03_rating_changes.ipynb

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   "metadata": {},
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   "source": [
    "# Загрузка нужных библиотек\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import Image, display\n",
    "\n",
    "from scipy.stats import shapiro\n",
    "import scipy.stats as stats\n",
    "\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Загрузка данных\n",
    "rate_2019 = pd.read_csv('data/rate_2019.csv')\n",
    "rate_2020 = pd.read_csv('data/rate_2020.csv')\n",
    "rate_2021 = pd.read_csv('data/rate_2021.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3. Являются ли изменения в оценках компаний статистически значимыми?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на распределение оценок компаний по каждому году.\n",
    "\n",
    "Видно, что распределения скошены вправо, значения распределены не нормально.\n",
    "\n",
    "В 2019 и 2021 годах явно видны пиковые знаяения, встречающиеся наиболее часто. В 2020 году значения распределены более равномерно.\n",
    "\n",
    "В 2020 и 2021 годах наиболее часто встречаются значения около 80, в 2019 - около 100."
   ]
  },
  {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 800
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(rows=1, cols=3, shared_yaxes=True)\n",
    "\n",
    "fig.append_trace(go.Histogram(x=rate_2019['points'], name='2019 год'), 1, 1)\n",
    "\n",
    "fig.append_trace(go.Histogram(x=rate_2020['points'], name='2020 год'), 1, 2)\n",
    "\n",
    "fig.append_trace(go.Histogram(x=rate_2021['points'], name='2021 год'), 1, 3)\n",
    "\n",
    "fig.update_layout(title='Распределение оценок компаний для каждого года',\n",
    "                  coloraxis=dict(colorscale='Bluered_r'),\n",
    "                  showlegend=True)\n",
    "fig.show()\n",
    "\n",
    "display(Image(filename='img/rating_distribution.png', width=800))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_test(groupA, groupB):\n",
    "    '''\n",
    "    Функция производит сравнение двух групп.\n",
    "    1) Проверяется нормальность распределения каждой группы. Если обе группы распределены нормально, для сравнения используется Ttest, в противном случае используется критерий Манна-Уитни.\n",
    "    2) Если группы рачпределены нормально, проверяется также гипотеза о гомогенности дисперсий двух групп.\n",
    "    '''\n",
    "    # Средние и медианы\n",
    "    print(f'1 группа - среднее: {np.mean(groupA)}, медиана: {np.median(groupA)}')\n",
    "    print(f'2 группа - среднее: {np.mean(groupB)}, медиана: {np.median(groupB)}')\n",
    "    print('\\n', '#########################################', '\\n')\n",
    "    \n",
    "    # Проверка данных на нормальность распределения\n",
    "    ntA = shapiro(groupA)[1] < 0.05\n",
    "    ntB = shapiro(groupB)[1] < 0.05\n",
    "    # H0: Нормальное распределение\n",
    "    # H1: Не нормальное распределение\n",
    "    \n",
    "    if (ntA == False) & (ntB == False): # H0: Нормальное распределение\n",
    "        # Параметрический тест\n",
    "        # Проверка гомогенности дисперсий\n",
    "        print('Обе группы имеют нормальное распределение, используется T-test', '\\n')\n",
    "        print(f'p-value критерия Шапиро-Уилка для первой группы: {shapiro(groupA)[1]}')\n",
    "        print(f'p-value критерия Шапиро-Уилка для второй группы: {shapiro(groupB)[1]}')\n",
    "        print('\\n', '#########################################', '\\n')\n",
    "        leveneTest = stats.levene(groupA, groupB)[1] < 0.05\n",
    "        # H0: Дисперсии гомогенны\n",
    "        # H1: Дисперсии гетерогенны\n",
    "        \n",
    "        if leveneTest == False:\n",
    "            # Дисперсии гомогенны, equal_var=True\n",
    "            print('Дисперсии групп гомогенны', '\\n')\n",
    "            print(f'p-value теста Левена: {stats.levene(groupA, groupB)[1]}')\n",
    "            print('\\n', '#########################################', '\\n')\n",
    "            test_result = stats.ttest_ind(groupA, groupB, equal_var=True)[1]\n",
    "            # H0: Средние равны\n",
    "            # H1: Средние не равны\n",
    "        else:\n",
    "            # Дисперсии гетерогенны, equal_var=False\n",
    "            print('Дисперсии групп гетерогенны', '\\n')\n",
    "            print(f'p-value теста Левена: {stats.levene(groupA, groupB)[1]}')\n",
    "            print('\\n', '#########################################', '\\n')\n",
    "            test_result = stats.ttest_ind(groupA, groupB, equal_var=False)[1]\n",
    "            # H0: Средние равны\n",
    "            # H1: Средние не равны\n",
