g8//KA777yToAMAAEJehXp2MjIytHr1ajVt2jTQ9QAh6x9/3itXmLG7jAoxRjr869S62rWk6jr64yuyNHRpHbvLAFDNVCjs9OzZU/fcc482btyo9PR0RURElFrfu3fvgBQHhBJXmJE7zO4qKs4ZAz7VM2wCsFeFws6gQYMkSQ899NAJ6yzLUlFRUeWqAgAACJAKhZ2Sl5oDAACEsgpNUC7JKZexAgAAZ6pQ2CkqKtJ//dd/6YwzzlBMTIy2bt0qSXrggQf04osvBrRAAACAyqhQ2Jk0aZJycnI0ZcoU1a5d2788PT1dL7zwQsCKAwAAqKwKhZ0ZM2bo+eefV1ZWlsLCfrs8pXXr1vr6668DVhwAAEBlVfimgs2aNTtheXFxsY4cOVLpogAAAAKlQmGnZcuW+ve//33C8n/961+64IILKl0UAABAoFTo0vPx48frlltu0Q8//KDi4mK99dZb2rx5s2bMmKF333030DUCAABUWIV6dnr16qU5c+bovffek2VZevDBB7Vp0ya988476tatW6BrBAAAqLDT7tl58skndccdd8jtdmv79u3q3r27454GDQAAnOe0e3ZGjRqlwsJCSVKTJk30008/Ba0oAACAQDntnp3U1FS9+eabuvLKK2WMUX5+fpl3T27UqFHACgQAAKiM0w4748aN0/DhwzVs2DBZlqULL7zwhG2MMTwIFAAAhJTTDjt33HGHbrzxRuXl5al169b68MMPlZiYGMzaAAAAKq1cl57HxsaqVatWevnll9WpUye5XK5g1QUAABAQFbr0vF+/fjp06JBeeOEFjR07Vj///LMk6fPPP9cPP/wQ0AIBAAAqo0I3FVy3bp26du2q+Ph4bdu2TYMGDVJCQoLmzp2rvLw8zZgxI9B1AgAAVEiFenbuuusu9e/fX1u2bJHb7fYv79Gjh5YuXRqw4gAAACqrQj07q1ev1vPPP3/C8jPOOEMFBQWVLgoAACBQKhR23G63/waDJW3evFn16tWrdFEAADiBMabMe9IF8hg+n0+S5HK5ZFlWUI/ndruDfoxAq1DYyczM1EMPPaR//vOfkiTLsrR9+3bdd9996tu3b0ALBACguvJ6vY57tJLH41FkZKTdZZRLhebsPPbYY/rpp59Uv359HTp0SJdeeqmaNWum2NhYTZo0KdA1AgAAVFiFenbi4uK0bNkyffzxx1qzZo2Ki4vVpk0bde3aNdD1AQBQbbndbnk8nqAew+v1KjMzU5I0f/78UhcOBUOw9x8M5Q47xcXFysnJ0VtvvaVt27bJsiw1adJEKSkp/sdFAACAY9M8qnLIx+12V7shpqpQrmEsY4x69+6tgQMH6ocfflB6erpatmypvLw89e/fX1dffXWw6gQAAKiQcvXs5OTkaOnSpVq0aJG6dOlSat1HH32kPn36aMaMGbr11lsDWiQAAEBFlatn5/XXX9ff/va3E4KOJF122WW67777NGvWrIAVBwAAUFnl6tlZt26dpkyZUub6Hj166Mknn6x0UaGsKu6ZUBVKtsEJ7ZGq570fEFqMMb+9OWpfHfhViXNQ6twA5VSusPPzzz8rOTm5zPXJycnas2dPpYsKZU68Z8LxWfzVXXW89wNCy/Ebs0lS2DthNlaC3/P5fIqKirK7DFRT5RrGKioqUnh42fkoLCxMR4/y5xAAAAgd5erZMcaof//+crlcJ11f8q+imuBgmyypVoVuVWQ/Y6TiX4NprXCpug7/FB9V9OfME0NglPzZVtSrqIJ3IkPAHP2th62s3zvA6SjXt3K/fv3+cJsadSVWrXApLMLuKiqhtt0FACGl1JyvcBF2Qgjz8VAZ5fpWfvnll4NVBwAAQFBU6NlYgbJ06VL16tVLqampsixL8+bNK7XeGKPs7GylpqYqMjJSnTt31oYNG0pt4/P5NHz4cCUlJSk6Olq9e/dWfn5+FbYCTlbyChBfkeTlZevLV3TycwMAp2JrJ+3Bgwd13nnn6bbbbjvp09KnTJmixx9/XDk5OWrRooUmTpyobt26afPmzYqNjZUkjRw5Uu+8845mz56txMREjR49WldddZXWrFmjsDCupkDllJyHNnRpXRsrwe9xdQ6A02Vr2OnRo4d69Ohx0nXGGD3xxBO6//77dc0110iSXnnlFSUnJ+u1117T4MGDtW/fPr344ot69dVX/Q8hnTlzptLS0vThhx867hJxAABQfiE7/S43N1cFBQXq3r27f5nL5dKll16qFStWaPDgwVqzZo2OHDlSapvU1FS1atVKK1asKDPs+Hy+Un+xFxYWBq8hqNZKXgHyjz/vkYvOQlv5in7rYePqHACnK2TDTkFBgSSdcBPD5ORk5eXl+bepXbu26tate8I2xz9/Mg8//LAmTJgQ4IrhRCWvAHGFSW7CTsjg6hwAp8vWCcqn4/c/0Iwxf/hD7o+2GTt2rPbt2+d/ff/99wGpFQAAhJ6QDTspKSmSdEIPza5du/y9PSkpKTp8+PAJj6gouc3JuFwuxcXFlXoBAABnCtmw06RJE6WkpOiDDz7wLzt8+LCWLFmijh07SpLatm2riIiIUtvs3LlTX331lX8bAABQs9k6Z+fAgQP69ttv/e9zc3O1du1aJSQkqFGjRho5cqQmT56s5s2bq3nz5po8ebKioqJ00003SZLi4+M1YMAAjR49WomJiUpISNDdd9+t9PR0/9VZAACgZrM17KxevVpdunTxvx81apSkY4+lyMnJ0ZgxY3To0CENGTJEe/bs0UUXXaSFCxf677EjSdOmTVN4eLiuu+46HTp0SJdffrlycnK4xw4AAJBkc9jp3LnzKe+CalmWsrOzlZ2dXeY2brdbTz31lJ566qkgVAgAAKq7kJ2zAwAAEAiEHQAA4GiEHQAA4GiEHQAA4GiEHQAA4Ggh+2wsALDVUbsLqAQjqejXr8MkVdfHiFXnc4CQQtgBgJMIe4d7dQFOQdgBANRIxhh5vV67y6i0km1wQnukY/fQ+6OHfpcHYQcAfuV2u+XxeOwuo9K8Xq8yMzMlSfPnz5fb7ba5osoLRhu8Xq8yMjICvl87HT/v1Z3H41FkZGTA9kfYAYBfWZYV0B+wocDtdjuuTUB5EXYAADXevZJq211EBRlJR379OkLVdz76YUmPBmnfhB0AQI1XW1LtahsTJJfdBQRE2c/KrCzuswMAAByNsAMAAByNsAMAAByNsAMAAByNsAMAAByNsAMAAByNsAMAAByN++yUkzEl7gNQdKTsDVE1SpyDUucGAIBfEXbKyefz+b+O/uI1GyvB7/l8PkVFRdldBgAgxDCMBQAAHI2enXJyuX67KffBC26SwiJsrAYqOuLvYSt5bgDgj5Qc+j58bIldpUDHz8ExgZ6WQNgpJ8sq8eyUsAjCTggpdW4A4A+UnJYQrAdQomICPS2BYSwAAOBo9OwAAGqkkkPf9+rYk89hn8P6rYct0NMSCDsAgBqp5NB3bUm1xVC4vX6bpxPoaQmEHeA0+YosVdcJjMZIh4uPfV27llRdpzcdOwcAUD6EHeA0DV1ax+4SAAAVwARlAADgaPTsAKfgdrvl8XjsLqPSvF6vMjMzJUnz58+X2+22uaLKc0IbAFQNwg5wCpZlKTIy0u4yAsrtdjuuTQBwKgxjAQAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARwu3u4Bqrfio3RVUnDG/1V8rXLIse+upqOp8DgAAVYKwUwnRn8+yuwQAAPAHGMYCAACORs9OObndbnk8HrvLqDSv16vMzExJ0vz58+V2u22uqPKc0AYAQOARdsrJsixFRkbaXUZAud1ux7UJAIDjGMYCAACORtgBAACORtgBAACORtgBAACORtgBAACORtgBAACORtgBAACOFtJhJzs7W5ZllXqlpKT