Investigate a dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Children per woman (total fertility rate)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Children per woman (total fertility rate) allover the world.\n",
    "\n",
    "`Sources`\n",
    "\n",
    "— 1800 to 1950 (and in some cases also years after 1950): Gapminder v6 which were compiled and documented by Mattias Lindgren, see previous versions further down on this page.\n",
    "\n",
    "— 1950 to 2014: In most cases we use the latest UN estimates from World Population Prospects 2017 published in the file with Annually interpolated demographic indicators, called WPP2017_INT_F01_ANNUAL_DEMOGRAPHIC_INDICATORS.xlsx , accessed on September 2, 2017.\n",
    "\n",
    "— 2015 – 2099: We use the UN forecast of future fertility rate in all countries, called median fertility variant.\n",
    "\n",
    "### • For more information about how the sources were combined and download link please visit this link:\n",
    "https://www.gapminder.org/data/documentation/gd008/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1st Dataset: countries rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1806</th>\n",
       "      <th>1807</th>\n",
       "      <th>1808</th>\n",
       "      <th>...</th>\n",
       "      <th>2091</th>\n",
       "      <th>2092</th>\n",
       "      <th>2093</th>\n",
       "      <th>2094</th>\n",
       "      <th>2095</th>\n",
       "      <th>2096</th>\n",
       "      <th>2097</th>\n",
       "      <th>2098</th>\n",
       "      <th>2099</th>\n",
       "      <th>2100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.60</td>\n",
       "      <td>4.60</td>\n",
       "      <td>...</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.99</td>\n",
       "      <td>...</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.94</td>\n",
       "      <td>6.94</td>\n",
       "      <td>...</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.52</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.43</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.99</td>\n",
       "      <td>4.99</td>\n",
       "      <td>4.99</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.97</td>\n",
       "      <td>4.97</td>\n",
       "      <td>...</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.82</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>179</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>5.63</td>\n",
       "      <td>5.63</td>\n",
       "      <td>5.64</td>\n",
       "      <td>5.64</td>\n",
       "      <td>5.64</td>\n",
       "      <td>5.65</td>\n",
       "      <td>5.65</td>\n",
       "      <td>5.65</td>\n",
       "      <td>5.66</td>\n",
       "      <td>...</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.70</td>\n",
       "      <td>...</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.88</td>\n",
       "      <td>...</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>6.71</td>\n",
       "      <td>6.71</td>\n",
       "      <td>6.71</td>\n",
       "      <td>6.71</td>\n",
       "      <td>6.71</td>\n",
       "      <td>6.71</td>\n",
       "      <td>6.71</td>\n",
       "      <td>6.71</td>\n",
       "      <td>6.71</td>\n",
       "      <td>...</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.75</td>\n",
       "      <td>6.75</td>\n",
       "      <td>...</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 302 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country  1800  1801  1802  1803  1804  1805  1806  1807  \\\n",
       "0            Afghanistan  7.00  7.00  7.00  7.00  7.00  7.00  7.00  7.00   \n",
       "1                Albania  4.60  4.60  4.60  4.60  4.60  4.60  4.60  4.60   \n",
       "2                Algeria  6.99  6.99  6.99  6.99  6.99  6.99  6.99  6.99   \n",
       "3                 Angola  6.93  6.93  6.93  6.93  6.93  6.93  6.93  6.94   \n",
       "4    Antigua and Barbuda  5.00  5.00  4.99  4.99  4.99  4.98  4.98  4.97   \n",
       "..                   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "179            Venezuela  5.63  5.63  5.64  5.64  5.64  5.65  5.65  5.65   \n",
       "180              Vietnam  4.70  4.70  4.70  4.70  4.70  4.70  4.70  4.70   \n",
       "181                Yemen  6.88  6.88  6.88  6.88  6.88  6.88  6.88  6.88   \n",
       "182               Zambia  6.71  6.71  6.71  6.71  6.71  6.71  6.71  6.71   \n",
       "183             Zimbabwe  6.75  6.75  6.75  6.75  6.75  6.75  6.75  6.75   \n",
       "\n",
       "     1808  ...  2091  2092  2093  2094  2095  2096  2097  2098  2099  2100  \n",
       "0    7.00  ...  1.74  1.74  1.74  1.74  1.74  1.74  1.74  1.74  1.74  1.74  \n",
       "1    4.60  ...  1.78  1.78  1.78  1.79  1.79  1.79  1.79  1.79  1.79  1.79  \n",
       "2    6.99  ...  1.86  1.86  1.86  1.86  1.86  1.86  1.86  1.86  1.86  1.86  \n",
       "3    6.94  ...  2.54  2.52  2.50  2.48  2.47  2.45  2.43  2.42  2.40  2.40  \n",
       "4    4.97  ...  1.81  1.81  1.81  1.81  1.81  1.81  1.81  1.82  1.82  1.82  \n",
       "..    ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "179  5.66  ...  1.78  1.78  1.78  1.79  1.79  1.79  1.79  1.79  1.79  1.79  \n",
       "180  4.70  ...  1.89  1.89  1.89  1.89  1.89  1.89  1.89  1.89  1.89  1.89  \n",
       "181  6.88  ...  1.68  1.68  1.69  1.69  1.69  1.69  1.70  1.70  1.70  1.70  \n",
       "182  6.71  ...  2.59  2.57  2.56  2.54  2.53  2.51  2.50  2.48  2.46  2.46  \n",
       "183  6.75  ...  1.85  1.85  1.85  1.84  1.84  1.84  1.83  1.83  1.83  1.83  \n",
       "\n",
       "[184 rows x 302 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"children_per_woman_total_fertility.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2nd Dataset: countries categorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Subregion</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Western Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DR Congo</td>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>French Guiana</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Falkland Islands</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country        Subregion      Continent  Unnamed: 3  Unnamed: 4  \\\n",
       "0             Nigeria   Western Africa         Africa         NaN         NaN   \n",
       "1            Ethiopia   Eastern Africa         Africa         NaN         NaN   \n",
       "2               Egypt  Northern Africa         Africa         NaN         NaN   \n",
       "3            DR Congo    Middle Africa         Africa         NaN         NaN   \n",
       "4            Tanzania   Eastern Africa         Africa         NaN         NaN   \n",
       "..                ...              ...            ...         ...         ...   \n",
       "183           Uruguay    South America  South America         NaN         NaN   \n",
       "184            Guyana    South America  South America         NaN         NaN   \n",
       "185          Suriname    South America  South America         NaN         NaN   \n",
       "186     French Guiana    South America  South America         NaN         NaN   \n",
       "187  Falkland Islands    South America  South America         NaN         NaN   \n",
       "\n",
       "     Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  Unnamed: 10  \\\n",
       "0           NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "1           NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "2           NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "3           NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "4           NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "..          ...         ...         ...         ...         ...          ...   \n",
