CorruptionCasesAPLU.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option(\"display.max_colwidth\", 10000)\n",
    "pd.options.mode.chained_assignment = None  # to not make too many copies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from datetime import datetime\n",
    "from dateutil.parser import parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "import pandas as pd\n",
    "\n",
    "def calculate_pvalues(df):\n",
    "    df = df.dropna()._get_numeric_data()\n",
    "    dfcols = pd.DataFrame(columns=df.columns)\n",
    "    pvalues = dfcols.transpose().join(dfcols, how='outer')\n",
    "    for r in df.columns:\n",
    "        for c in df.columns:\n",
    "            pvalues[r][c] = round(pearsonr(df[r], df[c])[1], 4)\n",
    "    return pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_folder = 'D:\\\\PhD\\\\Articles\\\\FCPA\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Addition for SAEE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gini =  pd.read_excel(report_folder + 'Gini-index.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_breaches =  pd.read_excel(report_folder + 'Breach-index.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ppp =  pd.read_excel(report_folder + 'API_NY.GDP.MKTP.PP.CD_DS2_en_excel_v2.xls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Restart Here for LAW conference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>APPEALS</th>\n",
       "      <th>CODE</th>\n",
       "      <th>COUNTRY_y</th>\n",
       "      <th>CSV</th>\n",
       "      <th>Count</th>\n",
       "      <th>Country</th>\n",
       "      <th>DATE</th>\n",
       "      <th>EXCEPTIONS &amp; REFUSALS</th>\n",
       "      <th>GDP (BILLIONS)</th>\n",
       "      <th>LAW</th>\n",
       "      <th>...</th>\n",
       "      <th>label</th>\n",
       "      <th>label_num</th>\n",
       "      <th>Jurisdiction</th>\n",
       "      <th>corrupt perception</th>\n",
       "      <th>Country Name_x</th>\n",
       "      <th>corporate transparency</th>\n",
       "      <th>Country Name_y</th>\n",
       "      <th>Reliability of transparency</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>disclosure_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Albania</td>\n",
       "      <td>36</td>\n",
       "      <td>Albania</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Albania</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DZA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>36</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>131.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10-80</td>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>15</td>\n",
       "      <td>Angola</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Angola</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.0</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>536.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10-80</td>\n",
       "      <td>3</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>32</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>AZE</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>77.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>29</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BHR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>51</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.0</td>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>186.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>25</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Benin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Benin</td>\n",
       "      <td>37</td>\n",
       "      <td>Benin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Benin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benin</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BOL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>34</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.0</td>\n",
       "      <td>BIH</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>38</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2244.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10-80</td>\n",
       "      <td>3</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>38</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.0</td>\n",
       "      <td>BGR</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>55.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>41</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14.0</td>\n",
       "      <td>BFA</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>38</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>BDI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>21</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TCD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Chad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Chad</td>\n",
       "      <td>22</td>\n",
       "      <td>Chad</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Chad</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Chad</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>23.0</td>\n",
       "      <td>CHL</td>\n",
       "      <td>Chile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Chile</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>264.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Chile</td>\n",
       "      <td>70</td>\n",
       "      <td>Chile</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.0</td>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>China</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10360.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10-80</td>\n",
       "      <td>3</td>\n",
       "      <td>China</td>\n",
       "      <td>37</td>\n",
       "      <td>China</td>\n",
       "      <td>9.0</td>\n",
       "      <td>China</td>\n",
       "      <td>6.0</td>\n",
       "      <td>China</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15.0</td>\n",
       "      <td>COL</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>400.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>37</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CRI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>55</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>29.0</td>\n",
       "      <td>HRV</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>57.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>51</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CYP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>61</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.0</td>\n",
       "      <td>CZE</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>205.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>56</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.0</td>\n",
       "      <td>DOM</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>33</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.0</td>\n",
       "      <td>ECU</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>100.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>32</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GNQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>0</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16.0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>France</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2902.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>France</td>\n",
       "      <td>70</td>\n",
       "      <td>France</td>\n",
       "      <td>8.0</td>\n",
       "      <td>France</td>\n",
       "      <td>8.0</td>\n",
       "      <td>France</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GAB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Gabon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Gabon</td>\n",
       "      <td>34</td>\n",
       "      <td>Gabon</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Gabon</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Gabon</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20.0</td>\n",
       "      <td>GEO</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>52</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15.0</td>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3820.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Germany</td>\n",
       "      <td>81</td>\n",
       "      <td>Germany</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GHA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>47</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NIC</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>27</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>17.0</td>\n",
       "      <td>NER</td>\n",
       "      <td>Niger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Niger</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Niger</td>\n",
       "      <td>34</td>\n",
       "      <td>Niger</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Niger</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Niger</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>594.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10-80</td>\n",
       "      <td>3</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>26</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>NaN</td>\n",
       "      <td>OMN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Oman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Oman</td>\n",
       "      <td>45</td>\n",
       "      <td>Oman</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Oman</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Oman</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>18.0</td>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>237.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>30</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>20.0</td>\n",
       "      <td>PAN</td>\n",
       "      <td>Panama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Panama</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>44.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Panama</td>\n",
       "      <td>39</td>\n",
       "      <td>Panama</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Panama</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Panama</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>14.0</td>\n",
       "      <td>PER</td>\n",
       "      <td>Peru</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Peru</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>208.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Peru</td>\n",
       "      <td>36</td>\n",
       "      <td>Peru</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Peru</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Peru</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>4.0</td>\n",
       "      <td>PHL</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>284.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>35</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>12.0</td>\n",
       "      <td>POL</td>\n",
       "      <td>Poland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>Poland</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>552.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Poland</td>\n",
       "      <td>62</td>\n",
       "      <td>Poland</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>NaN</td>\n",
       "      <td>QAT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>71</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>4.0</td>\n",
       "      <td>ROU</td>\n",
       "      <td>Romania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Romania</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>199.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Romania</td>\n",
       "      <td>46</td>\n",
       "      <td>Romania</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Romania</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Romania</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>10.0</td>\n",
       "      <td>RWA</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>54</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SAU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>777.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10-80</td>\n",
       "      <td>3</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>52</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>44</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>29.0</td>\n",
       "      <td>SRB</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>42.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>40</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>28.0</td>\n",
       "      <td>SVN</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>49.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>60</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>14.0</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>341.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>44</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>4.0</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>12.0</td>\n",
       "      <td>ESP</td>\n",
       "      <td>Spain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1400.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>58</td>\n",
       "      <td>Spain</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TZA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>30</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>14.0</td>\n",
       "      <td>THA</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>373.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10-80</td>\n",
       "      <td>3</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>38</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>15.0</td>\n",
       "      <td>TTO</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>39</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.0</td>\n",
       "      <td>TUR</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>813.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>42</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>NaN</td>\n",
