Analysing Deaths by causes (code).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ac5af6a9",
   "metadata": {},
   "source": [
    "# Using welch's t test to compare causes of death"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e1e59a",
   "metadata": {},
   "source": [
    "### Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c5ae923f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries to clean and preprocess the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statistics import variance\n",
    "import numpy as np\n",
    "import scipy.stats\n",
    "import seaborn as sns\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Color index for using it later on\n",
    "class color:\n",
    "    PURPLE = '\\033[95m'\n",
    "    CYAN = '\\033[96m' \n",
    "    DARKCYAN = '\\033[36m' \n",
    "    BLUE = '\\033[94m' \n",
    "    GREEN = '\\033[92m' \n",
    "    YELLOW = '\\033[93m' \n",
    "    RED = '\\033[91m'\n",
    "    BOLD = '\\033[1m' \n",
    "    UNDERLINE = '\\033[4m' \n",
    "    END = '\\033[0m'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b336b83",
   "metadata": {},
   "source": [
    "__Importing annual death by causes dataset (downloaded from oxford university's portal)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "c733fc1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset source : https://ourworldindata.org/causes-of-death (Oxford University)\n",
    "#importing the csv file into python\n",
    "df = pd.read_csv(\"annual_deaths_by_cause.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59dd9166",
   "metadata": {},
   "source": [
    "__pre-processing the dataset__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "46e8db89",
   "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>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Number of executions (Amnesty International)</th>\n",
       "      <th>Deaths - Meningitis - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Parkinson's disease - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Nutritional deficiencies - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Malaria - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Drowning - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>...</th>\n",
       "      <th>Deaths - Chronic kidney disease - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Poisonings - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Protein-energy malnutrition - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Terrorism (deaths)</th>\n",
       "      <th>Deaths - Road injuries - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Chronic respiratory diseases - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Digestive diseases - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Fire, heat, and hot substances - Sex: Both - Age: All Ages (Number)</th>\n",
       "      <th>Deaths - Acute hepatitis - Sex: Both - Age: All Ages (Number)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2007</td>\n",
       "      <td>15</td>\n",
       "      <td>2933.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2488.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>2439.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>7425.0</td>\n",
       "      <td>7222.0</td>\n",
       "      <td>3346.0</td>\n",
       "      <td>6458.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>3437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2008</td>\n",
       "      <td>17</td>\n",
       "      <td>2731.0</td>\n",
       "      <td>1424.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>7355.0</td>\n",
       "      <td>7143.0</td>\n",
       "      <td>3316.0</td>\n",
       "      <td>6408.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>3005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>1449.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4597.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>7290.0</td>\n",
       "      <td>7045.0</td>\n",
       "      <td>3291.0</td>\n",
       "      <td>6358.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>2663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2011</td>\n",
       "      <td>2</td>\n",
       "      <td>2327.0</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4785.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1805.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>7432.0</td>\n",
       "      <td>6916.0</td>\n",
       "      <td>3318.0</td>\n",
       "      <td>6370.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>2365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2012</td>\n",
       "      <td>14</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4846.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>7494.0</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>6398.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>2264.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Entity Code  Year Number of executions (Amnesty International)  \\\n",
       "0  Afghanistan  AFG  2007                                           15   \n",
       "1  Afghanistan  AFG  2008                                           17   \n",
       "2  Afghanistan  AFG  2009                                            0   \n",
       "3  Afghanistan  AFG  2011                                            2   \n",
       "4  Afghanistan  AFG  2012                                           14   \n",
       "\n",
       "   Deaths - Meningitis - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             2933.0          \n",
       "1                                             2731.0          \n",
       "2                                             2460.0          \n",
       "3                                             2327.0          \n",
       "4                                             2254.0          \n",
       "\n",
       "   Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             1402.0                                       \n",
       "1                                             1424.0                                       \n",
       "2                                             1449.0                                       \n",
       "3                                             1508.0                                       \n",
       "4                                             1544.0                                       \n",
       "\n",
       "   Deaths - Parkinson's disease - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                              450.0                   \n",
       "1                                              455.0                   \n",
       "2                                              460.0                   \n",
       "3                                              473.0                   \n",
       "4                                              482.0                   \n",
       "\n",
       "   Deaths - Nutritional deficiencies - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             2488.0                        \n",
       "1                                             2277.0                        \n",
       "2                                             2040.0                        \n",
       "3                                             1846.0                        \n",
       "4                                             1705.0                        \n",
       "\n",
       "   Deaths - Malaria - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                              393.0       \n",
       "1                                              255.0       \n",
       "2                                              239.0       \n",
       "3                                              390.0       \n",
       "4                                               94.0       \n",
       "\n",
       "   Deaths - Drowning - Sex: Both - Age: All Ages (Number)  ...  \\\n",
       "0                                             2127.0       ...   \n",
       "1                                             1973.0       ...   \n",
       "2                                             1852.0       ...   \n",
       "3                                             1775.0       ...   \n",
       "4                                             1716.0       ...   \n",
       "\n",
       "   Deaths - Chronic kidney disease - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             4490.0                      \n",
       "1                                             4534.0                      \n",
       "2                                             4597.0                      \n",
       "3                                             4785.0                      \n",
       "4                                             4846.0                      \n",
       "\n",
       "   Deaths - Poisonings - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                              512.0          \n",
       "1                                              495.0          \n",
       "2                                              483.0          \n",
       "3                                              483.0          \n",
       "4                                              482.0          \n",
       "\n",
       "   Deaths - Protein-energy malnutrition - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             2439.0                           \n",
       "1                                             2231.0                           \n",
       "2                                             1998.0                           \n",
       "3                                             1805.0                           \n",
       "4                                             1667.0                           \n",
       "\n",
       "   Terrorism (deaths)  \\\n",
       "0              1199.0   \n",
       "1              1092.0   \n",
       "2              1065.0   \n",
       "3              1525.0   \n",
       "4              3521.0   \n",
       "\n",
       "   Deaths - Road injuries - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             7425.0             \n",
       "1                                             7355.0             \n",
       "2                                             7290.0             \n",
       "3                                             7432.0             \n",
       "4                                             7494.0             \n",
       "\n",
       "   Deaths - Chronic respiratory diseases - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             7222.0                            \n",
       "1                                             7143.0                            \n",
       "2                                             7045.0                            \n",
       "3                                             6916.0                            \n",
       "4                                             6878.0                            \n",
       "\n",
       "   Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             3346.0                                          \n",
       "1                                             3316.0                                          \n",
       "2                                             3291.0                                          \n",
       "3                                             3318.0                                          \n",
       "4                                             3353.0                                          \n",
       "\n",
       "   Deaths - Digestive diseases - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                             6458.0                  \n",
       "1                                             6408.0                  \n",
       "2                                             6358.0                  \n",
       "3                                             6370.0                  \n",
       "4                                             6398.0                  \n",
       "\n",
       "   Deaths - Fire, heat, and hot substances - Sex: Both - Age: All Ages (Number)  \\\n",
       "0                                              481.0                              \n",
       "1                                              462.0                              \n",
       "2                                              448.0                              \n",
       "3                                              448.0                              \n",
       "4                                              445.0                              \n",
       "\n",
       "   Deaths - Acute hepatitis - Sex: Both - Age: All Ages (Number)  \n",
       "0                                             3437.0              \n",
       "1                                             3005.0              \n",
       "2                                             2663.0              \n",
       "3                                             2365.0              \n",
       "4                                             2264.0              \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "7bff14b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping Excecutions, as in most countries execution is illegal\n",
    "df.drop(['Number of executions (Amnesty International)'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "0a5a8bf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming the column names to more suitable names\n",
