Project-3-Harsh-Bhanderj.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 601 : Project 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By Harsh Bhanderi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic : Survey on fundamental rights of lesbian, gay, bisexual and transgender people in EU (2012)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last few years have been marked by a growing wave of hatred around the world, accompanied by the rise of conservatism. One of the main social groups affected by this phenomenon is LGBTQI +, which has been fighting for rights and inclusion for years; Recent achievements such as the legalization of the LGBT civil union throughout the United States represent a major achievement in the midst of this scenario, but the situation has not always been that way.\n",
    "\n",
    "With this in mind, I use data from the European Union in 2012 to analyze aspects such as the influence of discrimination on personal life, as well as the relationship between a country's HDI and the welfare of that group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions to the dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What is the influence of the Human Development Index (HDI) on the quality of life of LGBTs? Does this social minority benefit from less discrimination and more rights in countries with higher HDI?\n",
    "- Does sexism influence men to have less acceptance of sexuality compared to women? Is this more noticeable among bisexual men?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main database used for this analysis was taken from https://fra.europa.eu/en/publications-and-resources/data-and-maps/survey-fundamental-rights-lesbian-gay-bisexual-and and contains data from countries belonging to the European Union and Croatia in 2012. The basis for this was obtained after a survey, for which a series of questions about LGBT welfare, discrimination and violence were conducted. The questions also consider subgroups within LGBTs and separation by country of origin.\n",
    "\n",
    "It was also used the base available at http://hdr.undp.org/en/indicators/137506 to obtain the HDI of each of the countries present in the research."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pycountry in c:\\programdata\\anaconda3\\lib\\site-packages (19.8.18)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "!pip install pycountry\n",
    "import pycountry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data\n",
    "\n",
    "- Source :\n",
    "    - https://fra.europa.eu/en/publications-and-resources/data-and-maps/survey-fundamental-rights-lesbian-gay-bisexual-and (LGBT-Survey)\n",
    "    - http://hdr.undp.org/en/indicators/137506 (Human Development Recovery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly widespread</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly rare</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very rare</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Don`t know</td>\n",
       "      <td>4</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryCode   subset question_code  \\\n",
       "0     Austria  Lesbian          b1_a   \n",
       "1     Austria  Lesbian          b1_a   \n",
       "2     Austria  Lesbian          b1_a   \n",
       "3     Austria  Lesbian          b1_a   \n",
       "4     Austria  Lesbian          b1_a   \n",
       "\n",
       "                                      question_label             answer  \\\n",
       "0  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "1  In your opinion, how widespread is offensive l...  Fairly widespread   \n",
       "2  In your opinion, how widespread is offensive l...        Fairly rare   \n",
       "3  In your opinion, how widespread is offensive l...          Very rare   \n",
       "4  In your opinion, how widespread is offensive l...         Don`t know   \n",
       "\n",
       "  percentage notes  \n",
       "0          8   NaN  \n",
       "1         34   NaN  \n",
       "2         45   NaN  \n",
       "3          9   NaN  \n",
       "4          4   [0]  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DailyLife                    = pd.read_csv('Data/european-union-lgbt-survey-2012/LGBT_Survey_DailyLife.csv')\n",
    "df_RightsAwareness              = pd.read_csv('Data/european-union-lgbt-survey-2012/LGBT_Survey_RightsAwareness.csv')\n",
    "df_Discrimination               = pd.read_csv('Data/european-union-lgbt-survey-2012/LGBT_Survey_Discrimination.csv')\n",
    "df_ViolenceAndHarrassment       = pd.read_csv('Data/european-union-lgbt-survey-2012/LGBT_Survey_ViolenceAndHarassment.csv')\n",
    "df_SubsetSize                   = pd.read_csv('Data/european-union-lgbt-survey-2012/LGBT_Survey_SubsetSize.csv')\n",
    "df_TransgenderSpecificQuestions = pd.read_csv('Data/european-union-lgbt-survey-2012/LGBT_Survey_TransgenderSpecificQuestions.csv')\n",
    "\n",
    "# sample of survey data\n",
    "df_DailyLife.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(2017)</th>\n",
       "      <th>Country</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>...</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>168</td>\n",
       "      <td>Afghanistan</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>0.437</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.641</td>\n",
       "      <td>0.641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.773</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>Andorra</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>0.831</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>147</td>\n",
       "      <td>Angola</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>0.502</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  (2017)      Country   1990   1991   1992   1993   1994   1995   1996   1997  \\\n",
       "0    168  Afghanistan     ..     ..     ..     ..     ..     ..     ..     ..   \n",
       "1     68      Albania  0.645  0.626  0.610  0.613  0.619  0.632  0.641  0.641   \n",
       "2     85      Algeria  0.577  0.581  0.587  0.591  0.595  0.600  0.608  0.617   \n",
       "3     35      Andorra     ..     ..     ..     ..     ..     ..     ..     ..   \n",
       "4    147       Angola     ..     ..     ..     ..     ..     ..     ..     ..   \n",
       "\n",
       "   ...   2008   2009   2010   2011   2012   2013   2014   2015   2016   2017  \n",
       "0  ...  0.437  0.453  0.463  0.471  0.482  0.487  0.491  0.493  0.494  0.498  \n",
       "1  ...  0.724  0.729  0.741  0.752  0.767  0.771  0.773  0.776  0.782  0.785  \n",
       "2  ...  0.709  0.719  0.729  0.736  0.740  0.745  0.747  0.749  0.753  0.754  \n",
       "3  ...  0.831  0.830  0.828  0.827  0.849  0.850  0.853  0.854  0.856  0.858  \n",
       "4  ...  0.502  0.522  0.520  0.535  0.543  0.554  0.564  0.572  0.577  0.581  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_HDI = pd.read_csv('Data/Human Development Index Data from outside source/clean_hdi.csv')\n",
    "\n",
    "# The first column indicates the country's rank in 2017\n",
    "df_HDI.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>0.786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Romania</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>0.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>0.829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country   2012\n",
       "0  Bulgaria  0.786\n",
       "1   Romania  0.795\n",
       "2   Croatia  0.816\n",
       "3    Latvia  0.824\n",
       "4  Portugal  0.829"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just taking the HDI column for 2012\n",
    "df_HDI = df_HDI[['Country', '2012']]\n",
    "\n",
    "# Taking only the countries of the European Union\n",
    "countries = df_DailyLife['CountryCode'].unique()\n",
    "df_HDI = df_HDI.loc[df_HDI['Country'].isin(countries)]\n",
    "\n",
    "# Sorting in ascending order and restarting indexes\n",
    "df_HDI = df_HDI.sort_values(by='2012')\n",
    "df_HDI.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df_HDI.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Survey Data Cleaning\n",
    "\n",
    "- The cleaning will be as follows:\n",
    "    1. I will rename the CountryCode column to CountryName\n",
    "    2. I add the country ID\n",
    "    3. Taking unnecessary spaces in notes\n",
    "    4. Replacing notes that are [1] (not a number) with np.NaN, and removing this column from the data.\n",
    "    5. Converting float percentage values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = {}\n",
    "for country in pycountry.countries:\n",
    "    countries[country.name] = country.alpha_2\n",
    "\n",
    "def clean_data(df):\n",
    "    df = df.rename(columns={'CountryCode': 'CountryName'})\n",
    "    codes = [countries.get(country, 'Unknown code') for country in df['CountryName']]\n",
    "    df['CountryID'] = codes\n",
    "    df.loc[df['CountryName'] == 'Czech Republic', 'CountryID'] = 'CZ'\n",
    "    df.loc[df['notes'] == ' [1] ', 'notes'] = '[1]'\n",
    "    df.loc[df['notes'] == '[1]', 'percentage'] = np.NaN\n",
    "    df.drop('notes', axis=1, inplace=True)\n",
    "    df['percentage'] = df['percentage'].astype('float')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>CountryID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly widespread</td>\n",
       "      <td>34.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly rare</td>\n",
       "      <td>45.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very rare</td>\n",
       "      <td>9.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Don`t know</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian          b1_a   \n",
       "1     Austria  Lesbian          b1_a   \n",
       "2     Austria  Lesbian          b1_a   \n",
       "3     Austria  Lesbian          b1_a   \n",
       "4     Austria  Lesbian          b1_a   \n",
       "\n",
       "                                      question_label             answer  \\\n",
       "0  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "1  In your opinion, how widespread is offensive l...  Fairly widespread   \n",
       "2  In your opinion, how widespread is offensive l...        Fairly rare   \n",
       "3  In your opinion, how widespread is offensive l...          Very rare   \n",
       "4  In your opinion, how widespread is offensive l...         Don`t know   \n",
       "\n",
       "   percentage CountryID  \n",
       "0         8.0        AT  \n",
       "1        34.0        AT  \n",
       "2        45.0        AT  \n",
       "3         9.0        AT  \n",
       "4         4.0        AT  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DailyLife                    = clean_data(df_DailyLife)\n",
    "df_Discrimination               = clean_data(df_Discrimination)\n",
    "df_RightsAwareness              = clean_data(df_RightsAwareness)\n",
    "df_TransgenderSpecificQuestions = clean_data(df_TransgenderSpecificQuestions)\n",
    "df_ViolenceAndHarrassment       = clean_data(df_ViolenceAndHarrassment)\n",
    "\n",
    "df_DailyLife.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This work is based on a survey of LGBT people from Europe about various aspects of their lives. Data have 5 tables that deal with each of these aspects, 1 table that has an overall of respondents and 1 extra table that we took about the HDI of these countries at the time of the interviews (2012)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the default size of plots\n",
    "matplotlib.rcParams['figure.figsize'] = (15.0, 9.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryID</th>\n",
       "      <th>N</th>\n",
       "      <th>Lesbian women</th>\n",
       "      <th>Gay men</th>\n",
       "      <th>Bisexual women</th>\n",
       "      <th>Bisexual men</th>\n",
       "      <th>Transgender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EU Total</td>\n",
       "      <td>93079</td>\n",
       "      <td>15236</td>\n",
       "      <td>57448</td>\n",
       "      <td>6424</td>\n",
       "      <td>7200</td>\n",
       "      <td>6771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AT</td>\n",
       "      <td>2543</td>\n",
       "      <td>437</td>\n",
       "      <td>1558</td>\n",
       "      <td>164</td>\n",
       "      <td>217</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>2901</td>\n",
       "      <td>424</td>\n",
       "      <td>1998</td>\n",
       "      <td>132</td>\n",
       "      <td>182</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>1033</td>\n",
       "      <td>226</td>\n",
       "      <td>474</td>\n",
       "      <td>139</td>\n",
       "      <td>88</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CY</td>\n",
       "      <td>265</td>\n",
       "      <td>41</td>\n",
       "      <td>164</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CZ</td>\n",
       "      <td>2469</td>\n",
       "      <td>565</td>\n",
       "      <td>1432</td>\n",
       "      <td>163</td>\n",
       "      <td>139</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
       "      <td>20271</td>\n",
       "      <td>2445</td>\n",
       "      <td>13907</td>\n",
       "      <td>590</td>\n",
       "      <td>2000</td>\n",
       "      <td>1329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DK</td>\n",
       "      <td>1710</td>\n",
       "      <td>393</td>\n",
       "      <td>930</td>\n",
       "      <td>136</td>\n",
       "      <td>108</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EE</td>\n",
       "      <td>374</td>\n",
       "      <td>99</td>\n",
       "      <td>170</td>\n",
       "      <td>61</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GR</td>\n",
       "      <td>2760</td>\n",
       "      <td>399</td>\n",
       "      <td>1590</td>\n",
       "      <td>219</td>\n",
       "      <td>288</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ES</td>\n",
       "      <td>6388</td>\n",
       "      <td>897</td>\n",
       "      <td>4409</td>\n",
       "      <td>354</td>\n",
       "      <td>514</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FI</td>\n",
       "      <td>3439</td>\n",
       "      <td>820</td>\n",
       "      <td>1192</td>\n",
       "      <td>756</td>\n",
       "      <td>190</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FR</td>\n",
       "      <td>8375</td>\n",
       "      <td>1302</td>\n",
       "      <td>5720</td>\n",
       "      <td>326</td>\n",
       "      <td>592</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HU</td>\n",
       "      <td>2267</td>\n",
       "      <td>307</td>\n",
       "      <td>1455</td>\n",
       "      <td>135</td>\n",
       "      <td>236</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IE</td>\n",
       "      <td>1625</td>\n",
       "      <td>423</td>\n",
       "      <td>864</td>\n",
       "      <td>141</td>\n",
       "      <td>58</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IT</td>\n",
       "      <td>13255</td>\n",
       "      <td>2136</td>\n",
       "      <td>8668</td>\n",
       "      <td>805</td>\n",
       "      <td>996</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LT</td>\n",
       "      <td>821</td>\n",
       "      <td>177</td>\n",
       "      <td>439</td>\n",
       "      <td>100</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LU</td>\n",
       "      <td>318</td>\n",
       "      <td>51</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LV</td>\n",
       "      <td>501</td>\n",
       "      <td>150</td>\n",
       "      <td>200</td>\n",
       "      <td>83</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MT</td>\n",
       "      <td>358</td>\n",
       "      <td>109</td>\n",
       "      <td>173</td>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NL</td>\n",
       "      <td>3175</td>\n",
       "      <td>614</td>\n",
       "      <td>1937</td>\n",
       "      <td>214</td>\n",
       "      <td>159</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PL</td>\n",
       "      <td>2790</td>\n",
       "      <td>375</td>\n",
       "      <td>1754</td>\n",
       "      <td>264</td>\n",
       "      <td>138</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PT</td>\n",
       "      <td>2125</td>\n",
       "      <td>456</td>\n",
       "      <td>1168</td>\n",
       "      <td>268</td>\n",
       "      <td>146</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>RO</td>\n",
       "      <td>1260</td>\n",
       "      <td>139</td>\n",
       "      <td>690</td>\n",
       "      <td>70</td>\n",
       "      <td>214</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SE</td>\n",
       "      <td>2464</td>\n",
       "      <td>427</td>\n",
       "      <td>1053</td>\n",
       "      <td>341</td>\n",
       "      <td>269</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SI</td>\n",
       "      <td>636</td>\n",
       "      <td>160</td>\n",
       "      <td>345</td>\n",
       "      <td>64</td>\n",
       "      <td>38</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SK</td>\n",
       "      <td>1000</td>\n",
       "      <td>144</td>\n",
       "      <td>584</td>\n",
       "      <td>96</td>\n",
       "      <td>61</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>GB</td>\n",
       "      <td>6759</td>\n",
       "      <td>1285</td>\n",
       "      <td>3795</td>\n",
       "      <td>572</td>\n",
       "      <td>294</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>HR</td>\n",
       "      <td>1197</td>\n",
       "      <td>235</td>\n",
       "      <td>592</td>\n",
       "      <td>157</td>\n",
       "      <td>105</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CountryID      N  Lesbian women  Gay men  Bisexual women  Bisexual men  \\\n",
       "0   EU Total  93079          15236    57448            6424          7200   \n",
       "1         AT   2543            437     1558             164           217   \n",
       "2         BE   2901            424     1998             132           182   \n",
       "3         BG   1033            226      474             139            88   \n",
       "4         CY    265             41      164              18            17   \n",
       "5         CZ   2469            565     1432             163           139   \n",
       "6         DE  20271           2445    13907             590          2000   \n",
       "7         DK   1710            393      930             136           108   \n",
       "8         EE    374             99      170              61            20   \n",
       "9         GR   2760            399     1590             219           288   \n",
       "10        ES   6388            897     4409             354           514   \n",
       "11        FI   3439            820     1192             756           190   \n",
       "12        FR   8375           1302     5720             326           592   \n",
       "13        HU   2267            307     1455             135           236   \n",
       "14        IE   1625            423      864             141            58   \n",
       "15        IT  13255           2136     8668             805           996   \n",
       "16        LT    821            177      439             100            65   \n",
       "17        LU    318             51      187              16            26   \n",
       "18        LV    501            150      200              83            22   \n",
       "19        MT    358            109      173              40            18   \n",
       "20        NL   3175            614     1937             214           159   \n",
       "21        PL   2790            375     1754             264           138   \n",
       "22        PT   2125            456     1168             268           146   \n",
       "23        RO   1260            139      690              70           214   \n",
       "24        SE   2464            427     1053             341           269   \n",
       "25        SI    636            160      345              64            38   \n",
       "26        SK   1000            144      584              96            61   \n",
       "27        GB   6759           1285     3795             572           294   \n",
       "28        HR   1197            235      592             157           105   \n",
       "\n",
       "    Transgender  \n",
       "0          6771  \n",
       "1           167  \n",
       "2           165  \n",
       "3           106  \n",
       "4            25  \n",
       "5           170  \n",
       "6          1329  \n",
       "7           143  \n",
       "8            24  \n",
       "9           264  \n",
       "10          214  \n",
       "11          481  \n",
       "12          435  \n",
       "13          134  \n",
       "14          139  \n",
       "15          650  \n",
       "16           40  \n",
       "17           38  \n",
       "18           46  \n",
       "19           18  \n",
       "20          251  \n",
       "21          259  \n",
       "22           87  \n",
       "23          147  \n",