       "183         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "184         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "185         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "186         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "187         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "\n",
       "     Unnamed: 11  Unnamed: 12  Unnamed: 13  Unnamed: 14  Unnamed: 15  \n",
       "0            NaN          NaN          NaN          NaN          NaN  \n",
       "1            NaN          NaN          NaN          NaN          NaN  \n",
       "2            NaN          NaN          NaN          NaN          NaN  \n",
       "3            NaN          NaN          NaN          NaN          NaN  \n",
       "4            NaN          NaN          NaN          NaN          NaN  \n",
       "..           ...          ...          ...          ...          ...  \n",
       "183          NaN          NaN          NaN          NaN          NaN  \n",
       "184          NaN          NaN          NaN          NaN          NaN  \n",
       "185          NaN          NaN          NaN          NaN          NaN  \n",
       "186          NaN          NaN          NaN          NaN          NaN  \n",
       "187          NaN          NaN          NaN          NaN          NaN  \n",
       "\n",
       "[188 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zones = pd.read_csv(\"world_zones.csv\")\n",
    "df_zones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing and Cleaning the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### restructuring the dataset to make it tidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>year</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1800</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1800</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55379</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2100</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55380</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2100</td>\n",
       "      <td>1.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55381</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2100</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55382</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2100</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55383</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2100</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55384 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country  year  rate\n",
       "0              Afghanistan  1800  7.00\n",
       "1                  Albania  1800  4.60\n",
       "2                  Algeria  1800  6.99\n",
       "3                   Angola  1800  6.93\n",
       "4      Antigua and Barbuda  1800  5.00\n",
       "...                    ...   ...   ...\n",
       "55379            Venezuela  2100  1.79\n",
       "55380              Vietnam  2100  1.89\n",
       "55381                Yemen  2100  1.70\n",
       "55382               Zambia  2100  2.46\n",
       "55383             Zimbabwe  2100  1.83\n",
       "\n",
       "[55384 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c = df.copy()\n",
    "df_c = pd.melt(df_c, id_vars=[\"country\"], \n",
    "                  var_name=\"year\", value_name=\"rate\")\n",
    "df_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>55384.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.508159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.047657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.870000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               rate\n",
       "count  55384.000000\n",
       "mean       4.508159\n",
       "std        2.047657\n",
       "min        1.120000\n",
       "25%        2.170000\n",
       "50%        5.100000\n",
       "75%        6.390000\n",
       "max        8.870000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### removing the weird unnamed columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7',\n",
       "       'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12',\n",
       "       'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_z_c = df_zones.copy()\n",
    "false_cols = df_z_c.columns[3:]\n",
    "false_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in false_cols:\n",
    "    df_z_c.drop([c],axis = 1,inplace = True)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>subregion</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Western Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DR Congo</td>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>French Guiana</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Falkland Islands</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              country        subregion      continent\n",
       "0             Nigeria   Western Africa         Africa\n",
       "1            Ethiopia   Eastern Africa         Africa\n",
       "2               Egypt  Northern Africa         Africa\n",
       "3            DR Congo    Middle Africa         Africa\n",
       "4            Tanzania   Eastern Africa         Africa\n",
       "..                ...              ...            ...\n",
       "183           Uruguay    South America  South America\n",
       "184            Guyana    South America  South America\n",
       "185          Suriname    South America  South America\n",
       "186     French Guiana    South America  South America\n",
       "187  Falkland Islands    South America  South America\n",
       "\n",
       "[188 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_z_c.columns = map(str.lower, df_z_c.columns)    \n",
    "df_z_c "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### editing the `year` column type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_c['year']=df_c['year'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 55384 entries, 0 to 55383\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   country  55384 non-null  object \n",
      " 1   year     55384 non-null  int32  \n",
      " 2   rate     55384 non-null  float64\n",
      "dtypes: float64(1), int32(1), object(1)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_c.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## creating the master dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>year</th>\n",
       "      <th>rate</th>\n",
       "      <th>subregion</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1800</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1800</td>\n",
       "      <td>4.60</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.99</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.93</td>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>1800</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55379</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2100</td>\n",
       "      <td>1.79</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55380</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2100</td>\n",
       "      <td>1.89</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55381</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2100</td>\n",
       "      <td>1.70</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55382</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2100</td>\n",