       "      <td>TKM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>18</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>11.0</td>\n",
       "      <td>UGA</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>25</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>19.0</td>\n",
       "      <td>UKR</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>134.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2-5</td>\n",
       "      <td>1</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>27</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ARE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>416.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>70</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>7.0</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>8.0</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>23.0</td>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2848.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>81</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>NaN</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17420.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0-2</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>5.4</td>\n",
       "      <td>United States</td>\n",
       "      <td>7.6</td>\n",
       "      <td>United States</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>7.0</td>\n",
       "      <td>UZB</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>63.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>19</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>9.0</td>\n",
       "      <td>VNM</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>187.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5-10</td>\n",
       "      <td>2</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>31</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    APPEALS CODE               COUNTRY_y  CSV  Count                 Country  \\\n",
       "0      23.0  ALB                 Albania  NaN      2                 Albania   \n",
       "1       NaN  DZA                     NaN  NaN      3                 Algeria   \n",
       "2      18.0  AGO                  Angola  NaN     17                  Angola   \n",
       "3      13.0  ARG               Argentina  NaN     18               Argentina   \n",
       "4      19.0  AZE              Azerbaijan  NaN      4              Azerbaijan   \n",
       "5       NaN  BHR                     NaN  NaN      3                 Bahrain   \n",
       "6      23.0  BGD              Bangladesh  NaN      9              Bangladesh   \n",
       "7       NaN  BEN                     NaN  NaN      3                   Benin   \n",
       "8       NaN  BOL                     NaN  NaN      2                 Bolivia   \n",
       "9      16.0  BIH  Bosnia and Herzegovina  NaN      1  Bosnia and Herzegovina   \n",
       "10     22.0  BRA                  Brazil  NaN     23                  Brazil   \n",
       "11      3.0  BGR                Bulgaria  NaN      3                Bulgaria   \n",
       "12     14.0  BFA            Burkina Faso  NaN      1            Burkina Faso   \n",
       "13      NaN  BDI                     NaN  NaN      1                 Burundi   \n",
       "14      NaN  TCD                     NaN  NaN      5                    Chad   \n",
       "15     23.0  CHL                   Chile  NaN      4                   Chile   \n",
       "16     13.0  CHN                   China  NaN     78                   China   \n",
       "17     15.0  COL                Colombia  NaN      7                Colombia   \n",
       "18      NaN  CRI                     NaN  NaN      7              Costa Rica   \n",
       "19     29.0  HRV                 Croatia  NaN      5                 Croatia   \n",
       "20      NaN  CYP                     NaN  NaN      1                  Cyprus   \n",
       "21     10.0  CZE          Czech Republic  NaN      2          Czech Republic   \n",
       "22      3.0  DOM      Dominican Republic  NaN      4      Dominican Republic   \n",
       "23      8.0  ECU                 Ecuador  NaN      8                 Ecuador   \n",
       "24      NaN  GNQ                     NaN  NaN      3       Equatorial Guinea   \n",
       "25     16.0  FRA                  France  NaN      1                  France   \n",
       "26      NaN  GAB                     NaN  NaN      1                   Gabon   \n",
       "27     20.0  GEO                 Georgia  NaN      1                 Georgia   \n",
       "28     15.0  DEU                 Germany  NaN      3                 Germany   \n",
       "29      NaN  GHA                     NaN  NaN      2                   Ghana   \n",
       "..      ...  ...                     ...  ...    ...                     ...   \n",
       "59     14.0  NIC               Nicaragua  NaN      3               Nicaragua   \n",
       "60     17.0  NER                   Niger  NaN      5                   Niger   \n",
       "61      4.0  NGA                 Nigeria  NaN     45                 Nigeria   \n",
       "62      NaN  OMN                     NaN  NaN      1                    Oman   \n",
       "63     18.0  PAK                Pakistan  NaN      2                Pakistan   \n",
       "64     20.0  PAN                  Panama  NaN      7                  Panama   \n",
       "65     14.0  PER                    Peru  NaN      2                    Peru   \n",
       "66      4.0  PHL             Philippines  NaN      4             Philippines   \n",
       "67     12.0  POL                  Poland  NaN      9                  Poland   \n",
       "68      NaN  QAT                     NaN  NaN      1                   Qatar   \n",
       "69      4.0  ROU                 Romania  NaN      4                 Romania   \n",
       "70     10.0  RWA                  Rwanda  NaN      3                  Rwanda   \n",
       "71      NaN  SAU                     NaN  NaN     12            Saudi Arabia   \n",
       "72      NaN  SEN                     NaN  NaN      3                 Senegal   \n",
       "73     29.0  SRB                  Serbia  NaN      4                  Serbia   \n",
       "74     28.0  SVN                Slovenia  NaN      1                Slovenia   \n",
       "75     14.0  ZAF            South Africa  NaN      2            South Africa   \n",
       "76     12.0  ESP                   Spain  NaN      1                   Spain   \n",
       "77      NaN  TZA                     NaN  NaN      2                Tanzania   \n",
       "78     14.0  THA                Thailand  NaN     15                Thailand   \n",
       "79     15.0  TTO     Trinidad and Tobago  NaN      1     Trinidad and Tobago   \n",
       "80      5.0  TUR                  Turkey  NaN      7                  Turkey   \n",
       "81      NaN  TKM                     NaN  NaN      4            Turkmenistan   \n",
       "82     11.0  UGA                  Uganda  NaN      2                  Uganda   \n",
       "83     19.0  UKR                 Ukraine  NaN      5                 Ukraine   \n",
       "84      NaN  ARE                     NaN  NaN     10    United Arab Emirates   \n",
       "85     23.0  GBR          United Kingdom  NaN      2          United Kingdom   \n",
       "86      NaN  USA                     NaN  NaN      1           United States   \n",
       "87      7.0  UZB              Uzbekistan  NaN      9              Uzbekistan   \n",
       "88      9.0  VNM                 Vietnam  NaN      8                 Vietnam   \n",
       "\n",
       "      DATE  EXCEPTIONS & REFUSALS  GDP (BILLIONS)  LAW        ...         \\\n",
       "0   1999.0                   27.0           13.40  NaN        ...          \n",
       "1      NaN                    NaN          227.80  NaN        ...          \n",
       "2   2002.0                   18.0          131.40  NaN        ...          \n",
       "3   2004.0                   12.0          536.20  NaN        ...          \n",
       "4   2005.0                   24.0           77.91  NaN        ...          \n",
       "5      NaN                    NaN           34.05  NaN        ...          \n",
       "6   2009.0                   20.0          186.60  NaN        ...          \n",
       "7      NaN                    NaN            9.24  NaN        ...          \n",
       "8      NaN                    NaN           34.08  NaN        ...          \n",
       "9   2000.0                   19.0           19.55  NaN        ...          \n",
       "10  2011.0                   16.0         2244.00  NaN        ...          \n",
       "11  2000.0                   20.0           55.08  NaN        ...          \n",
       "12  2015.0                    8.0           13.38  NaN        ...          \n",
       "13     NaN                    NaN            3.04  NaN        ...          \n",
       "14     NaN                    NaN           15.84  NaN        ...          \n",
       "15  2008.0                   14.0          264.10  NaN        ...          \n",
       "16  2007.0                   15.0        10360.00  NaN        ...          \n",
       "17  1985.0                   23.0          400.10  NaN        ...          \n",
       "18     NaN                    NaN           50.46  NaN        ...          \n",
       "19  2003.0                   26.0           57.18  NaN        ...          \n",
       "20     NaN                    NaN           21.34  NaN        ...          \n",
       "21  1999.0                   14.0          205.60  NaN        ...          \n",
       "22  2004.0                   11.0           64.05  NaN        ...          \n",
       "23  2004.0                   14.0          100.50  NaN        ...          \n",
       "24     NaN                    NaN           15.40  NaN        ...          \n",
       "25  1978.0                   14.0         2902.00  NaN        ...          \n",
       "26     NaN                    NaN           20.68  NaN        ...          \n",
       "27  1999.0                   18.0           16.13  NaN        ...          \n",
       "28  2005.0                   11.0         3820.00  NaN        ...          \n",
       "29     NaN                    NaN           35.48  NaN        ...          \n",
       "..     ...                    ...             ...  ...        ...          \n",
       "59  2007.0                   27.0           11.85  NaN        ...          \n",
       "60  2011.0                   13.0            8.29  NaN        ...          \n",
       "61  2011.0                   22.0          594.30  NaN        ...          \n",
       "62     NaN                    NaN           80.54  NaN        ...          \n",
       "63  2002.0                   11.0          237.50  NaN        ...          \n",
       "64  2002.0                   16.0           44.69  NaN        ...          \n",
       "65  2003.0                   17.0          208.20  NaN        ...          \n",
       "66  2016.0                    4.0          284.60  NaN        ...          \n",
       "67  2001.0                   14.0          552.20  NaN        ...          \n",
       "68     NaN                    NaN          212.00  NaN        ...          \n",
       "69  2001.0                   13.0          199.00  NaN        ...          \n",
       "70  2013.0                   21.0            8.00  NaN        ...          \n",
       "71     NaN                    NaN          777.90  NaN        ...          \n",
       "72     NaN                    NaN           15.88  NaN        ...          \n",
       "73  2003.0                   26.0           42.65  NaN        ...          \n",
       "74  2003.0                   25.0           49.93  NaN        ...          \n",
       "75  2000.0                   25.0          341.20  NaN        ...          \n",
       "76  2013.0                   20.0         1400.00  NaN        ...          \n",
       "77     NaN                    NaN           36.62  NaN        ...          \n",
       "78  1997.0                   13.0          373.80  NaN        ...          \n",
       "79  1999.0                   20.0           29.63  NaN        ...          \n",
       "80  2003.0                   21.0          813.30  NaN        ...          \n",
       "81     NaN                    NaN           43.50  NaN        ...          \n",
       "82  2005.0                   22.0           26.09  NaN        ...          \n",
       "83  1992.0                   25.0          134.90  NaN        ...          \n",
       "84     NaN                    NaN          416.40  NaN        ...          \n",
       "85  2000.0                   13.0         2848.00  NaN        ...          \n",
       "86     NaN                    NaN        17420.00  NaN        ...          \n",
       "87  1997.0                   13.0           63.08  NaN        ...          \n",
       "88  2016.0                   14.0          187.80  NaN        ...          \n",
       "\n",
       "    label  label_num            Jurisdiction  corrupt perception  \\\n",
       "0     0-2          0                 Albania                  36   \n",
       "1     2-5          1                 Algeria                  36   \n",
       "2   10-80          3                  Angola                  15   \n",
       "3   10-80          3               Argentina                  32   \n",
       "4     2-5          1              Azerbaijan                  29   \n",
       "5     2-5          1                 Bahrain                  51   \n",
       "6    5-10          2              Bangladesh                  25   \n",
       "7     2-5          1                   Benin                  37   \n",
       "8     0-2          0                 Bolivia                  34   \n",
       "9     0-2          0  Bosnia and Herzegovina                  38   \n",
       "10  10-80          3                  Brazil                  38   \n",
       "11    2-5          1                Bulgaria                  41   \n",
       "12    0-2          0            Burkina Faso                  38   \n",
       "13    0-2          0                 Burundi                  21   \n",
       "14    2-5          1                    Chad                  22   \n",
