Investigate_a_Dataset.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "\n",
    "# Relationship between life expenctacy, Fertility Rate and Economical growth for all countries - grouped by income level\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "Is there a relationship between how long people live, their number of children and the economic growth of their country? Based on datasets from Gapminder https://www.gapminder.org/data/ on all countries, the goal is to uncover if correlations exisit between those how long people live, how much babies they make and economic growth. \n",
    "I am working with 4 datasets: <br> \n",
    "1. income_per_person_gdppercapita_ppp_inflation_adjusted.csv <br> the GDP or Gross Domestic Product. It has often been used to classify countries by Income. this dataset is in american dollars <br>\n",
    "2. life_expectancy_years.csv <br> This dataset show how much people live in average in each country\n",
    "3. children_per_woman_total_fertility.csv \n",
    "*\"This entry focuses on the number of births per woman in a population. The most commonly used metric is the Total Fertility Rate (TFR) – or often simply 'fertility rate' – which measures the average number of children per woman\"* \n",
    "<br> \n",
    "https://ourworldindata.org/fertility-rate <br>\n",
    "4. gdp_total_yearly_growth.csv <br> this dataset shows economical growth for each countries in percentage of the GDP(Gross Domestic product). \n",
    "\n",
    "<p> The data will be analyzed since the year 1950. the year 1950 is selected because it is after just after the 2nd world war II which has affected most countries in the west. It is also since that time that many countries all over the world could benefit from economical growth for example many asians countries. Almost all African countries gain their independance from the 1960's. \n",
    "    And equally relevant to this analysis, with women going to work and other major change in lifestyles,the need to question traditionally big family arised in the following decades.<br>\n",
    "<p> what is the relationship between fertility, longevity and economical growth for countries? is there even a relationship? \n",
    "Through the analysis that I am going to make, we will get to discover what data says about different countries from all income levels. <br> \n",
    "    \n",
    "   #### steps to follow: \n",
    "   \n",
    "   * we are first going to Group countries by GDP or Income Level\n",
    "   * The datasets provided have Data from the year 1800 which is more 200 years of data. Since we are only trying to understand changes since the 1950's , we are going to reduce our data from the year 1950 to  2018. \n",
    "  \n",
    "   * we will then group countries in each datasets -Fertility Rate, Life Expectancy and economic Growth based on their income level\n",
    "   * Compare the average life expectancy of countries based on their income level\n",
    "   * Compare How much children are born in country based on their income level\n",
    "   * Finally is there a relationship between growth, how much babies are born and economical growth in a country? \n",
    "   \n",
    "   Let's begin by importing all the packages that will be needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages needed for the analysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "In this section I am going to load the datasets that will be used. <br>\n",
    "1.children_per_woman_total_fertility.csv <br>\n",
    "2.gdp_total_yearly_growth.csv <br>\n",
    "3.income_per_person_gdppercapita_ppp_inflation_adjusted.csv <br>\n",
    "4.life_expectancy_years.csv <br>\n",
    "\n",
    "These datasets are already neat and clean. In this section I will get a look at the data to prepare it for the actual analysis. \n",
    "The data will be loaded, checked for null values, uneccessary columns will be droped. etc..\n",
    "\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Income per person GDP\n",
    "<br>\n",
    "This dataset will be used to group countries by income or GDP Per Capita. \n",
    "In order to do that, I could simply use the Quartiles in the data which simply to use group countries by the GDP income in 4 groups based on the most recent year. or to use the New Country Classification by income level  from the World bank. https://blogs.worldbank.org/opendata/new-country-classifications-income-level-2018-2019. but we will get in to that later. for now let's have a quick look at the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "<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>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>...</td>\n",
       "      <td>1530</td>\n",
       "      <td>1610</td>\n",
       "      <td>1660</td>\n",
       "      <td>1840</td>\n",
       "      <td>1810</td>\n",
       "      <td>1780</td>\n",
       "      <td>1750</td>\n",
       "      <td>1740</td>\n",
       "      <td>1800</td>\n",
       "      <td>1870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>667</td>\n",
       "      <td>667</td>\n",
       "      <td>667</td>\n",
       "      <td>667</td>\n",
       "      <td>667</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>...</td>\n",
       "      <td>9530</td>\n",
       "      <td>9930</td>\n",
       "      <td>10200</td>\n",
       "      <td>10400</td>\n",
       "      <td>10500</td>\n",
       "      <td>10700</td>\n",
       "      <td>11000</td>\n",
       "      <td>11400</td>\n",
       "      <td>11900</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>715</td>\n",
       "      <td>716</td>\n",
       "      <td>717</td>\n",
       "      <td>718</td>\n",
       "      <td>719</td>\n",
       "      <td>720</td>\n",
       "      <td>721</td>\n",
       "      <td>722</td>\n",
       "      <td>723</td>\n",
       "      <td>...</td>\n",
       "      <td>12600</td>\n",
       "      <td>12900</td>\n",
       "      <td>13000</td>\n",
       "      <td>13200</td>\n",
       "      <td>13300</td>\n",
       "      <td>13500</td>\n",
       "      <td>13700</td>\n",
       "      <td>14000</td>\n",
       "      <td>13800</td>\n",
       "      <td>13700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1210</td>\n",
       "      <td>1210</td>\n",
       "      <td>1210</td>\n",
       "      <td>1210</td>\n",
       "      <td>1220</td>\n",
       "      <td>...</td>\n",
       "      <td>41700</td>\n",
       "      <td>39000</td>\n",
       "      <td>42000</td>\n",
       "      <td>41900</td>\n",
       "      <td>43700</td>\n",
       "      <td>44900</td>\n",
       "      <td>46600</td>\n",
       "      <td>48200</td>\n",
       "      <td>49800</td>\n",
       "      <td>51500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>618</td>\n",
       "      <td>620</td>\n",
       "      <td>623</td>\n",
       "      <td>626</td>\n",
       "      <td>628</td>\n",
       "      <td>631</td>\n",
       "      <td>634</td>\n",
       "      <td>637</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>5910</td>\n",
       "      <td>5900</td>\n",
       "      <td>5910</td>\n",
       "      <td>6000</td>\n",
       "      <td>6190</td>\n",
       "      <td>6260</td>\n",
       "      <td>6230</td>\n",
       "      <td>6030</td>\n",
       "      <td>5940</td>\n",
       "      <td>5850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 220 columns</p>\n",
       "</div>"
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       "       country  1800  1801  1802  1803  1804  1805  1806  1807  1808  ...    \\\n",
       "0  Afghanistan   603   603   603   603   603   603   603   603   603  ...     \n",
       "1      Albania   667   667   667   667   667   668   668   668   668  ...     \n",
       "2      Algeria   715   716   717   718   719   720   721   722   723  ...     \n",
       "3      Andorra  1200  1200  1200  1200  1210  1210  1210  1210  1220  ...     \n",
       "4       Angola   618   620   623   626   628   631   634   637   640  ...     \n",
       "\n",
       "    2009   2010   2011   2012   2013   2014   2015   2016   2017   2018  \n",
       "0   1530   1610   1660   1840   1810   1780   1750   1740   1800   1870  \n",
       "1   9530   9930  10200  10400  10500  10700  11000  11400  11900  12400  \n",
       "2  12600  12900  13000  13200  13300  13500  13700  14000  13800  13700  \n",
       "3  41700  39000  42000  41900  43700  44900  46600  48200  49800  51500  \n",
       "4   5910   5900   5910   6000   6190   6260   6230   6030   5940   5850  \n",
       "\n",
       "[5 rows x 220 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the income_per_person_gdppercapita_ppp_inflation_adjusted.csv file\n",
    "#running df.head() for a quick view at the data\n",
    "df_gdp = pd.read_csv('income_per_person_gdppercapita_ppp_inflation_adjusted.csv')\n",
    "df_gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193 entries, 0 to 192\n",
      "Columns: 220 entries, country to 2018\n",
      "dtypes: int64(219), object(1)\n",
      "memory usage: 331.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking the composition of the dataframe\n",
    "df_gdp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 220)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of columns and rows\n",
    "df_gdp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list is very important because we will group all the othes datasets based on income level.\n",
    "checking for unique values guarantees that each country is only repeated once, which is what we need to ensure that our analysis is right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking unique values in the GDP dataset\n",
    "unique = df_gdp.country.unique()\n",
    "len(unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the shape of our data showed that the data has 193. we can see from the command above that all countries are only included one in the dataset. great! let's continue by checking for null values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gdp.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can that the dataset has data on 193 countries. From the year 1800 to 2018. With no missing value. <br>\n",
    "before dropping columns,let's first load the other datasets in order to understand better the data and the periods covered by all the datasets. since we will be comparing countries, it's good to know that the same countries and periods are covered. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GDP Yearly Growth\n",
    "\n",
    "This data set describre the increase in GDP by year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>country</th>\n",
       "      <th>1801</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.55</td>\n",
       "      <td>12.40</td>\n",
       "      <td>4.56</td>\n",
       "      <td>13.600</td>\n",
       "      <td>2.50</td>\n",
       "      <td>20.20</td>\n",
       "      <td>8.04</td>\n",
       "      <td>6.98</td>\n",
       "      <td>14.80</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.418</td>\n",
       "      <td>...</td>\n",
       "      <td>5.97</td>\n",
       "      <td>5.53</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.850</td>\n",
       "      <td>7.24</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.64</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.356</td>\n",
       "      <td>...</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.38</td>\n",
       "      <td>1.72</td>\n",
       "      <td>3.420</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3.57</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.166</td>\n",
       "      <td>...</td>\n",
       "      <td>8.64</td>\n",
       "      <td>7.80</td>\n",
       "      <td>4.97</td>\n",
       "      <td>0.161</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-5.06</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.425</td>\n",
       "      <td>...</td>\n",
       "      <td>11.20</td>\n",
       "      <td>20.50</td>\n",
       "      <td>18.60</td>\n",
       "      <td>23.200</td>\n",
       "      <td>13.80</td>\n",
       "      <td>2.39</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.87</td>\n",
       "      <td>4.96</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 214 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country   1801   1802   1803   1804   1805   1806   1807   1808   1809  \\\n",
       "0  Afghanistan  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000  0.000   \n",
       "1      Albania  0.418  0.418  0.418  0.418  0.418  0.418  0.418  0.418  0.418   \n",
       "2      Algeria  0.356  0.356  0.356  0.356  0.356  0.356  0.356  0.356  0.356   \n",
       "3      Andorra  0.166  0.166  0.166  0.166  0.166  0.166  0.166  0.166  0.166   \n",
       "4       Angola  0.425  0.425  0.425  0.425  0.425  0.425  0.425  0.425  0.425   \n",
       "\n",
       "   ...    2004   2005   2006    2007   2008   2009  2010  2011   2012  2013  \n",
       "0  ...    6.55  12.40   4.56  13.600   2.50  20.20  8.04  6.98  14.80  4.47  \n",
       "1  ...    5.97   5.53   5.77   5.850   7.24   3.28  3.36  2.86   2.64  2.06  \n",
       "2  ...    5.41   5.38   1.72   3.420   2.02   1.70  3.57  2.31   2.58  2.99  \n",
       "3  ...    8.64   7.80   4.97   0.161  -4.22  -5.06 -3.43 -2.83    NaN   NaN  \n",
       "4  ...   11.20  20.50  18.60  23.200  13.80   2.39  3.45  3.87   4.96  3.79  \n",
       "\n",
       "[5 rows x 214 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the income_per_person_gdppercapita_ppp_inflation_adjusted.csv file\n",
    "#running df.head() for a quick view at the data\n",
    "df_growth = pd.read_csv('gdp_total_yearly_growth.csv')\n",
    "df_growth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 194 entries, 0 to 193\n",
      "Columns: 214 entries, country to 2013\n",
      "dtypes: float64(213), object(1)\n",
      "memory usage: 324.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_growth.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(194, 214)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_growth.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if there is any missing values\n",
    "df_growth.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking how many null values are in the dataframe. depending on the volumes of missing values, we will decide of the next step\n",
    "df_growth.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_rows = df_growth.isnull().any().any()\n",
    "nan_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The growth by GDP Dataset had 194 entries or countries. with 214 columns. As we can see from the df_growth.info() command, the period covered goes from 1801 to 2013. \n",
    "he previous dataset on gdp/income had had a shape of (193,220). This dataset has a shape of (194,214). \n",
    "Since our exploration will be a comparaison, after we load all the dataset, we need to make sure that the countries and periods covered are the same. or at least understand and notice the irregularities and includes them in our conclusions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Life Expectancy \n",
    "\n",
    "This data set describre the average life expectancy in years.\n",
    "we are going to import the file (life_expectancy_years.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>...</td>\n",
       "      <td>55.7</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.7</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.7</td>\n",
       "      <td>57.8</td>\n",
       "      <td>57.9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.4</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>...</td>\n",
       "      <td>75.9</td>\n",
       "      <td>76.3</td>\n",
       "      <td>76.7</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.2</td>\n",
       "      <td>77.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>77.7</td>\n",
       "      <td>77.9</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>...</td>\n",
       "      <td>76.3</td>\n",
       "      <td>76.5</td>\n",
       "      <td>76.7</td>\n",
       "      <td>76.8</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.1</td>\n",
       "      <td>77.3</td>\n",
       "      <td>77.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>77.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</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>...</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.5</td>\n",
       "      <td>82.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>59.3</td>\n",
       "      <td>60.1</td>\n",
       "      <td>60.9</td>\n",
       "      <td>61.7</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.7</td>\n",
       "      <td>64.9</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1800  1801  1802  1803  1804  1805  1806  1807  1808  ...   \\\n",
       "0  Afghanistan  28.2  28.2  28.2  28.2  28.2  28.2  28.1  28.1  28.1  ...    \n",
       "1      Albania  35.4  35.4  35.4  35.4  35.4  35.4  35.4  35.4  35.4  ...    \n",
       "2      Algeria  28.8  28.8  28.8  28.8  28.8  28.8  28.8  28.8  28.8  ...    \n",
       "3      Andorra   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "4       Angola  27.0  27.0  27.0  27.0  27.0  27.0  27.0  27.0  27.0  ...    \n",
       "\n",
       "   2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "0  55.7  56.2  56.7  57.2  57.7  57.8  57.9  58.0  58.4  58.7  \n",
       "1  75.9  76.3  76.7  77.0  77.2  77.4  77.6  77.7  77.9  78.0  \n",
       "2  76.3  76.5  76.7  76.8  77.0  77.1  77.3  77.4  77.6  77.9  \n",
       "3  82.7  82.7  82.6  82.6  82.6  82.6  82.5  82.5   NaN   NaN  \n",
       "4  59.3  60.1  60.9  61.7  62.5  63.3  64.0  64.7  64.9  65.2  \n",
       "\n",
       "[5 rows x 220 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the dataset\n",
    "#checking the head\n",
    "df_expectancy = pd.read_csv('life_expectancy_years.csv')\n",
    "df_expectancy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 220)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the shape of the dataframe\n",
    "df_expectancy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 187 entries, 0 to 186\n",
      "Columns: 220 entries, country to 2018\n",
      "dtypes: float64(219), object(1)\n",
      "memory usage: 321.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#the dataframe has 220 columns, and 187 rows. datatypes are float which is quite normale and expected for age.\n",
    "df_expectancy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if there is any null values\n",
    "df_expectancy.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    0\n",
       "1800       3\n",
       "1801       3\n",
       "1802       3\n",
       "1803       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_expectancy.isnull().sum().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "516"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#counting null values\n",
    "df_expectancy.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "516 seems to be too big of a number to just drop everything. this dataframe covers period from 1800 to 2018. In order to respond to our analysis questions we will only need the period from 1950. we will come back to this later. now let's load the fertility dataset.\n",
    "The null values will be handled once the data is grouped. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Fertility Rate or Number of children per woman\n",
    "\n",
    "This data set describre the average number of children per woman in all countries. \n",
    "we are going to import the file (children_per_woman_total_fertility.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>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>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</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>6.04</td>\n",
       "      <td>5.82</td>\n",
       "      <td>5.60</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.17</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.48</td>\n",
       "      <td>4.33</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>1.65</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.71</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>2.83</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2.71</td>\n",
       "      <td>2.64</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>6.24</td>\n",
       "      <td>6.16</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.92</td>\n",
       "      <td>5.84</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.62</td>\n",
       "      <td>5.55</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>2.15</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  1800  1801  1802  1803  1804  1805  1806  1807  1808  \\\n",
       "0          Afghanistan  7.00  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  4.60   \n",
       "2              Algeria  6.99  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  6.94   \n",
       "4  Antigua and Barbuda  5.00  5.00  4.99  4.99  4.99  4.98  4.98  4.97  4.97   \n",
       "\n",
       "   ...   2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "0  ...   6.04  5.82  5.60  5.38  5.17  4.98  4.80  4.64  4.48  4.33  \n",
       "1  ...   1.65  1.65  1.67  1.69  1.70  1.71  1.71  1.71  1.71  1.71  \n",