       "      <td>2.46</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55383</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2100</td>\n",
       "      <td>1.83</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55384 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country  year  rate           subregion      continent\n",
       "0              Afghanistan  1800  7.00       Southern Asia           Asia\n",
       "1                  Albania  1800  4.60     Southern Europe         Europe\n",
       "2                  Algeria  1800  6.99     Northern Africa         Africa\n",
       "3                   Angola  1800  6.93       Middle Africa         Africa\n",
       "4      Antigua and Barbuda  1800  5.00                 NaN            NaN\n",
       "...                    ...   ...   ...                 ...            ...\n",
       "55379            Venezuela  2100  1.79       South America  South America\n",
       "55380              Vietnam  2100  1.89  South-Eastern Asia           Asia\n",
       "55381                Yemen  2100  1.70        Western Asia           Asia\n",
       "55382               Zambia  2100  2.46      Eastern Africa         Africa\n",
       "55383             Zimbabwe  2100  1.83      Eastern Africa         Africa\n",
       "\n",
       "[55384 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master = pd.merge(df_c,df_z_c,on='country',how='left')\n",
    "df_master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 55384 entries, 0 to 55383\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   country    55384 non-null  object \n",
      " 1   year       55384 non-null  int32  \n",
      " 2   rate       55384 non-null  float64\n",
      " 3   subregion  46354 non-null  object \n",
      " 4   continent  46354 non-null  object \n",
      "dtypes: float64(1), int32(1), object(3)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_master.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## another round of Assessing and cleanning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## checking the nun values if it is okay to neglect them, then dropping them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_null_country = df_master[df_master['continent'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country       30\n",
       "year         301\n",
       "rate         663\n",
       "subregion      0\n",
       "continent      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_null_country.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Antigua and Barbuda', 'Bahamas', 'Barbados', 'Belize',\n",
       "       'Cape Verde', 'Congo, Dem. Rep.', 'Congo, Rep.', 'Costa Rica',\n",
       "       \"Cote d'Ivoire\", 'Cuba', 'Czech Republic', 'Dominican Republic',\n",
       "       'El Salvador', 'Grenada', 'Guatemala', 'Haiti', 'Honduras',\n",
       "       'Jamaica', 'Kyrgyz Republic', 'Lao', 'Mexico',\n",
       "       'Micronesia, Fed. Sts.', 'Nicaragua', 'Palestine', 'Panama',\n",
       "       'Sao Tome and Principe', 'Slovak Republic', 'St. Lucia',\n",
       "       'St. Vincent and the Grenadines', 'Trinidad and Tobago'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_null_country.country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 46354 entries, 0 to 55383\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   country    46354 non-null  object \n",
      " 1   year       46354 non-null  int32  \n",
      " 2   rate       46354 non-null  float64\n",
      " 3   subregion  46354 non-null  object \n",
      " 4   continent  46354 non-null  object \n",
      "dtypes: float64(1), int32(1), object(3)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_master_1= df_master.copy()\n",
    "df_master_1.dropna(inplace = True)\n",
    "df_master_1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>year</th>\n",
       "      <th>rate</th>\n",
       "      <th>subregion</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1800</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1800</td>\n",
       "      <td>4.60</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.99</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.93</td>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.80</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  rate        subregion      continent\n",
       "0  Afghanistan  1800  7.00    Southern Asia           Asia\n",
       "1      Albania  1800  4.60  Southern Europe         Europe\n",
       "2      Algeria  1800  6.99  Northern Africa         Africa\n",
       "3       Angola  1800  6.93    Middle Africa         Africa\n",
       "5    Argentina  1800  6.80    South America  South America"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      154\n",
       "year         301\n",
       "rate         737\n",
       "subregion     17\n",
       "continent      6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master_1.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_c['rate'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    55384.000000\n",
       "mean         4.508159\n",
       "std          2.047657\n",
       "min          1.120000\n",
       "25%          2.170000\n",
       "50%          5.100000\n",
       "75%          6.390000\n",
       "max          8.870000\n",
       "Name: rate, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c['rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAFXCAYAAAB3M5DuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABI30lEQVR4nO3dfXxcdZn//9fVECDFmxQtrg2UAmpBrDQSpWxVbty1IncRhargKq6wX9efS5HtWlaWgouCdlXW9W5R8Q7UFqkRBG1dAW+QgsWklAr1FgopSpUGlAaaptfvj3NOO5mcM3Nm5py5Sd7Px2MeSc7dfObkZHLN53Od62PujoiIiIjkZ0qjGyAiIiIy0SngEhEREcmZAi4RERGRnCngEhEREcmZAi4RERGRnCngEhEREcmZAi6RBjOzWWbmZvblouVfDpfPKret1M7Mjg3P7SWNbkvezOwBM3ug0e0ACM/5bY1uh0jeFHDJpBG+sZd6vCPn574tx+OPC85ahZndZma5FwRUsFq9Ws5dMwV3Io20R6MbINIAlyYsH6hnIwoMAocBj2e8rUgrOAzY1uhGiORNAZdMOu5+SaPbUMjdR4D7s95WpBW4u65nmRQ0pCgSw8z2NbPLzew+Mxs2s8fN7Idm9tqYbd8RDUma2evCIbLHC5ZFw2XHFA1hXhLun3q4Jm7b8PhvD3/8fcHxHwjXrzGz0aThRjP713D7C1I8/yXhtsea2VvN7E4z+2vhkFH4mq83s9+F5+4JM7vdzM6Key3AMdHrKHjcVrTt/mb2qfCYT5vZn83sBjN7ebk2R+0Gfh/++PZyQ8lmNtfMbjKzITPbZmY/MrO/rfJ8PN/MPh0OrW03sy1mttLMjix1vJh1ideJmb0oPOdbzexJM/uZmZ1YeG0mnJepZrbMzDaF5/U3ZvZ+M7Nqz13BfseGv98DgQOL9vtywXZxv+/C8/oWM7s7/D1sNrOPm9le4XbHh39vT4Sv/Wtm9pyE9tR0DYnUSj1cIkXM7EDgNmAW8BPg+8A+wEnA983sn9z98zG7vgl4HfA94HPh/gMEQ5hLgQeBLxdsf1tGTb4U6AWOAP4bGAqXR18/A3wFOAf4QMz+7wKeDrdJ6wLg74EbgVuBZxes+yzwS+DHwCPAc4DXA18zs9nu/h8F7bsUeAfBP+XCod4Hom/M7GXAamBfYBWwEnguwWv+qZm9wd1vLtPe24BO4DxgHdBXsG6gaNse4N+AO4AvADOBNwI/NLO57r4x5vix58PMDgJ+CswAbgG+ARwAnA6caGZvdPfvlml7SWZ2KHA7wfm5CbgHOBj4NlDqvLQTnNcZBNfsDoJzegWwN7t/H7eR/twVeiA8xqLw5ytT7lfovcAJ4XPeBrwWOB/Y18y+A3yT4DVfBfwtcBbBtXFC4UEyuoZEauPueugxKR6Ah49LYh7vKNjuNmAn8Oai/TsJ/lEMA88rWP6O8Lg7gdeVeO7bEtbNCtd/uWj5l8Pls6rZtmDdXsCfCIKf9qJ1x4b7XZvyHF4Sbv8k0J2wzSExy/YEfgiMAF1F624L3opij7UH8BvgKeCYonUzCHLaHgH2StH22HMXcy688HoI1/1TuPwzlZwPgn/uDnygaPnfEgQ4fwaeEXO8Yyu4Tn4YLn930fITSryeB8LlNwMdBcv3IwiEhwqvlXLnrsx5fwB4oMzf5W1Fy6Lz8DhwWNG1vAEYDc/dMQXrpgA/CPebm8c1pIcetTw0pCiT0dKYxzsAzOwIgiGu6939m4U7uftQuO3eBD0exb7j7t/PrdVVcvengS8BfwOcUrT6n8Kv/1vhYa9y9/6E5/ttzLLtwKcJ/vm9poLnORE4BPgfd/9R0TE3Ax8leF2VHLOc2939y0XLriYIkF6RsM+482Fm+xP0yGwK27mLu/+MoLdrX+C0ahtqZgcAxxMEFGN+h+7+PeD/yhziX9x9uGCfR4HvEPTQza62XRn6pLvfF/0QXsvLCYKrmwqvCXffCVwT/nhEwTEacQ2JjKMhRZl03N1KrD46/Ppsi6/HND38eljMurtqaVfOPksw7PVPwPUAZvZc4A3Afe7+4wqPl/hazWwm8H6Cf2AzgY6iTboqeJ7o93Fgwu/jheHXwyg9fFaJtcUL3H3EzP4ITEvYJ+58dIdff+LBzQ7FbiEYAusGvlpNQ4G54dc7woCj2E+Bv0vY93F3/03M8ofCr0mvtZ7G/S6AzeHXu2PWDYZf9y9Y1ohrSGQcBVwiY0UJt38fPpI8I2bZH7JvTjbc/XdmtgpYYGaHhL1Q7yAYoqm0dwsSXquZHUwQfEwjyH9bTTAsNEowLPX28DnTin4fp5fZLu73Ua2hhOU7gLaEdXHnI8preyRhn2h5Z6pWxYue448J65OWQ+nXCcmvtZ7iyp/sSLGuvWBZI64hkXEUcImMFb2Jn+fun6xw39yLd9boswRJ/ecASwiS5Z+iut6VpNf6PoJ/cGcXD8uZ2VvYfTdlWtHv41R3v6HCfesp7nxEbf+bhH2eX7QdBHmAEP/e3Bmz7Inw6/MSniNp+WTSKteQTHDK4RIZa0349VUZH3cn+fYYjIZfSz3Hdwnyic62oLzFbGCFu2/NsB0vCL9eH7PumIR9RgHMLK7tWf4+0pyjLEU5Xa80s7gA6rjw6y8KlkW/iwNitu8p8RxHm1nc+/kry7YynVrO3WiV+2Ulr79pkYoo4BIp4O5rCYbCTjOzd8ZtY2ZzzGy/Cg/9Z+L/iWblz+HXmUkbhDk+VxHciXZ1uPhzGbfjgfDrsYULzWwBQY9anFJt/w7wW+A9Zvb6uJ3N7Ggzm5qibVsJeqISz1GW3P1hgrvmZrG7NAIAZnYU8NawTd8uWBXlgp1dGKSFyfEXxzzHQwR3eb6A3TdARPu8juT8rUrVcu7+DEw3s+JcvnrJ8hoSqZqGFEXGeytBQvMXzexfgDsJ8l32B14KvIQgEffRCo75Q+DNZnYjQbLvDuDHVSSrlzr+YuDzZvYt4K/AkLt/qmi7LxD84+4C1rv7HRk9f+QzwNnAdWZ2PUES80sIhjJXAAsT2n46sNLMbiYou/Ggu38tTFY/jaC8wk1m9jOC0hzbCALYlxPUnHo+ZaaHcfe/mtmdwKvM7FrgVwS9Lze4+z21vexE/4+gRtaysFdxLbvrcO0kGHr9S0Eb7zSzHwOvBu4ys1sIhgVPJjgHcUH7e8Ln+EwYUER1uN5IEGycyu6hyqrUeO5+SPB7+n742p4G1rn7jbW0Ka0sryGRWijgEini7g+HVcDfS/BP60yCIZE/EBT0/B9gfYWHPY+gh+A1BEVApxAUhcwk4HL3VRZUij+HoDDkngSFVj9VtN0fw6Cml+qS5cu14x4zOw64jOB17kFQLPM0gqA1LuD6AkHh0zcTFBzdA/gR8LWCYx5BkB92EkFAt5Mg6byfoFTHn1I28W3AJwgCwLcABjxMEKRkLrxZoQe4iOB8HEuQd/V94EPu/vOY3U4FloVf3wv8muC8rAbOiHmOX5rZ0cCHCUpEHE/wet5AcOfdqezO9apFtefuMoL8s5OB+QR/S18hKBJbFxlfQyJVMfdmz/MVkayEeT6/Ieg1eb67Z/GPWJpU2Bv1VuBQj6+QLyJ1ohwukcnlTcBBwFcVbE0MZjbFzMbdCWlmryHoUfylgi2RxlMPl8gkYGZLCKqan0swZHe4uz/Y2FZJFsxsb+AvBHM43k+QH3g4QR257QTTTd3WsAaKCKCAS2RSMDMnmMfwl8Bid/9Bg5skGQnLaVxJkLu1PzCVIB/px8AVSVMwiUh9KeASERERyZlyuERERERy1vRlIZ773Of6rFmzGt0MERERkbLuvvvuP7n79OLlTR9wzZo1i7Vr4yaMFxEREWkuZhZ7Q5KGFEVERERypoBLREREJGd1D7jM7Dwzu9fMNpjZono/v4iIiEi91TXgMrOXEMz19grgCOAkM3thPdsgIiIiUm/17uE6DFjj7tvcfQfBBLVvqHMbREREROqq3gHXvcCrzew5ZjYVeD1wQJ3bICIiIlJXdS0L4e73mdlHgB8AfwXWEcz7NYaZnUsw5xszZ86sZxNFREREMtfQqX3M7MPAw+7+maRtenp6XHW4REQq19c/yLJVG9k8NMyMzg4WL5hNb3dXo5slMqGZ2d3u3lO8vO6FT81sP3d/1MxmAqcBR9e7DSIiE1EUYA0ODWNA4cfpwaFhzl8+wNoHH+Oy3jmNaqLIpNWISvPXm9lzgBHgPe6+tQFtEBGZUC7qW8+1azbtCrLixi4cuGbNJm665xGWnny4ertE6qihQ4ppaEhRRCRZX/8gl9ywgaHhkYr3nTa1XYGXSMaaZkhRRERqV0ugFdm6bYQLV64HUNAlkjMFXCIiLaavf5ALV65neGS05mMNj4xywYp1gIIukTxpLkURkRZz6Y0bMgm2IqPuXLhyPX39g5kdU0TGUsAlItJC+voH2bot/TBiZ0c7U9vLv9UPj4yybNXGWpomIiVoSFFEpIWkCYrikuHT5HxtHhrOpI0iMp4CLhGRFtHXP8hgiaCo1F2Hvd1d9HZ30dc/yAUr1jEac4f6szvaM22viOymIUURkRYQJcon6exop//i15ZNfO/t7uJjZxxB+xQbt+7J7TuUxyWSEwVcIiItYNmqjYmJ8h3tbVxyyuGpj9Xb3cUz9h4/wDEy6srjEsmJAi4RkSZXbijx8tPmVFzSYSgh8V55XCL5UMAlItLEyg0lQlAmotKhwBmdHbHLp5hpWFEkBwq4RESaWKmhxMjWbSMsWj5A9wdXpw6WFi+YTUd727jlqsklkg8FXCIiTazUUGKxSgKv3u4uLj9tDm02PnleNblEsqeAS0SkCfX1DzL30tVV7RvNkZgm6NoZUx4ClMslkjXV4RIRaSJZTEoNQS/VhSvvKZtMP6OzI7YXLSnHS0Sqox4uEZEmEPVoLVo+UHOwFRke2clFfaUT7uNyuTra21i8YHYmbRCRgHq4REQa7KK+9Vy7ZhPxg3u1uWbNJgAu650Tuz7qAVu2aiODQ8O0mY3J4aq03ISIxFPAJSLSILUMHxqkDtDSBl0Xrly/647IwaHhXeUoFHSJ1E4Bl4hIHfT1D+7qRaokWCrW3mbs2TaFJ7eXLhVR7Jo1m+g5cN/E4Cmu/ETU06WAS6R2CrhERFIq7pGaYrDToc2MUfcxgVS0Li64qjbYajNj4csP2NVjVanF160D4nusku5K1N2KItlQwCUiUkbS0N/OMHIaDUsreMy6LPOydrpz6/1bqt5/ZKdzyQ0bYgMu3a0oki8FXCIiMbIqz5ClZ3e0lyyEeuXCuax98LGSPWBJr2fxgtljcrhAdyuKZGlSB1xJORVxQwGllqUdTpjox50IryGv46a1z55tfOgNlU9ELOlFf/ebh4aZ0dnB4gWzx5zvZgy0ANqnGE9u35G4vrOjnd7url2vpdJhx8K7FZPOjYhUzzyhynCz6Onp8bVr12Z+3GhC2HJzlIk0WhS0dekfYE2SAqmO9jYuPy24e68ZAy0IPgg8q2MPtm6Lb1v0Ggqvje4Prk7c/sqFcxOvo3IBqYiUZmZ3u3vPuOXVBFxm1ubuVUUqZnY+8C6CToD1wNnu/lTS9nkFXPOvuKWiOcpEmomCsPTS9Fh1tE/hqZGdudTBqoe4AKqvf5BFywdit+/q7OD2JcePWx73QTQumBORZEkBV9lK82Y2zczebWbXm9lDZvY0sN3MHjezn5vZlWb2ypSN6AL+Behx95cAbcCbK3wtmdCdN9LKomHKwaFhFi0fYNaSmzj4wpuYteQm5l9xS9k59CaLi/rWc36Kyu3DLRxsdXV2xAZDpQKkpA+bpUpDiEhtEnO4zGwWsJQgINoKrAG+APwJeBroBGYB84D3mNnvgMuAa7x0t9keQIeZjQBTgc21vohqJN2RI9KqioOwwt6NaVPbWXry4ZOql6KvfzCX6u3TprZz4kufz/V3DzY8JaFcUnuUkxi3PI5KQ4jkp1TS/Hrgm8DfufvtpQ5iZs8B3gQsAfYHLo/bzt0Hzey/gE3AMLDa3VdX0/Baxd2RIzJRbd02MqmCsL7+QS5YsS7TYCs6XwAXrFgXG8jUW7mhvqQ2jrrT1z84bl+VhhDJT2IOl5nNcPeKe5/M7G/c/Q8J66YB1wMLgSHgOuBb7n5N0XbnAucCzJw588gHH3yw0makMtHuUhSpVasHYVnfYVh8PprpZpukPKxCpXJV43KzlMMlUrtMk+ZraMTpwOvc/R/Dn/8BmOfu/5y0T15J882mMPiLAi0FXM0hi+C5VHJ7s5YhiLRCEJb1OTTgzHkzx809WOrOv3oqDILKfXAsJS5o012KIrVJCrgqqsNlZnsBXcDexevc/ZcpDrEJmGdmUwmGFF8DTJhoKi5oqiZYioYB0lavVrCVLMvAKC9R7aRmDbyi4cj3rRgY0/PaLHdIXtS3PtNcrTYzPnbGEbF3/TVDsAWw/7TgLXjupavHXC+Vvi/E5WYV1vISkeyk6uEysxnAVcAJcasBd/e2VE9odinBkOIOoB94l7s/nbR9nj1cF/Wt5xt3PtQUuRiSDw0P10fha6i1R6xcj010Ljs72tm+Y5RtIzszeAWBpOGzKCeskveK9inGSI6/zLg5Gis+hsEnzoivyaWeLpHq1DSkaGY3Ay8jSIb/JbC9eBt3/1EG7Rwnr4Dror71VU8AK9LMSk2aXC/tbcayN43vJSqn0TlSSfWsqmnTlQvn8v7r7+HpHdkFhHkqDJSVyyVSvVoDrseBc9x9RR6NKyWvgOuQC29Wz5ZMaIV31cX1GOUtTVJ3oWp6kbKUlM9UTZvOmjcTqHx6nWZX6e9UZDKqNYfrUYKcqwlDwZZMdFHuVdwQX9KwXZYqqd2UdR5WpeLqWUW9PAq2dhscGubgC2/SLAciVUgbcF0MvN/MfuTuT+TZoHpJKggoMtFs3TbC+csHWPvgY7vuuotLjM46CEtbuymvAqVpJeWcfeDblQ8jRgVFmyHYyisfMK7AbnS9KAgTSZZ2SPE64CjgmcDPCWpoFXJ3X5h561AOl0iWKk1oj7vzNsvnasQwYtsUY7QgEolKQPQcuO+u11qtRubNzT9kX6495+hxyxtx96vm+pTJrNYcrlvLbePux1XZtpJa9S7F6I13IhRUbcbnmkzHLWVq+5Sq7tKr5U7CcuVPCpVKtK53gvzU9ik8tWNnS94Fmlapkh2lJrOuBwVfMlk0ReHTakyWwqciSUpVC4+SmKvpxUgq7pl1O7NMRq9G+xTIsHJEy4j7/TbLzUKtUExXpFpJAdeURjRGRNIrNTnx4NDwrjnxBpa+lisXzqWzoz3VcZ0g16j7g6vp6x+suZ1pJj7u6x9k7qWrWbR8oG7/+CdjsAXB7/faNZvG/G5LnfOuOs6XGN3QkdW1J9IKUvdwmdkzgVOBFxFfaf7fsm1aQD1cMlmUKjRZakqZuGG7au76q7XXoVwPV6NrbE1WUeV8SJ50u3Cbxd9ax8ho/XvB1OslE0WtOVyHALcDU4F9gC3AvgR3OW4FHnf3gzNtcSjPgGuiTV7d6ONOhNcQd9x6/CMoV2iyXLASNx1NvYcZk9oYnb9Lb9zQNFPjyHhGUHm+0TluCryk1dUacN1AMPx4OvAk0AOsI5ii53LgTe7+80xbHMor4NKnbalUnv8I0uQ/lUt6LjUtTaWBV7WvNem58p7mRiYeBV7SqmrN4XoF8DkgmvNwT3cfdfevAx8D/jubZtbPslUbFWxJRarNO+nrH2T+Fbdw0JKbmH/FLeP27esfTEyKL8x/6u3uKplnMzwyyrJVG8c957JVG7nklMM5a95MLGWbq32tvd1d7LPX+PJ+CrakUlu3jXDhyvXK8ZIJI23AtTfwhLvvBB4DZhSsuxc4IuuG5a2SKtgihSr5RxD1pA4ODeMESe6F+0brkxQXD128YDYd7cnzxEdJ9HHP2XPgvnyigqR62F009aK+5DYW09+WxJnaXvk9WsMjo1xyw4YcWiNSf2mHFO8C/sfdv2ZmPyBIbzkFGAW+DLzC3V+YRwPzGlIsdau9SFrlaguVGyosdR1GeWRRXllnRztmlMyDMqBzanvsNsXDk3kNM+pvS+IUJubXa4hbpBFqHVL8JjA3/P4/CKrOPwH8hSCP69IM2lhX5XoKRNIo7rEqVq5UQqnAJPooFN1VNjQ8Ujbp3EkOyDaHvV/zr7iF85cPsM9eezD/kH0zH2YsVcZCJq9R9129uZWWMFEZCZkIqip8amYHACcQDDXe4u73Zt2wiO5SbJ3jToTXUK5yepK4uwShdA/X4gWzOX/5QN2mgulon8JTIzvHPF9HextvPLKL7657pOKpX5Imxa73NDLSWmq9o7bU7AUizUCV5kWqUMk/griSCqXKPdQ6b19WsqpWrzt/Ja2koKlUvblCnR3tDCx9bV7NE6lJJgGXmc0GuogvfHpzTS1MoB6u1jluM7+GOJUetxq17NsIrdZeaV1JUz6lDdqV1yXNKingGn//dvzOc4BvAIdBbMqHAy2VEFX8h134Pyb6h5N2WZRjU8sxJsJxm/k1xKn0uNVoteCl1dorrSuudzcKntL0tEZ3CxfuJ9LM0ibNXw2MACcBs4GDih65VJnPk+pwiYjUpppSDxGD2AT4SuYFHR4Z5YIV65RILy0h7V/LYcASd/+eu//a3R8sfuTZyDw0Q+6MiEgrc4yz5s2sqLbb7n3ZVag3ThR4TZta+tjR3Y8KuqTZpQ247gJm5tmQepuS9l54ERGJNTwyyrVrNmFVvp9GhXpLWXry4WVL+KinS1pBqhwu4FzgG2a2DbgVGCrewN23Zdiu3ClXRUSkdk7pYrzllMvDSpvXVVjnSzld0ozS9nD9CXgA+CrwEEHB0+KHiIhIRdL0ThXmdbWV6E7TVEDSzNL2cF0DHA38F/AbYHs1TxaWlVhesOhg4GJ3v7Ka44mISOtL2zsVrStVOmJoeIS+/kH1cknTSRtwHQec4+5fr+XJ3H0j4RRBZtYGDALfruWYIiLS+oZHRlm2amPZQClaf8GKdbvKuBS7YMW6MduKNIO0AdcDQNY5Wq8BftuKdziKiEj20t49HgVSi5YPxK5XPpc0o7Q5XIuBD5jZrAyf+80ExVRFREQSa3PF6e3uKlkyQncuSrNJG3BdSlAW4ldm9iszu6v4UcmTmtmewCnAdQnrzzWztWa2dsuWLZUcWkREWlS52lzFypWMUI0uaSZpA657gZuBa4HbgQ0xj0qcAPzC3f8Yt9Ldr3L3HnfvmT59eoWHFhGRVpWmNlekt7uLy0+bozsXpSWkyuFy97Mzft63oOFEERGJUUn+le5clFZR8URYZvZcM3uhmT2nmic0s6nA3wMrq9lfREQmtuiOxbTS9HRVcjyRPKQOuMxsoZndB/wRuB941MzuM7PTK3lCd9/m7s9x98crbKuIiEwSlQwtQhB0feyMIzI7nkjWUgVcZhYNAf4OOBt4ffj1d8A3zezNubVQREQmpUoT3svduagEemmktD1cHwCucvcT3f2r7r4q/Hoi8HngovyaKCIik1GlQ4tQ+s5FlYqQRkobcL0AuD5h3fXhehERkUylLYYaifK5kqhUhDRK2oDrj0BPwrqecL2IiEimKimGGunt7qKrsyNxvUpFSCOkDbi+BFxiZheZ2aFmNs3MZpvZRcBS4Or8migiIpNVpcVQI4sXzC5ZFDUqFSFSL2nnUvwg0A4sIag6HxkG/itcLyIikrnNFQ4rQrpJrtNMli2SlVQ9XO6+090/ABwAHEtQuPRY4AB3v8g94WoWERGp0RSzqnqjVCpCmknashB7A7j7Vnf/ibuvCL