Descriptive stat Fediushkin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d3e72e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy import stats as st\n",
    "\n",
    "from numpy import mean\n",
    "from numpy import var\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "008f6bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#all_rating_processed = pd.read_csv(r'C:/Users/Armit/uni_pass/Scripts/Project/all_rating_processed.csv', sep = '\\t', encoding='utf-8')\n",
    "# we sinchronise dictionaries so new processed file must be used\n",
    "all_rating_processed = pd.read_csv(r'C:/Users/Armit/uni_pass/Scripts/Project/all_rating_processed_adj_dict.csv', sep = '\\t', encoding='utf-8')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "caf2f8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "914172"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_rating_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "65914ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_rating_processed['Country'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6ee3874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Undefined', 'UnitedStates', 'France', 'Poland', 'Belgium',\n",
       "       'UnitedKingdom', 'Sweden', 'Finland', 'Austria', 'Norway',\n",
       "       'Germany', 'Netherlands', 'Canada', 'Switzerland', 'Italy',\n",
       "       'Russia', 'Spain', 'Portugal', 'Lithuania', 'Denmark', 'England',\n",
       "       'Scotland', 'Ireland', 'Hungary', 'Greece', 'Wales', 'Bulgaria',\n",
       "       'Croatia', 'Belarus', 'Malta', 'Serbia', 'Slovakia', 'Ukraine',\n",
       "       'Romania', 'Estonia', 'Luxembourg', 'CzechRepublic', 'Iceland',\n",
       "       'Turkey', 'Slovenia', 'Latvia', 'SanMarino', 'Georgia', 'Andorra',\n",
       "       'Moldova', 'IsleofMan', 'Cyprus', 'Macedonia',\n",
       "       'BosniaandHerzegovina', 'Other-EasternEurope', 'VaticanCity',\n",
       "       'FaroeIslands', 'Monaco', 'Greenland', 'Montenegro', 'Albania'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rating_processed['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35dc31f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Undefined', 'NorthAmerica', 'Europe'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rating_processed['Continent'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3e5727",
   "metadata": {},
   "source": [
    "As an input we have data about 37 counties + Undefined, all of which are mapped into Continents (NorthAmerica, Undefined and Europe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6cc94bc",
   "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>Unnamed: 0</th>\n",
       "      <th>Game_name</th>\n",
       "      <th>Gamer</th>\n",
       "      <th>Date</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Location</th>\n",
       "      <th>file</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Gloomhaven</td>\n",
       "      <td>guile678</td>\n",
       "      <td>Mar2020</td>\n",
       "      <td>8.70000</td>\n",
       "      <td>Undefined</td>\n",
       "      <td>0_174430_20_pages.csv</td>\n",
       "      <td>Undefined</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Gloomhaven</td>\n",
       "      <td>purplephoenixgames</td>\n",
       "      <td>Mar2020</td>\n",
       "      <td>-0.00001</td>\n",
       "      <td>Milan,Illinois·UnitedStates</td>\n",
       "      <td>0_174430_20_pages.csv</td>\n",
       "      <td>UnitedStates</td>\n",
       "      <td>NorthAmerica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Gloomhaven</td>\n",
       "      <td>oneover</td>\n",
       "      <td>Mar2020</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>Undefined</td>\n",
       "      <td>0_174430_20_pages.csv</td>\n",
       "      <td>Undefined</td>\n",
       "      <td>Undefined</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Game_name               Gamer     Date   Rating  \\\n",
       "0           0  Gloomhaven            guile678  Mar2020  8.70000   \n",
       "1           1  Gloomhaven  purplephoenixgames  Mar2020 -0.00001   \n",
       "2           2  Gloomhaven             oneover  Mar2020  8.00000   \n",
       "\n",
       "                      Location                   file       Country  \\\n",
       "0                    Undefined  0_174430_20_pages.csv     Undefined   \n",
       "1  Milan,Illinois·UnitedStates  0_174430_20_pages.csv  UnitedStates   \n",
       "2                    Undefined  0_174430_20_pages.csv     Undefined   \n",
       "\n",
       "      Continent  \n",
       "0     Undefined  \n",
       "1  NorthAmerica  \n",
       "2     Undefined  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rating_processed.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d94cfb44",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_rating_processed.drop(['Unnamed: 0','Location','file'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359cb4c9",