       "2  ...   2.83  2.89  2.93  2.94  2.92  2.89  2.84  2.78  2.71  2.64  \n",
       "3  ...   6.24  6.16  6.08  6.00  5.92  5.84  5.77  5.69  5.62  5.55  \n",
       "4  ...   2.15  2.13  2.12  2.10  2.09  2.08  2.06  2.05  2.04  2.03  \n",
       "\n",
       "[5 rows x 220 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import the children_per_woman_total_fertility.csv dataset\n",
    "df_birth = pd.read_csv('children_per_woman_total_fertility.csv')\n",
    "df_birth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 184 entries, 0 to 183\n",
      "Columns: 220 entries, country to 2018\n",
      "dtypes: float64(219), object(1)\n",
      "memory usage: 316.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking the dataframe\n",
    "df_birth.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(184, 220)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the shape of the dataframe\n",
    "df_birth.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values\n",
    "df_birth.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values in the Fertility Rate Dataset. the dataset covers from the year 1800 to the year 2018.\n",
    "The shape of the dataframe is 184 rows and 220 columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping by GDP Levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we are going to check if the same countries are present in all 4 datasets, check the period covered, group by income levels. after this step we will proceed with the Exploratoy Data Analysis. \n",
    "<ul>\n",
    "<li>group the countries by income level (4 levels) </li>\n",
    "<li>create a new daframe for each of those levels that will be used for Exploratory Data Analysis later</li>\n",
    "<li>check the countries in the dataframe</li>\n",
    "<li>Adjust the periods covered by all 4 datasets </li>\n",
    "</ul>\n",
    "\n",
    "#### Comparings Datasets shapes\n",
    "\n",
    "By comparing the 4 datasets shapes we can see that the number of columns is not the same in all 4 datasets. The columns represents the years. the rows the countries included.As you can see in the table below.<br> \n",
    "But for now let's focus on grouping the countries by Income Level based on the GDP Dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset shape     | Gdp_Income_Level    | Life Expectancy     | Fertility Rate     | Gdp Growth |\n",
    "  ---             |----------           |------               |------              |----------  |\n",
    "columns|   193               |     187             |    184             | 194        |\n",
    " rows    |  220                |     220             |     220            |    214     |\n",
    " \n",
    " let's make sure that the countries compared are the same, we are going to compare the columns from all the 4 datasets. \n",
    " we will look into that later. let's proceed with the grouping by income first. since the grouping of the other dataset will also be done based on gdp."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping by Income Level\n",
    "\n",
    "the file income_per_person_gdppercapita_ppp_inflation_adjusted.csv from Gapminder gives data from 1800 to 2018. \n",
    "since we are trying to group and observe what happened in the last decades, we are going to use the latest year to group countries. we are going to group countries based on their 2018 GDP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>...</td>\n",
       "      <td>1530</td>\n",
       "      <td>1610</td>\n",
       "      <td>1660</td>\n",
       "      <td>1840</td>\n",
       "      <td>1810</td>\n",
       "      <td>1780</td>\n",
       "      <td>1750</td>\n",
       "      <td>1740</td>\n",
       "      <td>1800</td>\n",
       "      <td>1870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1800  1801  1802  1803  1804  1805  1806  1807  1808  ...   \\\n",
       "0  Afghanistan   603   603   603   603   603   603   603   603   603  ...    \n",
       "\n",
       "   2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "0  1530  1610  1660  1840  1810  1780  1750  1740  1800  1870  \n",
       "\n",
       "[1 rows x 220 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2018 is the latest year\n",
    "df_gdp.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       193.000000\n",
       "mean      18241.502591\n",
       "std       19664.365483\n",
       "min         629.000000\n",
       "25%        3670.000000\n",
       "50%       12100.000000\n",
       "75%       25500.000000\n",
       "max      121000.000000\n",
       "Name: 2018, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Trying to understand the distribution of the gdp per capita based on the Quartiles. \n",
    "df_gdp['2018'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if we consider the number below, the 25% of low income countries have 3670 Dollars and below <br>\n",
    "the second group in the 50 would have a GDP of 12100  per capita and below, <br>\n",
    "the third group 25500 Dollars  of GDP per Capita <br>\n",
    "the last and richest group would have 121000 Dollars of GDP per capita. <br>\n",
    "\n",
    "Below we added an image from the world bank Group whith the most recent classification of countries based on income level.\n",
    "the classification is from 2018, this year.\n",
    "So I think its might be a more realistic categorisation of countries by income level. <br> \n",
    "After some time looking for officials classifications of countries by Income Level, its seemed like the world bank would  be a more credible authority. \n",
    "The Value below are the GNI or Gross National Income. they are not exactly the GDP but might be a more realistic way to compare countries than Quartiles. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"new country classification by GDP.PNG\">\n",
    "https://blogs.worldbank.org/opendata/new-country-classifications-income-level-2018-2019#comments\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the classification above to continue our analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Low Income Countries\n",
    "countries with less than 995 USD in GDP per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Niger</td>\n",
       "      <td>949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  2018\n",
       "27                    Burundi   691\n",
       "32   Central African Republic   689\n",
       "38           Congo, Dem. Rep.   751\n",
       "94                    Liberia   801\n",
       "123                     Niger   949\n",
       "154                   Somalia   629"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping countries by income using loc and only keeping the country column\n",
    "low_income = df_gdp.loc[df_gdp['2018'] < 995, ['country', '2018']]\n",
    "low_income\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lower Middle Income Countries\n",
    "countries with GDP per capita between 996 and 3895 USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>3720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benin</td>\n",
       "      <td>2170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>1710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>3830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  2018\n",
       "0    Afghanistan  1870\n",
       "13    Bangladesh  3720\n",
       "18         Benin  2170\n",
       "26  Burkina Faso  1710\n",
       "28      Cambodia  3830"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping countries by income using loc and only keeping the country column\n",
    "lower_middle_income = df_gdp.loc[(df_gdp['2018'] > 995) & (df_gdp['2018'] < 3896), ['country', '2018']]\n",
    "lower_middle_income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Upper Middle Income Countries\n",
    "countries with GDP between 3896 and 12055 USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>5850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>8660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belize</td>\n",
       "      <td>7710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>9930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>6980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  2018\n",
       "4    Angola  5850\n",
       "7   Armenia  8660\n",
       "17   Belize  7710\n",
       "19   Bhutan  9930\n",
       "20  Bolivia  6980"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping countries by income using loc and only keeping the country column\n",
    "upper_middle_income = df_gdp.loc[(df_gdp['2018'] >= 3896) & (df_gdp['2018'] <= 12055), ['country', '2018']]\n",
    "upper_middle_income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High Income Countries\n",
    "Countries with GDP Per capita which is above 12055 USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>13700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>51500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>18900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country   2018\n",
       "1              Albania  12400\n",
       "2              Algeria  13700\n",
       "3              Andorra  51500\n",
       "5  Antigua and Barbuda  21000\n",
       "6            Argentina  18900"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping countries by income using loc and only keeping the country column\n",
    "high_income = df_gdp.loc[df_gdp['2018'] > 12055, ['country', '2018']]\n",
    "high_income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verifying that the count all the countries included in the income file found a group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    193\n",
       "2018       193\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#veryfing that the count of our 4 new dataframes are equals to the total of gdp_df they were extracted from. \n",
    "#The number match our original GDP file\n",
    "low_income.count()+ lower_middle_income.count() + upper_middle_income.count() + high_income.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the count match our originial GDP file. meaning that each country is grouped. perfect! we are now going to group countries in the life expectancy dataset in the income level group as set above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Life Expectancy Grouping by Income Level, from the year 1950\n",
    "Now we are going to group the other datasets by Income. \n",
    "let's start by the life expectancy Dataset <br>\n",
    "based on the classification by countries's income, we are equally going to classify the other datasets in 4 groups. <br>\n",
    "we are going to create 4 groups within the life expectancy group: \n",
    "* low_income_expectancy\n",
    "* lower_middle_income_expectancy\n",
    "* upper_middle_income_expectancy\n",
    "* high_income_expectancy\n",
    "\n",
    "#### Limiting the dataset from the year 1950\n",
    "this dataset has data from the year 1800. lets remove the 150 years that are not necessary to answer our questions and only keep the decades that are of interest to us. But that still leaves us with 68 years of data or meaning 68 rows per country. In order to properly understand the progress, we are going to divide the data in decades from 1950 to 2018. Since we want to globally understand the tendancy, randomly picking a point in time every decade should give an idea of the general tendancy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>34.4</td>\n",
       "      <td>35.1</td>\n",
       "      <td>35.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>37.2</td>\n",
       "      <td>...</td>\n",
       "      <td>55.7</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.7</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.7</td>\n",
       "      <td>57.8</td>\n",
       "      <td>57.9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.4</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>54.5</td>\n",
       "      <td>54.7</td>\n",
       "      <td>55.2</td>\n",
       "      <td>55.8</td>\n",
       "      <td>56.5</td>\n",
       "      <td>57.3</td>\n",
       "      <td>58.3</td>\n",
       "      <td>59.3</td>\n",
       "      <td>60.4</td>\n",
       "      <td>...</td>\n",
       "      <td>75.9</td>\n",
       "      <td>76.3</td>\n",
       "      <td>76.7</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.2</td>\n",
       "      <td>77.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>77.7</td>\n",
       "      <td>77.9</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>46.9</td>\n",
       "      <td>47.1</td>\n",
       "      <td>47.6</td>\n",
       "      <td>48.1</td>\n",
       "      <td>48.6</td>\n",
       "      <td>49.2</td>\n",
       "      <td>49.7</td>\n",
       "      <td>50.3</td>\n",
       "      <td>50.9</td>\n",
       "      <td>...</td>\n",
       "      <td>76.3</td>\n",
       "      <td>76.5</td>\n",
       "      <td>76.7</td>\n",
       "      <td>76.8</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.1</td>\n",
       "      <td>77.3</td>\n",
       "      <td>77.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>77.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</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>...</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.5</td>\n",
       "      <td>82.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>36.6</td>\n",
       "      <td>36.9</td>\n",
       "      <td>37.5</td>\n",
       "      <td>38.1</td>\n",
       "      <td>38.7</td>\n",
       "      <td>39.3</td>\n",
       "      <td>39.9</td>\n",
       "      <td>40.5</td>\n",
       "      <td>41.2</td>\n",
       "      <td>...</td>\n",
       "      <td>59.3</td>\n",
       "      <td>60.1</td>\n",
       "      <td>60.9</td>\n",
       "      <td>61.7</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.7</td>\n",
       "      <td>64.9</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950  1951  1952  1953  1954  1955  1956  1957  1958  ...   \\\n",
       "0  Afghanistan  32.0  32.4  33.0  33.7  34.4  35.1  35.8  36.5  37.2  ...    \n",
       "1      Albania  54.5  54.7  55.2  55.8  56.5  57.3  58.3  59.3  60.4  ...    \n",
       "2      Algeria  46.9  47.1  47.6  48.1  48.6  49.2  49.7  50.3  50.9  ...    \n",
       "3      Andorra   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "4       Angola  36.6  36.9  37.5  38.1  38.7  39.3  39.9  40.5  41.2  ...    \n",
       "\n",
       "   2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "0  55.7  56.2  56.7  57.2  57.7  57.8  57.9  58.0  58.4  58.7  \n",
       "1  75.9  76.3  76.7  77.0  77.2  77.4  77.6  77.7  77.9  78.0  \n",
       "2  76.3  76.5  76.7  76.8  77.0  77.1  77.3  77.4  77.6  77.9  \n",
       "3  82.7  82.7  82.6  82.6  82.6  82.6  82.5  82.5   NaN   NaN  \n",
       "4  59.3  60.1  60.9  61.7  62.5  63.3  64.0  64.7  64.9  65.2  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping irrelevant columns.  from the year 1800 to 1949\n",
    "cols=  df_expectancy.columns[1:151] \n",
    "df_expectancy.drop(cols,axis=1, inplace=True)\n",
    "df_expectancy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the dataset above would still be too much unnecessary data. so we are only to randomly select one year every decade from 1950."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>45.8</td>\n",
       "      <td>43.3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>51.6</td>\n",
       "      <td>56.2</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>54.5</td>\n",
       "      <td>62.7</td>\n",
       "      <td>67.4</td>\n",
       "      <td>72.3</td>\n",
       "      <td>73.9</td>\n",
       "      <td>74.4</td>\n",
       "      <td>76.3</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>46.9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>62.1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>73.9</td>\n",
       "      <td>76.5</td>\n",
       "      <td>77.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>78.7</td>\n",
       "      <td>79.5</td>\n",
       "      <td>81.8</td>\n",
       "      <td>82.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>36.6</td>\n",
       "      <td>42.4</td>\n",
       "      <td>48.7</td>\n",
       "      <td>49.9</td>\n",
       "      <td>50.2</td>\n",
       "      <td>53.4</td>\n",
       "      <td>60.1</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "0  Afghanistan  32.0  38.6  45.8  43.3  51.6  51.6  56.2  58.7\n",
       "1      Albania  54.5  62.7  67.4  72.3  73.9  74.4  76.3  78.0\n",
       "2      Algeria  46.9  52.0  57.5  62.1  71.0  73.9  76.5  77.9\n",
       "3      Andorra   NaN   NaN  76.0  78.7  79.5  81.8  82.7   NaN\n",
       "4       Angola  36.6  42.4  48.7  49.9  50.2  53.4  60.1  65.2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting the years 1950, 1960, 1970, 1980, 1990, 2000, 2010, 2018\n",
    "df_expectancy = df_expectancy[['country','1950', '1960', '1970', '1980', '1990', '2000', '2010', '2018']]\n",
    "df_expectancy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Life Expectancy grouping For Low Income Income Countries\n",
    "we are going to use the isin to group countries which are in low income country dataframe created earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>44.7</td>\n",
       "      <td>46.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>47.9</td>\n",
       "      <td>57.7</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "27  Burundi  39.0  42.0  44.7  46.3  48.0  47.9  57.7  61.1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping life for low income countries\n",
    "low_income_expectancy = df_expectancy.loc[df_expectancy.country.isin(low_income.country.tolist())]\n",
    "low_income_expectancy.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Life Expectancy grouping For Lower Middle Income Countries\n",
    "Grouping Lower Middle Income Countries Life Expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>45.8</td>\n",
       "      <td>43.3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>51.6</td>\n",
       "      <td>56.2</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "0  Afghanistan  32.0  38.6  45.8  43.3  51.6  51.6  56.2  58.7"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping life for lower middle income countries\n",
    "lower_middle_income_expectancy = df_expectancy.loc[df_expectancy.country.isin(lower_middle_income.country.tolist())]\n",
    "lower_middle_income_expectancy.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Life Expectancy grouping For Upper Middle Income Countries\n",
    "Grouping Life Expectancy data for Upper Middle Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>36.6</td>\n",
       "      <td>42.4</td>\n",
       "      <td>48.7</td>\n",
       "      <td>49.9</td>\n",
       "      <td>50.2</td>\n",
       "      <td>53.4</td>\n",
       "      <td>60.1</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "4  Angola  36.6  42.4  48.7  49.9  50.2  53.4  60.1  65.2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping life for upper middle income countries\n",
    "upper_middle_income_expectancy = df_expectancy.loc[df_expectancy.country.isin(upper_middle_income.country.tolist())]\n",
    "upper_middle_income_expectancy.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Life Expectancy grouping For High Income Countries\n",
    "Grouping Life Expectancy Data for High Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>54.5</td>\n",
       "      <td>62.7</td>\n",
       "      <td>67.4</td>\n",
       "      <td>72.3</td>\n",
       "      <td>73.9</td>\n",
       "      <td>74.4</td>\n",
       "      <td>76.3</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "1  Albania  54.5  62.7  67.4  72.3  73.9  74.4  76.3  78.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping life for High income countries\n",
    "high_income_expectancy = df_expectancy.loc[df_expectancy.country.isin(high_income.country.tolist())]\n",
    "high_income_expectancy.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fertility Rate Grouping by Income Level, from the year 1950\n",
    "based on the classification by countries's income, we are equally going to classify this  dataset in 4 groups. <br>\n",
    "we are going to create 4 groups within the life expectancy group: \n",
    "* low_income_birth\n",
    "* lower_middle_income_birth\n",
    "* upper_middle_income_birth\n",
    "* high_income_birth\n",
    "\n",
    "#### Limiting the dataset from the year 1950\n",
    "this dataset has data from the year 1800. lets remove the 150 years that are not necessary to answer our questions and only keep the decades that are of interest to us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7.57</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.55</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.53</td>\n",
       "      <td>7.52</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.48</td>\n",
       "      <td>...</td>\n",
       "      <td>6.04</td>\n",
       "      <td>5.82</td>\n",
       "      <td>5.60</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.17</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.80</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.48</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>5.87</td>\n",