9uzbd5IiIy2dWS6K5SEdIs0uZwPW5mPzOzj5jZSWY2LddWiYiIFKimRESkXKkIVaGXekgbcL0VuAv4O+DbwBYzu8fMPm1mbzaz/XNroYiICNUPAZYrFaGhRamHtDlc17v7Inc/EtgXOAn4LkFJiGuBB3JroYiISKiWocVSpSI0tCh5q2jy6nDi6VcA88LHS4C/AKuzb5qIiMhYtQwBlioVodpckre0SfPLzOxOYAi4Bjgc6APmA9Pc/fV5NVBERKRQpdXnI+WGFlWbS/KUtofrAoLerM8BC9z9dHf/pLsPqCSEiIjUUzXV5yPlhhaVQC95SRtwvQ74BHAEcKeZ/dnMbjCzfzWzV5hZcjlfERGRDFVbfT6yeMHsxHVKoJe8pE2aXx0WOD0GeDZwKrAm/HoHoHpcIiJSN7UERqrNJY1QadL8c4ATgN7wMY+gd/fhrBsmIiJSSi2BkWpzSb2lTZr/rJltAB4FvgUcA/wUOAPYz91fnF8TRURExqslMFJtLqm3tD1cLwZWEvRuTXP3l7v7+9z92+7+p/yaJyIikqzWoUXV5pJ6SZvDdYy7/0eYy/XXvBslIiKSVi2BUbnaXBesWKegSzJRUQ6XiIhIs8lzaLGWibNFCingEhGRlpfn0KKS6CULCrhERGRCyGtoEaqvbi8SqXvAZWadZvYtM7vfzO4zs6Pr3QYREamv9jbL/TmyGFpss/h21lLdXgRSBFxmtreZrTazYzN6zv8Gvu/uhxJUrr8vo+OKiEiTGhmtzyxwtfRE9XZ38bEzjiAu5HJQAr3UpGzA5e5PAS8Hap6+x8yeBbwa+GJ47O3uPlTrcUVERKD2nqje7i6SQkMl0Est0g4p3kBQWb5WBwNbgC+ZWb+ZfcHM9snguCIiIjXPswgogV5ykTbgWgWcFuZevdPMTjSz1xc+Uh5nD+BlwGfdvRt4ElhSvJGZnWtma81s7ZYtW1IeWkREpPYq8WkS6NXLJZXaI+V214RfTwsfxZx0Q44PAw+7+53hz98iJuBy96uAqwB6enrqM/AvIiITxoUr1wPBEGGlon0uWLGOUY//F1TL8WVyStvDdVCZx8FpDuLufwAeMrPZ4aLXAL+spMEiIiLl1Dr0FyXQa4JryUqqHi53fzDD53wvcK2Z7Qn8Djg7w2OLiIgAu4f+qu2FivZbtHwg8fgiaaWuw2Vme5nZu83si2GZiBeGyxea2WFpj+PuA+7e4+4vdfded99aTcNFRETKqfWuwlJV6FWbSyqRKuAysxcBvwIuB2YRDAU+M1z9KuDCPBonIiJSiyyG/hYvmK3aXFKztD1cnwQ2EQRbC2DMtfcj4JXZNktERCQbtQ79qTaXZCFtwPUq4PKwSGnxdfdH4PlZNkpERCQrWQz9qTaX1CptwPUUkHS1dQFDmbRGREQkY1kUQ1VtLqlV2oDrB8C/m9mzC5a5me1FcNfhzZm3TEREJCO1BkTlJreG2hP0ZWJLG3AtBqYDvwG+RvCB4WJgPTAD+EAurRMREclIFncsqjaXVCtVwOXuDwFHAJ8jSJz/LUHe1nXAkWFBUxERkaaVRUAU9XQlUW0uSZK6Dpe7b3X3/3D3v3X3F7n7PHf/gLv/Oc8GioiIZCWLXCvV5pJqpA64AMys08xeaWanm9l8M+vMqV0iIiK5yCLXSrW5pFJpC5/uYWYfIZh8+sfAcuAnwMNm9lEza8+xjSIiIpnJamhRtbmkEml7uD4OnAd8GHgx8Nzw6+XAvwAfy6V1IiIiOcgi10q1uaQSaQOutwH/7u4fdvf73f2x8OuHCO5QfFt+TRQREclWFrlWqs0llUgbcO0ENiSsu5fx1edFRESaVhbFUFWbSyqRNuD6GvCuhHXnANdk0xwREZH6yOqORdXmkjT2SLndg8AbzWwDcAPwKLAfcCrwTOBjZvbP4bbu7p/NvKUiIiIZu3DleiAInKoV7bto+UDsetXmEkgfcEVJ8V3AYTHrP17wvQMKuEREpOkNj4xywYp1QO1B17JVG2ODqyhfrJbjS+tLW2l+SgWP5AxCERGRJpNVGQfV5pJSKip8OpF0tE/aly4iIkVUm0vyNmmjjtGdurFSRER2U20uydOkDbi2jyrgEhGR3VSbS/I0aQMuERGRQqrNJXlSwCUiIhJSbS7JiwIuERGRAln0QEU9XUk0tDj5pK3DhZnNAE4C9gf2Llrt7v7+lMd5APgLMArscPeetG0QERHJWz1qc0E2RVeldaQKuMzsDcA3gDaCKvPbizZxIFXAFTrO3f9UwfYiIiJ1E5VxgNoCosULZnPhyvUMj4yOWzc8MsolN2xQwDVJpB1S/DCwGnieu3e5+0FFj4NzbKOIiEjdZVWbq9TQ4tDwiIYWJ4m0AdcBwCfd/bEMntOB1WZ2t5mdm8HxREREcpFVEn2p+lxKoJ8c0gZcPwNmZ/Sc8939ZcAJwHvM7NXFG5jZuWa21szWbtmyJaOnFRERqVxW0/4kUQL95JA24HofcK6Zvd3MZpjZ1OJH2id0983h10eBbwOviNnmKnfvcfee6dOnpz20iIhI5qJcq1r0dncxbWp74nrV5pr40gZc9wBzgC8BDxHcZVj8KMvM9jGzZ0bfA68F7q2wzSIiInWVRa7V0pMPL1mbSxNcT2xpy0K8ExLn5KzE84BvW1CBdw/g6+7+/QyOKyIikqtaS0VE+y1aPhC7Pqs7I6U5pQq43P3LWTyZu/8OOCKLY4mIiNRTFgFRudpc0Z2RCrgmnooqzZvZi83sbWb272b2N+GyF0TDhCIik9GU5GnzZILJolREmgmuZeJJFXCZ2TPMbAVBvtUXgP8EZoSrPwwszad5+Skxr6iISEU+fsZc9JYyedR6V2G5Ca4NlMs1AaXt4fo48LfAa4Bnwpj3lpuB12Xcrtx5FhlpIjJpJN1h1tXZQW93F2fOm6mgaxKp9a7CaILruGvGQQn0E1DagOs04P3ufivBHIiFHgQOzLRVdZD0yUJEmkNH+5SmCWCM+DvMOtrbdtVXuqx3Di/Yb58GtE4aIasq9Emf/aN8MQVdE0fagKsD+HPCumcyPghreqPq4hJpalPMMrk1OgvO7mGgrs4OjKBn6/LT5uxKbv77j9/Grx99sqHtlPrKIteqVAV6lYqYWNKWhfg58A9AXAmHNxFUom8p06a2s3XbSKObISIJntzeXJ/jDrnwZkbdMYIAbHBomEtv3F0MU8HW5BPlWuU1uTWoVMREkjbgugj4PzP7P+A6gveb15vZ+QQB17jpeZqdOrhEpBJRr3jhW8fWbSO8b8UAz+5IriAuE1eUawW11+a6YMW6xJEXlYqYGFINKbr7TwkS5vcCPkUQ2F8KHAz8nbv/PLcW5uTxYfVuiUjtdjrqLZ/Essi1ihLoVSpiYisbcJnZXmZ2JvCou78KeBawP/BMd5/v7rfn3cg8zCgxbi4iIpJWFrlWKhUx8ZUNuNz9aYLaWzPCn4fdfbO7b8u7cXk67lBNii0iItnIsqdLpSImprR3Ka4HXpRnQ+rt1vu3NLoJIiIygQyPjHLJDRvKb1iCSkVMXGkDrvOBfzOzk8wsbaJ9U9N4uIiIZG1oeKTmgKhcqYha639JY6QNuPoIhhS/AzxlZlvM7NHCR24tzInmPhMRkTzUOvSXZq5F9XK1nrS9VZ+GpqlBmImdE+rViIhIs6i1dlaaUhGqzdV6UgVc7n5Jzu0QERGZMGqtnRXtl1QUNcoXU8DVOtIOKU44zTyiGA13dnV2cOXCuTxwxYm7HlcunBs7n1vSxLrF2125cG5Tv3YRkYmi1qG/qFREkizyxaR+Enu4zOyWSg7k7sfX3pz6adSIYnubsfDlB3Dr/VvYPDTMjM4OFi+YXfJTSl//IMtWbdy1/RuP7Bq3//nLB8o+d/SHO8VMc0mKiNRBrUN/vd1dLFu1MfFGr1or3Uv9mCf84zWz64oWHQ08D7gbeBTYD3gZ8EfgDnc/I48G9vT0+Nq1azM/7vwrbmnYnYrRXGwQ9Gbt9LHLKhUdQ0REmk9nRzsDS19b9f59/YMsKvGhuqO9bcxE6tJYZna3u/cUL08cUnT306MHwaTVjwGHuPs8dz/F3ecBLwC2Aj/Iq+F5aWTh08LYKAqUaomXFGyJiDSvWof+eru7SqaNZFHpXvKXNofrA8DF7r6pcGH481Lg37NuWN5U+FREROql1oBo6cmHlywVoaKozS9twPU3BBNXx9mLYHixpajwqYiI1EutAVG5uRZBPV3NLm3AdRvwETMbMyZpZi8HPgL8KON25a7URSsiIpK1WgOiaK5F9XS1prQB17kEOVx3mtlmMxsws83AmnD5uXk1MC+6S08kO4UfX9KUKBGZrOrV01XrnI6SvVQBl7s/7O4vA04G/he4I/x6srt3u/vDlTypmbWZWb+ZfbfiFotI03Fgnz2DT91PDO9obGNEmlw9erpUo6v5VDQRtbvfDNycwfOeB9wHPCuDY4lIE3hye1ANW73HIuXVY/of1ehqLok9XGY2tZJH2ic0s/2BE4EvZPECJorCzuGo0nzUZVxNtlnek3PHtVdZcSIi6dU69Bf1dCVRPldzKdXD9VcqKw+V3Lc51pXAvwHPrODYmeton8LwyM7cn6crRSX5Ss1aclPiuqiAapa1uR644sTU25ZqW7Guzg62bd/B1m0j1TRLRKTlDQ2P0P3B1Sw9+fCqe7ouvXFD4vtoNHwZbSuNUyrgeicZz4BjZicBj7r73WZ2bIntziVMxJ85c2aWTdjl8tNeyvuWD5B3yPWHx59i0fIBlq3amFng1dXZkVjWIuvBnK7Ojoq2nza1PXUAtXjBbCB5clYRkclg67aRmoYXl558eMn30VqHLyUbiVP75PJkZpcDbwN2AHsT5HCtdPezkvbJa2of2D1HYb1rcsVN7VNJT1hf/2DmQcrU9imM7HRGRndfD9VMF9HXP8jib60bc5wkBnRWEKCJiExkbWZ87IwjqgqK+voHS+ZzQe1TDEk6SVP71DXgGvPEQQ/Xv7r7SaW2yzPgihy05KaGTWYdJ00QluaPq5xpU9tZevLhALsCz7ZwYutahkKzaJuIyGTU3mYse1P1QVe5D+NXLpyrXq6cVTyXopndZWYvDr//efhz4iPPxudtRsphs67ODs6aNzP35PAo/2pwaJhFywc4+MKbmLXkJuZfccuu5Mc0twUn6ers4MqFc+m/OPikc+HK9bt6+Ubd6Whvq2n4s7e7i511CraUqC8iE8nIqPPvK++pat80NbpUib5xSuVwbQCGC77P9D+ou99GUMG+4RYvmF32U0FhEHJZ7xwu6lvPNWs2JW6fpeIAbNHygV09UZ0d7Ty9Y7RskvwUg7ceNZPLeueMWb5s1cZxr3t4ZJRlqzbW9CloRok8s6wUDs2KiEwU20Z20tc/WFO5iEXLB2LXK5+rcRIDLnc/u+D7d9SlNQ0SXXTLVm1k89AwMzo7OO7Q6dx6/5ZdPxf2+PT1D3L93Y39hBAN1w0Nl85/Kjc0uDkhKEpanlaaILYW0bCrSCl77TGFp3fkfzeySNYWLR9g7YOPjfuQnIbuXGxOFRU+nch6u7tSX3hxvULNZp892/jQG8onvCf1RKUdZk0SPe8lN2woGxRWQ8GWpKFgS1rZNWs28e1fDKZ6Ly+mOxebT+qk+XDi6tOA/QnuMBzD3c/ItmmBeiTNV6raJPtoGLCeQ2HlEvDjkiyruTsxyfwrbqn7XaAiIhNJtYn0unOxMZKS5lP1cJnZu4FPAX8Gfg1sz7Z5rSWplMG0qe1M3XOP2ACjq7OD25ccH3u8vv7B3HuCCvO/CoOvuOHULAu11jo0WUlw2tXZwSOPD6v3S0QmlJFRZ/F1AxW/L0fbl+rpiuZcVC9X/lL1cJnZb4Fbgf/n7nWdmbYZe7jmXro6Njjq7GjnklPGd+NWW88q7xph1dQAq1QtPVzTprZz4kufz/V3D5Ydwo0C2koq3YuItBIDzpw3/uancsr1dNVS/0vGq7gsRJH9gG/UO9hqVo8n9EQ9Pjyy67bcrs4OjCAQqGZ4rre7i9uXHM8DV5zIlQvn7qr4Xup230oV934Vl57IwuIFs6su3fDXp3bQc+C+Y85nZ0c77W1jj9jR3sZxh05n/hW31NxeEZFm5QR5XRf1ra9oP8252BzS9nCtAAbc/cP5N2msZuzhSuq1KTVsmLXCHrC8csKy6gGrpdcpLr+gHq89DZWlEJFGOauKnq7uD64uObOHerqyUXGl+ajoaWg6cBXwdeAHwFDx9u7+y0xaWqQZA660ieZRYJBHblRcm/LKAysUBWGFwUZUsT7ptSUFqJ0d7fzlqR1lK9J3drRjFsw31ogbD+J0tLfxxiO7xpUOgfzuzBQRKTT/kH259pyjU2+fphJ9tcOWsls1AddOxv5Pi8ZxincwwN298pLnKTRjwAXlg6m87/5L07ZG3h1YGISVOhfQWpNXG6QKnnV3pojUQ7kPvMXS3LlowCc0BVDVqgm4jqnkCdz9R1W2raRmDbjKaYZhR2j88Fua+RrXPvhY3ar216L4d1cq6E4qHWLA7684kb7+Qc5fMYCmmxSRLFQSeKXp6dLwYvWabvLqtFo14Cr3D7dRGtX7lXTHYfsUAwtue252hZO+luvBTBNw9/UPJk6/ISJSqfYpxrLT0wVJaXq6oPIeNKmuh2sf4IPAd9391oRtjgNOAi5x979k2N5dWjXgapYerlIa3fs1EUW/37RDyuWSWEVEKmEGnzgj3XBgX/8g5y8fKPveX690mImimrIQ7wZ6gdtLbPMz4BTgvJpaNwEtXjCbjvaxaW3RBNjNorD0xO/D8hOdHe2NblZLiwq9pi0PsvTkw8ddJyIi1XIP5mFMUzqit7uLM+fNLFu6Z3hklEtu2JBNAyexUj1c64Gr3f0TJQ9gtgg4292Ti3zUoFV7uKC+dynmpfjux7i7FGW3anow63WHqYhMLmmHAzW8mK1qhhSHgRPc/bYyBz4WuNndp9bezPFaOeCaDFphWDKpXYXDf1nlUlVTGyeiwEtE8pAmUEqTSA8aXkyjmiHF7cCeKY69J6AK9JNU8bDkA3Uampw2tX3XUN20qcnP1dXZwZnzZpYc3u3t7tpVyb9W3133SNX79nZ3aRJZEcnc1m0jnF9mmDFKgyj33j08MsoFK9apKn0VSgVc9wJ/l+IYfx9uKwLsDhyqCbzSTgP0xPAOPrFwLrcvOT42D6qjvY0rw/WX9c4pm08Vl3NXjaHhEbo/uLqmN6Ok4K/NjPmH7Fv1cUVk8oqmBSr1/hS9d5f6EAvBVECLlg/U/F432ZQaUnwX8EngxBJ3KR4L3AS8192vzqOBzTqkOBHys+ol7dQ+cdXbS5WvKOzazuL3kTaPIY1aut3L3eF4Ud96rl2zqSmHb0WkNZQaZkw7vAiqTB+nmhwuA1YAbwBWAquATQSB8kxgAXAasNLdF+bU7qYMuBpZRb4Vpam6njRfY7l9sy6zkWU+V9rCgYV5cFFR2Gg6o6FtI4kB5OEXf58nt7dGhX4RaT6lgqVKckpVmX6sinO4PIjEzgDOB44EPk8QdK0GvhAuOx94cx4NbmbLVm0cF/kPj4yybNXGBrWouZUbrjPg9iXHx/6