2. Spanish Translation AB Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b984d4e5",
   "metadata": {},
   "source": [
    "# Goal of Analysis: confirm the old version with only one translation performs better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e8be798",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "069e8bd4",
   "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>user_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>765821</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>343561</td>\n",
       "      <td>F</td>\n",
       "      <td>27</td>\n",
       "      <td>Nicaragua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118744</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>Colombia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>987753</td>\n",
       "      <td>F</td>\n",
       "      <td>27</td>\n",
       "      <td>Venezuela</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>554597</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452862</th>\n",
       "      <td>756215</td>\n",
       "      <td>F</td>\n",
       "      <td>27</td>\n",
       "      <td>Venezuela</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452863</th>\n",
       "      <td>36888</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452864</th>\n",
       "      <td>800559</td>\n",
       "      <td>M</td>\n",
       "      <td>28</td>\n",
       "      <td>Bolivia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452865</th>\n",
       "      <td>176584</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452866</th>\n",
       "      <td>314649</td>\n",
       "      <td>M</td>\n",
       "      <td>24</td>\n",
       "      <td>Mexico</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>452867 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id sex  age    country\n",
       "0        765821   M   20     Mexico\n",
       "1        343561   F   27  Nicaragua\n",
       "2        118744   M   23   Colombia\n",
       "3        987753   F   27  Venezuela\n",
       "4        554597   F   20      Spain\n",
       "...         ...  ..  ...        ...\n",
       "452862   756215   F   27  Venezuela\n",
       "452863    36888   M   18  Argentina\n",
       "452864   800559   M   28    Bolivia\n",
       "452865   176584   M   19      Chile\n",
       "452866   314649   M   24     Mexico\n",
       "\n",
       "[452867 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_df = pd.read_csv('user_table.csv')\n",
    "user_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fa51d1a",
   "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>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>source</th>\n",
       "      <th>device</th>\n",
       "      <th>browser_language</th>\n",
       "      <th>ads_channel</th>\n",
       "      <th>browser</th>\n",
       "      <th>conversion</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>315281</td>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>497851</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>Google</td>\n",
       "      <td>IE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>848402</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>290051</td>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Other</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>Android_App</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>548435</td>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>Google</td>\n",
       "      <td>FireFox</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453316</th>\n",
       "      <td>425010</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FireFox</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453317</th>\n",