       "      <td>5.97</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.37</td>\n",
       "      <td>6.45</td>\n",
       "      <td>6.50</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.54</td>\n",
       "      <td>...</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.44</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.41</td>\n",
       "      <td>7.41</td>\n",
       "      <td>7.41</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.45</td>\n",
       "      <td>...</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2.71</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>7.11</td>\n",
       "      <td>7.13</td>\n",
       "      <td>7.15</td>\n",
       "      <td>7.17</td>\n",
       "      <td>7.20</td>\n",
       "      <td>7.23</td>\n",
       "      <td>7.27</td>\n",
       "      <td>7.31</td>\n",
       "      <td>7.36</td>\n",
       "      <td>...</td>\n",
       "      <td>6.24</td>\n",
       "      <td>6.16</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.92</td>\n",
       "      <td>5.84</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.62</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.46</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.52</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.51</td>\n",
       "      <td>4.49</td>\n",
       "      <td>...</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  1950  1951  1952  1953  1954  1955  1956  1957  1958  \\\n",
       "0          Afghanistan  7.57  7.56  7.55  7.54  7.53  7.52  7.51  7.49  7.48   \n",
       "1              Albania  5.87  5.97  6.13  6.27  6.37  6.45  6.50  6.53  6.54   \n",
       "2              Algeria  7.49  7.47  7.44  7.43  7.41  7.41  7.41  7.43  7.45   \n",
       "3               Angola  7.11  7.13  7.15  7.17  7.20  7.23  7.27  7.31  7.36   \n",
       "4  Antigua and Barbuda  4.45  4.46  4.50  4.52  4.53  4.53  4.53  4.51  4.49   \n",
       "\n",
       "   ...   2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "0  ...   6.04  5.82  5.60  5.38  5.17  4.98  4.80  4.64  4.48  4.33  \n",
       "1  ...   1.65  1.65  1.67  1.69  1.70  1.71  1.71  1.71  1.71  1.71  \n",
       "2  ...   2.83  2.89  2.93  2.94  2.92  2.89  2.84  2.78  2.71  2.64  \n",
       "3  ...   6.24  6.16  6.08  6.00  5.92  5.84  5.77  5.69  5.62  5.55  \n",
       "4  ...   2.15  2.13  2.12  2.10  2.09  2.08  2.06  2.05  2.04  2.03  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=  df_birth.columns[1:151] \n",
    "df_birth.drop(cols,axis=1, inplace=True)\n",
    "df_birth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7.57</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.49</td>\n",
       "      <td>5.82</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>5.87</td>\n",
       "      <td>6.49</td>\n",
       "      <td>4.91</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.52</td>\n",
       "      <td>7.64</td>\n",
       "      <td>6.79</td>\n",
       "      <td>4.73</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>7.11</td>\n",
       "      <td>7.48</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.50</td>\n",
       "      <td>7.25</td>\n",
       "      <td>6.64</td>\n",
       "      <td>6.16</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.43</td>\n",
       "      <td>3.68</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.32</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "0          Afghanistan  7.57  7.45  7.45  7.45  7.47  7.49  5.82  4.33\n",
       "1              Albania  5.87  6.49  4.91  3.62  2.98  2.16  1.65  1.71\n",
       "2              Algeria  7.49  7.52  7.64  6.79  4.73  2.51  2.89  2.64\n",
       "3               Angola  7.11  7.48  7.60  7.50  7.25  6.64  6.16  5.55\n",
       "4  Antigua and Barbuda  4.45  4.43  3.68  2.12  2.06  2.32  2.13  2.03"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting the years 1950, 1960, 1970, 1980, 1990, 2000, 2010, 2018\n",
    "df_birth = df_birth[['country','1950', '1960', '1970', '1980', '1990', '2000', '2010', '2018']]\n",
    "df_birth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fertility grouping For Low Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>6.82</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.29</td>\n",
       "      <td>7.42</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.01</td>\n",
       "      <td>6.26</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "26  Burundi  6.82  6.95  7.29  7.42  7.51  7.01  6.26  5.53"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data for low income country\n",
    "low_income_birth = df_birth.loc[df_birth.country.isin(low_income.country.tolist())]\n",
    "low_income_birth.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fertily grouping For Lower Middle Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7.57</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.49</td>\n",
       "      <td>5.82</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "0  Afghanistan  7.57  7.45  7.45  7.45  7.47  7.49  5.82  4.33"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data for lower middle income country\n",
    "lower_middle_income_birth = df_birth.loc[df_birth.country.isin(lower_middle_income.country.tolist())]\n",
    "lower_middle_income_birth.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fertily grouping For Upper Middle Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>7.11</td>\n",
       "      <td>7.48</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.25</td>\n",
       "      <td>6.64</td>\n",
       "      <td>6.16</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "3  Angola  7.11  7.48   7.6   7.5  7.25  6.64  6.16  5.55"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data for lower middle income country\n",
    "upper_middle_income_birth = df_birth.loc[df_birth.country.isin(upper_middle_income.country.tolist())]\n",
    "upper_middle_income_birth.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fertily grouping For High Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>5.87</td>\n",
       "      <td>6.49</td>\n",
       "      <td>4.91</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "1  Albania  5.87  6.49  4.91  3.62  2.98  2.16  1.65  1.71"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data for High income country\n",
    "high_income_birth = df_birth.loc[df_birth.country.isin(high_income.country.tolist())]\n",
    "high_income_birth.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Economic Growth Grouping by Income Level, from the year 1950\n",
    "based on the classification by countries's income, we are equally going to classify this  dataset in 4 groups. <br>\n",
    "we are going to create 4 groups within the Economic Growth Dataset: \n",
    "* low_income_growth\n",
    "* lower_middle_income_growth\n",
    "* upper_middle_income_growth\n",
    "* high_income_growth\n",
    "\n",
    "#### Limiting the dataset from the year 1950\n",
    "this dataset has data from the year 1801. lets remove the 149 years that are not necessary to answer our questions and only keep the decades that are of interest to us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.800</td>\n",
       "      <td>3.26</td>\n",
       "      <td>5.97</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.83</td>\n",
       "      <td>4.32</td>\n",
       "      <td>-0.182</td>\n",
       "      <td>5.42</td>\n",
       "      <td>...</td>\n",
       "      <td>6.55</td>\n",
       "      <td>12.40</td>\n",
       "      <td>4.56</td>\n",
       "      <td>13.600</td>\n",
       "      <td>2.50</td>\n",
       "      <td>20.20</td>\n",
       "      <td>8.04</td>\n",
       "      <td>6.98</td>\n",
       "      <td>14.80</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.87</td>\n",
       "      <td>6.460</td>\n",
       "      <td>2.53</td>\n",
       "      <td>6.77</td>\n",
       "      <td>5.84</td>\n",
       "      <td>8.52</td>\n",
       "      <td>4.11</td>\n",
       "      <td>9.760</td>\n",
       "      <td>7.83</td>\n",
       "      <td>...</td>\n",
       "      <td>5.97</td>\n",
       "      <td>5.53</td>\n",
       "      <td>5.77</td>\n",
       "      <td>5.850</td>\n",
       "      <td>7.24</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.64</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.955</td>\n",
       "      <td>4.29</td>\n",
       "      <td>1.50</td>\n",
       "      <td>7.12</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.78</td>\n",
       "      <td>11.400</td>\n",
       "      <td>3.74</td>\n",
       "      <td>...</td>\n",
       "      <td>5.41</td>\n",
       "      <td>5.38</td>\n",
       "      <td>1.72</td>\n",
       "      <td>3.420</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.70</td>\n",
       "      <td>3.57</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>4.48</td>\n",
       "      <td>12.100</td>\n",
       "      <td>12.40</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.40</td>\n",
       "      <td>12.20</td>\n",
       "      <td>12.000</td>\n",
       "      <td>11.80</td>\n",
       "      <td>...</td>\n",
       "      <td>8.64</td>\n",
       "      <td>7.80</td>\n",
       "      <td>4.97</td>\n",
       "      <td>0.161</td>\n",
       "      <td>-4.22</td>\n",
       "      <td>-5.06</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>-2.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4.090</td>\n",
       "      <td>4.19</td>\n",
       "      <td>4.13</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>8.38</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>9.790</td>\n",
       "      <td>5.46</td>\n",
       "      <td>...</td>\n",
       "      <td>11.20</td>\n",
       "      <td>20.50</td>\n",
       "      <td>18.60</td>\n",
       "      <td>23.200</td>\n",
       "      <td>13.80</td>\n",
       "      <td>2.39</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.87</td>\n",
       "      <td>4.96</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950    1951   1952   1953   1954   1955   1956    1957  \\\n",
       "0  Afghanistan  1.42   2.800   3.26   5.97   1.97   1.83   4.32  -0.182   \n",
       "1      Albania  0.87   6.460   2.53   6.77   5.84   8.52   4.11   9.760   \n",
       "2      Algeria  1.70   0.955   4.29   1.50   7.12   2.68   9.78  11.400   \n",
       "3      Andorra  4.48  12.100  12.40  12.50  12.50  12.40  12.20  12.000   \n",
       "4       Angola  4.04   4.090   4.19   4.13  -2.31   8.38  -1.59   9.790   \n",
       "\n",
       "    1958  ...    2004   2005   2006    2007   2008   2009  2010  2011   2012  \\\n",
       "0   5.42  ...    6.55  12.40   4.56  13.600   2.50  20.20  8.04  6.98  14.80   \n",
       "1   7.83  ...    5.97   5.53   5.77   5.850   7.24   3.28  3.36  2.86   2.64   \n",
       "2   3.74  ...    5.41   5.38   1.72   3.420   2.02   1.70  3.57  2.31   2.58   \n",
       "3  11.80  ...    8.64   7.80   4.97   0.161  -4.22  -5.06 -3.43 -2.83    NaN   \n",
       "4   5.46  ...   11.20  20.50  18.60  23.200  13.80   2.39  3.45  3.87   4.96   \n",
       "\n",
       "   2013  \n",
       "0  4.47  \n",
       "1  2.06  \n",
       "2  2.99  \n",
       "3   NaN  \n",
       "4  3.79  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=  df_growth.columns[1:150] \n",
    "df_growth.drop(cols,axis=1, inplace=True)\n",
    "df_growth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.42</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.94</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.885</td>\n",
       "      <td>1.74</td>\n",
       "      <td>8.04</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.87</td>\n",
       "      <td>8.40</td>\n",
       "      <td>6.33</td>\n",
       "      <td>2.81</td>\n",
       "      <td>1.940</td>\n",
       "      <td>6.76</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1.70</td>\n",
       "      <td>6.87</td>\n",
       "      <td>10.10</td>\n",
       "      <td>2.02</td>\n",
       "      <td>-1.450</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.57</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>4.48</td>\n",
       "      <td>11.40</td>\n",
       "      <td>9.36</td>\n",
       "      <td>2.21</td>\n",
       "      <td>3.830</td>\n",
       "      <td>1.42</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4.09</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2.73</td>\n",
       "      <td>-0.445</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950   1960   1970  1980   1990  2000  2010  2013\n",
       "0  Afghanistan  1.42   3.37   1.94 -0.28 -0.885  1.74  8.04  4.47\n",
       "1      Albania  0.87   8.40   6.33  2.81  1.940  6.76  3.36  2.06\n",
       "2      Algeria  1.70   6.87  10.10  2.02 -1.450  2.45  3.57  2.99\n",
       "3      Andorra  4.48  11.40   9.36  2.21  3.830  1.42 -3.43   NaN\n",
       "4       Angola  4.04   4.09   6.50  2.73 -0.445  3.01  3.45  3.79"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting the years 1950, 1960, 1970, 1980, 1990, 2000, 2010, 2018\n",
    "df_growth = df_growth[['country', '1950', '1960' , '1970','1980', '1990', '2000', '2010', '2013']]\n",
    "df_growth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GDP Growth grouping For Low Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>0.855</td>\n",
       "      <td>1.47</td>\n",
       "      <td>26.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.63</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country   1950  1960  1970  1980  1990  2000  2010  2013\n",
       "27  Burundi  0.855  1.47  26.3   6.4  3.63 -1.15  3.98  3.91"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data for low income countries\n",
    "low_income_growth = df_growth.loc[df_growth.country.isin(low_income.country.tolist())]\n",
    "low_income_growth.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GDP Growth grouping For Lower middle Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.42</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.94</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.885</td>\n",
       "      <td>1.74</td>\n",
       "      <td>8.04</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950  1960  1970  1980   1990  2000  2010  2013\n",
       "0  Afghanistan  1.42  3.37  1.94 -0.28 -0.885  1.74  8.04  4.47"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data for lower middle income countries\n",
    "lower_middle_income_growth = df_growth.loc[df_growth.country.isin(lower_middle_income.country.tolist())]\n",
    "lower_middle_income_growth.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GDP Growth grouping For Upper middle Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4.09</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.73</td>\n",
       "      <td>-0.445</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  1950  1960  1970  1980   1990  2000  2010  2013\n",
       "4  Angola  4.04  4.09   6.5  2.73 -0.445  3.01  3.45  3.79"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data for upper middle income countries\n",
    "upper_middle_income_growth = df_growth.loc[df_growth.country.isin(upper_middle_income.country.tolist())]\n",
    "upper_middle_income_growth.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GDP Growth grouping For High Income Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.87</td>\n",
       "      <td>8.4</td>\n",
       "      <td>6.33</td>\n",
       "      <td>2.81</td>\n",
       "      <td>1.94</td>\n",
       "      <td>6.76</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  1950  1960  1970  1980  1990  2000  2010  2013\n",
       "1  Albania  0.87   8.4  6.33  2.81  1.94  6.76  3.36  2.06"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the data for upper middle income countries\n",
    "high_income_growth = df_growth.loc[df_growth.country.isin(high_income.country.tolist())]\n",
    "high_income_growth.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "so Now we have grouped all the countries in 4 categories based on income level. \n",
    "From now on we are going to compare Low income countries, lower middle income countries, upper middle income countries and high income countries on 3 metrics: Life expectancy, Fertility Rate and Economical Growth since the 1950's.\n",
    "\n",
    "Since the data is already cleaned and grouped, we are going to plot all the different metrics to have a visual representation of what is going on in each group for each metric\n",
    "\n",
    "### comparaison on Life Expectancy: How much does the Income Level of  a country influence the life expectancy of its habitants? Compared to countries from different income Level\n",
    "* low_income_expectancy \n",
    "* lower_middle_income_expectancy\n",
    "* upper_middle_income_expectancy\n",
    "* high_income_expectancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Life Expectancy In Countries with Low Income (Less than 995 USD of GNI per Capita in 2018)\n",
    "In order to analyze the data we need to put first change its format from wide shape to  long range. <br>\n",
    "we will then calculate the mean life expectancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burundi</th>\n",
       "      <td>39.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>44.7</td>\n",
       "      <td>46.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>47.9</td>\n",
       "      <td>57.7</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central African Republic</th>\n",
       "      <td>36.5</td>\n",
       "      <td>41.4</td>\n",
       "      <td>48.3</td>\n",
       "      <td>49.6</td>\n",
       "      <td>48.2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>47.5</td>\n",
       "      <td>51.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Congo, Dem. Rep.</th>\n",
       "      <td>42.8</td>\n",
       "      <td>46.5</td>\n",
       "      <td>50.3</td>\n",
       "      <td>52.6</td>\n",
       "      <td>53.8</td>\n",
       "      <td>53.4</td>\n",
       "      <td>57.5</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liberia</th>\n",
       "      <td>38.2</td>\n",
       "      <td>41.3</td>\n",
       "      <td>47.5</td>\n",
       "      <td>51.5</td>\n",
       "      <td>49.9</td>\n",
       "      <td>54.5</td>\n",
       "      <td>60.8</td>\n",
       "      <td>65.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niger</th>\n",
       "      <td>40.5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>43.9</td>\n",
       "      <td>47.1</td>\n",
       "      <td>52.2</td>\n",
       "      <td>58.8</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somalia</th>\n",
       "      <td>38.9</td>\n",
       "      <td>44.2</td>\n",
       "      <td>49.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>50.7</td>\n",
       "      <td>52.1</td>\n",
       "      <td>53.1</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          1950  1960  1970  1980  1990  2000  2010  2018\n",
       "country                                                                 \n",
       "Burundi                   39.0  42.0  44.7  46.3  48.0  47.9  57.7  61.1\n",
       "Central African Republic  36.5  41.4  48.3  49.6  48.2  44.5  47.5  51.6\n",
       "Congo, Dem. Rep.          42.8  46.5  50.3  52.6  53.8  53.4  57.5  62.4\n",
       "Liberia                   38.2  41.3  47.5  51.5  49.9  54.5  60.8  65.4\n",
       "Niger                     40.5  42.0  43.6  43.9  47.1  52.2  58.8  62.5\n",
       "Somalia                   38.9  44.2  49.8  50.8  50.7  52.1  53.1  58.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setting the country column as the index\n",
    "low_income_expectancy.set_index('country', inplace=True)\n",
    "low_income_expectancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verifying if there is any null values in the dataframe. <br>\n",
    "checking and handling null values now (if any) because now I can use the mean value of the given group to fill in the missing values. The mean value of the same group might a more realistic value than the mean off all group combined. <br> \n",
    "we are going to apply the same method for all groups. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifying if there is null values. there is none\n",
    "low_income_expectancy.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.316667</td>\n",
       "      <td>42.90000</td>\n",
       "      <td>47.366667</td>\n",
       "      <td>49.116667</td>\n",
       "      <td>49.616667</td>\n",
       "      <td>50.766667</td>\n",
       "      <td>55.900000</td>\n",
       "      <td>60.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.144217</td>\n",
       "      <td>2.05329</td>\n",
       "      <td>2.709736</td>\n",
       "      <td>3.347487</td>\n",
       "      <td>2.437553</td>\n",
       "      <td>3.799825</td>\n",
       "      <td>4.829493</td>\n",
       "      <td>4.832667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>36.500000</td>\n",
       "      <td>41.30000</td>\n",
       "      <td>43.600000</td>\n",
       "      <td>43.900000</td>\n",
       "      <td>47.100000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>51.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.375000</td>\n",
       "      <td>41.55000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>47.125000</td>\n",