       "15    2-5          1                   Chile                  70   \n",
       "16  10-80          3                   China                  37   \n",
       "17   5-10          2                Colombia                  37   \n",
       "18   5-10          2              Costa Rica                  55   \n",
       "19    2-5          1                 Croatia                  51   \n",
       "20    0-2          0                  Cyprus                  61   \n",
       "21    0-2          0          Czech Republic                  56   \n",
       "22    2-5          1      Dominican Republic                  33   \n",
       "23   5-10          2                 Ecuador                  32   \n",
       "24    2-5          1       Equatorial Guinea                   0   \n",
       "25    0-2          0                  France                  70   \n",
       "26    0-2          0                   Gabon                  34   \n",
       "27    0-2          0                 Georgia                  52   \n",
       "28    2-5          1                 Germany                  81   \n",
       "29    0-2          0                   Ghana                  47   \n",
       "..    ...        ...                     ...                 ...   \n",
       "59    2-5          1               Nicaragua                  27   \n",
       "60    2-5          1                   Niger                  34   \n",
       "61  10-80          3                 Nigeria                  26   \n",
       "62    0-2          0                    Oman                  45   \n",
       "63    0-2          0                Pakistan                  30   \n",
       "64   5-10          2                  Panama                  39   \n",
       "65    0-2          0                    Peru                  36   \n",
       "66    2-5          1             Philippines                  35   \n",
       "67   5-10          2                  Poland                  62   \n",
       "68    0-2          0                   Qatar                  71   \n",
       "69    2-5          1                 Romania                  46   \n",
       "70    2-5          1                  Rwanda                  54   \n",
       "71  10-80          3            Saudi Arabia                  52   \n",
       "72    2-5          1                 Senegal                  44   \n",
       "73    2-5          1                  Serbia                  40   \n",
       "74    0-2          0                Slovenia                  60   \n",
       "75    0-2          0            South Africa                  44   \n",
       "76    0-2          0                   Spain                  58   \n",
       "77    0-2          0                Tanzania                  30   \n",
       "78  10-80          3                Thailand                  38   \n",
       "79    0-2          0     Trinidad and Tobago                  39   \n",
       "80   5-10          2                  Turkey                  42   \n",
       "81    2-5          1            Turkmenistan                  18   \n",
       "82    0-2          0                  Uganda                  25   \n",
       "83    2-5          1                 Ukraine                  27   \n",
       "84   5-10          2    United Arab Emirates                  70   \n",
       "85    0-2          0          United Kingdom                  81   \n",
       "86    0-2          0           United States                   0   \n",
       "87   5-10          2              Uzbekistan                  19   \n",
       "88   5-10          2                 Vietnam                  31   \n",
       "\n",
       "            Country Name_x  corporate transparency          Country Name_y  \\\n",
       "0                  Albania                     7.0                 Albania   \n",
       "1                  Algeria                     2.0                 Algeria   \n",
       "2                   Angola                     4.0                  Angola   \n",
       "3                Argentina                     7.0               Argentina   \n",
       "4               Azerbaijan                     6.0              Azerbaijan   \n",
       "5                  Bahrain                     4.0                 Bahrain   \n",
       "6               Bangladesh                     6.0              Bangladesh   \n",
       "7                    Benin                     2.0                   Benin   \n",
       "8                  Bolivia                     4.0                 Bolivia   \n",
       "9   Bosnia and Herzegovina                     6.0  Bosnia and Herzegovina   \n",
       "10                  Brazil                     8.0                  Brazil   \n",
       "11                Bulgaria                     9.0                Bulgaria   \n",
       "12            Burkina Faso                     2.0            Burkina Faso   \n",
       "13                 Burundi                     1.0                 Burundi   \n",
       "14                    Chad                     2.0                    Chad   \n",
       "15                   Chile                     2.0                   Chile   \n",
       "16                   China                     9.0                   China   \n",
       "17                Colombia                     6.0                Colombia   \n",
       "18              Costa Rica                     2.0              Costa Rica   \n",
       "19                 Croatia                     6.0                 Croatia   \n",
       "20                  Cyprus                     7.0                  Cyprus   \n",
       "21          Czech Republic                     5.0          Czech Republic   \n",
       "22      Dominican Republic                     5.0      Dominican Republic   \n",
       "23                 Ecuador                     2.0                 Ecuador   \n",
       "24       Equatorial Guinea                     2.0       Equatorial Guinea   \n",
       "25                  France                     8.0                  France   \n",
       "26                   Gabon                     2.0                   Gabon   \n",
       "27                 Georgia                     8.0                 Georgia   \n",
       "28                 Germany                     7.0                 Germany   \n",
       "29                   Ghana                     2.0                   Ghana   \n",
       "..                     ...                     ...                     ...   \n",
       "59               Nicaragua                     4.0               Nicaragua   \n",
       "60                   Niger                     2.0                   Niger   \n",
       "61                 Nigeria                     7.0                 Nigeria   \n",
       "62                    Oman                     4.0                    Oman   \n",
       "63                Pakistan                     5.0                Pakistan   \n",
       "64                  Panama                     6.0                  Panama   \n",
       "65                    Peru                     3.0                    Peru   \n",
       "66             Philippines                     7.0             Philippines   \n",
       "67                  Poland                     7.0                  Poland   \n",
       "68                   Qatar                     4.0                   Qatar   \n",
       "69                 Romania                     7.0                 Romania   \n",
       "70                  Rwanda                     4.0                  Rwanda   \n",
       "71            Saudi Arabia                     7.0            Saudi Arabia   \n",
       "72                 Senegal                     2.0                 Senegal   \n",
       "73                  Serbia                     6.0                  Serbia   \n",
       "74                Slovenia                     6.0                Slovenia   \n",
       "75            South Africa                     4.0            South Africa   \n",
       "76                   Spain                     8.0                   Spain   \n",
       "77                Tanzania                     2.0                Tanzania   \n",
       "78                Thailand                     7.0                Thailand   \n",
       "79     Trinidad and Tobago                     2.0     Trinidad and Tobago   \n",
       "80                  Turkey                     7.0                  Turkey   \n",
       "81            Turkmenistan                     0.0            Turkmenistan   \n",
       "82                  Uganda                     5.0                  Uganda   \n",
       "83                 Ukraine                     9.0                 Ukraine   \n",
       "84    United Arab Emirates                     7.0    United Arab Emirates   \n",
       "85          United Kingdom                     8.0          United Kingdom   \n",
       "86           United States                     5.4           United States   \n",
       "87              Uzbekistan                     5.0              Uzbekistan   \n",
       "88                 Vietnam                     7.0                 Vietnam   \n",
       "\n",
       "   Reliability of transparency            Country Name  disclosure_index  \n",
       "0                          0.0                 Albania               9.0  \n",
       "1                          5.0                 Algeria               4.0  \n",
       "2                          0.0                  Angola               4.0  \n",
       "3                          5.0               Argentina               7.0  \n",
       "4                          4.0              Azerbaijan              10.0  \n",
       "5                          5.0                 Bahrain               8.0  \n",
       "6                          0.0              Bangladesh               6.0  \n",
       "7                          0.0                   Benin               7.0  \n",
       "8                          6.0                 Bolivia               1.0  \n",
       "9                          6.0  Bosnia and Herzegovina               3.0  \n",
       "10                         5.0                  Brazil               5.0  \n",
       "11                         5.0                Bulgaria              10.0  \n",
       "12                         0.0            Burkina Faso               7.0  \n",
       "13                         0.0                 Burundi               8.0  \n",
       "14                         0.0                    Chad               7.0  \n",
       "15                         6.0                   Chile               8.0  \n",
       "16                         6.0                   China              10.0  \n",
       "17                         6.0                Colombia               9.0  \n",
       "18                         7.0              Costa Rica               2.0  \n",
       "19                         5.0                 Croatia               5.0  \n",
       "20                         8.0                  Cyprus               8.0  \n",
       "21                         8.0          Czech Republic               2.0  \n",
       "22                         0.0      Dominican Republic               5.0  \n",
       "23                         6.0                 Ecuador               2.0  \n",
       "24                         0.0       Equatorial Guinea               7.0  \n",
       "25                         8.0                  France               8.0  \n",
       "26                         2.0                   Gabon               7.0  \n",
       "27                         6.0                 Georgia               9.0  \n",
       "28                         8.0                 Germany               5.0  \n",
       "29                         0.0                   Ghana               7.0  \n",
       "..                         ...                     ...               ...  \n",
       "59                         4.0               Nicaragua               1.0  \n",
       "60                         0.0                   Niger               7.0  \n",
       "61                         0.0                 Nigeria               7.0  \n",
       "62                         6.0                    Oman               8.0  \n",
       "63                         0.0                Pakistan               6.0  \n",
       "64                         7.0                  Panama               4.0  \n",
       "65                         6.0                    Peru               9.0  \n",
       "66                         6.0             Philippines               2.0  \n",
       "67                         7.0                  Poland               7.0  \n",
       "68                         6.0                   Qatar               2.0  \n",
       "69                         7.0                 Romania               9.0  \n",
       "70                         0.0                  Rwanda               7.0  \n",
       "71                         6.0            Saudi Arabia               8.0  \n",
       "72                         0.0                 Senegal               7.0  \n",
       "73                         5.0                  Serbia               4.0  \n",
       "74                         8.0                Slovenia               5.0  \n",
       "75                         0.0            South Africa               8.0  \n",
       "76                         8.0                   Spain               5.0  \n",
       "77                         4.0                Tanzania               2.0  \n",
       "78                         6.0                Thailand              10.0  \n",
       "79                         6.0     Trinidad and Tobago               4.0  \n",
       "80                         5.0                  Turkey               9.0  \n",
       "81                         0.0            Turkmenistan               NaN  \n",
       "82                         4.0                  Uganda               3.0  \n",
       "83                         6.0                 Ukraine               6.0  \n",