41xtjlJ2drdTUVEVGRqpz587asGGDjRUDAIBQE/I3FWzZsqU+/PBD//uwsDD/11OmTNHjjz+unJwctWjRQhMnTlS3bt20efNmxcbG2lEuAJySMUZerzeoxyi5/2AfSzp2Y1Kruj5M+FeHJUnG5ioqxkg68uvXEZKq65k4HMR9h3zYCQ8PL9Wbc5wxRk888YTuv/9+XXPNNZKkV155RcnJyXrttdc0ePDgMvfp8/nk8/n87wsLCwNfOACchNfrVUZGRpUd7/hjYYLJ4/FU+7uwP2p3AQiqkB7GkqQtW7YoNTVVTZo00Q033KCtW7dKknJzc1VQUKDu3bv7t3W5XLr00ku1YsWKU+7z4YcfVnx8vP+VlpYW1DYAAAD7hHTPzkUXXaQZM2aoRYsW+vHHHzVx4kR17NhRGzZsUEFBgSQpOTm51GeSk5OVl5d3yv2OHTtWo0aN8r8vLCwk8ACoElXxMGFjjL/32uVyBX2Iqbo+hJcHO4euQLchpMNOjx49/F+np6erQ4cOOuuss/TKK6/o4osvlqQTvomNMX/4je1yueRyuQJfMAD8gap6mHBUVFTQj1Hd8WDnmiPkh7FKio6OVnp6urZs2eKfx3O8h+e4Xbt2ndDbAwAAaq5qFXZ8Pp82bdqkBg0aqEmTJkpJSdEHH3zgX3/48GEtWbJEHTt2tLFKAAAQSkJ6GOvuu+9Wr1691KhRI+3atUsTJ05UYWGh+vXrJ8uyNHLkSE2ePFnNmzdX8+bNNXnyZEVFRemmm26yu3QAABAiQjrs5Ofn68Ybb9T//d//qV69err44ou1cuVKNW7cWJI0ZswYHTp0SEOGDNGePXt00UUXaeHChdxjBwAA+IV02Jk9e/Yp11uWpezsbGVnZ1dNQQAAoNqpVnN2AAAAyouwAwAAHI2wAwAAHI2wAwAAHI2wAwAAHI2wAwAAHC2kLz0HagJjjLxeb1CPUXL/wT6WdOz5PMF++CQAnC7CDmAzr9erjIyMKjve8acjB5PH4+FhhABCBsNYAADA0ejZAWzmdrvl8XiCegxjjHw+nyTJ5XIFfYjJ7XYHdf8AUB6EHcBmlmVVyZBPVFRU0I8BAKGIYSwAAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBohB0AAOBo4XYXgBMZY+T1eoN6jJL7D/axJMntdsuyrKAfBwCA3yPshCCv16uMjIwqO15mZmbQj+HxeBQZGRn04wAA8HsMYwEAAEejZycEud1ueTyeoB7DGCOfzydJcrlcQR9icrvdQd0/AABlIeyEIMuyqmTIJyoqKujHAADAbgxjAQAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAARyPsAAAAR+PZWAAABIkxRl6vN6jHKLn/YB9LOvZg52A/PDrQCDsAAASJ1+tVRkZGlR0vMzMz6MfweDxV8rDqQGIYCwAAOBo9OwAABInb7ZbH4wnqMYwx8vl8kiSXyxX0ISa32x3U/QcDYQcAgCCxLKtKhnyioqKCfozqjGEsAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaDz1XJIxRpJUWFhocyUAAOB0Hf+9ffz3eFkIO5L2798vSUpLS7O5EgAAUF779+9XfHx8mest80dxqAYoLi7Wjh07FBsbK8uy7C6nyhQWFiotLU3ff/+94uLi7C4HQcb5rlk43zVLTT3fxhjt379fqampqlWr7Jk59OxIqlWrlho2bGh3GbaJi4urUd8cNR3nu2bhfNcsNfF8n6pH5zgmKAMAAEcj7AAAAEcj7NRgLpdL48ePl8vlsrsUVAHOd83C+a5ZON+nxgRlAADgaPTsAAAARyPsAAAARyPsAAAARyPsICiys7N1/vnn210GKoBzB/4PwGkIO9VAQUGBRowYoWbNmsntdis5OVmXXHKJnn32Wf3yyy92l4cq1r9/f1mWJcuyFB4erkaNGumvf/2r9uzZY3dpCLBdu3Zp8ODBatSokVwul1JSUpSRkaFPPvlEkmRZlubNm2dvkagS/fv3V58+fewuo9riDsohbuvWrerUqZPq1KmjyZMnKz09XUePHtU333yjl156Sampqerdu/cJnzty5IgiIiJsqBhV4YorrtDLL7+so0ePauPGjbr99tu1d+9evf7663aXhgDq27evjhw5oldeeUVNmzbVjz/+qEWLFunnn3+2uzSgWqFnJ8QNGTJE4eHhWr16ta677jqdc845Sk9PV9++fbVgwQL16tVL0rG/8J599lllZmYqOjpaEydOlCRNnz5dZ511lmrXrq2zzz5br776qn/f27Ztk2VZWrt2rX/Z3r17ZVmWFi9eLElavHixLMvSokWL1K5dO0VFRaljx47avHlzqTofeeQRJScnKzY2VgMGDJDX6w3uP0wNd/yv/IYNG6p79+66/vrrtXDhQknHnvX20EMPqWHDhnK5XDr//PP1/vvvl/p8fn6+brjhBiUkJCg6Olrt2rXTp59+etJj5ebmqlmzZvrrX/+q4uLioLcNx+zdu1fLli3To48+qi5duqhx48Zq3769xo4dq549e+rMM8+UJF199dWyLMv/XpLeeecdtW3bVm63W02bNtWECRN09OhR//p9+/bpjjvuUP369RUXF6fLLrtMX375ZRW3EBVVXFysRx99VM2aNZPL5VKjRo00adIk//offvhB119/verWravExERlZmZq27Zt9hUcAgg7IWz37t1auHChhg4dqujo6JNuU/LBpePHj1dmZqbWr1+v22+/XXPnztWIESM0evRoffXVVxo8eLBuu+02ffzxx+Wu5f7779fUqVO1evVqhYeH6/bbb/ev++c//6nx48dr0qRJWr16tRo0aKBnnnmm/A1GhWzdulXvv/++vyfvv//7vzV16lQ99thjWrdunTIyMtS7d29t2bJFknTgwAFdeuml2rFjh95++219+eWXGjNmzEmDzFdffaVOnTrp2muv1fTp00/5oD0EVkxMjGJiYjRv3jz5fL4T1q9atUqS9PLLL2vnzp3+9x6PRzfffLPuvPNObdy4Uc8995xycnL8vwyNMerZs6cKCgr03nvvac2aNWrTpo0uv/xyeoyqibFjx+rRRx/VAw88oI0bN+q1115TcnKyJOmXX35Rly5dFBMTo6VLl2rZsmWKiYnRFVdcocOHD9tcuY0MQtbKlSuNJPPWW2+VWp6YmGiio6NNdHS0GTNmjDHGGElm5MiRpbbr2LGjGTRoUKll1157rbnyyiuNMcbk5uYaSeaLL77wr9+zZ4+RZD7++GNjjDEff/yxkWQ+/PBD/zYLFiwwksyhQ4eMMcZ06NDB/Od//mep41x00UXmvPPOq3DbUbZ+/fqZsLAwEx0dbdxut5FkJJnHH3/cGGNMamqqmTRpUqnPXHjhhWbIkCHGGGOee+45Exsba3bv3n3S/Y8fP96cd955ZsWKFSYhIcH8/e9/D26DUKY33njD1K1b17jdbtOxY0czduxY8+WXX/rXSzJz584t9Zk//elPZvLkyaWWvfrqq6ZBgwbGGGMWLVpk4uLijNfrLbXNWWedZZ577jljzG//BxA6+vXrZzIzM01hYaFxuVzmf/7nf0663YsvvmjOPvtsU1xc7F/m8/lMZGSk8Xg8VVVuyOHPtGqgZO+NJH322Wdau3atWrZsWeovvnbt2pXabtOmTerUqVOpZZ06ddKmTZvKXUPr1q39Xzdo0EDSscmTx4/ToUOHUtv//j0Cq0uXLlq7dq0+/fRTDR8+XBkZGRo+fLgKCwu1Y8eOU573tWvX6oILLlBCQkKZ+9++fbu6du2qcePG6e677w5qW1C2vn37+nvgMjIytHjxYrVp00Y5OTllfmbNmjV66KGH/D1DMTExGjRokHbu3KlffvlFa9as0YEDB5SYmFhqm9zcXH333XdV1zhUyKZNm+Tz+XT55ZefdP2aNWv07bffKjY21n9uExIS5PV6a/T5ZYJyCGvWrJksy9LXX39dannTpk0lSZGRkaWWn2yo6/dByRjjX3Z8SMKUeGLIkSNHTlpLycnOxz/P/A37REdHq1mzZpKkJ598Ul26dNGECRN0zz33SDr1ef/9/5uTqVevnlJTUzV79mwNGDBAcXFxAW4BTpfb7Va3bt3UrVs3Pfjggxo4cKDGjx+v/v37n3T74uJiTZgwQddcc81J91VcXKwGDRr45+WVVKdOncAWj4D7o+/f4uJitW3bVrNmzTphXb169YJVVsijZyeEJSYmqlu3bnr66ad18ODBcn/+nHPO0bJly0otW7Fihc455xxJv/3H37lzp399ycnK5TnOypUrSy37/XsE1/jx4/XYY4/pwIEDSk1NPeV5b926tdauXXvK+RmRkZF699135Xa7lZGRof379we1fpy+c8891//zICIiQkVFRaXWt2nTRps3b1azZs1OeNWqVUtt2rRRQUGBwsPDT1iflJRkR5NQDs2bN1dkZKQWLVp00vVt2rTRli1bVL9+/RPOb3x8fBVXGzoIOyHumWee0dGjR9WuXTvNmTNHmzZt0ubNmzVz5kx9/fXXCgsLK/Oz99xzj3JycvTss89qy5Ytevzxx/XWW2/5hyUiIyN18cUX65FHHtHGjRu1dOlSjRs3rtw1jhgxQi+99JJeeuklffPNNxo/frw2bNhQ4Taj/Dp37qyWLVtq8uTJuueee/Too49qzpw52rx5s+677z6tXbtWI0aMkCTdeOONSklJUZ8+fbR8+XJt3bpVb775pv/eLcdFR0drwYIFCg8PV48ePXTgwAE7mlZj7d69W5dddplmzpypdevWKTc3V//61780ZcoUZWZmSpLOPPNMLVq0SAUFBf77LD344IOaMWOGsrOztWHDBm3atElz5szxf2937dpVHTp0UJ8+feTxeLRt2zatWLFC48aN0+rVq21rL06P2+3WvffeqzFjxmjGjBn67rvvtHLlSr344ouSpKysLCUlJSkzM1P//ve/lZubqyVLlmjEiBHKz8+3uXob2TxnCKdhx44dZtiwYaZJkyYmIiLCxMTEmPbt25u///3v5uDBg8aYk09UNMaYZ555xjRt2tRERESYFi1amBkzZpRav3HjRnPxxRebyMhIc/7555uFCxeedILynj17/J/54osvjCSTm5vrXzZp0iSTlJRkYmJiTL9+/cyYMWOY4Bgkxycq/t6sWbNM7dq1zbZt28yECRPMGWecYSIiIsx5551n/vd//7fUttu2bTN9+/Y1cXFxJioqyrRr1858+umnxpgTJ6fu37/fdOzY0fzpT38yBw4cCGbTUILX6zX33XefadOmjYmPjzdRUVHm7LPPNuPGjTO//PKLMcaYt99+2zRr1syEh4ebxo0b+z/7/vvvm44dO5rIyEgTFxdn2rdvb55//nn/+sLCQjN8+HCTmppqIiIiTFpamsnKyjLbt283xjBBORSV/L4vKioyEydONI0bNzYRERGmUaNGpSal79y509x6660mKSnJuFwu07RpUzNo0CCzb98+m6q3n2VMiQkbAAAADsMwFgAAcDTCDgAAcDTCDgAAcDTCDgAAcDTCDgAAcDTCDgAAcDTCDgAAcDTCDgAAcDTCDoBqz7IszZs3z+4yAIQowg6AkFdQUKDhw4eradOmcrlcSktLU69evcp8GCIAlBRudwEAcCrbtm1Tp06dVKdOHU2ZMkWtW7fWkSNH5PF4NHToUH399dd2lwggxNGzAyCkDRkyRJZl6bPPPtNf/vIXtWjRQi1bttSoUaO0cuXKk37m3nvvVYsWLRQVFaWmTZvqgQce0JEjR/zrv/zyS3Xp0kWxsbGKi4tT