       "      <td>48.050000</td>\n",
       "      <td>48.950000</td>\n",
       "      <td>54.200000</td>\n",
       "      <td>58.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.950000</td>\n",
       "      <td>42.00000</td>\n",
       "      <td>47.900000</td>\n",
       "      <td>50.200000</td>\n",
       "      <td>49.050000</td>\n",
       "      <td>52.150000</td>\n",
       "      <td>57.600000</td>\n",
       "      <td>61.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.125000</td>\n",
       "      <td>43.65000</td>\n",
       "      <td>49.425000</td>\n",
       "      <td>51.325000</td>\n",
       "      <td>50.500000</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>58.525000</td>\n",
       "      <td>62.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.800000</td>\n",
       "      <td>46.50000</td>\n",
       "      <td>50.300000</td>\n",
       "      <td>52.600000</td>\n",
       "      <td>53.800000</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>60.800000</td>\n",
       "      <td>65.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1950      1960       1970       1980       1990       2000  \\\n",
       "count   6.000000   6.00000   6.000000   6.000000   6.000000   6.000000   \n",
       "mean   39.316667  42.90000  47.366667  49.116667  49.616667  50.766667   \n",
       "std     2.144217   2.05329   2.709736   3.347487   2.437553   3.799825   \n",
       "min    36.500000  41.30000  43.600000  43.900000  47.100000  44.500000   \n",
       "25%    38.375000  41.55000  45.400000  47.125000  48.050000  48.950000   \n",
       "50%    38.950000  42.00000  47.900000  50.200000  49.050000  52.150000   \n",
       "75%    40.125000  43.65000  49.425000  51.325000  50.500000  53.100000   \n",
       "max    42.800000  46.50000  50.300000  52.600000  53.800000  54.500000   \n",
       "\n",
       "            2010       2018  \n",
       "count   6.000000   6.000000  \n",
       "mean   55.900000  60.166667  \n",
       "std     4.829493   4.832667  \n",
       "min    47.500000  51.600000  \n",
       "25%    54.200000  58.775000  \n",
       "50%    57.600000  61.750000  \n",
       "75%    58.525000  62.475000  \n",
       "max    60.800000  65.400000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_income_expectancy.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see the mean age for this group. in 1950 it was 39.31 years, 42.9 years 10 years later in 1960. it went up to 60 years in 2018. below we will  plot this to more easily understand the evolution in this group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can also see that the life expectancy for this group went from less than 40 years in 1950 to 60 years in 2018. there was a clear and consistent increase in life expectancy. \n",
    "<br> Now lets continue with the Lower Middle Income Countries. We will go through the same steps. first shapping the data. and then  plotting the mean of the two groups so that a comparaison can be made. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Life Expectancy In Countries with Lower Middle Income  (Countries with GNI per Capita between 996 and 3895 USD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>45.8</td>\n",
       "      <td>43.3</td>\n",
       "      <td>51.6</td>\n",
       "      <td>51.6</td>\n",
       "      <td>56.2</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "0  Afghanistan  32.0  38.6  45.8  43.3  51.6  51.6  56.2  58.7"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if the dataframe looks ok\n",
    "lower_middle_income_expectancy.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 47 entries, 0 to 186\n",
      "Data columns (total 9 columns):\n",
      "country    47 non-null object\n",
      "1950       46 non-null float64\n",
      "1960       46 non-null float64\n",
      "1970       47 non-null float64\n",
      "1980       47 non-null float64\n",
      "1990       47 non-null float64\n",
      "2000       47 non-null float64\n",
      "2010       47 non-null float64\n",
      "2018       46 non-null float64\n",
      "dtypes: float64(8), object(1)\n",
      "memory usage: 3.7+ KB\n"
     ]
    }
   ],
   "source": [
    "lower_middle_income_expectancy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see that they some missing values. let's look more in to that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    0\n",
       "1950       1\n",
       "1960       1\n",
       "1970       0\n",
       "1980       0\n",
       "1990       0\n",
       "2000       0\n",
       "2010       0\n",
       "2018       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if there is any missing values\n",
    "lower_middle_income_expectancy.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There 3 missing values. one in 1950 and one in 1960 and  a last one in 2018. we are going to replace those missing values by the mean value of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:3660: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "country    0\n",
       "1950       0\n",
       "1960       0\n",
       "1970       0\n",
       "1980       0\n",
       "1990       0\n",
       "2000       0\n",
       "2010       0\n",
       "2018       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing missing values with means of each columns\n",
    "#cheking if there any missing values left\n",
    "lower_middle_income_expectancy['1950'].fillna((lower_middle_income_expectancy['1950'].mean()), inplace=True)\n",
    "lower_middle_income_expectancy['1960'].fillna((lower_middle_income_expectancy['1960'].mean()), inplace=True)\n",
    "lower_middle_income_expectancy['2018'].fillna((lower_middle_income_expectancy['2018'].mean()), inplace=True)\n",
    "lower_middle_income_expectancy.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.543478</td>\n",
       "      <td>45.747826</td>\n",
       "      <td>51.385106</td>\n",
       "      <td>55.151064</td>\n",
       "      <td>57.293617</td>\n",
       "      <td>57.600000</td>\n",
       "      <td>61.180851</td>\n",
       "      <td>65.041304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.083072</td>\n",
       "      <td>6.060124</td>\n",
       "      <td>6.689706</td>\n",
       "      <td>6.643855</td>\n",
       "      <td>6.140224</td>\n",
       "      <td>6.747334</td>\n",
       "      <td>7.620500</td>\n",
       "      <td>4.710238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23.800000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>43.300000</td>\n",
       "      <td>46.600000</td>\n",
       "      <td>44.100000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>51.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.950000</td>\n",
       "      <td>41.550000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>50.100000</td>\n",
       "      <td>52.150000</td>\n",
       "      <td>52.250000</td>\n",
       "      <td>57.050000</td>\n",
       "      <td>61.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.400000</td>\n",
       "      <td>44.800000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>54.900000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>57.600000</td>\n",
       "      <td>61.700000</td>\n",
       "      <td>64.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>44.750000</td>\n",
       "      <td>50.350000</td>\n",
       "      <td>56.050000</td>\n",
       "      <td>59.650000</td>\n",
       "      <td>61.350000</td>\n",
       "      <td>63.300000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>68.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52.500000</td>\n",
       "      <td>57.300000</td>\n",
       "      <td>67.400000</td>\n",
       "      <td>70.800000</td>\n",
       "      <td>71.600000</td>\n",
       "      <td>73.300000</td>\n",
       "      <td>76.200000</td>\n",
       "      <td>73.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1950       1960       1970       1980       1990       2000  \\\n",
       "count  47.000000  47.000000  47.000000  47.000000  47.000000  47.000000   \n",
       "mean   40.543478  45.747826  51.385106  55.151064  57.293617  57.600000   \n",
       "std     6.083072   6.060124   6.689706   6.643855   6.140224   6.747334   \n",
       "min    23.800000  33.000000  39.800000  43.300000  46.600000  44.100000   \n",
       "25%    35.950000  41.550000  45.600000  50.100000  52.150000  52.250000   \n",
       "50%    40.400000  44.800000  51.000000  54.900000  57.500000  57.600000   \n",
       "75%    44.750000  50.350000  56.050000  59.650000  61.350000  63.300000   \n",
       "max    52.500000  57.300000  67.400000  70.800000  71.600000  73.300000   \n",
       "\n",
       "            2010       2018  \n",
       "count  47.000000  47.000000  \n",
       "mean   61.180851  65.041304  \n",
       "std     7.620500   4.710238  \n",
       "min    32.100000  51.100000  \n",
       "25%    57.050000  61.200000  \n",
       "50%    61.700000  64.600000  \n",
       "75%    66.000000  68.150000  \n",
       "max    76.200000  73.400000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking general statistics about countries in the lower middle income group \n",
    "lower_middle_income_expectancy.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By having a quick look at the data returned above, we can see that the mean age is in general higher than in the Lower middle income countries than it is in the Low income Countries. In 1950 the  mean life expectancy in this group was 40 and in 2018 it is 65 years old. Against about 39 and 60 years in the low income group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see that the life expectancy is consistently higher in the lower middle income countries (cyan line) if we compare to lower income countries (red line). <br> \n",
    "let's continue our camparison by adding Upper Middle Income Countries to the next plot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Life Expectancy in Upper Middle Income Countries (GNI Per capita between 3896 and 12,055 USD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>36.6</td>\n",
       "      <td>42.4</td>\n",
       "      <td>48.7</td>\n",
       "      <td>49.9</td>\n",
       "      <td>50.2</td>\n",
       "      <td>53.4</td>\n",
       "      <td>60.1</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "4  Angola  36.6  42.4  48.7  49.9  50.2  53.4  60.1  65.2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the shape of this dataframe\n",
    "upper_middle_income_expectancy.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 41 entries, 4 to 183\n",
      "Data columns (total 9 columns):\n",
      "country    41 non-null object\n",
      "1950       40 non-null float64\n",
      "1960       40 non-null float64\n",
      "1970       41 non-null float64\n",
      "1980       41 non-null float64\n",
      "1990       41 non-null float64\n",
      "2000       41 non-null float64\n",
      "2010       41 non-null float64\n",
      "2018       40 non-null float64\n",
      "dtypes: float64(8), object(1)\n",
      "memory usage: 3.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking for missing values \n",
    "upper_middle_income_expectancy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    0\n",
       "1950       1\n",
       "1960       1\n",
       "1970       0\n",
       "1980       0\n",
       "1990       0\n",
       "2000       0\n",
       "2010       0\n",
       "2018       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_middle_income_expectancy.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:3660: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "country    0\n",
       "1950       0\n",
       "1960       0\n",
       "1970       0\n",
       "1980       0\n",
       "1990       0\n",
       "2000       0\n",
       "2010       0\n",
       "2018       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing missing values with means of each columns\n",
    "#cheking if there any missing values left\n",
    "upper_middle_income_expectancy['1950'].fillna((upper_middle_income_expectancy['1950'].mean()), inplace=True)\n",
    "upper_middle_income_expectancy['1960'].fillna((upper_middle_income_expectancy['1960'].mean()), inplace=True)\n",
    "upper_middle_income_expectancy['2018'].fillna((upper_middle_income_expectancy['2018'].mean()), inplace=True)\n",
    "upper_middle_income_expectancy.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47.455000</td>\n",
       "      <td>53.070000</td>\n",
       "      <td>58.224390</td>\n",
       "      <td>62.631707</td>\n",
       "      <td>65.595122</td>\n",
       "      <td>66.687805</td>\n",
       "      <td>69.285366</td>\n",
       "      <td>71.652500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.388443</td>\n",
       "      <td>7.654548</td>\n",
       "      <td>7.112376</td>\n",
       "      <td>6.399900</td>\n",
       "      <td>6.171060</td>\n",
       "      <td>6.942521</td>\n",
       "      <td>6.050147</td>\n",
       "      <td>4.364286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31.900000</td>\n",
       "      <td>39.700000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>47.900000</td>\n",
       "      <td>50.200000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>58.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.100000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>57.700000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>62.600000</td>\n",
       "      <td>66.200000</td>\n",
       "      <td>68.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>53.070000</td>\n",
       "      <td>58.300000</td>\n",
       "      <td>64.800000</td>\n",
       "      <td>67.200000</td>\n",
       "      <td>68.600000</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>72.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.200000</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>70.200000</td>\n",
       "      <td>71.700000</td>\n",
       "      <td>73.100000</td>\n",
       "      <td>74.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65.800000</td>\n",
       "      <td>69.400000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>73.100000</td>\n",
       "      <td>74.600000</td>\n",
       "      <td>75.600000</td>\n",
       "      <td>77.400000</td>\n",
       "      <td>78.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1950       1960       1970       1980       1990       2000  \\\n",
       "count  41.000000  41.000000  41.000000  41.000000  41.000000  41.000000   \n",
       "mean   47.455000  53.070000  58.224390  62.631707  65.595122  66.687805   \n",
       "std     8.388443   7.654548   7.112376   6.399900   6.171060   6.942521   \n",
       "min    31.900000  39.700000  42.200000  47.900000  50.200000  47.500000   \n",
       "25%    42.100000  47.000000  51.500000  57.700000  61.500000  62.600000   \n",
       "50%    46.000000  53.070000  58.300000  64.800000  67.200000  68.600000   \n",
       "75%    55.200000  59.500000  64.500000  67.500000  70.200000  71.700000   \n",
       "max    65.800000  69.400000  69.500000  73.100000  74.600000  75.600000   \n",
       "\n",
       "            2010       2018  \n",
       "count  41.000000  41.000000  \n",
       "mean   69.285366  71.652500  \n",
       "std     6.050147   4.364286  \n",
       "min    47.000000  58.600000  \n",
       "25%    66.200000  68.800000  \n",
       "50%    71.300000  72.400000  \n",
       "75%    73.100000  74.800000  \n",
       "max    77.400000  78.700000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking generals statistic for this group\n",
    "upper_middle_income_expectancy.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By Only looking at the mean value for each year we can see that this group has higher life expectancy than previous groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Life Expectancy in High Income Countries (GNI Per capita > 12,055 USD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>54.5</td>\n",
       "      <td>62.7</td>\n",
       "      <td>67.4</td>\n",
       "      <td>72.3</td>\n",
       "      <td>73.9</td>\n",
       "      <td>74.4</td>\n",
       "      <td>76.3</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "1  Albania  54.5  62.7  67.4  72.3  73.9  74.4  76.3  78.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_income_expectancy.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    0\n",
       "1950       1\n",
       "1960       1\n",
       "1970       0\n",
       "1980       0\n",
       "1990       0\n",
       "2000       0\n",
       "2010       0\n",
       "2018       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values\n",
    "high_income_expectancy.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:3660: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing missing values with means of each columns\n",
    "#cheking if there any missing values left\n",
    "high_income_expectancy['1950'].fillna((high_income_expectancy['1950'].mean()), inplace=True)\n",
    "high_income_expectancy['1960'].fillna((high_income_expectancy['1960'].mean()), inplace=True)\n",
    "high_income_expectancy['2018'].fillna((high_income_expectancy['2018'].mean()), inplace=True)\n",
    "high_income_expectancy.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.678261</td>\n",
       "      <td>62.081522</td>\n",
       "      <td>66.296774</td>\n",
       "      <td>69.516129</td>\n",
       "      <td>72.023656</td>\n",
       "      <td>73.575269</td>\n",
       "      <td>76.135484</td>\n",
       "      <td>77.718478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.592001</td>\n",
       "      <td>9.078521</td>\n",
       "      <td>6.711079</td>\n",
       "      <td>5.573780</td>\n",
       "      <td>4.643331</td>\n",
       "      <td>5.796225</td>\n",
       "      <td>5.339998</td>\n",
       "      <td>4.331255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.700000</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>39.100000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>54.300000</td>\n",
       "      <td>63.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.600000</td>\n",
       "      <td>57.700000</td>\n",
       "      <td>64.600000</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>71.700000</td>\n",
       "      <td>74.400000</td>\n",
       "      <td>75.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>58.400000</td>\n",
       "      <td>63.700000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>77.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>69.100000</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>73.200000</td>\n",
       "      <td>75.300000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>80.100000</td>\n",
       "      <td>81.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>71.600000</td>\n",
       "      <td>74.200000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>78.700000</td>\n",
       "      <td>79.500000</td>\n",
       "      <td>81.800000</td>\n",
       "      <td>82.800000</td>\n",
       "      <td>84.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1950       1960       1970       1980       1990       2000  \\\n",
       "count  93.000000  93.000000  93.000000  93.000000  93.000000  93.000000   \n",
       "mean   56.678261  62.081522  66.296774  69.516129  72.023656  73.575269   \n",
       "std     9.592001   9.078521   6.711079   5.573780   4.643331   5.796225   \n",
       "min    29.700000  30.900000  39.100000  48.200000  48.200000  46.700000   \n",
       "25%    51.600000  57.700000  64.600000  67.500000  70.000000  71.700000   \n",
       "50%    58.400000  63.700000  68.000000  70.500000  72.100000  74.500000   \n",
       "75%    64.000000  69.100000  70.900000  73.200000  75.300000  77.500000   \n",
       "max    71.600000  74.200000  76.000000  78.700000  79.500000  81.800000   \n",
       "\n",
       "            2010       2018  \n",
       "count  93.000000  93.000000  \n",
       "mean   76.135484  77.718478  \n",
       "std     5.339998   4.331255  \n",
       "min    54.300000  63.500000  \n",
       "25%    74.400000  75.700000  \n",
       "50%    76.500000  77.900000  \n",
       "75%    80.100000  81.300000  \n",
       "max    82.800000  84.200000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_income_expectancy.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the table above shows that for the high income countries too, life expectancy has been consistently raising. with the standard deviation also getting smaller. 9.5 in the 1950 and 4.33 in 2018. which is pretty good. \n",
    "<p> Below we are going to plot the mean values for all the 4 group and visually see the changes which occured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Life Expectancy (Grouped By Income Level)')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8240a3ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = low_income_expectancy.mean()\n",
    "B = lower_middle_income_expectancy.mean()\n",
    "C = upper_middle_income_expectancy.mean()\n",
    "D = high_income_expectancy.mean()\n",
    "\n",
    "plt.plot(D, 'g-', label='High Income Countries')\n",
    "plt.plot(C, 'b-', label='Upper Middle Income Countries')\n",
    "plt.plot(B, 'c-', label='Lower Middle Income Countries')\n",