    "new_column_names = ['country', 'code', 'year', 'meningitis', \"alzheimer's_diesease\",\n",
    "       \"parkinson's_disease\", 'nutritional_deficiency', 'malaria', \"drowning\",\n",
    "       'interpersonal_violence', 'maternal_disorders', 'hiv/aids',\n",
    "       'drug_use_disorders', 'tuberculosis', 'cardiovascular_diseases',\n",
    "       'lower_respiratory_infections', 'neonatal_disorders',\n",
    "       'alcohol_use_disorders', 'self_harm', 'exposure_to_forces_of_nature',\n",
    "       'diarrheal_diseases', 'environmental_heat_and_cold_exposure',\n",
    "       'neoplasms', 'conflict_and_terrorism', 'diabetes_mellitus',\n",
    "       'chronic_kidney_disease', 'poisonings', 'protein_energy_malnutrition',\n",
    "       'terrorism', 'road_injuries', 'chronic_respiratory_diseases',\n",
    "       'chronic_liver_diseases', 'digestive_diseases',\n",
    "       'fire_heat_hot_substance', 'acute_hepatitis']\n",
    "\n",
    "df.set_axis(new_column_names, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "530d9f98",
   "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>country</th>\n",
       "      <th>code</th>\n",
       "      <th>year</th>\n",
       "      <th>meningitis</th>\n",
       "      <th>alzheimer's_diesease</th>\n",
       "      <th>parkinson's_disease</th>\n",
       "      <th>nutritional_deficiency</th>\n",
       "      <th>malaria</th>\n",
       "      <th>drowning</th>\n",
       "      <th>interpersonal_violence</th>\n",
       "      <th>...</th>\n",
       "      <th>chronic_kidney_disease</th>\n",
       "      <th>poisonings</th>\n",
       "      <th>protein_energy_malnutrition</th>\n",
       "      <th>terrorism</th>\n",
       "      <th>road_injuries</th>\n",
       "      <th>chronic_respiratory_diseases</th>\n",
       "      <th>chronic_liver_diseases</th>\n",
       "      <th>digestive_diseases</th>\n",
       "      <th>fire_heat_hot_substance</th>\n",
       "      <th>acute_hepatitis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2007</td>\n",
       "      <td>2933.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2488.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>3657.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>2439.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>7425.0</td>\n",
       "      <td>7222.0</td>\n",
       "      <td>3346.0</td>\n",
       "      <td>6458.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>3437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2008</td>\n",
       "      <td>2731.0</td>\n",
       "      <td>1424.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>3785.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>7355.0</td>\n",
       "      <td>7143.0</td>\n",
       "      <td>3316.0</td>\n",
       "      <td>6408.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>3005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2009</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>1449.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4597.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>7290.0</td>\n",
       "      <td>7045.0</td>\n",
       "      <td>3291.0</td>\n",
       "      <td>6358.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>2663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2011</td>\n",
       "      <td>2327.0</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>4170.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4785.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1805.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>7432.0</td>\n",
       "      <td>6916.0</td>\n",
       "      <td>3318.0</td>\n",
       "      <td>6370.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>2365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2012</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>4245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4846.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>7494.0</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>6398.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>2264.0</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>7268</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2015</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>3019.0</td>\n",
       "      <td>2518.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2108.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>2990.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>2751.0</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>4202.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7269</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2016</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>3056.0</td>\n",
       "      <td>2050.0</td>\n",
       "      <td>801.0</td>\n",
       "      <td>1342.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>3027.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2436.0</td>\n",
       "      <td>2788.0</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>4264.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2017</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2990.0</td>\n",
       "      <td>2116.0</td>\n",
       "      <td>818.0</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2196.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2473.0</td>\n",
       "      <td>2818.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>4342.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7271</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2018</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>2918.0</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2890.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2509.0</td>\n",
       "      <td>2849.0</td>\n",
       "      <td>2030.0</td>\n",
       "      <td>4377.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7272</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2019</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>2884.0</td>\n",
       "      <td>2068.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>2855.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2554.0</td>\n",
       "      <td>2891.0</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7273 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country code  year  meningitis  alzheimer's_diesease  \\\n",
       "0     Afghanistan  AFG  2007      2933.0                1402.0   \n",
       "1     Afghanistan  AFG  2008      2731.0                1424.0   \n",
       "2     Afghanistan  AFG  2009      2460.0                1449.0   \n",
       "3     Afghanistan  AFG  2011      2327.0                1508.0   \n",
       "4     Afghanistan  AFG  2012      2254.0                1544.0   \n",
       "...           ...  ...   ...         ...                   ...   \n",
       "7268     Zimbabwe  ZWE  2015      1439.0                 754.0   \n",
       "7269     Zimbabwe  ZWE  2016      1457.0                 767.0   \n",
       "7270     Zimbabwe  ZWE  2017      1460.0                 781.0   \n",
       "7271     Zimbabwe  ZWE  2018      1450.0                 795.0   \n",
       "7272     Zimbabwe  ZWE  2019      1450.0                 812.0   \n",
       "\n",
       "      parkinson's_disease  nutritional_deficiency  malaria  drowning  \\\n",
       "0                   450.0                  2488.0    393.0    2127.0   \n",
       "1                   455.0                  2277.0    255.0    1973.0   \n",
       "2                   460.0                  2040.0    239.0    1852.0   \n",
       "3                   473.0                  1846.0    390.0    1775.0   \n",
       "4                   482.0                  1705.0     94.0    1716.0   \n",
       "...                   ...                     ...      ...       ...   \n",
       "7268                215.0                  3019.0   2518.0     770.0   \n",
       "7269                219.0                  3056.0   2050.0     801.0   \n",
       "7270                223.0                  2990.0   2116.0     818.0   \n",
       "7271                227.0                  2918.0   2088.0     825.0   \n",
       "7272                232.0                  2884.0   2068.0     827.0   \n",
       "\n",
       "      interpersonal_violence  ...  chronic_kidney_disease  poisonings  \\\n",
       "0                     3657.0  ...                  4490.0       512.0   \n",
       "1                     3785.0  ...                  4534.0       495.0   \n",
       "2                     3874.0  ...                  4597.0       483.0   \n",
       "3                     4170.0  ...                  4785.0       483.0   \n",
       "4                     4245.0  ...                  4846.0       482.0   \n",
       "...                      ...  ...                     ...         ...   \n",
       "7268                  1302.0  ...                  2108.0       381.0   \n",
       "7269                  1342.0  ...                  2160.0       393.0   \n",
       "7270                  1363.0  ...                  2196.0       398.0   \n",
       "7271                  1396.0  ...                  2240.0       400.0   \n",
       "7272                  1434.0  ...                  2292.0       405.0   \n",
       "\n",
       "      protein_energy_malnutrition  terrorism  road_injuries  \\\n",
       "0                          2439.0     1199.0         7425.0   \n",
       "1                          2231.0     1092.0         7355.0   \n",
       "2                          1998.0     1065.0         7290.0   \n",
       "3                          1805.0     1525.0         7432.0   \n",
       "4                          1667.0     3521.0         7494.0   \n",
       "...                           ...        ...            ...   \n",
       "7268                       2990.0        NaN         2373.0   \n",
       "7269                       3027.0        NaN         2436.0   \n",
       "7270                       2962.0        0.0         2473.0   \n",
       "7271                       2890.0        NaN         2509.0   \n",
       "7272                       2855.0        NaN         2554.0   \n",
       "\n",
       "      chronic_respiratory_diseases  chronic_liver_diseases  \\\n",
       "0                           7222.0                  3346.0   \n",
       "1                           7143.0                  3316.0   \n",
       "2                           7045.0                  3291.0   \n",
       "3                           6916.0                  3318.0   \n",
       "4                           6878.0                  3353.0   \n",
       "...                            ...                     ...   \n",
       "7268                        2751.0                  1956.0   \n",
       "7269                        2788.0                  1962.0   \n",
       "7270                        2818.0                  2007.0   \n",
       "7271                        2849.0                  2030.0   \n",
       "7272                        2891.0                  2065.0   \n",
       "\n",
       "      digestive_diseases  fire_heat_hot_substance  acute_hepatitis  \n",
       "0                 6458.0                    481.0           3437.0  \n",
       "1                 6408.0                    462.0           3005.0  \n",
       "2                 6358.0                    448.0           2663.0  \n",
       "3                 6370.0                    448.0           2365.0  \n",
       "4                 6398.0                    445.0           2264.0  \n",
       "...                  ...                      ...              ...  \n",
       "7268              4202.0                    632.0            146.0  \n",
       "7269              4264.0                    648.0            146.0  \n",
       "7270              4342.0                    654.0            144.0  \n",
       "7271              4377.0                    657.0            139.0  \n",
       "7272              4437.0                    662.0            136.0  \n",
       "\n",
       "[7273 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03da3278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7273 entries, 0 to 7272\n",
      "Data columns (total 35 columns):\n",
      " #   Column                                Non-Null Count  Dtype  \n",
      "---  ------                                --------------  -----  \n",
      " 0   country                               7273 non-null   object \n",
      " 1   code                                  6206 non-null   object \n",
      " 2   year                                  7273 non-null   int64  \n",
      " 3   meningitis                            6840 non-null   float64\n",
      " 4   alzheimer's_diesease                  6840 non-null   float64\n",
      " 5   parkinson's_disease                   6840 non-null   float64\n",
      " 6   nutritional_deficiency                6840 non-null   float64\n",
      " 7   malaria                               6840 non-null   float64\n",
      " 8   drowning                              6840 non-null   float64\n",
      " 9   interpersonal_violence                6840 non-null   float64\n",
      " 10  maternal_disorders                    6840 non-null   float64\n",
      " 11  hiv/aids                              6840 non-null   float64\n",
      " 12  drug_use_disorders                    6840 non-null   float64\n",