27Zt/U/8zsvLU69evVS3bl1FR0erZcuWeu+996qkrQCCg54dACHr559/1vvvv69JkyYpOjr6hPV16tQ56ediY2OVk5Oj1NRUrV+/XoMGDVJsbKzGjBkjScrKytIFF1yg6dOnKywsTGvXrlVERIQkaejQoTp8+LCWLl2q6Ohobdy4UTExMUFrI4DgI+wACFnffvutjDH6j//4j3J9bty4cf6vzzzzTI0ePVpz5szxh53t27frnnvu8e+3efPm/u23b9+uvn37Kj09XZLUtGnTyjYDgM0YxgIQsowxko5dbVUeb7zxhi655BKlpKQoJiZGDzzwgLZv3+5fP2rUKA0cOFBdu3bVI488ou+++86/7s4779TEiRPVqVMnjR8/XuvWrQtMYwDYhrADIGQ1b95clmVp06ZNp/2ZlStX6oYbblCPHj307rvv6osvvtD999+vw4cP+7fJzs7Whg0b1LNnT3300Uc699xzNXfuXEnSwIEDtXXrVt1yyy1av3692rVrp6eeeirgbQNQdSxz/E8nAAhBPXr00Pr167V58+YT5u3s3btXderUkWVZmjt3rvr06aOpU6fqmWeeKdVbM3DgQL3xxhvau3fvSY9x44036uDBg3r77bdPWDd27FgtWLCAHh6gGqNnB0BIe+aZZ1RUVKT27dvrzTff1JYtW7Rp0yY9+eST6tChwwnbN2vWTNu3b9fs2bP13Xff6cknn/T32kjSoUOHNGzYMC1evFh5eXlavny5Vq1apXPOOUeSNHLkSHk8HuXm5urzzz/XRx995F8HoHpigjKAkNakSRN9/vnnmjRpkkaPHq2dO3eqXr16atu2raZPn37C9pmZmbrrrrs0bNgw+Xw+9ezZUw888ICys7MlSWFhYdq9e7duvfVW/fjjj0pKStI111yjCRMmSJKKioo0dOhQ5efnKy4uTldccYWmTZtWlU0GEGAMYwEAAEdjGAsAADgaYQcAADgaYQcAADgaYQcAADgaYQcAADgaYQcAADgaYQcAADgaYQcAADgaYQcAADgaYQcAADgaYQcAADja/wNHygfRouhRRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Class\", y=\"Defense\", data=best_defence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "67087993",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Отклонить Н0\n"
     ]
    }
   ],
   "source": [
    "f, p = stats.f_oneway(best_defence[best_defence['Class'] == 'Rock']['Defense'], best_defence[best_defence['Class'] == 'Ground']['Defense'], best_defence[best_defence['Class'] == 'Steel']['Defense'], best_defence[best_defence['Class'] == 'Ice']['Defense'])  # дисперсионный анализ (ДА) в python\n",
    "a = 0.05\n",
    "\n",
    "if p < a:\n",
    "    print('Отклонить Н0')\n",
    "else:\n",
    "    print('Не отклоняем H0, классы не различаются')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f644c1b",
   "metadata": {},
   "source": [
    "![](scipy.png)"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}