xltt3c8YFXHu7u5jaXmqkPb1Rdy5cub5kl3sUvEdBZdS7NjQ8wtZtI3RObY8Ntvr6B9m+Y2cm7RSRyanUMGNhakiblU72cFBeVwol/7N44H/c/RCCXq2jw8dMd3+Bu3/Km71UfQ6S/sln/c9/ooiSMZP+aGeUSFivZd9qffi0lwYV8DNQrn7NpTduKNltv3XbSGzQtmzVRkZ2Tro/PRHJwdZtI4k5Wb3dXXzsjCPK5rgqr6u8PdJu6O4PAw/n2JaWkZRblMc//4ki6qGJG4otVwy22n2rzeuKtsmq3MXQ8Ah9/YNjcs0qOW5h0NboSclFZOKK7mZc++BjY4YZo/fENEOM0YfEwv0kUCqH6z+AT7r746kPZnY8sI+735hR+5TDNcHUktxeyb5Z/47SJv4niXKzmrVWmYhIoaSk+rQ37XR2tE/aMjfVJM3fCBwDfAe4DrjD3bcUbdMOzAFOABYC04G3u/vqhGPuDfwY2Iugd+1b7r60VMObMeAC3aXY7LKeyzJN4r+IyEQTF3ipMn1pFQdc4U5HAe8luFNxb+BP4eNpoBOYAbQDG4CrgavcfVuJ4xlBD9hfw2Dtp8B57r4maZ9mDbikuR205KbYT2AG/P6KEys+XiW3SYuITESFd5NX8p442QKvairN4+53uvtZwPOA1wNXAv8H/BxYDvw/4FB3f6m7X1kq2AqP5+7+1/DH9vChERbJXFI+XbV5dnGTUc8/ZN/UhVrroZnaIiITz+DQ8K7EeCBVZXpIV+l+MijZw5XLE5q1AXcDLwA+7e7vL7W9erikGvXKsytMgm+UuAKwGv4UkbxNMXjrUTO56Z5H2Lot3Rywk6G3q6ohxTyZWSfwbYIq9fcWrTsXOBdg5syZRz744IP1b6C0vHrn2fX1D7L4unV1LdeQVDA2ak9WRVxFRJI875l78sRToxWlXEzkwKvpAi4AM1sKPOnu/5W0jXq4pJVUUp25VlPbp/DL/zyh5DZK9heRZjVRpwWqKocrh0ZMD3u2MLMOgsmx769nG0TyFFVnrnTS7mpsG9lZtsBgVpNyi4hkLc2E2hNJXQMu4PnArWZ2D0Hi/Q/c/bt1boNI7i455fDMqtWXUm46qXKV+kVEGq1UpfuJpKFDimloSFFaVXEO2XGHTufW+7eweWiYZ3e0ZzrsWCqXK2rL+csHdEuwiDS9Vs/vyiSHy8xOAHqAA4DL3H2Tmb0a+I27b86stQUUcMlElXV+Vbm7MNNWiBYRabRWzu+qKYfLzJ5nZncCNwJvB/4ReG64+mzgP7JqqMhkEZdf1T7FaG+rbvhveGSURcsHmH/FLbHd8pf1zuETC+eOqSV21ryZdGkOUBFpMhMxvytVD5eZrQAOB04FHgC2Az3u/gszOxNY6u4vyqOB6uGSiSyudAXUPkl1pTXHkirzi4g0g1YaZqxpSNHMniCYI/HbYeHSEXYHXMcAN7v7Ppm3GgVcMnnFFW+tZPLrNjN2uqeqQabyESLSKsrlrDZaFmUhkiqaPRfQO7VIxuKmEzpz3szUZR5G3XGC6TguXLk+sVu+r3+QJ5/ekV3DRURyVDjFUCsNN6bt4boJ2BN4XbhoBDjS3fvDdU+6+xl5NFA9XCJj1TJ9T/Enw0ZUxxcRydJZTZZcX+uQ4kuAnwKPEEzH837gf4GXhI957v6rTFscUsAlEi9uyDGNwvyuuZeurktVfBGRemgz4y1HHdDQAKymIcVwrsMeYC3wDoLhxdOAh4Cj8gq2RCRZ4ZBjJYZHRncVTFWwJSITyag716zZxKwlNyXesd0oKnwqMkFUeqdhmxmjTf73LyJSqykGbz2qfsOOtdbhusXMDk1Y9yIzu6XWBopIbWZU2NOlYEtEJoOdzq5er4OW3MRFfesb0o60dykeCzwrYd2zgFdn0hoRqZomqhYRKS0qqNqIoKuSshDjPg6b2Z7A8cAfMmuRiFSlOKdL01WLiMT7xp0P1f0590haYWZLgYvDHx1YY5b4Fr4s43aJSBV6u7vGlHyIqtinGTzszHhCbRGRZtWIlIrEgAu4GfgTwQflTwIfI5jWp9B24H53/0kurRORqhUGX2kqyW/fUVl5CRERSS8x4HL3nwM/BzCzvwA3ufuf6tUwEcnO4gWzy9bs2jays44tEhGZXEr1cO3i7l/JuyEikp+op6twouxt23ewdZuGEEVE6iFVwAVgZguBc4AXAXsXr3f3/TJsl4hkrHCIEYK6XSIiUh9p63C9FfgK8Btgf+AG4Lvh/k8An8qrgSKSj0rrdomISPXSloVYDPwn8J7w58+4+zuBgwgS67fl0DYRyVGtdbumTW1n2tT2DFskIjJxpQ24Xgjc7u6jBPMoPgvA3f8CfAT4//JpnojkpbBulxGUhUgTQE2b2s6VC+fSf/FrWXry4fk3VERkAkibw/U4sFf4/SBwGHBb+LMBz8m2WSJSD8V5XQAX9a3n2jWbxtXumja1naUnHz5m+97uLhYtH8i/oSIiLS5twLUWeCmwiiB/62Iz20FQh+ti4M58mici9dTXP8j1dw+OCbYMOHNe8sSvmgRbRKS8tEOKlwObwu8vBu4CPgN8iSCH65+yb5qI1NuyVRvH1epy4LvrHknc5y1HHZBzq0REWl+qgMvd17j78vD7IXc/FXgG0OnuR7n7b9Mcx8wOMLNbzew+M9tgZudV33QRydrmhGr0Q8Mj9PUPxq67rHcOZ82bmWezRERaXiWTV4/h7k+7+xNmdpyZfS/lbjuAC9z9MGAe8B4ze3G1bRCRbJUqFbFs1cbEdZf1zqGzQ3csiogkKRlwmVmnmb3ZzBab2ZvMrL1g3elmthb4IUF5iLLc/RF3/0X4/V+A+4Cu0nuJSL0sXjA7cV1S71fkklMOp31K4gT3IiKTWmLAZWZzCAKirxOUflgB3GFmB5rZ7cA3Ce5cPBOouJfKzGYB3cQk3JvZuWa21szWbtmypdJDi0iVeru7EktDlCuU2tvdxbLTj8ijWSIiLa9UD9eHCarIHw1MJSgF8RjBhNYvAd7u7nPc/RvuXtGst2b2DOB6YJG7P1G83t2vcvced++ZPn16JYcWkRotPfnwcQVRO9rbSvZ+RYpLTIiISKBUWYge4Dx3j3qgNprZu4FfA+e6+zXVPGE4LHk9cK27r6zmGCKSn7iJrhcvmJ06mOrsaGdoWJNii4gUKhVwPQ94oGhZ9PO6ap7MzAz4InCfu3+8mmOISP7iCqKmdckph/O+5QNU1O0tIjLBlbtLMama4Y4qn28+8DbgeDMbCB+vr/JYItKEeru7+PjCuS1512KU8t9mSv4XkWyVqzS/KqwoX+yHxcvdfb9yT+buP2X3e5qITFBRD9lFfeu5Zs2m8js0gTYzPnbGEWN69vr6B1m2aiODQ8MYyZ9ARUTKKRVwXVq3VojIhBRNBxQ3N2OzKQ62YPzQal//IJfcsEE5aiJSMfMmnwOtp6fH165d2+hmiEiNmrm3a/4h+3LtOUdXtE9h75eItJ4Hrjgxl+Oa2d3u3lO8PO3k1SIiNek5cF+W3/UQIzsb9yFvikHh07eZ8ZajDkicmLuU4t6vMz9/B7f/9rFUz6/hSZHJRwGXiNTFslUbGxJsdXV2cPuS43N/nmvPOXpXr1e5chp9/YNcsGIdo00+wiAi2VHAJSKJ0gYQaZSbGigPaQu2ZiVtOY3e7i7OXz6Qf4NEpGko4BKRceKSwweHhrlw5XqguoryMzo76prvtM+ebXzoDXOatvp9vc+HiDRWuTpcIjLJ9PUPcuHK9bF34g2PjLJs1caqjrt4wexxUwZVaq890r9ldU7ds6bnylsW50NEqnPWvJl1f071cIlMcsXDhtu272B4ZDRx+2qHBqOepkVVDqW1TzFO79mf6+8eLNm+SK09cnmr9XyISOUMOHPezKpulKmVAi6RSSzqzYoCmDRDXDM6O6p+vt7urqpLKYzsdG69fwuXnzZnTIB43KHTufX+LbHHjHrkmjHggtrOh0gziLvzttJlbWaMule8fVeNeaX1poBLZBJbtmpjqt6iSBZJ6IsXzB4T5BXr6uxg89BwbNmEzUPDiYnpBy25KXGfZrZ4wWzOXz6gMhFSd43s7amljt3O8O7ewaFh3rdigEXLB1IHctOmtrP05MMbEqQp4BKZxCoJRrJ6o4r2j6vYHgV0SW/EpXrXkpLQa+mRq4fe7i4NK0ouStWZiwKea9ZsGjMTRJY9UeWWRaLyKIXLowoytSyLO+7WbSMs/tY6oP6pBgq4RCaxpCCls6OdffbaI5NyEHGiXqpSZSeKe8HK9a7F9ZzVuyxEtbp0x6JUqVQQtNOda9Zs4po1m0oGPLUEMLUERo0yMuoNSTVQwCUyicUFKe1TDDNyC7YKJQ0PRssqqQFWzT7NIilYvPy0OXz61l/z60efbGDrpJnVEgRNZo1INdBciiKTXGEv07M72nly+w5GRne/L0T/+FshcGllpXr7mnkeSpFWlOcMFElzKSrgEpFd5l9xS+zQVr2mx5HSLupbPybfRkQq195mLHvTEbl9iEwKuFT4VER2Sepmb/Y7/SaLy3rn8PsrTuTKhXPp7GhvdHNEMjHFgq+W8bI2s3Hrpk1tzzXYKkU5XCICBENaU8IE3GLNfqffZBOX+6beL8lCPe9SbGSJhkZQwCUiuwqgxgVbrXKn32R3We8ceg7cd1dJjeifY2dHO9t3jLJtZGejmygp5F00dDIHPI2mgEtEEgugtpkpYb6FJN31CWMLTcaVBpDaVRsYtVrFdKmOAi4RSczR2umufwITRHEwpiHI0tQ7JFlTwCUiLVulXapXPAQ52Xu95h+yL9eec3SjmyETmAIuEWnpKu1SveJeryCX7x6GJ3i+1z57tvGhN2ioXOqrrgGXmV0NnAQ86u4vqedzi0iyVq7SLtkpDMD6+gdj57tshM6OdsyCefDaEu6kTaLgSppFXQufmtmrgb8CX00bcKnwqYhI49Q78FIiubS6pMKnde3hcvcfm9msej6niIhUL+7OxzM/fwe3//axVPtHuWFKQpfJTjlcIiJSkWvPOXpMmYniYb42M95y1AFc1junga0UaS5NGXCZ2bnAuQAzZ85scGtERKRYqZpfIjJeU86l6O5XuXuPu/dMnz690c0RERERqUlTBlwiIiIiE0ldAy4z+wZwBzDbzB42s3+s5/OLiIiINEK971J8Sz2fT0RERKQZaEhRREREJGcKuERERERypoBLREREJGcKuERERERyVte5FKthZluAB3N+mucCf8r5OSYTnc/s6ZxmT+c0Wzqf2dM5zVa9zueB7j6uiGjTB1z1YGZr4yaalOrofGZP5zR7OqfZ0vnMns5pthp9PjWkKCIiIpIzBVwiIiIiOVPAFbiq0Q2YYHQ+s6dzmj2d02zpfGZP5zRbDT2fyuESERERyZl6uERERERyNiEDLjO72sweNbN7C5bNNbM1ZjZgZmvN7BUF6y40s9+Y2UYzW1Cw/EgzWx+u+6SZWb1fS7Oo5Jya2SwzGw6XD5jZ5wr20Tkl8XweYWZ3hOfnRjN7VsE6XaNlVHJOdY2WZ2YHmNmtZnafmW0ws/PC5fua2Q/M7Nfh12kF++g6LaHSc6rrtLQS5/P08OedZtZTtE/jrlF3n3AP4NXAy4B7C5atBk4Iv389cFv4/YuBdcBewEHAb4G2cN1dwNGAAd+L9p+MjwrP6azC7YqOo3OafD5/DhwTfv9O4D/D73WNZn9OdY2WP5/PB14Wfv9M4FfhtfhRYEm4fAnwkfB7XafZn1Ndp9Wdz8OA2cBtQE/B9g29RidkD5e7/xh4rHgxEPUYPBvYHH5/KvBNd3/a3X8P/AZ4hZk9H3iWu9/hwW/jq0Bv7o1vUhWe01g6p7slnM/ZwI/D738AvDH8XtdoChWe01g6p7u5+yPu/ovw+78A9wFdBNfjV8LNvsLu86PrtIwqzmksndNA0vl09/vcfWPMLg29RidkwJVgEbDMzB4C/gu4MFzeBTxUsN3D4bKu8Pvi5bLbIuLPKcBBZtZvZj8ys1eFy3ROS7sXOCX8/nTggPB7XaPVSzqnoGs0NTObBXQDdwLPc/dHIPiHB+wXbqbrtAIpzynoOk2l6Hwmaeg1OpkCrncD57v7AcD5wBfD5XHjtF5iueyWdE4fAWa6ezfwPuDrYe6Mzmlp7wTeY2Z3E3SPbw+X6xqtXtI51TWakpk9A7geWOTuT5TaNGaZrtMYFZxTXacptMo1OpkCrrcDK8PvrwOipPmHGfupd3+CobGHw++Ll8tusec07K79c/j93QTj5C9C57Qkd7/f3V/r7kcC3yA4b6BrtGpJ51TXaDpm1k7wj+xad4/+1v8YDsFEQ1uPhst1naZQyTnVdVpewvlM0tBrdDIFXJuBY8Lvjwd+HX5/A/BmM9vLzA4CXgjcFXbr/sXM5oV3K/wD8J16N7rJxZ5TM5tuZm3h9wcTnNPf6ZyWZmb7hV+nABcB0R1JukarlHROdY2WF77+LwL3ufvHC1bdQPBhi/DrdwqW6zotodJzquu0tBLnM0ljr9Gss/Cb4UHwSfYRYIQgcv1H4JXA3QR3KNwJHFmw/QcIPjlspODOBKCHIAfkt8CnCAvFTsZHJeeUIDF5Q7j8F8DJOqepzud5BHfZ/Aq4ovDc6BrN9pzqGk11Pl9JMKxyDzAQPl4PPAf4IcEHrB8C+xbso+s0w3Oq67Tq8/mG8D3gaeCPwKpmuEZVaV5EREQkZ5NpSFFERESkIRRwiYiIiORMAZeIiIhIzhRwiYiIiORMAZeIiIhIzhRwiYiIiORMAZeIiIhIzhRwiYhUyMw6Gt0GEWktCrhEpOWY2YlmtjOcnqNw+UHh8lPCn081s7Vm9pSZ/cHMPhrOvRZtf6iZfdPMHjKzbWa2wcwWhVMBRdsca2ZuZgvM7AYz+ytBJWoRkdQUcIlIK/o+wVyeby9a/g5gC3CzmZ1BMLn6XcApwKXAucDlBdt3EUzx8c8EU4J8Ptzu/THP+UWCKVZOCb8XEUlNU/uISEsys8uAM4GD3d3DSWd/D3wLWAw8ANzi7mcX7PNO4NPA/u7+56LjGdAG/BvwLnc/OFx+LHArcKW7n5/zyxKRCUo9XCLSqq4GDgSODX8+Lvz5S8CLgJnACjPbI3oAtwB7Ay8BMLO9zexSM/sNwUS3I8CHgIPC7QvdlPPrEZEJTAGXiLQkd/8dcBsQ9WCdDdzl7huA54bLbiYIoqLH78PlB4RfPwL8K3AVwZDiy4HLwnV7Fz3lH7N9BSIymRR/ghMRaSVfAD5vZhcCpwEXhMsfC7+eC/TH7BcFXqcD/+PuH41WmNmJCc+l/AsRqZoCLhFpZSsJcrK+SdBj/81w+UZgEJjl7p8vsX8HwVAiAGbWBrw5n6aKyGSmgEtEWpa7P2Vm1wLvAb7h7kPh8p1mdgHwNTN7FvA9YDtwMNALvMndtwE/AN4T5nA9Fh5nr7q/EBGZ8JTDJSKtri/8enXhQndfDpwKzAWuI+gN+2fgFwTBF8B7gZ8Q9JJdDdzL2LIRIiKZUFkIEWlpZvZRYCFwkLvvbHR7RETiaEhRRFqSmc0GXgy8G7hUwZaINDP1cIlISzKz24CjgBuAt7n79tJ7iIg0jgIuERERkZwpaV5EREQkZwq4RERERHKmgEtEREQkZwq4RERERHKmgEtEREQkZwq4RERERHL2/wO5D1K0lT3W/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5));\n",