      " 13  tuberculosis                          6840 non-null   float64\n",
      " 14  cardiovascular_diseases               6840 non-null   float64\n",
      " 15  lower_respiratory_infections          6840 non-null   float64\n",
      " 16  neonatal_disorders                    6840 non-null   float64\n",
      " 17  alcohol_use_disorders                 6840 non-null   float64\n",
      " 18  self_harm                             6840 non-null   float64\n",
      " 19  exposure_to_forces_of_nature          6840 non-null   float64\n",
      " 20  diarrheal_diseases                    6840 non-null   float64\n",
      " 21  environmental_heat_and_cold_exposure  6840 non-null   float64\n",
      " 22  neoplasms                             6840 non-null   float64\n",
      " 23  conflict_and_terrorism                6840 non-null   float64\n",
      " 24  diabetes_mellitus                     6840 non-null   float64\n",
      " 25  chronic_kidney_disease                6840 non-null   float64\n",
      " 26  poisonings                            6840 non-null   float64\n",
      " 27  protein_energy_malnutrition           6840 non-null   float64\n",
      " 28  terrorism                             2891 non-null   float64\n",
      " 29  road_injuries                         6840 non-null   float64\n",
      " 30  chronic_respiratory_diseases          6840 non-null   float64\n",
      " 31  chronic_liver_diseases                6840 non-null   float64\n",
      " 32  digestive_diseases                    6840 non-null   float64\n",
      " 33  fire_heat_hot_substance               6840 non-null   float64\n",
      " 34  acute_hepatitis                       6840 non-null   float64\n",
      "dtypes: float64(32), int64(1), object(2)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#Overview of the variables in the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0451ce3f",
   "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>year</th>\n",
       "      <th>meningitis</th>\n",
       "      <th>alzheimer's_diesease</th>\n",
       "      <th>parkinson's_disease</th>\n",
       "      <th>nutritional_deficiency</th>\n",
       "      <th>malaria</th>\n",
       "      <th>drowning</th>\n",
       "      <th>interpersonal_violence</th>\n",
       "      <th>maternal_disorders</th>\n",
       "      <th>hiv/aids</th>\n",
       "      <th>...</th>\n",
       "      <th>chronic_kidney_disease</th>\n",
       "      <th>poisonings</th>\n",
       "      <th>protein_energy_malnutrition</th>\n",
       "      <th>terrorism</th>\n",
       "      <th>road_injuries</th>\n",
       "      <th>chronic_respiratory_diseases</th>\n",
       "      <th>chronic_liver_diseases</th>\n",
       "      <th>digestive_diseases</th>\n",
       "      <th>fire_heat_hot_substance</th>\n",
       "      <th>acute_hepatitis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7273.000000</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6.840000e+03</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6.840000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>6.840000e+03</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>2891.000000</td>\n",
       "      <td>6.840000e+03</td>\n",
       "      <td>6.840000e+03</td>\n",
       "      <td>6.840000e+03</td>\n",
       "      <td>6.840000e+03</td>\n",
       "      <td>6840.000000</td>\n",
       "      <td>6840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2004.425134</td>\n",
       "      <td>8213.149561</td>\n",
       "      <td>2.736319e+04</td>\n",
       "      <td>6493.627339</td>\n",
       "      <td>11006.353947</td>\n",
       "      <td>18903.160234</td>\n",
       "      <td>8619.055117</td>\n",
       "      <td>10636.648684</td>\n",
       "      <td>6131.066959</td>\n",
       "      <td>2.814125e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>2.506893e+04</td>\n",
       "      <td>2145.455848</td>\n",
       "      <td>9531.528801</td>\n",
       "      <td>349.235905</td>\n",
       "      <td>3.063496e+04</td>\n",
       "      <td>9.130390e+04</td>\n",
       "      <td>3.189892e+04</td>\n",
       "      <td>5.652551e+04</td>\n",
       "      <td>3006.043275</td>\n",
       "      <td>3112.066520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.623572</td>\n",
       "      <td>34982.933895</td>\n",
       "      <td>1.113146e+05</td>\n",
       "      <td>25767.447063</td>\n",
       "      <td>48289.687100</td>\n",
       "      <td>97346.780570</td>\n",
       "      <td>35879.406010</td>\n",
       "      <td>40888.793762</td>\n",
       "      <td>26355.210370</td>\n",
       "      <td>1.391973e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>9.600839e+04</td>\n",
       "      <td>8433.507625</td>\n",
       "      <td>41689.675155</td>\n",
       "      <td>1917.143788</td>\n",
       "      <td>1.166999e+05</td>\n",
       "      <td>3.772013e+05</td>\n",
       "      <td>1.194387e+05</td>\n",
       "      <td>2.109884e+05</td>\n",
       "      <td>11211.885249</td>\n",
       "      <td>14440.219516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1990.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.287500e+02</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.140000e+02</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.077500e+02</td>\n",
       "      <td>3.685000e+02</td>\n",
       "      <td>2.047500e+02</td>\n",
       "      <td>4.000000e+02</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>150.500000</td>\n",
       "      <td>8.455000e+02</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>1.870000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>1.080000e+03</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.239500e+03</td>\n",
       "      <td>2.104000e+03</td>\n",
       "      <td>1.541000e+03</td>\n",
       "      <td>2.778500e+03</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1622.250000</td>\n",
       "      <td>4.251000e+03</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>1017.250000</td>\n",
       "      <td>1014.250000</td>\n",
       "      <td>1391.250000</td>\n",
       "      <td>1244.500000</td>\n",
       "      <td>3.836500e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>4.497500e+03</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>2120.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>5.786250e+03</td>\n",
       "      <td>9.013250e+03</td>\n",
       "      <td>5.670500e+03</td>\n",
       "      <td>9.594000e+03</td>\n",
       "      <td>593.250000</td>\n",
       "      <td>264.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>432524.000000</td>\n",
       "      <td>1.623276e+06</td>\n",
       "      <td>362907.000000</td>\n",
       "      <td>757152.000000</td>\n",
       "      <td>961129.000000</td>\n",
       "      <td>460665.000000</td>\n",
       "      <td>463129.000000</td>\n",
       "      <td>302586.000000</td>\n",
       "      <td>1.844490e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427232e+06</td>\n",
       "      <td>92101.000000</td>\n",
       "      <td>656314.000000</td>\n",
       "      <td>44490.000000</td>\n",
       "      <td>1.285039e+06</td>\n",
       "      <td>3.974315e+06</td>\n",
       "      <td>1.472012e+06</td>\n",
       "      <td>2.557689e+06</td>\n",
       "      <td>129705.000000</td>\n",
       "      <td>166405.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              year     meningitis  alzheimer's_diesease  parkinson's_disease  \\\n",
       "count  7273.000000    6840.000000          6.840000e+03          6840.000000   \n",
       "mean   2004.425134    8213.149561          2.736319e+04          6493.627339   \n",
       "std       8.623572   34982.933895          1.113146e+05         25767.447063   \n",
       "min    1990.000000       0.000000          0.000000e+00             0.000000   \n",
       "25%    1997.000000      19.000000          1.287500e+02            37.000000   \n",
       "50%    2004.000000     150.500000          8.455000e+02           209.000000   \n",
       "75%    2012.000000    1622.250000          4.251000e+03           984.000000   \n",
       "max    2019.000000  432524.000000          1.623276e+06        362907.000000   \n",
       "\n",
       "       nutritional_deficiency        malaria       drowning  \\\n",
       "count             6840.000000    6840.000000    6840.000000   \n",
       "mean             11006.353947   18903.160234    8619.055117   \n",
       "std              48289.687100   97346.780570   35879.406010   \n",
       "min                  0.000000       0.000000       0.000000   \n",
       "25%                 11.000000       0.000000      39.000000   \n",
       "50%                159.000000       0.000000     253.000000   \n",
       "75%               2204.000000    1017.250000    1014.250000   \n",
       "max             757152.000000  961129.000000  460665.000000   \n",
       "\n",
       "       interpersonal_violence  maternal_disorders      hiv/aids  ...  \\\n",
       "count             6840.000000         6840.000000  6.840000e+03  ...   \n",
       "mean             10636.648684         6131.066959  2.814125e+04  ...   \n",
       "std              40888.793762        26355.210370  1.391973e+05  ...   \n",
       "min                  0.000000            0.000000  0.000000e+00  ...   \n",
       "25%                 48.000000            6.000000  1.400000e+01  ...   \n",
       "50%                342.000000           70.500000  1.870000e+02  ...   \n",
       "75%               1391.250000         1244.500000  3.836500e+03  ...   \n",
       "max             463129.000000       302586.000000  1.844490e+06  ...   \n",
       "\n",
       "       chronic_kidney_disease    poisonings  protein_energy_malnutrition  \\\n",
       "count            6.840000e+03   6840.000000                  6840.000000   \n",
       "mean             2.506893e+04   2145.455848                  9531.528801   \n",
       "std              9.600839e+04   8433.507625                 41689.675155   \n",
       "min              0.000000e+00      0.000000                     0.000000   \n",
       "25%              2.140000e+02      8.000000                     6.000000   \n",
       "50%              1.080000e+03     72.000000                   120.000000   \n",
       "75%              4.497500e+03    371.000000                  2120.000000   \n",
       "max              1.427232e+06  92101.000000                656314.000000   \n",
       "\n",
       "          terrorism  road_injuries  chronic_respiratory_diseases  \\\n",
       "count   2891.000000   6.840000e+03                  6.840000e+03   \n",
       "mean     349.235905   3.063496e+04                  9.130390e+04   \n",
       "std     1917.143788   1.166999e+05                  3.772013e+05   \n",
       "min        0.000000   0.000000e+00                  1.000000e+00   \n",
       "25%        0.000000   2.077500e+02                  3.685000e+02   \n",
       "50%        5.000000   1.239500e+03                  2.104000e+03   \n",
       "75%       60.000000   5.786250e+03                  9.013250e+03   \n",
       "max    44490.000000   1.285039e+06                  3.974315e+06   \n",
       "\n",
       "       chronic_liver_diseases  digestive_diseases  fire_heat_hot_substance  \\\n",
       "count            6.840000e+03        6.840000e+03              6840.000000   \n",
       "mean             3.189892e+04        5.652551e+04              3006.043275   \n",
       "std              1.194387e+05        2.109884e+05             11211.885249   \n",
       "min              0.000000e+00        0.000000e+00                 0.000000   \n",
       "25%              2.047500e+02        4.000000e+02                21.000000   \n",
       "50%              1.541000e+03        2.778500e+03               152.000000   \n",
       "75%              5.670500e+03        9.594000e+03               593.250000   \n",
       "max              1.472012e+06        2.557689e+06            129705.000000   \n",
       "\n",
       "       acute_hepatitis  \n",
       "count      6840.000000  \n",
       "mean       3112.066520  \n",
       "std       14440.219516  \n",
       "min           0.000000  \n",
       "25%           2.000000  \n",
       "50%          21.500000  \n",
       "75%         264.000000  \n",
       "max      166405.000000  \n",
       "\n",
       "[8 rows x 33 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A statistical summar of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86f952f3",
   "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>country</th>\n",
       "      <th>code</th>\n",
       "      <th>year</th>\n",
       "      <th>meningitis</th>\n",
       "      <th>alzheimer's_diesease</th>\n",
       "      <th>parkinson's_disease</th>\n",
       "      <th>nutritional_deficiency</th>\n",
       "      <th>malaria</th>\n",
       "      <th>drowning</th>\n",
       "      <th>interpersonal_violence</th>\n",
       "      <th>...</th>\n",
       "      <th>chronic_kidney_disease</th>\n",
       "      <th>poisonings</th>\n",
       "      <th>protein_energy_malnutrition</th>\n",
       "      <th>terrorism</th>\n",
       "      <th>road_injuries</th>\n",
       "      <th>chronic_respiratory_diseases</th>\n",
       "      <th>chronic_liver_diseases</th>\n",
       "      <th>digestive_diseases</th>\n",
       "      <th>fire_heat_hot_substance</th>\n",