       "84                         8.0    United Arab Emirates              10.0  \n",
       "85                         8.0          United Kingdom              10.0  \n",
       "86                         7.6           United States               7.4  \n",
       "87                         8.0              Uzbekistan               8.0  \n",
       "88                         3.0                 Vietnam               7.0  \n",
       "\n",
       "[89 rows x 32 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freedom_dgp_freind_cp_cti = pd.read_excel(report_folder + 'df_freedom_dgp_freind_cp_cti_rel.xlsx')\n",
    "df_freedom_dgp_freind_cp_cti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti = pd.merge(left = df_freedom_dgp_freind_cp_cti, right = df_gini, on = 'Country', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti = pd.merge(left = df_freedom_dgp_freind_cp_cti, right = df_breaches, on = 'Country', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti = pd.merge(left = df_freedom_dgp_freind_cp_cti, right = df_ppp, on = 'Country', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti.rename(columns={'2016':'ppp'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['APPEALS', 'CODE', 'COUNTRY_y', 'CSV', 'Count', 'Country', 'DATE',\n",
       "       'EXCEPTIONS & REFUSALS', 'GDP (BILLIONS)', 'LAW',\n",
       "       'PROMOTIONAL MEASURES', 'RANKING POSITION', 'REQUESTING PROCEDURES',\n",
       "       'RIGHT OF ACCESS', 'SANCTIONS & PROTECTIONS', 'SCOPE', 'TOTAL',\n",
       "       'label-H', 'label_num_h', 'mean', 'sum', 'text', 'label', 'label_num',\n",
       "       'Jurisdiction', 'corrupt perception', 'Country Name_x',\n",
       "       'corporate transparency', 'Country Name_y',\n",
       "       'Reliability of transparency', 'Country Name', 'disclosure_index',\n",
       "       'GINI', 'Breaches_2017', 'ppp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freedom_dgp_freind_cp_cti.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti['ppp'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti['ppp'] = df_freedom_dgp_freind_cp_cti['ppp']/1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti['Breaches_2017'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti.loc[df_freedom_dgp_freind_cp_cti['Country']=='Myanmar', 'GDP (BILLIONS)'] = 67.43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       89.000000\n",
       "mean      1061.146125\n",
       "std       3133.814420\n",
       "min          0.000000\n",
       "25%         37.497127\n",
       "50%        168.386922\n",
       "75%        688.636986\n",
       "max      21409.404740\n",
       "Name: ppp, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freedom_dgp_freind_cp_cti['ppp'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freedom_dgp_freind_cp_cti_gini = df_freedom_dgp_freind_cp_cti[pd.isna(df_freedom_dgp_freind_cp_cti['GINI']) == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_freedom_dgp_freind_cp_cti_gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_freedom_dgp_freind_cp_cti.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Count</th>\n",
       "      <th>GDP (BILLIONS)</th>\n",
       "      <th>PROMOTIONAL MEASURES</th>\n",
       "      <th>sum</th>\n",
       "      <th>corrupt perception</th>\n",
       "      <th>corporate transparency</th>\n",
       "      <th>Reliability of transparency</th>\n",
       "      <th>disclosure_index</th>\n",
       "      <th>GINI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2434</td>\n",
       "      <td>0.0334</td>\n",
       "      <td>0.4397</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP (BILLIONS)</th>\n",
       "      <td>0.0017</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9141</td>\n",
       "      <td>0.6791</td>\n",
       "      <td>0.3423</td>\n",
       "      <td>0.0612</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.1187</td>\n",
       "      <td>0.6509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROMOTIONAL MEASURES</th>\n",
       "      <td>0.0249</td>\n",
       "      <td>0.9141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5322</td>\n",
       "      <td>0.9501</td>\n",
       "      <td>0.0721</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.9997</td>\n",
       "      <td>0.8007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>0</td>\n",
       "      <td>0.6791</td>\n",
       "      <td>0.5322</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.3478</td>\n",
       "      <td>0.5109</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.0401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corrupt perception</th>\n",
       "      <td>0.2434</td>\n",
       "      <td>0.3423</td>\n",
       "      <td>0.9501</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2704</td>\n",
       "      <td>0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corporate transparency</th>\n",
       "      <td>0.0334</td>\n",
       "      <td>0.0612</td>\n",
       "      <td>0.0721</td>\n",
       "      <td>0.3478</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reliability of transparency</th>\n",
       "      <td>0.4397</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.5109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4149</td>\n",
       "      <td>0.1086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disclosure_index</th>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.1187</td>\n",
       "      <td>0.9997</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.2704</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.4149</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GINI</th>\n",
       "      <td>0.0698</td>\n",
       "      <td>0.6509</td>\n",
       "      <td>0.8007</td>\n",
       "      <td>0.0401</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.1086</td>\n",
       "      <td>0.2135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Count GDP (BILLIONS) PROMOTIONAL MEASURES  \\\n",
       "Count                             0         0.0017               0.0249   \n",
       "GDP (BILLIONS)               0.0017              0               0.9141   \n",
       "PROMOTIONAL MEASURES         0.0249         0.9141                    0   \n",
       "sum                               0         0.6791               0.5322   \n",
       "corrupt perception           0.2434         0.3423               0.9501   \n",
       "corporate transparency       0.0334         0.0612               0.0721   \n",
       "Reliability of transparency  0.4397         0.0199                0.582   \n",
       "disclosure_index             0.0223         0.1187               0.9997   \n",
       "GINI                         0.0698         0.6509               0.8007   \n",
       "\n",
       "                                sum corrupt perception corporate transparency  \\\n",
       "Count                             0             0.2434                 0.0334   \n",
       "GDP (BILLIONS)               0.6791             0.3423                 0.0612   \n",
       "PROMOTIONAL MEASURES         0.5322             0.9501                 0.0721   \n",
       "sum                               0             0.0523                 0.3478   \n",
       "corrupt perception           0.0523                  0                 0.0009   \n",
       "corporate transparency       0.3478             0.0009                      0   \n",
       "Reliability of transparency  0.5109                  0                      0   \n",
       "disclosure_index              0.202             0.2704                 0.0034   \n",
       "GINI                         0.0401             0.0079                 0.0004   \n",
       "\n",
       "                            Reliability of transparency disclosure_index  \\\n",
       "Count                                            0.4397           0.0223   \n",
       "GDP (BILLIONS)                                   0.0199           0.1187   \n",
       "PROMOTIONAL MEASURES                              0.582           0.9997   \n",
       "sum                                              0.5109            0.202   \n",
       "corrupt perception                                    0           0.2704   \n",
       "corporate transparency                                0           0.0034   \n",
       "Reliability of transparency                           0           0.4149   \n",
       "disclosure_index                                 0.4149                0   \n",
       "GINI                                             0.1086           0.2135   \n",
       "\n",
       "                               GINI  \n",
       "Count                        0.0698  \n",
       "GDP (BILLIONS)               0.6509  \n",
       "PROMOTIONAL MEASURES         0.8007  \n",
       "sum                          0.0401  \n",
       "corrupt perception           0.0079  \n",
       "corporate transparency       0.0004  \n",
       "Reliability of transparency  0.1086  \n",
       "disclosure_index             0.2135  \n",
       "GINI                              0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_pvalues(df_freedom_dgp_freind_cp_cti[['Count',\n",
    "        'GDP (BILLIONS)',\n",
    "       'PROMOTIONAL MEASURES', 'sum', 'corrupt perception',\n",
    "       'corporate transparency',\n",
    "       'Reliability of transparency', 'disclosure_index', 'GINI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>EXCEPTIONS &amp; REFUSALS</th>\n",
       "      <th>GDP (BILLIONS)</th>\n",
       "      <th>PROMOTIONAL MEASURES</th>\n",
       "      <th>REQUESTING PROCEDURES</th>\n",
       "      <th>RIGHT OF ACCESS</th>\n",
       "      <th>SANCTIONS &amp; PROTECTIONS</th>\n",
       "      <th>SCOPE</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>corrupt perception</th>\n",
       "      <th>corporate transparency</th>\n",
       "      <th>Reliability of transparency</th>\n",
       "      <th>disclosure_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EXCEPTIONS &amp; REFUSALS</th>\n",
       "      <td>0</td>\n",
       "      <td>0.6088</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3473</td>\n",
       "      <td>0.9463</td>\n",
       "      <td>0.4822</td>\n",
       "      <td>0.6598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP (BILLIONS)</th>\n",
       "      <td>0.6088</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5043</td>\n",
       "      <td>0.8698</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.2094</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.3346</td>\n",
       "      <td>0.0784</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROMOTIONAL MEASURES</th>\n",
       "      <td>0</td>\n",
       "      <td>0.5043</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.1439</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1386</td>\n",
       "      <td>0.2293</td>\n",
       "      <td>0.3852</td>\n",
       "      <td>0.5763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REQUESTING PROCEDURES</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.8698</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2744</td>\n",
       "      <td>0.1184</td>\n",
       "      <td>0.4321</td>\n",
       "      <td>0.1781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RIGHT OF ACCESS</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.1439</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.3578</td>\n",
       "      <td>0.3007</td>\n",
       "      <td>0.8757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANCTIONS &amp; PROTECTIONS</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.2094</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5809</td>\n",
       "      <td>0.1751</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.3233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCOPE</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1829</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.7407</td>\n",
       "      <td>0.8375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>0</td>\n",
       "      <td>0.3346</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6059</td>\n",
       "      <td>0.7276</td>\n",
       "      <td>0.6436</td>\n",
       "      <td>0.8408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corrupt perception</th>\n",
       "      <td>0.3473</td>\n",
       "      <td>0.0784</td>\n",
       "      <td>0.1386</td>\n",
       "      <td>0.2744</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.5809</td>\n",
       "      <td>0.1829</td>\n",
       "      <td>0.6059</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corporate transparency</th>\n",
       "      <td>0.9463</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.2293</td>\n",
       "      <td>0.1184</td>\n",
       "      <td>0.3578</td>\n",
       "      <td>0.1751</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.7276</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reliability of transparency</th>\n",
       "      <td>0.4822</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.3852</td>\n",
       "      <td>0.4321</td>\n",
       "      <td>0.3007</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.7407</td>\n",
       "      <td>0.6436</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disclosure_index</th>\n",
       "      <td>0.6598</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>0.5763</td>\n",
       "      <td>0.1781</td>\n",
       "      <td>0.8757</td>\n",
       "      <td>0.3233</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>0.8408</td>\n",
       "      <td>0.4594</td>\n",
       "      <td>0.0176</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            EXCEPTIONS & REFUSALS GDP (BILLIONS)  \\\n",
       "EXCEPTIONS & REFUSALS                           0         0.6088   \n",
       "GDP (BILLIONS)                             0.6088              0   \n",
       "PROMOTIONAL MEASURES                            0         0.5043   \n",
       "REQUESTING PROCEDURES                      0.0005         0.8698   \n",
       "RIGHT OF ACCESS                              0.01          0.008   \n",
       "SANCTIONS & PROTECTIONS                    0.0011         0.2094   \n",