       "      <td>826793</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>ES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Android_App</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453318</th>\n",
       "      <td>514870</td>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>ES</td>\n",
       "      <td>Bing</td>\n",
       "      <td>Android_App</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453319</th>\n",
       "      <td>785224</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>ES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Android_App</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453320</th>\n",
       "      <td>241662</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>453321 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id        date  source  device browser_language ads_channel  \\\n",
       "0        315281  2015-12-03  Direct     Web               ES         NaN   \n",
       "1        497851  2015-12-04     Ads     Web               ES      Google   \n",
       "2        848402  2015-12-04     Ads     Web               ES    Facebook   \n",
       "3        290051  2015-12-03     Ads  Mobile            Other    Facebook   \n",
       "4        548435  2015-11-30     Ads     Web               ES      Google   \n",
       "...         ...         ...     ...     ...              ...         ...   \n",
       "453316   425010  2015-12-04     SEO     Web               ES         NaN   \n",
       "453317   826793  2015-12-01     SEO  Mobile               ES         NaN   \n",
       "453318   514870  2015-12-02     Ads  Mobile               ES        Bing   \n",
       "453319   785224  2015-12-04     SEO  Mobile               ES         NaN   \n",
       "453320   241662  2015-12-04     Ads     Web               ES    Facebook   \n",
       "\n",
       "            browser  conversion  test  \n",
       "0                IE           1     0  \n",
       "1                IE           0     1  \n",
       "2            Chrome           0     0  \n",
       "3       Android_App           0     1  \n",
       "4           FireFox           0     1  \n",
       "...             ...         ...   ...  \n",
       "453316      FireFox           0     0  \n",
       "453317  Android_App           0     1  \n",
       "453318  Android_App           0     0  \n",
       "453319  Android_App           0     1  \n",
       "453320       Chrome           0     1  \n",
       "\n",
       "[453321 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('test_table.csv')\n",
    "test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7963f9",
   "metadata": {},
   "source": [
    "#### I will first merge these two tables, but they have different sizes, which means some of the rows will have null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b4fa5a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged = pd.merge(user_df, test_df, how = 'right', on='user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "053411cd",
   "metadata": {
    "scrolled": true
   },
   "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>user_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>date</th>\n",
       "      <th>source</th>\n",
       "      <th>device</th>\n",
       "      <th>browser_language</th>\n",
       "      <th>ads_channel</th>\n",
       "      <th>browser</th>\n",
       "      <th>conversion</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>315281</td>\n",
       "      <td>M</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>497851</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>Google</td>\n",