    "    else: # H1: Не нормальное распределение\n",
    "        # Непараметрический тест\n",
    "        print('Какая-то из групп (либо обе группы) имеет не нормальное распределение, используется критерий Манна-Уитни', '\\n')\n",
    "        print(f'p-value критерия Шапиро-Уилка для первой группы: {shapiro(groupA)[1]}')\n",
    "        print(f'p-value критерия Шапиро-Уилка для второй группы: {shapiro(groupB)[1]}')\n",
    "        print('\\n', '#########################################', '\\n')\n",
    "        test_result = stats.mannwhitneyu(groupA, groupB)[1]\n",
    "        # H0: Средние равны\n",
    "        # H1: Средние не равны\n",
    "    \n",
    "    # Print Hypothesis\n",
    "    print('Тестируемые гипотезы', '\\n')\n",
    "    print('H0: Группы принадлежат одному распределению, различия НЕ ЗНАЧИМЫ')\n",
    "    print('H1: Группы принадлежат разным распределениям, различия ЗНАЧИМЫ')\n",
    "    print('\\n', '#########################################', '\\n')\n",
    "\n",
    "    if test_result < 0.05:\n",
    "        print('Есть основания отклонить нулевую гипотезу, различия между группами значимы')\n",
    "        print(f'Полученное значение p-value: {test_result}')\n",
    "    else:\n",
    "        print('Нет оснований отклонить нулевую гипотезу, различия между группами не значимы')\n",
    "        print(f'Полученное значение p-value: {test_result}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 группа - среднее: 99.15110320284697, медиана: 100.43\n",
      "2 группа - среднее: 77.66434710743802, медиана: 79.51\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Какая-то из групп (либо обе группы) имеет не нормальное распределение, используется критерий Манна-Уитни \n",
      "\n",
      "p-value критерия Шапиро-Уилка для первой группы: 3.822173844020971e-38\n",
      "p-value критерия Шапиро-Уилка для второй группы: 5.834929673159698e-31\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Тестируемые гипотезы \n",
      "\n",
      "H0: Группы принадлежат одному распределению, различия НЕ ЗНАЧИМЫ\n",
      "H1: Группы принадлежат разным распределениям, различия ЗНАЧИМЫ\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Есть основания отклонить нулевую гипотезу, различия между группами значимы\n",
      "Полученное значение p-value: 2.8396651559288137e-177\n"
     ]
    }
   ],
   "source": [
    "# Сравнение оценок в 2019 и 2020\n",
    "compare_test(rate_2019['points'], rate_2020['points'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 группа - среднее: 77.66434710743802, медиана: 79.51\n",
      "2 группа - среднее: 80.38958199356914, медиана: 80.4\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Какая-то из групп (либо обе группы) имеет не нормальное распределение, используется критерий Манна-Уитни \n",
      "\n",
      "p-value критерия Шапиро-Уилка для первой группы: 5.834929673159698e-31\n",
      "p-value критерия Шапиро-Уилка для второй группы: 8.758115402030107e-43\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Тестируемые гипотезы \n",
      "\n",
      "H0: Группы принадлежат одному распределению, различия НЕ ЗНАЧИМЫ\n",
      "H1: Группы принадлежат разным распределениям, различия ЗНАЧИМЫ\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Есть основания отклонить нулевую гипотезу, различия между группами значимы\n",
      "Полученное значение p-value: 1.1307682922647809e-46\n"
     ]
    }
   ],
   "source": [
    "# Сравнение оценок в 2020 и 2021\n",
    "compare_test(rate_2020['points'], rate_2021['points'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 группа - среднее: 99.15110320284697, медиана: 100.43\n",
      "2 группа - среднее: 80.38958199356914, медиана: 80.4\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Какая-то из групп (либо обе группы) имеет не нормальное распределение, используется критерий Манна-Уитни \n",
      "\n",
      "p-value критерия Шапиро-Уилка для первой группы: 3.822173844020971e-38\n",
      "p-value критерия Шапиро-Уилка для второй группы: 8.758115402030107e-43\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Тестируемые гипотезы \n",
      "\n",
      "H0: Группы принадлежат одному распределению, различия НЕ ЗНАЧИМЫ\n",
      "H1: Группы принадлежат разным распределениям, различия ЗНАЧИМЫ\n",
      "\n",
      " ######################################### \n",
      "\n",
      "Есть основания отклонить нулевую гипотезу, различия между группами значимы\n",
      "Полученное значение p-value: 5.662891214952993e-170\n"
     ]
    }
   ],
   "source": [
    "# Сравнение оценок в 2019 и 2021\n",
    "compare_test(rate_2019['points'], rate_2021['points'])"
   ]
  }
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