       "SCOPE                                      0.0005         0.0016   \n",
       "TOTAL                                           0         0.3346   \n",
       "corrupt perception                         0.3473         0.0784   \n",
       "corporate transparency                     0.9463         0.0073   \n",
       "Reliability of transparency                0.4822         0.0395   \n",
       "disclosure_index                           0.6598         0.0462   \n",
       "\n",
       "                            PROMOTIONAL MEASURES REQUESTING PROCEDURES  \\\n",
       "EXCEPTIONS & REFUSALS                          0                0.0005   \n",
       "GDP (BILLIONS)                            0.5043                0.8698   \n",
       "PROMOTIONAL MEASURES                           0                0.0088   \n",
       "REQUESTING PROCEDURES                     0.0088                     0   \n",
       "RIGHT OF ACCESS                           0.1439                0.0023   \n",
       "SANCTIONS & PROTECTIONS                   0.0002                0.0094   \n",
       "SCOPE                                     0.0362                0.0086   \n",
       "TOTAL                                          0                     0   \n",
       "corrupt perception                        0.1386                0.2744   \n",
       "corporate transparency                    0.2293                0.1184   \n",
       "Reliability of transparency               0.3852                0.4321   \n",
       "disclosure_index                          0.5763                0.1781   \n",
       "\n",
       "                            RIGHT OF ACCESS SANCTIONS & PROTECTIONS   SCOPE  \\\n",
       "EXCEPTIONS & REFUSALS                  0.01                  0.0011  0.0005   \n",
       "GDP (BILLIONS)                        0.008                  0.2094  0.0016   \n",
       "PROMOTIONAL MEASURES                 0.1439                  0.0002  0.0362   \n",
       "REQUESTING PROCEDURES                0.0023                  0.0094  0.0086   \n",
       "RIGHT OF ACCESS                           0                  0.0064  0.0131   \n",
       "SANCTIONS & PROTECTIONS              0.0064                       0  0.0471   \n",
       "SCOPE                                0.0131                  0.0471       0   \n",
       "TOTAL                                0.0005                       0       0   \n",
       "corrupt perception                    0.023                  0.5809  0.1829   \n",
       "corporate transparency               0.3578                  0.1751   0.836   \n",
       "Reliability of transparency          0.3007                  0.0277  0.7407   \n",
       "disclosure_index                     0.8757                  0.3233  0.8375   \n",
       "\n",
       "                              TOTAL corrupt perception corporate transparency  \\\n",
       "EXCEPTIONS & REFUSALS             0             0.3473                 0.9463   \n",
       "GDP (BILLIONS)               0.3346             0.0784                 0.0073   \n",
       "PROMOTIONAL MEASURES              0             0.1386                 0.2293   \n",
       "REQUESTING PROCEDURES             0             0.2744                 0.1184   \n",
       "RIGHT OF ACCESS              0.0005              0.023                 0.3578   \n",
       "SANCTIONS & PROTECTIONS           0             0.5809                 0.1751   \n",
       "SCOPE                             0             0.1829                  0.836   \n",
       "TOTAL                             0             0.6059                 0.7276   \n",
       "corrupt perception           0.6059                  0                 0.0119   \n",
       "corporate transparency       0.7276             0.0119                      0   \n",
       "Reliability of transparency  0.6436                  0                 0.0003   \n",
       "disclosure_index             0.8408             0.4594                 0.0176   \n",
       "\n",
       "                            Reliability of transparency disclosure_index  \n",
       "EXCEPTIONS & REFUSALS                            0.4822           0.6598  \n",
       "GDP (BILLIONS)                                   0.0395           0.0462  \n",
       "PROMOTIONAL MEASURES                             0.3852           0.5763  \n",
       "REQUESTING PROCEDURES                            0.4321           0.1781  \n",
       "RIGHT OF ACCESS                                  0.3007           0.8757  \n",
       "SANCTIONS & PROTECTIONS                          0.0277           0.3233  \n",
       "SCOPE                                            0.7407           0.8375  \n",
       "TOTAL                                            0.6436           0.8408  \n",
       "corrupt perception                                    0           0.4594  \n",
       "corporate transparency                           0.0003           0.0176  \n",
       "Reliability of transparency                           0            0.534  \n",
       "disclosure_index                                  0.534                0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_pvalues(df_freedom_dgp_freind_cp_cti[[\n",
    "       'EXCEPTIONS & REFUSALS', 'GDP (BILLIONS)',\n",
    "       'PROMOTIONAL MEASURES',  'REQUESTING PROCEDURES',\n",
    "       'RIGHT OF ACCESS', 'SANCTIONS & PROTECTIONS', 'SCOPE', 'TOTAL',\n",
    "        'corrupt perception',\n",
    "       'corporate transparency',\n",
    "       'Reliability of transparency', 'disclosure_index']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Out of all factors considered, the following are correlated with GD ra, scope cti, rt, di\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi = pd.read_excel(report_folder + '2018_Statistical_Annex_Table_2.xls', sheet_name='Score')\n",
    "df_hdi['Country'] = df_hdi['Country'].str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_group= pd.read_excel(report_folder + '2018_Statistical_Annex_Table_2.xls', sheet_name='Groups')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_with_all = pd.merge(left = df_freedom_dgp_freind_cp_cti, right=df_hdi, left_on = 'Country', right_on='Country', how = 'left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    89.000000\n",
       "mean      0.709762\n",
       "std       0.152088\n",
       "min       0.353931\n",
       "25%       0.589920\n",
       "50%       0.751704\n",
       "75%       0.824850\n",
       "max       0.938410\n",
       "Name: Score, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hdi_with_all['Score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdi_group_limits = df_hdi_group['Unnamed: 1'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hdi_group(x):\n",
    "    if x >= hdi_group_limits[0]:\n",
    "        return 'very high'\n",
    "    if x >= hdi_group_limits[1]:\n",
    "        return 'high'\n",
    "    if x >= hdi_group_limits[2]:\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'low'\n",
    "    "
   ]
  },
  {
   "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>APPEALS</th>\n",
       "      <th>CODE</th>\n",
       "      <th>COUNTRY_y</th>\n",
       "      <th>CSV</th>\n",
       "      <th>Count</th>\n",
       "      <th>Country</th>\n",
       "      <th>DATE</th>\n",
       "      <th>EXCEPTIONS &amp; REFUSALS</th>\n",
       "      <th>GDP (BILLIONS)</th>\n",
       "      <th>LAW</th>\n",
       "      <th>...</th>\n",
       "      <th>Country Name_x</th>\n",
       "      <th>corporate transparency</th>\n",
       "      <th>Country Name_y</th>\n",
       "      <th>Reliability of transparency</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>disclosure_index</th>\n",
       "      <th>GINI</th>\n",
       "      <th>Breaches_2017</th>\n",
       "      <th>ppp</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GHA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>7.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.075939</td>\n",
       "      <td>0.591738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    APPEALS CODE COUNTRY_y  CSV  Count Country  DATE  EXCEPTIONS & REFUSALS  \\\n",
       "29      NaN  GHA       NaN  NaN      2   Ghana   NaN                    NaN   \n",
       "\n",
       "    GDP (BILLIONS)  LAW    ...     Country Name_x  corporate transparency  \\\n",
       "29           35.48  NaN    ...              Ghana                     2.0   \n",
       "\n",
       "    Country Name_y  Reliability of transparency  Country Name  \\\n",
       "29           Ghana                          0.0         Ghana   \n",
       "\n",
       "    disclosure_index  GINI Breaches_2017         ppp     Score  \n",
       "29               7.0  42.3           0.0  121.075939  0.591738  \n",
       "\n",
       "[1 rows x 36 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hdi_with_all[df_hdi_with_all['Country'] == 'Ghana']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_with_all['HDIGroup'] = df_hdi_with_all['Score'].apply(lambda x: get_hdi_group(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_with_all.sort_values(by='Score',ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_with_all = df_hdi_with_all[['Count', 'Country', 'GDP (BILLIONS)', 'PROMOTIONAL MEASURES', 'Reliability of transparency',\n",
    "                                   'RIGHT OF ACCESS', 'SANCTIONS & PROTECTIONS', 'SCOPE', 'TOTAL',\n",
    "                                  'disclosure_index', 'corporate transparency', 'Score', 'HDIGroup']]\n",
    "\n",
    "df_hdi_with_all.columns = ['Count', 'Country', 'gdp', 'pm', 'rt',\n",
    "                           'ra','sp', 'scope', 'total_inf',\n",
    "                                  'di', 'cti', 'score', 'HDIGroup']\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#since RTI ratings are absent for countries that has not passed RTI laws (http://www.rti-rating.org/country-data/) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>score</td>      <th>  R-squared:         </th> <td>   0.695</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   97.83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 07 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>7.01e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:06:36</td>     <th>  Log-Likelihood:    </th> <td>  94.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    89</td>      <th>  AIC:               </th> <td>  -183.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    86</td>      <th>  BIC:               </th> <td>  -175.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.5028</td> <td>    0.020</td> <td>   25.140</td> <td> 0.000</td> <td>    0.463</td> <td>    0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rt</th>        <td>    0.0304</td> <td>    0.003</td> <td>    8.701</td> <td> 0.000</td> <td>    0.023</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cti</th>       <td>    0.0176</td> <td>    0.004</td> <td>    3.924</td> <td> 0.000</td> <td>    0.009</td> <td>    0.026</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.037</td> <th>  Durbin-Watson:     </th> <td>   1.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.982</td> <th>  Jarque-Bera (JB):  </th> <td>   0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.017</td> <th>  Prob(JB):          </th> <td>   0.909</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.776</td> <th>  Cond. No.          </th> <td>    16.3</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  score   R-squared:                       0.695\n",
       "Model:                            OLS   Adj. R-squared:                  0.688\n",
       "Method:                 Least Squares   F-statistic:                     97.83\n",
       "Date:                Sun, 07 Oct 2018   Prob (F-statistic):           7.01e-23\n",
       "Time:                        11:06:36   Log-Likelihood:                 94.622\n",
       "No. Observations:                  89   AIC:                            -183.2\n",
       "Df Residuals:                      86   BIC:                            -175.8\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.5028      0.020     25.140      0.000       0.463       0.543\n",
       "rt             0.0304      0.003      8.701      0.000       0.023       0.037\n",
       "cti            0.0176      0.004      3.924      0.000       0.009       0.026\n",
       "==============================================================================\n",
       "Omnibus:                        0.037   Durbin-Watson:                   1.212\n",
       "Prob(Omnibus):                  0.982   Jarque-Bera (JB):                0.190\n",
       "Skew:                           0.017   Prob(JB):                        0.909\n",
       "Kurtosis:                       2.776   Cond. No.                         16.3\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_score = smf.ols(formula='score ~ rt + cti', data=df_hdi_with_all).fit()\n",
    "lm_score.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Count</td>      <th>  R-squared:         </th> <td>   0.356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   24.09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 07 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>4.72e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:06:40</td>     <th>  Log-Likelihood:    </th> <td> -334.35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    89</td>      <th>  AIC:               </th> <td>   672.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    87</td>      <th>  BIC:               </th> <td>   677.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gdp</th> <td>    0.0018</td> <td>    0.000</td> <td>    3.672</td> <td> 0.000</td> <td>    0.001</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pm</th>  <td>    0.7322</td> <td>    0.149</td> <td>    4.912</td> <td> 0.000</td> <td>    0.436</td> <td>    1.028</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>54.097</td> <th>  Durbin-Watson:     </th> <td>   1.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 272.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.847</td> <th>  Prob(JB):          </th> <td>6.77e-60</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>10.735</td> <th>  Cond. No.          </th> <td>    307.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Count   R-squared:                       0.356\n",