       "      <td>IE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>848402</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>290051</td>\n",
       "      <td>F</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Other</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>Android_App</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>548435</td>\n",
       "      <td>M</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>Google</td>\n",
       "      <td>FireFox</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453316</th>\n",
       "      <td>425010</td>\n",
       "      <td>M</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FireFox</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453317</th>\n",
       "      <td>826793</td>\n",
       "      <td>F</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>ES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Android_App</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453318</th>\n",
       "      <td>514870</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>ES</td>\n",
       "      <td>Bing</td>\n",
       "      <td>Android_App</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453319</th>\n",
       "      <td>785224</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>ES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Android_App</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453320</th>\n",
       "      <td>241662</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Web</td>\n",
       "      <td>ES</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>453321 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id sex   age   country        date  source  device  \\\n",
       "0        315281   M  32.0     Spain  2015-12-03  Direct     Web   \n",
       "1        497851   M  21.0    Mexico  2015-12-04     Ads     Web   \n",
       "2        848402   M  34.0     Spain  2015-12-04     Ads     Web   \n",
       "3        290051   F  22.0    Mexico  2015-12-03     Ads  Mobile   \n",
       "4        548435   M  19.0    Mexico  2015-11-30     Ads     Web   \n",
       "...         ...  ..   ...       ...         ...     ...     ...   \n",
       "453316   425010   M  50.0    Mexico  2015-12-04     SEO     Web   \n",
       "453317   826793   F  20.0  Colombia  2015-12-01     SEO  Mobile   \n",
       "453318   514870   M  25.0     Spain  2015-12-02     Ads  Mobile   \n",
       "453319   785224   M  21.0  Colombia  2015-12-04     SEO  Mobile   \n",
       "453320   241662   M  27.0    Mexico  2015-12-04     Ads     Web   \n",
       "\n",
       "       browser_language ads_channel      browser  conversion  test  \n",
       "0                    ES         NaN           IE           1     0  \n",
       "1                    ES      Google           IE           0     1  \n",
       "2                    ES    Facebook       Chrome           0     0  \n",
       "3                 Other    Facebook  Android_App           0     1  \n",
       "4                    ES      Google      FireFox           0     1  \n",
       "...                 ...         ...          ...         ...   ...  \n",
       "453316               ES         NaN      FireFox           0     0  \n",
       "453317               ES         NaN  Android_App           0     1  \n",
       "453318               ES        Bing  Android_App           0     0  \n",
       "453319               ES         NaN  Android_App           0     1  \n",
       "453320               ES    Facebook       Chrome           0     1  \n",
       "\n",
       "[453321 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c18fb16",
   "metadata": {},
   "source": [