    "\n",
    "x=df_c['year']\n",
    "y=df_c['rate']\n",
    "plt.scatter(x, y);\n",
    "plt.xlabel('year',fontsize=15);\n",
    "plt.ylabel('Rate (Children per woman)',fontsize=15);\n",
    "plt.title('Fertility rate throught time',fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make chunks of time so it's easier to make analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>year</th>\n",
       "      <th>rate</th>\n",
       "      <th>subregion</th>\n",
       "      <th>continent</th>\n",
       "      <th>timeline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1800</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>early 18's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1800</td>\n",
       "      <td>4.60</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Europe</td>\n",
       "      <td>early 18's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.99</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>early 18's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.93</td>\n",
       "      <td>Middle Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>early 18's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1800</td>\n",
       "      <td>6.80</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>early 18's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  rate        subregion      continent    timeline\n",
       "0  Afghanistan  1800  7.00    Southern Asia           Asia  early 18's\n",
       "1      Albania  1800  4.60  Southern Europe         Europe  early 18's\n",
       "2      Algeria  1800  6.99  Northern Africa         Africa  early 18's\n",
       "3       Angola  1800  6.93    Middle Africa         Africa  early 18's\n",
       "5    Argentina  1800  6.80    South America  South America  early 18's"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_edges=[1800,1850,1900,1950,2000,2050,2100]\n",
    "bin_names=[\"early 18's\",\"late 18's\",\"early 19's\",\"late 19's\",\"early 20's\",\"late 20's\"]\n",
    "df_master_1['timeline']=pd.cut(df_master_1['year'],bin_edges,labels=bin_names,include_lowest=True)\n",
    "df_master_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df_master_1['timeline']\n",
    "y=df_master_1['rate']\n",
    "plt.scatter(x, y);\n",
    "plt.xlabel('year',fontsize=12);\n",
    "plt.ylabel('Rate (Children per woman)',fontsize=12);\n",
    "plt.title('Fertility rate throught time',fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['early 18's', 'late 18's', 'early 19's', 'late 19's', 'early 20's', 'late 20's']\n",
       "Categories (6, object): ['early 18's' < 'late 18's' < 'early 19's' < 'late 19's' < 'early 20's' < 'late 20's']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master_1.timeline.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# preparing the data to create the master graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th>timeline</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Africa</th>\n",
       "      <th>early 18's</th>\n",
       "      <td>6.415102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 18's</th>\n",
       "      <td>6.412347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 19's</th>\n",
       "      <td>6.407506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 19's</th>\n",
       "      <td>6.342988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 20's</th>\n",
       "      <td>3.819767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 20's</th>\n",
       "      <td>2.288992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Asia</th>\n",
       "      <th>early 18's</th>\n",
       "      <td>6.410584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 18's</th>\n",
       "      <td>6.380062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 19's</th>\n",
       "      <td>6.210898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 19's</th>\n",
       "      <td>5.075613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 20's</th>\n",
       "      <td>2.247404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 20's</th>\n",
       "      <td>1.821858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Australia</th>\n",
       "      <th>early 18's</th>\n",
       "      <td>6.218715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 18's</th>\n",
       "      <td>6.061822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 19's</th>\n",
       "      <td>5.621778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 19's</th>\n",
       "      <td>5.036733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 20's</th>\n",
       "      <td>2.927244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 20's</th>\n",
       "      <td>2.095756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Europe</th>\n",
       "      <th>early 18's</th>\n",
       "      <td>5.271341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 18's</th>\n",
       "      <td>4.955897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 19's</th>\n",
       "      <td>3.697573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 19's</th>\n",
       "      <td>2.273470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 20's</th>\n",
       "      <td>1.646427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 20's</th>\n",
       "      <td>1.792168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">North America</th>\n",
       "      <th>early 18's</th>\n",
       "      <td>6.092647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 18's</th>\n",
       "      <td>4.828200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 19's</th>\n",
       "      <td>3.204500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 19's</th>\n",
       "      <td>2.412400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 20's</th>\n",
       "      <td>1.768100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 20's</th>\n",
       "      <td>1.831100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">South America</th>\n",
       "      <th>early 18's</th>\n",
       "      <td>6.268807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 18's</th>\n",
       "      <td>6.166150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 19's</th>\n",
       "      <td>5.801333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 19's</th>\n",
       "      <td>4.543417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>early 20's</th>\n",
       "      <td>2.150117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>late 20's</th>\n",
       "      <td>1.798867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              rate\n",
       "continent     timeline            \n",
       "Africa        early 18's  6.415102\n",
       "              late 18's   6.412347\n",
       "              early 19's  6.407506\n",
       "              late 19's   6.342988\n",
       "              early 20's  3.819767\n",
       "              late 20's   2.288992\n",
       "Asia          early 18's  6.410584\n",
       "              late 18's   6.380062\n",
       "              early 19's  6.210898\n",
       "              late 19's   5.075613\n",
       "              early 20's  2.247404\n",
       "              late 20's   1.821858\n",
       "Australia     early 18's  6.218715\n",
       "              late 18's   6.061822\n",
       "              early 19's  5.621778\n",
       "              late 19's   5.036733\n",