       "Model:                            OLS   Adj. R-squared:                  0.342\n",
       "Method:                 Least Squares   F-statistic:                     24.09\n",
       "Date:                Sun, 07 Oct 2018   Prob (F-statistic):           4.72e-09\n",
       "Time:                        11:06:40   Log-Likelihood:                -334.35\n",
       "No. Observations:                  89   AIC:                             672.7\n",
       "Df Residuals:                      87   BIC:                             677.7\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "gdp            0.0018      0.000      3.672      0.000       0.001       0.003\n",
       "pm             0.7322      0.149      4.912      0.000       0.436       1.028\n",
       "==============================================================================\n",
       "Omnibus:                       54.097   Durbin-Watson:                   1.880\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              272.486\n",
       "Skew:                           1.847   Prob(JB):                     6.77e-60\n",
       "Kurtosis:                      10.735   Cond. No.                         307.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# create a fitted model in one line\n",
    "lm_count = smf.ols(formula='Count ~ gdp + pm -1 ', data=df_hdi_with_all).fit() # works very well\n",
    "lm_count.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because GDP is a difficult to change factor, let's build a model while normalising for GDP. The good thing is that in this case, small but significnt  relationship appears between the count of cases and HDI,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_with_all['Calc Count'] = lm_count.predict(df_hdi_with_all[['gdp','pm']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_with_all['Calc Score']  = lm_score.predict(df_hdi_with_all[['rt','cti']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalize actual GDP: count, ra, scope, cti, rt, di were found related and therefor excluded from the model\n",
    "df_hdi_with_all['NormCount'] = df_hdi_with_all['Count'].astype(float)/df_hdi_with_all['gdp']*df_hdi_with_all['gdp'].max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Count', 'Country', 'gdp', 'pm', 'rt', 'ra', 'sp', 'scope', 'total_inf',\n",
       "       'di', 'cti', 'score', 'HDIGroup', 'Calc Count', 'Calc Score',\n",
       "       'NormCount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hdi_with_all.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>NormCount</td>    <th>  R-squared:         </th> <td>   0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   13.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 07 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>0.000439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:06:47</td>     <th>  Log-Likelihood:    </th> <td> -573.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    60</td>      <th>  AIC:               </th> <td>   1149.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    59</td>      <th>  BIC:               </th> <td>   1151.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_inf</th> <td>   18.0891</td> <td>    4.856</td> <td>    3.725</td> <td> 0.000</td> <td>    8.371</td> <td>   27.807</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>76.797</td> <th>  Durbin-Watson:     </th> <td>   1.666</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 743.134</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.698</td> <th>  Prob(JB):          </th> <td>4.27e-162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>18.574</td> <th>  Cond. No.          </th> <td>    1.00</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              NormCount   R-squared:                       0.190\n",
       "Model:                            OLS   Adj. R-squared:                  0.177\n",
       "Method:                 Least Squares   F-statistic:                     13.87\n",
       "Date:                Sun, 07 Oct 2018   Prob (F-statistic):           0.000439\n",
       "Time:                        11:06:47   Log-Likelihood:                -573.65\n",
       "No. Observations:                  60   AIC:                             1149.\n",
       "Df Residuals:                      59   BIC:                             1151.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "total_inf     18.0891      4.856      3.725      0.000       8.371      27.807\n",
       "==============================================================================\n",
       "Omnibus:                       76.797   Durbin-Watson:                   1.666\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              743.134\n",
       "Skew:                           3.698   Prob(JB):                    4.27e-162\n",
       "Kurtosis:                      18.574   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score works but we cannot use it because it is dependent on GDP\n",
    "#lm_norm_count = smf.ols(formula='NormCount ~ score-1', data=df_hdi_with_all).fit()\n",
    "lm_norm_count = smf.ols(formula='NormCount ~   total_inf   -1', data=df_hdi_with_all).fit()\n",
    "#pm + score\n",
    "lm_norm_count.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the coefficients are so small and R-squared indicated that only about a third of the data set could be explained. Let's plot the relationship betweeb score and norm count to see what could be the proble,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create legend for the initial IV for count and score\n",
    "df_hdi_with_all['Legend'] = df_hdi_with_all.apply(lambda row: 'pm %.1f<br>cti %.1f<br>rt %.1f'%(row['pm'], row['cti'], row['rt']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = df_hdi_with_all['Calc Count'],\n",
    "    y = df_hdi_with_all['score'],\n",
    "    text = df_hdi_with_all['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=10, color='black', symbol='diamond'),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    x = df_hdi_with_all['Count'],\n",
    "    y = df_hdi_with_all['score'],\n",
    "    text = df_hdi_with_all['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='black', symbol = \"circle-open\"),\n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        title = 'FCPA cases'\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title = 'HDI'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "plotly.offline.init_notebook_mode(connected=True)\n",
    "\n",
    "plotly.offline.iplot({'data':data, 'layout': layout}, filename='HDI vs fcpa', config={'showLink': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the chart above shouws that particularly in the areas of medium and high and very high HDI, the model predictions are significatnly incorrent. \n",
    "# In an attempt to improve the model, let's view these areas separately "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjust slices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hdi_medium =  df_hdi_with_all[df_hdi_with_all['HDIGroup'] == 'medium']\n",
    "len(df_hdi_medium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tetya\\anaconda3\\envs\\semantics\\lib\\site-packages\\scipy\\stats\\stats.py:1394: UserWarning:\n",
      "\n",
      "kurtosistest only valid for n>=20 ... continuing anyway, n=13\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Count)</td>  <th>  R-squared:         </th> <td>   0.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.818</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   20.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 07 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>0.000138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:13:08</td>     <th>  Log-Likelihood:    </th> <td> -13.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    13</td>      <th>  AIC:               </th> <td>   33.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    10</td>      <th>  BIC:               </th> <td>   34.80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sp</th>    <td>   -0.3124</td> <td>    0.119</td> <td>   -2.630</td> <td> 0.025</td> <td>   -0.577</td> <td>   -0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>di</th>    <td>    0.2584</td> <td>    0.102</td> <td>    2.522</td> <td> 0.030</td> <td>    0.030</td> <td>    0.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>score</th> <td>    2.0947</td> <td>    0.851</td> <td>    2.461</td> <td> 0.034</td> <td>    0.198</td> <td>    3.991</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.844</td> <th>  Durbin-Watson:     </th> <td>   1.911</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.656</td> <th>  Jarque-Bera (JB):  </th> <td>   0.768</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.409</td> <th>  Prob(JB):          </th> <td>   0.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.135</td> <th>  Cond. No.          </th> <td>    27.8</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Count)   R-squared:                       0.860\n",
       "Model:                            OLS   Adj. R-squared:                  0.818\n",
       "Method:                 Least Squares   F-statistic:                     20.44\n",
       "Date:                Sun, 07 Oct 2018   Prob (F-statistic):           0.000138\n",
       "Time:                        11:13:08   Log-Likelihood:                -13.553\n",
       "No. Observations:                  13   AIC:                             33.11\n",
       "Df Residuals:                      10   BIC:                             34.80\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sp            -0.3124      0.119     -2.630      0.025      -0.577      -0.048\n",
       "di             0.2584      0.102      2.522      0.030       0.030       0.487\n",
       "score          2.0947      0.851      2.461      0.034       0.198       3.991\n",
       "==============================================================================\n",
       "Omnibus:                        0.844   Durbin-Watson:                   1.911\n",
       "Prob(Omnibus):                  0.656   Jarque-Bera (JB):                0.768\n",
       "Skew:                           0.409   Prob(JB):                        0.681\n",
       "Kurtosis:                       2.135   Cond. No.                         27.8\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In medium band, the model stresses the importance of SP and DI\n",
    "#lm_medium = smf.ols(formula='NormCount ~  gdp+ pm+rt+ra+sp+ scope +total_inf + di + cti + score -1 ', data=df_hdi_medium).fit()\n",
    "lm_medium = smf.ols(formula='np.log(Count) ~ sp+  di + score -1 ', data=df_hdi_medium).fit()\n",
    "#lm_medium = smf.ols(formula='NormCount ~ sp +di -1 ', data=df_hdi_medium).fit()\n",
    "lm_medium.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_medium['Calc Count'] = lm_medium.predict(df_hdi_medium[['sp', 'di', 'score']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_medium.dropna(subset=['sp'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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        "xaxis": {
         "title": "FCPA cases"
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        "yaxis": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = np.exp(df_hdi_medium['Calc Count']),\n",
    "    y = df_hdi_medium['score'],\n",
    "    text = df_hdi_medium['Legend'],\n",
    "    mode='markers',\n",
    "    marker = dict(symbol='diamond', color='black', size=10),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    x = df_hdi_medium['Count'],\n",
    "    y = df_hdi_medium['score'],\n",
    "    text = df_hdi_medium['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='black', symbol = \"circle-open\"),\n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        title = 'FCPA cases'\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title = 'HDI'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "\n",
    "plotly.offline.iplot({'data':data, 'layout': layout}, filename='HDI vs fcpa: Medium HDI', config={'showLink': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_hdi_medium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_hdi_with_all['Country'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_hdi_medium_sorted = df_hdi_medium.sort_values(by='Count')\n",
    "\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = df_hdi_medium_sorted['Country'],\n",
    "    y = np.exp(df_hdi_medium_sorted['Calc Count']),\n",
    "    text = df_hdi_medium_sorted['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(symbol = 'diamond', color ='black',size=10),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    y = df_hdi_medium_sorted['Count'],\n",
    "    x = df_hdi_medium_sorted['Country'],\n",
    "    text = df_hdi_medium_sorted['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='black', symbol = \"circle-open\"),   \n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(\n",
    "        title = 'FCPA case'\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title = 'Country'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "\n",
    "plotly.offline.iplot({'data': data, 'layout': layout}, filename='HDI vs fcpa: medium HDI', config={'showLink': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## High"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_high =  df_hdi_with_all[df_hdi_with_all['HDIGroup'] == 'high']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Count)</td>  <th>  R-squared:         </th> <td>   0.677</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   46.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 07 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>8.12e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:33:40</td>     <th>  Log-Likelihood:    </th> <td> -34.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    23</td>      <th>  AIC:               </th> <td>   71.80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    22</td>      <th>  BIC:               </th> <td>   72.94</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>score</th> <td>    2.1099</td> <td>    0.311</td> <td>    6.784</td> <td> 0.000</td> <td>    1.465</td> <td>    2.755</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.586</td> <th>  Durbin-Watson:     </th> <td>   2.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.452</td> <th>  Jarque-Bera (JB):  </th> <td>   0.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.463</td> <th>  Prob(JB):          </th> <td>   0.661</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.074</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Count)   R-squared:                       0.677\n",