       "      <th>acute_hepatitis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2007</td>\n",
       "      <td>2933.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>2488.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>3657.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>2439.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>7425.0</td>\n",
       "      <td>7222.0</td>\n",
       "      <td>3346.0</td>\n",
       "      <td>6458.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>3437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2008</td>\n",
       "      <td>2731.0</td>\n",
       "      <td>1424.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>3785.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>7355.0</td>\n",
       "      <td>7143.0</td>\n",
       "      <td>3316.0</td>\n",
       "      <td>6408.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>3005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2009</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>1449.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>3874.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4597.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>7290.0</td>\n",
       "      <td>7045.0</td>\n",
       "      <td>3291.0</td>\n",
       "      <td>6358.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>2663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2011</td>\n",
       "      <td>2327.0</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1775.0</td>\n",
       "      <td>4170.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4785.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>1805.0</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>7432.0</td>\n",
       "      <td>6916.0</td>\n",
       "      <td>3318.0</td>\n",
       "      <td>6370.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>2365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2012</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>4245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4846.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>7494.0</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>6398.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>2264.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country code  year  meningitis  alzheimer's_diesease  \\\n",
       "0  Afghanistan  AFG  2007      2933.0                1402.0   \n",
       "1  Afghanistan  AFG  2008      2731.0                1424.0   \n",
       "2  Afghanistan  AFG  2009      2460.0                1449.0   \n",
       "3  Afghanistan  AFG  2011      2327.0                1508.0   \n",
       "4  Afghanistan  AFG  2012      2254.0                1544.0   \n",
       "\n",
       "   parkinson's_disease  nutritional_deficiency  malaria  drowning  \\\n",
       "0                450.0                  2488.0    393.0    2127.0   \n",
       "1                455.0                  2277.0    255.0    1973.0   \n",
       "2                460.0                  2040.0    239.0    1852.0   \n",
       "3                473.0                  1846.0    390.0    1775.0   \n",
       "4                482.0                  1705.0     94.0    1716.0   \n",
       "\n",
       "   interpersonal_violence  ...  chronic_kidney_disease  poisonings  \\\n",
       "0                  3657.0  ...                  4490.0       512.0   \n",
       "1                  3785.0  ...                  4534.0       495.0   \n",
       "2                  3874.0  ...                  4597.0       483.0   \n",
       "3                  4170.0  ...                  4785.0       483.0   \n",
       "4                  4245.0  ...                  4846.0       482.0   \n",
       "\n",
       "   protein_energy_malnutrition  terrorism  road_injuries  \\\n",
       "0                       2439.0     1199.0         7425.0   \n",
       "1                       2231.0     1092.0         7355.0   \n",
       "2                       1998.0     1065.0         7290.0   \n",
       "3                       1805.0     1525.0         7432.0   \n",
       "4                       1667.0     3521.0         7494.0   \n",
       "\n",
       "   chronic_respiratory_diseases  chronic_liver_diseases  digestive_diseases  \\\n",
       "0                        7222.0                  3346.0              6458.0   \n",
       "1                        7143.0                  3316.0              6408.0   \n",
       "2                        7045.0                  3291.0              6358.0   \n",
       "3                        6916.0                  3318.0              6370.0   \n",
       "4                        6878.0                  3353.0              6398.0   \n",
       "\n",
       "   fire_heat_hot_substance  acute_hepatitis  \n",
       "0                    481.0           3437.0  \n",
       "1                    462.0           3005.0  \n",
       "2                    448.0           2663.0  \n",
       "3                    448.0           2365.0  \n",
       "4                    445.0           2264.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacaa2ca",
   "metadata": {},
   "source": [
    "__User Defined Function : Filling NA values__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01973b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to fill NA values\n",
    "def fillNa(df):\n",
    "    columnNames = list(df.columns)\n",
    "    for col in columnNames: \n",
    "        df[col] = df[col].fillna(df[col].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56c2118",
   "metadata": {},
   "source": [
    "__User Defined Function : Creating subsets based on country__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "313c1f99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Functio to create a subset based ont the country name\n",
    "def getSubset(df, countryName):\n",
    "    \n",
    "    df_new = df[df[\"country\"]==countryName] #Creating a subset with only the specific country's data\n",
    "    #Removing the redundant columns\n",
    "    df_new.index = df_new[\"year\"] #Setting year as the index valus\n",
    "    df_new.drop([\"country\",\"code\",\"year\"],inplace=True, axis=1) # Dropping all categorical columns\n",
    "    fillNa(df_new) #Filling NA or missing values\n",
    "    #Typecasting deaths to integers\n",
    "    df_new[list(df_new.columns)] = df_new[list(df_new.columns)].astype(\"int64\") \n",
    "    \n",
    "    return df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e796800",
   "metadata": {},
   "source": [
    "__User Defined Function : Creating a subset of all countries except queried__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "638827ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a subset with countries except specified country\n",
    "#row : rest of the world\n",
    "def rowSubset(df,df_to_be_removed):\n",
    "    df_row = df.drop(df_to_be_removed.index)\n",
    "    #Filling the na values of Rest of the world dataframe\n",
    "    fillNa(df_row.iloc[:,3:35])\n",
    "    df_row.dropna(inplace=True)\n",
    "    df_row['year'] = df_row['year'].astype(\"object\")\n",
    "    df_row = df_row.groupby(['year']).mean().round()\n",
    "    df_row[list(df_row.columns)] = df_row[list(df_row.columns)].astype(\"int64\")\n",
    "    \n",
    "    return df_row"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560d1de6",
   "metadata": {},
   "source": [
    "__User Defined Function : conducting Welch's t-test for all variables of two datasets__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6ec3c16",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Welch's t test of unequal variance\n",
    "def t_test(df1, df2):\n",
    "    p_values = []\n",
    "    for col in list(df1.columns):\n",
    "        x = scipy.stats.ttest_ind(a=np.array(df1[col]), b=np.array(df2[col]), equal_var=False)\n",
    "        p_values.append([col,x[0],x[1]])\n",
    "    \n",
    "    return p_values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c03071",
   "metadata": {},
   "source": [
    "__User Defined Function : to check whether the p values are greater than level of significance__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8c01dfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def acceptedVals(arr,level_of_sig):\n",
    "    acceptance = 1-lev\n",
    "    for x in level_of_sig:\n",
    "        if x[2]>0.05:\n",
    "            print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8672f1",
   "metadata": {},
   "source": [
    "__User Defined Function : For header of the generalised t-test result__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d5c228e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def headerResult(country1,country2,level_of_sig,variable,statistic,p_value):\n",
    "    header = (color.BLUE+\" Welch's T Test for comparing \"+country1+\n",
    "    \"'s and \"+country2+\"'s \"+variable+\" deaths. \"+color.END)\n",
    "    print(header.center(110,\"-\"))\n",
    "\n",
    "    print(\"\\n -> \"+color.UNDERLINE+\"Null Hypothesis (Ho) [µO=µ1]\" +color.END +\n",
    "        \"\\nThere is no significant difference in the average of deaths caused by \"\n",
    "         +variable+\" in \",country1 + \" and \" + country2 + \".\" )\n",
    "\n",
    "    print(\"\\n -> \"+color.UNDERLINE+ \"Allternate Hypothesis (H1) [µO≠µ1]\" +color.END +\n",
    "        \"\\nThere is a significant difference in the average of deaths caused by \"\n",
    "         +variable+\" in \",country1 + \" and \" + country2 +\".\")\n",
    "    \n",
    "    print(\"\\n -> \"+color.UNDERLINE+ \"Confidence Level Interval : \" +color.END + str(1-level_of_sig))\n",
    "\n",
    "    print(color.BOLD+\"\\n- Statistic (t) value : \" +color.END , round(float(statistic),9))\n",
    "    print(color.BOLD+\"- Probability (p) value : \" +color.END , \"{:.40f}\". format(p_value))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e572ab",
   "metadata": {},
   "source": [
    "__User Defined Function : to plot a line graph and a bar plot of the values of given variable__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "b685a2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotGraph(df1, df2, variable,country1,country2):\n",
    "    \n",
    "    palette = sns.color_palette()\n",
    "\n",
    "    c1 = random.choice(palette)\n",
    "    idx = palette.index(c1)\n",
    "    palette.pop(idx)\n",
    "    c2 = random.choice(palette)\n",
    "    \n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.title(str(variable + \" (DEATHS PER 100,000 OF POPULATION)\"))\n",
    "    plot_one = sns.lineplot(data = df1, x = 'year', y = variable, color = c1 , label = country1)\n",
    "    plot_two = sns.lineplot(data = df2, x = 'year', y = variable, color = c2, label = country2)\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.title(\"Average Deaths per 100,000 from 1990-2021\")\n",
    "    plt.bar(x=[country1,country2],height=[df1[variable].mean(),\n",
    "                                          df2[variable].mean()],color=[c1,c2])\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012d5014",
   "metadata": {},
   "source": [
    "__User Defined Function : To properly display the Welch's t-test result and visualisations__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "a0473db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def finalResult(df1,df2,values,country1,country2,level_of_sig,show_only_nnullAcceptance = False):\n",
    "    for val in values:\n",
    "        variable = val[0]\n",
    "        statistic = val[1]\n",
    "        p_value = val[2]\n",
    "        \n",
    "\n",
    "        #Rejecting null hypothesis\n",
    "        if p_value<level_of_sig:\n",
    "            \n",
    "            if show_only_nnullAcceptance:\n",
    "                pass\n",
    "            \n",
    "            else:\n",
    "                headerResult(country1,country2,level_of_sig,variable,statistic,p_value)\n",
    "                print(color.BOLD + \"\\n- Decision\" +color.END)\n",
    "                print(\"As p value (\", \"{:.40f}\". format(p_value) , \n",
    "                      \") is less than level of significance (\" , level_of_sig,\n",
    "                      \"),\\nWe Reject the Null Hypothesis(Ho) {Accept Alternate Hypothesis(H1)}\")\n",
    "\n",
    "                print(color.BOLD + \"\\n- Statistical Inference\" +color.END)\n",
    "                print(\"There is a significant difference in the average of no. deaths caused by \"\n",
    "                 +variable+\" in \",country1 + \" and \" + country2 +\".\"+\n",
    "                      \"\\nTime period : Past 30 years (1991-2019)\\n\")\n",
    "                \n",
    "\n",
    "                plotGraph(df1,df2,variable,country1,country2)\n",
    "                plt.pause(0.0001)\n",
    "                \n",
    "                print(\"\\n\")\n",
    "                print(\"-\"*100)\n",
    "                print(\"\\n\")\n",
    "                \n",
    "        \n",
    "        else:\n",
    "            \n",
    "            headerResult(country1,country2,level_of_sig,variable,statistic,p_value)\n",
    "            print(color.BOLD + \"\\n- Decision\" +color.END)\n",
    "            print(\"As p value (\", \"{:.40f}\". format(p_value) , \n",
    "                  \") is greater than level of significance (\" , level_of_sig,\n",