       "24          374  \n",
       "25           29  \n",
       "26          115  \n",
       "27          813  \n",
       "28          108  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SubsetSize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 1) Data Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is the table that represents the overall data sorted in descending order by number of respondents (N) by country. The following categories refer to which LGBT subgroup the person is considered to belong to (Lesbian Woman, Gay Man, Bisexual Woman, Bisexual Man, and Transgender). Without a very detailed analysis it can be seen that the number of respondents per country is not well distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>N</th>\n",
       "      <th>Lesbian women</th>\n",
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       "      <th>0</th>\n",
       "      <td>EU Total</td>\n",
       "      <td>93079</td>\n",
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       "      <td>6424</td>\n",
       "      <td>7200</td>\n",
       "      <td>6771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
       "      <td>20271</td>\n",
       "      <td>2445</td>\n",
       "      <td>13907</td>\n",
       "      <td>590</td>\n",
       "      <td>2000</td>\n",
       "      <td>1329</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IT</td>\n",
       "      <td>13255</td>\n",
       "      <td>2136</td>\n",
       "      <td>8668</td>\n",
       "      <td>805</td>\n",
       "      <td>996</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FR</td>\n",
       "      <td>8375</td>\n",
       "      <td>1302</td>\n",
       "      <td>5720</td>\n",
       "      <td>326</td>\n",
       "      <td>592</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>GB</td>\n",
       "      <td>6759</td>\n",
       "      <td>1285</td>\n",
       "      <td>3795</td>\n",
       "      <td>572</td>\n",
       "      <td>294</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ES</td>\n",
       "      <td>6388</td>\n",
       "      <td>897</td>\n",
       "      <td>4409</td>\n",
       "      <td>354</td>\n",
       "      <td>514</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FI</td>\n",
       "      <td>3439</td>\n",
       "      <td>820</td>\n",
       "      <td>1192</td>\n",
       "      <td>756</td>\n",
       "      <td>190</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NL</td>\n",
       "      <td>3175</td>\n",
       "      <td>614</td>\n",
       "      <td>1937</td>\n",
       "      <td>214</td>\n",
       "      <td>159</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>2901</td>\n",
       "      <td>424</td>\n",
       "      <td>1998</td>\n",
       "      <td>132</td>\n",
       "      <td>182</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PL</td>\n",
       "      <td>2790</td>\n",
       "      <td>375</td>\n",
       "      <td>1754</td>\n",
       "      <td>264</td>\n",
       "      <td>138</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GR</td>\n",
       "      <td>2760</td>\n",
       "      <td>399</td>\n",
       "      <td>1590</td>\n",
       "      <td>219</td>\n",
       "      <td>288</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AT</td>\n",
       "      <td>2543</td>\n",
       "      <td>437</td>\n",
       "      <td>1558</td>\n",
       "      <td>164</td>\n",
       "      <td>217</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CZ</td>\n",
       "      <td>2469</td>\n",
       "      <td>565</td>\n",
       "      <td>1432</td>\n",
       "      <td>163</td>\n",
       "      <td>139</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SE</td>\n",
       "      <td>2464</td>\n",
       "      <td>427</td>\n",
       "      <td>1053</td>\n",
       "      <td>341</td>\n",
       "      <td>269</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HU</td>\n",
       "      <td>2267</td>\n",
       "      <td>307</td>\n",
       "      <td>1455</td>\n",
       "      <td>135</td>\n",
       "      <td>236</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PT</td>\n",
       "      <td>2125</td>\n",
       "      <td>456</td>\n",
       "      <td>1168</td>\n",
       "      <td>268</td>\n",
       "      <td>146</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DK</td>\n",
       "      <td>1710</td>\n",
       "      <td>393</td>\n",
       "      <td>930</td>\n",
       "      <td>136</td>\n",
       "      <td>108</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IE</td>\n",
       "      <td>1625</td>\n",
       "      <td>423</td>\n",
       "      <td>864</td>\n",
       "      <td>141</td>\n",
       "      <td>58</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>RO</td>\n",
       "      <td>1260</td>\n",
       "      <td>139</td>\n",
       "      <td>690</td>\n",
       "      <td>70</td>\n",
       "      <td>214</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>HR</td>\n",
       "      <td>1197</td>\n",
       "      <td>235</td>\n",
       "      <td>592</td>\n",
       "      <td>157</td>\n",
       "      <td>105</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>1033</td>\n",
       "      <td>226</td>\n",
       "      <td>474</td>\n",
       "      <td>139</td>\n",
       "      <td>88</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SK</td>\n",
       "      <td>1000</td>\n",
       "      <td>144</td>\n",
       "      <td>584</td>\n",
       "      <td>96</td>\n",
       "      <td>61</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LT</td>\n",
       "      <td>821</td>\n",
       "      <td>177</td>\n",
       "      <td>439</td>\n",
       "      <td>100</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SI</td>\n",
       "      <td>636</td>\n",
       "      <td>160</td>\n",
       "      <td>345</td>\n",
       "      <td>64</td>\n",
       "      <td>38</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LV</td>\n",
       "      <td>501</td>\n",
       "      <td>150</td>\n",
       "      <td>200</td>\n",
       "      <td>83</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EE</td>\n",
       "      <td>374</td>\n",
       "      <td>99</td>\n",
       "      <td>170</td>\n",
       "      <td>61</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MT</td>\n",
       "      <td>358</td>\n",
       "      <td>109</td>\n",
       "      <td>173</td>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LU</td>\n",
       "      <td>318</td>\n",
       "      <td>51</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CY</td>\n",
       "      <td>265</td>\n",
       "      <td>41</td>\n",
       "      <td>164</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CountryID      N  Lesbian women  Gay men  Bisexual women  Bisexual men  \\\n",
       "0   EU Total  93079          15236    57448            6424          7200   \n",
       "6         DE  20271           2445    13907             590          2000   \n",
       "15        IT  13255           2136     8668             805           996   \n",
       "12        FR   8375           1302     5720             326           592   \n",
       "27        GB   6759           1285     3795             572           294   \n",
       "10        ES   6388            897     4409             354           514   \n",
       "11        FI   3439            820     1192             756           190   \n",
       "20        NL   3175            614     1937             214           159   \n",
       "2         BE   2901            424     1998             132           182   \n",
       "21        PL   2790            375     1754             264           138   \n",
       "9         GR   2760            399     1590             219           288   \n",
       "1         AT   2543            437     1558             164           217   \n",
       "5         CZ   2469            565     1432             163           139   \n",
       "24        SE   2464            427     1053             341           269   \n",
       "13        HU   2267            307     1455             135           236   \n",
       "22        PT   2125            456     1168             268           146   \n",
       "7         DK   1710            393      930             136           108   \n",
       "14        IE   1625            423      864             141            58   \n",
       "23        RO   1260            139      690              70           214   \n",
       "28        HR   1197            235      592             157           105   \n",
       "3         BG   1033            226      474             139            88   \n",
       "26        SK   1000            144      584              96            61   \n",
       "16        LT    821            177      439             100            65   \n",
       "25        SI    636            160      345              64            38   \n",
       "18        LV    501            150      200              83            22   \n",
       "8         EE    374             99      170              61            20   \n",
       "19        MT    358            109      173              40            18   \n",
       "17        LU    318             51      187              16            26   \n",
       "4         CY    265             41      164              18            17   \n",
       "\n",
       "    Transgender  \n",
       "0          6771  \n",
       "6          1329  \n",
       "15          650  \n",
       "12          435  \n",
       "27          813  \n",
       "10          214  \n",
       "11          481  \n",
       "20          251  \n",
       "2           165  \n",
       "21          259  \n",
       "9           264  \n",
       "1           167  \n",
       "5           170  \n",
       "24          374  \n",
       "13          134  \n",
       "22           87  \n",
       "7           143  \n",
       "14          139  \n",
       "23          147  \n",
       "28          108  \n",
       "3           106  \n",
       "26          115  \n",
       "16           40  \n",
       "25           29  \n",
       "18           46  \n",
       "8            24  \n",
       "19           18  \n",
       "17           38  \n",
       "4            25  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_SubsetSize.sort_values('N', ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1) Analysis of the number of gays and lesbians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_country = df_SubsetSize.sort_values('N')\n",
    "sorted_country = sorted_country[:-1]\n",
    "\n",
    "sns.lineplot(x='CountryID', y='N', marker='o',\n",
    "                data=sorted_country, label='Number of Respondents', sort=False)\n",
    "sns.barplot(x='CountryID', y='Gay men',\n",
    "                data=sorted_country, label='Gay')\n",
    "sns.barplot(x='CountryID', y='Lesbian women', color=\"black\",\n",
    "               data=sorted_country, label='Lesbian')\n",
    "\n",
    "plt.title('Comparison of Gays vs. Lesbians')\n",
    "plt.xlabel('Nation')\n",
    "plt.ylabel('')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above represents in the line graph the total number of respondents (y axis) by country (x axis). The colored bar graph represents the number of people who fall into the \"Gay Men\" category and the black color into the \"Lesbian Woman\" category.\n",
    "With the visual analysis of the graph I have the following conclusions:\n",
    "- It is noticeable that the number of gays is very high if we analyze the total amount, almost always appear to be above or close to 50% of respondents.\n",
    "- The number of lesbians shows not to grow so much when comparing each country.\n",
    "- Because of the above two points and because we have a growth in the number of respondents, it is clear that the proportion of lesbians in relation to the number of gays is quite varied throughout the country plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2) Bisexual Number Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NZ511LnvvvQ+zZ8/i8ssvZtmyZey4406Nta2pnkceeZjnnnuaxYsXM2vWe4wc+UnOOee7/OAH1zN//jzOP/8cLrpoPOPHX8iCBQsAGDfuREaN2m+DvxuSJElSa+nwYe+pl2cz+T+z17rPsQcO54e//hfVNcsAqK5ZxlUTnueMw0YwcdJrazxu1C6D2Gfn5q28ePrpZzFmzJH885/PMnLkJ5t1zNtvv8kFF1zCtttux1FH/Q/9+w/g9tvv5Z577uD3v/8tZ555NgBbbjmECy8czzPPTObKKy/l3nvv4+abb+BLX/oyo0btx/z58zn11OOZMOE+AFauXMHEib/+0PWuuOJijj12LPvttz+vvPIyF110Hr/85W854YRTeOmlF1Yb9CZPfoKxY4+mtraWFSuWM3LkXlx99Y0A/PSndxPCcC666DJqa3Occso4dthhJx544D6OPPJY9t//ACZN+gOvvvoyQ4YMZdGiGnbaaRfOPfcCHnzwfn7wg+v53veuY/z4C7jiimsYPnxH/va3xxg//kLuuutnANTV1XP77fcyefIT3HnnT9h77334/vev46CDDuGQQw7lT3/6I//3f79daz0AL7/8HyZOfICysixHH/013n7765x11rmcccbJXH31DUya9Ac233ww119/M2++Gfnzn/9k2JMkSdJGrcOHvebo1rW8Meg1qK5ZRrduLfd4evbsxXnnXdQ4nLM5Nt20H9tttz0AAwYMZPfdRwKw+eaDeOml/87TO+SQQwHYe+9RXH75JSxZsoQpU55n+vTp3HXX7QDU19fz3nszARoDTlNLly5l5syZ7Lff/gDstNPOVFZWMmPG9LXW2DCMs7Y2x7nnnsWQIUMZOnQYAFOmPM+KFcv54x9/D8Dy5ct599132Hvvfbjpput47rmn2Weffdlnn08DUFHRlS9+8UsAHHjgwdx++4+pqppO7969GT58RwD23/8ArrvuSnK5HAB77bU3ANts8zGWLFkMwEsvvdA45PTznz+Qa665Yq31AOy88y6NQ2wHD96CxYvfp0ePHo33udNOu3D77T9m/vxq9t57FGPHHr/W5yJJkiSVWocPe/vs/NG9b8VsGQP7dv9A4BvYtzub9u7Kecfs1mK17LnnJxuHczZYdQXH+vr6xj936dLlA9uy2exqz9u0vVgsUl5eTj5f4JZbfkJlZR8A5s+fT9++fXniiX/QtWvXD52jWPzwcNViEfL5fDPuLAmzF110GWPGHMEee+zJTjvtQqGQ5+KLryCEJLAuXLiAyso+lJeXs9NOu/DUU0/ywAP38cwzkznvvIsoK8s0Po9CoUg2m6VQKK7makUKhaSuiooKIHmOxWLDvpnG4zKZDGVl2fScq6/nz3+eREXFB5/Jf8+VGDJkKPfd9yDPPvsMTz31BPffP5GJE39NWZlrHEmSJGnj5E+qJIn3grF7MrBvd4DGOXutkYRPP/0snn/+GRYsmA9Anz6bMGvWe6xYsYLFi9/n3/9+aZ3P+ec//wmAxx//O1tttTXdu3dn99334Le/TYZqvvvuO4wZcwQrVixf4zl69uzF4MFb8PjjfwPglVdeZuHCBWyzzceaXcfgwVvwta8dwc0330CxWGS33Uby0EMPAknY/MY3jmLu3Dlccsn5vPbaVA499GuccMIpxPg6kPS0TZ78BACPPPJ79trrUwwdOoz333+f1157FYC//vUvbLbZoMYQuzp77LEnjz76SPpM/sbKlSsA1ljPmmSz2caw+5vf/Iq7776d/fc/gLPP/i41NTXU1tau8VhJkiSp1Dp8z15z5PMF+vbowtWn7tMqq3E21TCc89vfPh1Ihh/uvfc+jB59OIMGDeYTn9h1nc9ZVTWdsWOPpkePHo0rY37rW9/huuuu5BvfOJJiscjFF1/+kSuBXnLJFVx//VXcffftdOlSwZVXXveh3sWPMnr0cfzxj//HX/7yJ8aNO5Ebb7yW0aMPp1AocOqpZ7LFFlsyevRxXHvt95gw4U7Ky7twzjnfbTz+739/jDvvvJV+/QZw0UXjqaio4PLLr+amm65j+fJlVFb24fLLr15rDd/+9ne44opL+P3vf8f22w9vvO811bOmgL3ppv3YbLPNOeOMk7nmmhsZP/5Cxow5gmw2y2mnnUnv3r3X6dlIkiRJbSmz6nC1dmQr4N0FC3IfGuo3Z850Nt98WEmK0vobNWoPJk+eUuoyNojfPUmSJLWVsrIM/fr1AtgamPah7W1dkCRJkiSp9Rn2tNFo7716kiRJ0rrIZssoZsuoz2QoZsvIZls2njlnT5IkSZLaWDZbRs3SOq6a8DzVNcsaF4ns26NLi60dYs+eJEmSJLWxemgMepC85/uqCc9Tv/bD1olhT5IkSZLaWL5Q/MB7viEJfPnVvmd6/Rj2JEmSJKmNZcsyje/5bjCwb3eyZZkWu4ZhT5IkSZLaWDlwwdg9GwNfw5y9llxUpVnnCiFcChyefvxjjPE7IYQDgJuA7sCvYowXpfuOAO4CKoEngFNijPUhhKHARGAgEIFjYoy5EMImwC+AbYB5wOExxjktdodAz55ZevTo0ZKnBGDp0qXU1ubXus+NN17Lyy//m/r6OmbOrGKrrbYB4LDDjuRLX/ryao+ZObOKX/zip5x33kVrPO/MmVWcffYZ/OpXD32g/fLLL2avvfbmC1846APtjz/+d9555y2OO+7E5txai1tTXZIkSVJnlM8X6NO9nBMP3Zn+fbpT2bML5Wl7S/nInr001H0e2BUYAeweQjgKuAf4CjAcGBlCODA9ZCJweoxxOyADNKSLW4FbY4zbA1OAi9P27wFPxhiHA3cCN7fEjTXVo0cPMplMi//XnAB59tnnMWHCfVx//c307z+ACRPuY8KE+9YY9ABmz57FrFmzWvIRsN9+ny1Z0JMkSZL0YbPm5bjy3ud5+Y1qMvlCiwY9aN4wztnA2THGlTHGOuA1YDvgzRjjuzHGepKAd1gIYRjQPcb4bHrshLS9C7Av8GDT9vTPXyLp2QP4JXBgun+Ht3TpUi699AJGjz6cb3zjSP7850kA3HzzDUyd+go/+MH11NfXc/XVl3PSSWM57LAvc8EF57JixYq1nvfJJ//BuHHHMGbMEfzjH38F4OGHH+Kaa64A4JZbbuQb3ziK4447mgkT7kprqeWKKy5h3LhjOe64o/nrX/8CwA9+cD1XXXUZAJMm/YFTTz2BQqHAV796ENXVcwH45z+f43//91QAXnjhn3zzm+MYN+4YDj/8K0ye/MQHaluyZAlnn30m48Ydy7hxx/L005Nb4lFKkiRJ7U5VdQ6AoZv1apXzf+Qwzhjjqw1/DiF8nGQ45w9JQmCD2cCWwOA1tPcHFqfBsGk7TY9Jh3suBgYALdu1tRG6++7b6N+/P5dddhU1NQs58cRv8PGPB/73f89h4sSfctZZ5/Lii1Po3r07d9wxgUKhwGmnncjzzz/D1lt/bI3nXbmyjjvv/BkLFsznhBPG8IlP7Na47b33ZvLCC1P46U9/ydKlS7nmmiuoq6vjnnvuZMcdd+biiy8nl8txyinj2HHHnTjllDMYO/ZoHnvsUe666zZ++MPbKStb8+8IHnzwV1xwwXiGDBnK888/y09+cgujRu3buP3xx//KkCFDufHGW3j99df4+98f41OfGtUyD1SSJElqR2bMzZEtyzCoX89WOX+z5/+FEHYE/gicS/JaiO2abM4ABZKewmIz2knbG/ZpKtNk20fq1+/DKbi6uozy8rZZe6a518lmyz60/4svTuGyy66kvLyMAQP6M2rUvvz73y8ydOgwMplk3z333JN+/TbloYd+zbRp05g9+z1WrFhONptp3KepTAYOPvjLdO3ahcGDB7HDDjvyxhtTKStLhp4OHjyIbDbLaaedwD777MsZZ/wv3bt35YUXnufZZ+t4+OHfAbB8+TJmzJjGlltuwUUXXco3v3kC3/nO+QwdOuQD915eXkY2W9ZYy/e+dzWTJz/B3/72Z1555WWWLVtGeXmyvawsw4gRu3LXXbcxb95cPvWpUYwbd3yb/V21hbKyMgYM6F3qMiRJktQOzF20jCGb9WbwoD6tcv7mLtCyD/Ab4KwY4/0hhP2AQU122ZykJ27mGtqrgT4hhGyMMZ/u09Bz916638wQQjnQG1jQ3BtYsCBHYZV3URQKBerrW3a865o09zoN42+b7t9QZ0NboVCgrq6efL5AsZjs+/jjf2fChDs57LCjOPDAQ5g/fz75fIF8vti4T1PFImQymQ+cM5PJUigUKRaLZDJZ7rhjAi+99ALPPvs0J5zwDW699W7y+Tzjx1/Fttt+HICFCxdQWdmH+voC06a9yyab9GXq1Nc45JDkvJlMhrq6PPX1BVasWEmxCHV1eU488ThGjtyLESN2Y8SI3bnqqsuor0/up1AosuWWw7jvvt/wzDNP8dRTT/LLX/6Cn//8V2QyLbfEbCkVCgXmzVtS6jIkSZLUDrw1cxE7DNt0vX9+LCvLrLbzq3H7R50ghDAEeAg4OsZ4f9r8XLIpbBtCyAJHA5NijNOB5Wk4BBidttcBTwJHpO1jgEnpnx9JP5NufzLdv8PbbbeR/PGP/wdATU0Nkyc/wYgRu5HNZsnnkxGv//zncxxwwBc46KBD6NmzJ//614sUCmsPmH/5y6MUi0VmzXqPN998g+2336Fx2+uvT+Wss05l111354wzvsWQIcOYMWM6u+02koceSqZUzptXzZgxRzJ//jzmzp3DPffcye2338vUqS/z/PPJdMw+ffrw7rvvADB58uMALFpUw+zZszj++JP55Cc/xZNPPv6hWh944JdMmHAX/+//fZ5zzvku8+dXs2zZ0hZ4mpIkSVL7sbh2Je/nVrbafD1oXs/eOUA34KYQQkPbbcBYkt6+biSBrWHxlWOAO0MIlcCLwC1p+6nAT0MIFwEzgKPS9ouBCSGEV4FF6fGdwvHHn8wNN1zNmDFHUCgUGDfuRD7+8e2oqalh0aJFXHnleA4//Cguv/xiHn30Ebp0qWCXXUYwa9Z77LzzJ9Z43m7dunH88ceSz+c577yLqKysbNy2/fY7EML2jBlzBN26deMTn9iNPff8JDvv/IkP1HLGGd9i880H8e1vn84xx4xh8OAtOOec8xk//kJ++tP7Of74U7jxxmvp06cPI0fuBcykb99N+cIXDmL06MPJZrPsvvue1NbWsmLF8sbrH3jgwYwffyFjxhxBeXk5Z555Nj16tM4YZUmSJGlj1bA4y5CBrRf2MsXiqlPp2o2tgHdXN4xzzpzpbL75sMbPpXzPnjqXVb97kiRJ0ur86bkZPPD3t7j5zFH07lGxXudoMoxza2Daqttb8gXtG63a2jy1tc6jkiRJkrRxmFG9hL69u6530GuOjrMMoiRJkiS1E1XVuVYdwgmGPUmSJElqU3X1eeYsWGrYkyRJkqSOZNb8peQLRcOeJEmSJHUkM6qT9USGbta7Va9j2JMkSZKkNlQ1N0dFlzIGbtK9Va/TKVbj3LRPN7IVXVr8vPmVdSx8f/la93nxxSmcd9632GKLIRSLRerr6/jKV77G4Ycnrxk855wz+e53L6Z//wEtXt9HmT17FmeccTIPPvhwm19bkiRJ6qyqqnMMGdCLsrJMq16nU4S9bEUX5v1kYoufd8A3jwXWHvYAQhjOj350BwBLl9Zy7LGHM3LkXmy99TbccMMtH3G0JEmSpI6iWCwyozrHXsMHtvq1OkXY25isWLGCsrIyevVKJmN+/euH8MMf3k5tbS3XXXcl+XyeiooKLrjgUoYMGcqzzz7N3XffRn19PYMGbcF5513I8uXLOf740fzoR3ewxRZbcsIJozn55NNZtKiGl156gQsvHA/A6aefxLhxJ7HLLiO48cZreOedt1m4cCHbbrst48dfucYar7xyPN26deeNN14nl1vCSSedxqOPPsJbb73Bpz/9Gc4441vk83luvfVmXnrpBfL5AgcddDBHHHEML744hZ///F66devGtGnv8rGPbcull15Jly4t37MqSZIktTcLFi9n2Yp6hrTyfD0w7LWJGF9j7NijKRYLzJxZxf77f+5DwzYfeOA+jjzyWPbf/wAmTfoDr776Mr169ea2237ELbfcRmVlJQ899Js5G1cAACAASURBVBt+8pMf8t3vXsw3v3kGN9xwNTvv/Al22mkXPvWpUTzyyOqHY77yyn8oL+/C7bffS6FQ4MwzT+GZZ54ihOFrrHn+/Hncfvu9TJr0B66++jJ++cvf0rVrVw499CCOO+5EHnvsTwDcc88vWLlyJd/+9ulsv/0Ojdf7xS8epH//AZx88liee+4ZRo3at4WepiRJktR+Vc3NAbT6Spxg2GsTTYdx1tbmOPvsM5k4cQKjRx/XuM/ee+/DTTddx3PPPc0+++zLPvt8mmeffZq5c+dw5pmnAFAo5Kms7APAl770Zf72t8d47LFH+dnPfrXW648YsRuVlX34zW8eYMaMacycWcWyZcvWeswnP/kpADbbbHO23vpj9O27KQCVlZUsWbKYKVOe58033+CFF6YAsGzZUt5++y222mprtt76YwwcuBkAw4ZtzZIli9f1kUmSJEkdUlV1jgyw5YCerX4tw14b69mzF/vv/zmmTHnuA+2f/ewB7LTTLjz11JM88MB9PPPMZD71qVHssssnuPba7wPJENCGkLZixQqqq+eQz+eZN28uQ4duRSaToVgsNp4zn68HYPLkx7nrrts57LAjOeigL7No0aIP7Lc6TYddZrPZD23P5wuceuqZ7Lff/gAsWrSI7t278+qrL1NRUdG436o1SZIkSZ1ZVXWOgX27062i9aOYr15oY/l8npdeeoHtttv+A+2XXHI+r702lUMP/RonnHAKMb7ODjvsxKuvvsyMGdMBmDDhLn784x8AcNddt7H77iM544xvceWVl5HP5+nTZxOmT3+XYrHIrFnv8dZbbwEwZcrz7L//AXzpS1+mV69evPTSCxQK+Q26j91334Pf//4h6uvrWbp0KaeeejyvvvryBp1TkiRJ6uhmVC9pkyGcYM9em2iYs5fJQH19Pdtuux3HHPOND+wzevRxXHvt95gw4U7Ky7twzjnfpV+//nz3u5dwySXnUyjkGTBgMy655HJeeeVl/v73x/jZz+6nR4+eTJr0B+6/fyKHHXYUf/zj/3HUUV9j2LBh7LLLCAAOOeSrXHbZhTz22KOUl3dh5513YdasWey++/rf06GHfp2ZM6s47rijyefzHHTQIey22x68+OKUDXlUkiRJUoe1bEU98xYtZ9Qug9vkepl2PMRuK+DdBQtyFAofvIc5c6az+ebDGj+X8j176lxW/e5JkiRJDd6oWsQ1v3iRM7++CyO27b/B5ysry9CvXy+ArYFpq27vFD17SSAzlEmSJEkqnarqZCXOoW00jNM5e5IkSZLUBqqql9CzWzl9e3dtk+sZ9iRJkiSpDVRV5xi6WW8ymUybXK/Dhr12PBdR7ZTfOUmSJK1JvlBg5rzaNluJEzpo2Csvr6C2drE/fKvNFItFamsXU15e8dE7S5IkqdOZu3AZdfWFNg17HXKBlr59B1BTM49cblGpS1EnUl5eQd++A0pdhiRJkjZCM6qXABj2NlQ2W07//oNKXYYkSZIkAcl8vWxZhsH9e7bZNTvkME5JkiRJ2phUVecY3L8n5dm2i2CGPUmSJElqZVVzc206hBMMe5IkSZLUqt6vXcn7tSvb7GXqDQx7kiRJktSKqkqwOAsY9iRJkiSpVVVV5wAYslnvNr2uYU+SJEmSWlHV3Bx9e3elV/cubXpdw54kSZIktaKq6lybz9cDw54kSZIktZq6+jyzFyxlyGaGPUmSJEnqMN6bX0uhWGTowLadrweGPUmSJElqNVVz08VZHMYpSZIkSR3HjOocXbtkGdC3e5tf27AnSZIkSa2kqjrHlgN7UpbJtPm1DXuSJEmS1AqKxSJV1TmGlGC+Hhj2JEmSJKlVLHh/OctW1JfktQtg2JMkSZKkVjGjunSLs4BhT5IkSZJaRVV1jgyw5QDDniRJkiR1GDPmLmHgpj3oWpEtyfUNe5IkSZLUCqqqcyWbrweGPUmSJElqcUuX1zP//eUlm68Hhj1JkiRJanEz5yWLswzdzLAnSZIkSR1GVeNKnKV5xx4Y9iRJkiSpxc2Yu4Re3buwSa+KktVg2JMkSZKkFlZVnWPIwF5kMpmS1WDYkyRJkqQWlC8UmDmvtqSLs4BhT5IkSZJa1JyFy6jPF0q6OAsY9iRJkiSpRVXNXQKUdnEWgPLm7hhCqASeBg4GdgCuarJ5C+C5GOPBIYRLgXFATbrtzhjjj0MII4C7gErgCeCUGGN9CGEoMBEYCETgmBhjbgPvS5IkSZJKoqo6R3k2w6B+PUpaR7N69kIIewGTge0AYoyPxBhHxBhHAF8EFgPfSnffAziyYXuM8cdp+0Tg9BjjdkAGODFtvxW4Nca4PTAFuLgF7kuSJEmSSmJGdY7B/XpSni3tQMrmXv1E4DRg1mq2XQ/cFmN8M/28B3BBCOE/IYQfhRC6hRCGAd1jjM+m+0wADgshdAH2BR5s2r7utyFJkiRJG4eq6hxDSjxfD5oZ9mKMJ8QYn1y1PYTwceAzwC3p517AS8C5wG7AJiQ9dYOB2U0OnQ1sCfQHFscY61dplyRJkqR25/3cChbXriz5fD1Yhzl7a3ASyRDMFQDpXLuDGjaGEG4E7gEeAYpNjssABZKw2bSdtL3Z+vUrfWKWJEmSJICqBcsA2GW7gQwY0E4WaFmDQ4HPN3xIF1s5IMZ4T9qUAeqAmcCgJsdtTjIktBroE0LIxhjz6T6rGyq6RgsW5CgUVs2LkiRJktT2XnmzGoDeXcuYN29Jq16rrCyz1s6v9Z4xGELoTzIP790mzcuA60IIW4cQMiTz/H4XY5wOLA8h7JPuNxqYFGOsA54EjkjbxwCT1rcmSZIkSSqlGdU5Nq3sSs9uXUpdyga9Z28bkh67RjHGecDJwMMkr1HIADemm48Bvh9CeB3oRTrPDzgVOCmEMBX4NHDRBtQkSZIkSSVTVZ1j6EYwXw8gUyy22yGQWwHvOoxTkiRJ0sZgZV2eb970OF/aeyv+Z99tWv16TYZxbg1M+9D2Vq