       "              early 20's  2.927244\n",
       "              late 20's   2.095756\n",
       "Europe        early 18's  5.271341\n",
       "              late 18's   4.955897\n",
       "              early 19's  3.697573\n",
       "              late 19's   2.273470\n",
       "              early 20's  1.646427\n",
       "              late 20's   1.792168\n",
       "North America early 18's  6.092647\n",
       "              late 18's   4.828200\n",
       "              early 19's  3.204500\n",
       "              late 19's   2.412400\n",
       "              early 20's  1.768100\n",
       "              late 20's   1.831100\n",
       "South America early 18's  6.268807\n",
       "              late 18's   6.166150\n",
       "              early 19's  5.801333\n",
       "              late 19's   4.543417\n",
       "              early 20's  2.150117\n",
       "              late 20's   1.798867"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master_2world=df_master_1.copy()\n",
    "df_master_2world.drop(['year'],axis = 1,inplace = True)\n",
    "df_master_2world.groupby(['continent','timeline']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"early 18's\",\n",
       " \"late 18's\",\n",
       " \"early 19's\",\n",
       " \"late 19's\",\n",
       " \"early 20's\",\n",
       " \"late 20's\"]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = list(df_master_2world.timeline.unique())\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.2688071895425015,\n",
       " 6.166150000000002,\n",
       " 5.801333333333324,\n",
       " 4.543416666666667,\n",
       " 2.1501166666666705,\n",
       " 1.7988666666666593]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = \"South America\"\n",
    "list(df_master_2world.groupby(['continent','timeline']).mean().query('continent == \"{}\"'.format(n))['rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Asia', 'Europe', 'Africa', 'South America', 'Australia', 'North America']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = list(df_master_2world.continent.unique())\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Asia': [6.410583877995682,\n",
       "  6.380062222222233,\n",
       "  6.210897777777798,\n",
       "  5.075613333333317,\n",
       "  2.2474044444444385,\n",
       "  1.821857777777795],\n",
       " 'Europe': [5.271340752517201,\n",
       "  4.955897297297296,\n",
       "  3.697572972972977,\n",
       "  2.2734702702702703,\n",
       "  1.6464270270270243,\n",
       "  1.7921675675675728],\n",
       " 'Africa': [6.415102040816261,\n",
       "  6.4123469387754595,\n",
       "  6.40750612244895,\n",
       "  6.342987755102049,\n",
       "  3.819767346938772,\n",
       "  2.2889918367346973],\n",
       " 'South America': [6.2688071895425015,\n",
       "  6.166150000000002,\n",
       "  5.801333333333324,\n",
       "  4.543416666666667,\n",
       "  2.1501166666666705,\n",
       "  1.7988666666666593],\n",
       " 'Australia': [6.218714596949899,\n",
       "  6.061822222222231,\n",
       "  5.6217777777777815,\n",
       "  5.036733333333334,\n",
       "  2.927244444444443,\n",
       "  2.0957555555555505],\n",
       " 'North America': [6.0926470588235375,\n",
       "  4.828199999999999,\n",
       "  3.2044999999999986,\n",
       "  2.4124000000000008,\n",
       "  1.7680999999999991,\n",
       "  1.831099999999999]}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_dict={}\n",
    "for a in t:\n",
    "    rate_dict[a]=list(df_master_2world.groupby(['continent','timeline']).mean().query('continent == \"{}\"'.format(a))['rate'])    \n",
    "rate_dict    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 =rate_dict[\"Asia\"]\n",
    "y2 =rate_dict[\"Europe\"]\n",
    "y3 =rate_dict[\"Africa\"]\n",
    "y4 =rate_dict[\"South America\"]\n",
    "y5 =rate_dict[\"Australia\"]\n",
    "y6 =rate_dict[\"North America\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x2880 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10));\n",
    "m='o'\n",
    "ms = 4\n",
    "plt.plot(x,y1,marker = m,label = 'Asia',markersize=ms);\n",
    "plt.plot(x,y2,marker = m,label = 'Europe',markersize=ms);\n",
    "plt.plot(x,y3,marker = m,label = 'Africa',markersize=ms);\n",
    "plt.plot(x,y4,marker = m,label = 'South America',markersize=ms);\n",
    "plt.plot(x,y5,marker = m,label = 'Australia',markersize=ms);\n",
    "plt.plot(x,y6,marker = m,label = 'North America',markersize=ms);\n",
    "\n",
    "plt.legend(prop={'size':16});\n",
    "plt.xlabel('Timeline',fontsize=20);\n",
    "plt.ylabel('Rate (Children per woman)',fontsize=20);\n",
    "plt.title('Fertility rate throught time',fontsize=20);\n",
    "plt.figure(figsize=(20,40));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# closer look to the end of the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x2880 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10));\n",
    "m='D'\n",
    "ms = 5\n",
    "plt.plot(x,y1,marker = m,label = 'Asia',markersize=ms);\n",
    "plt.plot(x,y2,marker = m,label = 'Europe',markersize=ms);\n",
    "plt.plot(x,y3,marker = m,label = 'Africa',markersize=ms);\n",
    "plt.plot(x,y4,marker = m,label = 'South America',markersize=ms);\n",
    "plt.plot(x,y5,marker = m,label = 'Australia',markersize=ms);\n",
    "plt.plot(x,y6,marker = m,label = 'North America',markersize=ms);\n",
    "\n",
    "plt.legend(prop={'size':16});\n",
    "plt.xlabel('Timeline',fontsize=20);\n",
    "plt.ylabel('Rate (Children per woman)',fontsize=20);\n",
    "plt.title('Fertility rate throught time',fontsize=20);\n",
    "plt.ylim(1.5,2.4)\n",
    "plt.figure(figsize=(20,40));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Future Expectations of mean fertility rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeline</th>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">late 20's</th>\n",
       "      <th>Africa</th>\n",
       "      <td>2.288992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>2.095756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>1.831100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>1.821858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>1.798867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>1.792168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             rate\n",
       "timeline  continent              \n",
       "late 20's Africa         2.288992\n",
       "          Australia      2.095756\n",
       "          North America  1.831100\n",
       "          Asia           1.821858\n",
       "          South America  1.798867\n",
       "          Europe         1.792168"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master_3world=df_master_1.copy()\n",
    "df_master_3world = df_master_3world.drop(['year'],axis = 1)\n",
    "df_master_3world = df_master_3world.groupby(['timeline','continent']).mean()\n",
    "df_master_3world.query('timeline == \"late 20\\'s\"').sort_values('rate',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_master_3world=df_master_1.copy()\n",
    "df_master_3world = df_master_3world.drop(['year'],axis = 1)\n",
    "df_master_3world = df_master_3world.groupby(['timeline','continent']).mean()\n",
    "df_master_3world.query('timeline == \"late 20\\'s\"').sort_values('rate',ascending=False).plot(kind='bar');\n",
    "plt.xlabel('continent',fontsize=12);\n",
    "plt.ylabel('Rate (Children per woman)',fontsize=12);\n",
    "plt.title(' Predicted Fertility rate throught time',fontsize=15);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}