    "plt.plot(A, 'r-', label='Low Income Countries')\n",
    "\n",
    "\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12,12\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('Life Expectancy in years')\n",
    "plt.title('Life Expectancy (Grouped By Income Level)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so this graph clearly shows that richer the country, longer the life expectancy. since the 1950, we can see that red line (low income countries) are also the countries with the shorted life expectancy. and the life expectancy is directly linked to the income level. richer the country, longer is the life expectancy. But thats not really a suprise. \n",
    "<p> now let's compare each income group by fertility Rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### comparaison on Fertility Rate (Number of Babies per woman): How do country from different Income Level differs when its come to the number of babies per woman? \n",
    "\n",
    "Earlier we created 4 Dataframes for the fertility rate\n",
    "* low_income_birth\n",
    "* lower_middle_income_birth\n",
    "* upper_middle_income_birth\n",
    "* high_income_birth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Babies per Woman In Countries with Low Income  (Countries with GNI per Capita between < 995)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>6.82</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.29</td>\n",
       "      <td>7.42</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.01</td>\n",
       "      <td>6.26</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "26  Burundi  6.82  6.95  7.29  7.42  7.51  7.01  6.26  5.53"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the dataframe\n",
    "low_income_birth.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null Values\n",
    "low_income_birth.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.458333</td>\n",
       "      <td>6.650000</td>\n",
       "      <td>6.816667</td>\n",
       "      <td>6.963333</td>\n",
       "      <td>7.020000</td>\n",
       "      <td>6.775000</td>\n",
       "      <td>6.221667</td>\n",
       "      <td>5.638333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.704086</td>\n",
       "      <td>0.667263</td>\n",
       "      <td>0.641301</td>\n",
       "      <td>0.655062</td>\n",
       "      <td>0.733975</td>\n",
       "      <td>0.899906</td>\n",
       "      <td>0.952836</td>\n",
       "      <td>0.976390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.400000</td>\n",
       "      <td>5.840000</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>5.810000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.020000</td>\n",
       "      <td>4.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.055000</td>\n",
       "      <td>6.102500</td>\n",
       "      <td>6.332500</td>\n",
       "      <td>6.685000</td>\n",
       "      <td>6.657500</td>\n",
       "      <td>6.150000</td>\n",
       "      <td>5.480000</td>\n",
       "      <td>4.922500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.535000</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>6.940000</td>\n",
       "      <td>6.990000</td>\n",
       "      <td>7.265000</td>\n",
       "      <td>6.985000</td>\n",
       "      <td>6.365000</td>\n",
       "      <td>5.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.985000</td>\n",
       "      <td>7.175000</td>\n",
       "      <td>7.262500</td>\n",
       "      <td>7.317500</td>\n",
       "      <td>7.482500</td>\n",
       "      <td>7.467500</td>\n",
       "      <td>6.770000</td>\n",
       "      <td>6.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.250000</td>\n",
       "      <td>7.450000</td>\n",
       "      <td>7.570000</td>\n",
       "      <td>7.840000</td>\n",
       "      <td>7.770000</td>\n",
       "      <td>7.680000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>7.130000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1950      1960      1970      1980      1990      2000      2010  \\\n",
       "count  6.000000  6.000000  6.000000  6.000000  6.000000  6.000000  6.000000   \n",
       "mean   6.458333  6.650000  6.816667  6.963333  7.020000  6.775000  6.221667   \n",
       "std    0.704086  0.667263  0.641301  0.655062  0.733975  0.899906  0.952836   \n",
       "min    5.400000  5.840000  5.950000  5.950000  5.810000  5.500000  5.020000   \n",
       "25%    6.055000  6.102500  6.332500  6.685000  6.657500  6.150000  5.480000   \n",
       "50%    6.535000  6.680000  6.940000  6.990000  7.265000  6.985000  6.365000   \n",
       "75%    6.985000  7.175000  7.262500  7.317500  7.482500  7.467500  6.770000   \n",
       "max    7.250000  7.450000  7.570000  7.840000  7.770000  7.680000  7.490000   \n",
       "\n",
       "           2018  \n",
       "count  6.000000  \n",
       "mean   5.638333  \n",
       "std    0.976390  \n",
       "min    4.450000  \n",
       "25%    4.922500  \n",
       "50%    5.725000  \n",
       "75%    6.040000  \n",
       "max    7.130000  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_income_birth.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this group, the change is relatively small. in 1950 the average number of children per child was 6.45. in 2018 it is 6.45. with a pick in 1990. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Babies per Woman In Countries with Lower Middle Income  (Countries with GNI per Capita between 996 and 3895 USD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7.57</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.49</td>\n",
       "      <td>5.82</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "0  Afghanistan  7.57  7.45  7.45  7.45  7.47  7.49  5.82  4.33"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_middle_income_birth.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values, there is none\n",
    "lower_middle_income_birth.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.406522</td>\n",
       "      <td>6.666957</td>\n",
       "      <td>6.742609</td>\n",
       "      <td>6.567826</td>\n",
       "      <td>6.026957</td>\n",
       "      <td>5.281957</td>\n",
       "      <td>4.575000</td>\n",
       "      <td>4.016522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.987257</td>\n",
       "      <td>0.681038</td>\n",
       "      <td>0.773366</td>\n",
       "      <td>1.019141</td>\n",
       "      <td>1.082499</td>\n",
       "      <td>1.214224</td>\n",
       "      <td>1.129454</td>\n",
       "      <td>0.989047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.950000</td>\n",
       "      <td>4.580000</td>\n",
       "      <td>4.320000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>2.290000</td>\n",
       "      <td>1.990000</td>\n",
       "      <td>1.980000</td>\n",
       "      <td>1.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.992500</td>\n",
       "      <td>6.280000</td>\n",
       "      <td>6.240000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>5.317500</td>\n",
       "      <td>4.317500</td>\n",
       "      <td>3.592500</td>\n",
       "      <td>3.342500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.555000</td>\n",
       "      <td>6.785000</td>\n",
       "      <td>6.820000</td>\n",
       "      <td>6.685000</td>\n",
       "      <td>6.195000</td>\n",
       "      <td>5.460000</td>\n",
       "      <td>4.830000</td>\n",
       "      <td>4.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.942500</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.235000</td>\n",
       "      <td>7.130000</td>\n",
       "      <td>6.720000</td>\n",
       "      <td>6.070000</td>\n",
       "      <td>5.355000</td>\n",
       "      <td>4.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.920000</td>\n",
       "      <td>8.190000</td>\n",
       "      <td>8.230000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>8.610000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>6.590000</td>\n",
       "      <td>5.880000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1950       1960       1970       1980       1990       2000  \\\n",
       "count  46.000000  46.000000  46.000000  46.000000  46.000000  46.000000   \n",
       "mean    6.406522   6.666957   6.742609   6.567826   6.026957   5.281957   \n",
       "std     0.987257   0.681038   0.773366   1.019141   1.082499   1.214224   \n",
       "min     1.950000   4.580000   4.320000   2.770000   2.290000   1.990000   \n",
       "25%     5.992500   6.280000   6.240000   6.250000   5.317500   4.317500   \n",
       "50%     6.555000   6.785000   6.820000   6.685000   6.195000   5.460000   \n",
       "75%     6.942500   7.000000   7.235000   7.130000   6.720000   6.070000   \n",
       "max     7.920000   8.190000   8.230000   8.750000   8.610000   7.490000   \n",
       "\n",
       "            2010       2018  \n",
       "count  46.000000  46.000000  \n",
       "mean    4.575000   4.016522  \n",
       "std     1.129454   0.989047  \n",
       "min     1.980000   1.890000  \n",
       "25%     3.592500   3.342500  \n",
       "50%     4.830000   4.140000  \n",
       "75%     5.355000   4.700000  \n",
       "max     6.590000   5.880000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_middle_income_birth.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Babies per Woman In Countries with Upper Middle Income  (Countries with GNI per Capita between 3895 and 12055 USD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>7.11</td>\n",
       "      <td>7.48</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.25</td>\n",
       "      <td>6.64</td>\n",
       "      <td>6.16</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "3  Angola  7.11  7.48   7.6   7.5  7.25  6.64  6.16  5.55"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_middle_income_birth.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values, there is none\n",
    "upper_middle_income_birth.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Babies per Woman In Countries with High Income countries  (Countries with GNI per Capita > 12055 USD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>5.87</td>\n",
       "      <td>6.49</td>\n",
       "      <td>4.91</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  1950  1960  1970  1980  1990  2000  2010  2018\n",
       "1  Albania  5.87  6.49  4.91  3.62  2.98  2.16  1.65  1.71"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_income_birth.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values, there is none\n",
    "high_income_birth.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.587391</td>\n",
       "      <td>4.550326</td>\n",
       "      <td>4.040652</td>\n",
       "      <td>3.285652</td>\n",
       "      <td>2.730978</td>\n",
       "      <td>2.115435</td>\n",
       "      <td>1.995000</td>\n",
       "      <td>1.918913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.771416</td>\n",
       "      <td>1.843716</td>\n",
       "      <td>1.847032</td>\n",
       "      <td>1.677433</td>\n",
       "      <td>1.265315</td>\n",
       "      <td>0.840647</td>\n",
       "      <td>0.669322</td>\n",
       "      <td>0.557554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.870000</td>\n",
       "      <td>1.910000</td>\n",
       "      <td>1.830000</td>\n",
       "      <td>1.460000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>1.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.935000</td>\n",
       "      <td>2.707500</td>\n",
       "      <td>2.370000</td>\n",
       "      <td>2.022500</td>\n",
       "      <td>1.807500</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.517500</td>\n",
       "      <td>1.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.490000</td>\n",
       "      <td>4.155000</td>\n",
       "      <td>3.540000</td>\n",
       "      <td>2.515000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.935000</td>\n",
       "      <td>1.870000</td>\n",
       "      <td>1.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.157500</td>\n",
       "      <td>6.450000</td>\n",
       "      <td>5.627500</td>\n",
       "      <td>4.212500</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>2.565000</td>\n",
       "      <td>2.222500</td>\n",
       "      <td>2.032500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.110000</td>\n",
       "      <td>7.560000</td>\n",
       "      <td>8.130000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>7.170000</td>\n",
       "      <td>5.830000</td>\n",
       "      <td>5.210000</td>\n",
       "      <td>4.510000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1950       1960       1970       1980       1990       2000  \\\n",
       "count  92.000000  92.000000  92.000000  92.000000  92.000000  92.000000   \n",
       "mean    4.587391   4.550326   4.040652   3.285652   2.730978   2.115435   \n",
       "std     1.771416   1.843716   1.847032   1.677433   1.265315   0.840647   \n",
       "min     1.870000   1.910000   1.830000   1.460000   1.300000   1.120000   \n",
       "25%     2.935000   2.707500   2.370000   2.022500   1.807500   1.500000   \n",
       "50%     4.490000   4.155000   3.540000   2.515000   2.250000   1.935000   \n",
       "75%     6.157500   6.450000   5.627500   4.212500   3.240000   2.565000   \n",
       "max     8.110000   7.560000   8.130000   8.300000   7.170000   5.830000   \n",
       "\n",
       "            2010       2018  \n",
       "count  92.000000  92.000000  \n",
       "mean    1.995000   1.918913  \n",
       "std     0.669322   0.557554  \n",
       "min     1.190000   1.240000  \n",
       "25%     1.517500   1.570000  \n",
       "50%     1.870000   1.770000  \n",
       "75%     2.222500   2.032500  \n",
       "max     5.210000   4.510000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_income_birth.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By quicly having a look at the table above, we can se a much shaper decrease in  number of birth. from 4.5 in 1950 to 1.9 in  2013. with a shaper decrease from the 1980's.\n",
    "\n",
    "<p> let's plot all 4 groups to see how the differences and similiraties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Number of Babies Per Woman (Countries of all Income Level)')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8240a34a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = low_income_birth.mean()\n",
    "B = lower_middle_income_birth.mean()\n",
    "C = upper_middle_income_birth.mean()\n",
    "D = high_income_birth.mean()\n",
    "plt.plot(A, 'r--', label='Low Income Countries')\n",
    "plt.plot(B, 'c--', label='Lower Middle Income Countries')\n",
    "plt.plot(C, 'b--', label='Upper Middle Income Countries')\n",
    "plt.plot(D, 'g--', label='High Income Countries')\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12,12\n",
    "plt.legend(loc='lower left')\n",
    "plt.ylabel('Number Of Babies Per Woman')\n",
    "plt.title('Number of Babies Per Woman (Countries of all Income Level)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that since the 1950's there has been a decrease in the number of birth in all countries. up to late 1960's the low income and lower income countries had same numbers of birth. but as time went by, birth in  lower middle income countries become lower than countries from the Low Income Group. \n",
    "<p> so richer countries have longer lives and fewer children. there has been negative correlation between Life expectancy and number of births, at least from the 1950's. \n",
    " <P> does that impact the economic growth? growth is based upon so many metrics but todays we will just focus on life expectancy and number of birth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### comparaison on Growth : So richer countries  all have long life expectacncy and fewer babies. How is their growth going going? is there any  hope for the future for people from Low Income Countries \n",
    "* low_income_growth\n",
    "* lower_middle_income_growth\n",
    "* upper_middle_income_growth\n",
    "* high_income_growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see that richer countries consistently have higher Life Expectancy and Fewer children. But isn't that at some point affecting growth? In some of the richest countries, like Japan, retreat age is being pushed and foreigners are welcome to fill in jobs because nationals are no longer enough. Growth Is dependent on so many things. but is there a relationship between growth and Fertility Rate and the life Expectancy of a country? \n",
    "let's find out in this section\n",
    "<p> as previously done, we are going to use the data that we grouped. visualize and then draw conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>0.855</td>\n",
       "      <td>1.47</td>\n",
       "      <td>26.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.63</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country   1950  1960  1970  1980  1990  2000  2010  2013\n",
       "27  Burundi  0.855  1.47  26.3   6.4  3.63 -1.15  3.98  3.91"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the dataframe\n",
    "low_income_growth.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values\n",
    "low_income_growth.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.42</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1.94</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.885</td>\n",
       "      <td>1.74</td>\n",
       "      <td>8.04</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1950  1960  1970  1980   1990  2000  2010  2013\n",
       "0  Afghanistan  1.42  3.37  1.94 -0.28 -0.885  1.74  8.04  4.47"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_middle_income_growth.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    0\n",
       "1950       0\n",
       "1960       0\n",
       "1970       0\n",
       "1980       0\n",
       "1990       0\n",
       "2000       0\n",
       "2010       1\n",
       "2013       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking where null values are\n",
    "lower_middle_income_growth.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:3660: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing missing values in 2010 and 2013 with the group mean Growth of the year\n",
    "lower_middle_income_growth['2010'].fillna((lower_middle_income_growth['2010'].mean()), inplace=True)\n",
    "lower_middle_income_growth['2013'].fillna((lower_middle_income_growth['2013'].mean()), inplace=True)\n",
    "lower_middle_income_growth.isnull().sum().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    0\n",
       "1950       0\n",
       "1960       0\n",
       "1970       0\n",
       "1980       0\n",
       "1990       0\n",
       "2000       0\n",
       "2010       0\n",
       "2013       5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_income_growth.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1950    2.117500\n",
       "1960    3.902333\n",
       "1970    8.180500\n",
       "1980    1.417667\n",
       "1990   -9.010000\n",
       "2000    2.975167\n",
       "2010    5.713333\n",
       "2013   -1.505000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking for mean growth value for each decade for all the 4 groups\n",
    "low_income_growth.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:3660: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_income_growth['2013'].fillna((high_income_growth['2013'].mean()), inplace=True)\n",