    "                  \"),\\nWe Fail to reject the Null Hypothesis(Ho) {Accept Null Hypothesis(Ho)}\")\n",
    "            \n",
    "            print(color.BOLD + \"\\n- Statistical Inference\" +color.END)\n",
    "            print(\"There is no significant difference in the average of no. deaths caused by \"\n",
    "             +variable+\" in \",country1 + \" and \" + country2 +\".\"+\n",
    "                  \"\\nTime period : Past 30 years (1991-2019)\\n\")\n",
    "            \n",
    "            plotGraph(df1,df2,variable,country1,country2)\n",
    "            plt.pause(0.0001)\n",
    "        \n",
    "            print(\"\\n\")\n",
    "            print(\"-\"*100)\n",
    "            print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff406c6",
   "metadata": {},
   "source": [
    "__User Defined Function : Standardize the datasets according to deaths per 100,000 people of a population__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "664f448d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a user-defined function to standardize the data for every 100,000 people in the population\n",
    "def normalizeDf(df_cont,pop_arr):\n",
    "    for index in range(0,len(df_cont)):\n",
    "        df_cont.iloc[index] = (df_cont.iloc[index]/pop_arr[index])*100000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e011b40",
   "metadata": {},
   "source": [
    "__Creating subsets using previously created user-defined function, according to country name__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "fb2b8cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting subsets (samples) of data for 36 variables, 30 years for these specific countries\n",
    "df_india = getSubset(df,\"India\").sort_index(ascending=True)\n",
    "df_china = getSubset(df,\"China\").sort_index(ascending=True)\n",
    "df_usa = getSubset(df, \"United States\").sort_index(ascending=True)\n",
    "df_uk = getSubset(df, \"United Kingdom\").sort_index(ascending=True)\n",
    "df_som = getSubset(df, \"Somalia\").sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8bd5e71",
   "metadata": {},
   "source": [
    "__Importing the file that holds population of the above countries (downloaded from world statistics forum)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "5e40961d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the population of the 5 countries to be analysed\n",
    "df_population = pd.read_excel(\"population.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "b77a2ca0",
   "metadata": {
    "scrolled": false
   },
   "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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>1990</td>\n",
       "      <td>870452165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>1991</td>\n",
       "      <td>888941756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>1992</td>\n",
       "      <td>907574049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>1993</td>\n",
       "      <td>926351297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>1994</td>\n",
       "      <td>945261958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>2015</td>\n",
       "      <td>13763906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>2016</td>\n",
       "      <td>14292847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>2017</td>\n",
       "      <td>14864221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>2018</td>\n",
       "      <td>15411094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>2019</td>\n",
       "      <td>15981300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Year  Population\n",
       "0      India  1990   870452165\n",
       "1      India  1991   888941756\n",
       "2      India  1992   907574049\n",
       "3      India  1993   926351297\n",
       "4      India  1994   945261958\n",
       "..       ...   ...         ...\n",
       "145  Somalia  2015    13763906\n",
       "146  Somalia  2016    14292847\n",
       "147  Somalia  2017    14864221\n",
       "148  Somalia  2018    15411094\n",
       "149  Somalia  2019    15981300\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_population"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b856b3",
   "metadata": {},
   "source": [
    "__Standrdizing data according to their respective populations__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "8d832e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Population of countries in order from 1990 to 2019\n",
    "india_population_arr = list(df_population[df_population[\"Country\"]==\"India\"][\"Population\"])\n",
    "som_population_arr = list(df_population[df_population[\"Country\"]==\"Somalia\"][\"Population\"])\n",
    "uk_population_arr = list(df_population[df_population[\"Country\"]==\"UK\"][\"Population\"])\n",
    "usa_population_arr = list(df_population[df_population[\"Country\"]==\"USA\"][\"Population\"])\n",
    "china_population_arr = list(df_population[df_population[\"Country\"]==\"China\"][\"Population\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "cd97740a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalising the dataset to make the data uniform to deaths per 1000 of the population\n",
    "normalizeDf(df_india,india_population_arr)\n",
    "normalizeDf(df_som,som_population_arr)\n",
    "normalizeDf(df_uk,uk_population_arr)\n",
    "normalizeDf(df_usa,usa_population_arr)\n",
    "normalizeDf(df_china,china_population_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009c2bfb",
   "metadata": {},
   "source": [
    "__Performing t-test with the help of pre-defined functions__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "a6894b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Welch's t-test on specific country combinations using user-defined functions\n",
    "us_china_t_test = t_test(df_usa,df_china)\n",
    "us_uk_t_test = t_test(df_usa,df_uk)\n",
    "usa_som_t_test = t_test(df_usa,df_som)\n",
    "usa_india_t_test = t_test(df_india,df_usa)\n",
    "ind_uk_t_test = t_test(df_india,df_uk)\n",
    "ind_chn_t_test = t_test(df_india,df_china)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08056851",
   "metadata": {},
   "source": [
    "# Final tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "126ce349",
   "metadata": {},
   "source": [
    "## Comparing India and USA's causes of death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "c4192009",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------\u001b[94m Welch's T Test for comparing USA's and INDIA's hiv/aids deaths. \u001b[0m------------------\n",
      "\n",
      " -> \u001b[4mNull Hypothesis (Ho) [µO=µ1]\u001b[0m\n",
      "There is no significant difference in the average of deaths caused by hiv/aids in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mAllternate Hypothesis (H1) [µO≠µ1]\u001b[0m\n",
      "There is a significant difference in the average of deaths caused by hiv/aids in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mConfidence Level Interval : \u001b[0m0.95\n",
      "\u001b[1m\n",
      "- Statistic (t) value : \u001b[0m 0.494069489\n",
      "\u001b[1m- Probability (p) value : \u001b[0m 0.6231231832055129649106106626277323812246\n",
      "\u001b[1m\n",
      "- Decision\u001b[0m\n",
      "As p value ( 0.6231231832055129649106106626277323812246 ) is greater than level of significance ( 0.05 ),\n",
      "We Fail to reject the Null Hypothesis(Ho) {Accept Null Hypothesis(Ho)}\n",
      "\u001b[1m\n",
      "- Statistical Inference\u001b[0m\n",
      "There is no significant difference in the average of no. deaths caused by hiv/aids in  USA and INDIA.\n",
      "Time period : Past 30 years (1991-2019)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "-----------\u001b[94m Welch's T Test for comparing USA's and INDIA's conflict_and_terrorism deaths. \u001b[0m-----------\n",
      "\n",
      " -> \u001b[4mNull Hypothesis (Ho) [µO=µ1]\u001b[0m\n",
      "There is no significant difference in the average of deaths caused by conflict_and_terrorism in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mAllternate Hypothesis (H1) [µO≠µ1]\u001b[0m\n",
      "There is a significant difference in the average of deaths caused by conflict_and_terrorism in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mConfidence Level Interval : \u001b[0m0.95\n",
      "\u001b[1m\n",
      "- Statistic (t) value : \u001b[0m 1.508839815\n",
      "\u001b[1m- Probability (p) value : \u001b[0m 0.1374514382619866081913784228163422085345\n",
      "\u001b[1m\n",
      "- Decision\u001b[0m\n",
      "As p value ( 0.1374514382619866081913784228163422085345 ) is greater than level of significance ( 0.05 ),\n",
      "We Fail to reject the Null Hypothesis(Ho) {Accept Null Hypothesis(Ho)}\n",
      "\u001b[1m\n",
      "- Statistical Inference\u001b[0m\n",
      "There is no significant difference in the average of no. deaths caused by conflict_and_terrorism in  USA and INDIA.\n",
      "Time period : Past 30 years (1991-2019)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFNCAYAAAAAWhivAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABbDUlEQVR4nO3dd3hb5fnw8e8t2ZZkO44znD2cBIcMRoBAoIwwW0bZ/bWEDaWUPUoplN3SQUvhhVJooWxaQimjQBkJG0qBEkbIIjtOnB2P2I4t2Zbu949z5MiO5RVZku37c126bJ356Fg+93m2qCrGGGOMJ9UJMMYYkx4sIBhjjAEsIBhjjHFZQDDGGANYQDDGGOOygGCMMQawgGCMMcaVkoAgjsdEpFxE/icih4pIScz6BSJyaCrS1h4iskpEjkx1OtpDRP4iIjcn4Tw+EVkoIkO7+lzGmPYTkbtE5OJ2bayqSX8BBwMlQI77/lCgpBPHUWCXFKR/FXBkG9vcBvwtFdc3RX/Ty4G/xLx/HKgDqtzXfOC3QN+Ybc4FwkB1s9ewZsd+DygHfO77v8RsWwfUx7x/HSh0vxsZzY7zOPCrmPc/BL5x07cReA3oE+fzvQcE3XNsAV4Ahjb7rLGfYa67LpqW6PJVwPVtXMvbgXlAA3BbC+tPB4qBbcC/gP4x6/oDL7rrioHTWzmPAL8DSt3X7wCJWT8F+ByocX9Oae++LZwrH/gzsME93jzgvBb+r2pb+y642x0KRNz1VcDi2GMBPpzv2mr3eEuBa5t9ttb+nu8BF7RwzpKY963ee4Bc99ivxyxbEPO5wjHnrwZuwPl/+E+z45zrXqsa99r9GciPWX+bm5bvxyzLcJcVuu+HAmuArLb+j1NVZDQaWKWq21J0/rQnIhktLPN28Bgd2n4nXQQ81WzZ71W1D1AAnAfsD3wkIjkx23ysqrnNXuuiK0WkEOcBQoETAFT1oui2wG+Af8Tse0x7Eisi0919Z7hpnAj8o43dLnPPOR7nBvf/mn3W2M+wZ7N98919vwfcLCJHtXKeZcDPgFdbSPdk4EHgLGAwzo3igZhN7scJToOBM4A/u/u05ELgJGBPYA/geODH7nmygJeAvwH9gCeAl9zlre7bQpqzgLdw/u8PAPri3KDvEJGfNNv8+HjfhWbWudczD7gO+KuITHLX/RM4AjgW6INzrS4E7m12jNb+njvrVCAEHCUiQwBUdXLM9/bD6Pnd12+aH0BErsEJtNfiXLP9ca7hmzF/B4Ay4Bfx/t9VdT3Og88JbSW6zYAgIiNF5AUR2SwipSLyJ3e5R0RuEpFiEdkkIk+KSF93XaGIqIicIyKrRWSLiNzorvsh8DBwgIhUi8gvWjhnY5GMiHhF5AYRWS4iVSLyuZumD9zN57rH+UErn6GfiPzb/Qzl7u8jYta/JyK3i8hH7jlmi8jAmPVnuZ+zNPo52rhmR+NE/B+4aZvrLu8rIo+IyHoRWSsiv4r+EUXkXPf8/09ESoHbRORxEfmziLwmItuAw0RkopveCrdo7YSY87a0/eMi8it3/UD3s1eISJmIfCginphrfq2IfC0i29x0DhaR191r8paI9IvzeUcBY4FPW1qvqkFV/QznCzkAJzi019nAJzhP4ed0YL+27IsTjL5001imqk+oalVbO6pqGfA8sFtHT6qqc3CeFKe0ss0Tqvo6ztNvc2cAr6jqB6paDdwMnCIifdxAeypws6pWq+p/gJdxbogtOQe4S1VLVHUtcBfOEyk4T8QZwD2qGlLVP+LkCg5vx77NnQWMAv5PVVeqar2qvgFcAfxSRPLiXYu2qONfODnISSJyBPBt4FRVna+qDar6CXAmcKmI7NLCMTr992zFOTg52a/dc3eIe01+AVyuqm+412wV8H2cXGfsMd/AeQho7TzvAce1dd5WA4J7s/o3TtazEBgOPOOuPtd9HYZzM8gF/tTsEAcBu+JE61tEZKKqPoLzNBl9Mry1jTT+BJiBE+3zgPOBGlU9xF2/p3uc1p7uPMBjONF1FE42snlaT8e5UQ0CsoCfutdgEk427SxgGM4NbQStcL/ssU+u0afFx3GKAXYB9sL54l4Qs+s0YAXO092vY9L1a5wnnU+BV4DZbjovB/4uIrs2+xzR7f/TLGnX4BTVFbjnuAHnyTvqVOAonCem43GKX25wt/fg/AO3ZHdghao2xFkPgHuzfRPnib+9zgb+7r6+IyKDO7Bvaz51j/cLETlQRHzt3dF9WDgV+LKjJxWR/XFuPMs6uq9rMjA3+kZVl+PcDMa7rwZVXRKz/Vx3nzaP1WzbycDX6pY5uL5utj7evs0dhVN00rxE4HnAj5Nr6BT3wfRknCf8ee65PlXVNbHbqeqnON/9I1o4Rqf/nnHSNBonoEa/t2d34jDfwrk2L8QudB8CXsP5nI2LcR4MbhWRzDjHW4STm2tVWzmE/XBugteq6jb3SS96kzkDuFtVV7iJ/DlwmjQt6viFqtaq6lycL0ybCWrBBcBNqrrYfRqYq6qlHTmAqpaq6vOqWuPelH4NTG+22WOqukRVa4Fn2f4E9z3g3+4TWQjnwkc6+iHcG9mxwFXutdyEk0U9LWazdap6n/tUU+sue0lVP1LViJumXOAOVa1T1XdwAvaMmGM0bq+qwWbJqMcpTxztPnF82Owf/j5V3eg+8X2I84/1pXucF3GCWEvyaflptiXrcMq5o/Z3cyzR1/LoChE5CCeIP6uqnwPLcQJee22JPXbsvqr6IXAKsDdO0UypiNwtrRez/dE9zlxgPc7DStRPm32OJ1pISy3wMU4Rz7868Dli5QJbmy3bivMAkAtUxlnXnmNtBXJFRNo4T1v7NjcQ53o14T5AbHHXR/0r5hr+K066AYa5f4stwK3AWaq6ON65XOubnau1v+fOOAsnmC7EeYCeLCLx/nfiGQhsifOQ1fxzoKovA5tp+oAZqwrn/7RVO5RTNzMSKI6TqGE4OYeoYvd4sU9wG2J+r8H5EnXUSJwbQaeJSDbOzfdonPJQgD4i4lXVsPs+XlqH4VTIAKCq29winY4aDWQC62P+Zzyxx272e0vLhgFr3OAQVYyTc2vtGFF34lRCzXbT8JCq3hGzfmPM77UtvI/39ysn/k2nueE4ZZ5Rn6jqQXG2PQeYrapb3PdPu8vaW9Y7MPa7KyKPx650i2Ved4vNDsMpe16MU0bfkitU9eE46/6gqje1lhacJ7krcQJTJs6TfUdV4+SUY+Xh/MNHWlnXnmPlAdWqqiLS2nla3beF82zBeRBpwn14HOiujzpJVd+Kk95Y61S1pZz6FqAozj5Dm50r3t+zAefvEysT54GqPc4G/gqgqmtF5H2c721HciBbgIEiktHC/bf554i6CackpHldHjj/nxVtnbStHMIaYJS0UMGJ86Q3Oub9KJwLubGFbXfGGmDcTh7jGpyiq2mqmgdEi5taepppbj1OUHJ2cILLgHbs1/wfYw1OJdNAVc13X3mqOrmVfZovWweMjJb7u0YBa9s4hrNCtUpVr1HVsTjl+T9xy1x31tfAmDjfk0YikgsciZP7aJWIBHDKS6eLyAYR2QBcDewpIp3Jacbl5qbeBt4hseXIzc8TVtW7cVqXXNLJwywgJqctImNxWtUscV8ZIhJ7Q9zT3afNYzXbdgGwR7Mn/j2arY+3b3NvAcdI08YEsL3i9ZM4+3XGW8A0ERkZu1BEpuH8H7/TjmOsxikijzWGpg/ALRKRb+EEpJ/HfG+nAae39f/RzMc41+aUZsfPBY4B3m6+g6q+iVMU2dJ3ayJNi/ha1FZA+B/ODfEOEckREb+IHOiumwlcLSJj3ERGy8xbLUfuhIeB20WkSBx7iEj0hrwRp/6iLX1wnnArRKQ/ThazvZ4DvisiB4lTs/9L2td/YyNQGL15q1PTPxu4S0Ty3LLPceK0dmmvT3FyLz8TkUxx+mocz/Z6nVaJyHdFZBf3n3wrTtO3Dhd/NaeqJThfxP3inNcnIvvgFJOU4zzFtOUkN32TcIrKpuB8qT+kc2WyzdN0ooicJk6DAxGR/XCKERN5c4rnDpy/oT9O2jLddR6cG7w/pijr78DxInKwe4P9JfCCG+y34ZQ5/9L9fz0QOBH3iVG2N/YodI/1JM5DwXARGYbz4PS4u+49nOt/hfv3u8xd/k479m3uKZzy+3+6acgUke8Af8RpVtu8aKrT3NzF28DzIjJZnEYp++O0lvqzqi5tx2H+AZwnIvu5343xOA8jzf/Psty/jT/mb3QOTj1Z7Pd2NyCAcyNv7+fYilOpfJ+IHO1es0Kc4uwSWs4FANyI00Ktuek4dYKtavXG5hanHI9TCbraTUi0Nc+jbqI+AFbiPPVc3tYJO+FunIswG6d89BGciwtO8ccTbnnj91s5xj3uPltw/uHfaO/JVXUBcClOccV6nBtaSas7Of7p/iwVkS/c38/GqbBe6B7nOVrISreSljqcv8cxOJ/lAeBsVf2mnYcownmCqsYty1bVd9t7/jZEm0LG+pmIVOG0U38Spy37t5pVLkZbm8W+9sX5x3pMVVer6oboC6cxwBkdfNpqSTnwI5w26pU4N4w7VfXvnTzez5p9hpay9FGvxpy/JX/FeYCZgfMPXot7bd3v40U4gWETzsNO7BPhJTjf9U04D20Xu/uAWwTM9hzlgziNFObh9BN51V0W/a6dhPOdrcBpzHGSu7zVfZtz696OxMklf4pzve8GblTVO+Ncg51xKvAuzv95Nc7f9hHaeX9S1VnA9TgPLltxKnGfAB5qtukCnL9N9HUxTq72vtjvrKquxLlXdqiVnKr+HqdRxx9wrtmnONfwCPeatrTPRzgP8o3E6Sw6iXbUW0nLRX7GdIw4rXS+xPmyxqvUMykkIjcBm1U1Xh2J6YFE5C5guao+0Oa2FhCMMcZADxrcTpzOa82LHqpFpM1ys5045+txznlDV53TGGO6iuUQjDHGAD0oh2CMMWbn7GxLjbQ2cOBALSwsTHUyjDE9zOeff75FVQtSnY5E69EBobCwkDlz5qQ6GcaYHkZE2uyk1h1ZkZExxhjAAoIxxhiXBQRjjDFAD69DMMbsvPr6ekpKSggGm4+m3vP5/X5GjBhBZma8aQZ6FgsIxphWlZSU0KdPHwoLC5EWpzvomVSV0tJSSkpKGDNmTKqTkxRWZGSMaVUwGGTAgAG9KhgAiAgDBgzoVTkjCwjGmDb1tmAQ1ds+twUEY0zaW7VqFbvt1nTuottuu40//OEPfPLJJ0ybNo0pU6YwceJEbrvttibbXXXVVQwfPpxIZKen/ujxrA7BGNOtnXPOOTz77LPsueeehMNhFi9e3LguEonw4osvMnLkSN5//30OO+ywFKY0/VkOwaQFVaX6Px+h9hRnOmjTpk0MHerMM+X1epk0aVLjuvfee4/Jkydz8cUXM3PmzFQlsduwgGDSQnDBQtZccAE1//ss1Ukx3czVV1/Nrrvuysknn8yDDz7YpBJ45syZzJgxg5NPPplXX32V+vr6FKY0/VmRkUkL4a0Vzs/KhE2va7rAht/8htCi9s7Y2j6+iRMYckPrU4jEq9wVEW655RbOOOMMZs+ezdNPP83MmTN57733qKur47XXXuPuu++mT58+TJs2jVmzZvHd7343oenvSSwgmLSgoVCTn8bEGjBgAOXl5U2WlZWVNfYPGDduHBdffDE/+tGPKCgooLS0lP/+979UVFSw++67A1BTU0MgELCA0AoLCCYtqJvNj/SiNt/dUVtP8l0lNzeXoUOH8s4773D44YdTVlbGG2+8wZVXXsmrr77Ksccei4iwdOlSvF4v+fn5zJw5k4cffpgZM2YAsG3bNsaMGUNNTQ3Z2dkp+RzpzuoQTFqIBN0cQtByCKZlTz75JLfffjtTpkzh8MMP59Zbb2XcuHE89dRT7LrrrkyZMoWzzjqLv//974RCId544w2OO+64xv1zcnI46KCDeOWVV1L4KdKb5RBMWtBQsMlPY5qbNGkS77777g7Ln3nmmRa3Lysr22HZCy+8kPB09SSWQzBpIZpDiNRaQDAmVSwgmLSgwVrnp+UQjEkZCwgmLTTmEKwOwZiUsYBg0kK0lZHlEIxJHQsIJi1EQtFmp5ZDMCZVLCCYtKCNzU5rU5wSY3ovCwgmLVgOwbQmNzcXcIbBFhHuu+++xnWXXXYZjz/+OADnnnsuY8aMYc8992T8+PGcffbZlJSUNG5bWFjIli1bGt//61//QkT45pvEDsfRXaVFQBCRR0Vkk4jMb2O7fUWkQUS+l6y0meTYnkOwOgTTukGDBnHvvfdSV1fX4vo777yTuXPnsnjxYvbaay8OP/zwuNvOnDmTgw46yEZCdaVFQAAeB45ubQMR8QK/A2YnI0EmuSJuUVHExjIybSgoKOCII47giSeeaHU7EeHqq69myJAhvP766zusr66u5j//+Q+PPPJI3M5tvU1aBARV/QDYsVthU5cDzwObuj5FJtksh2A64rrrruMPf/gD4XC4zW333nvvFouEXnrpJY4++mjGjx/PgAED+Pzzz7siqd1Ktxi6QkSGAycDhwH7pjg5pgtsr0OwgJDObl5awvzqxFb875Yb4PaiER3aZ+zYsUybNo2nn366zW1VtcXlM2fO5MorrwTgtNNOY+bMmeyzzz4dSkdP0y0CAnAPcJ2qRtqa9FpELgQuBBg1alTXp8wkhOUQTEfdcMMNfO9732P69Omtbvfll19yxBFHNFlWVlbGO++8w7x58xARwuEwIsKdd94Zd+6F3qC7BISpwDPuH2ogcKyINKjqv5pvqKoPAQ8BTJ06teVHA5N2Goe/tjqEtNbRJ/muNGHCBCZNmsQrr7zCvvvuWHCgqtx3332sX7+eo49uWkX53HPPcdZZZ/Hggw82Lps+fToffvghhxxySJenPV2lRR1CW1R1jKoWqmoh8BxwSUvBwHRf0UBgOQTTETfeeGOTZqUA1157bWOz088++4x3332XrKysJtvMnDmTk08+ucmyU089tde3NkqLHIKIzAQOBQaKSAlwK5AJoKp/SWHSTJJsH7oihEYiiKdbPKuYJKmurgacfgTz529vnb7nnnsSiUQa30f7I8SzatUqgBaH0b7iiit2PqHdXFoEBFWd0YFtz+3CpJgUiQSD4PVCOIyGQkggkOokGdPr2GOYSTmtr4dwGG/fvoC1NDImVSwgmJSL1h9EA4JaxbIxKWEBwaRctP6gMSBYDiHtxGvL39P1ts9tAcGkXHRAO29+vvPecghpxe/3U1pa2utujqpKaWkpfr8/1UlJmrSoVDa9W3RSnMYcQq0NgZ1ORowYQUlJCZs3b051UpLO7/czYkT69L3oahYQTMpFK5Ebcwg2BHZayczMZMyYMalOhkkCKzIyKddYh5AfrVS2OgRjUsECgkm5aA7B09js1HIIxqSCBQSTctFmphlukZHlEIxJDQsIJuW0eQ6h1gKCMalgAcGkXGOz0775gOUQjEkVCwgm5RqbneZbHYIxqWQBwaTc9hyC9VQ2JpUsIJiU06DTEc3j9yM+X+N0msaY5LKAYFIuEgyB14tkZiJ+P2qVysakhAUEk3IaDOLx+QDwWA7BmJSxgGBSLhIKIu4AYuL3o1apbExKWEAwKafBEOJ3cwh+v+UQjEkRCwgm5SKhIB6/M2Wm5RCMSR0LCCblmuQQfD4iQRv+2phUsIBgUi4SrMXjszoEY1LNAoJJuaZ1CD4busKYFLGAYFIuEgrG5BACNnSFMSliAcGknJNDcAKCx++zoSuMSZG0CAgi8qiIbBKR+XHWnyEiX4vIPBH5r4jsmew0mq4T2zFNfP7GCXOMMcmVFgEBeBw4upX1K4Hpqro7cDvwUDISZZIjEgohAcshGJNqaREQVPUDoKyV9f9V1XL37SfAiKQkzCSFk0Nw6xB8frS+Hg2HU5wqY3qftAgIHfRD4PVUJ8IkTiS4fegKj5tTiE6raYxJnoxUJ6AjROQwnIBwUCvbXAhcCDBq1Kgkpcx0ltbXQziMx7+9DgGcYiRPdnYqk2ZMr9NtcggisgfwMHCiqpbG205VH1LVqao6taCgIHkJNJ0ScXMC0tjs1AkMWmu9lY1Jtm4REERkFPACcJaqLkl1ekziRCuQYwe3A5tG05hUSIsiIxGZCRwKDBSREuBWIBNAVf8C3AIMAB4QEYAGVZ2amtSaRIre+LdXKrs5BOutbEzSpUVAUNUZbay/ALggSckxSRS98Ucrkz0BZ9RT64tgTPJ1iyIj03NFaqNFRs1zCFZkZEyyWUAwKRXNIUQDQWMdglUqG5N0FhBMSkWLhjz+7R3TwHIIxqSCBQSTUtqs2Wm0P4LVIRiTfBYQTEppYw7B7ZjmTqVpk+QYk3wWEExKRZudxg5/Ddbs1JhUsIBgUqqx2Wm0DqGxUtkCgjHJZgHBpNQOzU6zskCEiOUQjEk6CwgmpRpzCNEJckQQn8/qEIxJAQsIJqUiwRB4vUhmZuMyj99vdQjGpIAFBJNSsdNnRonfb4PbGZMCFhBMSkVC2yfHifL4bBpNY1LBAoJJKQ2GGoe+jnJyCBYQjEk2CwgmpSKhIB63M1qU+C2HYEwqWEAwKaW1wR1yCB5/oHEmNWNM8lhAMCkVCQUbJ8eJshyCMamR0AlyRMQLHAcUxh5bVe9O5HlMz9FSHYLH56femp0ak3SJnjHtFSAIzAMiCT626YEioSCZffs2WSZ+H2pDVxiTdIkOCCNUdY8EH9P0YE4OoXmzU7/VIRiTAomuQ3hdRL6d4GOaHqzFjmkBv9UhGJMCic4hfAK8KCIeoB4QQFU1L8HnMT1EJBRCApZDMCYdJDog3A0cAMxTVU3wsU0PpLW1LbYyor4ebWhAMhL9FTXGxJPoIqM1wHwLBqa9IqEW6hCicyLYeEbGJFWiH79WAO+JyOtA43+zNTs1LdH6egiHG2dJi4rOr6yhIOTmpCJpxvRKic4hrATeBrKAPjGvVonIoyKySUTmx1kvIvJHEVkmIl+LyN4JTbVJiWg9gTQrMvK4dQpWsWxMciU0h6Cqv4j+7lYs56pqZTt2fRz4E/BknPXHAEXuaxrwZ/en6caiN/wdBrdzA4RVLBuTXAnNIYjI0yKSJyI5wHxgoYhc29Z+qvoBUNbKJicCT6rjEyBfRIYmJtUmVaJ1BM0Ht4sWIVkOwZjkSnSR0SQ3R3AS8DowBjgrAccdjlNhHVXiLjPdWOP0mfFyCBYQjEmqRAeETBHJxAkIL6tqPZDUFkcicqGIzBGROZs3b07mqU0HRWqjRUbNWxlZDsGYVEh0QHgQWAXkAB+IyGigPXUIbVkLjIx5P8JdtgNVfUhVp6rq1IKCggSc2nSVaA5BdphC0ylCsmanxiRXQgOCqv5RVYer6rFueX8xcFgCDv0ycLbb2mh/YKuqrk/AcU0KRYuEPPFyCDbiqTFJlZBWRiJypqr+TUR+EmeTVvshiMhM4FBgoIiUALcCmQCq+hfgNeBYYBlQA5yXiHSb1NI4zU7FOqYZkxKJanYa7T3UZp+DlqjqjDbWK3BpZ45t0pcG41UqR+sQapOeJmN6s4QEBFV90J0cp1JV/18ijml6vmgOQHZodmo5BGNSIWF1CKoaBlp90jcmVrxmp9GAYHUIxiRXoscy+khE/gT8A9gWXaiqXyT4PKYHiNfslMxM8HisH4IxSZbogDDF/fnLmGUKHJ7g85geoDGH0LzZqQji96NWZGRMUiV6LKNENDE1vUQkGAKvF8nM3GGdx+cjYpXKxiRVoscy6isid0d7CovIXSLSt+09TW/U0vSZUZZDMCb5Et1T+VGgCvi++6oEHkvwOUwPEQkFd6w/cHn8fiJWqWxMUiW6DmGcqp4a8/4XIvJVgs/RJYLhCCWhOnbJbvkGZRJPg6EdeilHWQ7BmORLdA6hVkQOir4RkQOBblEQ/HDJZg769Bu21jekOim9Rqs5BJ/Pmp0ak2SJziFcBDwZU29QDpyT4HN0iTHZTln2qmAde2baxO7JoLXBHSbHiRK/v7FZqjEmORJ253N7Kp+lqnuKSB5AO2dLSwuFATcg1IbYs092ilPTO0RCQTy++DmE+sqtSU6RMb1bwgKCqoajxUXdKRBEjfZnAVBcW5filPQeGgzFzyEEAlaHYEySJbps5EsReRn4J017Kr+Q4PMkXG6Gl4KsDFbW2k0oWSKhIJl9W26V7PH5bIIcY5Is0QHBD5TStGeyAmkfEAAK/T5WWUBIGieHEL+VUSRkfwtjkinRdQilqvrTRB0z2Qqzs/iovDrVyeg1NBiM2+zU4/ehtd2igZoxPUaiRzs9MFHHS4VCv491oXpqw5FUJ6VXiARbaWXksxyCMcmW6CKjr7prHQJAYcCpWF4drGPXHOug1tWcoSvi5BACfgiH0fr6Fsc6MsYkntUhxBjjNj0trg1ZQEiCSKiVOgQ3UERCIbwWEIxJikSPdtqt5zoe7QYEa2nU9bS+HsLhHSbHiYoWJWkwCLm5yUyaMb1Wokc7HS8ib4vIfPf9HiJyUyLP0ZX6Z3rp4/WwyvoidLlo/YDE7ZgWnUbTmp4akyyJHsvor8DPgXoAVf0aOC3B5+gyIsKYgDU9TYZoH4P4Q1f4mmxnjOl6iQ4I2ar6v2bLutVocaMtICRFxO2F7PEHWlzvCQSabGeM6XqJDghbRGQcTkUyIvI9YH2Cz9GlCgNZrAnW0RDRVCelR1N3NrS4dQjuxDk24qkxyZPoVkaXAg8BE0RkLbASOCPB5+hSYwI+GhTWhuoaK5lN4kWf/FubIMfZzgKCMcmS6ByCquqRQAEwQVUPau85RORoEVksIstE5PoW1o8SkXdF5EsR+VpEjk1w2gEY7fZFsIrlrhV98pd4U2i6lcpWh2BM8iQ6IDwPoKrbVLXKXfZcWzu5w17cDxwDTAJmiMikZpvdBDyrqnvhVFQ/kLBUxxgTMwy26TrRJ//Whq6I3c4Y0/USUmQkIhOAyUBfETklZlUeTme1tuwHLFPVFe7xngFOBBbGbKPu8QD6Aut2Nt0tGeLLxOcRCwhdTNtodipuZbMNgW1M8iSqDmFX4LtAPnB8zPIq4Eft2H84sCbmfQkwrdk2twGzReRyIAc4spNpbZVHhFH+LCsy6mLamENoucioMYdglcrGJE1CAoKqvgS8JCIHqOrH8bYTkZ+r6m87eZoZwOOqepeIHAA8JSK7qWqTkehE5ELgQoBRo0Z16kTWF6Hrba9UbrnZabSy2XIIxiRPQusQWgsGrv+Ls3wtMDLm/Qh3WawfAs/GnMcPDGwhDQ+p6lRVnVpQUNCudDdXGPCxqrYOVWt62lUibTQ79fh8TbYzxnS9RFcqt0XiLP8MKBKRMSKShVNp/HKzbVYDRwCIyEScgLC5KxI5OpBFbSTCprpu1aeuW9E2mp1KZiZ4vZZDMCaJkh0QWnzkVtUG4DJgFrAIpzXRAhH5pYic4G52DfAjEZkLzATO1S56hLeWRl0v2uzUE6fZKTgtkKxjmjHJk+iOaW2Jl0NAVV8DXmu27JaY3xeSpAl4ChsDQh3T8pNxxt4nEgyB19vqXAfi99vQFcYkUbJzCP9M8vk6ZYQ/Ew+WQ+hKzuQ4rfcE9/h81jHNmCRKVD+E+4hTHASgqle4P3+TiPN1tSyPhxH+LAsIXSgSCsatP4hycggWEIxJlkTlEOYAn+NU9O4NLHVfU4CsBJ0jqQoDWay0vghdRoOhuL2Uo8RvOQRjkilR/RCeABCRi4GD3EpiROQvwIeJOEeyFQZ8vLKpItXJ6LEiwbZzCB5/oHEiHWNM10t0HUI/tg8vAZDrLut2CgM+yhvCVNRb09OuoMFg3MlxojyWQzAmqRLdyugO4EsReRenRdEhOENOdDuFMaOeTslMdmOsni8SCjZOkxmP+PxEyiuSkyBjTGIDgqo+JiKvs30coutUdUMiz5EssX0RpuRlpzg1PY8GQ23mEMTvQ2utp7IxydIVzU69OD2Iy4HxInJIF5yjy41ycwjFVrHcJdqTQ/D4/FaHYEwSJTSHICK/A34ALACig84p8EEiz5MMOV4vg7IyWGlNT7uEk0Noo8go4Lc6BGOSKNGF4ycBu6pqj7iL2qinXUeDwTabnVoOwZjkSnSR0Qog/lgE3czoQBbFQSsy6gqRdrQyivZDsFFnjUmOROcQaoCvRORtoPHRLtpTubspDPh4dkM5teEIAW+yR/no2ZyhK9rqh+CHSATq6yGrW/ZvNKZbSXRAeJkdh63utqItjYqDISbktDyRi+mcSKgddQhuwIgEg3gtIBjT5RLd7PSJRB4v1UbHtDSygJA4Wl8P4XDcyXGiPIGYgJCX1+q2xpidl+hWRkXAb4FJOOMaAaCqYxN5nmSJDoO9ssYqNhMpWlEs7eiYBqBWsWxMUiS6YPwx4M9AA3AY8CTwtwSfI2n6ZXjpm+FllVUsJ1S0KWl7hq6I3d4Y07USHRACqvo2IKparKq3Accl+BxJIyJOSyNreppQ0UlvPP7Wi+G21yHY9TcmGRJdqRwSEQ+wVEQuA9biDHDXbY0J+JhbVZPqZPQoGnSGo2izDqExh2DDVxiTDInOIVwJZANXAPsAZwLnJPgcSVUY8FESrKMhYm3hEyX6xN/2BDmBJtsbY7pWolsZfeb+Wg2c13y9iNynqpcn8pxdbXQgiwaFtaE6Rgdaf6I17aMhtw6hrSk0ozmEkNUhGJMMye5tdWCSz7fTon0RbEyjxIlOi9n2jGlWh2BMMln32zbEzotgEkPb2ezU47McgjHJZAGhDYOzMvF7xAa5SyBtzCG0NZaRm0OotYBgTDIkOyBIks+30zwijLZRTxNqe6Vy681Oo0VKlkMwJjkSGhBE5P/aWHZvnP2OFpHFIrJMRK6Ps833RWShiCwQkacTlOR2KQxkWZFRAkXa2ex0ex2CBQRjkiHROYSft7ZMVR9vvlJEvMD9wDE4Q17MEJFJzbYpco9zoKpOBq5KXJLbVuj3UVwbsmGYE0RbaHYaDEe4r3gjNeFI4zLxeiEzs3F7Y0zXSkizUxE5BjgWGC4if4xZlYczjEVr9gOWqeoK91jPACcCC2O2+RFwv6qWA6jqpkSku70Ks33URpSNdQ0M8fWY6R5SJloE5Ilpdvrq5gp+vWI9I/xZnDy4X+Nyj89HxIqMjEmKROUQ1gFzgCDweczrZeA7bew7HFgT877EXRZrPM78zB+JyCcicnRCUt1Ohf5oSyN7Uk2ESDAEXi+SuT24vllaCcCi6qa9ksXvR61S2ZikSEgOQVXnAnNF5EVgm6qGobE4KBG9uTKAIuBQYATwgYjsrqoVzTcUkQuBCwFGjRqVgFPDmGznI6yqDbF/frceiSMtOJPjbP9aNESUd8uqAFi4renN3+P3Ww7BmCRJdB3CbCC26UgAeKuNfdYCI2Pej3CXxSoBXlbVelVdCSzBCRA7UNWHVHWqqk4tKCjoUOLjGe7LwivWFyFRIqFgk/qDOZXb2NoQJj/D20IOwWd1CMYkSaIDgl9Vq6Nv3N+z29jnM6BIRMaISBZwGjvOuvYvnNwBIjIQpwhpRYLS3KZMjzDCl2VFRgmitcEmvZTfKq0kQ+DsYQNYG6qnon57tZPHZzkEY5Il0QFhm4jsHX0jIvsArQ5VqaoNwGXALGAR8KyqLhCRX4rICe5ms4BSEVkIvAtcq6qlCU57q8YEfJZDSJDm02e+VVrJtL65THOL4xbFFBuJ3285BGOSJNHDX18F/FNE1uF0QhsC/KCtnVT1NeC1ZstuifldgZ+4r5QYHcjiq00VqTp9j6LBYOPkOCXBOr7ZFuTWccOYlOsEiYXVtRzgBgePz0d4W3XcYxljEifho52KyARgV3fRYlWtT+Q5UqUw4KOiIUxFfQP5mYmOo71LJBTE445j9JbbuujIAXkMycokP8PLN7E5hEAALU1qZtCYXitR/RAOV9V3ROSUZqvGiwiq+kIizpNK0VFPV9XWMcUCwk7RYKgxh/BWaSWj/Vnsku1DRJiY62dhTMWyx+ezKTSNSZJE3dmmA+8Ax7ewToFuHxBGB7b3RZiS11Y9uWlNJBQks29fasMRPiqv4vShAxBxhrmalBNg5oYyIqp4RBC/n0jI6hCMSYZE9UO41f25w6Q4PcXowPa+CGbnODkEPx9VVFMbUY4ckNe4blJugJpwhNXBOgoDPjx+yyEYkyyJKjJqtbJXVe9OxHlSKdvrYUhWJitbaGnUEFFqIhFqwxGCkQgj/Fl4pdsN7Jo0kWAtHr+ft0orCXg8jRXIABNjKpYLAz7E57fB7YxJkkQVGfVJ0HHSWmEgi39vrmDO1m3URCLUhJ0gUNds0LurRw/murFDU5TK9KfBEPh9vFW6lUP65+L3bm/9vGuOHwEWVQc5tgA8AT8aDKKqjcVKxpiukaiAkK2q14nI/6nqPxN0zLRz/ogC/rmhjGyvx3l5nJ+BmN/vX72Jr6pqUp3UtKbBICvzB1ISrOfK0YObrMvxehkT8LFwm1OxHJ1VTevq2pyD2RizcxIVEI515zH4OdBjA8IJg/I5YVB+q9t8snUb/9tq7eZbEwmF+LDAyUEd0T9vh/UTc/0sqm46q5oGg2ABwZgulaieym8A5cAeIlIZ86oSkcoEnaNbKMr2URKsZ1tDONVJSUtaXw/hMB/mF7BbboBh7kiysSbmBFhZG2JbONyYQ4hYb2VjulxCAoKqXquq+cCrqpoX8+qjqjs+AvZgRdnODWyZtUZqUSQUoio7h68CfZq0Loo1KdePAou3be/RrMFWR0AxxiRAQscyUtUTE3m87mh8jhMQlm6zljEt0WCQzybtQViklYDgDJi7qDqIx5132XIIxnS9RM+pfIqILBWRrb21yGhMwEeGwBILCC2KBIN8stte9IuE2StOB79R/iyyvR4WbavdnkOwEU+N6XKJHoPh98DxqroowcftNjI9wpiAj6U19kTbkobaWj6dPIVDI6G4fTU8IkzM8bOwevsw2dYXwZiul+jhrzf25mAQNT7Hz9Iau4G15MvqIJW5fTjMG2l1u4k5AWeynKxoDsECrDFdLdE5hDki8g+cCW0a/4N7wuB2HVGU7eeNLVupi0TI8iQ65nZv79TU4wkrh/hb72Q2MdfP39aH2ewGhEitVSob09USfbfKA2qAb+MMdHc88N0EnyPtFWX7CCussJZGO3inXtltxRL6BfytbhetWP5GnGcWyyEY0/USPR9Cjx3criO2tzQKMSEn0MbWvcf6UB2L1MuF879EvrVHq9tOdK/hYvUwGqtDMCYZEt3KaISIvCgim9zX8yIyIpHn6A7GZTvj8VhLo6beLq0CYP/5Xzb2QI4nPzOD4b5MvmlwxomyaTSN6XqJLjJ6DHgZGOa+XnGX9SrZXg8j/FlWsdzMW6VbGRaup3BdCeJvO+c0MTfAN0Fnwj1rdmpM10t0QChQ1cdUtcF9PQ4UJPgc3UJRts8CQoxgOMIH5dVMD1Yh0GYOAWBSjp+lwTrqvV4itXYtjelqiQ4IpSJypoh43deZQK+cEHd8jp9lNSHCzYbG7q0+rqimJhzhkKpyAMTfeqUyODmEBoU1Iwsth2BMEiQ6IJwPfB/YAKwHvgecm+BzdAvjs/2EIsqa4I4T6vRGzmQ4wr5btwDOXMltiU6Ws2L0WBu6wpgkSHRA+CVwjqoWqOognADxiwSfo1soclvJWMUyqCpvlVZyYL8+ZNXWgteLZGa2ud+4gJ8sEVaMGGU5BGOSINEBYQ9VLY++UdUyYK8En6NbKMp2noAtIMCymhDFwTqOHJCHBoPtyh2AMwzI+Bw/K4aOsByCMUmQ6IDgEZF+0Tci0p929nUQkaNFZLGILHMn24m33akioiIyNQHp7TL5mRkMysqwMY2AN0ud8Q2PGpBHJBRsV/1B1MRcP8sHD7Phr41JgkQHhLuAj0XkdhG5HfgvzoB3rRIRL3A/cAwwCZghIpNa2K4PcCXwaUJT3UWKsm1MI4A3S7cyOdfPcH8WWrt9wLr2mJQTYEtuHmVq8ykb09USPR/Ck8ApwEb3dYqqPtWOXfcDlqnqClWtA54BWppb4Xbgd0C3uMuOz/GzZJszQXxvVVHfwP+2buPIAX0BZ4KcjuYQAJYG+nRJ+owx2yV6cDtUdSGwsIO7DQfWxLwvAabFbiAiewMjVfVVEbl251KZHEXZPqrDETbU1TPUt+NUkb3Be2VVhNUpLgJnghxpRx+EqEnu0B9L8/K7InnGmBjdYihOEfEAdwPXtGPbC0VkjojM2bx5c9cnrhWxYxr1Vm+WVtI/09s4GU4kFMTja38OoSArg37BWpblD+iqJBpjXOkSENYCI2Pej3CXRfUBdgPeE5FVwP7Ayy1VLKvqQ6o6VVWnFhSktpP0eHd+5SW9tB6hIaK8U1rJEQPyGifD0WCoQzkEEaGoqoLl/Xtlh3djkipdAsJnQJGIjBGRLOA0nDGRAFDVrao6UFULVbUQ+AQ4QVXnpCa57VOQlUHfDG+vbXr6eeU2yhvCHOXWH0DHcwgARbVVrBg42Hp9G9PF0iIgqGoDcBkwC1gEPKuqC0TklyJyQmpT13ki0qvHNHqztJIMgUP7b68QdnIIHQsI44M1hLKyWGXzSxjTpRJeqdxZqvoa8FqzZbfE2fbQZKQpEcbn+Jm1pTLVyUiJN0sr2b9vLnkZ3sZlkWBth5qdAuza4ATUhdVBxmV3bF9jTPulRQ6hJyvK9lNa30BZfUOqk5JUxbUhFm8LctTAvCbLO1qHADAuEsYTibCwqiaRSTTGNGMBoYsVNbY06l3FRm819k7u22S5M3RFx57yA74sRmxcbwHBmC5mAaGLjY+OadTL6hHeKq1kXMDH2OymuYGOdkwD8Pj9jF27mm96WVA1JtksIHSxEf4sAh5Pr+qLsK0hzEfl1RzZvLiovh7C4XZNjhNL/D7GrV1NcV0D1Q3hRCa1SwUXLqRuzZq2NzQmTVhA6GIeEXbpZS2NPiyvpk61sXdyVCTkBEXpYJFRNIcApH0uQSMRqt59l+Izz2LlKadScvkVqU6SMe1mASEJomMa9RZvlm6lj9fDtL65TZZr0LkGHa1UFt/2gLCwOjmjnmq4YzmRSF0dFc8/z4rjT6Dk4kuoW7eWnIMPJvTNN9SvX99FqTQmsdKm2WlPVpTt4/mN5WxrCJMT0wSzJ4q4k+EcNiCPTE/TEUojbkDw+AMdOqbH72Nw2Rb6AAu7ILBGgkGCixYRnDef2nnzCM6bR11xMZkjRuCfOBH/pIn4J07EN3EimYMGNdk3XFlJ+T/+QfmTT9GweTO+CRMYdufvyTv6aOqKi1nx4YdUv/8B/U77QcLTbUyiWUBIgsaWRjUhprhj+vRU86pr2VjXsENxEWzPIXS8DiGAAOM9ERYlIIdQv24d2z7+mNqvnZt/cMkSaHCaBWcUFODfYw/6HHUUdWvWEFy0kKrZsxv39Q4c6ASJiRPRUJCK554nsm0bOd/6FkPv+C053/oW4g7TkTVuHJnDh1P9/vsWEEy3YAEhCYpixjTq6QHhzS2VCHB4/x0DQnTWs463MnICyK6Rel6prqUuEiHL07nSznBFBStOPIlIVRWePn0I7L4bA84/n8Aeu+PffXcyBw/ecZ/qakLffENw4UKCCxcRXLSI0o8/BlXyjjmGAeefh3/SDtN3ICLkTp9OxYsvEgmF2j1TnDGpYgEhCcYEfGRI7+iL8GbpVqbm5TAga8evVnReZOngjTEaQI6or+VpfPxq+Xp+WTS8U+nb+tJLRKqqGPX4Y2Tvtx/SjsDizc0le+pUsqduH0sxUleH1tbi7du3lT0h99DplD/9NDX/+x+5Bx/cqTQbkyxWqZwEmR5hTKDntzTaGKpnblXtDr2To7bXIXQwh+AGkIOClfxw+EAeKtnMrC1bO5w+VaX8mX8QmDKFnP33b1cwiJumrKw2gwHgBB2/n+r33u/0uYxJFgsISeK0NOrZfRHejpk7uSXayWan0RxCJBjkll2GsUdugCsXraYkWNeh49T87zPqVq4kP4nl+R6/n5z996f6/fd79cx5pnuwgJAk47P9rKoNEYpEUp2ULvNmaSXDfZlMyGn5hh+pdSqEO1qpHM1RaDCIz+PhwcmFNKhy0YJV1Efaf5Ot+MczePr2Je/oozt0/p2Ve+h06ktKqFuxIqnnNaajLCAkSVGOnwiwoqZn5hKC4Qjvl1dx5IC8xlY2zWljpXLHmp1uzyE4+4/J9vGHXUcyp7KG361sXxv/hi1bqHzzLfJPOqnDRVY7K/eQQwCs2MikPQsISVLkjumztIcGhI8rqqkJRzhqYPxy9Uiok81O3TqEaLNVgJMG9+PsYQP40+pNjUVVral44UWoryf/B8lv/pk5bBi+8eOpft8CgklvFhCSZFy2H4Ee22P5zdJKAh7hwPzcuNtoJ5udigji8zUGlKhf7DKcSTl+Ll9UzPpQ/PoEjUSo+Mc/yJ42Dd/YMR06d6LkTj+Emi++IFxVlZLzG9MeFhCSJNvrYaQ/q0e2NFJV3iyt5OB+fQh443+los1OO9MeX/z+xoASFfA69QnBiHLxgmIa4tQnbPvoI+rXrk1p57Dc6dOhoYFtH32UsjQY0xYLCElUlO3vkX0RFtcEWROsi9vcNCoSDIHXi2RmdvgcHr+fSHDHXspFOX5+N34En2zdxl2rNrS4b/kz/8A7YAB9jjiiw+dNlMCUKXj69qX6/Q9SlgZj2mIBIYnG5/hYXhvqcZPFv+lOEXpknOamUc7kOJ3rrSt+3w45hKj/G9Kf04b0557ijXxY1rRIpn7DBqrffZf8U09FsrI6de5EkIwMcg88kOoPPkB7cEsz071ZQEiiohw/oYiyurZj7efTWVVDmCfXlbJHnwBDfa3fcCPBYIfrD6I8Pv8OdQixfj1+OLtk+7hkUTGb6+obl1f88zlQJf/7/9ep8yZS7qHTCZeWElywINVJMaZFFhCSaHx2dJC7nlNs9PMlJawN1vGrXdoeSkKDwU43+WypDiFWjtfLQ5MLqW4I86P5q6gNR9CGBir++U9yDj6IrBEjOnXeeFbWhNrVuqlJGg8+GESs+alJWxYQkija9HRxD6lHeH5DGc9tLOcnhUPYr5XWRVGdmT4zyuPzNWl22pKJuQH+34RRfLp1Gz9esIry996jYdMm+p12WqfO2ZLlNUEuX1TMQf9bxBlfr+CWpWuJtLMIMKNfPwJ77mnNT03asoCQRH0zMxicldEjcgjFtSGuW1LCfn1zuGr0jiOEtkSDwQ5PjhMlAX/jWEitOWlwP+4YP4LZpZVctaYMGTq0sWPYzli6LcilC4s5+NNv+PemCi4YUcD57rhKP15QTDDcvnqB3EOnE5w/n4bNm3c6TcYkmo12mmROS6Pu3TmtIaJcsrAYj8D9k0aT4Wm5Z3JzkVAQTwfHMYry+Pw0tFKHEOuc4QMpLy3jDsaTf9m13Ovt/KREi7cF+X+rNvDSpgr8Hg8XjRzExaMKKMhyWkqN8mdx2/J1bK6r5/Hdx5Cf2fq/VO706Wy+516qP/iQ/FNP6XS6jOkKaZFDEJGjRWSxiCwTketbWP8TEVkoIl+LyNsiMjoV6UyE8Tl+ltYEu/VAZ3et2sDnlTX8fvxIRvrb33JHg6HO5xD8/sahK9rj9Ddf4fTZL/Ns/6H8ZkXHp7BcVF3Lj+av4tD/fcObpZVcNmoQnx0wiVt2GdYYDAAuGjWIv0wazReVNRz/xdI2B9zzTZhAxqBBVL//PtUNYZ7bUMbXVTUdTp8xXSHlOQQR8QL3A0cBJcBnIvKyqi6M2exLYKqq1ojIxcDvgW45BVVRjp/qcIT1oXqGdeBmmi4+qajm3uKNfH9IP04a3K9D+0ZCQTLbMWR0Szz+tusQGs9TV0fF8y9w9T57kzFsAPet3kRehpfL21G09c22Wu5auZFXNleQ6/Vw5ejBXDiygP6tPPmfNLgfBVkZnDd/Jcd9voSn9xzH5NyWx2sSESq+cwx/bPDyxn8XUOUWNR2Un8slowZxWP8+cceCMqarpUMOYT9gmaquUNU64BngxNgNVPVdVY0+Rn0CJLbJSBKNdyuWr/xmNc+sL6W8viFlaaluCPP65grK2pmGivoGLl1YzKhAFr8p6vifQGs73+xUfH4iofblEKrefJNwWRn9fnAad4wfwSmD+/HrFet5Yu2WuPssqwly8YJVHPa/xbxTVslVowfz2QGTuH7s0FaDQdSB/frw0l5FeEU48YulfNCsP4Sq8lF5FefOW8EJB3yHFw48jEOp54Upu3DzuGEsqwlx+tcrOPyzxTy7oYw666tgUiDlOQRgOLAm5n0JMK2V7X8IvN6lKepC+/bN4eKRBby8qYKrvllDhqzhwPw+HFvQl2MG9mWQr+O9eDuqJhzhsbVbuH/1Rsrqw+R4PZw/fCA/HjmIgS3MdAbODe1nS0rYWFfPy3sXkZvR8XL5SKjzzU49AT9a2775lCue+QeZI0eS860DEBHunTCKqoYw1y8poU+Gl1NicjYra0LctWoDL2wsx+/1cNmoQVw8alC7gkBzE3MD/HvvIs74egWnf72ceyaM4rsF+by4qZy/rtnMwm1B+md6uXxYfw6+4GzGf/dYBh+yD9/ql8uPRgzkxY0VPLBmE1csWs1vV6znwhEFnDlsAH06ca2N6Yx0CAjtJiJnAlOB6a1scyFwIcCoUaOSlLL2y/J4uHWX4dwybhhzq2p5dXMFr27eynVLSrjebbVzXEFfjivIZ3iCi5SC4Qh/W1/KvcUb2VzXwKH9+nDO8AG8vKmCP63exMMlWzhn+AAuGTloh8D0zIYyXt5UwY1jh7J3Xk6nzr9TdQg+P1pfj4bDSCuVxKHly6n57DMG/fSaxhnRMj3CQ5MLOePrFVy+qJhcr4ddc/zcU7yRZzeUkSXCj0cWcMmoQU3qBzpjmD+Ll/Yu4rx5K7ls0WpuXrqW8oYwE3L83L3rSE4e3I+A18Pq8btQ/f77DL7+OsD5XvxgaH++P6Qfb5dV8cDqTfxi+TruXrWBc4YP5JrCIa2OE2VMIqRDQFgLjIx5P8Jd1oSIHAncCExX1bhlB6r6EPAQwNSpU9O25lZEmJKXzZS8bG4YO5RvtgV5dfNWXttcwS3L1vGr5ev548RRHS6nb0ldJMIz68u4p3gj60L1HJCfw8OTC5nm9h04piCfnxQGubd4Iw+t2czja7dw1rABXDpqMEN8mayoCXHj0rUc6JZzd5YzdEUncwhuINFQCMnOjrtd+T/+AZmZ9D2laQuegNfDk7uP4XtfLeeC+auIoHhF+OHwAi4btWMA3Bl5GV6e3nMsNy1dy5a6Bn44YiAH5uc2qRvIPXQ6G3/zW+pWryYr5sFFRDhyQB5HDsjjq8oaHliziftXb2LJtiCP7DaGzGYtumoXLKD8ySepW7uWgRdemJAmtqb3SoeA8BlQJCJjcALBacDpsRuIyF7Ag8DRqrop+UnsWiLCxNwAE3MD/HTMEFbUhPjJN6u5aGExa0P1XDKyoFMVjQ0R5bmNZdy1aiNrgnVMzcvm3gmjOKhf7g7HK8rx86dJo7mmcAj3Fm/k0bVbeHJdKTOGDuCLym34RLhv4ii8O1HhuTMd06LTbkZCITxxAoKGw1S++hp9DjuMjP79d1if696oL11YzJiAjytGOwGvK/g8Hu7cdWTc9bnTnYBQ/f4H9D/rzBa3mZKXzUOTC3k8fwvXLynh6m9W88eJo5BIhKp33qH8iSepmTMHT3Y23n79WHPhj8k55GAGX3cdvnHjuuRzmZ4t5QFBVRtE5DJgFuAFHlXVBSLyS2COqr4M3AnkAv90b2SrVfWElCW6i43N9vHMnuO44pvV3L58HSXBOn5VNLxDN+P/lldz3ZI1LK0JsUefAHeMH8vh7WjBMibbxz0TR3F14WD+tHoTf19XSr0qj+5WuFOtorS+HsLhDk+OExUtamqtpVHtF18QLi0l75j4U2T2z8xg5p6pv1lmjR5NVmEh1e+/HzcgRJ07fCDl9Q38buUGAsuWcOF9d9KwZg2Zw4Yx6LrryP/eqXh8Psqefpot9z/AihNOpN+MGQy89BIy+u18DtP0HikPCACq+hrwWrNlt8T8fmTSE5Vifq+Hv0wazXBfJn9es5n1oToemFRIdhvlyKV1Dfxy+Tr+saGMUf4sHt2tkGMG9u1wDmN0wMedu47kytGDWVET4pD+fXbm4zS2EJJOFxk5zTgjtfEDQuWs2YjP122KTXKnH0L5zGeI1NTEz/WoUl9SwoyZf2d5fQZPHXwkgW+fwE93L6LPkUcgGdv/hQecey59TzyRLffdR/nTT7P1lVcouPRS+s04rVNDjpveJy0CgmmZR4RbdxnOCH8WNy1dy/e+WsaTu49tsSWQqvLshnJ+sXwtlQ1hrhg1iKsKh7QZQNoywp/FiARUbkdbCHW+Y1q0DqHlgKCRCFWzZ5N7yMF4cjpX6Z1sudOnU/bEk6y/5VY8OTmEK7cSqawiXFVFZOtWwlXO79TXQ0YG133nO4Sz4KGpBzFm/AjOy9jxe5DRrx9DbrmF/NNOY9Mdv2Pjb35D+cyZDL7+OmeSHmNaYQGhG/jhiAKG+jK5ZGExx3+xhKf3GMeY7O031uU1QX62uISPKqrZNy+H3+86golxOkalSjSHEH3S76hoc9V44xnVfjWXhk2b6PPt73QugSmQPXUqmcOHUzlrFt68PLx5eXjy+uDNyyNrxAjn9z55eAf0J+/oo8kcMoQ/RpRt81dyw5IS8jO8nByn0YF//HhGPvIw1e+9x6bf/Z41P76IvGOPZdjv7uhVuQVVtY5+HWABoZs4tiCf56Zkcva8FRz3xRKe2n0su/UJ8KfiTdxbvBG/V/j9+BGcOWwAnjT8B4iW/Xe6DsG3vZVRS6pmzUIyM8k97NBOHT8VJCuLcW+96fzezr9Zpkd4cHIhM+Yu5/JFxeRleDkizsREIkKfww4j98AD2fLww2z5431ofR3D77orpZMFJcvty9fx5pZKXp9aRM5OjGfVm1jD5m5kat8c/r33ePIyvHzvq2Uc9r/F3LlqA8cW9OU/+03k7OED0zIYAI3jEHV6+OtWcgiqSuWbs8k58EC8uW0Pw51ORKTDT7ABr4cn9xjLxJwAF8xfyf8qqls/R1YWBZdcwuAbbqDqzbco+clP0LqeM0lTS17YWO40160J8mhJ/B7qpikLCN3M2Gwfr+xdxOTcAGFVnt5jLH+ZXJiUHs47I1r2L52eQtMJCC21MgrOn0/DuvX0+U73KS7aWdG+DsN8WZw1byULq9vuxd3/7LMYfOONVL/1NiVX99ygsKi6lmu+WcO0vjkc3r8Pf1q9iYoUDhHTnVhA6IYKsjJ5Ze8iPt5/Ioe3MY9xuog0FhklPodQNWsWZGTQ5/DDOp/AbqggK5Nnpowj2+vhpC+X8pvl61gfav0m3/+sMxl8001Uv90zg0JVQ5gL5q8iN8PDQ5MLuXHcMLY2hHlgdY/rvtQlLCB0UyKStsVDLdGdbHYa3a/5NJqqSuWs2eQccADeTo6k2p2N9Gfx/JRdOCi/D/et3sS+Hy/k0oXFzG1lSO3+Z57B4JvdoHDV1T0mKKgqV32zmlXBEA9NLmSwL5PJuQFOHpTPX0u2sClU3/ZBejkLCCYpIm6z085WKnviNDsNLVpE/Zo15H3n2zuXwG5sbLaPR3cfwyf7T+S84QN5Y8tWvjNnCSd9sZTXNlcQbmHujf5nnMHgW26m+p13KLnyKiJpFhTqIpEOzxnylzWbeXXzVm4aO4wDYqZ0vXbMUOo0wj3FGxOdzB7HAoJJCm2sVO5cs1NpLDJqmkOonDUbvF5yjzhi5xLYA4wO+Li9aARffmsyt40bRkmojvPnr+Jbnyzir2s2U90QbrJ9/9NPZ8itt1D97rusveLKtAkKc6tq2O2j+Xzvq+Usbef84x9XVPOrFes4rqAvF40saLJubLaP04cO4Kl1pRTXdu/ZCruaBQSTFJHQTjY7zcoCESLB7ZWnqkrVrFlk77evDdEQIy/Dy0WjBvHJtEn8dXIhg7IyuXnZWvb+eAG/Wb6uSdFJvxkznKDw3ntpERSKa0Oc+fUKsj1e5lfXcvhni/ntivXUtDJn9cZQPT9esIpCv497JoxqsdXWTwoH4xFntj8TnwUEkxS6k81ORQTx+5vUIYSWLqVu1SryelHroo7I8AjHD8rnlX2KeG2fIg7p59YzfLKQaxevYXmNE6T7zZjBkNtupfq99yi55FIiNamZ0rO0roHT566gPqI8O2Uc/5k2gZMH53Nv8Uam/+8bZm/ZusM+9RHlwgWrqGqI8MjuhXHnjhjqy+K84QN5bkM5i9uZ6+iNLCCYpIiW/Xs62ew0um9sHULVrNkgQp8je91QVx22d14OD+82ho+mTeQHQ/rz7IYyDvr0G344fyVfVG6j32mnMfTXv2Lbf//L6gt+RLiyslPn+XzrNu5fvYlgK0/0LakJRzhn3gpKQnU8ufsYxuf4KcjK5I8TR/PiXrsQ8Hg4e95Kzp23gjUx81b/asU6Pt26jbsmjGRCTuvFkZePGky218PvOjHHdm9hAcEkRSQYAq93p4ZNEL+/SR1C1exZZE+dSsbAgYlIYq8wNtvH73cdyZwDJnHF6MH8p7yaYz9fyilfLuPzQ49i2N13UztvHsXnnEtDaWmHjv3PDWWc/OUybl++ju9+sbTd5f9hVS5dWMznlTU8MGk0++U37Vx4QH4ub++7KzeNHcr7ZdUc8uki7iveyAsby3lwzWbOHz6wySx48QzIyuCikYN4bctWvqxMTS4o3VlAMEnhTI7T+dwBuDkEtx9CaPlyQkuX9arOaIlUkJXJz8cO5fMDJvGLXYaxqjbEGV+v4IIh4/De/2fqVq6k+IwzqV+3rs1jRVS5Y8V6Ll+0mql9c/jzpNGsC9Xx7TlLeHpdaauthVSVG5eu5fUtW7m9aDjHFeS3uF2mR7hs9GA+nDaBw/rn8esV67lkYTH75GVz2y7D2v25LxpZQP9ML79d0fbn6o0sIJikiASDna4/iJJAoLFjWtXs2QD0OeqonU5bb5ab4eXHIwfxyf4T+e34EXxRWcOx9OGTPz9C/ZYtrDrjTEIrV8bdvzYc4aKFxdxTvJEZQ/vzzJ5jOXlwP97ed1f2zsvmJ4vXcNHCYrbG6Sn8p9WbeHztFi4ZOYgLRhS0uE2sEf4sHt19DE/uPoYTBuXz18mFZHnafxvLzfBy5ejBfFBezX/Kq9q9X29hAaEbioRCrLnkUladeaYzPHIXKn3kEZZ/52hCy5bt1HE0GOx0L+Wo2BxC5ew3Cey1F5mDOz+lp9kuy+PhvOEDeWffXdm9T4Drgx5+ce/DbM7yU3zmWQQXLdphn02hek75chmvbKrg5nHDuHvXkY0356G+LJ6dMo4bxg7l35srOGLOYj7buq3J/v/cUMavV6znlMH9uGnc0A6l99sD+/LQ5M5N2nTOsIEM82XymxXrO9zXoaezgNABlbNmU/roYyltmqd1day94kqq33mH2q/msubHF3VZq5CKF15k051/oG7NGlafdz51xcWdPtbOTJ8ZJX4/kVCIuuJiQosW0acXd0brKqMDPp6fsgu/KhrOJw1w3g2/5a29prHq7HOo+eLLxu0WVtdyzOdL+GZbkEd3K+TSUYN2aO7pFeGK0YN5ea8iBOGkL5dyz6oNhFX5oKyKq79ZzUH5udwzYWRSe937vR6uKRzCF5U1zNrSucrznsoCQjttffll1l51FZt+/3tWnnAi2z7+OOlp0Pp61l5zDdXvv8+Q225j+B/upParryi57LLG+QYSpfrDD1l/883kfOsAxjz/HNrQQPG551FXsrZzaQ8GOz05TpT4nRxCpVtclPdtCwhdwSPCBSMKeGvfXdklN8AvTz2LW394BXOvvIrqjz7irdJKjv9iKWGFl/behWPilPtH7dM3h7f33ZXjC/K5Y+UGTvlyGefPX8n4bD+P7j6mQ0U+ifKDIf0ZG/Bxx8r1Lfbk7q0sILRD5RtvsO76n5M9bRoj/vwAqhFWn3c+a396LQ2bNyclDRoOs+6666l68y0G3/Bz+p32A/KOPpqhv/412/77MWuvutqZtzgBaucvoOTKq/AVFTH8j3/EP3Eiox59hMi2baw+7zzqN3Z8CIBIKIink+MYRXl8fiKhIFWzZuPfYw8yh7W/MtF03LhsPy/vXcRNY4fy8YTdOO/633Dtmx9x9tzljMkQXp9axB59Wp76s7m8DC9/njSaeyaM5OuqWvpmePn7nmPJi9NvoKtleISfjRnCN9uCvLixPCVpSEcWENpQ9fbbrP3ptQT22ouRD9xPn8MOY+zLLzPw0kupmjWL5cceR9nf/46Gw20frJM0EmH9jTdR+dprDPrpNfQ/++zGdfknn7R9+IGf/Wyn01G3Zg1rfvxjMvLzGfngg43zC/gnTmTUIw8TLitj9bnn0bClY2PMazC00zkET8BP/br1BOfP79VjFyWTV5zWPbOm7srIAf14cfq3OWDeF/z+0nOJ3HgjwcVL2n0sEeG0oQP4eP+JzJq6K0N9qZ2k54RB+eyWG2Dm+rKUpiOdWEBoRfUHH1By1dX4J09i5IN/aZwI3ePzUXD5ZYx5+SUCu+/Gxtt/xaofnEbt/AUJT4OqsuG2X7D1X/9i4OWXMeCCC3bYpt+MGQy69lqqXn+D9TfdjEY61ikoqqG8nDUX/AhtaGDkXx/aocI2sPvujHzoQeo3bGD1eefTUN7+J6tE5BDE50fd+pI+VlyUVBNzA7y230Re3GsX/v6D4xh+2g+oeucdVp54Imt+fBE1X3zR7mMN8WW2OC94snlEeGS3Qp7ec2yqk5I2LCDEse3jjym57HL8RUWM+utfW5yJyzdmDCMfeYRhf/gD9Rs2sOr732fDr35N7dy5hJYto37DBsJVVZ1+aldVNv7mt1Q8+ywDLryQgZdcEnfbAT88n4GXXsrWF19k469+3eHWE5HaWkouupj6DRsY+ecH8I0b1+J22fvsw8g/P0Dd6tWs/uEP292jVWsT0OzUzWH4Jk0ka+TInTqW6bhMj3BAfi7+IUMYfN3PKHrnbQZecTm1c+dSfPoZrDrzTKrff79btdwZHfDhS0EdRrpKfZhOQzWffcaaiy8hq7CQkY88jDcv/iQ0IkLf7x5H7iEHs/meeyn/+98p/9vfdtwuEMCTk4M3JwdPXh6+oiICe+yOf7fd8Y8v2mGOW1Vl0x/+QPlTT9H/nHMouPqqNqdaHHjZpURqayl79FE82QEKrrmmXdMzakMDa6/5KbVff83wP95L9t57t7p9zv77M+JP97HmkktZ86MLGfnII3hzc1rdJxJKRLNTZ/+8b1tntHTgzc+n4JJLGHDuuVQ89zyljz3Gmh9fROawYWSOGkXGoAIyBw0iY9BgMgYNImPQIDIHDyKjoKBXzOncHVlAaKb2q68av9SjHn2k3aNoevPyGHLLzfQ760zqV68mXF1NZNs2IttqnJ+N77cRrqig+t132frCC4Azkqdv4gQCu+2Of/fdCOyxB5X//jdljzxK/ozTGHT9de26sYsIg679KZHaGkoffgTJzqaglVwFuEVSt/+K6nfeYfDNN5HXzo5euQcfzIj/dzclV15FyUUXMfKvD+EJxB9LJiF1CDlOkZ01N00vnuxs+p99Fv1mnMbWf79K9fvv07BpE7Wff0HVpk0tNnbw9uvXGCQyBhU4waLx/fZXR+ebNjtH0iV7JyJHA/cCXuBhVb2j2Xof8CSwD1AK/EBVV7V2zKlTp+qcOXPanYba+QtYfd55ePv1Y/RTT3VppydVpX7tWoLz5lE7b77zc8GCxjJygL6nnsLQ229HOpil1UiE9T+/ga0vvYR/0iS8AweQ0a8f3n798fbrh7d/P/d9P7Z99BFbHvgzA370IwZd85MOf47K115j7U+vxZObi7dPHyTgx+PzI34/Hv/2n5WzZ9P/9NMZ/PPrO3yOqPqNG6mZM4e+xx3X6WOY5FJVwhUVNGza5Lw2bqR+40YaNm+mYdPm7cu3bIFmdV/e/v0J7LUX2XvvRWCvvfHvNhlPmuQsRORzVZ2a6nQkWloEBBHxAkuAo4AS4DNghqoujNnmEmAPVb1IRE4DTlbVH7R23I4EhODixaw++xw8OTmM/ttTKWnSqOEwdStWUDtvPhpuIP+UUxBv55rlaUMDm++/n+D8BYTLygiXl9NQXo7W7jgZe94JxzPsd7/r9NNY1XvvUf3Ou2goSCQYIhKsRYMhNBgkEgw6P+vrGHzd9dY6yLRIw2EaSksbg0T9+nUE582n5ssvqC9eDTg5af/uuzcGiMCUPfH269eh720kFKJu1SpCy5ZRt3w5oWXLyRo7hkFXXdWh9PbUgJAuRUb7ActUdQWAiDwDnAgsjNnmROA29/fngD+JiGiCIlpw/gIkEGDU44+lrH27eL34iorwFRXt/LEyMhh05ZU7LI/U1jYGh3BZORpuIPfAA3cqa97n0EPpc+ihO5Fa09uJ10umW2wEk5usa9iyhZovv6T28y+o+fILSh9/Av76sLNfVhbeAQPI6N8f74D+ZPQf0OQnQN3yFYSWL6du+XLq1qzZnhPxeMgaOZLMESOS+VHTWroEhOHAmpj3JcC0eNuoaoOIbAUGAB1rEB9H/qmnkHf0d/DktF452t15AgE8gYB16jLdRsbAgeQddVRj/VYkGGwsYg1v2UJDaRkNZaWES8sILVtGuLQMje25n5FBVuFofBMmkHfccfh2GUfWuF3IKhy90yPw9jTpEhASRkQuBC4EGDVqVIf27enBwJiewOP3k73vvmTvu2+L61UVramhoawMrW8ga+SInZqHozdJl4CwFohtWD7CXdbSNiUikgH0xalcbkJVHwIeAqcOoUtSa4xJWyKC5OSQZQ94HZYuPTI+A4pEZIyIZAGnAS832+Zl4Bz39+8B7ySq/sAYY0ya5BDcOoHLgFk4zU4fVdUFIvJLYI6qvgw8AjwlIsuAMpygYYwxJkHSIiAAqOprwGvNlt0S83sQ+L9kp8sYY3qLdCkyMsYYk2IWEIwxxgAWEIwxxrgsIBhjjAEsIBhjjHFZQDDGGANYQDDGGONKi+Gvu4qIbAaKO7DLQBI0WF6SWHq7VndKb3dKK3T/9I5W1YJUJaar9OiA0FEiMqc7jXFu6e1a3Sm93SmtYOlNV1ZkZIwxBrCAYIwxxmUBoamHUp2ADrL0dq3ulN7ulFaw9KYlq0MwxhgDWA7BGGOMq8cHBBF5VEQ2icj8mGV7isjHIjJPRF4RkTx3eZaIPOYunysih8bss4+7fJmI/FF2Zlb6rk/reyKyWES+cl+DEp1W9zwjReRdEVkoIgtE5Ep3eX8ReVNElro/+7nLxb12y0TkaxHZO+ZY57jbLxWRc+KdM43SG465vs0nc0pFWie435OQiPy02bGOdr8Py0Tk+kSntQvSu8r9Xn8lInPSJL1nuN+BeSLyXxHZM+ZYXX59k0ZVe/QLOATYG5gfs+wzYLr7+/nA7e7vlwKPub8PAj4HPO77/wH7AwK8DhyTxml9D5iahGs7FNjb/b0PsASYBPweuN5dfj3wO/f3Y91rJ+61/NRd3h9Y4f7s5/7eL13T666rTrNrOwjYF/g18NOY43iB5cBYIAuYC0xK1/S661YBA9Ps+n4r+p0Ejon57ibl+ibr1eNzCKr6Ac4Ma7HGAx+4v78JnOr+Pgl4x91vE1ABTBWRoUCeqn6izrfgSeCkdExrotPUGlVdr6pfuL9XAYuA4cCJwBPuZk+w/VqdCDypjk+AfPfafgd4U1XLVLUc53Mencbp7XIdTauqblLVz4D6ZofaD1imqitUtQ54xj1GuqY3KTqR3v+6302AT3DmfYckXd9k6fEBIY4FbP+j/R8w0v19LnCCiGSIyBhgH3fdcKAkZv8Sd1k6pjXqMTfLfbNI4ou3mhORQmAv4FNgsKqud1dtAAa7vw8H1sTsFr2O8Zana3oB/CIyR0Q+EZGT0iCt8aTrtW2NArNF5HMRubBrUrldJ9L7Q5ycI6Tg+nal3hoQzgcuEZHPcbKLde7yR3H+oHOAe4D/AuFUJDBGZ9J6hqruDhzsvs7qygSKSC7wPHCVqlbGrnNzVGnVlC1B6R2tTs/V04F7RGRc4lPaa6/tQaq6N07RzKUickjiU+roaHpF5DCcgHBdV6UplXplQFDVb1T126q6DzATpwwQVW1Q1atVdYqqngjk45QtrmV7FhH397VpmlZUda37swp4Gidb2yVEJBPnH+rvqvqCu3hjtGjF/bnJXb6WprmY6HWMtzxd0xt7jVfg1NnsleK0xpOu1zaumGu7CXiRLvr+djS9IrIH8DBwoqqWuouTdn2ToVcGBHFb3YiIB7gJ+Iv7PltEctzfjwIaVHWhm4WsFJH93eKXs4GX0jGtbhHSQHd5JvBdYH6LB9/5tAnwCLBIVe+OWfUyEG0pdA7br9XLwNni2B/Y6l7bWcC3RaSf26rj2+6ytEyvm06fe8yBwIHAwhSnNZ7PgCIRGSMiWcBp7jESKlHpFZEcEekT/R3nu5Dw729H0ysio4AXgLNUdUnM9km5vknTVbXV6fLCeapej1N5VYKT3bsS52l6CXAH2zvoFQKLcSqY3sIpFogeZyrOF3M58KfoPumWViAHp8XR1zj1D/cC3i66tgfhZKm/Br5yX8cCA4C3gaVu2vq72wtwv3sN5xHTEgqnaGyZ+zovndOL0+JkHk49zjzgh2mQ1iHud6YSp4FBCU5DCNz9lrif48Y0ubYtphentc5c97UgjdL7MFAes+2cmGN1+fVN1st6KhtjjAF6aZGRMcaYHVlAMMYYA1hAMMYY47KAYIwxBrCAYIwxxmUBwRhjDGABwZgOERFvqtNgTFexgGB6LBH5pYhcFfP+1yJypYhcKyKfuePb/yJm/b/cAdUWxA6qJiLVInKXiMwFDkjupzAmeSwgmJ7sUZxhRqJDf5yGM4JlEc74OFOAfWIGTztfnTGjpgJXiMgAd3kOzvj3e6rqf5KYfmOSKiPVCTCmq6jqKhEpFZG9cIYx/hJnUpZvu78D5OIEiA9wgsDJ7vKR7vJSnFFkn09m2o1JBQsIpqd7GDgXZ+ycR4EjgN+q6oOxG4kzBemRwAGqWiMi7wF+d3VQVVM9DLoxXc6KjExP9yLO7Gv74oygOgs43x0HHxEZ7o4o2xcod4PBBJwpM43pVSyHYHo0Va0TkXeBCvcpf7aITAQ+dieSqwbOBN4ALhKRRTijyH6SqjQbkyo22qnp0dzK5C+A/1PVpalOjzHpzIqMTI8lIpNw5ld424KBMW2zHIIxxhjAcgjGGGNcFhCMMcYAFhCMMca4LCAYY4wBLCAYY4xxWUAwxhgDwP8HPGzX0o4pU4cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUIAAAE/CAYAAAAzEcqDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYsklEQVR4nO3ceZRcZZ3G8e9DQoIQloQ0iNmaJQ4EFBjDclQQZQsyEhkBE1DCCBM9Z3BDmEEdIUZwXEfHERXUGGRHECZIEFGWI4NCggQwQCCEJQkBsrCF1cBv/njfhktNdXd1ujrV8D6fc+p03fu+761f3br91F2qShGBmVnJ1mt1AWZmreYgNLPiOQjNrHgOQjMrnoPQzIrnIDSz4jkICyFppqTTWl1HiSQdKmmxpNWSdm11