9AkiRJkjqB9+bXUizC0IEbxyKShj1JkiRJagFV1TmAjeIde2DYkyRJkqQWUTU3R9eKLAM26V7qUgDDniRJkiS1iBnVSxgyoBdlmUypSwEMe5IkSZK0wYrFIjPn5TaaIZxg2JMkSZKkDTb//eUsW5FnyEayOAsY9iRJkiRpg82YmyzOsrG8Yw8Me5IkSZK0waqql5DJwBYDepa6lEaGPUmSJEnaQFXVOTbr24OuXbKlLqWRYU+SJEmSNlBVdY6hG9HiLGDYkyRJkqQNsnR5HfPfX75RLc4Chj1JkiRJ2iBV1cniLEM2osVZwLAnSZIkSRtkRmPYs2dPkiRJkjqMquocvXt0YZNeFaUu5QMMe5IkSZK0Aarm5hgysBeZTKbUpXyAYU+SJEmS1lO+UOC9+bUb1cvUGxj2JEmSJGk9zVmwlPp8YaObrweGPUmSJElab42Ls2xk79gDw54kSZIkrbeq6hzl2Qybb9qj1KV8iGFPkiRJktZT1dwlDO7fk/LsxhetNr6KJEmSJKkdKBaLzKjObZSLs4BhT5IkSZLWy/u1K1mytG6jXJwFDHuSJEmStF6q0sVZhm6Ei7OAYU+SJEmS1suMuUsA2NKePUmSJEnqOKqqc/Sr7EbPbl1KXcpqGfYkSZIkaT1UVec22vl6YNiTJEmSpHW2si7PnIVLN9r5emDYkyRJkqR19t78WopF7NmTJEmSpI6kYXGWIZttnO/YA8OeJEmSJK2zquoc3Sqy9O/TrdSlrJFhT5IkSZLW0YzqHFsO7EVZJlPqUtbIsCdJkiRJ66BQLDKzOsfQjXi+Hhj2JEmSJGmdzF+0jOUr8xv14ixg2JMkSZKkdVJVnQNg6Ea8OAsY9iRJkiRpncyYmyOTgS369yx1KWtl2JMkSZKkdVBVnWPzTXtQ0SVb6lLWyrAnSZIkSeugqjq30c/XA8OeJEmSJDVb7fI6FixevtHP1wPDniRJkiQ128x0cRZ79iRJkiSpA5kxN12J07AnSZIkSR1HVXWOyh5d6NOra6lL+UiGPUmSJElqphnVS9rFEE4w7EmSJElSs9TnC8yaX8uQdrA4Cxj2JEmSJKlZ5ixYSn2+aM+eJEmSJHUkVdXtZ3EWMOxJkiRJUrNUVecoz5axeb8epS6lWQx7kiRJktQMM6qXsMWAnmTL2keMah9VSpIkSVIJFYtFqqpz7Wa+Hhj2JEmSJOkjLcqtZMnSunYzXw+gvLk7hhAqgaeBg2OM00II9wKjgNp0l8tijL8LIRwA3AR0B34VY7woPX4EcBdQCTwBnBJjrA8hDAUmAgOBCBwTY8y1zO1JkiRJ0oZrWJylw/XshRD2AiYD2zVp3gPYN8Y4Iv3vdyGE7sA9wFeA4cDIEMKB6f4TgdNjjNsBGeDEtP1W4NYY4/bAFODiDb0pSZIkSWpJVdVLgA4Y9kiC2WnALIAQQg9gKHBPCOE/IYTLQghlwJ7AmzHGd2OM9SQB77AQwjCge4zx2fR8E9L2LsC+wINN2zf8tiRJkiSp5VRV5+jfpxs9unUpdSnN1qxhnDHGEwBCCA1NmwN/A04F3gf+ABwP5IDZTQ6dDWwJDF5De39gcRoMm7ZLkiRJ0kZjxtz2tTgLrMOcvaZijO8AX234HEL4ITCGpIeu2GTXDFAg6UFsTjtpe7P169e+HrgkSZKk9mX5inrm1izls3sMYcCA3qUup9nWK+yFEHYGtosx/iZtygB1wExgUJNdNycZ+rmm9mqgTwghG2PMp/vMWpdaFizIUSismhclSZIkqWW8Pet9ikXYtGcF8+YtKXU5jcrKMmvt/FrfVy9kgB+EEPqm8+5OAn4HPAeEEMK2IYQscDQwKcY4HVgeQtgnPX502l4HPAkckbaPASatZ02SJEmS1OIaVuIculn7GlW4XmEvxvgf4GrgKWAq8K8Y4y9jjMuBscBv0vbX+e/iK8cA3w8hvA70Am5J208FTgohTAU+DVy0frciSZIkSS2vqjpH965Z+vfpVupS1kmmWGy3QyC3At51GKckSZKk1nTVz1+gLAPfPXb3UpfyAU2GcW4NTPvQ9rYuSJIkSZLai0KxSNW8HEMGtp+FWRoY9iRJkiRpDeYtWsaKlXmGtLP5emDYkyRJkqQ1qpqbLM7S3t6xB4Y9SZIkSVqjGdU5MhnYon/PUpeyzgx7kiRJkrQGM6tzDOrXk4ou2VKXss4Me5IkSZK0BjOql7TLIZxg2JMkSZKk1cotq2Ph4hUMNexJkiRJUscxs7r9Ls4Chj1JkiRJWq0ZDWFvs/b3jj0w7EmSJEnSalVVL6GyZwV9elaUupT1YtiTJEmSpNWomptrt/P1wLAnSZIkSR9Sny8wa0Ftu52vB4Y9SZIkSfqQ2QuWUp8vMmQzw54kSZIkdRhV1UsAGDKwfS7OAoY9SZIkSfqQGXNzlGfL2HzT7qUuZb0Z9iRJkiRpFVXVObYc0JNsWfuNTO23ckmSJElqBcVikarqXLtenAUMe5IkSZL0AYtyK8ktq2NoO32ZegPDniRJkiQ18d/FWezZkyRJkqQOY8bcHABbDjDsSZIkSVKHUVWdo3+fbvToVl7qUjaIYU+SJEmSmphRnWv38/XAsCdJkiRJjVaszFO9cGm7n68Hhj1JkiRJajRzXo4iMNSwJ0mSJEkdR1V1sjiLPXuSJEmS1IHMqM7RvWs5/fp0K3UpG8ywJ0mSJEmpquolDBnYi0wmU+pSNphhT5IkSZKAQrHIzOraDjGEEwx7kiRJkgTAvJplrKjLd4jFWcCwJ0mSJElAk8VZNjPsSZIkSVKHMaN6CWWZDFv071nqUlqEYU+SJEmSgKq5OQb160GX8mypS2kR5aUuQJIkSZJKKZstox44+ovDyReKZLNl5POFUpe1wQx7kiRJkjqtbLaMmqV1XDXheaprljGwb3cuGLsnfXt0afeBz2GckiRJkjqtemgMegDVNcu4asLz1Je2rBZh2JMkSZLUaeULxcag16C6Zhn5QrFEFbUcw54kSZKkTitblmFg3+4faBvYtzvZskyJKmo5hj1JkiRJnVY5cP7YPRsDX8OcvY6wuElHuAdJkiRJWi/5fIHqeUs44Ss7s+WAnnStyFKetrd39uxJkiRJ6tSeeXUuN933AuUUyeQLHSLogWFPkiRJUic39d2FbD+0L+XZjhWPOtbdSJIkSdI6mLdoGdWLljF8q76lLqXFGfYkSZIkdVqvTa8BYIetNi1xJS3PsCdJkiSp05o6bSGb9KpgcL8epS6lxRn2JEmSJHVKhWKRqdNq2GGrTclk2v979VZl2JMkSZLUKVXNzZFbVscOHXC+Hhj2JEmSJHVSU6cvBGD4sI43Xw8Me5IkSZI6qanTatiif0/69u5a6lJaRXlzdwwhVAJPAwfHGKeFEE4CzgSKwBTg5BjjyhDCpcA4oCY99M4Y449DCCOAu4BK4AnglBhjfQhhKDARGAhE4JgYY66F7k+SJEmSPqSuPs+bVYvYd8TgUpfSaprVsxdC2AuYDGyXft4OOBf4FLBLep7T0t33AI6MMY5I//tx2j4ROD3GuB2QAU5M228Fbo0xbk8SGi/e4LuSJEmSpLV4a+b7rKwvdMhXLjRobs/eiSRh7ufp5xXAqTHGxQAhhJeBoem2PYALQgjDSHrwzgE2A7rHGJ9N95kAXBZCuAvYFzi0SfvjwHnreT+SJEmS9JGmTq8hW5YhDNmk1KW0mmaFvRjjCQAhhIbP04HpadsA4HRgbAihF/ASSa/fWyTh7WLgD8DsJqecDWwJ9AcWxxjrV2mXJEmSpFYzddpCthlcSfeuzZ7Z1u5s0J2FELYAJgF3xxj/kTYf1GT7jcA9wCMkc/saZIACyfDPpu2k7c3Wr1+vdStakiRJUqeWW7qSaXOWcNTnAgMG9C51Oa1mvcNeCGF74FHglhjjjWnbUOCAGOM96W4ZoA6YCQxqcvjmwCygGugTQsjGGPPpPrPWpY4FC3IUCqvmRUmSJElavRdiNcUiDBvYk3nzlpS6nPVWVpZZa+fXer16IYTQG/gzcFFD0EstA64LIWwdQsiQzPP7XTrsc3kIYZ90v9HApBhjHfAkcETaPoakp1CSJEmSWsWr02roWpFl60GVpS6lVa1vz94JJIuunB1CODtt+32M8ZIQwsnAw0AFyQqeDWHwGODO9BUOLwK3pO2nAj8NIVwEzACOWs+aJEmSJOkjTZ22kO2HbEJ5tmO/djxTLLbbIZBbAe86jFOSJElSc81ftIzv3PYMRx3wcT63x5BSl7NBmgzj3BqY9qHtbV2QJEmSJJXK1Ok1AB36/XoNDHuSJEmSOo2p0xbSp1cFg/v1KHUprc6wJ0mSJKlTKBSLTJ1Www7DNiWTyZS6nFZn2JMkSZLUKcyszpFbVseOW/ctdSltwrAnSZIkqVOYOi2Zrzd8WMefrweGPUmSJEmdxNRpCxncvyd9e3ctdSltwrAnSZIkqcOrq8/zRtUidhjWOYZwgmFPkiRJUifw1nuLWVlf6BSvXGhg2JMkSZLU4U2dtpCyTIYwdJNSl9JmDHuSJEmSOryp02rYZotKunctL3UpbcawJ0mSJKlDq11ex7Q5izvVfD0w7EmSJEnq4F6fXkOxSKearweGPUmSJEkd3NRpNXStyLLN4MpSl9KmDHuSJEmSOrSp0xay/ZBNKM92rvjTue5WkiRJUqcy//1lzK1Z1umGcIJhT5IkSVIHNnVaDQA7bNW5FmcBw54kSZKkDmzqtIX06VXB4P49S11KmzPsSZIkSeqQCsUir02vYYdhm5LJZEpdTpsz7EmSJEnqkGZW51iytK5TDuEEw54kSZKkDuq/8/U63+IsYNiTJEmS1EFNnbaQQf160Ld311KXUhKGPUmSJEkdTl19gTeqFrFjJ+3VA8OeJEmSpA7o7ffeZ2V9odMO4QTDniRJkqQOaOr0hZRlMoShm5S6lJIx7EmSJEnqcF59t4ZtBlfSvWt5qUspGcOeJEmSpA6ldnkd0+Ys7rSvXGhg2JMkSZLUobw+fRHFYud95UIDw54kSZKkDmXq9IV0rciyzeDKUpdSUoY9SZIkSR3K1HcXEoZsQnm2c8edzn33kiRJkjqU+e8vY27Nsk4/hBMMe5IkSZI6kNem1QCwYydfnAUMe5IkSZI6kKnTa+jTs4LB/XuWupSSM+xJkiRJ6hAKxSJTpy1kh636kslkSl1OyRn2JEmSJHUIM6tzLFla53y9lGFPkiRJUocwNZ2vZ9hLGPYkSZIkdQhTpy9kUL8e9O3dtdSlbBQMe5IkSZLavbr6Am9ULbJXrwnDniRJkqR27+333mdlXYEdfOVCI8OeJEmSpHZv6vSFlGUybD/UsNfAsCdJkiSp3Zs6rYZtBlfSvWt5qUvZaBj2JEmSJLVrS5fX8e7sxQ7hXIVhT5IkSVK79vqMRRSLvnJhVYY9SZIkSe3aq9MW0rVLlm0GV5a6lI2KYU+SJElSuzZ1Wg1h6CaUZ403Tfk0JEmSJLVbC95fztyFSx3CuRqGPUmSJEnt1tTpCwFcnGU1DHuSJEmS2q2p02ro07OCLfr3LHUpGx3DniRJkqR2qVAsMnXaQnbYqi+ZTKbU5Wx0DHuSJEmS2qX35tWyZGmd8/XWoFmvlw8hVAJPAwfHGKeFEA4AbgK6A7+KMV6U7jcCuAuoBJ4ATokx1ocQhgITgYFABI6JMeZCCJsAvwC2AeYBh8cY57ToHUqSJEnqkKZOS+brDR/mfL3V+cievRDCXsBkYLv0c3fgHuArwHBgZAjhwHT3icDpMcbtgAxwYtp+K3BrjHF74P+3d+dxcteF/cdfc+x9JJtNNgcBEkA+XEHOeCCKBx61iqKW/jxR8ai1Vau2P6nWai38Wuvx01JtSy0o9SqUn1ZFoSrVoki45Ih8QEkwIXeyOfbenfn+/vjObjbJbq6d2dnj9XwQZuc73/1+PjM7x/c9n+tu4COl7Z8AfhpjPBX4Z+D/luNOSZIkSZr5Vq/tZHF7I/Na66tdlSnpcLpxvg34Q2BD6fpK4LEY45oY4xBpwHtNCOF4oCHGeGdpv+tK22uAZwM3jt5e+vmlpC17AF8DXlLaX5IkSZLGNThUJK7r5LTj7cI5nkN244wxXgEQQhjetATYOGqXjcDSg2yfD+wuBcPR2/c5Vqm7525gAXuD5SG1tzcf7q6SJEmSZogHf7ONgcEiz3jqEhYsaKl2daakwxqzt58skIy6ngGKR7Cd0vbhfUbLjLrtsGzf3kWxuP/hJUmSJM1kP7v/SbKZDIvm1LN1655qV6cqstnMQRu/jmY2zvXA4lHXF5G2xI23fQswJ4SQK21fzN6WuydL+xFCyAMtwPajqJMkSZKkWeRXa3ewfEkLjfVH0341OxxN2PsFEEIIJ5UC3GuBW2KMTwB9IYQLSvu9obR9EPgpcFlp+xuBW0o/f690ndLtPy3tL0mSJElj6ukb5PGNux2vdwhHHPZijH3A5cBNwGrgEfZOvvI64DMhhEeAZuBzpe3vAt4eQlgNXAh8uLT9I8DTQwgPl/b5w6O7G5IkSZJmi0d+u5MkgdOXG/YOJpMk03a82zJgjWP2JEmSpNnlhlsjdzy4ic+/90LyuaPprDgzjBqztxxYe8Dtk10hSZIkSZqI1Ws7CcfNndVB73D46EiSJEmaNnbs7mPTjh5OO76t2lWZ8gx7kiRJkqaNh9fuAOC0ZY7XOxTDniRJkqRp41drO2ltquWYBU3VrsqUZ9iTJEmSNC0kScLqtTs4bVkbmUym2tWZ8gx7kiRJkqaFJ7d2s7tn0PX1DpNhT5IkSdK0sHe8npOzHA7DniRJkqRpYfXaTha3NzKvtb7aVZkWDHuSJEmSpryhQpG4rtMunEfAsCdJkiRpyvvNk7sYGCzahfMIGPYkSZIkTXmr13aSzWQIxxn2DpdhT5IkSdKUt3rtDpYvaaGxPl/tqkwbhj1JkiRJU1pP3xCPb9zteL0jZNiTJEmSNKXF33aSJC65cKQMe5IkSZKmtNVrO6mtyXLiMXOqXZVpxbAnSZIkaUp7eO0OwrFt5HPGlyPhoyVJkiRpytqxu49NO3o43S6cR8ywJ0mSJGnKWr22E4DTljk5y5Ey7EmSJEmaslY/sYPWxhqOWdBU7apMO4Y9SZIkSVNSkiSsXtvJacvmkclkql2dacewJ0mSJGlKenJrN7u7B+zCeZQMe5IkSZKmpNVrdwCur3e0DHuSJEmSpqTVT3SyaF4j81rrq12VacmwJ0mSJGnKGSoUib/daaveBBj2JEmSJE05v3lyF/2DBcfrTYBhT5IkSdKUs3ptJ5kMnHKcLXtHy7AnSZIkacpZ/cQOTljcSmN9vtpVmbYMe5IkSZKmlJ6+IdZs2MOpduGcEMOeJEmSpCklruukmCSc7uQsE5JJkqTadThay4A127d3USxO2/sgSZIkaZRcLsumnX0Uk4QFcxuoyUChUKx2taakbDZDe3szwHJg7f632wFWkiRJ0pSQy2Xp7BnkU1+9hy2dvXS0NXDl5Stpa6wx8B0Fu3FKkiRJmhKGgKuuu4stnb0AbOns5arr7mKoutWatgx7kiRJkqaEoUIyEvSGbenspeCwraNi2JMkSZJUdYVikS2dPXS0NeyzvaOtgVw2U6VaTW+GPUmSJElVd+Ptv+GGWx7hg68/byTwDY/Zc6KRo+PjJkmSJKmqfv7wJn5w1zqef+5SOlrruPpdF1AoJuSyGfI4G+fRMuxJkiRJqponNu3hulseIRw7l8uedxKFQpEMpaBSSChUuX7Tmd04JUmSJFXF7u4BPv8fD9DaWMMfvPIM8jnjSTn5aEqSJEmadEOFIl/4fw+xp2eQd196Jq2NtdWu0oxj2JMkSZI06b7xo18T1+3k8pecwvGLWqpdnRnJsCdJkiRpUv30gQ388J71vGjlsTzj9EXVrs6MZdiTJEmSNGl+s2EXX/lB5LRlbbz6ohOrXZ0ZzbAnSZIkaVLs6urnmv94wHBbtgAAIABJREFUkLnNdbzzkjPIZY0jleSjK0mSJKnihgpFrrn5IXr6h/ijV51Jc0NNtas04xn2JEmSJFXcV297lF8/uYu3vvQ0ju1ornZ1ZgXDniRJkqSKuv2+J7n9/g289BnHc/4pHdWuzqxh2JMkSZJUMY+t38m/3fYoK05o55UXnlDt6swqhj1JkiRJFdG5p59rbn6I+XPqecfLTyObzVS7SrNK/mh/MYRwBfDuUZuWA18BmoBnAd2l7R+LMd4cQngB8GmgAfhGjPHDpeOcBVwLtAI/Ad4ZYxw62npJkiRJqr7BoQJ//x8P0j9Y4IP/62wa652QZbIddctejPHaGONZMcazgNcBW4C/BM4Dnj18WynoNQBfAi4BTgXODyG8pHSoG4B3xxhPBjLA247+7kiSJEmqtiRJ+PIPIms27uZtv3sax8xvqnaVZqWjbtnbzxeAK4Ee4DjgSyGEY4CbgY8BK4HHYoxrAEIINwCvCSGsBhpijHeWjnNdaf8vlKlekiRJkibZj+59kjse3MTLL1jGOScvqHZ1Zq0Jj9krdc9siDH+O7AI+BHwFuDpwIXAW4ElwMZRv7YRWHqQ7ZIkSZKmoUee6ORr//UYZ500n5c/a3m1qzOrlaNl7x2kY/GIMT4OvHL4hhDC54E3AjcCyajfyQBF0rA51vbD1t7uGh2SJEnSVLCls4cvfvthlixo4kNvXuk4vSqbUNgLIdQCzwEuL11fAZwcY7yptEsGGATWA4tH/eoiYMNBth+27du7KBaTQ+8oSZIkqWL6BwtcfcM9DA4VeNcrzqB7Tx/de/qqXa0ZLZvNHLTxa6LdOM8EHo0xDs+8mQE+G0JoCyHUAG8nHbf3CyCEEE4KIeSA1wK3xBifAPpCCBeUfv8NwC0TrJMkSZKkSZQkCdd//xHWbe7i7S87nUXzGqtdJTHxsHcCaescADHGB4CrgTuA1cD9McavxRj7SFv/biptf4S0ayekM3l+JoTwCNAMfG6CdZIkSZI0iW5dtY47H97MK599Ak89aX61q6OSTJJM2y6Qy4A1duOUJEmSqufhNTv49Dfv59yTF/AHrziDTMaF0yfLqG6cy4G1B9w+2RWSJEmSNDNs2dnLF7/1EEvmN/GWl55q0JtiDHuSJEmSjljfwBB/f9MDAPzRpSuory3XEt4qF8OeJEmSpCOSJAlf+u6veHJbN++45HQ62pyQZSoy7EmSJEk6It+78wnujlt5zUUnccby9mpXR+Mw7EmSJEk6bA/8Zhv/8d+P87TTFvKilcdWuzo6CMOeJEmSpMOyaUcP//jt1Rzb0czlLznFCVmmOMOeJEmSpEPq7R/i8zc9QC6b4d2vWkFdTa7aVdIhGPYkSZIkHVQxSbj2O6vZvKOXP3jFGcyf01DtKukwGPYkSZIkHdR/3rGW+x7bxmXPP4lTj2+rdnV0mFwMQ5IkSdIBcrksQ0Bvf4HTT1pATT7LC85dWu1q6QgY9iRJkiTtI5fL0tkzyFXX3cWWzl462hq48vKV5PM5CoVitaunw2Q3TkmSJEkjisWEroHCSNAD2NLZy1XX3cVQleumI2PLniRJkjTL7e4Z4OHHd/Dg49t5aM0O/vzNK0eC3rAtnb0UiokBYhrxbyVJkiTNMsViwpqNu3nw8e08+Ph21m7cQwK0NNaw4oR2GuvydLQ17BP4OtoayGUzUEiqV3EdkUySTNs/1jJgzfbtXRSL0/Y+SJIkSZNid/cAD63ZzoOP7+DhNTvo6h0kk4ETlrSy4oR2VpzQzvGLWshmMuOO2WtrrHHM3hSSzWZob28GWA6s3f92w54kSZI0AxWLCY9v3M2Dv0lb757YlLbetTbWcEYp3J2+fB7NDTVj/v7wbJyFYkIumyEPBr0p5lBhz26ckiRJ0gyxq3uAh0pdMx9es4PuviEyGThxyRxeceFyVpzYznEL09a7QykUimQoBYZCQqHSlVfZGfYkSZKkKexgLWzFYsLjG3bzwON7W+8AWptqOeuk+aw4sZ3Tlo3feqeZzbAnSZIkTVFjjZ370JtWsn7TLn76yw0HtN698sLlnHnifI5d2HxYrXea2Qx7kiRJ0hQ1BAesd3f19XdxxSUreOS3O22900EZ9iRJkqQpqlBMxlzv7tiOZj797gtsvdNBZatdAUmSJEljy2UzdLQ17LOto62B2pqsQU+HZNiTJEmSpqpikfdcdvZI4Bte787ueTocPk8kSZKkKerOhzbxk19u5CNveRp1tTnXu9MRMexJkiRJU1CSJNy2ah19AwUa8hmySeJ6dzoiduOUJEmSpqDH1u9i7aY9XHzeUsfn6agY9iRJkqQp6LZV62iqz/PMMxZXuyqapgx7kiRJ0hSzdWcv9z62leecdQx1tblqV0fTlGFPkiRJmmL+6+71ZDMZnn/u0mpXRdOYYU+SJEmaQnr7h/jpAxs4/5QO2lrqql0dTWOGPUmSJGkK+ekvN9A3UODi84+tdlU0zRn2JEmSpCmiWEz4r3vW85Slc1i+uLXa1dE0Z9iTJEmSpoh7H93Ktl19vNBWPZWBYU+SJEmaIm69ex3z59Rz9lMWVLsqmgEMe5IkSdIUsGbjbn69fhcvOO9YslkXUdfEGfYkSZKkKeC2Veuor81x4Zkuoq7yMOxJkiRJVda5p59Vj2zhwjOX0FCXr3Z1NEMY9iRJkqQq++E96ykmCS84z0XUVT6GPUmSJKmK+gcK/Pf9T3LOUxawYG5DtaujGcSwJ0mSJFXRzx7aSHffkIuoq+wMe5IkSVKVFJOEW+9ez7JFLTxl6ZxqV0czjGFPkiRJqpIHf7OdzTt6eOH5x5LJuNyCysuwJ0mSJFXJravW0dZSx3mndFS7KpqBDHuSJElSFazb0sWvnujkeeccQz7nabnKz2eVJEmSVAW3rVpHbT7Lc846ptpV0Qxl2JMkSZIm2e7uAe5cvYlnrlhMc0NNtaujGcqwJ0mSJE2yH9/3JEOFhItdRF0VZNiTJEmSJtHgUIEf37ueM09sZ3F7U7WroxnMsCdJkiRNojtXb2Z3z6CLqKvi8hP55RDCj4EOYLC06R3AicCHgRrgszHGa0r7vgD4NNAAfCPG+OHS9rOAa4FW4CfAO2OMQxOplyRJkjQVJUnCbavWsXRBE6cd31bt6miGO+qWvRBCBjgZeGqM8awY41nAeuCvgWcBZwFvDyGcFkJoAL4EXAKcCpwfQnhJ6VA3AO+OMZ4MZIC3HfW9kSRJkqawXz3Ryfqt3Vx8nouoq/Im0rIXSpe3hhDagX8G9gA/ijHuAAgh3Ai8Gvhv4LEY45rS9huA14QQVgMNMcY7S8e6DvgY8IUJ1EuSJEmakm5dtY7WxhqefvrCaldFs8BEwl4b8EPgj0i7bN4OfAPYOGqfjcBKYMkY25ceZPtha29vPsJqS5IkSZNv/ZY9PPCb7fyvFwaWLJ5b7epoFjjqsBdj/Dnw8+HrIYR/IR2T94lRu2WAIml30eQIth+27du7KBaTQ+8oSZIkVdE3b43kcxlWhgVs3bqn2tXRDJDNZg7a+DWRMXvPCiE8f9SmDLAWWDxq2yJgA+lYviPZLkmSJM0YXb2D3PHQRp522kLmNNVWuzqaJSay9MJc4JMhhPoQQgvwJuD1wPNDCAtCCI3Aq4DvA78AQgjhpBBCDngtcEuM8QmgL4RwQemYbwBumUCdJEmSpCnnJ7/cwMBgkYvPc7kFTZ6jDnsxxu8A3wXuA+4BvhRjvAP4c+DHwP3AV2OMd8UY+4DLgZuA1cAjwI2lQ70O+EwI4RGgGfjc0dZJkiRJmmqGCkV+eM96Tj2+jeMWtlS7OppFMkkybce7LQPWOGZPkiRJU9mdqzfxT99ezR+/+kzOOml+taujGWTUmL3lpEPq9r19siskSZIkzRbDi6gvnNfImSe2V7s6mmUMe5IkSVKF/PrJXazZuIeLz1tK1kXUNckMe5IkSVKF3LpqHU31eS44Y/Ghd5bKzLAnSZIkVcDWnb3c++hWnn3WEupqc9WujmYhw54kSZJUAT+8Zz3ZTIbnn7O02lXRLGXYkyRJksqst3+In/xyA+ed0sG81vpqV0ezlGFPkiRJKrOfPrCRvoGCi6irqgx7kiRJUhkViwn/dfc6TjpmDicsaa12dTSLGfYkSZKkMrrvsW1s29XHC8+3VU/VZdiTJEmSyui2Vb+lvbWes0+eX+2qaJYz7EmSJEllsnbTbh5dv4sXnLeUXNZTbVWXz0BJkiSpTG5dtY662hwXnrmk2lWRDHuSJElSOXTu6WfVr7Zw4ZmLaazPV7s6kmFPkiRJKocf3bueYjHhBS63oCnCsCdJkiRNUP9ggdvve5KzT15Ax9yGaldHAgx7kiRJ0oT97KFNdPcNcfF5S6tdFWmEYU+SJEmagGKScNuqdRy/sIWTj51b7epIIwx7kiRJ0gQ89Ph2Nu3o4YXnH0smk6l2daQRhj1JkiRpAm5btY45zbWcf2pHtasi7cOwJ0mSJB2l9Vu7eHhtJ88/Zyn5nKfWmlp8RkqSJElH6bZV66jNZ7no7GOqXRXpAIY9SZIk6Sjs7h7g5w9v5plnLKK5oaba1ZEOYNiTJEmSjsLt9z3JUKHIxee7iLqmJsOeJEmSdIQGh4r86L4nWXFCO4vbm6pdHWlMhj1JkiTpCP1i9WZ2dw/wQlv1NIUZ9iRJkqQjkCQJt65axzHzmzhtWVu1qyONy7AnSZIkHYFHnuhk/dYuLnYRdU1xmSRJql2Ho7UMWLN9exfF4rS9D5IkSZomcrksQ0Dnnn52dQ1w/IJGsoY9VVE2m6G9vRlgObB2/9vzk10hSZIkabrJ5bJ09gxy1XV3saWzl462Bq68fCVtjTUUCsVqV08ak904JUmSpEMYLCYjQQ9gS2cvV113F0NVrpd0MLbsSZIkSaMMDhX47ZYu1m7cw9pNu1m7aQ/vfs1ZI0Fv2JbOXgrFxBNqTVk+NyVJkjRrDRWKrN86Ktht3MOT27oplOaEaGmsYfniVvK5LB1tDfsEvo62BnLZDBScP0JTkxO0SJIkaVYYKhTZsK2btZv2pP827mb91i6GSmGtqT7PssWtLFvUwrJFrSxf3EJbSx2ZTMYxe5qSDjVBi2FPkiRJ08rwrJiFYkIumyEPBwSuYjFh4/ZSsCu12v12SxeDQ+l+DXU5li0qBbtSwJs/p/6gSykcTrnSZHI2TkmSJM0Y47WwFQaHeOSJzpFg98TmPQwMpkGsrjbH8QtbeO7Zx7BscQvLF7WyoK3hiJdNKBSKZCidQBcSCmW/d1J52bInSZKkaSPJZfnQP9xxwNi5Ky5ZwVXX3UVtPstxC1tKLXZpd8xF8xrJZl0PTzOPLXuSJEmatvb0DLBm4x7WbNzNmo27ee2LTx1zVszF7Y18/C0rWTy/kVzW1cUkMOxJkiRpiugfLPDbzXtYs2E3j5fC3dadfQBkgCXzm0iSZMxZMRvr8zR1NFep5tLUZDdOSZIkTbpCsciGbT0jLXZrNuxm/dZuiqVz0/bWOpYvbmX5klZOWNzKcQtbaKjLOyumNIqzcUqSJKmqkiRh+66+kda6NRt2s3bUBCqNdXmWL2ll+eI02C1f3MKc5rpxj+esmFLKMXuSJEmqiPFCV1fv4EioGw54e3oGAcjnshy/qJlnP3XJSLjraGs46JIH+3NWTOnwGPYkSZJ0xMbqTvknrz2Xm3/8GL9YvRnYO87uqSfOH+mOecyCJvI5J1CRJoNhT5IkaZorV7fGJEno7S+wp2eA3T0D7OkZTC+7B9jdM5hu7063v+XlZ/CPNz8wMlHKls5ePv3Ve3jP75/NcR3NLF/cyvGL0nF2kqrDV58kSVKZVGMs2aEmLBkYLOwNbT0D7O4eHAlzu7sH2dM7wJ7uvbcPFcaeC6GhLk9rYw0tTbUsnNdIW2vdmEsgLJjbwEuefnxF77Okw2PYkyRJKoNDha5iMWGoUGSokDBULFIopNcLpe3D14cKRYaKCYXhfcfcJ6FQTC+fdfZSPnnD3fu0sF113V2889Iz+Zuv3E3/wNgj2mrzWVoaa2ltqmFOcy3HdjTT0lRDS0O6rbWxtnR7Lc0NNdTk9+16meSyYy6BkMtmYJzAKGlyGfYkSZKOUpIk7O4e4Mlt3SxZ2Mr/+fKqA0LX216xgquuu4tKTYD+9DOXjNnCNre5juc8dQktjTVpaGuspaUU4loba6mrzU2o3Dxw5eUrDwi3eXDCFGmKMOxJkiQdwuhQ9+S2bjaWLjds66a7bwiAq991wbjdGl/6jGXkcxnyuWzavTOXJZfLkM9m924vXeazGXK5bPpzbvjnzMjv7X+MTD43ZgvbnOZafv/5T6nYY1IoFGlrrOHqd13gEgjSFGXYkyRJKhkd6jaU/u0f6gCa6vMsmd/E+ad0sHh+E8fMb6K9tX7M0NXSWMOlzz6hYnXOUb0WNpdAkKa2CS2qHkL4KPB7pavfjTH+aQjhX4FnAd2l7R+LMd4cQngB8GmgAfhGjPHDpWOcBVwLtAI/Ad4ZYxzi0JbhouqSJGkMh5ooZTjUjQ5zwz+PFeqOmd80EuqOmd9Ea1PtAevCHWrMXjXvr6SZ6VCLqh912CuFt48BzwUS4PvA3wMfB14YY9w4at8GIALPAdYB3wU+G2O8JYTwEHBFjPHOEMK/AHfHGL9wGFVYhmFPkiTtZ6zQ9WdvPJ816zt56PEdbNjaNW6oG/53TOlyzhih7lBlG7okTZZDhb2JdOPcCLw/xjgAEEL4FXBc6d+XQgjHADeTBsKVwGMxxjWlfW8AXhNCWA00xBjvLB3zutL+hxP2JEnSEahWEJlIucUkoa+/QN/AED39Q/T1F9LL/a/3D9Hbn2679HlP4fPfvH+fiVL+5suruOKSFaz61WaWzG/ivFM6JhTqxmO3RkkATU05GhsbK3b8np4eursP/Q5z1GEvxvjw8M8hhKeQdue8ELgIeBewC/gO8FagizQcDtsILAWWjLNdkiSV0VitXR9600rymYT+/rSFK0kgIaH0H0lpY3pZ2lbqEZQkh74doKEhT21tzcgslR1tDfzp689jzYadPLmli97+IXr7C+nlQBrYRge6vv4Ch+q/kwHq6/I01uWor8tTX5sfc6KU4xY187n3XFiWUCdJB9PY2FjR95okSeju3nPI/SY8QUsI4XTSbpkfjDFG4JWjbvs88EbgRtjnvToDFIHsONsPW6nZUpIklQwOFdLZIrd2sX5LF09u7eKic5Ye0Np19fV3ccUl6bIAlXLl5Su59uv7lvu3N9zNFZes4JqbHqS2JkdTfZ7G+jyN9TW0NNbRMS9PU33NyLbRl3u352lqqKGhFO6y2b0nVZ17+sacKKWxvoa2lvqK3VdJmkwLFrQccp8Jhb0QwgXATcB7Y4xfDyGsAE6OMd5U2iUDDALrgcWjfnURsOEg2w+bY/YkSbNRkiTs7Bpg046e9N/20uWObrbt6ttnTbc5TbW8+OnLxmztWtTeyJteHEa+gc6U/pchw/CX0pnS9dJ/Y9yeYThqZUo7ZEr7LpnfNGa5xy9q4Z8+eBH53L4LdR+J4sAQ3QNDIzPCDcvlsmPOTpkMFti69dDfhEvSRB1OEJuorVv3jB6zN6ajDnshhGOB/wdcFmP8UWlzBvhsCOFHpF033w5cD/wi/ZVwErAGeC3wpRjjEyGEvhDCBTHGO4A3ALccbZ0kSZoOjmQMW9/AEJt39LJxRzebd/TuDXadPfQP7B2vUVuTZVFbI8sWtfL00xaxqL2RRfMaWdjWSGN9niSXHbO1q6k+z3POOqZi93W8cvO5DBmOPugdjOu/SVJqIi17HwDqgU+HEIa3fRG4GrgDqAFuijF+DSCEcDlpK2A98D3Srp0ArwP+OYTQCtwLfG4CdZIkaUobb3r+pFjkiQ27R1rqNpcuO/f0j/xuBmifU8+ieY08ZenikUC3aF4jc1vqyB5kfEie6qzFVq1ynShFkia4zl6VLcOlFyRJ00CxmLC7Z4DOPf0saG/iE/961wEtXaPHzjXV50dC3KL2tHUuvWygJp876npMx9k4JWk6WrCgpeITtOzXjbPsSy9IkjStlSOEFIpFdnUNsGNPP517+unc3bf35z39dO7pY2fXAIXSF5NXv+uCMcewLW5v5EOvP4dF8xppbqipyElCtVq7bGWTpOow7EmSZqXxulO2NdaMBL7BoSKdXWmAGw5vO0aFuM49/ezqHmD/TjK1NVnmtdTT1lJHOK6NtpY65rXU0dZSz5ym2nFmiszzlKVzJ/MhkCTNcHbjlCTNSkkuy4f+4Y4DQtd7Ljubf7z5ATr39LOnZ/CA32uoy5eC295/81rr9/7cUkdDXX7clrnDCZmSpOnNbpySJFVBoVjk0XW7WLKwdczulDU1OeY213HC4tZSgKunrTUNcXOb0yA3ofKdKVKSNEkMe5KkGW9wqMDDazu5N27l/l9vo6t3kD9/88oxu1O2t9bx3tc8taL1cQybJGkyGPYkSTNSb/8QDz6+nXviVh54fDv9AwUa6vKcdVI755zcwbKOpqosCSBJ0mQx7EmSZow9PQPc/9g27nl0K6vX7mCokNDaVMszTlvIOWEBpxzXRj63dyFvu1NKkiohGSpQyblRkqHD+1rSsCdJmtZ27O7j3ke3cu+jW4nrdpIkMH9OPc87ZynnhgWcuGQO2ezYg+TtTilJqoRMPseWv/9OxY7f8e7fPaz9DHuSpGln044e7olbuPfRrazZuAeAY+Y38bvPWMa5YQHHdjRXdBY0SZKmA8OeJGnKS5KE327u4p5SC96Gbd0ALF/cyqsvOpFzTl7AonmNVa6lJElTi2FPklR1uVyWIdhn7NzgYIFfP7lrpIvmtl19ZDIQjp3Lcy8+mbOfMp95rfXVrrokSVOWYU+SVFVjLTL+/teey9dvfYT7HttGPpfh9GXzeNkFyzjrpPm0NNZWu8qSpGmkqSlHY2Nlen/09PTQ3T11R3wb9iRJI8ZqYZvI7JTFYkJP/xDdfYP09A3R0zfq59L255x7LJ/+6r0j691t6ezlU1+9h/f+/tk87bSFrDihfcILmUuSZq/GxsaKjeNOkoTu7j0VOXY5+OkpSQLGbmG78vKVtNRl2d01QHcprPX0D478vH+I6+0fKt2W7tM3cPBvO/O5DM8597h9FjaHNPDNn9vAojl205Qk6WgZ9iRJAPQVkpGgB2nguuq6u7jikhVcdd1d4/5eXU2Oxvo8jfV5murytLfWc1xHMw31eZrqa9Lt9Xka60b9XNpem89CPkdHW8M+ga+jrYFcNgOFyq1RJEnSTGfYk6RZanCoyGPrd/LQ4zt4cM123v2as8ZsYVs4r4E3vPDkkYDWOBzi6tKfRy9SfjRywJWXrzygRTEPrnsnSdIEGPYkaRbZ3NmThrvHt/PIbzsZGCySz2V4ytK51NZkx2xha26o4bnnLK1YnQqFIm2NNVz9rgvKNlZQkiQZ9iRpRusbGOKR3+7koce389DjO9iyMw1yHW0NXLhiCWecMI9TjmujrjZHLpetWgtboVAkQ+lDqZDYoidJUhkY9iRpBkmShCe3dvPgmjTcPbpuJ4ViQl1NjlOPb+Pi84/ljBPmsbDtwCmobWGTJGlmMexJ0jTX1TvI6rU7eGjNDh56fDs7uwYAWLqgiYvPP5YVy+dx0tK51OQPPbbOFjZJkmYOw54kTUEHW++uWExYu2kPDz2+nQfXbOfxDbtJEmisy3P68nmcsXweZ5zQTltLXVXvgyRJqi7DniRNMWOtd/ehN61k/aZd/M8DG3l4zQ66egfJAMsWt/KyZy7jjBPaWb64hVx2YjNjSpKkmcOwJ0kHcbAWtolKkoSBwSJ9gwX6BoboHyjQN1DgmEWtB6x3d/X16Xp3v3qik6ee2M4ZJ7Rz2rI2Whpry1IXSZI08xj2JGkcY7awXb6SbFJk157+NJwNFujrL9A/OETfQGEksPUN7N22z/ZRwa5/oMBYS4Zf/a4Lxlzv7tiOZj797gvIZjKT8wBIkqRpzbAnSaTj4HZ29bNtVx9bd/ayfVcfTztzCZ/52r37trBdl7awXXXdXeMeKwPU1eaoq81RX5unviZHfW2OOc21LKxNf66ryVNf+rm+tG9dTZ76uhytTbVjrndXW5Ml48yYkiTpMBn2JM0KxSRhV9cA23b1sm1XH9t2li539bF9Vx/bd/dRKO7bzvaMpx4zZgvb4vZG/uAVZ1BXs29YGw52tTVZMhNofavmeneSJGnmMOxJOmKVHMd2tGUmScLu7gG27upj2660ZW50qNu+u4+hwr5hrrWplgVz6lm2uIXzTulg/tx65s+pZ/6cBtpb68jX1YzZwtZYn+f8Uzoqdl9d706SJJWDYU/SERlrHNuVl6+krbGmLGEkSRIKxYShQpGhQnqZy2cpkOH/XL9qpMwPvP5c7v3VZh56fDvbdqZhbnBo3/JbGmuYP6eB4xa2cM7JC9IgN7eB+XPqmddaT11N7uD3FarWwuZ6d5IkaaIMe5IO28BggaTIATNFXnXdXXzgdedy651rRwLaSFgbKjJULJYuS9f32Wf09YRCoXjApCVXXr6Sa7/14D5l/t0N9/COV57Jzx/axNIFTZx10nza59TvDXSt9dTVHjzMHYotbJIkaToz7EnaR3ffIFs6e9N/O3vZ2tnLls4etuzsZWfXwLgzRRaKCXc8uImaXIZcLktNLksulyldZqkp/dxYlyeXzVCTz5LLZqnJj7f/3uuL2xvHnp1yYTMfvfz8ij4etrBJkjS9JUNFkmSs+a/Lc+ypzLAnTWNHM3YuSRJ2dg2MBLitO3tHwt3Wnb109w3ts/+c5lo65jZw+rJ56Xi1uvyY49g62hq45n3PrsC9hCSXHbPMXDYDhcq8eUuSpJkhk8+y+XM/qcixF/5xZc59ysWwJ01TBxs71z8wxPbdfWmr3H5hbuvOXgZGfQuVzWRon1NHx9wGlp+6kAVz0+DWMbeBBXMbDugKWY3+uotPAAAX/0lEQVSZIvNUb+ycJGnma2rK0djYWLHj9/T00N3tJ5aP8+Qz7EnT1GCSjDl27p2XnsknvnQXxVHdFWrz2ZEQd/ryeSNhrqOtgXmt9eRz2cMutxrj2Bw7J0mqpMbGxgktmXMoSZLQ3b3ngO3VCj/VKrdaj/NsZtiTprD+wQJbO3vZ3NnLlp09bN6Rjp/b3NnLn77hvDHHsc1pruN3nnEcC+Y2sLCtkQVzG5jbXFvWN9dqjGNz7JwkaaapVvgxdM0ehj2pyvoHC6VulmmI29LZw5ZSwOvc07/Pvi2N6bpvpx7fRk1+7HFsc5trufTZJ0723ZAkSdIUY9iTyuBQE6X0DxTYPBLiekbG0G3u7GFn18A+x2ptrKGjrZHTjm+jo62BhfMaR7pdNtbX7FOm49gkSZI0HsOeNEFjTZTygdefy6qHN/HLR7eyeWcvu/YPdE21o8bPNbKwbW+Xy8b6w3tZOo5NkiRJB2PYkw5hqFBkV9cAO7v66dzTT2dXPzu7+tm5p5+dXQP83sWBL9z0yzEX/L7/0a2sWN6+t4WuNClKQ115XnqOY5MkSdJ4DHuaUY5k3bkkSejqHaSzFNqGA1znfpd7egbZfyW3XDbD3OY62lrqaG2qHXfB7w+9/tyK3E9JGs3pzCVJYzHsacYYqzvln73xfDZv3cOajbvZuWdgv1a5fobGWJC7pbGGtuY65rbUsWxRK20tdcxtri1dptubG2rIlmaxcsFvSdXmzHqSpoNkqEiSVO7cKBlyKMv+DHuaNpIkobe/wK7utCVuV1fpsrufXV0DvPTCE/j8N+/fpzvl33x5FVdcsoJ///FvqKvJMbeljrbmWk5aOmck0A1fzm2uZW5z3RGtOQcu+C1Jko5OcSipaPgpDk2tL50z+SybP3N/xY6/8H1nVezY05Vhb4Y7km6N1Sq3mCR09Qyys6ufXd1pd8pdXQPpv+5+dnanwW5X1wADY3xjU5vPMqe5lvra/NjdKTuaueZ9zy7bOLn9OVGKJM18dpVVJWTzGR69ZnPFjn/yHy6s2LE1PRj2Jkk1QtdY3RqvvHwlbY01FSu7mCRkspkDyv3TN5zHuo27WLNhN7u6B0YmPNnVPcDu7gEKxQO/eWqoyzO3uZY5TbWcuGQOc5prmdOUtsDNaS5dNtXRUJcjk8mM252ytiZLJlvZp7oTpUjSzGZXWUnT0awLe1M5dCVJQqGYMFQoMjhU+jf651HXhw5121CRi59+PJ/+2r37dGu86rq7+KPfO4uvfv8RisWEYlL6V0woFtn3egJJMa3T8PaktH3kd0cu09+FtEvjtd96cJ9y//Yrd3PFJSv49h0P0NJYMxLajlnQxNzmOuY0pV0o55SC3JymWupqckf0ONudUtIwW2E0U/hcljQRsyrsHW1LVzFJGBws0j9UYGCgQP9QkYHBAgODBfoHi6XLUdeHhq8X6R8s8DsXLOezX7/vgND1zkvP5HPfuG+foDbRbtsZoCafpSaf5eJnLBuzW2N9bZ7BQpFsJkMum6EmmyWbzZDNlP5lM2Qz7N02cgnZTIbMqH1z2QyZ0vbhfRfOaxiz3OMWNvNPH7zoiMfEHS67U0oaVq1WGE/MK2+2PcazrUVxtv19pUqbVWFvCEaCHuwNXe+57Gy+dusj4wa5scaJHUo+l6WuJkttTY6XPfvEMcPP3OY6zjyxnZpcjpp8lnwppNXksiOBbfjnA24btU9+1PVcNjPyoTBet8Z5rXVcWcElAcYrtyafJVPh91e7U0qqJk/My2usE/PZ9hhXS7VCl39fqbxmVdgrFJMxQ1dNPkt37xB1NVnmNNVSW5MbCWp1NTlq81nqanPU5kvXR99Wky1d7t23tiZLLru39Wq88DOnuZbLX3Jqxe5vtbo12p1S00G1TmT81lqVMFAoVHRGv4HC2M+p2XRiPlCo7JTxA1OsF8ps+tvORkmFZwFNptgsoLPZrAp7uWxmzNDVPqeej7zpvIqVW63wU61ujXan1JGYbd8e5ys0K+w+x59CYW+2hdtqha7aXI6X3Xhzxcr9z1e/smLHPlLVCl21uSy/d9MjFSv3m686pWLHlvaXyWfY9MknKnb8RR88vmLH1pGZEmEvhPBa4MNADfDZGOM1lShntoWu4bKr0a3R7pQ6XLPt2+PaXC0v+dabKnb8Wy65Hug/YPtsC9XVUpvL8bs3/lvFjv+dV7+uYseeLmpzWS696WcVO/5/vOqZFTv2dFIoVLblp1CYWi0/s229O80eVQ97IYRjgL8GziU9Q/lZCOHHMcbV5S5rNoYuTU8zsYvhVGuBmW1mW+gaKAxVuPVnqGLHnk6q1ZI5mwxWOHQNjhO6crkM1//H1oqV+6ZLF4y5vVohM5vPcOd1WypW7tMv76jYsaWDqXrYA14A/CjGuAMghHAj8Grg45UozNA1MTMxhEzFcqt1Yl7Jcscrc6gwUNEP9qHCwJjbBytc7uA45c421Qpdtbk8L73p2oqV+91XXVGxY08ntbkcl9z4g4od/1uvflHFjj1d1OQyfPTmDRU7/sdeuaRixz4auVyGW76xrWLHf8ll8yt2bGkqmgphbwmwcdT1jcDKw/i9HKTLAwA0NuZoaGgoe+WG9fb20tMz1kn5bCu3kWXLllWs3LVr19Lb2zVlyq2rrexLpK42T2/vgS3LhaHKBpHC0MDIa2e0SgavocLYZeZztXzhP99YkTIB/uBlXyabPTB41eRq+dgtletO+dGXXD9muQAdDZU92RjrcR4oDFY4dA2OWW5tLs+bv/+lipX7ry9+y5jlAnQ0NlesXBj7cU7LbapSuZX7Yurg5dZPerkLGusmvcy03JqqlDu38cjWnS1XuU2NlVkq6VDlNlSp3Lrm6pSbb6lOubnW6jyvsq211Sm3pXLvG+OXWblz9eFyR5U95h80U8kP/8MRQvhzoD7G+JHS9bcB58YY33mIX30W8NNK10+SJEmSprgLgf/Zf+NUaNlbT1q5YYuAw+mvsKr0extxRn9JkiRJs08OWEyajQ4wFVr2jiFNoSuBbuBnwNtjjHdVtWKSJEmSNI1VtpPwYYgxPgn8OfBj4H7gqwY9SZIkSZqYqrfsSZIkSZLKr+ote5IkSZKk8jPsSZIkSdIMZNiTJEmSpBnIsCdJkiRJM5BhT5IkSZJmoKmwqHpFhRBagauB5wBDQCfwfuAtwKIY46tH7ftC4IvAU2OMeyZY7kXAX8YYLxq1bRlwe4xx2X77JjHGzETKG3X8NcA/xRjfMWr7WcB9pPf5PaXNpwG/BgaAO2KMf1iGsh8FVu930z8DHwR6SmUNuy/G+OaJlFkq9yIOfJz/B/j7GOPXR21rAn4LhBjjtomWO+q4rwY+RPpaygJfjjF+MoRwHenf+rpylVUq7yIOvL/XABcAtcBJ7P0b/N8Y47+Wqdzx7uda4KIY49rSfh8ELgeeF2PcPIHyLgK+Q/oczZDety/GGP9v6fbnAR8FFpEuJno/8N4Y4/qjLbN03GWM8xoFnjuqTpTKbQb+Jsb4xTKUO/r1kwVagetjjB8NITQDfwO8iHQ90t2kz4MfTqTc/eqQxBgzB3stxxivKVNZ+5fRQLrG6v8GTmXUczyE0ALcRvo+9f4yl52QPrc2ADcA7yvtdhKwCegC1sQYX1mGMg/2vDqsz4qjLHu81+7twFLS+zhsc4zxRRMts1TueJ+9TcDn99t9BfCmGOMNZSr7DOBB4NUxxptCCC8iff1Amf+2pfLywJ8Bryd9TuWA60nv/0eBd5bKBKgjfTzeGWO8Y4LlLmPs5/KbSdcvHnlvLqeDvT9X8nk16hznhTHG20ZtXwv8JXD56NdRuR3kvfEm4MQY4+WTUNY/Az8FPgu0k76ufw68J8bYXYZyD/Zc/lfgecAO0veSXuBtMcYHylDuMsa+v/cAL6UCr58x6jD8GTgZ51TLGP8zsIEyfwbP6LAXQsgC3yNdw++sGONQCOG5wC2ki7jfHkJ4eYzx26Uw8AXgLRMNelW2HXhxCCEXYyyUtl0GbAWSGONZMPLm+Dtl/iDYMHz80UoBoNxlHcyXgNcBXx+17VLgR2UOescAnwLOiTFuL52Q/3cIIZarjMMxHNRHnSAe8DeYiMO9nyGE9wFvAJ4bY9xShqLv3u+Ef3UI4TbSD7gbgEtjjHeWbv9D4Gbg/DKUe1h1KpV7FrAqhPDVGOPuCR57n9dPCGEJ8FgI4evAP5AG2tNijAMhhLOB74YQXhtjvH2C5R6yLhWyYdT7UQa4CrgR+MjwDqXn2vdJn9f/uxJll8r5FPDiUfW5nTSA3V7GMifdYbx2r6jEfTzEZ+9p+z327wPeRPq3L5e3AP8OvAO4Kcb4A+AHpfJup/x/238AFgLPiDHuLAXdm4Fdpdu/GGP8y+GdQwjvBT4NPK0MZY/1XP5kGY57KOO9P0OFnlclg8A/hxBWVOlc7YD3xhDC5cCJk1FWqbxfkZ6r/rz0WrsG+CvgT8pQ5qGey38x/CV2COEVwLWk59PlMNZj+5dU9vVzgEqfU40y3mfgGyjzZ/BM78b5XOA44KMxxiGAGOOPSb/16gXeBvx9Keh9HPj2dP9wJ/027T7g2aO2vRD4r+pUpyq+CVwQQpg3atsbSENgOc0HaoBGgBhjF+lJy/7fxkx3h7yfIYQ/Bt5I2qJXjqC3vwagQPqB8xHgE8NBr1Sna4BvhBDqKlD2wSwjbWnrr8CxF5N+a34ucDzwJzHGAYAY433AJxgVjKazGGNC2gJyBnAmjLTGf4/0S5pyBr2x/LhU9kxTrfeog3325oZ3CiFcCPw58KoYY185Cg4h1JB+2fdh4JwQQiVOwkeXt5S0FeTyGONOgNIXP3/I3taI0ftngWNJW0cqoRrP5dHvz5W2gbSl/1OTUNZUtYi9r+ki8DHS854JOdLnMjAHOOoePEdjEl4/VbHfZ2BruY8/o1v2gLOB+0svhhExxu+VfvyvEMIPSJumT6V8305U2zeBVwM/DiGcDzxAetJYaUtCCPfvt+0NpcvvhRBGd+MsW3P4/mKMXSGEbwGvAf6x1EISgFvLXM4vS+U8HkK4j/RD9qsxxl+HEMpZVFUdxv18F/AB4K3lbDkFzis9n7KkXSm+SfpB/3TG+AYzxvh3ZSp3rOfx/nVqAtqA24GLY4zlCHvD5daTnqSvAl5JGn7uLn0YjPYT4P+UodyD1WW0N8QYH6xQeZRaLB8jPaloJO0mtgJ4RaXKhJFw8GrSrlCVdLDnVUUcxmv32hDC6O52/x5j/OsyFH2oz15CCB3A10i7gf2mDGUOeynwRIzx0RDC/wPeTtotrVJWAqtjjJ2jN8YYHwEeCSGsAN5ZagVpI30/+w5p62NZ7fdcfmG5j7+f8d6foXLPq2HvBx4MIVw8ujvnJNn/dfxvpD2nJqMsSM+p3gd8O4SwgfQ1/a0Y43fLUN6hnssvAz5eallrIv1C5+VlKHfYWI8tTNLrp9pGfQa+mDJ/Bs/0sFcEDvVt4ftJx3K9IsbYOwn12Uep6Xb/k7iJ+jbwidI3IJcB3wB+v8xljGW8Lgcwud04IQ3wfwX8I+m3vF8Z1a21bGKMfxBC+ATpB+uLgDtDCK8rdznVdoj7+Vzgd4EvhxB+GGP8bZmKHd1NqJW0O99wC09S2l4L3FXaNg/4/RjjzyZY7lhdSYZfo3fHGC8qtSB+BdgdY1w1wfL2Kbf0uv0U6bja20gDz1jv1bWU/71jn7pU6NgHk5D2ujiftNXyEdJuQpeWuZzRH6R1pM+hSrcejve8OuBzgfTLubG2H7FDvHYr1d3uoJ+9pef4vwFfizHeXOay30waIiH97Pu3EMJHhlvFK2TkdRjSMZIfJm3B7AO+S6kbWghhEfAj4M4Y48YylT3ec7nSYe9g78+V7MZJjHF3COFtlLpzVqqccYzXjXNSyip5MIRwE/CC0r/rQgj/FmN8bxnKPNhz+WH27cb5TOD7IYSnxhjXlKHsg3bjrNDrZ6oZ/gy0G+cRuJu0G8c+rVohhKtK4weGm6h3AmsnoT6dpM3eo3WUtpdNqavOL4FnkQ6mnU1dOAGIMf4EWBRCOJa0W0LZWxFDCC8NIVwWY3wyxvivMcbfB/4YeGu5y6qmw7ifryt9Y/+PwFdDCLlxD3aUSq/Tb5AOml5VuiTGOBBjPKv0pvg4aQCquFJL3tuAl4UQfq/Mxy6STmp0DGmL6S9Iv0Wv2W/XZ5C+x80IpeAeSN8Tfx5j/ATpl3FnhBDecdBfPnIbhp83McZTY4xvijFWq1tQJzB3v21l+Vyo4nvUoT57P0oaTD5UzkJLrYUvAd4f0nHp15K2BpT7y4LR7gZOKwUeYow3lt6PXgYsGL1jjHETcAXp8JHlZSq/6s/l/d6fJ6vMW5mF3TlDCE8pfXmxJ8Z4c2l82QWkn0cTddjP5dLtPwN+QzrUoOIq9PqZMkZ9Bn7vUPseqZke9n4KbAE+OnwCGtJZud5MFcZVxXQw8WMhhFeN2vx2KhPGvknaxevu4TETs9CXSceD7ChzN6FhPcDVpUG8w620wzOfziSHup/D35j/JenYjb8odwVKr9+LgHtJTxT/IoTwtFG3nwmcQDpuZFLEGHeV6vJ3IYSGMh97iDTofQR4jPQb1c8OB74Qwrmk37j+VTnLrZZSS8/HgDtJTx6Gxyb2kHZb+mQI4bTq1bCiVgPzhp/PpcfiCsrzuVCt96iDffZ2kN6/yyrw2fQG4IcxxqUxxmUxxuOBvyadDbMiSj0ZvgJcH0KYCyMzGv4uY7wflU6Qvwv8baXqNNn2e3+eTO8nba1ePMnlVtNW4D0hnZF62NmU4TV9pM/lEMLxwHLSxoVJMRNfP3DAZ2DZz2NmdDfOGGMSQng58BngoRDCILCNtEvhZAwqvXC/fus3kLYyfSGE8BekrRAPkA5+Lbf/BP6FyZ3AYaw+xj8pXe4/Zq8nxvjMMpV7wOMcY3wncB1pi21F+nbHGH8cQvgY8J1RrS4/YG/30UoZ7/5WxCHu5+tH7TcYQng96eyUPyy1rk7E8JiQhHSSiV+SLnPQHUK4jLSr8kLS5Q/WAe+PMf50gmUeqWtJW0r+hPSksmxijN8PIfyc9HG+tHT8h0IIBdLB6a+vYFepMV/LMcY/rlAZOdKTlf8FPHX0TjHGX4QQPgN8PYSwMpZpIo+pIsZYKLUOf7b0pUEDaVelj5Xh2Ad77f6AA8dWQTpd/84JljvuZy9pS0weuCXsO7b5i3GCS5iQLvty5X7brgH+NIRwSmnsUSW8i/Q94Mel4NNCOp73JcBrx9j/Q8CvQgjPijH+T4XqBPDwqC7oxBiby3jsMd+fSSeHq8jzan+junP+oLRpUj8bJ8F451QvBf42hHAt6RdjkfS9sxwO9ly+kr1j9gqk48s/EGN8rExlH67Jev1U2nifgXMo82dwJkkqNeRDkiRJklQtM70bpyRJkiTNSoY9SZIkSZqBDHuSJEmSNAMZ9iRJkiRpBjLsSZIkSdIMZNiTJM1aIYRlIYQkhPDW/bZ/IIRw3SF+9y9CCJeUfv54COGNFayqJElHzLAnSZrtisCnwn4Lvx2G55GuMUaM8S9ijF8ue80kSZqAGb2ouiRJh6GXdLHvr4YQnhFjHBi+IYRwMunC3C3AYuB+4DLgrcB5wCdLC91fAjwUY/y7EMKFwCeBRtJFjz8cY/x+COFy4JWk4fIpQA/wphjjrybnbkqSZhtb9iRJgr8GuoCr9tv+NuD6GOPTgZOA5cBLY4zXAHcDH4wx3jy8cwihHbgReE+M8UzgTcANIYTlpV2eA/xRjPEM4BfA/67gfZIkzXKGPUnSrBdjLAKvB94cQrh41E1/BmwNIfwp8AVgCdB8kEM9Dfh1jPEXpeM+DNwBXFS6/Z4Y4/rSz/cC88p2JyRJ2o9hT5IkIMa4DngHcD0wv7T5a8DbgSeAz5AGtMxBDpMDkv22ZSmN7SPtMjosOcSxJEmaEMOeJEklMcYbgVuA95Y2vQj4eIzxG6XrTyMNdABD7A1xw34OnBJCWAkQQjgdeDZwewWrLUnSmJygRZKkff0x8KzSz1cCN4cQuoFdwH+Tjt0D+DZwdQihdvgXY4zbQgivAT4fQmgknYzlzTHGR0MIz5y0eyBJEpBJkv17m0iSJEmSpju7cUqSJEnSDGTYkyRJkqQZyLAnSZIkSTOQYU+SJEmSZiDDniRJkiTNQIY9SZIkSZqBDHuSJEmSNAMZ9iRJkiRpBvr/enp0MhJypAEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_country_bi = df_SubsetSize.sort_values('N')\n",
    "sorted_country_bi = sorted_country_bi[:-1]\n",
    "sorted_country_bi['Bisexual'] = sorted_country_bi['Bisexual men'] + sorted_country_bi['Bisexual women']\n",
    "\n",
    "sns.lineplot(x='CountryID', y='N', marker='o',\n",
    "                data=sorted_country_bi, label='Number of Respondents', sort=False)\n",
    "sns.barplot(x='CountryID', y='Bisexual', color=\"black\",\n",
    "                data=sorted_country_bi, label='Total bisexuals')\n",
    "sns.barplot(x='CountryID', y='Bisexual men',\n",
    "               data=sorted_country, label='Bisexual men')\n",
    "\n",
    "plt.title('Total Comparison of Bisexuals vs. Bisexual Men')\n",
    "plt.xlabel('Nation')\n",
    "plt.ylabel('')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above represents in the line graph the total number of respondents (y axis) by country (x axis). The colored bar graph represents the number of people who fall into the \"Bisexual Man\" category and the black color the total Bisexual People (\"Bisexual Man\" + Bisexual Woman \"category).\n",
    "With the visual analysis of the graph I have the following conclusions:\n",
    "- The total number of bisexuals in relation to the total number of respondents is low.\n",
    "- There is not much standard for the percentage of bisexual men in relation to the total number of bisexuals. For some countries it has more than half, for others less and for others it is close to 50%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3) Analysis of Transgender Quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='CountryID', y='N', marker='o',\n",
    "                data=sorted_country, label='Number of Respondent', sort=False)\n",
    "sns.barplot(x='CountryID', y='Transgender',\n",
    "                data=sorted_country, label='Transgender')\n",
    "\n",
    "plt.title('Transgender Quantity Analysis')\n",
    "plt.xlabel('Nation')\n",
    "plt.ylabel('')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above represents in the line graph the total number of respondents (y axis) by country (x axis). The color bar chart represents the number of people who fall into the \"Transgender\" category. With the visual analysis of the graph I have the following conclusions:\n",
    "- The number of transgenders interviewed is small in all countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4) Final Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_country_sum = df_SubsetSize.sort_values('N')\n",
    "sorted_country_sum = sorted_country_sum[:-1]\n",
    "sorted_country_sum['Bisexual women'] = sorted_country_sum['Bisexual men'] + sorted_country_sum['Bisexual women']\n",
    "sorted_country_sum['Lesbian women'] = sorted_country_sum['Bisexual women'] + sorted_country_sum['Lesbian women']\n",
    "sorted_country_sum['Gay men'] = sorted_country_sum['Lesbian women'] + sorted_country_sum['Gay men']\n",
    "sorted_country_sum['Transgender'] = sorted_country_sum['Gay men'] + sorted_country_sum['Transgender'] \n",
    "\n",
    "# sns.lineplot(y='CountryID', x='N', marker='o',\n",
    "#                 data=sorted_country_sum, label='Número de entrevistados', sort=False)\n",
    "sns.barplot(y='CountryID', x='Transgender', color=\"yellow\",\n",
    "                data=sorted_country_sum, label='Transgender')\n",
    "sns.barplot(y='CountryID', x='Gay men', color=\"red\",\n",
    "                data=sorted_country_sum, label='Gay')\n",
    "sns.barplot(y='CountryID', x='Lesbian women', color=\"green\",\n",
    "                data=sorted_country_sum, label='Lesbian')\n",
    "sns.barplot(y='CountryID', x='Bisexual women', color='dodgerblue',\n",
    "               data=sorted_country_sum, label='Bisexual women')\n",
    "sns.barplot(y='CountryID', x='Bisexual men', color=\"indigo\",\n",
    "                data=sorted_country_sum, label='Bisexual men')\n",
    "\n",
    "plt.title('Comparison of number of categories by country')\n",
    "plt.xlabel('Nation')\n",
    "plt.ylabel('')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, in the chart above, an overview of each category by country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Question Table Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are five tables that deal with the different aspects of LGBT life. They all have a common format of ContryName (name of country you belong to), subset (LGBT category that answered the question), question_code (code of the question being asked), question_label (the question being asked), answer (the answer for the question), percentage (the percentage of people in this country in this category who answered this question), and CountryID."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1) Daily Life"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This set of 50 questions deals with LGBT daily life in your country. Some questions like, \"How common is it to see same-sex partners holding hands in public in the country you live in?\", \"What would allow you to feel more comfortable living as a lesbian, gay or bisexual in the country you live in?\" or \"How many people in your family (other than your partner) know that you are L, G, B or T?\" are examples of what I find in the Daily Life chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>CountryID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly widespread</td>\n",
       "      <td>34.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly rare</td>\n",
       "      <td>45.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very rare</td>\n",
       "      <td>9.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Don`t know</td>\n",
       "      <td>4.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian          b1_a   \n",
       "1     Austria  Lesbian          b1_a   \n",
       "2     Austria  Lesbian          b1_a   \n",
       "3     Austria  Lesbian          b1_a   \n",
       "4     Austria  Lesbian          b1_a   \n",
       "\n",
       "                                      question_label             answer  \\\n",
       "0  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "1  In your opinion, how widespread is offensive l...  Fairly widespread   \n",
       "2  In your opinion, how widespread is offensive l...        Fairly rare   \n",
       "3  In your opinion, how widespread is offensive l...          Very rare   \n",
       "4  In your opinion, how widespread is offensive l...         Don`t know   \n",
       "\n",
       "   percentage CountryID  \n",
       "0         8.0        AT  \n",
       "1        34.0        AT  \n",
       "2        45.0        AT  \n",
       "3         9.0        AT  \n",
       "4         4.0        AT  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DailyLife.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2) Discrimination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This set of 32 questions is about how LGBTs view discrimination for being LGBT in their country. Questions such as: \"In the last 12 months in your country have you felt discriminated against or harassed because of your sexual orientation?\", \"In your last 5 years as an employee, have you heard or seen negative comments or conduct because a co-worker is known to be L, G, B and / or T? \" or \"During school time, before you were 18, did you hide or disguise that you were L, G, B or T?\" These are examples of what I find in the Discrimination table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>CountryID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Difficulty in gaining access to healthcare</td>\n",
       "      <td>2.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Having to change general practitioners or othe...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Receiving unequal treatment when dealing with ...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Foregoing treatment for fear of discrimination...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Specific needs ignored (not taken into account)</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian           c10   \n",
       "1     Austria  Lesbian           c10   \n",
       "2     Austria  Lesbian           c10   \n",
       "3     Austria  Lesbian           c10   \n",
       "4     Austria  Lesbian           c10   \n",
       "\n",
       "                                      question_label  \\\n",
       "0  Have you ever experienced any of the following...   \n",
       "1  Have you ever experienced any of the following...   \n",
       "2  Have you ever experienced any of the following...   \n",
       "3  Have you ever experienced any of the following...   \n",
       "4  Have you ever experienced any of the following...   \n",
       "\n",
       "                                              answer  percentage CountryID  \n",
       "0         Difficulty in gaining access to healthcare         2.0        AT  \n",
       "1  Having to change general practitioners or othe...         8.0        AT  \n",
       "2  Receiving unequal treatment when dealing with ...         3.0        AT  \n",
       "3  Foregoing treatment for fear of discrimination...         5.0        AT  \n",
       "4    Specific needs ignored (not taken into account)        12.0        AT  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Discrimination.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3) Rights Awareness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This set of 10 questions addresses LGBTs' knowledge of their rights in their country. Questions such as: \"To the best of your knowledge, in the country where you live, can same-sex couples be legally married and / or have registered partners?\" a non-governmental organization on discrimination against gay, lesbian or bisexual people? \" or \"Is there a law in your country that prohibits discrimination against people because of their gender identity when applying for a job?\" These are examples of what I find in the Rights Awareness table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>CountryID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>53.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>Don`t know</td>\n",
       "      <td>37.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>45.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian            d1   \n",
       "1     Austria  Lesbian            d1   \n",
       "2     Austria  Lesbian            d1   \n",
       "3     Austria      Gay            d1   \n",
       "4     Austria      Gay            d1   \n",
       "\n",
       "                                      question_label      answer  percentage  \\\n",
       "0  In the country where you live, is there a law ...         Yes        53.0   \n",
       "1  In the country where you live, is there a law ...          No        10.0   \n",
       "2  In the country where you live, is there a law ...  Don`t know        37.0   \n",
       "3  In the country where you live, is there a law ...         Yes        45.0   \n",
       "4  In the country where you live, is there a law ...          No        16.0   \n",
       "\n",
       "  CountryID  \n",
       "0        AT  \n",
       "1        AT  \n",
       "2        AT  \n",
       "3        AT  \n",
       "4        AT  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_RightsAwareness.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4) Violence and Harassment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This set of 47 questions deals with violence and harassment that LGBTs have suffered. Questions such as: \"Do you avoid holding hands in public with a same-sex partner for fear of being assaulted, threatened or harassed?\", \"For the past 5 years, have you been: physically / sexually assaulted or threatened with violence at home or in anywhere else (street, public transportation, workplace, etc.) for any reason? \" or \"Do you think the MOST SERIOUS harassment incident happened partly or completely because you were seen as L, G, B or T?\" These are examples of what I find in the Violence and Harrassment table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>CountryID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>33.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>No</td>\n",
       "      <td>53.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>I do not have a same-sex partner</td>\n",
       "      <td>12.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>Don`t know</td>\n",
       "      <td>2.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>51.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian            e1   \n",
       "1     Austria  Lesbian            e1   \n",
       "2     Austria  Lesbian            e1   \n",
       "3     Austria  Lesbian            e1   \n",
       "4     Austria      Gay            e1   \n",
       "\n",
       "                                      question_label  \\\n",
       "0  Do you avoid holding hands in public with a sa...   \n",
       "1  Do you avoid holding hands in public with a sa...   \n",
       "2  Do you avoid holding hands in public with a sa...   \n",
       "3  Do you avoid holding hands in public with a sa...   \n",
       "4  Do you avoid holding hands in public with a sa...   \n",
       "\n",
       "                             answer  percentage CountryID  \n",
       "0                               Yes        33.0        AT  \n",
       "1                                No        53.0        AT  \n",
       "2  I do not have a same-sex partner        12.0        AT  \n",
       "3                        Don`t know         2.0        AT  \n",
       "4                               Yes        51.0        AT  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ViolenceAndHarrassment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5) Transgender Specific Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This set of 23 questions are specific to transgender people. Questions such as, \"Have you ever sought psychological or medical help for being a trans person?\", \"Have you gone abroad or considered going abroad for medical treatment to alter your physical appearance, including the purchase of hormones over the Internet from other countries?\" or \"As far as you know, in the country in which you live, can a person change their official documents to match the gender they identify with?\" These are examples of what I find in the Trangender Specific Questions table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>CountryID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Transgender</td>\n",
       "      <td>tr1</td>\n",
       "      <td>Have you ever sought psychological or medical ...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>35.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Transgender</td>\n",
       "      <td>tr1</td>\n",
       "      <td>Have you ever sought psychological or medical ...</td>\n",
       "      <td>No</td>\n",
       "      <td>60.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Transgender</td>\n",
       "      <td>tr1</td>\n",
       "      <td>Have you ever sought psychological or medical ...</td>\n",
       "      <td>Don`t know</td>\n",
       "      <td>5.0</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>Transgender</td>\n",