    "### EDA to explore my dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c717316d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable     Count of unique value    content\n",
      "user_id              453321\t [315281 497851 848402 290051 548435 540675 863394 527287 261625  10427]\n",
      "sex                     3\t ['M' 'F' nan]\n",
      "age                    53\t [32. 21. 34. 22. 19. 35. 31. 33. 37. 29.]\n",
      "country                18\t ['Spain' 'Mexico' 'Venezuela' 'Bolivia' 'Colombia' 'Uruguay' 'El Salvador'\n",
      " 'Nicaragua' 'Peru' 'Costa Rica']\n",
      "date                    5\t ['2015-12-03' '2015-12-04' '2015-11-30' '2015-12-01' '2015-12-02']\n",
      "source                  3\t ['Direct' 'Ads' 'SEO']\n",
      "device                  2\t ['Web' 'Mobile']\n",
      "browser_language        3\t ['ES' 'Other' 'EN']\n",
      "ads_channel             6\t [nan 'Google' 'Facebook' 'Bing' 'Yahoo' 'Other']\n",
      "browser                 7\t ['IE' 'Chrome' 'Android_App' 'FireFox' 'Iphone_App' 'Safari' 'Opera']\n",
      "conversion              2\t [1 0]\n",
      "test                    2\t [0 1]\n"
     ]
    }
   ],
   "source": [
    "print('Variable', '   ', 'Count of unique value','  ', 'content')\n",
    "for column in df_merged.columns:\n",
    "    uniques = df_merged[column].unique()\n",
    "    print('{0:20s} {1:4d}\\t'.format(column, len(uniques)), uniques[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c33e4f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='country'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J9DsDdxe2V+V9g54zJknzJQ1LGl6zZs0gbzUzszGUCfTqsS8qnDOmiFgYEbMjYvbQ0NAgbzUzszGUCfSrgF0L27sA91Q4x8zMJkCZQL8UmClpN0lbAYcCi7rOWQQcnkff7A+sjYjVDdfVzMwq6BvoI2IdcAywBLgV+HZErJB0lKSj8mmLgZXACPBV4B8775d0FnAN8BxJqyQd2fA1mJnZGEolNYuIxaRgXty3oPB1AEeP8t55dSpoZmb1eGasmVnLOdCbmbWcA72ZWcs50JuZtZwDvZlZyznQm5m1nAO9mVnLOdCbmbWcA72ZWcs50JuZtZwDvZlZyznQm5m1nAO9mVnLOdCbmbWcA72ZWcs50JuZtZwDvZlZy5UK9JIOlHS7pBFJx/U4Lkkn5OPLJe1d9r1mZja++gZ6SdOBE4E5wCxgnqRZXafNAWbm13zgpAHea2Zm46jMHf2+wEhErIyIh4Czgbld58wFTo/kWmB7STuVfK+ZmY0jpXW9xzhBegNwYET8Q95+G7BfRBxTOOdC4LMR8eO8fQnwP4AZ/d5bKGM+6WkA4DnA7X3qviPw234XOIBHU3mTuW5NlzeZ69Z0eZO5bk2XN5nrNlHlPT0ihnod2KLEN1CPfd2fDqOdU+a9aWfEQmBhifqkbygNR8Tssue7vPEpa7KXN5nr1nR5k7luTZc3mes2GcsrE+hXAbsWtncB7il5zlYl3mtmZuOoTBv9UmCmpN0kbQUcCizqOmcRcHgefbM/sDYiVpd8r5mZjaO+d/QRsU7SMcASYDpwSkSskHRUPr4AWAwcBIwADwBHjPXehupeupnH5Y1rWZO9vMlct6bLm8x1a7q8yVy3SVde385YMzOb2jwz1sys5RzozcxazoHezKzlHOgfBSQNSfqCpMWSLu28apQ3XdI3GqrbNEk/aaIsm3wkPX+i67C5TOZrLTOOftKR9CRg6852RPyiYjlbA0cCf9lV3jsrlCXgrcAzIuJ4SU8DnhIR11es2xBpdvGsrrq9okJxZwLfAg4GjgLeDqypUq9ch4fzh8dWObVFZRHxiKSbJT2t6s9xNA3+nrwYWBYR90s6DNgb+GJE3FWjbk8G9smb10fEb2qU9URSnqnitV5ZtbyGLchDq78OfDMi/lC1IEmH99ofEadXLbNhjV0rgKSD2TQ2HV+lrCl1Ry/pEEk/A+4ArgDuBL5Xo8gzgKcAf5vL2wW4t2JZXwFeCMzL2/eSErpVdSZwK7Ab8GnStS6tWNYOEXEy8OeIuCJ/kO1fo27k+lwl6ROS/qnzqljWTsAKSZdIWtR5Va3YOPyenAQ8IGkP4MPAXUDl4CLpTcD1wBuBNwHX5VQjVcr6B+BK0hDmT+d/P1WjbvtLWirpPkkPSXpY0h+rlhcRf026AdoVGJb0TUmvrljcPoXXS0jXeUjVukl6vaSfSVor6Y+S7p0s1yppAfBm4D2kDANvBJ5etW5ExJR5ATcDOwA35e0DgIU1yuuUszz/uyVwacWybiyW2alvjbrdUKxb/vqKimVdm/9dQrqr3wv4ec2fxSd7vSqW9bJer0n0e9L52f4zcGRxX436PamwPVT1dwW4hXTHtyxvPxf4Vo26DQPPAm4izX05AviXOr8rudzpwN8DvyTdwNwGvL5mmU8AFtV4/wjwvLrXNh7XWohJnX+3BS6uWqep1nTz54j4XW7XnRYRl0n6XJ3y8r9/yO1rvyIlYqtUVk7LHLC+6eWRBuq2Oj/C3UN64qjif0l6AvAB4EvA44H316gbEfFpAEnbpc24r0ZZVzTZlEHzvyf3SvoIcBjw0vxz3rJGedO6ru93VH+6fjAiHpSEpMdExG2SnlOjbkTEiKTpEfEwcKqkq6uWJWl30ofFwcAPgNdGxI2SngpcA3ynRlUfIDVZVfXriLi1xvs30vC1/nf+94H8/t+Rnu4rmWqB/g+StiU9qp4p6TfAuhrlLcztm58gpWbYlnTXVsUJwPnAkyT9C/AG4OM16tZYcI6IC/OXa0l3t7XlD8YzgL/I278FDo8KM59zU8bngctJj6lfkvShiDi3YvWa/j15M/AW0t38r3L/y+drlPd9SUuAswrlV21aWiVpe+AC4AeSfk+9fFIP5HbmZZL+DVgNPK5GeV8Gvgp8NCI6wYuIuEfSQH8fkr7LhqSI00j9V9+uUbdhSd8i/d/9qVC3qh8+jV0rcGH+uX4euJF03V+rWK+pNTNW0uOAB0nB4K2kR7czI+J3E1qxTNJzgVeS6ndJk3cLdUg6DTg2cudQ/nD796jQ6Vwo82rgYxFxWd5+OfCvEfGiCmXdDLy6c5ebn4Z+GBF7VKzbpP49gdQ+DPw1qY5XRsT5DZT5MtK1fj8qdpJLejrwG9ITy/tzeV+JiJG69asrX1/HOuCuiFhVo7xTe+yOOn8X40HSY4CtI2Jt5TKmUqBviqTDIuIbo3UeRsR/DFDWX4x1PCL+a8C6fTgi/k3Sl+iR0jki3jtIebnMmyJir377Bizz5u5A3GtfybJuiYi/KmxPI7VZ/9UYb9tslBL1fQl4Hikj63Tgvoh4QsXydgNWR8SDefuxwJMj4s4KZT2t1/5oeARTVZJmAp9h09FjzxiwnOnAkoh4VbM1bE4T15pvAEZV9WljSjXd5P+EzwFPIt0JifQJ/PgBi+o8im7XQLVuYNPc+53tAAb6hSZ13kDqFGvKNElPjIjfw/oPp7o/+5WSPkFqvoHUfn1HxbJ6NWUsHrQQSffSe72Dqr8nHV8mZV49B5gNHE69tuFzgOKTz8N53z69Tx/TRWz4fdua1I57O2lY3sAkvQb4n6QRHltQ///uVFJH/f8mNRseQe91KsYUaUjvA5KeUOfOFsbnZipr4lpfO8axoGKfxpS6o5c0QurgaKRJRNJQRFQeT745SHo86Q+t6rDPzvjjjwCdNu83kkZSnDH6u/qW+UTScL71zQ/ApzofJhXK+3vgxZ2ymmjKaIryog+SlkfE7nnf1VWaqfJ7l0XEnl37Kj0N9Sh7b+DdEfHuiu8fAV4P3BINBAdJN0TEC4pPbZJ+FBEvqVDWt0nDgn8A3N/ZP2hglvTaiPiupLf3Oh4Rpw1at1xuY9fatCl1R0/DveTA1ZLuIE0m+k6VICXpuXmkw969jkfEjVUqJmk26Q5hu7SpPwDvjIgbBi0rIk6XNAy8ghRIXx8RP61Sr0KZvweq3vn0Ku884LymyoPmJkzRfAflGkmHRMSiXM+5NLTsXB7lUeXJoONu4CdNBPnswdwU9zOllOW/JD2RV3FRftUSEd/N/1YK6GNo7FrzKLR/BZ4aEXMkzQJeGGk+zODlTbE7+i+SJjhdQDO95Ejal/RY/jrgp8DZEVF6er+khRExX9JlPQ5HVJvJiqTlwNER8aO8/dekTrHdByjj8RHxx9H6EQbtP8hlFkc+9Cqz9ASWMZpaOmVVai6QdAjw78BTSR2LTwdujYiqzRlPB35Nap+v3UEp6ZmkCXFPJX3w3k0asTRweV39TNNIs3Z3iIi/rVi3fUhNN1ew8d9Y6X6rHuXdCmyfy30C8G8RcW3F8h4LPC0i+q0pXaasJmefN3qtkr5HutH7WETsIWkL0ryQSv1WUy3Qj1svuaQdgf8A3hoR0+uW10B9roqIF/fb16eMCyPiNfmppfiD7rS7Dtp/UBz58HrSh27nQ3EecGdEfLRCmceT5jCcwYaRMttFxL8NWlYu72bS08sPI2IvSQcA8yJifp+39iprOnBaRBxWpS59yt6W9DdYp1nuk4XNdaRZwOd1OnorlHcxcB9pItb6eSCR501MJEmvBb4AbBURu0naEzh+kJuLrvIuJj3Nf5BCapCI+B8NVbkySUsjYp/ioIleTX6ly5tKgb5puf3770h39M8kjYP/dpXmkVzei0gTrtY3icWAeTgKTUBvA7YhdVAGqYPy9xHxsSp1a5qkKyPipf32lSzruojYr9++AcrrtKnfDOwVKZ/O9RGxb8XylpD6hmrl9WlytNd4UfOLWl9G7w7Pge+aJd1A+gC/vBD8NhqxNWh5uU292PdyRUS8rN97RymvyWu9nDS79gcRsXce+fW5qnWbEm3049hLfjOpGej4iLimRhWRdAbpw2IZaRQFpLoOmhPl37u2i3dsjXwqK82c/GBEvKtGMUOSnhERK3OZu5Gm8lfxsKS3AmeTrnEeG/4Pq2h6wtSdpLw+i9i4E3DQwNzkaC8A1Dsn0FrSqK3/W+HO/oeS/iYiLq5fOyDdLXdsTQpeVX8W6yJirbTRQJY6fxNNzj6HZq/1n0iTOJ8p6SrS31alfEgwRe7ox7GXXE11Okm6FZjVYCdWbUpTsr9Aagu+gDQW/CvAfqQJU/+7RtkHktaxXJl3zSCN9lhSoawZwBdJo24CuAp4X1QYV57La3TCVFfzyHpNNmeoYibQ3G81xMZDU38FPBZ4fES8bcDy7iV9IP2JFAjrDq/s9T0q3TVLOhm4BDiOFETfC2wZEUdVrMdrgB+RkpB1Zp9/utNJ3oSaTwhbAM8h/Qxuj4g/93nL6GVNorjUl6Q3RsQ5/faVKOf/RMT7RutYrNLmJ+kc4L0RsXrQ945S3vak8doz2LgpqPTTi6TrSJkXrwEOJGVe/CbwiaptuF3lP4aURAvgtoj401jnb265aa74fzdw5/N4yI/l7+h8kOVOvK9FtclmozahSVpRtQO6KV0DAaYBLwBOiIiB8/FI2gb4GPA3edcS4H818bvchIavtdGUzFOi6abgI6SJJf329dMZP/6FuhUqfFhsB/xU0vVsPFqhahrVxcC1dHWKDegxEfH1/PXtkj4IHBcpWVUTZpLuOLYG9pBU6RdRDa4LkMt7N3A8KTHUI1B58lqnvMbaXrPPkCaJnQDsDMwhTa6pYkiFXP5KM2V3zMeqpkFoMr99cULhOtKkuiMHrM/WpM7SZ5H+Hl4YEXWa4jrlPoP0JPlC0u/JNcD7O82RFdS+1oLiENmtSalVbqRieuwpEeglzQEOAnbOfxwdj6dCG1inszUirmigeouAJ5MeAYteRhpHW9XWEVE1v/v6MiTtBetn590H7K7cyBkVx/jD+uaMl5OGpi0mBasfU+0X8QxSKte/JQXot7JhhnAVHwT+MiIaGZtOs22vRMQSSUeRJv78ltRh/KuKxX0A+LGkn5N+zrsB/5ibrwZu0lTKb38sqa16GWmC0jWkTtCBRUTljIsFp5GakX5E+j17HvC+Bsr9JmnNiL/L24eSmsAqDQJo6Fo7Zb2nuK2U4LD6BMep0HSjtODDnqQgUMwueS9wWQw40UnSLYw9VX6QseoXkrLVLe/aP5uUn32sKc1jlft+UmC+kI2fEEo3P6j32P5CUZXvSDv/h3uQxvbuoTTB42tVrrczhKwz+kHSlqS8JlXHM3+fNCnsgSrvL/k96rS9foK04Mh8YHfS2PwPRESlyUCFJjSRmtAqN2Xkn+s+pDUM9lRK1PfpiHhzxfJ65W5ZS5p5WyoVtTaeaboFKY11zwmKA9at12ivayOi0qI8TVzrGGVvScpN/7wq758Sd/QRcTNws6Rv1umQKHhNA2V0zOgO8gARMZw7Gat6iJSi9GNs+FAaqPkhIhpJSTyK/87DFtfltvDfULFphGbXBYDUnHd17qMofkhWGp01StvrU2rUb0dg30ipbK/JH0xfo/qsz2IT2u5Vm9CypvPbH0lqGuncdLyc1CT5bEnHR7k0HOv/5iNiXdeomzouk3QcG0Z7vRm4qPPzrtCn08S1Ahs1CcOGlMyDNlGvNyUCfcG+kj7FpgmXBgowUVjrU/UXvNh6jGOPHbCson8CntVg80PThnOH8VdJbZP3kZbHq6LXugCfqFG3/wtcSr3+jaIm216JiGMlPVbScyLi9vz7WHXJuSab0KD5/PaPkFZx+nWu75NJAwT2Iw1/LRP89tCGJf4EPDZv1x0R1HlK6Uyk63yCvJNqfTpNXGtHsf+wdkrmWktmbe4XqR13Dil/xA6dV43y3kRa//M00h/GHcAbBizjLOBdPfYfSb0l3RYB20z0/3nJus4Adq/x/ukN1+fqif4/6VO/15IyTN6Rt/ek4pJ4pA+zTlpnSP1F322oni8jrcm6VY0ybunaFimXDhSW3dzM///7AE8pbL89/72dAPzFZLhW0o3Orl375let25Roo+/o1aZWs7zaC17kT+3zSU0tnRm1s0l5Uf4uKnaySTqfNArlMhpofmiapJ4zYKPC6AxJvwC+T5qOfmnU/KVUWuHrLuC7VOzfyOWMS25wNTjDU3nGby7zAFK/1U+ixrBKpbQPT2bjoamVEsJJ+grwNDY0O/w9sAr4EHBhjG/z4mh1uhF4VUT8V/49Ppu0CPeepDvyqgu1N3atSpP8fkvKd3VZp95RsW9iqjXdXCbp86SczMU/4KqjR2qv3RnpMe1FSvlUnp93XxQRl1asU8cF+dWIhofMQfrl7dga2Jf0QVelA/U5pLvco4FTcvvk2RHx44p1e0v+9yOFfVUexTsdy08i5Y/v/EwPIC17WDWZXpMzPJtsQkPSe0izsX/NhmavIHUaV3E0KeB1UlCfTsrFE9DMspYVTC986L+ZtHD8ecB5kpbVKLfJa/0lMBc4R9K5EfF5GDi3/XpT7Y6+6QyRnyf9AhdnFS6PSZDUCNJsSeDZebPyzLjRhsxV/X8b5XvsSsrUN69mOU8kjW2eFMnlYP3IqndFngwnaSfgxIgY845/jPIameGZh8nuEhF35+0ZpNmwmwwOGKDMEWC/mETLLjZN0k+APSN17N5GahK5snMsIp4/dgnjrzASbWtSO/+2wF9FxHP7vLWnqivPT4iIOKDHq3KwiogPkTrudicNFVw4iYL8y4Gfkcb5fgX4z9GaS0o4ltQueVd+fNwLaHrBlVVseKIZmKSX5UffG0lPCG+qUdY2kj4uaWHenqk03b2qGbHxjOdfs+EDuIr3kJrl/kS6yfgjFcaF5zvFCwrbd9YJ8tndpCGBjZC0v6Slku6T9JCkhwsdqxPlLOAKSf+PNKmukwr8WdS4dkn3Svpjfj1Y81qHASLiwYg4gvQEuVXluk2xO/pGk/F3lb0j8Lu67cNNyW2ub4mcd1vSs4GzIuIFFcrqpDxdRrpb+5NqpDzNZRYTzE0jtW/eGRXS+SqlUV4GfJvUKXn/2O/oW963SM0Yh0fE85VymF9T9XolfZnU7NXJJHooMBJdk1omgqQTga9HxNKGyjuZ1JR2Ec3kox9m02UYnxUTnIVVKRvkTsDFnd+3/De2bY2m4O7v8TrSMNqBU3fn9zeWe3+qtdF/nZyMP2//J6kDb6BAn3/InwX+i7RAwBmksc3TJB0eEd9vqsI1bFn8AUfEf+ZJE1U0PWQONl7Tdh3pQ+iqimXtERFN3uU9MyLeLGkeQET8t2oMvo6IY3LHbGdJuIVRYalD9c40Wfw+VdJlHAC8W9JdpMyaA0/66/KL/NqKGneQRRExIml6pNQbp0q6uolya9Zpk8VAIuI/G/4eF+Rx+gNTIfc+sJtq5t6faoF+x4j4tqSPwPrJE1XytnwZ+Cgpq+GlwJyIuFZpFuBZpBEgE2043111xt6+lQ2jegYSEZ0p3p/K/RxPAL5Xs37nkibXPAxppIakbWKA2ajFp4JecbjGCKOH8t1Qp+xnUrg7rSKPsKm8kln2QlLTyFnAddToXCuY00AZ60XzC4w0vQzjpNU1Smsa6QmmagvBp0gDHC4HiIhlSqnAK5lqgf5+STuw4Q94f6q1qW0ROd+20oy1awEizQJsrLI1/X+kXvz3wvrFt79SpSBJZ0ROVxs5v49S/vyBUth2uQR4FWmUB6TJYReTRqeUNdz/lEo+Rfqw3lXSmaRREFWThnX+gD9HGn0jqk/UeQppYtQ80sigi0hPQiuq1i3y5D91rY9bldIQ4w+zaYK5qn1hbyMFvWNIqR52JXVAt1Ex/Udnta+5FctqNvd+TMCEhaov0nqYV5GC+1WkppuBJ+oAN/b6utd2G149rnE68NOaZS4rs28Cr3kH4GBSuosda5Y1Qhpf3WT9HgO8g9Qp/p4a5RxC6rS/nzTh7xFgRY3yLiZN9ruVNGHqFNLKRlXKmg58Y6J/FzbHK1/r+xss72TSzcByUv/Ql4AFVcubUnf0kVa4fxn1k/F3plQXp1OTt2vfFdWh0ROuARCDJVz7CKmJqvsaHyItGlLH/ZL2jtxxJekFpBEMA1PzizRfEhGvpJA7prCvil9HRJ1smsW6PYb0ATSPNKP4BOo1Cf1P0nDZjdbHrVHeDhFxsqRjIz39XSGpUpbXiHhY0pAqLqoyleRrPQSovJhPl/eQ+iI7I7OWkH7WlUypQK9NZyo+W9LA2eFikozPHkVjCdci4jPAZyR9JiI+0vcNg3kfaTJHp1N3J9LoiirOJHWqH0xhkeZBC8ljjrcBdszj8TvPvY8nrbJV1XAeyXMBG49EGShASzqNNAT1e6SMkD+pUaeOP0fE7yRNkzQtIi6T9Lk65eV/m1pe706aWYZxKrg6j9D6Fhtf68CjeCL1dX2MDQNPaplqwysvYpTscKQe6cr5micj1U+41innxaRmlfslHUZqAvtiFJK7VSx3SzY8Xd1W8ekKNbRIs6RjSR9ATyXNLOwE+j8CX42IL1es36k9dkcMuDCKpEfYEACKf3iVk3NJ+iHwOtJiJjuSsojuExGD9JUUy2t0eT1thmUYJwttmNDZ+dl2fq5VFgd/NmkdhBlsnIqi2lPuFAv03wX+ITbNDvcPwJUxCWa0NUXSm0hpii8n/cK8BPhQRJxboazlpAlhu5NG8ZxMytc+cD515YXa89cbLeMo6V+jwphh5RzgkpaQmjLuAc6NiGcOWlYu7z0R8aUq751qlBYY+W9Sh2fl9XG16SpOJ0cDqzg9GkjqLBDUubEI0hPpjyPijopl3gwsII20Wz+yMPKiSQOXN8UC/UaJn/LY6FsiTYq5KXKCqDZQAwnXCmXdGBF7S/pn4Je5DbZSgqTi+7rLqFFm44s0K+W1727zr5S6V9IuuV6dxct/DBwbddLGNiQPuVsdebGRPKz0yTHgwuq5aaq4itNdEXFsA/VrehTPpDPKU8tfkFZM+1REnF2hzBuiwuTI0UypNnrgR0p5R4rZ4a7MdzV/mLBajY/aCdcK7s0ds28DXqKUnbDq5CuN8nWv7VIi4sL85VoaSHSl5nO0n0padu6NefuwvK9SDvmGncPGQ1ofzvv26X36qGbFhlWcTqZGYrQunf6X11Cj/2UyG60ZSmkBkx+SsmMO6ruS/pGUGbdyBtaOqZbr5h9Jf2B7kvK1nE5K43l/TEC603H2fUlLJL1D0jtII0gWVyzrzaRflndGSpu8M6lZqIoY5ete22OS9C5JM/PXknSqpLWSliutdVvVG0iLKf8qUp6QPUjDGasaiohTI2Jdfn0dGKpRXpO2KI5oyV9XmdG60SpOTVQs2yFSipI/R8QVuV+j0lJ9U00OylUn5rydlCH2alLzzQ3UmHcyZe7oJU0jZZZ8PnDeRNdnvCglVnpyRHwojzL6a9IvyzWku6OBRcSv8sShfXIzyfVVmzFodmjqsaS0FpCGBO5OSiW8F6mt/iW939ZXk8scAvw2d2J3spzOIz1hTQZrJB3SaeaSNJeUx3xQ47WKU9OjeKYMSa8Afl/lvdHgQuOdAqfMixTonjbR9Rjna7yQHpPASNOpK60cRAMraY3TtS4rfP1NUrt3Z7vyxDXSDOLtSU0FPwNuAk6tUd7TSCsQrSF9aFwAPH2i//9y3Z5JGnn2C1J6hatJScMmvG65fq8hdRA/nzRa7gbgkImuV8PXeAtpYlPxtYrU/PXcimVuA3yclFcJ0qSp11St41TrjL2U1PZ4PYVhahFRdZrxpKMx8mF3d0YPUGZjHbtNUlrp52DSXc9dwCsipwOQdGtUXPG+63vMoGaO9qlA0rakwRX3TnRd4NE1ikfS07t2BSkTbuUsrGo4A+uUabrJip0eIjVr1FroYhIaj8XGm+zYbdI/k9odp5PSE3eC/MuAlVULVY+8/ZJeGgOuqKWNUzFvIiZwWUdJh0XENwpD+zr7gUkxIek0Nh7FM4vUVNc6UXM+yigazcA6pQJ9RFyhlK7zLaTmiDtIY03bZKmkd0XEV4s7JR1JxeyV5I5dNl5Jq272ytoi4sJ8N7RdRBTbModJdayqqWUOi51fnyYtsTdZdDJAbjehtRjdeI3iebRoNAPrlGi6ybPEDmVDJ9i3gA9GRPcj05Sn8VtsvNixe2VUyKc+VamBZQ7bNk9jvDU1x+LRRimFwlmkD/KPkZ6ELibN4XhHRFxeqdwpEugfIT0CHhkRI3nfyoioM5JiUtPGi42viAqLjRdG8FzVtf+lpIlTP69f08kvP/Iur9K/UShjUgWqPPltNBERlRNgNUFpnYhOG7VIzY4PUH8UT6sppfE4lJQ76lJSE+ZNwHURUWU0VSp3igT6vyNd/ItIecbPBr4WTQ9Bapk8ueyj3R2RkmYDn4yI1/Z+59SmTZc53Au4Iyosc1goc7IF+g/02P04UorhHSJi281cJWtQbtI8NL+2Jo1K+1ZUXAVrSgT6jjwD9nWkJpxXkDp8zo+8iIhtbDxG8DRJ0piBMyqu3Snp7Z0iyAtARMTAy9dJupcNHxjbkO5IYZLdlUrajtTReSRp3d1/j4oJ8GzyyZMHTyENu66UeXdKBfqiPL34jcCbo0V5M5okaSQinjXosc1FG7L99RKD/lzzZKFdIuLEvH09aQZrAB+OCgnhJrP8N/BPpGRmp5EyklaaoGOTi1Jm2ANJd/SvBK4grUZ2QaXypmqgt/4knQVcOsoInr+JiDojWyYdSVcBh0bE3Xl7GenJb1vShKmqC49MOpI+D7yetIDMiRFxX5+32BQgqbPU5MGkkUpnAxfUGZMPDvStNl4jeJqihlMeS1oaEfsUtr8cEcfkr6+NiNbkWMkDFP5EappqJLe9Tbz8lPtN4LyomMCsZ7kO9O3XxAie8aCGUx73aar6eVTMb2821U2pCVNWTURcxoZVuSaTplMeXzfKZLN34wk79ijmQG8TqbGUx9n7gQskvQXojNh5ASlF8esqlGfWCm66sQlTmFRTnFBD3t46IiotjpLTw/5l3pw0TVVmE8WB3sys5SZDBkMzMxtHDvRmZi3nQG9m1nIO9GY1SXqfpG0muh5mo3FnrFlNku4EZvdKIytpekQ8vPlrZbaB7+jtUUHS4ZKWS7pZ0hmSni7pkrzvEklPy+d9XdIbCu+7L//7ckmXSzpX0m2SzlTyXuCpwGWdJG2S7pN0vKTrgI9LOr9Q3qslfWezXrw96nnClLWepL8krdbz4oj4bc76eBpwekScJumdwAn0n1S1F2l8/j3AVbm8E/K6rQcU7ugfB/wkIv45L3pyq6ShiFgDHAGc2vQ1mo3Fd/T2aPAK4NxOIM7Jol5ISh4FcAZpmcV+ro+IVRHxCLAMmDHKeQ8D5+XvFbn8wyRtn7/vhK/Xa48uvqO3RwPRP6VC5/g68g1QvhvfqnBOcXHmhxn97+fBrnb5U4HvAg8C50TEupL1NmuE7+jt0eAS4E2SdoD1C3ZcTVrUAdLCHT/OX99Jyo8DMBcok4bhXmC70Q5GxD2k5p6PA18frOpm9fmO3lovIlZI+hfgipxf5ybgvcApkj4EdNrOAb4K/L+8OtUlbFjgeiwLge9JWh0RB4xyzpnAUET8tM61mFXh4ZVmm4GkLwM3RcTJE10Xe/RxoDcbZ5JuID0ZvDoi/tTvfLOmOdCbmbWcO2PNzFrOgd7MrOUc6M3MWs6B3sys5Rzozcxa7v8HxzD5J10rwfIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged.groupby('country')['conversion'].mean().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322c3b9e",
   "metadata": {},
   "source": [
    "apparently, Spain has the highest conversion rate and Uruguay has the lowest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a4212852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fix, ax = plt.subplots(figsize=(18,6))\n",
    "sns.barplot(x='country', y='conversion', hue='test', data = df_merged, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c5ae8a",
   "metadata": {},
   "source": [
    "because we have to keep Spain in our control group, so I will look at the test/control spreads without Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "558cc0dd",
   "metadata": {
    "scrolled": true
   },
   "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>conversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.048330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.043425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      conversion\n",
       "test            \n",
       "0       0.048330\n",
       "1       0.043425"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_spain = df_merged.loc[df_merged['country'] != 'Spain',:]\n",
    "df_no_spain.groupby(\"test\")[[\"conversion\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ddce784",
   "metadata": {},
   "source": [