Pfb/vWGCUNL1kp6QNLjVtfSWpGmS/ibpmXy7V9IPJW3VpOUfI+nGZixrXZN0vKS5kl6UNLNO+76S7pH0nKTrJI2ptA2WNEPS05IelXRCN4/1+dzv6TxucKWtPS//ufx4+zU6to7vAMdHxJCIuK3hldEEkgZJukTSg5JC0j417ZtJOlvS4/k2rab93ZJuydvpHZLeW9N+pKSHJD0r6XJJw7qoZYqkW/M6WyLpW5IGVtqHSbosL+shSUdW2g6WdKOkJ/N6/5mkjSvtR0i6Kb9e1/d0Pb0hglBSO7AXEMAhfbD8gd33arqLImJjYBhwKPBW4NZmhWF/p6Te9vcIcBowo86Y4cCvga+Q1ttc4KJKl2nAWGAM8H7gXyVN6OTxDwROBvbN/bcBvlrpcgFwG7A58GXgEkltDY6tNQaY30kd62LbuxH4GPBonbbvARsC7cDuwMcl/VOubRhwBfBtYDPgW8AVkobm9h2BM4GPA1sCzwE/6qKODYHPAcOBPUjr78RK+xnAS3lZRwE/zo8BsClpu3gbsAMwItfVYRXwfeAbXTx+5yKi39+AU4D/Bf4T+E2eNxh4Etip0q8NeB7YIk//AzAv97sJeGel74PAvwF3AC8CA0kb9/3AM8BdwKGV/gOA7wIrgAeA40nBPDC3bwr8HFgGLM0v2oBOns804NyaeQOA24HvVOZ1VX/dWkkbyQvAy8Bq4Mk8fyZpQ7syj7kZ2Da3ifQP8TjwNHBndb3W1Hk98B/ALbnv/wDDKu175lqfzM9nn5qxp+fX8nlguy5e89OAmTXzpgI3VaY3ysvZPk8/AhxQaf8acGEnyz8f+Hplel/g0Xz/7Xmb2LjS/kfgU92NrXmMwfk1COBZ4P4utr1DSGH5ZF5PO9Rsqyfl/s+StrMtgavya/l7YGgD/0dLqq9HnrcC2K0y/SXgj5Xtb35N/3uBY/P9rwPnV9q2JQXZxt3VkvufAFxReS1fAt5eaT8H+EYnY/8RuLPO/OOA63uaMW+IPULgaOC8fDtQ0pYR8SJp72Bypd8RwA0R8Xg+FzMD+CTpXf1MYFbNIcxk4GBgs4hYQwqWvUih9lXg3Moe2j8DBwG7AH8PfLimxpnAGmA7YFfgANKL0pCIeJkUKnsBNFB/3Voj4m7gU8CfIh2KbVZ5mEm571BgISmUyLXuTQqATUnrcWUX5R4NfALYKj/nH+SaR5CC9jTSHtuJwKUde1LZx0mBtjHwUGNr51U7ksIVgIh4lrQedsx7KVtV2/P9Halvxzp9t5S0eW5bFBHPdLKsrsa+KiJejIgheXLniNi20vzqtkfao7yAtLfUBswm7XkNqvT/CLA/6TX6ECkEv5T7rwd8ppPn2QjV3N+pk7ba9trX435ymDX4uHvz2p7y24E1EXFvpb2r1686ttf6fRDmcxJjgIsj4lbSht9x7uB80j93hyPzPEj/bGdGxM0R8XJEnE16992z0v8HEbE4Ip4HiIhfRcQjEfFKRFwE3Ec6XIAUDv8VEUsi4gkqu+CStgQ+CHwuIp6NiMdJe1jV2hrxCClAuq2/m1o7c1lE3JJD/zxSqAP8jRRM2wOKiLsjYlkXyzknIv6ag+grwBGSBpAOv2ZHxOxc1zWkw9cPVsbOjIj5EbEmIv7W0Fp5zRDgqZp5T+Xah1Sma9saWVbH/Y3rtNUuq6uxjapuex8FroyIa/I6+Q7wFuDdlf7/HRGPRcRS0t7pzRFxW0S8AFxGevNdG78FTpa0saTtSG9wG+a2PwFvkzRZ0vqSppD2+jrau1tPnZL0CWA86bl2LOvpRpYlaX9gCulIsSn6fRCSnvDvImJFnj4/zwO4DthQ0h75POIupI0CUnh+IZ9cfVLSk8Ao0jmGDourDyTpaEnzKv13Ip3PII9b3MnYMcD6wLLK2DOBLXr4XEeQznV0W383tXameo7oOXJ4RMS1wA9Jh86PSzpL0iZdLKf63B8iPffhuebDa2p+L2lPrd7YnloN1Na1CenwcHVluratkWV13H+mTlvtsroa26jqengblb3jiHglt4+o9Hmscv/5OtNDWDufyePvIx2RXEA6hCYiVgITSYewjwETSIfhS/LYTteTpKOUrpKvlnRVtYOkD5NOrxxU+b/ubp13jN2TlAGH1ew99kq/DkJJbyHtib0vXyl6FPg8sLOknfPh5MWkw4zJpPOHHStuMXB6RGxWuW0YERdUHiIqjzUG+Cnp3N/m+ZDyr7x2aLAMGFkZO6pyfzFpb2145bE2iYjOduvrPdf1SIc8f+yu/gZq7fFPCkXEDyLiXcA40mHKSV10rz730aQ9yhW55nNqat4oIqonsHvzc0fzgZ07JiRtRNpDmZ/30pdV2/P9zg6f5tfp+1j+558PbFO9KlmzrK7GNqq6Hh4hvYkA6UISaR0v7cHy1kpErIqIoyLirXl7XY90/rej/YaI2C0ihpFOa2xfaa99PbYhnRe9NyLOy6dmhkTEQZU+E0jb7oci4s5KKfcCAyWNrcx73euXTxfNAj4REX9oygrI+nUQks7DvUz659wl33YghcXRuc/5pEOLo3jtsBjSyv5U3luUpI3yJfjOdts3Im2cywHylbPquZKLgc9KGiFpM9LJbgDyYeTvgO9K2kTSepK2lfS+7p6gpIGSdiC9E7+VdEGou/q7q/UxYGTNOaauatgtP876pJPxLwCvdDHkY5LGSdoQmA5ckt+UzgU+JOlASQMkbSBpH0kju1hWbS0DJW1AunjUsYyOK6uXATtJ+kjucwpwR0Tck9t/Cfy7pKGStied151ZWXb14yO/BI7Nz2Mz4N87+uY9jXnAqfnxDwXeCVza3di1dDFwsNJHg9YHvkB6Y72pF8t8ldLHijbIk4Pyc1Ju21bS5vn1Ooh0Sua0ythd82HxJqTD2MURcXVuPo/0eu+V35SmA7+uObdareMDecxHIuKWals+zfJrYHre1t9D2hs9J4/diXQY/+mIuKLOsgfk5zgQWC8/x/UbXkk9vbqyLm/5iX+3zvwjSId5HVdsF5IOKQfV9JsAzCFdiVsG/Ip8RYt0JW6/mv6n5+WsIAXSDcBxuW0g6bzfStJV48+T9oSU2zcFfkw6bHiK9NGLSZ08r2l57GpS8NxH+tjBiB7U31Wtg0gXLVYBK/K8mcBplWXvAyyJ16563pHrWUHaWId0Uvv1vP6q8RWkPeGO9j1yLatIQX0lMLoy9rhuXvNppJCv3qZV2vcD7iEdzl0PtFfaBpMuMD1NejM4odI2Ks/fvDKv45DvaeAXwOBKW3te/vPAgjrbSqdj6zynoHKFnPrb3qGkq/9P5fW3Y2f9SW841XVyHPD7Lh7/wTrrtL3yv/QI6VTJPODAmrEX5JqeIn1UaYua9iOBh0nb8es+QVCnjutIF9dWV25XVdqHAZfnZT0MHFlp+wXpzbk6dn6l/Zg6z3Fmo1nT8U9sPZTfPX8SEWO67fwmovRh1XMj4metrqUnJH2MFC5fbHUt1v+04oPEb0j5fOX7SYfAWwKn8tqFGevnIuLcVtdg/Vd/P0fYn4j0GbwnSIe9d9PEy/dm1jo+NDaz4nmP0MyK5yA0s+L1u4slw4cPj/b29laXYWZvMrfeeuuKiGir19bvgrC9vZ25c+e2ugwze5OR1OmPfPjQ2MyK5yA0s+I5CM2seA5CMyueg9DMiucgNLPiOQjNrHgOQjMrnoPQzIrnIDSz4jkIzax4/e67xmbrwluvm9fqEqyXHn3/Lk1blvcIzax4DkIzK56D0MyK5yA0s+I5CM2seA5CMyueg9DMiucgNLPiOQjNrHgOQjMrnoPQzIrnIDSz4jkIzax4DkIzK56D0MyK5yA0s+I5CM2seA5CMyueg9DMiucgNLPiOQjNrHgOQjMrnoPQzIrnIDSz4jkIzax4DkIzK56D0MyK5yA0s+I5CM2seA5CMyueg9DMiucgNLPiOQjNrHgOQjMrXkNBKGmCpAWSFko6uU77CZLuknSHpD9IGlNpmyLpvnyb0szizcyaodsglDQAOAM4CBgHTJY0rqbbbcD4iHgncAnwrTx2GHAqsAewO3CqpKHNK9/MrPca2SPcHVgYEYsi4iXgQmBitUNEXBcRz+XJPwMj8/0DgWsiYlVEPAFcA0xoTulmZs3RSBCOABZXppfkeZ05FriqJ2MlTZU0V9Lc5cuXN1CSmVnzNPViiaSPAeOBb/dkXEScFRHjI2J8W1tbM0syM+tWI0G4FBhVmR6Z572OpP2ALwOHRMSLPRlrZtZKjQThHGCspK0lDQImAbOqHSTtCpxJCsHHK01XAwdIGpovkhyQ55mZ9RsDu+sQEWskHU8KsAHAjIiYL2k6MDciZpEOhYcAv5IE8HBEHBIRqyR9jRSmANMjYlWfPBMzs7XUbRACRMRsYHbNvFMq9/frYuwMYMbaFmhm1tf8zRIzK56D0MyK5yA0s+I5CM2seA5CMyueg9DMiucgNLPiOQjNrHgOQjMrnoPQzIrnIDSz4jkIzax4DkIzK56D0MyK5yA0s+I5CM2seA5CMyueg9DMiucgNLPiOQjNrHgOQjMrnoPQzIrnIDSz4jkIzax4DkIzK56D0MyK5yA0s+I5CM2seA5CMyueg9DMiucgNLPiOQjNrHgOQjMrnoPQzIrnIDSz4jkIzax4DkIzK56D0MyK5yA0s+I5CM2seA5CMyueg9DMiucgNLPiOQjNrHgOQjMrnoPQzIrXUBBKmiBpgaSFkk6u0763pL9IWiPpsJq2lyXNy7dZzSrczKxZBnbXQdIA4Axgf2AJMEfSrIi4q9LtYeAY4MQ6i3g+InbpfalmZn2j2yAEdgcWRsQiAEkXAhOBV4MwIh7Mba/0QY1mZn2qkUPjEcDiyvSSPK9RG0iaK+nPkj7ck+LMzNaFRvYIe2tMRCyVtA1wraQ7I+L+agdJU4GpAKNHj14HJZmZvaaRPcKlwKjK9Mg8ryERsTT/XQRcD+xap89ZETE+Isa3tbU1umgzs6ZoJAjnAGMlbS1pEDAJaOjqr6Shkgbn+8OB91A5t2hm1h90G4QRsQY4HrgauBu4OCLmS5ou6RAASbtJWgIcDpwpaX4evgMwV9LtwHXAN2quNpuZtVxD5wgjYjYwu2beKZX7c0iHzLXjbgLe0csazcz6lL9ZYmbFcxCaWfEchGZWvHXxOcI+d/f2O7S6BOulHe65u9UlWMG8R2hmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RoKQkkTJC2QtFDSyXXa95b0F0lrJB1W0zZF0n35NqVZhZuZNUu3QShpAHAGcBAwDpgsaVxNt4eBY4Dza8YOA04F9gB2B06VNLT3ZZuZNU8je4S7AwsjYlFEvARcCEysdoiIByPiDuCVmrEHAtdExKqIeAK4BpjQhLrNzJqmkSAcASyuTC/J8xrRm7FmZutEv7hYImmqpLmS5i5fvrzV5ZhZYRoJwqXAqMr0yDyvEQ2NjYizImJ8RIxva2trcNFmZs3RSBDOAcZK2lrSIGASMKvB5V8NHCBpaL5IckCeZ2bWb3QbhBGxBjieFGB3AxdHxHxJ0yUdAiBpN0lLgMOBMyXNz2NXAV8jhekcYHqeZ2bWbwxspFNEzAZm18w7pXJ/Dumwt97YGcCMXtRoZtan+sXFEjOzVnIQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEaCkJJEyQtkLRQ0sl12gdLuii33yypPc9vl/S8pHn59pMm129m1msDu+sgaQBwBrA/sASYI2lWRNxV6XYs8EREbCdpEvBN4KO57f6I2KW5ZZuZNU8je4S7AwsjYlFEvARcCEys6TMRODvfvwTYV5KaV6aZWd9pJAhHAIsr00vyvLp9ImIN8BSweW7bWtJtkm6QtFcv6zUza7puD417aRkwOiJWSnoXcLmkHSPi6WonSVOBqQCjR4/u45LMzF6vkT3CpcCoyvTIPK9uH0kDgU2BlRHxYkSsBIiIW4H7gbfXPkBEnBUR4yNifFtbW8+fhZlZLzQShHOAsZK2ljQImATMqukzC5iS7x8GXBsRIaktX2xB0jbAWGBRc0o3M2uObg+NI2KNpOOBq4EBwIyImC9pOjA3ImYBPwfOkbQQWEUKS4C9gemS/ga8AnwqIlb1xRMxM1tbDZ0jjIjZwOyaeadU7r8AHF5n3KXApb2s0cysT/mbJWZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFc9BaGbFcxCaWfEchGZWPAehmRXPQWhmxXMQmlnxHIRmVjwHoZkVz0FoZsVzEJpZ8RyEZlY8B6GZFa+hIJQ0QdICSQslnVynfbCki3L7zZLaK21fzPMXSDqwibWbmTVFt0EoaQBwBnAQMA6YLGlcTbdjgSciYjvge8A389hxwCRgR2AC8KO8PDOzfqORPcLdgYURsSgiXgIuBCbW9JkInJ3vXwLsK0l5/oUR8WJEPAAszMszM+s3GgnCEcDiyvSSPK9un4hYAzwFbN7gWDOzlhrY6gIAJE0FpubJ1ZIWtLKefmg4sKLVRfQpqdUVvNm86beZtdhixnTW0EgQLgVGVaZH5nn1+iyRNBDYFFjZ4Fgi4izgrAZqKZKkuRExvtV12BuHt5meaeTQeA4wVtLWkgaRLn7MqukzC5iS7x8GXBsRkedPyleVtwbGArc0p3Qzs+bodo8wItZIOh64GhgAzIiI+ZKmA3MjYhbwc+AcSQuBVaSwJPe7GLgLWAP8S0S83EfPxcxsrSjtuFl/JmlqPn1g1hBvMz3jIDSz4vkrdmZWPAdhi0lql/TXmnnTJJ0oac/8lcV5ku6WNK2m3/clLZXk1/FNTNLq/LddUkj6dKXth5KOyfdnSnpA0u2S7pX0S0kjK30flDS8Mv3hvLzt1+HT6Zf8D9S/nQ1MjYhdgJ2AizsacvgdSvrA+vtaUp21wuPAZ/MnOOo5KSJ2Bv4OuA24tou+k4Eb89+iOQj7ty2AZQAR8XJE3FVp2weYD/wYb8glWQ78gdc+rlZXJN8DHiX9TsDrSBoCvJf0OwGT+qDONxQHYf/2PWCBpMskfVLSBpW2ycAFwGXAwZLWb0mF1grfBE5s8AdM/gLUO/SdCPw2Iu4FVkp6VzMLfKNxELZeZ5ftIyKmA+OB3wFHAr8FyIc6HwQuj4ingZsB/8RZISJiEek1P7KB7p19E20y6QdUyH+LPqroF981LtxKYGjNvGHAAwARcT/wY0k/BZZL2hx4N7AZcGf6kR82BJ4HfrOOarbW+zrpl55u6KbfrqRD6VdJGgZ8AHiHpCB9USIknRSFfp7Oe4QtFhGrgWWSPgCvbqQTgBslHZx/zgzS1xNfBp4kvXsfFxHtEdEObA3sL2nDdV2/tUZE3EP6xtaH6rUr+QywFflIouIw4JyIGJO3oVGkN969+rLm/sxB2D8cDXxF0jzgWuCreU/w46RzhPOAc4CjgMGkoLyyY3BEPEu6+lf3n8LetE4n/ZBJ1bcl3Q7cC+wGvD//jmjVZNK55apLKfjw2N8sMbPieY/QzIrnIDSz4jkIzax4DkIzK56D0MyK5yA0s+I5CM2seA5CMyve/wHlAK473SytuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "-----------------\u001b[94m Welch's T Test for comparing USA's and INDIA's terrorism deaths. \u001b[0m------------------\n",
      "\n",
      " -> \u001b[4mNull Hypothesis (Ho) [µO=µ1]\u001b[0m\n",
      "There is no significant difference in the average of deaths caused by terrorism in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mAllternate Hypothesis (H1) [µO≠µ1]\u001b[0m\n",
      "There is a significant difference in the average of deaths caused by terrorism in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mConfidence Level Interval : \u001b[0m0.95\n",
      "\u001b[1m\n",
      "- Statistic (t) value : \u001b[0m 0.273373034\n",
      "\u001b[1m- Probability (p) value : \u001b[0m 0.7864347641236840713574451910972129553556\n",
      "\u001b[1m\n",
      "- Decision\u001b[0m\n",
      "As p value ( 0.7864347641236840713574451910972129553556 ) is greater than level of significance ( 0.05 ),\n",
      "We Fail to reject the Null Hypothesis(Ho) {Accept Null Hypothesis(Ho)}\n",
      "\u001b[1m\n",
      "- Statistical Inference\u001b[0m\n",
      "There is no significant difference in the average of no. deaths caused by terrorism in  USA and INDIA.\n",
      "Time period : Past 30 years (1991-2019)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "---------------\u001b[94m Welch's T Test for comparing USA's and INDIA's road_injuries deaths. \u001b[0m----------------\n",
      "\n",
      " -> \u001b[4mNull Hypothesis (Ho) [µO=µ1]\u001b[0m\n",
      "There is no significant difference in the average of deaths caused by road_injuries in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mAllternate Hypothesis (H1) [µO≠µ1]\u001b[0m\n",
      "There is a significant difference in the average of deaths caused by road_injuries in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mConfidence Level Interval : \u001b[0m0.95\n",
      "\u001b[1m\n",
      "- Statistic (t) value : \u001b[0m 0.076152578\n",
      "\u001b[1m- Probability (p) value : \u001b[0m 0.9397482316315335371115224916138686239719\n",
      "\u001b[1m\n",
      "- Decision\u001b[0m\n",
      "As p value ( 0.9397482316315335371115224916138686239719 ) is greater than level of significance ( 0.05 ),\n",
      "We Fail to reject the Null Hypothesis(Ho) {Accept Null Hypothesis(Ho)}\n",
      "\u001b[1m\n",
      "- Statistical Inference\u001b[0m\n",
      "There is no significant difference in the average of no. deaths caused by road_injuries in  USA and INDIA.\n",
      "Time period : Past 30 years (1991-2019)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "-------------\u001b[94m Welch's T Test for comparing USA's and INDIA's digestive_diseases deaths. \u001b[0m-------------\n",
      "\n",
      " -> \u001b[4mNull Hypothesis (Ho) [µO=µ1]\u001b[0m\n",
      "There is no significant difference in the average of deaths caused by digestive_diseases in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mAllternate Hypothesis (H1) [µO≠µ1]\u001b[0m\n",
      "There is a significant difference in the average of deaths caused by digestive_diseases in  USA and INDIA.\n",
      "\n",
      " -> \u001b[4mConfidence Level Interval : \u001b[0m0.95\n",
      "\u001b[1m\n",
      "- Statistic (t) value : \u001b[0m 1.277889417\n",
      "\u001b[1m- Probability (p) value : \u001b[0m 0.2073849539114311080201247250442975200713\n",
      "\u001b[1m\n",
      "- Decision\u001b[0m\n",
      "As p value ( 0.2073849539114311080201247250442975200713 ) is greater than level of significance ( 0.05 ),\n",
      "We Fail to reject the Null Hypothesis(Ho) {Accept Null Hypothesis(Ho)}\n",
      "\u001b[1m\n",
      "- Statistical Inference\u001b[0m\n",
      "There is no significant difference in the average of no. deaths caused by digestive_diseases in  USA and INDIA.\n",
      "Time period : Past 30 years (1991-2019)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "finalResult(df_usa,df_india,usa_india_t_test,\"USA\",\"INDIA\",0.05,show_only_nnullAcceptance = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2cd0009",
   "metadata": {},
   "source": [
    "## Comparing India and China's causes of death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "bc15480e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#finalResult(df_india,df_china,usa_som_t_test,\"INDIA\",\"CHINA\",0.05,show_only_nnullAcceptance = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1069f2",
   "metadata": {},
   "source": [
    "## Comparing USA and UK's causes of death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "81caadb5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#finalResult(df_usa,df_uk,us_uk_t_test,\"USA\",\"UK\",0.05,show_only_nnullAcceptance = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4977cff6",
   "metadata": {},
   "source": [
    "## Comparing USA and Somalia's causes of death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "b1632e6a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#finalResult(df_usa,df_som,usa_som_t_test,\"USA\",\"SOMALIA\",0.05,show_only_nnullAcceptance = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190978b4",
   "metadata": {},
   "source": [
    "## Comparing USA and China's causes of death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "4a9906bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#finalResult(df_usa,df_china,us_china_t_test,\"USA\",\"CHINA\",0.05,show_only_nnullAcceptance = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d1b930",
   "metadata": {},
   "source": [
    "## Comparing India and UK's causes of death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "c7b9732d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#finalResult(df_india, df_uk,ind_uk_t_test ,\"INDIA\",\"UK\",0.05,show_only_nnullAcceptance = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d37d3fcd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}