   "metadata": {},
   "source": [
    "## Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402274f1",
   "metadata": {},
   "source": [
    "### Histograms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a09e22bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>914172.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.971864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.325511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rating\n",
       "count  914172.000000\n",
       "mean        6.971864\n",
       "std         2.325511\n",
       "min        -0.000010\n",
       "25%         6.500000\n",
       "50%         7.500000\n",
       "75%         8.000000\n",
       "max        10.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rating_processed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c41e4303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_rating_processed['Rating'].hist(bins=15, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2153d058",
   "metadata": {},
   "source": [
    "We have a spike around 0 presumably caused by not given rating, which we marked as -0.00001.\n",
    "Let's filter it out "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af15e694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_rating_processed[all_rating_processed['Rating'] >= 0]['Rating'].hist(bins=15, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b3df3c",
   "metadata": {},
   "source": [
    "Assumption was correct. Finally for 3 Continents we see that data is left-skewed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3af5c699",
   "metadata": {},
   "outputs": [],
   "source": [
    "NA_data = all_rating_processed[all_rating_processed['Continent'] =='NorthAmerica']\n",
    "Undef_data = all_rating_processed[all_rating_processed['Continent'] =='Undefined']\n",
    "EU_data = all_rating_processed[all_rating_processed['Continent'] =='Europe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a2618b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.hist(NA_data['Rating'], bins=20, label='North America', alpha=0.4)\n",
    "plt.hist(Undef_data['Rating'], bins=20,  label='Undefined', alpha=0.5)\n",
    "plt.hist(EU_data['Rating'], bins=20,  label='Europe', alpha=0.5)\n",
    "plt.title(\"Rating distribution of continents\")\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend(prop ={'size': 15}) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb27adb",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "be2a0560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 6), sharex=True, sharey=True)\n",
    "\n",
    "sns.boxplot(x='Continent', y= 'Rating', \n",
    "            data=all_rating_processed, \n",
    "            showfliers=False, \n",
    "            showmeans=True, \n",
    "            meanprops={\"marker\":\"s\",\"markerfacecolor\":\"white\", \"markeredgecolor\":\"blue\"},\n",
    "            ax=ax[0])\n",
    "ax[0].set_title('Boxplot distribution with undefined rating')\n",
    "\n",
    "sns.boxplot(x='Continent', y= 'Rating', \n",
    "            data=all_rating_processed[all_rating_processed['Rating'] >= 0], \n",
    "            showfliers=False, \n",
    "            showmeans=True, \n",
    "            meanprops={\"marker\":\"s\",\"markerfacecolor\":\"white\", \"markeredgecolor\":\"blue\"},\n",
    "            ax=ax[1])\n",
    "ax[1].set_title('Boxplot distribution without undefined rating')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea16b81d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = 'Continent', y = 'Rating', \n",
    "            data = all_rating_processed[(all_rating_processed['Rating'] >= 0) & \n",
    "                                      (all_rating_processed['Continent'].isin(['NorthAmerica','Europe']))], \n",
    "            showfliers=False, \n",
    "            showmeans=True, \n",
    "            meanprops={\"marker\":\"s\",\"markerfacecolor\":\"white\", \"markeredgecolor\":\"blue\"})\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ea0e622",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e227909a",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_ratinng = all_rating_processed[(all_rating_processed['Rating'] >= 0) & \n",
    "                          (all_rating_processed['Continent'].isin(['NorthAmerica','Europe']))].groupby(['Continent']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1cf8d8d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_ratinng.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4cc16f89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   NaN\n",
       "1   NaN\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_ratinng[avg_ratinng['Continent'] == 'Europe']['Rating'] - avg_ratinng[avg_ratinng['Continent'] == 'NorthAmerica']['Rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f03a76d1",
   "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>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorthAmerica</th>\n",
       "      <td>-0.03026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Rating\n",
       "Continent            \n",
       "Europe            NaN\n",
       "NorthAmerica -0.03026"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_ratinng.set_index('Continent').diff() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d86be59d",
   "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>Continent</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Europe</td>\n",
       "      <td>7.478705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NorthAmerica</td>\n",
       "      <td>7.448446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Continent    Rating\n",
       "0        Europe  7.478705\n",
       "1  NorthAmerica  7.448446"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_ratinng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4da0c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03029999999999955"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7.4787 - 7.4484"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac65a04",
   "metadata": {},
   "source": [
    "## Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2f3d12",
   "metadata": {},
   "source": [
    "Formulate H0 (important: we formulate H0 in a way that there is no differece, that is why it is zero hypothesis).\n",
    "\n",
    "**H₀** for our case:\n",
    "\n",
    "**Average defined gamers' rating between Europe and North Americe has no difference.** \n",
    "\n",
    "The **alternative hypothesis H₁** will be:\n",
    "\n",
    "**Average defined gamers' rating between Europe and North Americe has difference.**\n",
    "\n",
    "A significance level of α(alpha) = 0.05, in other words we state that 100% of confidence in hypothesis test is difficult to reach, so that with declared significance level (5%) we state that risk of false positive difference discovcery is 5%.\n",
    "\n",
    "As our data is skewed, we cannot perform Student's test (ttest) , we should use non-parametric test (a.k.a distribution free methods) for two (unpaired) samples MannWhitney test (equivalent to Wilcoxon test).\n",
    "P.S. Unpaired means that both samples consist of distinct test subjects and we assume that gamers do not use two different locations (countries/continets) to provide feedback. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "321924e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "NA_data_wt_undef = NA_data[NA_data['Rating'] >=0]\n",
    "EU_data_wt_undef = EU_data[EU_data['Rating'] >=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e2f24a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NA_data_wt_undef.hist()\n",
    "#plt.title(\"Rating distribution NorthAmerica\")\n",
    "plt.title(\"\")\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Frequency')\n",
    "#plt.legend(prop ={'size': 15}) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "74cfa0dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "EU_data_wt_undef.hist()\n",
    "#plt.title(\"Rating distribution Europe\")\n",
    "plt.title(\"\")\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1f635d6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 5.648866776980333e-11\n",
      "Reject H₀\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "\n",
    "results = st.mannwhitneyu(\n",
    "    NA_data_wt_undef['Rating'], \n",
    "    EU_data_wt_undef['Rating'])\n",
    "\n",
    "print('p-value:', results.pvalue)\n",
    "\n",
    "if (results.pvalue < alpha):\n",
    "    print(\"Reject H₀\")\n",
    "else:\n",
    "    print(\"Accept H₀\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c8b67df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stat_calc(d1, d2):\n",
    "    #mean_diff = d1.mean() - d2.mean()\n",
    "    #results = st.mannwhitneyu(d1,d2)\n",
    "    \n",
    "    # calculate the size of samples\n",
    "    n1, n2 = len(d1), len(d2)\n",
    "    # calculate the variance of the samples\n",
    "    s1, s2 = var(d1, ddof=1), var(d2, ddof=1)\n",
    "    # calculate the pooled standard deviation\n",
    "    s = sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))\n",
    "    # calculate the means of the samples\n",
    "    u1, u2 = mean(d1), mean(d2)\n",
    "    effect_size = (u1 - u2) / s\n",
    "    # calculate the effect size\n",
    "    return effect_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92c8f326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.02181012986785638"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_calc(NA_data_wt_undef['Rating'],EU_data_wt_undef['Rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c4046e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}