       "      <td>tr1</td>\n",
       "      <td>Have you ever sought psychological or medical ...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>48.0</td>\n",
       "      <td>BE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>Transgender</td>\n",
       "      <td>tr1</td>\n",
       "      <td>Have you ever sought psychological or medical ...</td>\n",
       "      <td>No</td>\n",
       "      <td>37.0</td>\n",
       "      <td>BE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName       subset question_code  \\\n",
       "0     Austria  Transgender           tr1   \n",
       "1     Austria  Transgender           tr1   \n",
       "2     Austria  Transgender           tr1   \n",
       "3     Belgium  Transgender           tr1   \n",
       "4     Belgium  Transgender           tr1   \n",
       "\n",
       "                                      question_label      answer  percentage  \\\n",
       "0  Have you ever sought psychological or medical ...         Yes        35.0   \n",
       "1  Have you ever sought psychological or medical ...          No        60.0   \n",
       "2  Have you ever sought psychological or medical ...  Don`t know         5.0   \n",
       "3  Have you ever sought psychological or medical ...         Yes        48.0   \n",
       "4  Have you ever sought psychological or medical ...          No        37.0   \n",
       "\n",
       "  CountryID  \n",
       "0        AT  \n",
       "1        AT  \n",
       "2        AT  \n",
       "3        BE  \n",
       "4        BE  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_TransgenderSpecificQuestions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detailed Survey Questions and Answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the answers to each question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_question(df, df_name):\n",
    "    df_quest = df.copy()\n",
    "    df_quest.drop(['CountryName', 'subset', 'CountryID', 'percentage'], axis=1, inplace=True)\n",
    "    df_quest = df_quest.groupby(['question_code', 'answer'])\n",
    "\n",
    "    return df_quest.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing Table Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_questions(df):\n",
    "    questions = df['question_label'].unique()\n",
    "    for i, q in enumerate(questions):\n",
    "        print('-> Question {}: {}'.format(i+1, q))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample DailyLife Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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></th>\n",
       "      <th>question_label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>question_code</th>\n",
       "      <th>answer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">b1_a</th>\n",
       "      <th>Don`t know</th>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly rare</th>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly widespread</th>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very rare</th>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very widespread</th>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">b1_b</th>\n",
       "      <th>Don`t know</th>\n",
       "      <td>In your opinion, how widespread are casual jok...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly rare</th>\n",
       "      <td>In your opinion, how widespread are casual jok...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly widespread</th>\n",
       "      <td>In your opinion, how widespread are casual jok...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very rare</th>\n",
       "      <td>In your opinion, how widespread are casual jok...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very widespread</th>\n",
       "      <td>In your opinion, how widespread are casual jok...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">b1_c</th>\n",
       "      <th>Don`t know</th>\n",
       "      <td>In your opinion, how widespread are expression...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly rare</th>\n",
       "      <td>In your opinion, how widespread are expression...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly widespread</th>\n",
       "      <td>In your opinion, how widespread are expression...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very rare</th>\n",
       "      <td>In your opinion, how widespread are expression...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very widespread</th>\n",
       "      <td>In your opinion, how widespread are expression...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">b1_d</th>\n",
       "      <th>Don`t know</th>\n",
       "      <td>In your opinion, how widespread are assaults a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly rare</th>\n",
       "      <td>In your opinion, how widespread are assaults a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly widespread</th>\n",
       "      <td>In your opinion, how widespread are assaults a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very rare</th>\n",
       "      <td>In your opinion, how widespread are assaults a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very widespread</th>\n",
       "      <td>In your opinion, how widespread are assaults a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">b1_e</th>\n",
       "      <th>Don`t know</th>\n",
       "      <td>In your opinion, how widespread is same-sex pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly rare</th>\n",
       "      <td>In your opinion, how widespread is same-sex pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly widespread</th>\n",
       "      <td>In your opinion, how widespread is same-sex pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very rare</th>\n",
       "      <td>In your opinion, how widespread is same-sex pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very widespread</th>\n",
       "      <td>In your opinion, how widespread is same-sex pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">b1_f</th>\n",
       "      <th>Don`t know</th>\n",
       "      <td>In your opinion, how widespread is heterosexua...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly rare</th>\n",
       "      <td>In your opinion, how widespread is heterosexua...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairly widespread</th>\n",
       "      <td>In your opinion, how widespread is heterosexua...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very rare</th>\n",
       "      <td>In your opinion, how widespread is heterosexua...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very widespread</th>\n",
       "      <td>In your opinion, how widespread is heterosexua...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">h15</th>\n",
       "      <th>No</th>\n",
       "      <td>In the country where you have moved to (taken ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>In the country where you have moved to (taken ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">h16</th>\n",
       "      <th>A minority in terms of disability (excluding diagnosis of gender dysphoria/gender identity disorder)</th>\n",
       "      <td>In the country where you live, do you consider...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A religious minority</th>\n",
       "      <td>In the country where you live, do you consider...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A sexual minority</th>\n",
       "      <td>In the country where you live, do you consider...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>An ethnic minority (including of migrant background)</th>\n",
       "      <td>In the country where you live, do you consider...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None of the above</th>\n",
       "      <td>In the country where you live, do you consider...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other minority group</th>\n",
       "      <td>In the country where you live, do you consider...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">h9</th>\n",
       "      <th>No</th>\n",
       "      <td>Do any children (under the age of 18) live in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>Do any children (under the age of 18) live in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">h9_1</th>\n",
       "      <th>No</th>\n",
       "      <td>Are you a parent or legal guardian of a child ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>Are you a parent or legal guardian of a child ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">h9_2</th>\n",
       "      <th>No</th>\n",
       "      <td>Is someone in the household a parent or legal ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>Is someone in the household a parent or legal ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">i1</th>\n",
       "      <th>I read about it in a newspaper (online or printed)</th>\n",
       "      <td>how did you come to know about this survey?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I received an email from an organisation or online network</th>\n",
       "      <td>how did you come to know about this survey?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I saw an advertisement (banner) online, please specify where:</th>\n",
       "      <td>how did you come to know about this survey?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somebody told me about it or sent me the link</th>\n",
       "      <td>how did you come to know about this survey?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somewhere else</th>\n",
       "      <td>how did you come to know about this survey?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Through social media (facebook, twitter or etc.)</th>\n",
       "      <td>how did you come to know about this survey?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">open_at_school</th>\n",
       "      <th>Always open</th>\n",
       "      <td>Have you been open about you being L, G, B or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hide LGBT identity</th>\n",
       "      <td>Have you been open about you being L, G, B or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selectively open</th>\n",
       "      <td>Have you been open about you being L, G, B or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">open_at_work</th>\n",
       "      <th>Always open</th>\n",
       "      <td>Have you been open about you being L, G, B or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hide LGBT identity</th>\n",
       "      <td>Have you been open about you being L, G, B or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selectively open</th>\n",
       "      <td>Have you been open about you being L, G, B or ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">openness_cat4</th>\n",
       "      <th>Fairly open</th>\n",
       "      <td>4 levels of being open about LGBT background *</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never Open</th>\n",
       "      <td>4 levels of being open about LGBT background *</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rarely Open</th>\n",
       "      <td>4 levels of being open about LGBT background *</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Very open</th>\n",
       "      <td>4 levels of being open about LGBT background *</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                      question_label\n",
       "question_code  answer                                                                                               \n",
       "b1_a           Don`t know                                          In your opinion, how widespread is offensive l...\n",
       "               Fairly rare                                         In your opinion, how widespread is offensive l...\n",
       "               Fairly widespread                                   In your opinion, how widespread is offensive l...\n",
       "               Very rare                                           In your opinion, how widespread is offensive l...\n",
       "               Very widespread                                     In your opinion, how widespread is offensive l...\n",
       "b1_b           Don`t know                                          In your opinion, how widespread are casual jok...\n",
       "               Fairly rare                                         In your opinion, how widespread are casual jok...\n",
       "               Fairly widespread                                   In your opinion, how widespread are casual jok...\n",
       "               Very rare                                           In your opinion, how widespread are casual jok...\n",
       "               Very widespread                                     In your opinion, how widespread are casual jok...\n",
       "b1_c           Don`t know                                          In your opinion, how widespread are expression...\n",
       "               Fairly rare                                         In your opinion, how widespread are expression...\n",
       "               Fairly widespread                                   In your opinion, how widespread are expression...\n",
       "               Very rare                                           In your opinion, how widespread are expression...\n",
       "               Very widespread                                     In your opinion, how widespread are expression...\n",
       "b1_d           Don`t know                                          In your opinion, how widespread are assaults a...\n",
       "               Fairly rare                                         In your opinion, how widespread are assaults a...\n",
       "               Fairly widespread                                   In your opinion, how widespread are assaults a...\n",
       "               Very rare                                           In your opinion, how widespread are assaults a...\n",
       "               Very widespread                                     In your opinion, how widespread are assaults a...\n",
       "b1_e           Don`t know                                          In your opinion, how widespread is same-sex pa...\n",
       "               Fairly rare                                         In your opinion, how widespread is same-sex pa...\n",
       "               Fairly widespread                                   In your opinion, how widespread is same-sex pa...\n",
       "               Very rare                                           In your opinion, how widespread is same-sex pa...\n",
       "               Very widespread                                     In your opinion, how widespread is same-sex pa...\n",
       "b1_f           Don`t know                                          In your opinion, how widespread is heterosexua...\n",
       "               Fairly rare                                         In your opinion, how widespread is heterosexua...\n",
       "               Fairly widespread                                   In your opinion, how widespread is heterosexua...\n",
       "               Very rare                                           In your opinion, how widespread is heterosexua...\n",
       "               Very widespread                                     In your opinion, how widespread is heterosexua...\n",
       "...                                                                                                              ...\n",
       "h15            No                                                  In the country where you have moved to (taken ...\n",
       "               Yes                                                 In the country where you have moved to (taken ...\n",
       "h16            A minority in terms of disability (excluding di...  In the country where you live, do you consider...\n",
       "               A religious minority                                In the country where you live, do you consider...\n",
       "               A sexual minority                                   In the country where you live, do you consider...\n",
       "               An ethnic minority (including of migrant backgr...  In the country where you live, do you consider...\n",
       "               None of the above                                   In the country where you live, do you consider...\n",
       "               Other minority group                                In the country where you live, do you consider...\n",
       "h9             No                                                  Do any children (under the age of 18) live in ...\n",
       "               Yes                                                 Do any children (under the age of 18) live in ...\n",
       "h9_1           No                                                  Are you a parent or legal guardian of a child ...\n",
       "               Yes                                                 Are you a parent or legal guardian of a child ...\n",
       "h9_2           No                                                  Is someone in the household a parent or legal ...\n",
       "               Yes                                                 Is someone in the household a parent or legal ...\n",
       "i1             I read about it in a newspaper (online or printed)        how did you come to know about this survey?\n",
       "               I received an email from an organisation or onl...        how did you come to know about this survey?\n",
       "               I saw an advertisement (banner) online, please ...        how did you come to know about this survey?\n",
       "               Somebody told me about it or sent me the link             how did you come to know about this survey?\n",
       "               Somewhere else                                            how did you come to know about this survey?\n",
       "               Through social media (facebook, twitter or etc.)          how did you come to know about this survey?\n",
       "open_at_school Always open                                         Have you been open about you being L, G, B or ...\n",
       "               hide LGBT identity                                  Have you been open about you being L, G, B or ...\n",
       "               selectively open                                    Have you been open about you being L, G, B or ...\n",
       "open_at_work   Always open                                         Have you been open about you being L, G, B or ...\n",
       "               hide LGBT identity                                  Have you been open about you being L, G, B or ...\n",
       "               selectively open                                    Have you been open about you being L, G, B or ...\n",
       "openness_cat4  Fairly open                                            4 levels of being open about LGBT background *\n",
       "               Never Open                                             4 levels of being open about LGBT background *\n",
       "               Rarely Open                                            4 levels of being open about LGBT background *\n",
       "               Very open                                              4 levels of being open about LGBT background *\n",
       "\n",
       "[249 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_question(df_DailyLife, 'DailyLife')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Question 1: In your opinion, how widespread is offensive language about lesbian, gay, bisexual and/or transgender people by politicians in the country where you live?\n",
      "-> Question 2: In your opinion, how widespread are casual jokes in everyday life about lesbian, gay, bisexual and/or transgender people in the country you live?\n",
      "-> Question 3: In your opinion, how widespread are expressions of hatred and aversion towards lesbian, gay, bisexual and/or transgender in public in the country where you live?\n",
      "-> Question 4: In your opinion, how widespread are assaults and harassment against lesbian, gay, bisexual and/or transgender people in the country where you live?\n",
      "-> Question 5: In your opinion, how widespread is same-sex partners holding hands in public in the country where you live?\n",
      "-> Question 6: In your opinion, how widespread is heterosexual partners holding hands in public in the country where you live?\n",
      "-> Question 7: In your opinion, how widespread is public figures in politics, business, sports, etc being open about themselves being lesbian, gay, bisexual and/or transgender in the country where you live?\n",
      "-> Question 8: In your opinion, how widespread are positive measures to promote respect for the human rights of lesbian, gay or bisexual people in the country where you live? *\n",
      "-> Question 9: In your opinion, how widespread are positive measures to promote respect for the human rights of transgender people in the country where you live? *\n",