    "we can see that the control group without change of translation has conversion rate of 4.8% while has conversion rate of only 4.3% after change of translation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1af64a6",
   "metadata": {},
   "source": [
    "### use t-test to see if the test is negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a6eb262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-7.422512095954769, pvalue=1.15144687852198e-13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_in_test = df_no_spain.loc[test_df.test==1,\"conversion\"]\n",
    "conv_in_ctrl = df_no_spain.loc[test_df.test==0,\"conversion\"]\n",
    "\n",
    "ss.ttest_ind(conv_in_test,conv_in_ctrl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4002bb7b",
   "metadata": {},
   "source": [
    "based on the t-test, the local translation makes the situation even worse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dbc37d2",
   "metadata": {},
   "source": [
    "### Further Analysis to confirm my observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "523756b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country               Test Rate    Control Rate    P-Value\n",
      "-----------------------------------------------------------------\n",
      "Spain                       nan         0.07972        nan\n",
      "Mexico                  0.05119         0.04949   0.165544\n",
      "Venezuela               0.04898         0.05034   0.573702\n",
      "Bolivia                 0.04790         0.04937   0.718885\n",
      "Colombia                0.05057         0.05209   0.423719\n",
      "Uruguay                 0.01291         0.01205   0.879764\n",
      "El Salvador             0.04795         0.05355   0.248127\n",
      "Nicaragua               0.05418         0.05265   0.780400\n",
      "Peru                    0.05060         0.04991   0.771953\n",
      "Costa Rica              0.05474         0.05226   0.687876\n",
      "Chile                   0.05130         0.04811   0.302848\n",
      "Argentina               0.01373         0.01507   0.335147\n",
      "Ecuador                 0.04899         0.04915   0.961512\n",
      "Guatemala               0.04865         0.05064   0.572107\n",
      "Honduras                0.04754         0.05091   0.471463\n",
      "Paraguay                0.04923         0.04849   0.883697\n",
      "Panama                  0.04937         0.04680   0.705327\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "countries = [name for name in df_merged['country'].unique() if name is not np.nan]\n",
    "\n",
    "print('{0:15s} {1:>15s} {2:>15s} {3:>10s}'.format('Country', 'Test Rate', 'Control Rate', 'P-Value'))\n",
    "print('-' * 65)\n",
    "for country in countries:\n",
    "    test_val = df_merged[(df_merged['country'] == country) & (df_merged['test'] == 1)]['conversion'].values\n",
    "    cont_val = df_merged[(df_merged['country'] == country) & (df_merged['test'] == 0)]['conversion'].values\n",
    "    \n",
    "    test_mean = test_val.mean()\n",
    "    cont_mean = cont_val.mean()\n",
    "    p_val = ss.ttest_ind(test_val, cont_val, equal_var=False).pvalue\n",
    "    \n",
    "    print('{0:15s} {1:15.5f} {2:15.5f} {3:10f}'.format(country, test_mean, cont_mean, p_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e842134",
   "metadata": {},
   "source": [
    "After we control the country, we can see that the conversion rate has no significant difference, therefore the change of translation will not result in conversion rate drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebb5c42d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1da9db14",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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