    "high_income_growth.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Order to make a bar chart with the growth from all 4 groups. I simply used looked for the mean value for each decades(as done above). There must be different ways to do this, but since in this case we only have a small number of values, I simply copied/paste them from the cell above to the cell below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XI5YbSd57GBlCuDQmnUVsCvQFRlD7PnyPpHnjUyQ3zUeSfeIxG+i20k1sbYYAV4cQ3osxTsi8i3RgjPHvIYSdSZ5MjyU53pew4p8z+KHq5BwoF5JuxHNJEqNGIYQmwNLMObIpyY3/TJJ3Vy4DTsrMV5QZ9jJJgn40SZPXczKLvhd4I/O+3liSDlr+GVfq4KKS8uO8vJv2McD9IYQxJOfctSTvM02qYhvWAZ4B/hNjvLCKZY8CRoQQ7s1sy6Ukx1f5/I1IaiZygfzMPlgWYywNIfyWJFH5hO/fgXs3ft/xxxUkHeDsu/JKY4zPhhD+TdKs+o/AuyT7s7YmXrVdM98DfhuSDlg2ISmTlTvj+FMI4b8ktTFnZ+KGGo5hknf9bsts86PAH1mNhxaZ5pkHkRyz5R4h6T1yI7J/d6029wIXhxCOInmHaV1g80wzwQeBa0IIPUneQTyP5H3b1ZI5/ocCN4UQ+sTknahNgW1jjM9UM1tO5tipvJyZIYRngUEh6ehlAUlPkpvFGMtbKowh2U9fseJ1dSjJ787zJB1zNCNp9fBKDeeRVG98B0v6cR4LyR/sLf/3MEDmB/oakh+L+SQ/GOvHGJeSdHjRneSp4B1Azxhjre8xZJ4wH0Jyw/0VSUcUh8RMT1I/Uj+SngPfJnkS/RequD7E5H2p/UieiD4BfEfSM9vOJC8k/6DYM7VhY4EJmX0ESecEkzPt9MsNIOkRbV4Iod8P3trv3U3yHsQrJL3sLSF5ib5WmRuM/YGbY4yzKv37H0lis1p/kDdzk7oHmUQ8JD2QvUDyjsynWZT/ZSSJ/DcknaSMIXvlf3T5qyregaou3odJjpP7QwjfAR+QHNeQ1GgMzcQyORPvat/YVbHOujwHIKn1WUxSDn/NfC7vdGIrkgcBC0lqIi6M3//tpkYkvZvNITmvzyRpPhwzcU8geTBxL0mTwhYk3cFX5wqSRHteCOGoGOMLJOX7D5KkaCuqf//lcJLz8cSVrk1tMrE8TXJz/2+SspnMih1/DM1s9+9JbnAX8/17O5uSHNvzSa4XyzPrK7c5Sc+T1fktSTJ8D8nfwPqC5B2ig6qbIYtr5k0k7wrNJimXqmpU/wX8jyQZewIYlll2tcdw5pj6HUmNennzxpq2DeDo8v1Nci39D5W6wM/Ugv6DJKn4Zy3LykrmvbKDSc6JrzPbWF5beibJ8fo5yXutY0iueT/EBSQJ/5uZffU8yUOx6uxBcuxU/AvJu609+b7zj29Ielyt3Oy8PKHehOS9x/Lh75C8h3VbZr5PSTookhqknLKytFvcSJKkn5sQwnvA/j+wuWGdCD/ijzbXhRBCf6BDrOWPektau9lEUJIk/Wgxxq61T/XzFZI/M3ASK/bkJ+knyCaCkiRJdSgkf8JjKsmfdHiltuklrd1sIihJkiRJKbEGS5IkSZJS8pN4B2vOnPlrVTVcYWEBCxas3Pu11jTLoeGwLBoGy6FhsBwaDsuiYbAcGgbLYVWtWrXIqWq4NVj1ID9/df/4ueqC5dBwWBYNg+XQMFgODYdl0TBYDg2D5ZA9EyxJkiRJSokJliRJkiSlxARLkiRJklJigiVJkiRJKTHBkiRJkqSUmGBJkiRJUkpMsCRJkiQpJSZYkiRJkpSS/PoOQJIkSWuPwr5np7q8BYMG1zpNt25789xzr6a6XoCxY9/h/vvvYeDAm1NfdlrGjBnN448/Ql5eHrm5efTocSzdux+S2vLnz5/Pc889zW9/+7tqpznttN7cf//9qa3zp84ES5IkSWqAHnnkId55578MHTqS5s0LWbBgAa+++lKq61iwYD4PP/z3KhOs0tJS8vLyGDLk7lTX+VNngiVJkqS1zqxZMxkw4CrmzfuGoqL1uOiiy2nVqhU9evyWBx98hAULFnDwwb/i1lvvomvXHTjjjJO5+OLL2Wyzzatc3rBhdzF79ixmzJjO7NmzOeqo3/O73/UA4KmnHuf+++8Bcmjfvj2XXXZ1letv3bo111xzBQUFBUyePIlZs2Zx8cX9eeqpx5kw4X06dtyWSy65AoC33nqTYcPuYtmypWyyyWZcfPHlNGvWbIWYRo0azq233kXz5oUAFBYWVtRevfPOW9x++82Ulpbyi190pF+/i2jcuDFHHnkof/vbaIqKivjoow+57babue22v1a7fUOG3Mr06dPp1esYdt55V3bffU+GDx/KBhu05NNPP+aee/5Ot2578/bb/wNgzJhRvPji8yxbtpR99tmPk046lcWLF9O//4V8+eWXLF9eSq9eJ7P//gfWQamvHUywJEmStNa58caBHHTQr+ne/RAef/xfDB58PQMGDGLzzdvwxRefM3PmDELYhnHj3qVjx22ZM+fLapOrclOmTOaWW4awaNEijjnmCA4//EimTJnMqFF3c+edd1NUVMR3331b4/oB5s//jltuGcJrr73MBRecx513DmPLLdtx8sk9+eSTSKtWGzFy5DBuvvkOmjZtyj33jOCBB+7lxBNPqYhl0aKFLFq0iE033WyVOIuLi7n22iu5+eY7aNOmLVdf3Z9HHnmIo446ZrW377TTzuTzzz9jxIgxQNJscuLECYwa9QCbbLLpCvO/9dabTJ06laFDR1JWVsaFF57He++NZd68b2jZshXXX58091ywYEEtpffTZicXkiRJWutMmDCebt0OAuCgg37N+PHvAdClS1fGjXuXcePGcvzxvRg/fhwTJ37IL37RsdZl7r77njRu3JiioiLWW289vv76K8aOfZt9992foqIiANZZZ90a1w+w5577kJOTQ7t27Vl//fXZaqv25ObmsuWW7Zg5cyYTJrzPpEmfc/rpJ9Gr1zE8/fQTzJo1c4VYysrKyMnJqTLOKVMms/HGm9CmTVsAunc/hPfee/cHbV9Vttmm0yrJFSQJ1ttvv8mJJx5L797HMXnyJKZNm0K7du155523uOOOWxg37l0KCwtrjeWnzBosSZIkrfXKk5EuXbbnkUf+wdy5czjppNMYM2Y07777Dl27bl/rMho1alzxOTc3l9LSUsrKqDbRqWr9yXIaVSyj/PP3yywhNzeXnXbalSuvvLba5TVvXkjTpk2ZPn1aFbVYZdXOl5eXR1nZcgCKi5fWun1Vadq0aZXDy8rKOO64Xhx22BGrjBs2bDRvvPEfhgy5jV122W2F2rifG2uwJEmStNbZdtvteP75ZwB49tmn6Ny5KwAdO27LBx+MJzc3l4KCArbeugOPPvow221Xe4JVlR133JkXX3yeb7+dB1DRRLC69WejU6fOvP/+OKZNmwrAkiVLmDJl8irTHXdcL268cSALFyZN7hYuXMC//vVP2rTZgpkzZ1TM/8wzT9K16w4AtG69CR99NBGAl19+odZYmjVrxqJFi7KKe9ddd+eJJx6tmH7OnC/55puvmTt3DgUFTfi//zuY3//+eD7++KOslvdTZQ2WJEmSspZNt+ppW7JkCYcffnDF96OPPoZzzvkTAwZcxX33ja7oZAKgcePGbLjhRnTq1BmA7bbbnueff4attmr/g9bdrt1WnHBCb/r0+QO5uXl06BC45JIrql1/NtZbbz0uueQKrrjiEpYtS2qZTjnl9Iomf+UOP/xIFi9exMkn9yQ/P5/8/Hx69DiOgoICLr74ci677IKKTi7Ka5V69z6FAQOuZvTo4XTsuG2tsay7bhGdO3fh+OOPYrfd9mT33fesdtpddtmNSZO+4LTTTgSgadNm9O9/NdOmTeWOOwaTk5NLfn4+/fpdmPW++CnKKSurvopxbTFnzvy1aiOKipoxb152TwpUdyyHhsOyaBgsh4bBcmg4LIuGwXJoGCyHVbVq1aLKtqM2EZQkSZKklJhgSZIkSVJKTLAkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpcS/gyVJkqSs9Z2x6h/E/TEGbdK21mm6ddub5557NdX1rq6GEENN3njjP/ztb0NYsmQxZWVl7LHH3vTpc06q63jwwTH85je/pUmTJlWOv+66qzn66GPZcst2qa53bWOCJUmSJFVSUlJCfv7ac5v8+eefctNNA7n++sG0bbsFJSUlPProw6mv58EH7+PAAw+uMsEqLS3lwgsvS32da6O158iRJEmSMmbNmsmAAVcxb943FBWtx0UXXU6rVq3o0eO3PPjgIyxYsICDD/4Vt956F1277sAZZ5zMxRdfzgYbtOSmmwby+eefUVpaQu/ef2DvvfflyScf4/XXX2Pp0qUsWbKYW24ZUuV6x459h7vv/itFRUV8/vlnhLAN/ftfTU5ODhMnTmDw4EEsXryYxo0bMXjwneTl5TNo0HV89NGH5OXlceaZ57HDDjvx5JOP8eqrL1FaupwvvviMHj2OZdmyZTzzzJM0atSYG24YzDrrrMv06dMYNOgvzJv3DU2aNOGCCy6lbdstVojp3ntH0bNn74rh+fn5/Pa3v6t2P7Vu3ZprrrmCPfbYi/32OwD4voauuu27557RzJ07h7POOpV11y3i1lvvolu3vTn66GP573/foE+fcxk69A769DmHX/yiI2+99SbDht3FsmVL2WSTzbj44stp1qwZd955K//5zyvk5eWx8867pV7L1hCYYEmSJGmtc+ONAznooF/TvfshPP74vxg8+HoGDBjE5pu34YsvPmfmzBmEsA3jxr1Lx47bMmfOl2y22ebcddft7Ljjzlx88eXMnz+fU045gZ122hWACRPeZ+TI+1hnnXVrXPcnn0RGj36Qli1bcfrpJzF+/Dg6duxE//4Xc9VV17LNNp1YuHABjRsX8Pe/3w/AqFEPMHnyJM4994/cd98/Afj8888YPvxeiouX0qPHYZx++pkMHz6GW24ZxNNPP8FRRx3DwIHX0K/fRWy+eRsmTPiAQYOuWyX5SxK041ZrP63u9h133PGMGDGCW265i6KiIgAWL17Mlltuxcknn7bC/PPmzWPkyGHcfPMdNG3alHvuGcEDD9zLEUccxSuv/JsxY/5BTk4O8+fPrzGOtZUJliRJktY6EyaM59prrwfgoIN+zZ133gJAly5dGTfuXWbOnM7xx/fi0UcfoWvXHfnFLzoC8NZbb/Laay9z3333ALB0aTGzZ88CYOedd601uQLYZptObLjhRgBsvXUHZs2aQWFhIS1bbsA223QCoHnzQgDGj3+PI488GoC2bbegdeuNmTp1CgA77LATzZo1p1mz5jRvXsiee+4DQLt27fnss09ZtGgR778/nssuu7Bi3cuWLU1lP63u9lUlLy+Pfff9VRXrfJ9Jkz7n9NNPAqCkZBmdOnWmWbPmNG5cwHXXXc0ee+zFHnvsvVrbsrYwwZIkSdJaLycnB4AuXbbnkUf+wdy5czjppNMYM2Y07777Dl27bg9AWVkZ11wzkDZttlhh/g8//KDazhtW1rhx44rPubm5lJaWUlZWBuRUMXVZtctp1KjRCstp1KhxpWWWUFa2nBYtChkxYkyN8Wy5ZTtinMjWW3eoNfby/ZSXl8fy5UlsZWVlLFu2rGKaqravKo0bNyYvL2+V4WVlZey0065ceeW1q4wbOnQk//vfWzz//LP84x8PVtsUc21mN+2SJEla62y77XY8//wzADz77FN07twVgI4dt+WDD8aTm5tLQUEBW2/dgUcffZjttksSrF133Z2HHnogkxDBxx9/lEo8bdtuwdy5c5k4cQIAixYtpKSkhC5dtufZZ58CYMqUycyePYs2bWrvORGSWrCNN96UF198HkgSl08++XiV6X7/+56MHj2cKVOSHh6XL1/O/fcnNXTV7afWrTcmxokAvPrqy5SUlNQaT7NmzVi0aGGt03Xq1Jn33x/HtGlTAViyZAlTpkxm0aJFLFy4gN1334uzz+5b5bb8FFiDJUmSpKxl06162pYsWcLhhx9c8f3oo4/hnHP+xIABV3HffaMrOm+ApFZlww03olOnzgBst932PP/8M2y1VXsAevU6icGDB3HCCT0oKytj4403YeDAm390jI0aNeKqq67lppuup7i4mIKCAm6++Q4OP/x33HDDAHr2PJq8vDwuueSKFWqIatO//9XccMN1jBw5jNLSEvbf/8BVaqrat9+as87qyxVXXEJx8RJycnLYffe9AKrdT7/5zeFceGFfTjmlJzt1YY89AAAgAElEQVTuuAtNmzatNZbf/OZw+vU7iw02aMmtt95V7XTrrbcel1xyBVdccUlFk8ZTTjmdZs2ac9FF57F06VLKyso466zzst4Pa5Oc8ux9bTZnzvy1aiOKipoxb96i+g7jZ89yaDgsi4bBcmgYLIeGw7JoGCyHhsFyWFWrVi2qahNqE0FJkiRJSosJliRJkiSlxARLkiRJklJiJxeS6lxh37NrHJ9bkE9hcQkLBg1eQxFJkiTVDWuwJEmSJCklJliSJEmSlBKbCEqSJClrffsWpLq8QYOKaxw/ffp0TjvtVEaPfrBi2LBhd9G0aTOOOeb4VGOpSZ8+f2DGjOn84x+Pk5OT9M590UV9eeedt3juuVeZO3cON998PX/+88Aq5+3T5xx+8YuOKwx/8snH+OijDznvvAtWe5sqz9sQlZSUMHTonbz88os0atSIJk2a0Lv3qey++56prWPmzBm8//54DjzwoCrH11QmdckES5IkSaqkrKyMsrIycnNXbOzVokULxo8fR5cuXZk/fz5z586tGNeyZas1fiPfkA0deidffTWXUaMeoHHjxnz99Ve8++7YVNcxc+YMnn/+6SoTrJKSknorExMsSZIkrbX69PkDW28dmDhxAgsXLuCii/rTseO2DBt2FzNmTGPOnDl8+eVsjjmmJ7/5zeEAjBkzihdffJ5ly5ayzz77cdJJpzJz5gz69TuL7bffiQkTxjNgwCBat954hXXtv/+BvPDCM3Tp0pWXX36RX/5yPyZN+hxIbvbPP/8cRo9+kOLiJVx77ZVMmvQFbdtuSXHx97V0TzzxKKNHj6Bly5ZsvnkbGjVqtMo2TZ8+jUGD/sK8ed/QpEkTLrjgUtq23aLafXDNNVfQvHlzPvpoIl999RVnnHEm++13AAD33juSZ555kpycXHbbbQ9OP/1MPvkkcv31AyguXsImm2zGRRf1Z5111qFPnz/QoUMgxo+YN+8bLr30SkaPHsHnn3/KwQcfTM+epwDwzDNP8tBD97NsWQkdO3aib98LycvLq4hnyZIlPPbYI/z97/+icePGAKy//gbsv383AJ577mlGjx5OWVkZu+++F2eccRYA3brtzXPPvQrAv//9PK+//hqXXHJFtds3ZMhtTJ78Bb16HUP37r+mRYt1eP3111i6dClLlizmoov6V5RJaWkpQ4bcxrvv/o9ly5Zy+OG/47DDjmDu3LlcfvlFLFy4kNLSEvr1u4guXbbP/gCsgu9gSZIkaa22ZMlihgy5m759L2TAgKsqhn/66adcf/3NDBkynBEj/sbcuXN46603mTp1KkOHjmT48DHEOJH33ktqVqZMmcxBB/2a4cPHrJJcAey44y6MG/cupaWlvPDCs+y//4FVxvPwww9RUNCEkSPvp2fP3nz88UcAzJ07l2HD7uLOO4dx0023VyRnKxs48BrOPfdP3H33Pfzxj+cwaNB1te6DuXPncscdf2PgwJsYMuQ2AN544z+8+urL/PWvIxk58j6OPbYnAH/+8+WcfvqZjBx5P1tt1Z7hw4dWLCc/vxG33z6U//f/juDCC/ty3nkXMGrUA/zrX4/w7bfzmDTpC1544TnuvPNuRowYQ25uHs8++9QKsUybNpWNNtqI5s0Lq4hzDnfeeSuDBw9h+PAxfPTRh7zyyks/aPtOO60PXbpsz4gRYzj66GMBmDDhfS699ApuuWXICvM//vi/aN68OX/72yiGDh3FY489wowZ03nuuafZZZfdGDFiDCNG3MfWW3eoNZbaWIMlSZKkBivzulMVw78fccAB/wdA1647sHDhQubPnw/A3nv/koKCJhQUNGH77Xfkww8nMH78e7z99puceGJyQ7548SKmTZvCRhu1pnXrjdl2287VxpKXl0vnzl154YXnKC4uZuONN6lyunHj3uXII3sA0L791my1VXsAPvzwA7bffkfWW289AH71qwOZOnXyCvMuWrSI998fz2WXXVgxbNmypdXGVG6fffYlNzeXLbdsx9dffw3AO++8xcEHH0qTJk0AWGeddVmwYAHz589n++13BKB790O47LLv3+Paa699ANhqq/ZsuWU7WrZsCcBmm23Gl1/OZvz494hxIiefnCRrxcVLKrYnGxMnTlhhHxx44EGMGzeWffbZd7W3ryo777wr66yz7irD3377TT799FNeeulFABYuXMC0aVPZZpuODBhwFSUlJeyzz75svXXIeluqY4IlSZKkBquoqKgiYSo3f/53bLLJphXfc1bKwsq/rzo8eb/quON6cdhhR6wwbubMGRWJSE0OOOBALr74T/TufUqN06287tqGlysrW06LFoWMGDGm1lgqW7GpYVnF/7Wtb2XlTfpycnIqPiffcyktLaWsrIzu3Q/htNP6VLuMzTbbnNmzZ7No0UKaNWu+wriysmpmStZS8Wnp0hWTyqq3b1XVlWFZWRnnnvsndt1191XG3X77UF5//TWuvro/v//98XTvfkhNQdbKJoKSJElqsJo1a84GG7TknXfeAuC7777lzTffYLvtulZM88ILzwIwbtx7FBYWUliYNE179dWXKS4u5ttv5/Huu/9jm206seuuu/PEE4+yaNEiAObM+ZJvvqm+RmRlXbpsz3HH9eKAA6ruua58mvJmc59//imfffYpAB07bsu77/6Pb7+dR0lJCf/+9/OrzNu8eSEbb7wpL76YjCsrK+OTTz7OOr7Kdt55N5544lGWLFkCJPuusLCQFi3WYdy4dwF4+ukn6Np1h6yXueOOu/DSSy9U7LPvvvuWWbNmrjBNkyZNOOSQ33DzzTewbNkyIGni98wzT9Kx47a8995Y5s2bR2lpKc8992zF+tdff30mTfqC5cuX88or/641lmbNmleUY2122WV3HnnkIUpKSoCkOejixYuZNWsmRUXr8ZvfHM4hh/w/Pv44Zr0vqmMNliRJkrJWW7fqdeHSS6/kxhv/wm233QxA796nsOmmm1WMb9FiHU47rXdFJxflttmmE+effw6zZ8+iV6+TadmyFS1btmLSpC847bQTAWjatBn9+1+9So+B1cnJyam1K/XDDz+Sa6+9khNO6EH79h3YZptOALRs2ZLevf/Aqaf2pmXLlnTo8AuWLy9dZf7+/a/mhhuuY+TIYZSWlrD//gf+oHeDdtttDz755GNOPvl48vMbsfvue3LqqX/k0kuvqNTJxaZcdNHlWS9zyy3bccopp3PuuX0oK1tOXl4+5513wSrvrJ1yyhkMHXoHxx33Oxo3bkyTJk05+eTTaNmyJaee2oezzjo108nFnuy9975A8k7V+eefw4YbbkS7dluxePHiGmNp335r8vLyOOGE33PwwYfQosU61U576KGHMWvWTHr3PpaysjKKitZjwIBBvPvu/xgzZhT5+fk0bdqMSy+9Mut9UZ2csprr6dYKc+bMX6s2oqioGfPmZZdtq+5YDmtOYd+zaxzfuCCfpcUlLBg0eA1FpKp4TjQMlkPDYVk0DLWVQ3V/Y6o+/lbWT5nnw6patWpRZftLmwhKkiRJUkpsIihJkqS11m23/bXK4SeddOoajkRKWIMlSZIkSSkxwZIkSZKklJhgSZIkSVJKTLAkSZIkKSV2ciFJkqSs9X2p5j+9sboG7Vv7n+jo1m1vnnvu1YrvTz75GB999CHnnXcBjzzyEAUFTeje/ZBq5688fU2q6/K9oZgyZTK33DKIqVOnkJ+fT7t27Tn33D+x/vobpLaOV155ic03b8OWW7arcnw2+/vnzgRLkiRJa63DDjuyvkNYI4qLizn//HPo0+dc9tprHwDGjn2HefO+STXBevXVl9hjj72qTLBKSkp+Nvv7xzDBkiRJ0lqr8h8UnjhxAtdddzVNmjRlu+268uab/2H06AcBmDt3LueddyYzZkxjn3325Ywzaq6J69Ztb448sgevv/4aBQUFXHfdINZffwO+/vorrr9+ADNmTAegX78L6dy5C/fffw9PPPEoAIceehhHHXUMM2fOoG/fM9luu65MmPA+7dt34OCDD+Xuu+/im2++oX//q+nYcVsWL17MTTcN5PPPP6O0tITevf/A3nvvu0I8zz33NJ06da5IrgB22GEnIEm+Bg26jo8++pC8vDzOPPM8dthhp1Vq7s4//xx69DiOHXbYqcrtmz59Gq+99grvvTeWkSPv5pprBjJgwFV07tyFiRPfZ9dd92LRooUV+3v69GkMGvQX5s37hiZNmnDBBZfStu0WvPji8wwf/ldyc/MoLCzk9tuHplLWawsTLEmSJDVoxcXF9Op1TMX37777doVEo9y1117J+edfQufOXbjzzltXGPfJJx8zfPi9NGrUiGOOOYIjjjiajTZqXe06Fy9eTKdOnTn11D9yxx2DefTRh+nV62RuvvkGtt9+BwYMuIHS0lIWL17MRx9N5MknH+Ovfx1JWVkZf/hDL7p23YEWLdZh+vRpXH31Xzj//Es4+eSePPfc09xxxzBee+1lRo8ezoABgxg16m523HFnLr74cubPn88pp5zATjvtStOmTSvi+eKLzwhhmypj/ec//w7AqFEPMHnyJM4994/cd98/a9yn1W3fXnvtwx577MV++x1QMe38+fMZMWIU8+YtYtiwuyqGDxx4Df36XcTmm7dhwoQPGDToOm65ZQgjRgzlxhtvo1WrDZk/f36NcfwUmWBJkiSpQSsoKGDEiDEV38trZiqbP38+ixYtonPnLgB063YQr7/+/XtbO+20M4WFhQBssUU7Zs2aVWOC1ahRI/bcc28AQtiGt9/+LwBjx77NpZdeCUBeXlJDM378e+yzz34VCdEvf7kf48a9x1577cPGG2/CVlu1B2DLLdux0067kJOTQ7t27Zk5cyYAb731Jq+99jL33XcPAEuXFjN79iy22GLLrPbP+PHvceSRRwPQtu0WtG69MVOnTqlxnuq2ryr7799tlWGLFi3i/ffHc9llF1YMW7ZsKQCdO3fhmmuu4Fe/6sYvf7lfVtvwU2KCJUmSpLVeWVlZjeMbNWpU8TkvL5fS0pIap8/PzycnJweA3NxcSktLa1p7VuvNzc2t+J4ss6Qi9muuGUibNltUu5wtt2zHu++OXa315+XlsXz59+OKi5dWfF6d7atck1axxrLltGhRuELiW+5Pf7qYCRM+4I03XuPEE49l+PB7WXfdomqX/1NjN+2SJEla662zzjo0a9aMDz54H4AXXni2Ttaz444788gjDwFQWlrKwoUL6NJlB1599SWWLFnC4sWLeeWVf9OlS9esl7nrrrvz0EMPVCSJH3/80SrTdOt2EB98MJ7XX3+tYtibb77OZ599Spcu2/Pss08BSU+Ds2fPok2btrRuvQmffvoxy5cvZ/bsWUycOKHWWJo1a8aiRYtqna5580I23nhTXnzxeSBJEj/55GMApk+fRqdO23Lyyaex7rrr8uWXs2vfCT8h9VaDFUIIwAOVBrUD+scYb640zb7Av4AvMoP+GWO8ao0FKUmSpBVk0616fbnwwv4MHPhnmjRpyvbb71jRJDBNZ5/dj4EDr+Hxx/9Fbm4e/fpdyLbbbkf37odwyik9gaSTiw4dfsHMmTOyWmavXicxePAgTjihB2VlZWy88SYMHHjzCtMUFDRh4MCbGTx4ELfcMoj8/Hy22qo9Z5/dj8MP/x033DCAnj2PJi8vj0suuYLGjRuz3XZd2HjjTejZswft2m1Fhw6h1lj23/9ABg68hoceup8//3lgjdP27381N9xwHSNHDqO0tIT99z+QrbfuwO23D2batCmUlZWx44670L59h6z2w09FTm3VqWtCCCEPmA7sGmOcXGn4vkC/GGONHe3PmTO//jdiNRQVNWPevNqfDKhuWQ5rTmHfmntqalyQz9LiEhYMarg/2j8HnhMNg+XQcFgWDcPqlMOiRYto1qwZAKNHj+Crr+Zyzjn96jK8nw3Ph1W1atUip6rhDeUdrP2BzyonV5IkSdLqeOON1xg9egSlpSW0br0xF198RX2HpJ+hhpJg9QDuq2bc7iGEccAMktqsVRqPFhYWkJ+fV5fxpSovL5eiomb1HcbPnuWw5uQW1Hypyc1JarEsj/rlOdEwWA4Nh2XRMKxOORxxxGEcccRhdRzRz5PnQ/bqPcEKITQGfgNcVMXosUDbGOOCEMLBwCPA1itPtGBBcd0GmTKrWBsGy2HNKSyuuaemiiaClke98pxoGCyHhsOyaBgsh4bBclhVq1YtqhzeEHoR7A6MjTGu0r1IjPG7GOOCzOcngUYhhJZrOkBJkiRJykZDSLB+TzXNA0MIrUMIOZnPu5DE+9UajE2SJEmSslavTQRDCM2AbsCplYadBhBjHAIcCZweQigBFgM9YoxrVY+BkiRJkn4+6jXBijEuAjZYadiQSp9vA25b03FJkiRJ0g/REJoISpIkSdJPggmWJEmSJKXEBEuSJEmSUmKCJUmSJEkpMcGSJEmSpJSYYEmSJElSSkywJEmSJCklJliSJEmSlBITLEmSJElKiQmWJEmSJKXEBEuSJEmSUmKCJUmSJEkpMcGSJEmSpJSYYEmSJElSSkywJEmSJCklJliSJEmSlBITLEmSJElKiQmWJEmSJKXEBEuSJEmSUmKCJUmSJEkpMcGSJEmSpJTk13cA0k9N35fOzmq6QfsOruNIJEmStKZZgyVJkiRJKTHBkiRJkqSUmGBJkiRJUkpMsCRJkiQpJSZYkiRJkpQSEyxJkiRJSondtEv6ycqmy3y7y5ckSWmyBkuSJEmSUmINlqS1Tt++BdlNeGjdxiFJkrQya7AkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpcQES5IkSZJSYoIlSZIkSSkxwZIkSZKklJhgSZIkSVJKTLAkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpcQES5IkSZJSYoIlSZIkSSkxwZIkSZKklJhgSZIkSVJKTLAkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpcQES5IkSZJSYoIlSZIkSSkxwZIkSZKklJhgSZIkSVJKTLAkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpcQES5IkSZJSYoIlSZIkSSkxwZIkSZKklJhgSZIkSVJK8us7AGlt0bdvQXYTHlq3cUiSJKnhsgZLkiRJklJigiVJkiRJKTHBkiRJkqSUmGBJkiRJUkpMsCRJkiQpJSZYkiRJkpQSu2mXJElSvSrse3a143IL8iksLmHBoMFrMCLph7MGS5IkSZJSYoIlSZIkSSkxwZIkSZKklPgO1k9I35eqb79c2aB9bcMsSZIk1QVrsCRJkiQpJSZYkiRJkpQSEyxJkiRJSokJliRJkiSlxE4uJEmSJGWt74zJWU03aJO2dRxJw2QNliRJkiSlxARLkiRJklJigiVJkiRJKTHBkiRJkqSUmGBJkiRJUkpMsCRJkiQpJSZYkiRJkpQSEyxJkiRJSokJliRJkiSlxARLkiRJklJigiVJkiRJKTHBkiRJkqSUmGBJkiRJUkpMsCRJkiQpJfn1HYAkSZJUm74zJmc13aBN2tZxJFLNrMGSJEmSpJSYYEmSJElSSuq9iWAIYRIwHygFSmKMO600PgcYDBwMLAJ6xRjHruEwJUmSJKlW9Z5gZewXY5xbzbjuwNaZf7sCd2b+lyRJkqQGpaEkWDX5f8CoGGMZ8GYIoSiEsHGMcWZ9ByZJP2d9+xZkNd2gQcV1HIkkSQ1HQ0iwyoBnQwhlwF0xxr+uNH5TYGql79MywyoSrMLCAvLz8+o80LTk5eVSVNQs9eUWNM6uOOti3Wuj1S2HgoKc7Ca0HFaRW1DzPsnNgcYF+VnvkzTL4udUDrWpq3OiqGjtuT43BHX1G6HVV1dlccaTp2c13R0H35n6uhuqmn4nyn8jvM+pX5XPh4K5lkVNGkKCtWeMcUYIYUPguRDCRzHGVyqNr+oXvKzylwUL1q6no0VFzZg3b1HW02f7lJhDS7KabHXW/VO2uuVQXJxlOSy1HFZWWFzzPmlckM/S4hIWZLlP0iyLn1M51Kauzol589aua3R9W91yUN2pq7Io9ndiFTX9TpT/Rrjf6lfl88GySLRq1aLK4fXei2CMcUbm/y+Bh4FdVppkGrB5pe+bATPWTHSSJEmSlL16TbBCCM1DCC3KPwMHAh+sNNmjQM8QQk4IYTfgW9+/kiRJktQQ1XcTwY2Ah0MI5bGMiTE+HUI4DSDGOAR4kqSL9k9Jumk/sZ5ilSRJkqQa1WuCFWP8HOhSxfAhlT6XAX9ck3FJkiRJ0g9R3zVYUoPQd8bkLKbqUOdxSJIkae1W751cSJIkSdJPhQmWJEmSJKXEBEuSJEmSUmKCJUmSJEkpMcGSJEmSpJSYYEmSJElSSkywJEmSJCklJliSJEmSlBITLEmSJElKiQmWJEmSJKXEBEuSJEmSUpJf3wFIUrm+MyZnOWWHOo1DkiTph7IGS5IkSZJSYoIlSZIkSSkxwZIkSZKklJhgSZIkSVJKTLAkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpcQES5IkSZJSkl/fAUiSJElp6du3IKvpBg0qruNI9HNlDZYkSZIkpcQES5IkSZJSYoIlSZIkSSkxwZIkSZKklJhgSZIkSVJKTLAkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpcQES5IkSZJSYoIlSZIkSSkxwZIkSZKklJhgSZIkSVJKTLAkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpSS/vgOQ6lJh37OrHZdbkE9hcUnype95aygiSZIk/ZRZgyVJkiRJKbEGS5IkST87fV+qvpVLZYP2HVzHkfx09e1bUOs0gwYVr4FI1ixrsCRJkiQpJSZYkiRJkpQSEyxJkiRJSokJliRJkiSlxARLkiRJklJigiVJkiRJKTHBkiRJkqSUmGBJkiRJUkpMsCRJkiQpJSZYkiRJkpQSEyxJkiRJSokJliRJkiSlxARLkiRJklJigiVJkiRJKTHBkiRJkqSUmGBJkiRJUkpMsCRJkiQpJSZYkiRJkpQSEyxJkiRJSokJliRJkiSlxARLkiRJklJigiVJkiRJKTHBkiRJkqSUmGBJkiRJUkpMsCRJkiQpJSZYkiRJkpQSEyxJkiRJSokJliRJkiSlxARLkiRJklJigiVJkiRJKTHBkiRJkqSU5Nd3AJIkST8FffsWZDfhoXUbh6T6ZQ2WJEmSJKXEBEuSJEmSUmKCJUmSJEkpMcGSJEmSpJSYYEmSJElSSuxFUJIk/SwV9j27xvG5BfkUFpewYNDgNRSRpJ8Ca7AkSZIkKSUmWJIkSZKUEhMsSZIkSUqJCZYkSZIkpcQES5IkSZJSYoIlSZIkSSkxwZIkSZKklJhgSZIkSVJKTLAkSZIkKSUmWJIkSZKUkvz6DkCSJKkh6ztjcpZTdqjTOCStHazBkiRJkqSUWIMlSapTfV86O6vpBu07uI4jkSSp7lmDJUmSJEkpMcGSJEmSpJSYYEmSJElSSurtHawQwubAKKA1sBz4a4xx8ErT7Av8C/giM+ifMcar1mSckiRJkpSt+uzkogToG2McG0JoAfwvhPBcjPHDlaZ7NcZ4SD3EJ0mSJEmrpd6aCMYYZ8YYx2Y+zwcmApvWVzySJEmS9GM1iG7aQwhbANsD/61i9O4hhHHADKBfjHHCyhMUFhaQn59Xt0GmKC8vl6KiZllPX1CQk92EjbMrztVZ99out6D6fZKbA40z4wuy2Xc1LGsFlsMqaioH+L4ssioHSLUsfk7lUBuvTQ3D6paDfriGfG2Cn9c5kc3vteVQvypfmwrmplcWRUVrzz18tuo9wQohFAL/AM6JMX630uixQNsY44IQwsHAI8DWKy9jwYLiug80RUVFzZg3b1HW0xcXF2Q34dKSrCZbnXWv7QqLq98njQvyWZoZX5zNvqthWSuwHFZRUznA92WRVTlAqmXxcyqH2nhtahhWtxz0wzXkaxP8vM6JbH6vLYf6VfnalGZZzJu3dt3HV9aqVYsqh9drL4IhhEYkydW9McZ/rjw+xvhdjHFB5vOTQKMQQss1HKYkSZIkZaXeEqwQQg4wDJgYY7yxmmlaZ6YjhLALSbxfrbkoJUmSJCl79dlEcE/geOD9EMJ7mWEXA20AYoxDgCOB00MIJcBioEeMsaw+gpUkSZKk2tRbghVjfA2o8Q3pGONtwG1rJiJJkiRJ+nHqvZOLn7O+MyZnOWWHOo1DkiRJUjrqtZMLSZIkSfopMcGSJEmS/n979x5u6V3WB/87mUmGhEkcWoMcVV4Id0UKUQ7SF0vVIga0HFQgEZVKi2JBQs1VBKmiUAtSgkaLgi+neChQKGrAvCJggaLQEk4SDrdAXsQ01MhhgElgkknm/WM9I9vJ7D1rMr+19l4zn8917Wuv5/c8e+1773uvtfd3/57nt2AQAQsAAGAQAQsAAGAQAQsAAGAQAQsAAGAQAQsAAGAQAQsAAGAQAQsAAGCQHZtdwPFo1wXnb7j/pJ07smvf/uSCn15SRQAAwDKYwQIAABhEwAIAABhEwAIAABhEwAIAABhEwAIAABjEKoIAsEVdcNVfzXXchbf7hgVXAsC8zGABAAAMImABAAAMImABAAAM4hosAABgU1zw1vPnOu7C77howZWMYwYLAABgEDNYALDiLrhg5xGPufDCfUuoBAAzWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIMIWAAAAIPs2OwCAFiOXRecv+H+k3buyK59+7P3wouWVBEAHH8ELAA4AVzw1o0D9kEXfoeADXAsnCIIAAAwiIAFAAAwiIAFAAAwyIbXYFXVtyS5c5IPdfdHllMSAADAalp3Bquqfj7Jq5P8QJI/qqrHL+urwOUAACAASURBVK0qAACAFbTRKYKPTnJ2d5+X5D5Jfnw5JQEAAKymjQLWV7r72iTp7s8e4VgAAIAT3kbXYN25qi6Zbm87ZDvd/dCFVgYAALBiNgpYDztk+/mLLAQAAGDVrRuwuvttB29X1ZnT2N8uoygAAIBVtG7AqqptSX4+yU9ldorgSVW1P8mvd/ezllQfAADAytho4YqnJPn2JPfp7n/Y3bdK8m1J7l9V/3Yp1QEAAKyQjQLWjyY5r7v/v4MD3X1Fkh+e9gEAALDGRgHr5O7+zKGD03VYJy+uJAAAgNW0UcC67mbuAwAAOCFttEz7Pavqi4cZ35bkFguqBwAAYGVttEz79mUWAgAAsOo2msH6e6rq9kkOhq6runv/YkoCAABYTRu9DtbTM1vo4uBrXr0zyRcyW+Di4iTPWXx5AAAAq2OjRS4emeTCNduf7e5/nOSbk3zvQqsCAABYQRsFrHT3NWs2L5rGbkhy6iKLAgAAWEUbBaxdVfV3r3fV3a9IkqrameSMBdcFAACwcjYKWK9N8uKqOu3gQFXdMsmLpn0AAACssdEqgj+X5JeSfKqq/moa+/okL532AQAAsMZGr4N1Q5KnVdUvJrnLNPzx7v7yUioDAABYMUd8HawpUH1wCbUAAACstA1XEQQAAGB+6wasqrr/9H7n8soBAABYXRvNYP3a9P6dyygEAABg1W10Ddb1VfXyJLevql87dGd3P3lxZQEAAKyejQLW9yV5YJLvSvKe5ZQDAACwujZapv0zSV5VVR/p7g8ssSYAAICVdMRl2pN8tqp+P8n9kxxI8o4k53f3lQutDAAAYMXMs0z7y5NckuR2SW6f5PXTGAAAAGvMM4N16+5eG6heUVVPWVRBAAAAq2qegPW3VfXDSV45bZ+X5LOLKwkAAGA1zXOK4OOSPCrJ/0ny6SQ/OI0BAACwxhFnsLr7U0keuoRaAAAAVto8M1gAAADMQcACAAAYRMACAAAYZJ5VBJMkVXW/JP8xyc4k/6m7/2BhVQEAAKygdWewquo2hwz9dGaLXZyT5NmLLAoAAGAVbTSD9aKqek9ms1VfSbInyQ8luTHJF5dRHAAAwCpZdwarux+e5P1J3lBVP5LkKZmFq9OSPHw55QEAAKyODRe56O7XJ/meJLuTvG421L/W3X+7jOIAAABWyUbXYD20qt6R5E+TXJ7k3CSPqKpXVtWdl1UgAADAqtjoGqz/kOSfJDk1yaXdfd8kP11VZyX5pcwCFwAAAJONAtYXMgtRpya5+uBgd38swhUAAMBNbHQN1iMyW9Bif2arBwIAALCBdWewuvszSX59ibUAAACstA1XEQQAAGB+AhYAAMAgAhYAAMAgG60imCSpqu9P8stJbp1k2/R2oLvPWHBtAAAAK+WIASvJ85L8i+7+yOhPXlXnJLkoyfYkL+nu5x6yf2eS305yrySfTfLo7v7k6DoAAABGmOcUwb9ZULjanuSFSR6c5G5Jzququx1y2L9K8vnuvkuSX8lsJg0AAGBLmmcG67KqenWSP0iy7+Bgd7/uGD/3fZN8vLuvSJKqelWShyX58JpjHpbkF6bbr03yn6tqW3cfOMbPDQAAMNy2Awc2zipV9fLDDB/o7scdyyeuqh9Mck53/+tp+0eSfFt3P2nNMZdPx1w5bX9iOuYza+/ry1++7sCOHduPpZyl2r79pNxww42bXcYJTx+2Dr3YGg724d98/GNzHf8bdzlrwRUdv076Nz+5/r5tyY3Tr+Ybf+M3l1QRh+O5aWvQh+Xx3HR0Tj55+7bDjR9xBqu7f2x8OUlmi2Uc6tC0N88x2bt332EO27p27z4te/Zcu9llnPD0YevQi63hYB/2Xbd/ruP17ObbtW/97/EpO3fkumn/Xt/jTeW5aWvQh+Xx3HR0zjzz9MOOrxuwquqp3f28qvr1HCbUdPeTj7GmK5Pccc32HZJctc4xV1bVjiRfk+Rzx/h5AQAAFmKjGayDC1tctqDP/e4kZ1XVnZL87yTnJvmhQ465JMljk7wzyQ8m+VPXXwEAAFvVugGru18/vb94EZ+4u/dX1ZOSvDGzZdpf1t0fqqpnJbmsuy9J8tIkv1NVH89s5urcRdQCAAAwwjyrCC5Md1+a5NJDxn5+ze2vJHnksusCAAC4OeZ5HSwAAADmIGABAAAMcsRTBKvqzCSPT/KNa48/1tfBAgAAON7Mcw3WHyb5H0nenOSGxZYDAACwuuYJWKd1988svBIAAIAVN881WG+oqocsvBIAAIAVt+4MVlV9KcmBJNuS/GxV7Uty/bR9oLvPWE6JAAAAq2GjFxo+fZmFAAAArLojniJYVW+ZZwwAAOBEt9EpgrdIcsskX1tVt8rs1MAkOSPJ7ZZQGwAAwErZaBXBn0jylMzC1HvXjH8xyQsXWRQAAMAq2ugarIuSXFRVP9Xdv77EmgAAAFbSPK+D9aiquk1mLzb8Z939pQXXBAAAsJLmeR2sxybpJD+Q5M+r6rKq+pXFlgUAALB6jjiD1d1XVNWXk1w3vX1nkm9adGEAAACrZp5l2j+R5A+SfF2Slya5e3efs+jCAAAAVs08pwj+WpJPJTkvyZOTPLaq7rzQqgAAAFbQEQNWd1/U3Y9M8sAk70nyC0n+csF1AQAArJwjXoNVVRcm+fYku5K8K8nPZ7aiIAAAAGvMs0z7u5I8r7v/ZtHFAAAArLJ5VhF8TVU9tKoeMA29rbtfv+C6AAAAVs48qwg+J8n5ST48vT15GgMAAGCNeU4R/N4kZ3f3jUlSVRcneV+Spy+yMAAAgFUzzzLtSbJ7ze2vWUQhAAAAq26eGaznJHlfVf33JNuSPCBmrwAAAG5iw4BVVduSvCPJ/ZLcJ7OA9TPd/X+WUBsAAMBK2TBgdfeBqvqD7r5XkkuWVBMAAMBKmucarHdV1X0WXgkAAMCKm+carO9M8hNV9VdJrsnsNMED3X2PhVYGAACwYuYJWA9eeBUAAADHgSMGrO7+qySpqtsn2T4NX7XIogAAAFbRugGrqp6e5OTuftY09M4ke5KckuTizJZvBwAAYLLRIhePTHLhmu3PTtddfXOS711oVQAAACtow1UEu/uaNZsXTWM3JDl1kUUBAACsoo2uwdpVVSd39/VJ0t2vSJKq2pnkjCXUBsAmuPB237DZJQDAytpoBuu1SV5cVacdHKiqWyZ50bQPAACANTaawfq5JL+U5FPTa2Alydcneem0DwAAgDXWDVjTtVZPq6pfTHKXafjj3f3lpVQGAACwYuZ5HawvJ/ngEmoBAABYaRuuIggAAMD8BCwAAIBBjhiwquoRVfU1a7Z3V9XDF1sWAADA6plnBuuZ3f2FgxvdvSfJMxdXEgAAwGqaJ2Ad7pgjLo4BAABwopknKF1WVS9I8sIkB5L8VJL3LLQqAACAFTTPDNZPJbkuyauTvCbJV5I8cZFFAQAArKJ5XgfrmiRPW0ItAAAAK23dgFVVv9rdT6mq12d2auDf090PXWhlAAAAK2ajGazfmd4/fxmFAAAArLp1A1Z3v2d6/7bllQMAALC6jngNVlV9X5JnJ/mG6fhtSQ509xkLrg0AAGClzLNM+68m+f4kH+zum1yLBQAAwMw8y7T/dZLLhSsAAICNzTOD9dQkl1bV25LsOzjY3S9YWFUAAAAraJ6A9UtJ9ia5RZJTFlsOAADA6ponYP2D7n7QwisBAABYcfNcg/XmqhKwAAAAjmCeGawnJnlqVV2X5PppzDLtAAAAhzhiwOru05dRCAAAsHn2XnjRuvt27z4te/dcu8RqVtc8M1ipqocmecC0+dbufsPiSgIAAFhNR7wGq6qem+T8JB+e3s6fxgAAAFhjnhmshyQ5u7tvTJKqujjJ+5I8bZGFAQAArJp5VhFMkt1rbn/NIgoBAABYdfPMYD0nyfuq6r8n2ZbZtVhPX2hVAAAAK+iIM1jd/cok90vyuuntn3T3qxZdGAAAwKqZ9xTBk5J8Jsnnk9y1qh5whOMBAABOOEc8RbCqfjnJo5N8KMmN0/CBJG9fYF0AAAArZ55rsB6epLp736KLAQAAWGXznCJ4RZKTF10IAADAqptnBuvaJO+vqrck+btZrO5+8sKqAgAAWEHzBKxLpjcAAAA2cMSA1d0XV9UpSe761aG+frFlAQAArJ4jXoNVVd+R5GNJXpjkN5L8pWXaAQAAbmqeUwQvTPKg7u4kqaq7JnllknstsjAAAIBVM88qgicfDFdJ0t1/GasKAgAA3MQ8M1iXVdVLk/zOtP2YJO9ZXEkAAACraZ6A9ZNJnpjkyUm2JXl7ZtdiAQAAsMY8AWtHkou6+wVJUlXbk+xcaFUAAAAraJ5rsN6S5NQ126cmefNiygEAAFhd8wSsW3T33oMb0+3TFlcSAADAaponYF1TVd96cKOq7pXky4srCQAAYDXNcw3WU5K8pqqumrZvm+TcxZUEAACwmo4YsLr73VX1j5JUZqsIfrS7r194ZQAAACtm3VMEq+qpazYf3t2Xd/cHu/v6qvqPS6gNAABgpWx0Ddba0wCffsi+cxZQCwAAwErbKGBtW+f24bYBAABOeBsFrAPr3D7cNgAAwAlvo0Uu7llVX8xsturU6Xam7VssvDIAAIAVs27A6u7tyywEAABg1c3zQsMAAADMQcACAAAYRMACAAAYRMACAAAYRMACAAAYRMACAAAYRMACAAAYRMACAAAYZN0XGl6kqvpPSf5FkuuSfCLJj3X3nsMc98kkX0pyQ5L93X3vJZYJAABwVDZrButNSe7e3fdI8pdJnr7Bsd/Z3WcLVwAAwFa3KTNY3f0nazbfleQHN6MOAACAkTYlYB3icUlevc6+A0n+pKoOJHlxd//W4Q7atWtnduzYvqj6htu+/aTs3n3aZpdxwtOHrUMvtgZ9WJ6Tdq7/6/ekbckp03792FweE1uDPmwN+jC/hQWsqnpzktscZtczuvsPp2OekWR/kt9b527u391XVdWtk7ypqj7a3W8/9KC9e/eNKnspdu8+LXv2XLvZZZzw9GHr0IutQR+WZ9e+/evuO2Xnjlw37d+rH5vKY2Jr0IetQR9u6swzTz/s+MICVnc/cKP9VfXYJN+X5J9394F17uOq6f3VVfX7Se6b5CYBCwAAYCvYlEUuquqcJD+T5KHdfdgoXFW3rKrTD95O8qAkly+vSgAAgKOzWasI/uckp2d22t/7q+pFSVJVt6uqS6djvi7JO6rqA0n+V5I/6u4/3pxyAQAAjmyzVhG8yzrjVyV5yHT7iiT3XGZdAAAAx2KzZrAAAACOOwIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIAIWAADAIDs245NW1S8keXySv52Gfra7Lz3MceckuSjJ9iQv6e7nLq1IAACAo7QpAWvyK939/PV2VtX2JC9M8t1Jrkzy7qq6pLs/vKwCAQAAjsZWPkXwvkk+3t1XdPd1SV6V5GGbXBMAAMC6NjNgPamq/qKqXlZVtzrM/tsn+es121dOYwAAAFvSwk4RrKo3J7nNYXY9I8lvJnl2kgPT+wuTPO6Q47Yd5mMPHO5z7dq1Mzt2bL/5xS7Z9u0nZffu0za7jBOePmwderE16MPynLRz/V+/J21LTpn268fm8pjYGvRha9CH+S0sYHX3A+c5rqr+nyRvOMyuK5Pccc32HZJcdbj72Lt331HXt5l27z4te/Zcu9llnPD0YevQi61BH5Zn17796+47ZeeOXDft36sfm8pjYmvQh61BH27qzDNPP+z4ppwiWFW3XbP5iCSXH+awdyc5q6ruVFWnJDk3ySXLqA8AAODm2KxVBJ9XVWdndsrfJ5P8RJJU1e0yW479Id29v6qelOSNmS3T/rLu/tAm1QsAAHBEmxKwuvtH1hm/KslD1mxfmuQmr48FAACwFW3lZdoBAABWioAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwiIAFAAAwyI7N+KRV9eokNW3uTrKnu88+zHGfTPKlJDck2d/d915WjQAAAEdrUwJWdz/64O2qujDJFzY4/Du7+zOLrwoAAODYbErAOqiqtiV5VJLv2sw6AAAARtjUgJXknyb5m+7+2Dr7DyT5k6o6kOTF3f1bhzto166d2bFj+6JqHG779pOye/dpm13GCU8ftg692Br0YXlO2rn+r9+TtiWnTPv1Y3N5TGwN+rA16MP8FhawqurNSW5zmF3P6O4/nG6fl+SVG9zN/bv7qqq6dZI3VdVHu/vthx60d+++Yy94iXbvPi179ly72WWc8PRh69CLrUEflmfXvv3r7jtl545cN+3fqx+bymNia9CHrUEfburMM08/7PjCAlZ3P3Cj/VW1I8n3J7nXBvdx1fT+6qr6/ST3TXKTgAUAALAVbOYy7Q9M8tHuvvJwO6vqllV1+sHbSR6U5PIl1gcAAHBUNjNgnZtDTg+sqttV1aXT5tcleUdVfSDJ/0ryR939x0uuEQAAYG6btshFd//Lw4xdleQh0+0rktxzyWUBAADcbJs5gwUAAHBcEbAAAAAGEbA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd81f6b56a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data to plot\n",