       "Model:                            OLS   Adj. R-squared:                  0.662\n",
       "Method:                 Least Squares   F-statistic:                     46.02\n",
       "Date:                Sun, 07 Oct 2018   Prob (F-statistic):           8.12e-07\n",
       "Time:                        11:33:40   Log-Likelihood:                -34.902\n",
       "No. Observations:                  23   AIC:                             71.80\n",
       "Df Residuals:                      22   BIC:                             72.94\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "score          2.1099      0.311      6.784      0.000       1.465       2.755\n",
       "==============================================================================\n",
       "Omnibus:                        1.586   Durbin-Watson:                   2.209\n",
       "Prob(Omnibus):                  0.452   Jarque-Bera (JB):                0.827\n",
       "Skew:                           0.463   Prob(JB):                        0.661\n",
       "Kurtosis:                       3.074   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_high = smf.ols(formula='np.log(Count) ~  score -1', data=df_hdi_high).fit()\n",
    "#lm_high = smf.ols(formula='np.log(NormCount) ~  pm + rt + scope + di -1', data=df_hdi_high).fit()\n",
    "lm_high.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_high.dropna(subset=['ra'], inplace=True)\n",
    "\n",
    "len(df_hdi_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tetya\\anaconda3\\envs\\semantics\\lib\\site-packages\\scipy\\stats\\stats.py:1394: UserWarning:\n",
      "\n",
      "kurtosistest only valid for n>=20 ... continuing anyway, n=18\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Count)</td>  <th>  R-squared:         </th> <td>   0.780</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   17.71</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 07 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>3.42e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:37:07</td>     <th>  Log-Likelihood:    </th> <td> -25.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    18</td>      <th>  AIC:               </th> <td>   56.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    15</td>      <th>  BIC:               </th> <td>   58.85</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rt</th>    <td>    0.5313</td> <td>    0.153</td> <td>    3.471</td> <td> 0.003</td> <td>    0.205</td> <td>    0.858</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ra</th>    <td>    1.0077</td> <td>    0.359</td> <td>    2.807</td> <td> 0.013</td> <td>    0.243</td> <td>    1.773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>scope</th> <td>   -0.2112</td> <td>    0.083</td> <td>   -2.557</td> <td> 0.022</td> <td>   -0.387</td> <td>   -0.035</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.803</td> <th>  Durbin-Watson:     </th> <td>   2.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.149</td> <th>  Jarque-Bera (JB):  </th> <td>   1.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.753</td> <th>  Prob(JB):          </th> <td>   0.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.531</td> <th>  Cond. No.          </th> <td>    42.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Count)   R-squared:                       0.780\n",
       "Model:                            OLS   Adj. R-squared:                  0.736\n",
       "Method:                 Least Squares   F-statistic:                     17.71\n",
       "Date:                Sun, 07 Oct 2018   Prob (F-statistic):           3.42e-05\n",
       "Time:                        11:37:07   Log-Likelihood:                -25.090\n",
       "No. Observations:                  18   AIC:                             56.18\n",
       "Df Residuals:                      15   BIC:                             58.85\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "rt             0.5313      0.153      3.471      0.003       0.205       0.858\n",
       "ra             1.0077      0.359      2.807      0.013       0.243       1.773\n",
       "scope         -0.2112      0.083     -2.557      0.022      -0.387      -0.035\n",
       "==============================================================================\n",
       "Omnibus:                        3.803   Durbin-Watson:                   2.155\n",
       "Prob(Omnibus):                  0.149   Jarque-Bera (JB):                1.910\n",
       "Skew:                           0.753   Prob(JB):                        0.385\n",
       "Kurtosis:                       3.531   Cond. No.                         42.1\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In high band\n",
    "#lm_high = smf.ols(formula='np.log(NormCount) ~  pm+rt+ra+sp+ scope +total_inf + di + cti  -1 ', data=df_hdi_high).fit()\n",
    "lm_high = smf.ols(formula='np.log(Count) ~  rt+ra+scope  -1', data=df_hdi_high).fit()\n",
    "#lm_high = smf.ols(formula='np.log(NormCount) ~  pm + rt + scope + di -1', data=df_hdi_high).fit()\n",
    "lm_high.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_high['Calc Count'] = lm_high.predict(df_hdi_high[['rt','ra','scope']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = np.exp(df_hdi_high['Calc Count']),\n",
    "    y = df_hdi_high['score'],\n",
    "    text = df_hdi_high['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(symbol = 'diamond', color ='black', size=10),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    x = df_hdi_high['Count'],\n",
    "    y = df_hdi_high['score'],\n",
    "    text = df_hdi_high['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='black',symbol = \"circle-open\"),\n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        title = 'FCPA case'\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title = 'HDI'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "\n",
    "plotly.offline.iplot({'data': data, 'layout': layout}, filename='HDI vs fcpa: high HDI', config={'showLink': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_high_sorted = df_hdi_high.sort_values(by='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
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       "<div id=\"69fec1ac-9ffb-439a-b85d-599cb616b0da\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"69fec1ac-9ffb-439a-b85d-599cb616b0da\", [{\"marker\": {\"color\": \"black\", \"size\": 10, \"symbol\": \"diamond\"}, \"mode\": \"markers\", \"name\": \"Calculated count\", \"text\": [\"pm 9.0<br>cti 2.0<br>rt 6.0\", \"pm 9.0<br>cti 8.0<br>rt 6.0\", \"pm 12.0<br>cti 6.0<br>rt 6.0\", \"pm 10.0<br>cti 7.0<br>rt 0.0\", \"pm 8.0<br>cti 3.0<br>rt 6.0\", \"pm 16.0<br>cti 6.0<br>rt 5.0\", \"pm 3.0<br>cti 5.0<br>rt 0.0\", \"pm 12.0<br>cti 6.0<br>rt 4.0\", \"pm 8.0<br>cti 9.0<br>rt 6.0\", \"pm 4.0<br>cti 7.0<br>rt 5.0\", \"pm 11.0<br>cti 6.0<br>rt 7.0\", \"pm 9.0<br>cti 6.0<br>rt 6.0\", \"pm 9.0<br>cti 2.0<br>rt 6.0\", \"pm 0.0<br>cti 5.0<br>rt 8.0\", \"pm 5.0<br>cti 7.0<br>rt 6.0\", \"pm 13.0<br>cti 8.0<br>rt 5.0\", \"pm 14.0<br>cti 4.0<br>rt 6.2\", \"pm 13.0<br>cti 9.0<br>rt 6.0\"], \"x\": [\"Trinidad and Tobago\", \"Georgia\", \"Bosnia and Herzegovina\", \"Albania\", \"Peru\", \"Serbia\", \"Dominican Republic\", \"Azerbaijan\", \"Ukraine\", \"Turkey\", \"Panama\", \"Colombia\", \"Ecuador\", \"Uzbekistan\", \"Thailand\", \"Brazil\", \"Mexico\", \"China\"], \"y\": [3.1334738487709086, 2.417352746663842, 2.417352746663842, 0.9245856741556112, 2.9858183071469755, 3.8927515587322707, 1.8284365841636412, 3.491129829966735, 10.102777002052733, 1.8419960349413516, 17.186132290944013, 22.406367830223832, 4.555227510615507, 7.341376436288981, 8.58381041083279, 13.171464863796036, 20.17416599158202, 5.264997468167774], \"type\": \"scatter\", \"uid\": \"9f611f22-ca14-11e8-a1d1-c48e8ff48516\"}, {\"marker\": {\"color\": \"black\", \"size\": 13, \"symbol\": \"circle-open\"}, \"mode\": \"markers\", \"name\": \"Actual count\", \"text\": [\"pm 9.0<br>cti 2.0<br>rt 6.0\", \"pm 9.0<br>cti 8.0<br>rt 6.0\", \"pm 12.0<br>cti 6.0<br>rt 6.0\", \"pm 10.0<br>cti 7.0<br>rt 0.0\", \"pm 8.0<br>cti 3.0<br>rt 6.0\", \"pm 16.0<br>cti 6.0<br>rt 5.0\", \"pm 3.0<br>cti 5.0<br>rt 0.0\", \"pm 12.0<br>cti 6.0<br>rt 4.0\", \"pm 8.0<br>cti 9.0<br>rt 6.0\", \"pm 4.0<br>cti 7.0<br>rt 5.0\", \"pm 11.0<br>cti 6.0<br>rt 7.0\", \"pm 9.0<br>cti 6.0<br>rt 6.0\", \"pm 9.0<br>cti 2.0<br>rt 6.0\", \"pm 0.0<br>cti 5.0<br>rt 8.0\", \"pm 5.0<br>cti 7.0<br>rt 6.0\", \"pm 13.0<br>cti 8.0<br>rt 5.0\", \"pm 14.0<br>cti 4.0<br>rt 6.2\", \"pm 13.0<br>cti 9.0<br>rt 6.0\"], \"x\": [\"Trinidad and Tobago\", \"Georgia\", \"Bosnia and Herzegovina\", \"Albania\", \"Peru\", \"Serbia\", \"Dominican Republic\", \"Azerbaijan\", \"Ukraine\", \"Turkey\", \"Panama\", \"Colombia\", \"Ecuador\", \"Uzbekistan\", \"Thailand\", \"Brazil\", \"Mexico\", \"China\"], \"y\": [1.0, 1.0, 1.0, 2.0, 2.0, 4.0, 4.0, 4.0, 5.0, 7.0, 7.0, 7.0, 8.0, 9.0, 15.0, 23.0, 31.0, 78.0], \"type\": \"scatter\", \"uid\": \"9f611f23-ca14-11e8-88c0-c48e8ff48516\"}], {\"xaxis\": {\"title\": \"Country\"}, \"yaxis\": {\"title\": \"FCPA case\"}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = df_hdi_high_sorted['Country'],\n",
    "    y = np.exp(df_hdi_high_sorted['Calc Count']),\n",
    "    text = df_hdi_high_sorted['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(symbol = 'diamond', color ='black',size=10),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    y = df_hdi_high_sorted['Count'],\n",
    "    x = df_hdi_high_sorted['Country'],\n",
    "    text = df_hdi_high_sorted['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='black',symbol = \"circle-open\"),  \n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(\n",
    "        title = 'FCPA case',\n",
    "        #type='log',\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title = 'Country'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "\n",
    "plotly.offline.iplot({'data': data, 'layout': layout}, filename='HDI vs fcpa: high HDI', config={'showLink': False})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Very High"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hdi_vhigh =  df_hdi_with_all[df_hdi_with_all['HDIGroup'] == 'very high']\n",
    "len(df_hdi_vhigh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_vhigh.dropna(subset=['ra'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Count)</td>  <th>  R-squared:         </th> <td>   0.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   28.98</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 07 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>4.16e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:38:58</td>     <th>  Log-Likelihood:    </th> <td> -18.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>   42.20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    18</td>      <th>  BIC:               </th> <td>   45.33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ra</th> <td>    0.2844</td> <td>    0.076</td> <td>    3.738</td> <td> 0.002</td> <td>    0.125</td> <td>    0.444</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sp</th> <td>   -0.2582</td> <td>    0.069</td> <td>   -3.734</td> <td> 0.002</td> <td>   -0.403</td> <td>   -0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>di</th> <td>    0.1310</td> <td>    0.041</td> <td>    3.229</td> <td> 0.005</td> <td>    0.046</td> <td>    0.216</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.383</td> <th>  Durbin-Watson:     </th> <td>   1.598</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.826</td> <th>  Jarque-Bera (JB):  </th> <td>   0.514</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.239</td> <th>  Prob(JB):          </th> <td>   0.773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.401</td> <th>  Cond. No.          </th> <td>    5.24</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Count)   R-squared:                       0.828\n",
       "Model:                            OLS   Adj. R-squared:                  0.800\n",
       "Method:                 Least Squares   F-statistic:                     28.98\n",
       "Date:                Sun, 07 Oct 2018   Prob (F-statistic):           4.16e-07\n",
       "Time:                        11:38:58   Log-Likelihood:                -18.098\n",
       "No. Observations:                  21   AIC:                             42.20\n",
       "Df Residuals:                      18   BIC:                             45.33\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ra             0.2844      0.076      3.738      0.002       0.125       0.444\n",
       "sp            -0.2582      0.069     -3.734      0.002      -0.403      -0.113\n",
       "di             0.1310      0.041      3.229      0.005       0.046       0.216\n",
       "==============================================================================\n",
       "Omnibus:                        0.383   Durbin-Watson:                   1.598\n",
       "Prob(Omnibus):                  0.826   Jarque-Bera (JB):                0.514\n",
       "Skew:                          -0.239   Prob(JB):                        0.773\n",
       "Kurtosis:                       2.401   Cond. No.                         5.24\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In high band\n",
    "#lm_high = smf.ols(formula='np.log(NormCount) ~  pm+rt+ra+sp+ scope +total_inf + di + cti  -1 ', data=df_hdi_high).fit()\n",
    "lm_vhigh = smf.ols(formula='np.log(Count) ~  ra+sp + di -1 ', data=df_hdi_vhigh).fit()\n",