      "-> Question 10: What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live? Anti-discrimination policies referring to sexual orientation at the workplace?\n",
      "-> Question 11: What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live? Measures implemented at school to respect lesbian, gay and bisexual people?\n",
      "-> Question 12: What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live? Public figures in politics, business, sports, etc openly speaking in support of lesbian, gay and bisexual people?\n",
      "-> Question 13: What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live? National authorities who promote the rights of lesbian, gay and bisexual people?\n",
      "-> Question 14: What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live? Training of public servants (eg police, teachers) on the rights of lesbian, gay and bisexual people?\n",
      "-> Question 15: What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live? Better acceptance of differences in sexual orientations by religious leaders?\n",
      "-> Question 16: What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live? The possibility to marry and/or register a partnership?\n",
      "-> Question 17: What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live? The possibility to foster / adopt children?\n",
      "-> Question 18: Recognition of same-sex partnerships across the European Union - What would allow you to be more comfortable living as a lesbian, gay or bisexual person in the country where you live?\n",
      "-> Question 19: In your opinion, in the country where you live, how widespread is discrimination because a person is Lesbian?\n",
      "-> Question 20: In your opinion, in the country where you live, how widespread is discrimination because a person is Gay?\n",
      "-> Question 21: In your opinion, in the country where you live, how widespread is discrimination because a person is Bisexual?\n",
      "-> Question 22: In your opinion, in the country where you live, how widespread is discrimination because a person is Transgender?\n",
      "-> Question 23: For each of the following types of discrimination, could you please specify whether, in your opinion, it is very rare, fairly rare, fairly widespread or very widespread in the country where you live?\n",
      "-> Question 24: Does your current partner know that you are L, G, B or T?\n",
      "-> Question 25: In your opinion, how many people know that you are L, G, B or T? Family members (other than your partner(s))?\n",
      "-> Question 26: In your opinion, how many people know that you are L, G, B or T? Friends?\n",
      "-> Question 27: In your opinion, how many people know that you are L, G, B or T? Neighbours?\n",
      "-> Question 28: In your opinion, how many people know that you are L, G, B or T? Work colleagues / schoolmates?\n",
      "-> Question 29: To how many people among the following groups are you open about yourself being L, G, B or T? Family members (other than your partner(s))?\n",
      "-> Question 30: To how many people among the following groups are you open about yourself being L, G, B or T ? Friends ?\n",
      "-> Question 31: To how many people among the following groups are you open about yourself being L, G, B or T? Neighbours ?\n",
      "-> Question 32: To how many people among the following groups are you open about yourself being L, G, B or T? Work colleagues / schoolmates?\n",
      "-> Question 33: To how many people among the following groups are you open about yourself being L, G, B or T? Immediate superior/head of department\n",
      "-> Question 34: To how many people among the following groups are you open about yourself being L, G, B or T? Customers, clients, etc at work?\n",
      "-> Question 35: To how many people among the following groups are you open about yourself being L, G, B or T? Medical staff / health care providers?\n",
      "-> Question 36: You have been treated with less courtesy than other people - In the last six months, in your day-to-day life, how often have any of the following things happened to you because you are or are assumed to be lesbian, gay, bisexual and/or transgender?\n",
      "-> Question 37: You have been treated with less respect than other people - In the last six months, in your day-to-day life, how often have any of the following things happened to you because you are or are assumed to be lesbian, gay, bisexual and/or transgender?\n",
      "-> Question 38: You have received poorer services than others (eg in restaurants, shops) - In the last six months, in your day-to-day life, how often have any of the following things happened to you because you are or are assumed to be L,G,B,T\n",
      "-> Question 39: All things considered, how satisfied would you say you are with your life these days? *\n",
      "-> Question 40: In terms of your civil status in the country where you live, are you *\n",
      "-> Question 41: What best describes your current situation in the country where you live *\n",
      "-> Question 42: Have you ever moved to an EU country (and also taken up local residence) together with your same-sex partner, since you married or registered your partnership?\n",
      "-> Question 43: In the country where you have moved to (taken u p residence), have you or your partner been denied or restricted access to any benefits or services that would have been available for a different-sex spouse or partner because of you having a same-sex partner or spouse?\n",
      "-> Question 44: In the country where you live, do you consider yourself to be part of any of the following? *\n",
      "-> Question 45: Do any children (under the age of 18) live in your household?\n",
      "-> Question 46: Are you a parent or legal guardian of a child (or children)?\n",
      "-> Question 47: Is someone in the household a parent or legal guardian of a child (or children)?\n",
      "-> Question 48: how did you come to know about this survey?\n",
      "-> Question 49: Have you been open about you being L, G, B or T? *\n",
      "-> Question 50: 4 levels of being open about LGBT background *\n"
     ]
    }
   ],
   "source": [
    "get_questions(df_DailyLife)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrimination Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group_question(df_Discrimination, 'Discrimination')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_questions(df_Discrimination)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RightsAwareness Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group_question(df_RightsAwareness, 'RightsAwareness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_questions(df_RightsAwareness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## TransgenderSpecificQuestions Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group_question(df_TransgenderSpecificQuestions, 'TransgenderSpecificQuestions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_questions(df_TransgenderSpecificQuestions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## ViolenceAndHarassment Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group_question(df_ViolenceAndHarrassment, 'ViolenceAndHarassment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get_questions(df_ViolenceAndHarrassment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Counting Ratios and Weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I will convert categorical data (answers to questions) into number values, assigning a _score_ for each answer.\n",
    "\n",
    "\n",
    "- One problem I have with the data is that the answers are not always Yes / No for example. But yes, for example, binary (Yes / No), numeric (ranging from 1 to 10) or scaled (Always, Almost Never, Never)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the Proportion of Each Subset\n",
    "\n",
    "- I will use the values ​​in SubsetSize to calculate the proportion of each subset by country, assuming the overall ratio (first line of the SubsetSize.csv file) is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>CountryID</th>\n",
       "      <th>N</th>\n",
       "      <th>Lesbian</th>\n",
       "      <th>Gay</th>\n",
       "      <th>Bisexual women</th>\n",
       "      <th>Bisexual men</th>\n",
       "      <th>Transgender</th>\n",
       "      <th>CountryName</th>\n",
       "      <th>Lesbian ratio</th>\n",
       "      <th>Gay ratio</th>\n",
       "      <th>Bisexual women ratio</th>\n",
       "      <th>Bisexual men ratio</th>\n",
       "      <th>Transgender ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EU Total</td>\n",
       "      <td>93079</td>\n",
       "      <td>15236</td>\n",
       "      <td>57448</td>\n",
       "      <td>6424</td>\n",
       "      <td>7200</td>\n",
       "      <td>6771</td>\n",
       "      <td>EU Total</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AT</td>\n",
       "      <td>2543</td>\n",
       "      <td>437</td>\n",
       "      <td>1558</td>\n",
       "      <td>164</td>\n",
       "      <td>217</td>\n",
       "      <td>167</td>\n",
       "      <td>Austria</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>2901</td>\n",
       "      <td>424</td>\n",
       "      <td>1998</td>\n",
       "      <td>132</td>\n",
       "      <td>182</td>\n",
       "      <td>165</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>1033</td>\n",
       "      <td>226</td>\n",
       "      <td>474</td>\n",
       "      <td>139</td>\n",
       "      <td>88</td>\n",
       "      <td>106</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CY</td>\n",
       "      <td>265</td>\n",
       "      <td>41</td>\n",
       "      <td>164</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryID      N  Lesbian    Gay  Bisexual women  Bisexual men  Transgender  \\\n",
       "0  EU Total  93079    15236  57448            6424          7200         6771   \n",
       "1        AT   2543      437   1558             164           217          167   \n",
       "2        BE   2901      424   1998             132           182          165   \n",
       "3        BG   1033      226    474             139            88          106   \n",
       "4        CY    265       41    164              18            17           25   \n",
       "\n",
       "  CountryName  Lesbian ratio  Gay ratio  Bisexual women ratio  \\\n",
       "0    EU Total          0.164      0.617                 0.069   \n",
       "1     Austria          0.172      0.613                 0.064   \n",
       "2     Belgium          0.146      0.689                 0.046   \n",
       "3    Bulgaria          0.219      0.459                 0.135   \n",
       "4      Cyprus          0.155      0.619                 0.068   \n",
       "\n",
       "   Bisexual men ratio  Transgender ratio  \n",
       "0               0.077              0.073  \n",
       "1               0.085              0.066  \n",
       "2               0.063              0.057  \n",
       "3               0.085              0.103  \n",
       "4               0.064              0.094  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge between SubsetSize with DailyLife and renaming columns\n",
    "df_SubsetSize = df_SubsetSize.merge(df_DailyLife[['CountryID', 'CountryName']], how='left').drop_duplicates().reset_index(drop=True)\n",
    "df_SubsetSize.rename(columns={'Lesbian women': 'Lesbian', 'Gay men': 'Gay'}, inplace=True)\n",
    "df_SubsetSize.replace(np.nan, 'EU Total', inplace=True)\n",
    "\n",
    "# Calculating the ratio of each subset by country\n",
    "subsets = ['Lesbian', 'Gay', 'Bisexual women', 'Bisexual men', 'Transgender']\n",
    "for ss in subsets:\n",
    "    df_SubsetSize[ss + ' ratio'] = df_SubsetSize[ss] / df_SubsetSize['N']\n",
    "    df_SubsetSize[ss + ' ratio'] = df_SubsetSize[ss + ' ratio'].apply(lambda x: round(x, 3))\n",
    "\n",
    "\n",
    "# Displaying the dataframe so far\n",
    "df_SubsetSize.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Creating a Dataframe Containing Weights for Each Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing the weight for each country\n",
    "for ss in subsets:\n",
    "    df_SubsetSize[ss + ' weight'] = df_SubsetSize[ss + ' ratio'] / df_SubsetSize.loc[df_SubsetSize['CountryName'] == 'EU Total', ss + ' ratio'].values[0]\n",
    "    df_SubsetSize[ss + ' weight'] = df_SubsetSize[ss + ' weight'].apply(lambda x: round(x, 3))\n",
    "    \n",
    "# Creating a new table containing only the reasons and another table containing only the weights\n",
    "df_SubsetRatios  = df_SubsetSize.drop(['N'] + subsets + [ss + ' weight' for ss in subsets], axis=1)\n",
    "df_SubsetWeights = df_SubsetSize.drop(['N'] + subsets + [ss + ' ratio'  for ss in subsets], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying New Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>CountryID</th>\n",
       "      <th>CountryName</th>\n",
       "      <th>Lesbian ratio</th>\n",
       "      <th>Gay ratio</th>\n",
       "      <th>Bisexual women ratio</th>\n",
       "      <th>Bisexual men ratio</th>\n",
       "      <th>Transgender ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EU Total</td>\n",
       "      <td>EU Total</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CY</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryID CountryName  Lesbian ratio  Gay ratio  Bisexual women ratio  \\\n",
       "0  EU Total    EU Total          0.164      0.617                 0.069   \n",
       "1        AT     Austria          0.172      0.613                 0.064   \n",
       "2        BE     Belgium          0.146      0.689                 0.046   \n",
       "3        BG    Bulgaria          0.219      0.459                 0.135   \n",
       "4        CY      Cyprus          0.155      0.619                 0.068   \n",
       "\n",
       "   Bisexual men ratio  Transgender ratio  \n",
       "0               0.077              0.073  \n",
       "1               0.085              0.066  \n",
       "2               0.063              0.057  \n",
       "3               0.085              0.103  \n",
       "4               0.064              0.094  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SubsetRatios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>CountryID</th>\n",
       "      <th>CountryName</th>\n",
       "      <th>Lesbian weight</th>\n",
       "      <th>Gay weight</th>\n",
       "      <th>Bisexual women weight</th>\n",
       "      <th>Bisexual men weight</th>\n",
       "      <th>Transgender weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EU Total</td>\n",
       "      <td>EU Total</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>1.049</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.928</td>\n",
       "      <td>1.104</td>\n",
       "      <td>0.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0.890</td>\n",
       "      <td>1.117</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>1.335</td>\n",
       "      <td>0.744</td>\n",
       "      <td>1.957</td>\n",
       "      <td>1.104</td>\n",
       "      <td>1.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CY</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>0.945</td>\n",
       "      <td>1.003</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.831</td>\n",
       "      <td>1.288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryID CountryName  Lesbian weight  Gay weight  Bisexual women weight  \\\n",
       "0  EU Total    EU Total           1.000       1.000                  1.000   \n",
       "1        AT     Austria           1.049       0.994                  0.928   \n",
       "2        BE     Belgium           0.890       1.117                  0.667   \n",
       "3        BG    Bulgaria           1.335       0.744                  1.957   \n",
       "4        CY      Cyprus           0.945       1.003                  0.986   \n",
       "\n",
       "   Bisexual men weight  Transgender weight  \n",
       "0                1.000               1.000  \n",
       "1                1.104               0.904  \n",
       "2                0.818               0.781  \n",
       "3                1.104               1.411  \n",
       "4                0.831               1.288  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SubsetWeights.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answers Scoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- I will give a value for each answer given by the question asked, in each data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Auxiliary Functions\n",
    "\n",
    "- All answers will have a value that belongs to the range [-1, 1]. Neutral answers, such as \"Dont 'Know\", will have a value of NaN (not a number).\n",
    "\n",
    "\n",
    "- Basically, the answers to the questions are subdivided into 3 sets (this may vary the way the answer is given):\n",
    "    1. {Very widespread, Fairly widespread, Fairly rare, Very rare}\n",
    "    2. {Yes, No}\n",
    "    3. {Always, Often, Rarely, Never}\n",
    "    \n",
    "\n",
    "- The big difficulty is that not always answering Yes to a question will have a _score_ equal to 1. For example, in the question \"Have you personally felt discriminated against being harassed because of being perceived as Gay?\", The answer Yes will have _score_ -1; Already in the question \"Does your current partner know that you are L, G, B or T?\", the answer Yes will have _score_ 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_WidespreadRare_weight(df, questions_list, rare_negative=False):\n",
    "    \"\" \"The rare_negative parameter will say the Rare answer is a good or bad answer.\" \"\"\n",
    "    if rare_negative:\n",
    "        weight = -1\n",
    "    else:\n",
    "        weight = 1\n",
    "    for quesID in questions_list:\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Very widespread'), 'weight'] = -weight\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Fairly widespread'), 'weight'] = -weight/2\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Fairly rare'), 'weight'] = weight/2\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Very rare'), 'weight'] = weight\n",
    "        \n",
    "\n",
    "def set_YesNo_weight(df, questions_list, yes_negative=False):\n",
    "    \n",
    "    \"\" \"The yes_negative parameter will say the answer Yes is a good or bad answer\" \"\"\n",
    "    if yes_negative:\n",
    "        weight = -1\n",
    "    else:\n",
    "        weight = 1\n",
    "    for quesID in questions_list:\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Yes'), 'weight'] = weight\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'No'), 'weight'] = -weight\n",
    "        \n",
    "        \n",
    "def set_AlwaysNever_weight(df, questions_list, alsways_negative=False):\n",
    "    \n",
    "    \"\" \"The yes_negative parameter will say the answer Always is a good or bad answer\" \"\"\n",
    "    if alsways_negative:\n",
    "        weight = -1\n",
    "    else:\n",
    "        weight = 1\n",
    "    for quesID in questions_list:\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Always'), 'weight'] = weight\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Often'), 'weight'] = weight/2\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Rarely'), 'weight'] = -weight/2\n",
    "        df.loc[(df['question_code'] == quesID) & (df['answer'] == 'Never'), 'weight'] = -weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Auxiliary Function to Clear Data\n",
    "\n",
    "- I will create this function to clear the data after the calculations, leaving only the data we want to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_df(df):\n",
    "    \n",
    "    \"\" \"Data wipe function\" \"\"\n",
    "    \n",
    "    df = df.dropna()\n",
    "    df = df.drop('CountryID', axis=1)\n",
    "    df = df.reset_index(drop=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DailyLife"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_df(df):\n",
    "    \n",
    "    \"\" \"Cleaning Dataframes\" \"\"\n",
    "    \n",
    "    df = df.dropna()\n",
    "    df = df.drop(['CountryID'], axis=1)\n",
    "    df = df.reset_index(drop=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly widespread</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly rare</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very rare</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian          b1_a   \n",
       "1     Austria  Lesbian          b1_a   \n",
       "2     Austria  Lesbian          b1_a   \n",
       "3     Austria  Lesbian          b1_a   \n",
       "4     Austria      Gay          b1_a   \n",
       "\n",
       "                                      question_label             answer  \\\n",
       "0  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "1  In your opinion, how widespread is offensive l...  Fairly widespread   \n",
       "2  In your opinion, how widespread is offensive l...        Fairly rare   \n",
       "3  In your opinion, how widespread is offensive l...          Very rare   \n",