    "\n",
    "n_groups = 8\n",
    "\n",
    "low_mean_growth = (2.117500,3.902333,8.180500,1.417667, -9.010000,2.975167, 5.713333,-1.505000)\n",
    "lower_mean_growth = (2.354979,4.863750 , 4.566604, -0.938271,1.970963,2.698229,5.142915, 5.499795 )\n",
    "upper_mean_growth = (4.969780,5.869163,6.669439,5.017659, 1.820954,3.109122,4.343732, 3.857488 )\n",
    "high_mean_growth = (5.617388,6.867168,6.713541,2.651132,1.405147,5.011735, 3.866864, 1.881906)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.1\n",
    "opacity = 0.6\n",
    "\n",
    "rects1 = plt.bar(index, low_mean_growth, bar_width, \n",
    "                 alpha=opacity, color='r',label='Low Income Countries')\n",
    "rects2 = plt.bar(index + bar_width,lower_mean_growth, bar_width, \n",
    "                 alpha=opacity, color='c', label='Lower Income Countries')\n",
    "rects3 = plt.bar(index + bar_width + bar_width,upper_mean_growth, bar_width, \n",
    "                 alpha=opacity,  color='b', label='Upper Middle Income Countries')\n",
    "rects4 = plt.bar(index + bar_width + bar_width + bar_width,high_mean_growth, bar_width, \n",
    "                 alpha=opacity, color='g', label='High Income Countries')\n",
    "rcParams['figure.figsize'] = 12,12\n",
    "\n",
    "ax.set_xlabel('Years')\n",
    "ax.set_ylabel('Economic Growth in % of GDP')\n",
    "ax.set_title('Economic Growth for All Countries From 1950 to 2013, Grouped By Income Level')\n",
    "ax.set_xticks(index + bar_width / 4)\n",
    "ax.set_xticklabels(('1950', '1960', '1970', '1980', '1990', '2000',\n",
    "                           '2010', '2013'))\n",
    "\n",
    "ax.legend()\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>1950</th>\n",
       "      <th>1960</th>\n",
       "      <th>1970</th>\n",
       "      <th>1980</th>\n",
       "      <th>1990</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>0.855</td>\n",
       "      <td>1.470</td>\n",
       "      <td>26.300</td>\n",
       "      <td>6.400</td>\n",
       "      <td>3.63</td>\n",
       "      <td>-1.150</td>\n",
       "      <td>3.98</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>5.200</td>\n",
       "      <td>0.674</td>\n",
       "      <td>4.210</td>\n",
       "      <td>-0.894</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1.720</td>\n",
       "      <td>2.46</td>\n",
       "      <td>-36.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>1.990</td>\n",
       "      <td>9.540</td>\n",
       "      <td>-0.187</td>\n",
       "      <td>2.410</td>\n",
       "      <td>-6.58</td>\n",
       "      <td>-6.900</td>\n",
       "      <td>7.19</td>\n",
       "      <td>8.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2.200</td>\n",
       "      <td>2.900</td>\n",
       "      <td>6.410</td>\n",
       "      <td>-6.290</td>\n",
       "      <td>-51.00</td>\n",
       "      <td>22.400</td>\n",
       "      <td>7.26</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Niger</td>\n",
       "      <td>1.190</td>\n",
       "      <td>7.310</td>\n",
       "      <td>3.890</td>\n",
       "      <td>5.220</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-0.779</td>\n",
       "      <td>11.20</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>1.270</td>\n",
       "      <td>1.520</td>\n",
       "      <td>8.460</td>\n",
       "      <td>1.660</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>2.560</td>\n",
       "      <td>2.19</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country   1950   1960    1970   1980   1990    2000  \\\n",
       "27                    Burundi  0.855  1.470  26.300  6.400   3.63  -1.150   \n",
       "32   Central African Republic  5.200  0.674   4.210 -0.894   3.09   1.720   \n",
       "38           Congo, Dem. Rep.  1.990  9.540  -0.187  2.410  -6.58  -6.900   \n",
       "94                    Liberia  2.200  2.900   6.410 -6.290 -51.00  22.400   \n",
       "124                     Niger  1.190  7.310   3.890  5.220  -1.47  -0.779   \n",
       "155                   Somalia  1.270  1.520   8.460  1.660  -1.73   2.560   \n",
       "\n",
       "      2010   2013  \n",
       "27    3.98   3.91  \n",
       "32    2.46 -36.00  \n",
       "38    7.19   8.11  \n",
       "94    7.26   7.81  \n",
       "124  11.20   4.03  \n",
       "155   2.19   3.11  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking growth in the low income group\n",
    "#looking at the 1990 column in the low income group to understand \n",
    "low_income_growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so from the chart above shows growth for each group in the years 1950, 1960, 1970, 1980, 1990, 2000, 2010 and 2013. we can see that the low income group had a huge growth in 1970. and one really visible negative value in 1990. the data looked so strange that I had to look at the data once again. (check the table above)<br>\n",
    "and one big outlier was Liberia with -51.00 in the year 1990. By making a quick google search, I could notice that year the country knew a civil war. my first attempt was to remove this extreme value but since 4 out of 6 countries in that group had negative values that year, I decided to leave the plot as is. https://en.wikipedia.org/wiki/First_Liberian_Civil_War. \n",
    "<p> apart from negative growth in 1980 for lower middle income countries and another negative value in 2013 for the low income countries in 2013, all groups seemed to have enjoyed growth at different levels. \n",
    "Since I could not really observe any tendencies, I decided to also plot a line graph with the same data. as shown below\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Economic Growth for All Countries From 1950 to 2013, Grouped By Income Level')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd81f78ba90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = low_income_growth.mean()\n",
    "B = lower_middle_income_growth.mean()\n",
    "C = upper_middle_income_growth.mean()\n",
    "D = high_income_growth.mean()\n",
    "plt.plot(A, 'r-.', label='Low Income Countries')\n",
    "plt.plot(B, 'c-.', label='Lower Middle Income Countries')\n",
    "plt.plot(C, 'b-.', label='Upper Middle Income Countries')\n",
    "plt.plot(D, 'g-.', label='High Income Countries')\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 8,8\n",
    "plt.legend(loc='lower left')\n",
    "plt.ylabel('Economic Growth in % of GDP')\n",
    "plt.title('Economic Growth for All Countries From 1950 to 2013, Grouped By Income Level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graphs shows better the more erratic tendencies in growth. the green and blue line which the 2 richest group did not fall below 0 of growth but have benefited for less spectacular number in general. In 2013, which is how far the data is available, we can see that lower middle income are benefiting from the highest growth. <br>\n",
    "But at the opposite of the previous metrics, not patterns can be observed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "\n",
    ">* we are at the end of this analysis. we could see that people in richer country enjoyed a longer life in average compared to people with a smaller GDP. <br> And that people in less wealthy countries had more children in average comparing to people in richer countries. \n",
    "* Life expectancy and fertility rate are negatively correlated. even if this analysis could not show any causation. so this is a mere observation.\n",
    "* This analysis could only show that economic growth knows more pronounced changes than the other variables. but based on this analysis alone, it is hard to draw any conclusions on any relationships between longevity, fertiliy and economic growth as a trio. \n",
    "* GDP VS GNI : As previously explained the data used to group the country is the GDP of each country. but instead of using Quartiles,I used the world bank measurement which to be a more realistics measures. But Again, it is not a perfect measure so please take this classification with a grain of salt. the GNI is the measure used in 2018 by the world bank to classify countries by Income.\n",
    "* 2018 vs 2013 : the data in the life expectancy, number of babies per woman and countries's GDP were all up to 2018. the Growth Dataset only had data up to 2013. But again the grouping was made based on each countries 2018's GDP. <br> since the goal was to look how the different metrics have evolved in the last decades. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Personally this analysis left me a little sad. My secret hope had been that all  countries or almost all countries had been benefiting from a steady growth since the 1950' and 1960's.<br> \n",
    "So I went to look one more time at the proportion of each group. and it made me feel a little better. \n",
    "<p> As shown  on the pie chart below, more than 70% of countries are either in High income group (50.8%) or in the upper middle Income countries (21.2%)\n",
    "<br> And only 3.1% of the countries are in the Low income Group. <br>\n",
    "Again please take this classification with a grain of salt. \n",
    "The chart with proportions is below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportions of Each Income Level Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f9UsTNSeFXYyIKLiKZJwBYw4d+g/mzfsmJSWlYdeT5wzB57lqBy0J6kq87gNvDLsQkUKn4CqSQWbr1unEYo+xYMFbw66lgASdLlAHdJOwQKsGWpqhMSNVSa6bBnzPRM3/mKiZHXYxIoVKwVUkQ8y2betZtux55s07O+xaCkygEdfN8Xg3sIfBC7Q0XUBG8iq80dfrwi5EpBApuIpkgHnuuTewcuXfmDpV81knXioLtBxJwfWApgvI6OYAPzNR810TNUHnVItIGii4iqSZefbZKKtX/ycVFRVh11KgVpqkbgEj2EPCVAGAnQquEtzNeKOv6hIiMkEUXEXSxEQiReaZZ/6TNWvuoahIu++ExwBnBDz3ENCTeGCLpgpIahYCvzVR8ykTNcWjni0i46LgKpIG5l3vquJzn/sza9feFHYtAgSfLnAYiJPwWrgfTrRAU0aqknxVBHwM+L2JmnlhFyOSzxRcRcbJfPKTJ3HHHU9wyikvDbsW6RN0B61uYB/aQUvS42K8Xbc2hl2ISL5ScBUZB/OlL63lLW95jBUrbNi1yABBe7kCPE/SnNgDmi4gYzcP+IOJmo+ZqNGUIZE0U3AVGSPzzW++lDe84UFOOmlB2LXIINYkjaKOYA9Jr4W7NOIq41MMfAr4lYmaWWEXI5JPFFxFxsB8/evX8prX/JpZs6aHXYsMqQhYH/DcQSF1i4KrpMcr8aYOnBt2ISL5QsFVJAUmEjHmX//1Jm688QdMmVIVdj0yoqDTBWpJWqC1B5paoDkTRUnBWQg8YKLmdWEXIpIPFFxFAjKRSBEbNryLW275f0yeXBl2PTKqoAu0uvAWaA34Q6ROo66SPhXAD03UfELzXkXGR8FVJAATiZRw/vkf4rbb/onJkyeFXY8EkuoOWskLtBRcJd024QVYvYaIjJGCq8go/NB6N+95z8eprtYvnNyx2kDQkfEXgJLEA7vUWUAy40a8qQPzwy5EJBcpuIqMwEQipZx//se5/fYPK7TmnGLg9IDn1pC0g9ZTGnGVzDkHeMxETdAd3kTEp+AqMgwTiZRywQUf5/bbP0hVVUXY9ciYpLJACxJeE3dBYxu0pL8kEcBbtPUXEzXXhl2ISC5RcBUZgolESlm37sO8+913KrTmtKALtDqBAyQt0Dqs6QKSWZXA/5iouTXsQkRyhYKrSBITiZSxdOkH+MAH7tL0gJyX6gKtAZsWHNR0Acm8YuAbJmo+EHYhIrlAwVUkgYlEypg9+7189KN3MWPG5NFvIVnuVOO1IgriBaA08cBuBVeZGAb4goma/xt2ISLZTsFVxGcikRKqqt7Fxz9+B/PmzQi7HkmLEuC0gOcewtuIoM9WTRWQifUREzVfNVGj380iw9B/DhH8zQVKS9/MPfe8j6VL54Vdj6RV0OkCvQu0+hrE74DjbdCa/pJEhvV2vF6vpaOeKVKAFFyl4JlIxACv5iMf+RCnnLI47Hok7QJ1Ftgcj3cAB9EOWhK+G4Cfm6jRDn0iSRRcReCVvO99H+fss1eGXYhkxLh20DoENe3G8JrFi4ksWcJVS5bwpZkzB93w75Mmcf3ixZy6ciW/qe5f4/VCaSmv8m/7ZIU33bYLuOWkk2g12v5ThvUK4LcmaqpHPVOkgCi4SkEzkchFvPa1d3PJJevCrkUyZq2BsoDn7mbwAq1DZfE439m/n8179/K/e/fyUGUlWysGrvma39nJZ2Ixrm5qGnD8R9Om8YEjR/jSoUN8c/p0AH44bRrXNjUxKR4fMKdWJMlFwC9N1FSNeqZIgVBwlYJlIpHTOe+8j/KGN5wXdi2SUaVA0D9MakhaoPUU1Bigys+YXcbQZQzJQ6ULu7pY3dFBUVIWLYnHaTOGNmMoicdpLCriT1VVXNfYOKYnIwVnA1541bQBERRcpUCZSGQVS5d+lPe//2UUFxeHXY9kXNDpAjG8xVl9ufR5ONoObd3AtYsXc8GKFVzQ0sLpbW2B7vCmY8f49vTpbJo7l7c3NPDlmTO5raFhUPAVGcHLgF+YqFFfaSl4owZXa23zRBQySg1xa+33Ej4vsdbWWWt/4X8esdZ+eJjbDlm/tfbb1trX+B//2Vp7dgr19N02G1lr51lr/8tau9ta+5y19lfW2lVpfoyN1toLRvj6sP8mYTORyAKqqz/EPfdcwqRJGsUoDEF30GrHa4E14OfiCNQUA/ft28cDL7zA0xUV7CgLNvtgQVcX3ztwgB/t38+keJzDxcUs7+jgrnnziM5dMGMSkwZPmBUZ7GLgZyZqgk57EclLWTfiaq0tGeLwCWCttbb3r82X463+BcA5t9k599mJqC/bWWsN8DPgz865Fc65U4GPAnPT/FAbgSGDq7W2JFv/TUwkMhlj3scnPrGRWbNmhV2PTJhAnQV8O0haoJW4g9aUnh5e0tLCQ1WpTzv84qxZvLe+nu9Nm8Y1jY28ueFI4yxmvSzlO5JC9QrgRyZqhvo9KVIQxvTDb61dAnwTmA3UAbfiBcmdwApgKtAAbHTOPWitfcg/pwb4V7z5ZiXAJ5xz91lrbwGuwtvhpgq4ZIiH/bV/zk+A1wM/BF7q13MLcLZz7t3W2mXAD/z7/01CzcZ/7EuAF2HoK3XW2suBKFCOt1DjVufcsKPO1to9wHeAa/Dm0r3WObfdWlvtP97ZeHPmos65/7HWvh4vSBrgl865D/n30wx8GbgMOOqf80/AYuB9zrnN1tpi4LN4obEc+LJz7mtJJV0MdDrnvtp7wDm3NeF78E/AFX5Nn3bO/chauxG40zl3tX/evwGPO+e+PdTzA9qA24Bua+0bgfcAb8H7Nz8D2GKt3Ub/v8ls4Kv+c8F/Pn+11r4MuNc/Fgc2OOcGrmxJIxOJlAK38f73v5xVq5Zn6nEkK60zUBqHzgDn7sT7P9bnyZKS+tU9PUzp6aHNGB6urOStR4+mVMBjkyYxt6uLpZ2dtBYV9Y0aGNSvU1JyHfA9EzU3xTfFe8IuRmSijXXE9d+A7zrnTgO+D3zJOdeNN1JxKt5KyCeAl1pry4GFzrldwN3A/c65c/AC1uettb3DFucDb3bODRVaAf4LeJ21tgJvJ5xHhznvXuAr/mPEEo5fD1i80PxWhhgttNbOAj4GXOacOxN4HLhj1O8GHPHP/wpwp3/s48Bx59w6//t0v7V2AfA5vPC8HjjHWnudf34V3ijpWUAT8Gm8keXrgU/657zFv89zgHOAt/pBPdFavO/9UF7lP+7peAH589ba+ak+P+fcHrwg+kXn3Hrn3EP+eavwvnfJe27f6597DvBq4Ov+8TuBdznn1uP9EZKxRu9+r9bX8epXX8PGjesz9TiStcrx/m8EUQMMCASPlpU1vWnhQq5ZsoTXLF7MBS0tXHziBPfOnMkf/ZHXp8vL2bBsGb+ZPJlNc+dy1ZIlfbePA1+ZMYN31tcDcOPx4/x/s2bxqbnzZxzj2MNpeH5SWF6HNzAiUnDGernhfLwQBPA9vFE8gIfwVkAuAz6DFxAfAP7uf/1yIGKt7Q13FfSPwv3eOdcw3AM655621i7FG2391Qi1XYgXjnpr+5z/8Qbgh37APmStvX+I256HF7z/aq0Fr4XO30Z4rF4/9d8/Qf/35TK8F5fe+o9aazfghdM6AGvt9/26/hfooH+EeBvQ7pzr9Ecul/rHLwdOS5hfOxVYiTeCHMRF9H8Paq21D+AF4NGWNw/1/Iby3/59J7sMONX/ngJMsdZOBv4K/LP/ffipc+5AwOcxFhtZs+a13HTT+Rl8DMluZwJPBjgvhvdHvcHvMFDf0rLzv/ftay/zAnCf9/pBFOC09nYefHHo/4oG+NbBvtlNrOjo4Gf79rGnhMPLlrMfmJbaUxHhnSZq9sU3xT83+qki+SNd82R6+788hHcJeQFwD3AX3iW3B/2vG+DVzjmXeGNr7Uvw5rGOZjPwBf8+R1rQMFxvxNF6Jhq8AP36ALUkavffd9P/Pe37pZd0/8PpdM71nt/Te5/OuZ6Eeb8GeI9z7rcj3M+zwHALx4Z7/C4Gjr5XJH19qOc3lOH+DYuA851zySOqn7XW/hK4EnjEWnuZc277CPc/JiYSOYWqqrfwwQ9eQEmJ5oYVrrOAb4x20uZ4vC1iTAxvgdYJ8P4jH4HYAlgy4o1FJtZnTNTsj2+K/yDsQkQmylinCjxM/2jiTcBf/I8fxbsE3+OcawO24u273Hsp+bfAe/y5llhrz0jxcb8JfNI5t22Ec/6aVFuvB/GmGhT7l8cvHuK2jwAXWmtP9uurHMdq/N8B7+79xFo7He/78zJr7Sx/vurr8Uakg/ot8A5rbal/n6sSplr0uh8ot9a+NeGxz/Hnkz4I3Oh/D2bjjfY+BuzFGxEtt9ZOBS4NUEsTSQtYRpD8vVjvv1/hnNvmnPsc3rSM1QHvLzATicwHbufuu89g+nSt3i5sqeygtQMYsGPRIa/bgEg2McC3TNQMN8VOJO8ECa6V1toDCW93ALcDt1prnwZuBt4L4JxrB/bjBUDwAutkvEvfAJ/CW+DztLX2Gf/zwJxzB5xz945y2nuBd1lr/453Kb3Xz/AWXWzDm6s5KDD6l/BvAX7oP7dHGHuY+jQw3Vr7jLX2KeBi51wN8BHgT8BTwBbn3H0p3OfXgefwFj89A3yNpBFQf9T2euDlfjusZ4FP4P3S/RnwtP/Y9wMfdM7FnHP7gR/7X/s+wS6n/hy43lq71Vr70lHOvR0421r7tLX2ObxReYD3JXx/WvEW4KWNiUSqgPfyxjeuYu3aU9N535KTTjPBrzLtJGlawAsJnQVEskgZ8FMTNdr9TwqC0Y6Dko/8xVhvZ926y4lGr9EUAfGdHvf+QBtRxJhleAs19/ceWw+zPgnvSmcxe0o4uGw5X+c01rF2xPnjIqM5AJwf3xTP5FoBkdBlXR9XkTTZQFXVBu6666UKrZIgaD/XQTtoPQ31nd4iSpFstBD4tYmaqaOeKZLDFFwl75hIZAnwJj7+8XVMm6ZNBiRR0B20WoHDQN8Wmz0QPzKwxZ5ItlkL/NhEjbaxlryl4Cp5xZ/X+i5uumkxp54atG+nFI5UF2gNWIBYo3mukv0uB/5v2EWIZIqCq+QNf17rzSxbtpjrr98Qdj2SlU43EHQ0ageDF2ips4Dkgg+aqHlt2EWIZIKCq+STDRhzAR/60HmUlZWFXYxkpUqCdwqpIakX8zMacZXc8S0TNbrqJHlHwVXyQt+81ne+cwELFqhJvIwk6HSBGpI27dgKR7qgM/0liaRdFfC/Jmqmh12ISDopuErOM5FIBfAO1qwp59JLXxZ2PZL1AnUW2ByPtwB1aIGW5K4VwPdN1Oh3veQN/TBLPriO4uK5vP/9G9X6SgJIZYHWTrRAS3LbFaS42Y9INlNwlZxmIpFTgCt417sWMWfOSWHXIzlhvQn+2rcDqEg8sEfBVXLPR0zUXB92ESLpoOAqOctvffU21qzpYeNGTRGQoKqBVQHPPQT0JB54Rp0FJPcY4BsmahaFXYjIeCm4Sk7yW1/dgDGTuf32SzRFQFKUygKtAa+TT0JdF3SlvySRjJoO/Kfmu0qu0w+w5Ko1wEZuuWU28+cvDrsYyTlBd9A6ATSQsECrC+INUJupwkQyaAPw0bCLEBkPBVfJOf4UgX9k1qxmrrjisrDrkZwUqLOAbwfe9II+hzRdQHLXJhM154VdhMhYKbhKLnoNMJn3ve9cKioqwy5GctIZJqlH6wh2kDDiCrBXC7Qkd5UAPzBRMyXsQkTGQsFVcoqJRFYBl3DRRXHWrk2lrZFIoinAyoDnHiJpB61nFVwlty0DvhJ2ESJjoeAqOcNEIqXALRQVHePWW6+gqCjoiJnIUIJOFzhE0ujsFjjcDd3pL0lkwrzBRM3NYRchkioFV8klLwMW8Na3rmD27AVhFyM5L+gCrWbgKAn9XDugRwu0JA982USNtsiWnKLgKjnBRCIzgRuYP/8Yl112adj1SF5IZarJLrSDluSfycDXwi5CJBUKrpL1/J6trwXgPe/ZQHl5xci3EAnkjBTO3U7SAq096iwg+eEVmjIguUTBVXLBKcD5nHlmF6eeuj7sYiRvTDOwIuC5g3bQel4jrpI/vmiiZk7YRYgEoeAqWc1EImXALcBRbrnlUi3IkjQb8w5aT2ggqqHVAAAgAElEQVSBluSPmcCXwi5CJAgFV8l2lwFzuOyyKSxdGnR/eZGggnYWaAKOA+W9B9qg+ygczkhVIhPvRhM1kbCLEBmNgqtkLROJTAeuAw5x440vD7seyUtBOwvEGWKBVkzTBSS//Ls2JpBsp+Aq2ewqoIgbbljB3LkLwy5G8lIqW786YMBObXsUXCW/nAR8PuwiREai4CpZyUQi84FLKCmJcfXVan8lmTLDwNKA5x4gaQet59VZQPLPW03UXBR2ESLDUXCVbPUqoINbbz2dadNmhl2M5LVUFmgNWBz4ONT2JHUbEMlxBrjXRI3ygWQl/WBK1jGRyArgHKqr67n00o1h1yN5L+h0gUagmYQFWq3eAq26jFQlEp4zgX8IuwiRoSi4SlbxNxu4ATjBm950JpWV1WHXJHkvlQVaO4EBP5MxTReQ/PR/tFBLspGCq2SbtcBqysrqueiiC8IuRgpCKgu0tpO0QGuvFmhJfpoD3BN2ESLJFFwla5hIpAR4PXCUm28+nepq/bUvE2G2gUUBzz2YfEA7aEkeu91EjfpnS1ZRcJVsciawgJKS42zcqFWtMpGCLtA6RNICrScg1pPUbUAkT5QC/xx2ESKJFFwlK5hIpBh4NdDAjTeuYerUGWHXJAUlaHA9DpwAynoPNEPXMS3Qkvx1lYmaV4ZdhEgvBVfJFuuAORjTyMtf/tKwi5GCE2ieq3bQkgL1RRM1xWEXIQIKrpIFTCRShNe39TjXX7+KGTPmhF2TFJygI64Az5O0QGufgqvkt9XAm8IuQgQUXCU7nAIsBo5x5ZUbwi5GCtJc4213GcRBkua0bldLLMl/95ioKQ27CBEFVwmV37f1OqCJSy9dzJw5QcODSLoFbYtVQ9Jr5+NaoCX5bynwlrCLEFFwlbCdDKwE6rnyynPDLkYKWtDpAkeBVrwV1wA0Qmcj1GekKpHs8TETNRVhFyGFTcFVQuOPtkaAFhYvrmb58lPCrkkKWio7aA21QEvTBSTfnQS8PewipLApuEqYluB1E6jjda87i+Ji/TxKmFLdQasq8YAWaEmB+IiJmsrRTxPJDAUFCdNlQDulpYYzz0xlVbdIJiwwMC/gufuTDzgFVykMc4F3h12EFC4FVwmFiUSmAucDh3nNa06hsnLyaLcRmQBB/4AaFFIfhxqtzpIC8UETNXrNllAouEpYzsPbOrObjRvPCbsYEV/Q6QINQDsJC7SOQsdxLdCSwjATeGfYRUhhUnCVCWcikRLgCuAI55wzh/nzl4Rdk4gvlQVau4HqxOOHNV1ACsft6usqYVBwlTCsAaYCLVx7rUZbJZukukBrQHDdp84CUjgWAG8IuwgpPAquEoZXAs1UVpawevW6sIsRSbDIwOyA52qBlhS6O8IuQAqPgqtMKBOJnIS373UD115rKSsrD7umbFJSU8PCm29myRVXsOSqq5j2ne8M+Pr0b3yDVdZS1NAw5O1nff7zLLn6apZcfTXVv/pV3/F5H/gAS665hpn//M99x2Z8+ctU/eEPmXkiuS3oAq1DePO0+2iBlhSY00zUXB52EVJYFFxlor0U6ALinH/+aWEXk23ixcXUffjD7P31r9n3ox8x7Qc/oGzXLsALtZUPP0znggVD3rbqz3+m/Lnn2Pu//8u+H/+YGV//OkXNzZRt3w7A3p//nEmPP05RUxPFhw9TsW0bJy67bMKeWw5JdYFWSe+Bemhv8o6LFIoPhF2AFBYFV5kwJhKZBFwMHGbBgkoWLz457JqyTfecObSvWQNAvLqajuXLKamtBWD2Zz5D3V13gTFD3rZs1y5azzkHSkqIV1bSvno1lQ8+CKWlmPZ26OnBdHYSLypi1pe+RP3tt0/Y88oxQRdo9QAvkLSDVq2mC0hhudxEjaZ8yYRRcJWJtAYoAzq57rq1FBXp528EJQcOUP7887SdfjpVf/wjXXPm0LF69bDnt69eTdWDD2JaWylqaGDSo49SGovRsWIFXfPns/j662m+4grK9u2DeJz2U0+dwGeTU1LZDGPQDlr7FVyl8NwZdgFSOEpGP0UkbTYCzQCcddbpoVaS5cyJEyy4/XbqPvpR4sXFzPjqVzn4zW+OeJuWiy6iYts2Fr3udXTPmEHb+vXEi4sBqLv77r7zFtx2G7XRKDO+8hXKt2+n5cILOX7DDRl9PjlmiYEZ8WCX/PeRNM/VwaFLMlOXSLZ6vYmaj8Q3xdVVQzJOI14yIUwkMgM4FWhg/fpZzJ499ERNgc5OFtx+O43XXEPz5ZdTum8fpQcOsOTaa1l2ySWUxGIsedWrKK6rG3TThne8g3333cfBb33Lu6ulSwd8veoPf6Bt7VqKWlsp27mTmnvvZfJ992FaWyfimeWSMe+g9YRGXKXwlAJvC7sIKQwKrjJRzvDfx7niCi3KGk48zry776Zj+XKO3XorAB3W8sLf/saL99/Pi/ffT9e8eez96U/pnp3Utam7m6KjRwEo276dcuc4ceGF/V/v7GT6d7/L0be8BdPW1jdX1vhzX2WAoMH1CNBJwtWrw9DWBMcyUpVI9vpHEzXFYRch+U/BVTLORCIGuATwUtXatZrIP4yKJ55gyn33UfnIIyy+9loWX3stVQ88MOz55du2MdefBmC6ulh0000sufJK5t5zD7HPfx5K+mcDTfv+92m8/nrikybRYS3E4yy55hpazzyTnilTMv7cckygzgL+Aq0XSdqIoFYbEUjhOQm4KuwiJP9pjqtMhIV4u6zs5fzz5zF58rSwC8pWbWefzQ7nRjznxfvv7/u4fd06atd5fwfEy8vZm9C7NdmxW27p/8QYYgk9XWWQVBdorSJhlPUA1JzsTY0RKSRvBzaHXYTkN424ykQ4B+gGYMOG4ZfFi2SP5QaC/oE1aIHWDs1zlcL0ShM1i8IuQvKbgqtklIlESvC6CRwBYPUI/ZxEskvQjQgOAQM2zHpcUwWkMBUBt4RdhOQ3BVfJtOV48//asHYaM2fODbsgkYCCTheow7ui0LcwJQatzXA8I1WJZLdbTXSYXVJE0kDBVTLtdHqnCVx2mQ23FJGUpLKD1h6SFmgd1nQBKUzL8HZIFMkIBVfJGL+bwAX0NnJfu1bTBCSXBJ0qAPA8SVu/7td0ASlct4ZdgOQvBVfJpIXAVKCVuXMnMX/+4rALEknByQaC9gnbS9Lr6U6NuErhus5EzaSwi5D8pOAqmbS276NXvGIlRUX6eZNcYgg+6lpD0gKtLQquUriqgSvDLkLyk4KEZNIF9G46cPrpq8ItRWRMggbXwyQt0DoAJ05AY0aqEsl+N4RdgOQnBVfJCBOJzMLbSaUJY2DRomVh1yQyBkEXaHXjTReoSjyuBVpSwK4yUVMZdhGSfxRcJVP6F2Kde+5cKir0Aia5KJUdtBxJC7QOKLhK4apCW8BKBii4SqZcADQBcO65Gm2VXLXSJLW5GsGLJEwVANipzgJS2DRdQNJOwVXSzkQikwBL797tK1cquEquKgLOCHhuDdCTeOBJjbhKYbvSRE3V6KeJBKfgKpmw1H8fp6TEsGDBkjCLERmnVBZoxUl4Xd0LzS3QnJGqRLJfJXB12EVIflFwlUyw9LYGuuCCBZSVlYdbjsi4BF2g1QXsY/AOWpouIIVM0wUkrRRcJRPOpHef9rPO0jQByXWpLtAaEFwParqAFLbLTdSUhV2E5A8FV0krE4lU4+2Y5V0ePflkBVfJddZ4lzyDeBEoSTywS8FVCls1cFHYRUj+UHCVdFuGN00gTmlpEfPnLwq7IJFxKgbWBzz3EIN30NJUASl0rwy7AMkfCq6SbqvpXVl95pmzKSkpDbcckbQIOl1g0AKtF6GpFU5kpCqR3KDgKmmj4Crpdia9bbDWrTsp3FJE0iZQZ4HN8XgncADtoCWSaJ2JGv0+kLRQcJW0MZHINGAuvaNLy5YtCLUgkfRJZYHWdpJ20Dqk4CqiUVdJCwVXSafFJM7vmz9fwVXyxakGJgU89wUGL9DSPFcpdAqukhYKrpJOS+gNrpMmFTN9+txwyxFJm2LgtIDn1pC0QGurRlxFLjNRUzz6aSIjU3CVdDqV3jZY55wzj+Ji/XxJPgk6XSDmv+/7+d8Jx9ugJf0lieSMacB5YRchuU/BQtLCRCJFeK2wmgBYs0YT8SXfBN1Bq3eB1oDer3UadRW5JOwCJPcpuEq6zAZKgW4Ali7V/FbJN4E6C/h2kLRASztoiXBB2AVI7lNwlXQ5CTB9n82dOz+8UkQyYo2B8oDn7sL7Q67PbgVXkfNN1JjRTxMZnoKrpMsyekdbS0uLmDp1ZrjliKRdKakt0OpJPPCUOguITAXWhF2E5DYFV0mXU+md33rKKdMpLtbqUclHQacLxPCuQPSNLm2HY63QnpGqRHLHhWEXILlNwVXGzUQiJXg9XL2OAitXzgq1IJHMCbpAqwNvhHXADloxaMhEUSI5RMFVxkXBVdJhDt7IkndpdNEiBVfJV6nsoOWA6sQD++BIessRyTlaoCXjouAq6TCLgQuzFFwlX601UBbw3N0kLebaBfXpL0kkp6wwUaPNaWTMFFwlHXpHXD2zZim4Sr4qA9YGPPcQSQu0nlNwFQGNuso4KLhKOiwB2vo+mzZNHQUkn415gdYuaOrunQsuUri0g5aMmYKrpMMSerezXLiwivLySeGWI5JRQRdoteO1xerbQSsOtHhzX0UKWdC2ciKDKLjKuPhbvc6nN7iuXq3RVsl3qSzQGrSD1jHvmEghWxd2AZK7FFxlvKbh/Rx5c/nmz58aajUimbfOQEnAc3eStJirBranvySRnHKSiZrpYRchuUnBVcZrFt4VUM/MmVPCK0VkQlQQfPefGhL/fwBPaaqACGi6gIyRgquM1ywSf46mT1dwlUIQdLpADUkLtH4F+zsSFzOKFCZNF5AxUXCV8ToJ6Or7bMqUycOfKpI3AnUW2ByPtwG1QN+CxW4gVsL+DNUlkisUXGVMFFxlvOaSuP/65MkacZVCMK4FWi+Wsje95YjkHAVXGRMFVxmvWSQG16oqBVcpBKcbKA