    "#lm_high = smf.ols(formula='np.log(NormCount) ~  pm + rt + scope + di -1', data=df_hdi_high).fit()\n",
    "lm_vhigh.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra+sp + di"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_vhigh['Calc Count'] = lm_vhigh.predict(df_hdi_vhigh[['ra','sp', 'di']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_hdi_vhigh[pd.isnull(df_hdi_vhigh['ra'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = np.exp(df_hdi_vhigh['Calc Count']),\n",
    "    y = df_hdi_vhigh['score'],\n",
    "    text = df_hdi_vhigh['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(symbol = 'diamond', size=10),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    x = df_hdi_vhigh['Count'],\n",
    "    y = df_hdi_vhigh['score'],\n",
    "    text = df_hdi_vhigh['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='red', symbol = \"circle-open\"),   \n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        title = 'FCPA case'\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title = 'HDI'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "\n",
    "plotly.offline.iplot({'data': data, 'layout': layout}, filename='HDI vs fcpa: very high HDI', config={'showLink': False})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_vhigh_sorted = df_hdi_vhigh.sort_values(by='Count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
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      "text/vnd.plotly.v1+html": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = df_hdi_vhigh_sorted['Country'],\n",
    "    y = np.exp(df_hdi_vhigh_sorted['Calc Count']),\n",
    "    text = df_hdi_vhigh_sorted['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(symbol = 'diamond', color ='black',size=10),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    y = df_hdi_vhigh_sorted['Count'],\n",
    "    x = df_hdi_vhigh_sorted['Country'],\n",
    "    text = df_hdi_vhigh_sorted['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='black', symbol = \"circle-open\"),\n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(\n",
    "        title = 'FCPA case'\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title = 'Country'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "\n",
    "plotly.offline.iplot({'data': data, 'layout': layout}, filename='HDI vs fcpa: very high HDI', config={'showLink': False})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Low HDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_hdi_low =  df_hdi_with_all[df_hdi_with_all['HDIGroup'] == 'low']\n",
    "len(df_hdi_low)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hdi_low.dropna(subset=['sp'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tetya\\anaconda3\\envs\\semantics\\lib\\site-packages\\scipy\\stats\\stats.py:1394: UserWarning:\n",
      "\n",
      "kurtosistest only valid for n>=20 ... continuing anyway, n=8\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Count)</td>  <th>  R-squared:         </th> <td>   0.467</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 07 Oct 2018</td> <th>  Prob (F-statistic):</th>  <td>0.0426</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:40:04</td>     <th>  Log-Likelihood:    </th> <td> -13.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>     8</td>      <th>  AIC:               </th> <td>   28.80</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     7</td>      <th>  BIC:               </th> <td>   28.88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_inf</th> <td>    0.0140</td> <td>    0.006</td> <td>    2.474</td> <td> 0.043</td> <td>    0.001</td> <td>    0.027</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.389</td> <th>  Durbin-Watson:     </th> <td>   1.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.823</td> <th>  Jarque-Bera (JB):  </th> <td>   0.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.346</td> <th>  Prob(JB):          </th> <td>   0.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.367</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Count)   R-squared:                       0.467\n",
       "Model:                            OLS   Adj. R-squared:                  0.390\n",
       "Method:                 Least Squares   F-statistic:                     6.122\n",
       "Date:                Sun, 07 Oct 2018   Prob (F-statistic):             0.0426\n",
       "Time:                        11:40:04   Log-Likelihood:                -13.398\n",
       "No. Observations:                   8   AIC:                             28.80\n",
       "Df Residuals:                       7   BIC:                             28.88\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "total_inf      0.0140      0.006      2.474      0.043       0.001       0.027\n",
       "==============================================================================\n",
       "Omnibus:                        0.389   Durbin-Watson:                   1.477\n",
       "Prob(Omnibus):                  0.823   Jarque-Bera (JB):                0.294\n",
       "Skew:                           0.346   Prob(JB):                        0.863\n",
       "Kurtosis:                       2.367   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In low band\n",
    "#lm_high = smf.ols(formula='np.log(NormCount) ~  pm+rt+ra+sp+ scope +total_inf + di + cti  -1 ', data=df_hdi_high).fit()\n",
    "lm_low = smf.ols(formula='np.log(Count) ~  total_inf  -1 ', data=df_hdi_low).fit()\n",
    "#lm_high = smf.ols(formula='np.log(NormCount) ~  pm + rt + scope + di -1', data=df_hdi_high).fit()\n",
    "lm_low.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_hdi_low[pd.isnull(df_hdi_low['sp'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 13, 18, 21)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_hdi_low), len(df_hdi_medium),len(df_hdi_high), len(df_hdi_vhigh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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         "mode": "markers",
         "name": "Actual count",
         "text": [
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          "pm 6.0<br>cti 2.0<br>rt 0.0",
          "pm 4.0<br>cti 1.0<br>rt 0.0",
          "pm 16.0<br>cti 1.0<br>rt 0.0",
          "pm 6.0<br>cti 2.0<br>rt 0.0",
          "pm 6.0<br>cti 2.0<br>rt 0.0"
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         "type": "scatter",
         "uid": "fdf68ef7-ca14-11e8-89c4-c48e8ff48516",
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       "layout": {
        "xaxis": {
         "title": "FCPA case"
        },
        "yaxis": {
         "title": "HDI"
        }
       }
      },
      "text/html": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_hdi_low['Calc Count'] = lm_low.predict(df_hdi_low[['total_inf']])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = np.exp(df_hdi_low['Calc Count']),\n",
    "    y = df_hdi_low['score'],\n",
    "    text = df_hdi_low['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(symbol = 'diamond', size=10),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    x = df_hdi_low['Count'],\n",
    "    y = df_hdi_low['score'],\n",
    "    text = df_hdi_low['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='red', symbol = \"circle-open\"),    \n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        title = 'FCPA case'\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title = 'HDI'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "\n",
    "plotly.offline.iplot({'data': data, 'layout': layout}, filename='HDI vs fcpa: very high HDI', config={'showLink': False})\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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          "pm 6.0<br>cti 2.0<br>rt 0.0",
          "pm 5.0<br>cti 5.0<br>rt 4.0",
          "pm 3.0<br>cti 4.0<br>rt 0.0",
          "pm 4.0<br>cti 1.0<br>rt 0.0",
          "pm 6.0<br>cti 2.0<br>rt 0.0",
          "pm 6.0<br>cti 2.0<br>rt 0.0",
          "pm 11.0<br>cti 7.0<br>rt 0.0"
         ],
         "type": "scatter",
         "uid": "fede400a-ca14-11e8-ba85-c48e8ff48516",
         "x": [
          "Liberia",
          "Burkina Faso",
          "Uganda",
          "Rwanda",
          "Mozambique",
          "Niger",
          "Guinea",
          "Nigeria"
         ],
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        {
         "marker": {
          "color": "black",
          "size": 13,
          "symbol": "circle-open"
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         "mode": "markers",
         "name": "Actual count",
         "text": [
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          "pm 3.0<br>cti 4.0<br>rt 0.0",
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         ],
         "type": "scatter",
         "uid": "fede400b-ca14-11e8-8ddc-c48e8ff48516",
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          "Liberia",
          "Burkina Faso",
          "Uganda",
          "Rwanda",
          "Mozambique",
          "Niger",
          "Guinea",
          "Nigeria"
         ],
         "y": [
          1,
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          2,
          3,
          4,
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        }
       ],
       "layout": {
        "xaxis": {
         "title": "Country"
        },
        "yaxis": {
         "title": "FCPA case"
        }
       }
      },
      "text/html": [
       "<div id=\"c69b7662-1363-416f-aff7-599834f89e60\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"c69b7662-1363-416f-aff7-599834f89e60\", [{\"marker\": {\"color\": \"black\", \"size\": 10, \"symbol\": \"diamond\"}, \"mode\": \"markers\", \"name\": \"Calculated count\", \"text\": [\"pm 16.0<br>cti 1.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 5.0<br>cti 5.0<br>rt 4.0\", \"pm 3.0<br>cti 4.0<br>rt 0.0\", \"pm 4.0<br>cti 1.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 11.0<br>cti 7.0<br>rt 0.0\"], \"x\": [\"Liberia\", \"Burkina Faso\", \"Uganda\", \"Rwanda\", \"Mozambique\", \"Niger\", \"Guinea\", \"Nigeria\"], \"y\": [5.705915022711697, 3.032860316082704, 3.9051845722254908, 3.299505024206493, 2.322543487948138, 2.827192764296114, 2.456752716287183, 3.441493762319075], \"type\": \"scatter\", \"uid\": \"fee5dff6-ca14-11e8-aa3a-c48e8ff48516\"}, {\"marker\": {\"color\": \"black\", \"size\": 13, \"symbol\": \"circle-open\"}, \"mode\": \"markers\", \"name\": \"Actual count\", \"text\": [\"pm 16.0<br>cti 1.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 5.0<br>cti 5.0<br>rt 4.0\", \"pm 3.0<br>cti 4.0<br>rt 0.0\", \"pm 4.0<br>cti 1.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 11.0<br>cti 7.0<br>rt 0.0\"], \"x\": [\"Liberia\", \"Burkina Faso\", \"Uganda\", \"Rwanda\", \"Mozambique\", \"Niger\", \"Guinea\", \"Nigeria\"], \"y\": [1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 8.0, 45.0], \"type\": \"scatter\", \"uid\": \"fee5dff7-ca14-11e8-8ab5-c48e8ff48516\"}], {\"xaxis\": {\"title\": \"Country\"}, \"yaxis\": {\"title\": \"FCPA case\"}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\"})});</script>"
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      "text/vnd.plotly.v1+html": [
       "<div id=\"c69b7662-1363-416f-aff7-599834f89e60\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"c69b7662-1363-416f-aff7-599834f89e60\", [{\"marker\": {\"color\": \"black\", \"size\": 10, \"symbol\": \"diamond\"}, \"mode\": \"markers\", \"name\": \"Calculated count\", \"text\": [\"pm 16.0<br>cti 1.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 5.0<br>cti 5.0<br>rt 4.0\", \"pm 3.0<br>cti 4.0<br>rt 0.0\", \"pm 4.0<br>cti 1.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 11.0<br>cti 7.0<br>rt 0.0\"], \"x\": [\"Liberia\", \"Burkina Faso\", \"Uganda\", \"Rwanda\", \"Mozambique\", \"Niger\", \"Guinea\", \"Nigeria\"], \"y\": [5.705915022711697, 3.032860316082704, 3.9051845722254908, 3.299505024206493, 2.322543487948138, 2.827192764296114, 2.456752716287183, 3.441493762319075], \"type\": \"scatter\", \"uid\": \"fee5dff6-ca14-11e8-aa3a-c48e8ff48516\"}, {\"marker\": {\"color\": \"black\", \"size\": 13, \"symbol\": \"circle-open\"}, \"mode\": \"markers\", \"name\": \"Actual count\", \"text\": [\"pm 16.0<br>cti 1.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 5.0<br>cti 5.0<br>rt 4.0\", \"pm 3.0<br>cti 4.0<br>rt 0.0\", \"pm 4.0<br>cti 1.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 6.0<br>cti 2.0<br>rt 0.0\", \"pm 11.0<br>cti 7.0<br>rt 0.0\"], \"x\": [\"Liberia\", \"Burkina Faso\", \"Uganda\", \"Rwanda\", \"Mozambique\", \"Niger\", \"Guinea\", \"Nigeria\"], \"y\": [1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 8.0, 45.0], \"type\": \"scatter\", \"uid\": \"fee5dff7-ca14-11e8-8ab5-c48e8ff48516\"}], {\"xaxis\": {\"title\": \"Country\"}, \"yaxis\": {\"title\": \"FCPA case\"}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_hdi_low_sorted = df_hdi_low.sort_values(by='Count')\n",
    "\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "trace1 = go.Scatter(\n",
    "    x = df_hdi_low_sorted['Country'],\n",
    "    y = np.exp(df_hdi_low_sorted['Calc Count']),\n",
    "    text = df_hdi_low_sorted['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(symbol = 'diamond', color ='black',size=10),\n",
    "    name = 'Calculated count'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    y = df_hdi_low_sorted['Count'],\n",
    "    x = df_hdi_low_sorted['Country'],\n",
    "    text = df_hdi_low_sorted['Legend'],\n",
    "    mode='markers',\n",
    "    marker=dict(size=13, color ='black', symbol = \"circle-open\"),   \n",
    "    name = 'Actual count'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(\n",
    "        title = 'FCPA case'\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title = 'Country'\n",
    "    )\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "\n",
    "plotly.offline.iplot({'data': data, 'layout': layout}, filename='HDI vs fcpa: very high HDI', config={'showLink': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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   "execution_count": null,
   "metadata": {},
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
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  }
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