       "4  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "\n",
       "   percentage  weight  \n",
       "0         8.0    -1.0  \n",
       "1        34.0    -0.5  \n",
       "2        45.0     0.5  \n",
       "3         9.0     1.0  \n",
       "4         4.0    -1.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_DailyLife['weight'] = np.NaN\n",
    "df_DailyLife.loc[df_DailyLife['answer'] == 'Don`t know', 'weight'] = np.NaN\n",
    "\n",
    "set_WidespreadRare_weight(\n",
    "    df=df_DailyLife,\n",
    "    questions_list=[\n",
    "        'b1_a', 'b1_b', 'b1_c', 'b1_d', 'c1a_a', 'c1a_b', 'c1a_c', 'c1a_d', ''\n",
    "    ],\n",
    "    rare_negative=False\n",
    ")\n",
    "set_WidespreadRare_weight(\n",
    "    df=df_DailyLife,\n",
    "    questions_list=[\n",
    "        'b1_e', 'b1_g', 'b1_h', 'b1_i'\n",
    "    ],\n",
    "    rare_negative=True\n",
    ")\n",
    "\n",
    "# ***** Arranging dataframe-specific answers ***** #\n",
    "\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_a') & (df_DailyLife['answer'] == 'Never happened in the last sixth months'), 'weight'] = 1\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_a') & (df_DailyLife['answer'] == 'Happened only once in the last six months'), 'weight'] = 0.5\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_a') & (df_DailyLife['answer'] == '2-5 times in the last six months'), 'weight'] = -0.5\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_a') & (df_DailyLife['answer'] == '6 times or more in the last six months'), 'weight'] = -1\n",
    "\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_b') & (df_DailyLife['answer'] == 'Never happened in the last sixth months'), 'weight'] = 1\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_b') & (df_DailyLife['answer'] == 'Happened only once in the last six months'), 'weight'] = 0.5\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_b') & (df_DailyLife['answer'] == '2-5 times in the last six months'), 'weight'] = -0.5\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_b') & (df_DailyLife['answer'] == '6 times or more in the last six months'), 'weight'] = -1\n",
    "\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_c') & (df_DailyLife['answer'] == 'Never happened in the last sixth months'), 'weight'] = 1\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_c') & (df_DailyLife['answer'] == 'Happened only once in the last six months'), 'weight'] = 0.5\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_c') & (df_DailyLife['answer'] == '2-5 times in the last six months'), 'weight'] = -0.5\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'g4_c') & (df_DailyLife['answer'] == '6 times or more in the last six months'), 'weight'] = -1\n",
    "\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'h15') & (df_DailyLife['answer'] == 'Yes'), 'weight'] = -1\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'h15') & (df_DailyLife['answer'] == 'No'), 'weight'] = 1\n",
    "df_DailyLife.loc[(df_DailyLife['question_code'] == 'h15') & (df_DailyLife['answer'] == 'I did not need or use any benefits or services'), 'weight'] = np.NaN\n",
    "\n",
    "df_DailyLife = clean_df(df_DailyLife)\n",
    "df_DailyLife.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Right Awareness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Bisexual women</td>\n",
       "      <td>d1</td>\n",
       "      <td>In the country where you live, is there a law ...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName          subset question_code  \\\n",
       "0     Austria         Lesbian            d1   \n",
       "1     Austria         Lesbian            d1   \n",
       "2     Austria             Gay            d1   \n",
       "3     Austria             Gay            d1   \n",
       "4     Austria  Bisexual women            d1   \n",
       "\n",
       "                                      question_label answer  percentage  \\\n",
       "0  In the country where you live, is there a law ...    Yes        53.0   \n",
       "1  In the country where you live, is there a law ...     No        10.0   \n",
       "2  In the country where you live, is there a law ...    Yes        45.0   \n",
       "3  In the country where you live, is there a law ...     No        16.0   \n",
       "4  In the country where you live, is there a law ...    Yes        51.0   \n",
       "\n",
       "   weight  \n",
       "0     1.0  \n",
       "1    -1.0  \n",
       "2     1.0  \n",
       "3    -1.0  \n",
       "4     1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_RightsAwareness['weight'] = np.NaN\n",
    "df_RightsAwareness.loc[df_RightsAwareness['answer'] == 'Don`t know', 'weight'] = np.NaN\n",
    "df_RightsAwareness.loc[df_RightsAwareness['answer'] == 'No', 'weight'] = -1\n",
    "df_RightsAwareness.loc[df_RightsAwareness['answer'] == 'Yes', 'weight'] = 1\n",
    "\n",
    "df_RightsAwareness = clean_df(df_RightsAwareness)\n",
    "df_RightsAwareness.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Difficulty in gaining access to healthcare</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Having to change general practitioners or othe...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Receiving unequal treatment when dealing with ...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Foregoing treatment for fear of discrimination...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>c10</td>\n",
       "      <td>Have you ever experienced any of the following...</td>\n",
       "      <td>Specific needs ignored (not taken into account)</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian           c10   \n",
       "1     Austria  Lesbian           c10   \n",
       "2     Austria  Lesbian           c10   \n",
       "3     Austria  Lesbian           c10   \n",
       "4     Austria  Lesbian           c10   \n",
       "\n",
       "                                      question_label  \\\n",
       "0  Have you ever experienced any of the following...   \n",
       "1  Have you ever experienced any of the following...   \n",
       "2  Have you ever experienced any of the following...   \n",
       "3  Have you ever experienced any of the following...   \n",
       "4  Have you ever experienced any of the following...   \n",
       "\n",
       "                                              answer  percentage  weight  \n",
       "0         Difficulty in gaining access to healthcare         2.0    -1.0  \n",
       "1  Having to change general practitioners or othe...         8.0    -1.0  \n",
       "2  Receiving unequal treatment when dealing with ...         3.0    -1.0  \n",
       "3  Foregoing treatment for fear of discrimination...         5.0    -1.0  \n",
       "4    Specific needs ignored (not taken into account)        12.0    -1.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "set_YesNo_weight(\n",
    "    df=df_Discrimination,\n",
    "    questions_list=[\n",
    "        'c2a_a', 'c2a_b', 'c2a_c', 'c2a_d', 'c2_b', 'c2_c', 'c4_a', 'c4_b', \n",
    "        'c4_c', 'c4_d', 'c4_e', 'c4_f', 'c4_g', 'c4_h', 'c4_i', 'c4_j', 'c4_k', 'discrim1yr'\n",
    "    ],\n",
    "    yes_negative=True\n",
    ")\n",
    "\n",
    "set_AlwaysNever_weight(\n",
    "    df=df_Discrimination,\n",
    "    questions_list=[\n",
    "        'c8a_b', 'c8a_c', 'c8a_d', 'c8a_e', 'c8a_f', 'c9_b', 'c9_c', 'c9_d', 'c9_e'\n",
    "    ],\n",
    "    alsways_negative=True\n",
    ")\n",
    "\n",
    "set_AlwaysNever_weight(\n",
    "    df=df_Discrimination,\n",
    "    questions_list=[\n",
    "        'c8a_a', 'c9_a'\n",
    "    ],\n",
    "    alsways_negative=False\n",
    ")\n",
    "\n",
    "# ***** Arranging dataframe-specific answers ***** #\n",
    "\n",
    "df_Discrimination['weight'] = np.NaN\n",
    "df_Discrimination.loc[df_Discrimination['answer'] == 'Don`t know', 'weight'] = np.NaN\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'None of the above'), 'weight'] = 0\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'I have never accessed healthcare services'), 'weight'] = 0\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'Difficulty in gaining access to healthcare'), 'weight'] = -1\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'Having to change general practitioners or other specialists due to their negative reaction'), 'weight'] = -1\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'Receiving unequal treatment when dealing with medical staff'), 'weight'] = -1\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'Foregoing treatment for fear of discrimination or intolerant reactions'), 'weight'] = -1\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'Specific needs ignored (not taken into account)'), 'weight'] = -1\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'Inappropriate curiosity'), 'weight'] = -1\n",
    "df_Discrimination.loc[(df_Discrimination['question_code'] == 'c10') & (df_Discrimination['answer'] == 'Pressure or being forced to undergo any medical or psychological test'), 'weight'] = -1\n",
    "\n",
    "df_Discrimination = clean_df(df_Discrimination)\n",
    "df_Discrimination.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violence and Harassment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>No</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>No</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Bisexual women</td>\n",
       "      <td>e1</td>\n",
       "      <td>Do you avoid holding hands in public with a sa...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName          subset question_code  \\\n",
       "0     Austria         Lesbian            e1   \n",
       "1     Austria         Lesbian            e1   \n",
       "2     Austria             Gay            e1   \n",
       "3     Austria             Gay            e1   \n",
       "4     Austria  Bisexual women            e1   \n",
       "\n",
       "                                      question_label answer  percentage  \\\n",
       "0  Do you avoid holding hands in public with a sa...    Yes        33.0   \n",
       "1  Do you avoid holding hands in public with a sa...     No        53.0   \n",
       "2  Do you avoid holding hands in public with a sa...    Yes        51.0   \n",
       "3  Do you avoid holding hands in public with a sa...     No        25.0   \n",
       "4  Do you avoid holding hands in public with a sa...    Yes        18.0   \n",
       "\n",
       "   weight  \n",
       "0    -1.0  \n",
       "1     1.0  \n",
       "2    -1.0  \n",
       "3     1.0  \n",
       "4    -1.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_ViolenceAndHarrassment['weight'] = np.NaN\n",
    "\n",
    "df_ViolenceAndHarrassment.loc[df_ViolenceAndHarrassment['answer'] == 'Don`t know', 'weight'] = np.NaN\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'e1') & (df_ViolenceAndHarrassment['answer'] == 'I do not have a same-sex partner'), 'weight'] = np.NaN\n",
    "set_YesNo_weight(\n",
    "    df=df_ViolenceAndHarrassment,\n",
    "    questions_list=[\n",
    "        'e1', 'e2', 'f1_a', 'f1_b', 'fa1_5', 'fa2_5', 'fb1_5', 'fb2_5'\n",
    "    ],\n",
    "    yes_negative=True\n",
    ")\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fa1_3') & (df_ViolenceAndHarrassment['answer'] == 'More than ten times'), 'weight'] = -1\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fa1_3') & (df_ViolenceAndHarrassment['answer'] == 'Six to ten times'), 'weight'] = -0.86\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fa1_3') & (df_ViolenceAndHarrassment['answer'] == 'Five times'), 'weight'] = -0.71\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fa1_3') & (df_ViolenceAndHarrassment['answer'] == 'Four times'), 'weight'] = -0.57\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fa1_3') & (df_ViolenceAndHarrassment['answer'] == 'Three times'), 'weight'] = -0.43\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fa1_3') & (df_ViolenceAndHarrassment['answer'] == 'Twice'), 'weight'] = -0.29\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fa1_3') & (df_ViolenceAndHarrassment['answer'] == 'Once'), 'weight'] = -0.14\n",
    "\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fb1_3') & (df_ViolenceAndHarrassment['answer'] == 'More than ten times'), 'weight'] = -1\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fb1_3') & (df_ViolenceAndHarrassment['answer'] == 'Six to ten times'), 'weight'] = -0.86\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fb1_3') & (df_ViolenceAndHarrassment['answer'] == 'Five times'), 'weight'] = -0.71\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fb1_3') & (df_ViolenceAndHarrassment['answer'] == 'Four times'), 'weight'] = -0.57\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fb1_3') & (df_ViolenceAndHarrassment['answer'] == 'Three times'), 'weight'] = -0.43\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fb1_3') & (df_ViolenceAndHarrassment['answer'] == 'Twice'), 'weight'] = -0.29\n",
    "df_ViolenceAndHarrassment.loc[(df_ViolenceAndHarrassment['question_code'] == 'fb1_3') & (df_ViolenceAndHarrassment['answer'] == 'Once'), 'weight'] = -0.14\n",
    "\n",
    "df_ViolenceAndHarrassment = clean_df(df_ViolenceAndHarrassment)\n",
    "df_ViolenceAndHarrassment.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenating Question Data Frames in One"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An extra final_weight column is added which is the multiplication of the percentage of answers to a question and its weight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.concat([df_DailyLife, df_Discrimination, df_RightsAwareness, df_ViolenceAndHarrassment])\n",
    "\n",
    "df['final_weight'] = df.apply(lambda row: row.percentage * row.weight, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clearing to leave only the columns that interest us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>final_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>-17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  final_weight\n",
       "0     Austria  Lesbian          b1_a          -8.0\n",
       "1     Austria  Lesbian          b1_a         -17.0\n",
       "2     Austria  Lesbian          b1_a          22.5\n",
       "3     Austria  Lesbian          b1_a           9.0\n",
       "4     Austria      Gay          b1_a          -4.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['CountryName', 'subset', 'question_code', 'final_weight']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Passing categorical data to numeric data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>final_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>0</td>\n",
       "      <td>-17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset  question_code  final_weight\n",
       "0     Austria  Lesbian              0          -8.0\n",
       "1     Austria  Lesbian              0         -17.0\n",
       "2     Austria  Lesbian              0          22.5\n",
       "3     Austria  Lesbian              0           9.0\n",
       "4     Austria      Gay              0          -4.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_old = df['question_code'].unique()\n",
    "id_new = list(range(len(id_old)))\n",
    "    \n",
    "df = df.replace(id_old, id_new)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CountryName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>final_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>0</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>0</td>\n",
       "      <td>-17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Gay</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CountryName   subset  question_code  final_weight\n",
       "0            0  Lesbian              0          -8.0\n",
       "1            0  Lesbian              0         -17.0\n",
       "2            0  Lesbian              0          22.5\n",
       "3            0  Lesbian              0           9.0\n",
       "4            0      Gay              0          -4.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pais_old = df['CountryName'].unique()\n",
    "pais_new = list(range(len(pais_old)))\n",
    "    \n",
    "df = df.replace(pais_old, pais_new)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Grouping to the sum of final_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>final_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Bisexual men</td>\n",
       "      <td>0</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Bisexual men</td>\n",
       "      <td>1</td>\n",
       "      <td>-36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Bisexual men</td>\n",
       "      <td>2</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Bisexual men</td>\n",
       "      <td>3</td>\n",
       "      <td>43.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Bisexual men</td>\n",
       "      <td>4</td>\n",
       "      <td>-50.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CountryName        subset  question_code  final_weight\n",
       "0            0  Bisexual men              0          28.5\n",
       "1            0  Bisexual men              1         -36.0\n",
       "2            0  Bisexual men              2          14.5\n",
       "3            0  Bisexual men              3          43.5\n",
       "4            0  Bisexual men              4         -50.5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = df.groupby(by=['CountryName', 'subset', 'question_code']).sum().reset_index()\n",
    "group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making KNN to classify subset based on question_code and final_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = group['subset']\n",
    "x = group[['question_code', 'final_weight']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 5}\n",
      "0.2665036674816626\n"
     ]
    }
   ],
   "source": [
    "params = { \n",
    "    'n_neighbors': [3, 5, 7, 9, 11]\n",
    "}\n",
    "\n",
    "classifier = GridSearchCV(KNeighborsClassifier(), params)\n",
    "classifier.fit(x_train, y_train)\n",
    "\n",
    "y_pred = classifier.predict(x_test)\n",
    "\n",
    "print(classifier.best_params_)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2dbaa2493c8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion = confusion_matrix(y_test, y_pred)\n",
    "df_cm = pd.DataFrame(confusion, index = y.unique(), columns = y.unique())\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through a kNN classifier, I sought to find out whether it would be possible to predict to which subgroup a person belongs based on their responses. With this, I would seek to know if there is any bias in negative responses directed to one of the groups.\n",
    "\n",
    "After classification, I obtained a 26% feather accuracy for all classes. The confusion matrix can be seen as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this, I find that it is not possible to state that there is no more affected subgroup. However, the Bisexual man class obtained the best accuracy; This may indicate that this is the subgroup with the lowest self-acceptance rates, although we cannot say for sure. Therefore, my hypothesis that the bisexual men group is most affected by machismo cannot be confirmed or refuted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "  Bisexual men       0.25      0.37      0.30       222\n",
      "Bisexual women       0.25      0.27      0.26       238\n",
      "           Gay       0.24      0.24      0.24       250\n",
      "       Lesbian       0.30      0.22      0.25       280\n",
      "   Transgender       0.34      0.24      0.29       237\n",
      "\n",
      "      accuracy                           0.27      1227\n",
      "     macro avg       0.27      0.27      0.27      1227\n",
      "  weighted avg       0.27      0.27      0.27      1227\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred, target_names=y.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import stats as ss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modifying the Data\n",
    "\n",
    "- I will create an auxiliary column that means percentage * weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>weight</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly widespread</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly rare</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very rare</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian          b1_a   \n",
       "1     Austria  Lesbian          b1_a   \n",
       "2     Austria  Lesbian          b1_a   \n",
       "3     Austria  Lesbian          b1_a   \n",
       "4     Austria      Gay          b1_a   \n",
       "\n",
       "                                      question_label             answer  \\\n",
       "0  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "1  In your opinion, how widespread is offensive l...  Fairly widespread   \n",
       "2  In your opinion, how widespread is offensive l...        Fairly rare   \n",
       "3  In your opinion, how widespread is offensive l...          Very rare   \n",
       "4  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "\n",