547k6SdtB6pox96S9JJKesNVFjRj9NZCAFVxmvmfQG15ISw6RJ1SOfLpIXJgGnBDx30A5af6xUcJWCNxlYGnYRknsUXGXMTCRSClQDnQAsWlRNUZH+gpZCkcoCrQGvtT+vprbN+P9vRArXirALkNyj4CrjMRlvrYln0SJNE5BCEnQHrVagjoQdtLoM8b0lHMlUYSI5YlnYBUjuUXCV8RgYVKdPrxzmPJF8FKizgG8H3tWJPtvLqEtvOSI5Z2nYBUjuUXCV8ZhMQn9KqqvLhz9VJO+sN8FfQ3eQ0BILYGu5gqsUvKVhFyC5R8FVxmMKicG1qqoivFJEJlwVsDrguTUkLdB6aBKH016RSG5ZGnYBknsUXGU8ZpD4y7iyUiOuUmiCThc4ROIfecCDlRxtN3SkvySRnLE07AIk9yi4ynhMg4SV0ZWVGnGVQhN0gVYLUE/CdIFOQ/yFUmozVZhIDphvokYDHpISBVcZj2oSuwpUVOgFSApNKjto7SRpB62t5dSktxyRnGKAJWEXIblFwVXGowro6vtMwVUKz3qTNAVgBA4oTTzw50oOpb8kkZyyNOwCJLcouMp4JAdXTRWQQjMZWBXw3ENAc+KBP1ZSn/aKRHLLwrALkNyi4CrjUUniVIHyco24SiFKZQetpsQD3YZ4+ssRySkzwy5AcouCq4xHJYkjriUlZeGVIhKaQJ0FNsfjzUAsw7WI5JpZYRcguUXBVcbERCKG5OBaVBR0rp9IPkl1gZaI9NOIq6REwVXGqhTv56f/Uqcx+nmSQnRGCgu0fgJszWQxIjlGwVVSUhJ2AZKzyknaCQhjNOIqhWgqsALYNdqJm+PxYwDG6r+KiE9TBSQlGiGTsSqFpIUlCq5SuFKZLiAi/TTiKilRcJWxMiRfHlVwlcIVdOtXERlIwVVSouAqYzU4pCq4SmGqIXGRooikYoaJ6neHBKc5rjJWQ73Q6MVH8ldPT5zGxgZisToOHqzh0ks/BzwZh8NhlyaSw0rw5okfC7sQyQ0KrjJWhuQ5rkVFGsGX/NDV1cXRo4eJxWLs3x9j+/YatmyppbGxE+9nfxH33vtAfPPmtrBLFckDk1BwlYAUXGWsNLoq+aG9vZUjR2LU1MTYsyfGc8/FeOqpI3R29gxzi7j/Ng/YM2F1iuQv7boogSm4ylgNHl3t7tY8P8luzc3HqauLcfBgDbt3x3j66Rg7dx4f5Va9m21MBsrov9JQmslSRQqIdl2UwBRcZawGj7h2dXWGUIfIYD09PRw7doTa2hgHDtSwc2eMLVtiHD482qX9YqDafyumP6TWAM/i7XwVA2LxzZtPZKp8kQKjEVcJTMFVxmpwcO3sVHCVidfZ2UF9fS2xWIx9+2I8/3wNW7YcprW1e5RbluEF1KqEY914l///CuzFC6yH45s3d2SidBEBNOIqKVBwlfTRiKtkWmvrCerqajh0KMaLL8Z49tkYzzzTQE9PfJRbTsK71D8Jb8c3A5wAdgM7gAN4I6lH4ps3Dze3VUQyQyOuEpiCq4zV4PmsGnGVdInHobGxgbo671L/rl0xtm6NsW9f8yi3LMIbQa3GG8Xp8Y8dBrbihdQavJDaGN+8ebTAKyKZpxFXCUzBVcZqcEhVcJWx6O7uHtB6yrkanniiluPHR7s8X0L/fNTEqSv7gS3AC3ghtTa+eXNrRmoXkXTQiKsEpuAqY9Xbz7JfR4eCq4ysvb2N+vr+1lPPPx9j69Y6OjpGuzxfgRdQK/EWTBmgHXgRbxR1H15IrYtv3qzuFpIWy+5bRk9JD3EThyLY98p9FLUXMf+v8yltLqWzupOai2roKRv84zvryVlUHaqCOLTMa6HurDpMj2HBgwsoaSnh2MpjHF/lNbSY8+gcjq88TvuM9ol+itlCI64SmIKrjNXg4KoRV0nU3NzIkSNe66kXXvBaTzk3WpPx3tZT1XhhtXc+6nHgebyQegjvUn9DTl/q755czIkrF9B2wXyO/Wou/DbsimQI+y/dT09FfzCd8dwMWua2cPSSo0x/djoznp3BkTOODLhNRV0Fk+omsfeKvQAs+v0iJh2eRFFnEW0z2qjfWM/iXy/m+KrjlB0tw2AKObSCsoikQD8sMlaDg2t7u1ZeF6KenjjHjx/h8OEYBw7E2LGjhiefjBGLjXZ5vpj++agl9I+k1gCPMbD11GhzW7OaMcwAzgDWA2dQVHcxPdPnQ7H3f6jrmb1h1ifBVR+oZv9l+wFoXN7Ioj8sGhRcAUy3wfR4/7wmbuiu6MZ0G4q6irw/x3yznp5F7Tm1E1J7FhutA0hKrLXNzrnqhM9vAc52zr3bWnsb0OKc++4It+87f5TH+TNwp3Pu8bQUnmbW2lXAvwCr8H5nbwPe45xL2w+ctfY6YIdz7rlhvj7q9ztVCq4yJvHNm7tNJNJN4tavLS3a/jLfdXV1Ul9fS21tjL17YzgXY8uWWpqbR7s8P1zrqX3AI3gtqHpbT+X00JMxLKU3oPa/XzTgpJ5ZA29UcSx3R47z3MI/LQTg+MrjHD/5OMVtxXRP8nJW96RuituKB92mbXYbLXNbWP6z5QAcW3WMjqkddEzuYMqLU1j8u8UcPfUoVQeqaJvRRndlWnNbLpqw6T3Oua9O1GOFyVpbAfwSuMM593P/2MXAbCCdfyldB/wCGBRcrbUlmfh+K7jKeLTjjZp5LzrHj6shez5pa2uhri5GTU1NX+upp5+uD9h6qtp/33tuK0O3nsrZ39jGUAKcwsCAejowPeU7K2tMa22SHvtevo/uSi+cLrx/IR1Tgl1UKm0qpayxjBeuewGAhfcvpGV+C61zWoldGPNO6vFC8cENB5n9xGxKWkpoXNbIiYUF+TI6YcHVWvsJoNk59wVr7TnAN/Ba4/0FuMI5t9Y/dYG19jfACuBnzrkPjnK/zcC9wNV4r3fXOudqrbVzga8Cy/1T3+Gce9haewfwD/6xrzvn/sVauxT4jV/LecBTwLeAKDAHuMk595i1tgr4V2AdXo77hHPuvqSS3gD8rTe0Ajjn/uTXWgF8BTgb73t/h3PuT8kjzdbaXwBfcM79eajn539vIsDLrLUfA17tfz8fBi4ENltrJyd8v1cAX8YLzy3AW51z2621rwU24Q1mHHfObRjpe63gKuPRQWJwra/P6cu5BSseh6amo32tp3bvjvHUUzFefLFplFsW0T8ftZz++aj1eJekEuejHs/l+ajGUI0XShNHUteSrtXQFY2DN/SQ0PWOhHZXdNO8sJmK+gq6K7opbvVGXYtbi+muGPy3V/X+atpmthEv9X7kTyw4QcWRClrn9M+embZjGo3LGpl0ZBLx4jg1F9aw+HeLFVzTY5K1dmvC5zOAzUOc9y3gbX6Q/GzS13r/r7cDzlr7r865/SM8ZhXwiHPubmvtPwFvBT4NfAl4wDl3vbW2GKi21p4F3Aq8BO8181Fr7QPAUeBk4LXA24C/4wXQi/AC4kfxRjjvBu53zv2DtXYa8Ji19g/OucQfnrXAE8PU+i4A59w6a+1q4Hf+tIKRDHp+zrlPW2s3A79wzv0EwFoLMM059zL/808k3Md/ALc553Zaa18C/DtwCXAP8Arn3EH/+YxIwVXGox0vvHjq6wvyFTendHd3c+xYXV/rqR07atiypZaGhtEuzw/XeuogXn/UxNZTLRmpfYIYw1y8X1iJI6krSPxZT7cy/c2XbUyXgTjES+OYLkNlrJL6tfU0L2xmygtTOLrmKFNemELzwsH/dp1VnUzdNbVvLmvl4UqO2qN9Xy/qKKLqUBUHLz5I1cEq4sTB0DcntgCle31Eq3Nufe8nvSOJiSf4AWmyc+5h/9AP8EYTe/3ROXfcP/c5YAleq73hdOBdMgcvML7c//gS4E0Azrlu4Li19iK8UdwT/v3/FHgpXrh+0Tm3zT/+rF9H3Fq7DVjq3+flQMRae6f/eQWwGG8BaxAX4Y3Y4o947sWbBzuS4Z7fUH6UfMBaWw1cAPy3H26h/w//vwLfttb+GPjpaMUruMp4NAKz8C4bQCym4JpNOjra+1pP7d3b33qqrW20y/Pl9Lee6tWJ13pqJ95WqDG8+ag520nCGAze6EZiQF0PzJvwYspOZC4Uy5iUtJWw4MEF3idxaFrSRMuCFtpmtrHgLwuYunsqXVVdHLroEADl9eVM2zWN2pfU0ryomcraSpb8agkALfNbBoykztw2k4Y1DWC8r03bMY0lv1rC8ZOPT/jzzBJh9Fke7a+ExD/muxk9L3U653qvKo12/kiPnfi4PQmf9yTcpwFe7ZxzI9zPs8DLUnz8Lgb+gV6R8HEqz2+oLFAEHEv8g6KXc+42fwT2KmCrtXa9c65+uDtXcJXxOAos6Pvs4EEF17CcONFEXV2MQ4e8+ahPPx1j+/ajxEe8Om8YOB+191J/I15AdXgjqr2tp3J2K1RjKMe7dJYYUE/He+7hK2tVcM0yndWd7L1ycLOHnvIeDlx6YNDx9pnt1M7017wUweFzDw9733Vn1fV9HC+Oc/CSg+MvOLdN+MJe59xRa22TtfY859wjwOsy9FB/BN4B/Is/VaAKeBBvhPGzeK+51wM3p3CfvwXeY619jz8ae4Zz7smkc34AfMRae5Vz7pcA1tpX4r2mPwjcBNzvTxFYjPd6PwV4p7W2CDgJODdALU1422mPyDnXaK190Vr7Wufcf1trDXCac+4pa+0K59yjeFMmrsFbzKrgKhnRAJT2fdbW1k17exvl5RXD30TGpacnTmNjfV/rqZ07vdZThw6Ndnm+iP5L/Ymtp2rxLvvspH8r1OYcn486jcGjqKeQza93pW3ZW5tI5oXVkeYtwP+z1p4A/ozXLzrd3gv8h7X2LXgjle9wzv3NWvttvLZ/4C3OetJfnBXEp/DaXD3tB8A9DJzmgHOu1Vp7NV5g/he8q2ZP+/X8O/BVf/pBF3CLc67dWvtXvCtr24Bn8HYgHM1/4X0PbwdeM8q5NwFf8Rdylfq3fQr4vLV2Jd7vpD/6x4Zl4iOPyIgMy0QiLwdej9fSyPPd776badNmhlZUPunq6qKhYXDrqaam0S7Pl9Lfeqr3klAP3r/TTrwXpt7+qLneemoxg1tPLQm1qLG4o/oIU07MGv1Ekbx0UnxT/NBEP6i1tto51+x//GFgvnPuvRNdh6RGf+XLeAzu4dPa2qzgOgZtba0cOeK1ntqzp7f11BG6ukb7y7IC7zJNYuupNrzFUjvwFhPE8LZCzeXWU8XAaga3nsqPn7WSTr0WSyELa3LvVdbaj+Blob3ALSHVISnQi6WMxwkG7AEDNDU1MX9+ONXkiqamY9TVxTh4MMbu3TU8/XSMXbtGa+Rp6N9lKrH1VAPeJZ3E1lPHcvxSfyWDW0+tY+BCgfxS0qXXYilU7fFN8VDWRzjnfsQQK+Alu+nFUsZjcB+Y+vqjQ5xXmHp6ejh2rI7a2oGtp44cGW0+VzH981F7F+0YvEn1T+M18u9tPZXTC+KMYTaDW0+tJJOtp7JRcU9Z2CWIhGTYRTgiQ1FwlfE4QXJbjVisIZxSQtbR0UF9fYxYzJuPun17jCefPExr62iX58vwLvVX0X+pvwtvsv1f6G89VZsHraeWM3jR1IKRblcwihIWOYoUFgVXSYmCq4zH4OC6f3/+B9eWluYBrae2bYvx3HMNKbSeqqB/VX8zsIuBrafqc7z1VBmwhsGtp6aEWVfWKj3aiVFwlYJ1JOwCJLcouMp4tOKNDhbjtfmAXbvyZ6pAT0+cpqaGvtZTu3bVsHVrjP37R7s8X0T/fNRS+kNqHV57kV30z0dtyvH5qFMYvKr/VFAQC6y8vhN9v6RwacRVUqLgKmMW37w5biKRWrwRRC/M7dnTRFdXJyUlufWLuKuri6NHD1NbG2Pfvv7WU8ePj7YVYW/rqd6tUHtHSvfj9Ud9gf7WU2H1KkwLY1iIF04Tg/FWCAcAACAASURBVOoyRt+BRkZScSRnp4CIpIGCq6REwVXG6yDejkT9o5CNjUeZMWNOaBWNpr29bUDrqeeei/HUU0fo7Bzt8nwF/f1Re1f1t+H1RXUMbD3VlcFnkFHGUARY+gNqb+up2WHWlbcmNSi4SiFTcJWUKLjKeO0Hzhlw5NixhqwJrs3Nxwe0ntq2LcaOHaP1DDRAJf2tp+J4l/8bgOfxQmrvpf6jOX6pfxJwGoNbT1WGWVdBqajP2f66Immg4CopUXCV8aoj+VJxff1Rli+f2Cq81lNHOHzYaz21c2eMJ5+MUVvbOsoth2o9BV67qWfx5qP2tp4a3P4rhxjDTAa3nlqF9z2QsJQruEpBqwu7AMktCq4yXg0kb0JQW5vZv6A7OztpaKilpqaGffu81lNbthympWW0y/Nl9F/q79WN13rqYf99b+up0ea2ZjVjWMbg1lMLQy1KhlZxNGc7SIikwd6wC5DcouAq49VA8ojr7t3p+wu6tfVEX+upPXtiPPNMjGeeaaCnZ7TL872tpyrpn496Am+xlAMO4IXUIzneeqoUbxV/YkBdD0wNsy5JQUVDzv78iaTBnrALkNyi4CrjdZz+dk9emHzyyVricTApLDaPxxnUeuqpp2Ls3Tva5fmhWk8V4V1+egrYSf981MYcn486GW+RVHLrqfIw65JxqjiWsz+TIuPUibfAVyQwBVcZl/jmzd0mEqnHW3HvzSdtaGinufkYkydPG/JG3d3dA1pP7dgRY8uWGEePjnZ5voSB81F7R1L34/VHTWw9Ndrc1qxmDPMZfKl/BWo9lX/Kjyu4SqE6EN8U1xxvSYmCq6TDAbxFPv1h8ciRWiZPnkZ7exv19d581L17vdZTW7fW0dEx2uXRcvrno/aO6LbjXVZywD68kHo4D1pPrWTwpf65YdYlE6i8UX+MSKHaE3YBknsUXCUdduOFrf5FWd/61h9oa/sN27cfG+W2w7WeOoYXUHfQvxVqQ45f6q/A63mbOJJ6GgMXi0mhKc/pZhUi47En7AIk9yi4SjoMnqO09f9v787j5KoKtI//bq+VfQVCEpOA4AWigrKJ8jKOOog6NOLgqOMyA44zgzM6OqOO20vjy4w6Ojqi44KACDLsslSAsIUEskASyEJIyCFbZ+tsvaWX9FZd5/3j3ibdXdXp7nRVnbpVz/fzqU/CrVvVTy9Unj517jnr0u0/XcrR+ahlBG/1lxCU0tUE81H3A/sKYOmpKaS+1X8G+n9OBqps1YirFKsa1wEkevSPqGTCgTTH+m6F2quHYD7qSoLdpnqXnurMesIs8jzm0r+gvgOY4zSUREf5kZKhTxIpSDWuA0j0qLhKJhwieIt/LkcvmGonmEKwhaPzUetsPB7ZifieRxlwJv1L6tnAVJe5JOIqOrQBhBSrGtcBJHpUXGXUwpUFbidY4L+WYKepwxGfjzqO1KWn5hOsniCSOeUqrlK0NrsOINHjWRvZbiGSEZ7HSaTORz2N/lvAimTH18Y0ML5Do/ZSbA7aaqvVU2TENOIqRcPz8AgK6cD5qDNc5pIiV5Yodx1BxIENrgNINKm4SkHyPCpIv/TUBJe5RFKU9uh1WIqRiqscF71gSuR5HpM5unB/b1E9k2BlA5H8VmorXEcQcUDFVY6LiqtEiufxJlLf6p/nMpPIcStpT1KCLs6SYqTiKsdFxVXykudRCvj0L6hnA9Nd5hLJqFh9F1qpQopPEtjoOoREk4qr5KsfA191HUIkqypVXKUobbfV9ojrEBJNWu5H8tVa1wFEsi5Wn3AdQcSBV1wHkOhScZW84nlVpZ5XNQPuanOdRSTrYvWR3UlOZBTWuQ4g0aWpApIXPK/qMuBCYDbgwX0l8BfdUKmVAaRwqbhKcVrhOoBEl4qr5Iv3A2MJtotNBnP3G2rh5LluY4lkUaxRUwWk2PQAK12HkOjSVAHJFwbwCBpraE+tqzAiORFr0J7bUmw22Grb6jqERJeKq+QLA4zpf2i7iqsUtsqm5NAniRQUTROQUVFxlXyxl36jrQBr9zpJIpIrsSbXCURybbnrABJtKq6SL/YRTBXwjh7a1AhtLa4CiWRd5WFNFZBioxFXGRUVV8kL1sbbgQOkTBeo2eYij0hOVLZ4Q58kUjBqbbWtcR1Cok3FVfKJASb0P7Rhq5MkIrlQ2ariKsXkBdcBJPpUXCWfvA5U9j+0aDsk9XaqFKaKNr0GSzFZ5jqARJ9eNCWf1AADSuqBdqjT6gJSmMrbNeIqxeQp1wEk+lRcJZ/sA9pIGXXdoukCUpgqOrQJjBSL3bbabnIdQqJPxVXyhrVxC6wGpva/Z5Uu0JLCVNZV6jqCSI486TqAFAYVV8k3G4Dy/oeW7oHODidpRLKprFsjrlIsnnAdQAqDiqvkm97R1T5z/xIW9uxwkkYkm8p6yoc+SSTyEsAzrkNIYVBxlbxibbwF2EnKslgbNc9VCk9pUsVVisGLttoedh1CCoOKq+SjlcCk/oeWqLhKgUlCCRWuU4jkgKYJSMaouEo+MqmHtjZDU13uo4hkSUVjN1oMS4qDiqtkjIqr5KPdQDcpF2lt06irFI7K+i7XEURy4CCwxnUIKRwqrpJ3rI0ngHXAlP73rNWyWFI4YvXdriOI5MDDttpq90PJGBVXyVdrgVj/Q8/WQCLhIoxIxsXq9bMsxeB+1wGksKi4Sr7aCgNnALYmYK+WxZLCEKvvcR1BJMsOAYtdh5DCouIq+aoBOACM63/4xVdchBHJuFiDRlyl0D1oq61+QZOMUnGVvNRn+9cB81wf2gxdnS4yiWRUrCHpOoJIlt3nOoAUHhVXyWcbSZkucCQBr290kkYkk2JNumBFCtlB4DnXIaTwqLhKPttBsFXggGWxFq13EUYkoyobVVylkGmagGSFiqvkLWvjncByYHr/exbtguZGF5lEMiZ2WMVVCpmmCUhWqLhKvnsB0m2LuV6jrhJtlc2uE4hkywE0TUCyRMVV8t02ghUGxvY/vGA9aMBKIqyiVRu+SqH6g622uvhQskLFVfKatfEksIiU6QKbm2D/TheZRDKislWvv1KobnEdQAqXXjglCl4iWF1gwAjVSk0XkOiqOKIRVylEy2y1Na5DSOFScZW8Z238IMFOWpP73/PQRkhov3eJpvKOUtcRRLLgZtcBpLCpuEpULAIm9j/U2AXbNjtJIzJa5Z0qrlJoDgP3uw4hhU3FVaJiA8GargP+sX9O0wUkmsq6y1xHEMmwu2y1bXcdQgqbiqtEgrXxNoItYE/of8/j26GtxUUmkVEpS6i4SqHRNAHJOhVXiZKlQGX/Q0kLr77iJI3IaJT1pFmfWCSy1thqu9Z1CCl8Kq4SJVuAFiDW//CCtVrTVSKnxJYPfZJIZGi0VXJCxVUiw9p4AlhMynSBV+phhy7SkugoPdJDycD52iKR1QT8wXUIKQ4qrhI1q0j7c/vgspwnETlelfVdriOIZNDNttq2uQ4hxUHFVaKmFtgNTOp/+Pm9UFvjII/IyMXqtf6wFIoE8AvXIaR4qLhKpFgbt8CTpGxGAPCYRl0lGmL1CdcRRDLkQVttd7sOIcVDxVWi6CWgjdSLtLZB/X4XgURGpFLFVQrGT1wHkOKi4iqRY228E1gAnJh67zPLc51HZMRi9T2uI4hkwPO22q5yHUKKi4qrRNVyoBsYsBbmvRuhudFFIJFhizWouEoh+JHrAFJ8VFwlkqyNtxLMdT2p/z0JC8+vcJFJZNhijVp4WKLuVeBx1yGk+Ki4SpQtDv8csB7mH9bCkdacpylAZWX7mD37s8yd+yHmzv0IkyffDsD48QuZO/cjnH76GVRWbhjRYwGmT/8xc+dezowZ33jj2IQJD/c7p6DFGpOuI4iM0o9stdUvYJJzKq4SWdbGG4ElwIz+97T3wMqVDiIVHGtLOXTom+zcuZBdu+5l8uS7qKjYSlfXW6it/QXt7eeP+LElJS3EYmvZuXMB0ENFhcHzOpg06SGamv4qd5+cS7Em/YMvUWaAu1yHkOKk4ipR9zRQRsrP8h2roavTRaBC0tNzIp2d8wGwdjxdXadSVnaArq4309196nE91loPz+sGLJ7XibVlTJlyC42NnwWKZBfUymbXCURG43pbbTVPW5xQcZVIsza+n2A3rQErDNR3wrqXXGQqVGVle6isfI2OjrNH9Vhrx9Paeilz5nyU7u7ZJJMTiMVepa3tA1lInacqWzzXEUSO0wbgXtchpHiVuQ4gkgELgQsBD+jzFuwdL8K574JS7Qk/Sp7XxsyZX+bQoW+TTI4f9WMbG79AY+MXADjppO9QX/9lJk68n3HjltHZ6dPQ8MWMfw55paJVxVWiqlpzW8UljbhKIdgJbASm9z+8qxU2rnMRqLB0M3Pml2luvpzW1ksz+tjKyk0AdHXNY+LEh9m370YqKrZQXl4z+tj5rOKIXnslitbYavuQ6xBS3PTiKZEXbgO7ABiXeu8tz0G39oU/bpYZM75DV9epNDVdnfHHTpt2I3V1X8bzEnhe75S5EjyvY1Sp815Fu157JYqucx1ARC+eUigMUANM6X+4pgVWLHWQpyDEYi8zceIjjB37InPmXMGcOVcwbtxzjB//NKeccgmx2Fpmzfp7Zs36PAClpQeYNesLx3xsr3HjnqGj42309JxEMjmR9vZ3MHfu5QB0dZ2R+082l8o7NH1FouZFW20fcx1CxLOaqiIFwvOqzgG+QlBg+xhbBjf/I0yY7CCWSKp/HVfHhCPThz5RJG9caqvt065DiGjEVQrJBuAAMLH/4SMJWKAXXMkfZd1Fsu6XFIinVFolX6i4SsGwNt4D3A1MS733nk2wb2euM4mkVdqjFV0kGiwJ4KuuY4j0UnGVQrOeYIWBk1Lvuv1JSGpujLhXmqxwHUFkWDx+bavtJtcxRHqpuEpBCVcYuBsYAwy4AGbFPnhNy2OJY0koKZYtwiTSLPVAtesYIn2puErBsTa+G3gGODn13l8tgq6uXGcSeUNFQxfafkCiwKPaVttG1zFE+lJxlUK1AOgBYv0P726DZc+7CCQCQKxe6wpL/rO8CvzGdQyRgVRcpSBZGz8M3E/aua43vQjNGkUQN2J1Kq6S/zz+2VbbnqFPFMktFVcpZM8Dh4BJ/Q+398AjT7kIJEKsPuE6gsgQHrbV9lnXIUTSUXGVgmVtvAu4E5iaeu/9m2FvTY4jiUCsQcVV8pflCPAvrmOIDEbFVQrdhvCWZsrAbU9oeSzJucqGpOsIIoPy+L+22u5wHUNkMCquUtD6LI8VI2V5rFUHYMPLDmJJMYupuEqesrwE3Og6hsixqLhKwbM2vhd4CpiZeu9Pn4aWplxnkiIWa9QFL5J/LAk8rtEFWZLvVFylWDwGdJGyPFZjF9z2kKYMSM7E9HuS5KUf2Gq7wXUIkaGouEpRsDbeDNwHzEi995ld8NILuc4kRarysH5JkvySxODx765jiAyHiqsUk2VALWlXGfjJs9BwMNeBpAjFmrVvluQPS5ISrrHVVjsKSiSouErRsDbeDdwMTICBe8W398CvH4Qeze+S7KpodZ1ApK9f2Wq7wnUIkeFScZWiYm18B/BHYFbqvSsPwNIlOY4kxaaiVa+7kh+S1ODxLdcxREZCL6BSjJ4EtgMnpN718+Wwf3euA0kRqWjX6664Z+mhhE/Yaqu3ACRS9AIqRSecMnALUBne+khY+NlD0K35XpIdFe2lQ58kkmWW6221XeU6hshIqbhKUbI2XgvcRdq1XTc1wpNP5TqTFImyLr3uilsJXqSE77uOIXI89AIqxWwJwXawJ6fe9duXYeeWHOeRYlDeVT70SSJZ0kMrZXzcVlvt4CaRpOIqRcvaeBK4DbDA2NQzfhyHjvYcx5JCV5oocx1Bipjl87ba7nEdQ+R4qbhKUbM2Xk9QXmcAA9bX3NUKf3zUQSwpZGVJjbiKG938r73B3uc6hshoqLiKwCrgBdLOd713E2x+JdeBpICV2ArXEaQIJdhJOX/vOobIaKm4StGzNm6B/wXaCTYnGOCGR6F+f45jSSEqbeuhRK+7kmNJOinhcltt21xHERktvYCKANbGmwl21ZpOyv8XLd3w/XugXS/6MjqV9VpmTXKvm7+z37MbXMcQyQQVV5GjXgUWAbNT79pyGG66V1vCyqjE6hOuI0iROcIt9vv2DtcxRDJFxVUkFE4ZuB+oA6alnvHsbnj0sRzHkkIS04ir5FA76xjLP7iOIZJJKq4ifVgbbwd+AcRIu0TWrWthrXabkeMTq9OIveRGFw108UFbbfUzJwVFxVVkAGvju4FfAicBadbcvOEJ2LM9x7GkEIxpUImQ7EuSoJUq+1N70HUUkUxTcRVJw9r4OoJpA3NIWd81YeF7D0Bzg4NoEmWVKq6SA818zd5ol7uOIZINKq4ig3scWAG8KfWuA+3w03ugqzPXoSTCYg3aZlOyq5m77X/bG13HEMkWFVeRQYRbwv4e2E0wbWCANYfgzj9C0uY2mURWrEk/K5I9zaykic+4jiGSTSquIsdgbbyD4GKtBDAp9YyHt8CSRTmOJVFV2eQ6gRSqVmqo4wP2VqtRfSloKq4iQ7A2Xgf8nKC4Vqae8bPlYLS4twytslkjrpJ57TSwh/fa222r6ygi2abiKjIM1sa3AL8DZgGlqWdUx+FQbY5jSdTEWryhTxIZgS7a2c4V9m6703UUkVxQcRUZvuXAQtJerHUkAdffBU31uQ4lEVLequIqmdNDgh1cY++zy1xHEckVFVeRYeqzs9YGgpHXAXa3wXW3Q3NjjqNJVFQe0WuuZEYSyw6+a++y97iOIpJLehEVGQFr4wngt0AjMD31jJoW+N7t0Ho4x9EkCsrb9ZormbGbX7OKH7mOIZJrehEVGSFr4y3AjUA5MD71jC2H4Ybboa0lx9Ek31V0pJkfLTJCNdzLcr5kjdXFflJ0VFxFjoO18b3Az4CpwLjUM15rhB/cDkd0la8cVdaVZgthkRHYwWOs4HPWaNkrKU4qriLHydr4a8B/E0wZGJt6xiv18KM7oONIjqNJvipLqLjK8dvBc7zAJ62xXa6jiLii4ioyCtbGNxCs8XoiMCb1jDWH4Cd3QGd7jqNJPirtKXcdQSKqhpW8wFXWaK1WKW4qriKjZG18LfBLgm1hY6lnrDwAN94JXZ05jib5pjSp4iojt4s1rOBj1tg611FEXFNxFckAa+OrgZuAk0m7u9ayWvifO6Fbb/EVKy9hKUXFVUZmN+tZxhXWWG1wIoKKq0jGWBt/AbiVYI3XitQzluyB39wFie4cR5N8UNmg77uMzB42sJQqa+we11FE8oWKq0gGWRt/HrgNmA3pRtee3gk33w2JRI6jiWuV9Rptl+GrYR3Pc4U1dpfrKCL5RMVVJMOsjS8G7iTYGjbNVeQLd8Dv71V5LTKxOn2/ZXi2sIoVXGWN3eE6iki+UXEVyY6ngXuBOaQtr/Gt8AutNlBMKhtUXOXYLLCJFazmM9bYba7jiOQjFVeRLLA2boHHgQcIymuaHZMW74b/+J22hy0Ssfoe1xEkjyVJso7FrOPz1tgtruOI5CsVV5EsCcvrAuARYC5py+u6Ovj2rdBwILfpJOdiGnGVQfTQzWoW8hp/b43d7DqOSD5TcRXJorC8Pgg8SlBe00wbqGmBf70N9tbkNJzkVqxRW3RKqm46WM4jbOOfNNIqMjQVV5EsC8vr/Ryd85pmndf6TvjqnbBlY27TSc7EGq3rCJJn2mliMfexh69YY2tcxxGJAhVXkRywNm6tjT/G0U0KxqWe1dEDX3sAXlyW43iSC5VNKq5yVBO1PMnt1PF1a+xe13FEokLFVSSHrI0vB34CTA5vA88Avr8IHnoEenQxTyGJHVZxlcBeDE9wC0eotsYedB1HJEpUXEVyzNr4BuAHBPNdp6c/67Z18Ks/QIeWyyoUlc2e6wjimMWymVU8xy0k+U9rrFYUERkhFVcRB6yNbwf+HThCMHUgjad3wvdugab6HEaTbKlsdZ1AXOqhk1UsZg23AD+3xh5xHUkkilRcRRyxNr4f+D6wm+CirTQjchsb4F9ugT3aQSfqKlr1elusOjnMYp5kGz8FbrXGavtfkeOkF1IRh6yNNwE/Bl4E5pF2uay6DvjnO2H1imAOrERSRbteb4tRE7t4gkc5yP+zxj5mjdWyaCKj4FmrfwhFXPO8qhLgcuAvgFqgI/2ZVafBpz8KY9KsSiB57Z+m72d6/QzXMSRHLJbtvMwqlmP5mZa7EskMFVeRPOJ5VRcA/wAcDm9pzBkP37wSZp+aw2gyWl+dcIhJrSe4jiE50E0bq1lKDSsJ5rM2uI4kUihUXEXyjOdVnQZ8hWCL2EG2gi3x4CvvgUv+FEr0FnQUfD3WyLjOKa5jSJa1sIslrKSFJcBt1litDCKSQSquInnI86pOAK4FTiW4eGuQNV3fOxv+7ioYPyl36eS4fKushcqeCa5jSJZYYDerWI7B8kfgUWus1mIWybCiGqnxfX+e7/uvDjh2ve/7X8txjiW+7+/yfd/rc+xh3/dbw7/P9H3/gWM89rw0x//G9/3/Cf8+os+p72Pzke/75b7v/9D3/S2+77/q+/4q3/c/lOGPMc/3/b86xv2Dfk+ywdr4IeCHwGMEKw6MT3/mkj3w5d/A1k25yibHqTRZ4TqCZEmCdlazkGW8hOWH1thHVFpFsqOoiqsLvu97vu+n+zo3Ae8Jz5lMn7U8jTG1xpirchQxCm4g+Pq81RjzVoKLmDI9cjUPSFtcfd8vc/E9sTbeZW38foJVB8Yw6HqvdR3wL/fDo49BIpHDiDISpbbcdQTJgia2sZAFbGUpcJ01Vr9EimRRmqV3ipfv+0uAdcAFwETgGmPMKt/3rwfeDMwC3gT8yBhzc/iYrwN/CVQCDxljqn3fnwcsBBYDFwEfBXYO+HD3AJ8ElgEfAx4E5ofPOQ941BjzVt/3xwC3AWcBrxEUmN68VwPfAvYBrwOdaT6nNwO/BE4gWOz+C8aYzcf4GvweaAbOA2YA3zDGPBDe9w3gs0ASWGiM+abv++cAvwHGAtvCr1lj+LVcC5wbfuzPhVnfBtxrjPlu+JyfAb4MVAArgS8aY94YqfB9fyzwBeAUY0wngDHmAHBfeP+ngG8TrIH6mDHm38LjrcaY8eHfrwL+3BjzN8f4/H4InOn7/jrgdqAR+AgQA8b5vn9Nn+9JaXj+ewm+7780xtzk+/7JwL0EPztlwLXGmKWDfa2Hy9r4q55X9V3gb4G3AnuA7tQzf/sSrNkFX7oKpugioHxS1tKDR6nrGJJBPXTxOktYywEgDjxijU3z/6WIZJJGXFONM8a8G/gi8Ls+x99OUGQuAq4L3zq+FDidoOieA5zr+/4l4fk+cIcx5h3GmIGlFWARcElYgj5JUHjSuRY4Yox5O/AfBEWQsCR9j2DU9s8Iim06vwW+ZIw5F/ga8KuhvgAEI3sXA39OUNAI35r/KHChMeZs4EfhuXcA/xbm2wBU93meLmPMJQTF9hHgHwmK19/4vj/N9/0zgU8A7zHGnEMwj/PTA7KcBuwyxjQPDOn7/kzgP4H3EXz9z/d9/6PH8/kB3wSWGmPOMcb8d3jsIuCvjTHvG/D4zwOHjTHnA+cDX/B9/xSCEdsnw8/lbIJfgjLC2ngj8FOCn5NZwOT0Z750EP7xt7BhTaY+tmRAZUPKL5USYS3s4mnuZy3bgf+yxj6g0iqSG8VWXAe7Eq3v8bsBjDHPAxPDt/EBHjHGtBtj6ghGUi8ALg1va4E1wBkERRZgpzHmxWNk6SEYbf0EMMYYUzPIeZcAd4aZXgFeCY9fCCwxxhwyxnSRpvj6vj8eeDdwfziSeBODvt3cz8PGmKQxZhNwUnjsA8BtxpgjYZYG3/cnAZONMc+F59we5u0VD//cAGw0xuwLR023E4xcv5+giK8O872f4GKk4Tqfo1+DBPC/Az7+SD6/dJ42xqRbxuZS4HNh5pXANILv+2rg6nCE/m3GmJYRfC5DsjbeY238cYKtYpPAbNLuttWagO8sgLsfgA5d0ZwPYnWawlEIkiTYyjM8ymIaeJ1gasArQz5ORDKm2KYK1AMDl6OZCvTdTnNgubXHOO4BPzDG3NT3jvCt/rZh5LkHeAi4fojzhlO40ykBmsIRwJHoOzrk9flzpEtQ9D5PcsBzJgl+9jzgdmPMt47xHFuBOb7vT0hTBNOUtjf0zRobJNdQzzHY99AjGMV+cuAd4Yj7R4A/+L7/Y2PMHcd4/uNibXyr51VdRzD94l3AXtJME4G7N8Li7fDlD8D8dx77U5WsqqxXcY26NvbxIos4QA+wAFigrVtFcq+oRlyNMa3APt/33w/g+/5U4DKCkc9enwjvu5jg7eDeReCv8H0/5vv+NIK5jauBJ4FrwpFNfN+f5fv+iSOItBT4AeEo7yCeJ3z73Pf9txJMWYBgpO+94Vvu5cDH03y+zcAO3/c/Hj7e833/7BHk6+spgs91bPhcU8OvTaPv+/8nPOezwHODPUEai4Crer9mvu9P9X1/7oDP4QhwK/Bz3/crwvNODufGrgT+xPf96eGUi0/1+fgHfN8/M7ww7sphZGlh+Bd8PQlcG37d8X3/Lb7vjwuzHwznP98KvHOYzzdi1sZbgV+HH+dEYHr6M/e3w7cXwM9+Bw2DrAkrWRdTcY2sJAl2sJgFPMkB9gH/bo39o0qriBvFNuIKwSjVL33f/0n4398zxmzrc3+j7/srCC/O6nN8FUeXJrrBGFML1IbzNF/wfR+gFfgMg6652Z8xxgL/NcRpvwZu833/FYI5k6vCx+4L35J+geDirDWQ9uKPTwO/9n3/u0A5wSjv+uHkG5D1ifBCrJd83+8CHie4KOqvgd+EhXY7cPUInnNTmOupsGB2E8yDHTgn+LsEb49v8n2/g2Ak9Lrwa/AtgqkbHvC4MeaR8DHfBB4lWAP1VQZdTuoNrwAJ3/fXA78nuDhrMLcQJglfJAAACzZJREFUrEKwJlzS7BDB/N/3Al/3fb+b4Gfhc0N8zFGxNm6B5zyvajvBXOg5BBdupdkLffFuWHoTXHsh/MmfQoWWZsqlMQ0qrlHUxBZe5HkaKAeeBh7ShgIibmkDgj7CK+G/Zox5acDx64FWY8xQJVPECc+rihG8W/A+gtLdNPjZp0yAL10Gpw12QZ9k2rk37OHy62a7jiHD1EUzm3iCTbQSrELyW2vsa65jiUiRTRUQKVTWxjsIVnj4AcF813kES4ylsaMlWPf15juhWXuo50JlY5pRcMk7liR7WUGc29hEJ7AC+L8qrSL5QyOuIgXG86oqCFaB+BjBtJX9DHph3ZhS+NLF8K6LoawYpw7lxvuu3sklv5879IniTAu7eImF7KOCYKrP74D11ugfSZF8ouIqUqA8r+pEggvW3gnUEVyANoj5U+HaD8OcN+cmXZH58JU1XPDwPNcxJI0uDrOFZ1nPPoKNVJ4AHrXGDmdlGBHJMRVXkQLmeVUewWYIf02wacFe4BgXCn3yLLjiMhiX6S11i9uV76/h7GfnuY4hfSToYBfLeJn1dHMCwdJ7t1tjd7mOJiKDU3EVKQKeVzUG+DDBbmHtwMHBz55QDlefB+95D4wZl5uEBe6TF+zkjNWaKpAPkvSwn9WsZiltTAW6gLuAF6yxw1oRRkTcUXEVKSKeVzWbYL3dM4ADwJHBz55QDtecHxTY2NjcJCxQnztrF6e+Nsd1jKJmgXo2sIZnqaMEmAQsAR60xh4+5mNFJG+ouIoUGc+rKiHYsvizBLuK7SXt2q+9JpbD1Sqwo/G38/Ywe6eWw3KlmR2s52l20wKcQLBW9J3W2NcdJxOREVJxFSlSnlc1gWDjhPcBHQTTB47xgjCxHK65AN79bhXYEfriSbWceHCm6xhF5zDbeI3n2c5B4CSC9Y3vBl7WtACRaFJxFSlynlc1j2DprLdzdP7rMV4YJlUEBfaid0NsTC4yRt4/Tz7IlMMj2Q5ajpcFmtjMRpayi4PAyQTzWB8AllljO53mE5FRUXEVkd7VB04hGIEdQYH9/AXwLhXYofzr2HomtE9zHaOgWSz1bORVllJLHUFhLSHYnvopa+wxloMTkahQcRWRN4QF9lSCAvs2gou3DnHMAjulAq6+EC66CCpVYNP5t4rDjOme5DpGQUqSpI71vMIyDtJEMCWgAlgGPGKNrXMbUEQyScVVRFL0KbBXAvMJRmCHUWA//na46FyYNiMXOSPjO6VHKE9qXnAmddPGPl5mIy/RyBGCwloOvAAstMbudhtQRLJBxVVEBhUW2DdztMAe4ZhrwPa6ZBZ86Dzw50NZeVZDRsF1XoIStKVuJrSyh52sYiObSOABMwimBCwFnrDG7nMbUESyScVVRIbUp8B+jKDAthKMwA5hWiX85dlw4bkwtTgvTvK6LNWVnusYkZYkQT0b2cxKdrOPYGR1BsE7AM8CT1tjh/HzKCJRp+IqIsMWFtjTCEZgz+LoFIJjrAPb672z4bLz4C3zoax4Rh9jBzr55oxK1zEiqZMmanmZV1lDC0eAMQTrsPYATwKLrLGNTjOKSE6puIrIiPUpsH8GnAt4QB3H3Imr14kx+PjZcOF5MHl6NnPmhUmbWvnq/PGuY0RGD900sIkdrGMrNeHRqcAEoAVYCKzQblcixUnFVURGxfOqpgDnA5cBUwg2MxjmKOwH5sAHz4PTzoLS0mzmdOakpU1ce8lk1zHymsXSzA728gqvsYlOuoEy4ESCaQFbCArrq9bYbpdRRcQtFVcRyQjPqyoFfIKduN4ZHh7mKOz0GFx2OrzDh7mnQ0VF1oLm2tyHD3H1lSe4jpGX2qhlHxswvMphWsOj44BpBL/4PAcsAfZYo3+sRETFVUSywPOqpgIXEIzCTiKYC1vHsEZhY6Vw6SlwgQ+n+TB2QjazZp1/634+9bdaHgyCkdVWdnEQw3Y2c4je+amlBKOrFUAD8BiwWpsGiMhAKq4ikjXhKOyZBKOw54SHDxEU2eE8A3DxTLj4DPD9SK5McPaP93LlN2a5juFMkm4Os419bGYbW8KLrCD45k4mmLvaA7xIsGnAVmtsj6O0IpLnVFxFJCc8r2oa8C7ggwRlpQuoD/8cpjOnwAd8mH8GzJgDJfm/zNSF39rNh374JtcxcqqbNhp5nb1sZhvb6SLR594JBHOhPWAr8AywwRo7jCklIlLsVFxFJKc8r6oMOAM4j2A6QSXBepwNDGs+bK8ZY+CDb4FzfJg1F2L5uTPVn/zDTv70prmuY2RVgnaaqaGeGvawg30pa/yO52hZPQAsAtZpO1YRGSkVVxFxJiyx84CzgYsIlj0CaCJY+mgEL1Dzp8K5s+G02TBrNkw7CUpKMpv4OHzwUzu56J7CKq49dNLCTurZwV5q2MP+AWf0nQYAsA9YDmxAF1qJyCiouIpIXgjXhp1JMCf2YmBOeFcb0MiwLuzqa3wZvGsmzJ8Np7wJZsyGsblfT/XyD9dw7sJ5Of+4mWKxdHCIFmppopaD7GUP+0im/FJRSvCLx5jwv18HVgCbgUMqqyKSCSquIpKXwvVhzyCYFzufYD/63nmxx7mW5+mT4LzZ8JbZMHs2TD856+vHfvzincxfHo0R16Ck1tNKLYep5RC17GU/XWm/3h7BiOrE8O89wDpgFWC0IoCIZIOKq4jkPc+rGgucTjAv9jyCZZM8oBNoZtirFAw0phTeeSLMnQozpsD0KTBlCkycAuMmZeTir0+fs5PT1+dXcU2SoIN62qmnjTpaqKeJeg5wiI5BL5bzCOaqTgr/7gE1wFqC0dUaa2xnLuKLSPFScRWRSAnnxZ4MzAbOIphaMJWjUwlaCebHjnJJpYoSOH0ynDIFZk6BE6fAtCkwKSy2FZXDepprTt/NnK25XVUgSQ/dtNBNC1200EELbTRwmDrqqKeB4WyXWkpQVCdwtKjuAdZwtKhqJQARySkVVxGJPM+rmkhQZOcBbwXeTLBlqEcwvaCZEa1YMBwzxsDpU+DkSTAhBuNiMDbGmD1nYWfUk5zsYceP4VPvb2VWzQxKKH/j5jG8i8Z66CZJJz10kaSTRPj3nvDPBJ1000E7LbTRQistNNNC64hHoEs4WlJLCS6KSxKMqG7maFHV2/8i4pSKq4gUnHDjgxkEZdYnGJk9kaCQ9U4xaAc6GNE6ssNw8kt/wdiDT1Pa05zR580MD4gRXEA1lqCw2vC2i6CkbgdqgYPW2MQgzyMi4kSZ6wAiIplmbbwH2BveVgJ4XtV4YBbwJoJCO5Og3I7n6DSDEoIpBr2ltoORrmbg9ZThJTNbhkeugqCgjiVYJ7eHo2/31wPbgJ0EBbUWOGCNPc4L3kREckfFVUSKgrXxVsCEtzd4XlWMYM3RKeGtd6T2ZIJy63F0pLZ3tDaR5tYDFrxkKZ7NdHH1CN7CLyV43a4gKKQVHB0x7T3PI1hCbB+wm2AktY6gsNaroIpIlKm4ikhRszbeAewPb/14XlUJwXJPvaV2GkGZnUAwUjseGEcwslkBFmxJMx4zBz5V3w85jOO9+s6F7SAYCW4jGEk+FN4OE1yM1ntr1Vv8IlKoNMdVRCQDwtUOYpy4fgyTd5UB5X1uAwcJBnvh7SaYc9s54M9uLeAvIqLiKiIiIiIR4X4fbxERERGRYVBxFREREZFIUHEVERERkUhQcRURERGRSFBxFREREZFIUHEVERERkUhQcRURERGRSFBxFREREZFIUHEVERERkUhQcRURERGRSFBxFREREZFIUHEVERERkUhQcRURERGRSFBxFREREZFIUHEVERERkUhQcRURERGRSFBxFREREZFIUHEVERERkUj4/1pEK6y6viYXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd825498630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Low Income Countries', 'Lower Middle Income Countries', 'Upper Middle Income Countries', 'High Income Countries'\n",
    "sizes = [low_income.country.count(),lower_middle_income.country.count() ,\n",
    "         upper_middle_income.country.count(),high_income.country.count() ]\n",
    "colors = ('red', 'cyan', 'b', 'g')\n",
    "explode = (0.1, 0.1, 0, 0)  # only \"explode\" 1st & 2nd slice \n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90, colors=colors)\n",
    "rcParams['figure.figsize'] = 5,5\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the end, Thank you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}