       "   percentage  weight  score  \n",
       "0         8.0    -1.0 -0.080  \n",
       "1        34.0    -0.5 -0.170  \n",
       "2        45.0     0.5  0.225  \n",
       "3         9.0     1.0  0.090  \n",
       "4         4.0    -1.0 -0.040  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_score(df):\n",
    "    df['score'] = df.apply(lambda row: row['weight'] * (row['percentage'] / 100), axis=1)\n",
    "    \n",
    "create_score(df_DailyLife)\n",
    "create_score(df_Discrimination)\n",
    "create_score(df_RightsAwareness)\n",
    "create_score(df_ViolenceAndHarrassment)\n",
    "\n",
    "df_DailyLife.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clearing the Data\n",
    "\n",
    "- I will remove unnecessary columns for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>CountryName</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>-0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>-0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>-0.040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName  score\n",
       "0     Austria -0.080\n",
       "1     Austria -0.170\n",
       "2     Austria  0.225\n",
       "3     Austria  0.090\n",
       "4     Austria -0.040"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clean_df(df):\n",
    "    return df[['CountryName', 'score']]\n",
    "\n",
    "temp_df_DailyLife = clean_df(df_DailyLife)\n",
    "temp_df_Discrimination = clean_df(df_Discrimination)\n",
    "temp_df_RightsAwareness = clean_df(df_RightsAwareness)\n",
    "temp_df_ViolenceAndHarassment = clean_df(df_ViolenceAndHarrassment)\n",
    "\n",
    "temp_df_DailyLife.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Grouping data by country, adding _scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>-1.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>-4.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>12.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>-22.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>-26.450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              score\n",
       "CountryName        \n",
       "Austria      -1.540\n",
       "Average      -4.445\n",
       "Belgium      12.170\n",
       "Bulgaria    -22.960\n",
       "Croatia     -26.450"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_DailyLife             = temp_df_DailyLife.groupby('CountryName').sum()\n",
    "group_Discrimination        = temp_df_Discrimination.groupby('CountryName').sum()\n",
    "group_RightsAwareness       = temp_df_RightsAwareness.groupby('CountryName').sum()\n",
    "group_ViolenceAndHarassment = temp_df_ViolenceAndHarassment.groupby('CountryName').sum()\n",
    "\n",
    "group_DailyLife.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summing all _scores_, resulting in final _score_ for each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>CountryName</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>13.9665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Average</td>\n",
       "      <td>2.2569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>28.2459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>-34.6853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>-19.3698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName    score\n",
       "0     Austria  13.9665\n",
       "1     Average   2.2569\n",
       "2     Belgium  28.2459\n",
       "3    Bulgaria -34.6853\n",
       "4     Croatia -19.3698"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_weight_per_country = group_DailyLife + group_Discrimination + group_RightsAwareness + group_ViolenceAndHarassment\n",
    "final_weight_per_country = final_weight_per_country.reset_index()\n",
    "final_weight_per_country.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Creating Tuples (HDI, _score_) by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>CountryName</th>\n",
       "      <th>HDI-2012</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>0.786</td>\n",
       "      <td>-34.6853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Romania</td>\n",
       "      <td>0.795</td>\n",
       "      <td>-32.8909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>0.816</td>\n",
       "      <td>-19.3698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>0.824</td>\n",
       "      <td>-17.8596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>0.829</td>\n",
       "      <td>4.5956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName HDI-2012    score\n",
       "0    Bulgaria    0.786 -34.6853\n",
       "1     Romania    0.795 -32.8909\n",
       "2     Croatia    0.816 -19.3698\n",
       "3      Latvia    0.824 -17.8596\n",
       "4    Portugal    0.829   4.5956"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modifying the Country Column Name to CountryName\n",
    "df_HDI.rename(columns={'Country': 'CountryName', '2012': 'HDI-2012'}, inplace=True)\n",
    "\n",
    "\n",
    "# Merge between dataframes\n",
    "df_merged = pd.merge(df_HDI, final_weight_per_country)\n",
    "\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score [Parents, IDH, score]: ['Netherlands', '0.921', 39.946199999999976]\n",
      "Worse score [Parents, IDH, score]: ['Bulgaria', '0.786', -34.685300000000005]\n"
     ]
    }
   ],
   "source": [
    "print('Best score [Parents, IDH, score]:', df_merged.iloc[df_merged['score'].idxmax()].tolist())\n",
    "print('Worse score [Parents, IDH, score]:', df_merged.iloc[df_merged['score'].idxmin()].tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We realize that the country with the best total score is the Netherlands; the worst is Bulgaria."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Performing Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all survey questions the answer will have a value within the discrete range [-1, 1], except neutral answers (\"Don't know\"), which will have a value of NaN. Questions with a negative answer will have a value of -1, and questions with a positive answer will have a value of +1; In the end, the values ​​are added to a _score_ which generally defines the well-being of an individual.\n",
    "\n",
    "The answers to the questions are essentially divided into three sets:\n",
    "- Very widespread, Fairly widespread, Fairly rare, Very rare\n",
    "- Yes, No\n",
    "- Always, Often, Rarely, Never\n",
    "\n",
    "However, there is no direct relationship between the response type and its numerical value. For example, in a question with the answer \"yes\" the value added to _score_ can be +1 or -1 in case the question is positive or negative respectively. For this, functions created by the research author were used to group questions whose numerical and categorical answers have the same relation.\n",
    "\n",
    "From the _score_ obtained for each individual, we obtained the total _score_ for each country. Based on this, I could seek answers about the influence of a country's HDI on the general well-being of the LGBT population through a linear regression, where the HDI occupies the abscissa axis and the _score_ axis of ordinates. The results are as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged['HDI-2012'] = df_merged['HDI-2012'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27 entries, 0 to 26\n",
      "Data columns (total 3 columns):\n",
      "CountryName    27 non-null object\n",
      "HDI-2012       27 non-null float64\n",
      "score          27 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 864.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_merged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model slope: 521.760743712921\n",
      "Model intercept: -452.16056968129357\n",
      "Model rvalue: 0.846671525150669\n",
      "Model pvalue: 2.5996612706522572e-08\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Performing regression on data\n",
    "sns.regplot(x='HDI-2012', y='score', data=df_merged, n_boot=10000,\n",
    "            line_kws={'color':'magenta', 'lw':4},\n",
    "            scatter_kws={'edgecolor':'k', 's':80, 'alpha':0.8})\n",
    "\n",
    "plt.title('HDI x Score analysis')\n",
    "plt.xlabel('IDH')\n",
    "plt.ylabel('Score')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Retrieving model data (slope, intercept, pearson correlation, pvalue)\n",
    "model = ss.linregress(df_merged['HDI-2012'], df_merged['score'])\n",
    "print('Model slope:', model.slope)\n",
    "print('Model intercept:', model.intercept)\n",
    "print('Model rvalue:', model.rvalue)\n",
    "print('Model pvalue:', model.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I noticed that data has a Pearson correlation of $ \\ approx $ 0.85.\n",
    "- With this, I have that data have a positive correlation, and with the increase in the country's HDI, explains relatively well, an increase in the score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that a linear model describes the data well. Thus, I note that there is indeed a relationship between a country's HDI and the welfare of the LGBT population.\n",
    "\n",
    "Below are the regressions considering the different categories for _score_ measurement, such as _daily life_, _discrimination_, _harassment_ among others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Analyzing Correlation for Each Specific DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auxiliary Function for Easy Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_regression_result(group):\n",
    "    \n",
    "    # Modifying the Country Column Name to CountryName\n",
    "    df_HDI.rename(columns={'Country': 'CountryName', '2012': 'HDI-2012'}, inplace=True)\n",
    "\n",
    "    # Merge between dataframes\n",
    "    df_merged = pd.merge(df_HDI, group.reset_index())\n",
    "    df_merged['HDI-2012'] = df_merged['HDI-2012'].astype(float)\n",
    "    \n",
    "    print('Best score [Parent, IDH, score]:', df_merged.iloc[df_merged['score'].idxmax()].tolist())\n",
    "    print('Worst score [Parent, IDH, score]:', df_merged.iloc[df_merged['score'].idxmin()].tolist())\n",
    "    \n",
    "    # Performing regression on data\n",
    "    sns.regplot(x='HDI-2012', y='score', data=df_merged, n_boot=10000,\n",
    "                line_kws={'color':'magenta', 'lw':4},\n",
    "                scatter_kws={'edgecolor':'k', 's':80, 'alpha':0.8})\n",
    "\n",
    "    plt.title('HDI x Score analysis')\n",
    "    plt.xlabel('IDH')\n",
    "    plt.ylabel('Score')\n",
    "    plt.show()\n",
    "\n",
    "    # Retrieving model data (slope, intercept, pearson correlation, pvalue)\n",
    "    model = ss.linregress(df_merged['HDI-2012'], df_merged['score'])\n",
    "    print('Model slope:', model.slope)\n",
    "    print('Model intercept:', model.intercept)\n",
    "    print('Model rvalue:', model.rvalue)\n",
    "    print('Model pvalue:', model.pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DailyLife"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score [Parent, IDH, score]: ['Netherlands', 0.921, 17.445]\n",
      "Worst score [Parent, IDH, score]: ['Croatia', 0.816, -26.450000000000028]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model slope: 302.9383963002464\n",
      "Model intercept: -269.8304665419691\n",
      "Model rvalue: 0.8747502091834357\n",
      "Model pvalue: 2.4318474100523302e-09\n"
     ]
    }
   ],
   "source": [
    "show_regression_result(group_DailyLife)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I realize that the country with the best score for DailyLife data is the Netherlands; the worst is Croatia.\n",
    "- Pearson correlation value is $ \\ approx $ 0.87.\n",
    "- With that, I have that data have a positive correlation, and with the increase of the HDI of the country, explains relatively well, an increase of the score for the DailyLife data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discrimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score [Parent, IDH, score]: ['Malta', 0.849, -0.9]\n",
      "Worst score [Parent, IDH, score]: ['United Kingdom', 0.898, -3.7400000000000007]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model slope: -2.9084626892423557\n",
      "Model intercept: 0.11674851862565294\n",
      "Model rvalue: -0.19840616441617934\n",
      "Model pvalue: 0.3211615059012004\n"
     ]
    }
   ],
   "source": [
    "show_regression_result(group_Discrimination)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I realize that the country with the best score for Discrimination data is Malta; the worst is the UK.\n",
    "- Pearson correlation value equals $ \\ approx $ 0.12.\n",
    "- With this, I have that data have a positive correlation, but very low. Thus, an increase in the country's HDI cannot explain an increase in the score for Discrimination data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RightsAwareness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score [Parent, IDH, score]: ['Sweden', 0.908, 33.980000000000004]\n",
      "Worst score [Parent, IDH, score]: ['Greece', 0.854, -2.9399999999999995]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model slope: 161.23591607408719\n",
      "Model intercept: -122.82214682598119\n",
      "Model rvalue: 0.6364876791067299\n",
      "Model pvalue: 0.0003581300084342057\n"
     ]
    }
   ],
   "source": [
    "show_regression_result(group_RightsAwareness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- I realize that the country with the best score for RightsAwareness data is Sweden; the worst is Greece.\n",
    "- Pearson correlation value is $ \\ approx $ 0.64.\n",
    "- With that, I have that data have a positive correlation, but not so high. Thus, an increase in the country's HDI can reasonably explain an increase in the score for RightsAwareness data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ViolenceAndHarassment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score [Parent, IDH, score]: ['Luxembourg', 0.892, 1.7787000000000002]\n",
      "Worst score [Parent, IDH, score]: ['Croatia', 0.816, -13.6898]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model slope: 60.494894027829744\n",
      "Model intercept: -59.6247048319689\n",
      "Model rvalue: 0.6337424793827471\n",
      "Model pvalue: 0.0003866934384392864\n"
     ]
    }
   ],
   "source": [
    "show_regression_result(group_ViolenceAndHarassment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I realize that the country with the best score for ViolenceAndHarassment data is Luxembourg; the worst is Croatia.\n",
    "- Pearson correlation value is $ \\ approx $ 0.63.\n",
    "- With that, I have that data have a positive correlation, but not so high. Thus, an increase in the country's HDI can reasonably explain an increase in the score for ViolenceAndHarassment data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of df_DailyLife: 8426\n",
      "Size of df_Discrimination: 1221\n"
     ]
    }
   ],
   "source": [
    "print('Size of df_DailyLife:', len(df_DailyLife))\n",
    "print('Size of df_Discrimination:', len(df_Discrimination))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Concatenating the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of df_concat: 9647\n"
     ]
    },
    {
     "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>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>weight</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly widespread</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly rare</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very rare</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian          b1_a   \n",
       "1     Austria  Lesbian          b1_a   \n",
       "2     Austria  Lesbian          b1_a   \n",
       "3     Austria  Lesbian          b1_a   \n",
       "4     Austria      Gay          b1_a   \n",
       "\n",
       "                                      question_label             answer  \\\n",
       "0  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "1  In your opinion, how widespread is offensive l...  Fairly widespread   \n",
       "2  In your opinion, how widespread is offensive l...        Fairly rare   \n",
       "3  In your opinion, how widespread is offensive l...          Very rare   \n",
       "4  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "\n",
       "   percentage  weight  score  \n",
       "0         8.0    -1.0 -0.080  \n",
       "1        34.0    -0.5 -0.170  \n",
       "2        45.0     0.5  0.225  \n",
       "3         9.0     1.0  0.090  \n",
       "4         4.0    -1.0 -0.040  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_concat = pd.concat([df_DailyLife, df_Discrimination])\n",
    "\n",
    "print('Size of df_concat:', len(df_concat))\n",
    "df_concat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing total weight for each row (weight * percentage / 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>subset</th>\n",
       "      <th>question_code</th>\n",
       "      <th>question_label</th>\n",
       "      <th>answer</th>\n",
       "      <th>percentage</th>\n",
       "      <th>weight</th>\n",
       "      <th>score</th>\n",
       "      <th>total_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly widespread</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.170</td>\n",
       "      <td>-0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Fairly rare</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Lesbian</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very rare</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Gay</td>\n",
       "      <td>b1_a</td>\n",
       "      <td>In your opinion, how widespread is offensive l...</td>\n",
       "      <td>Very widespread</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>-0.040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName   subset question_code  \\\n",
       "0     Austria  Lesbian          b1_a   \n",
       "1     Austria  Lesbian          b1_a   \n",
       "2     Austria  Lesbian          b1_a   \n",
       "3     Austria  Lesbian          b1_a   \n",
       "4     Austria      Gay          b1_a   \n",
       "\n",
       "                                      question_label             answer  \\\n",
       "0  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "1  In your opinion, how widespread is offensive l...  Fairly widespread   \n",
       "2  In your opinion, how widespread is offensive l...        Fairly rare   \n",
       "3  In your opinion, how widespread is offensive l...          Very rare   \n",
       "4  In your opinion, how widespread is offensive l...    Very widespread   \n",
       "\n",
       "   percentage  weight  score  total_weight  \n",
       "0         8.0    -1.0 -0.080        -0.080  \n",
       "1        34.0    -0.5 -0.170        -0.170  \n",
       "2        45.0     0.5  0.225         0.225  \n",
       "3         9.0     1.0  0.090         0.090  \n",
       "4         4.0    -1.0 -0.040        -0.040  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_concat['total_weight'] = df_concat.apply(lambda row: row['weight'] * row['percentage'] / 100, axis=1)\n",
    "df_concat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a histogram for the total weight of bisexual men group data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following histogram refers to the response scores given by bisexual men. I wanted to see if actions (whether good or bad, such as machismo) influence participants' responses by shifting the plot to more negative or more positive values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_concat.loc[df_concat['subset'] == 'Bisexual men']['total_weight'], density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a histogram for the total weight of gay group data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram, on the other hand, refers to the response scores given by gay men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_concat.loc[df_concat['subset'] == 'Gay']['total_weight'], density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I see that for both cases we are highly likely to have a total weight = 0. Thus, I cannot conclude anything about actions (good or bad, such as machismo), leading to worse or better answers to the questions at hand. both groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression shows me that a country's human development index is a good indicator of the well-being of the LGBT population. This may be due to the fact that inhabitants of countries with higher HDI have more access to education, besides having a larger number of laws that guarantee the rights of this social minority.\n",
    "\n",
    "However, the classifier did not show promising results regarding the fact that bisexual men had less acceptance about sexuality. Therefore, it is possible that there is no major influence of machismo in this group, but it is also possible that the available data are insufficient to support this hypothesis."
   ]
  }
 ],
 "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": "3.7.3"
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}