female-labour-force-participation.ipynb

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    "# Project: Female labour force participation around the the globe\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",
    "Female Labour Participation Rates (FLPR) measure the percentages of the working female population in relation to the country's total female population. Several sources verify that female labour force participation are substantially lower than male labour participation almost everywhere in the world (ILO, World Bank). Regardless of the male labour force participation, the percentage of women working can vary significantly across the globe.  \n",
    " \n",
    "Throughout the report I will investigate female labour participation geographically and how it may have changed over time. Further, I'm going to analyse to what extend a country's economic situation may determine if women engage in the labour market. "
   ]
  },
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   "source": [
    "# Import packages \n",
    "import pandas as pd\n",
    "# allow to show complete tables throughout the notebook\n",
    "from IPython.display import display\n",
    "pd.options.display.max_columns = None\n",
    "from functools import reduce\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "The data used in this report comes from csv files, I downloaded from the Gapminder platform. Gapminder provides socio-economic and political statistics and information coming from third party databases like ilostat, World Bank, esa. Below, I will introduce the data and variables I will be using throughout the explorative analysis.\n",
    "  \n",
    "In this section, I will load my data into pandas dataframes and make myself familiar with the data we got. Throughout the process I am using functions like:  `Dataframe.head()`, `Dataframe.tail()`, `Dataframe.info()` and `Dataframe.describe()`. I\n",
    "\n",
    "https://www.gapminder.org/data/\n",
    "\n",
    "### Load Datasets"
   ]
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    "# load female labour force participation rates\n",
    "df_flpr = pd.read_csv('./female_labour_15+.csv')\n",
    "# load GNI per capita\n",
    "df_gni = pd.read_csv('./gnipercapita.csv')\n",
    "# load list of countries and their categories\n",
    "df_countries = pd.read_csv('./countries_by_category.csv')\n",
    "# load female population by country\n",
    "df_pop = pd.read_csv('./female_population.csv')\n",
    "# load female agriculture employment\n",
    "df_agr = pd.read_csv('./female_agriculture_workers.csv')\n",
    "# load female industry employment\n",
    "df_ind = pd.read_csv('./female_industry_workers.csv')\n",
    "# load female service employment\n",
    "df_serv = pd.read_csv('./female_service_workers.csv')"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### Properties Female Labour Participation Rates (FLPR)\n",
    "Source: Gapminder, provided by International Labour Organization (ilostat)\n",
    "\n",
    "**Description**  \n",
    "Central to this report are labour force participation rates of females aged over 15 years in countries around the globe. It states the percentage of the working female population in relation to the country's total female population. The FLPR counts all working females or females who are actively looking for labour. That includes self-employed and unemployed females. FLPRs  are likely to be underestimated as 'female' work (e.g. care work) is often not considered as employment.  https://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf\n",
    "\n",
    "**Properties**  \n",
    "The female labour particpation dataset contains data of 179 countries over the years 1990 to 2017 (28 years). The country column comes in the data format of a string and the years columns state the FLPR in the form of a float. ILO provides us with imputed estimate values. Hence, there are no null values.  "
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
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       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</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",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>14.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>14.8</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>16.1</td>\n",
       "      <td>15.7</td>\n",
       "      <td>15.2</td>\n",
       "      <td>14.9</td>\n",
       "      <td>14.7</td>\n",
       "      <td>14.7</td>\n",
       "      <td>15.3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>18.7</td>\n",
       "      <td>19.4</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>53.6</td>\n",
       "      <td>55.8</td>\n",
       "      <td>56.1</td>\n",
       "      <td>55.3</td>\n",
       "      <td>54.5</td>\n",
       "      <td>53.5</td>\n",
       "      <td>52.9</td>\n",
       "      <td>53.6</td>\n",
       "      <td>53.0</td>\n",
       "      <td>52.2</td>\n",
       "      <td>51.7</td>\n",
       "      <td>51.1</td>\n",
       "      <td>50.7</td>\n",
       "      <td>49.8</td>\n",
       "      <td>48.9</td>\n",
       "      <td>48.1</td>\n",
       "      <td>47.4</td>\n",
       "      <td>46.8</td>\n",
       "      <td>46.3</td>\n",
       "      <td>44.9</td>\n",
       "      <td>45.7</td>\n",
       "      <td>47.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>44.6</td>\n",
       "      <td>44.8</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.4</td>\n",
       "      <td>47.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>11.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>11.6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>11.7</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>12.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>12.8</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>13.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.9</td>\n",
       "      <td>15.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>15.1</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "       country  1990  1991  1992  1993  1994  1995  1996  1997  1998  1999  \\\n",
       "0  Afghanistan  14.8  14.9  14.9  14.9  14.9  15.0  14.8  14.7  14.6  14.5   \n",
       "1      Albania  53.6  55.8  56.1  55.3  54.5  53.5  52.9  53.6  53.0  52.2   \n",
       "2      Algeria  11.5  11.5  11.6  11.6  11.7  11.8  11.9  11.8  11.8  11.8   \n",
       "\n",
       "   2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "0  14.5  14.8  15.2  15.5  15.8  16.1  15.7  15.2  14.9  14.7  14.7  15.3   \n",
       "1  51.7  51.1  50.7  49.8  48.9  48.1  47.4  46.8  46.3  44.9  45.7  47.1   \n",
       "2  11.9  12.0  12.2  12.4  12.6  12.8  13.1  13.5  13.8  14.2  14.4  15.2   \n",
       "\n",
       "   2012  2013  2014  2015  2016  2017  \n",
       "0  16.0  16.9  17.8  18.7  19.4  19.5  \n",
       "1  48.8  44.6  44.8  47.7  47.4  47.2  \n",
       "2  16.2  16.9  15.3  15.2  15.1  15.2  "
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    "df_flpr.head(3)"
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178 entries, 0 to 177\n",
      "Data columns (total 29 columns):\n",
      "country    178 non-null object\n",
      "1990       178 non-null float64\n",
      "1991       178 non-null float64\n",
      "1992       178 non-null float64\n",
      "1993       178 non-null float64\n",
      "1994       178 non-null float64\n",
      "1995       178 non-null float64\n",
      "1996       178 non-null float64\n",
      "1997       178 non-null float64\n",
      "1998       178 non-null float64\n",
      "1999       178 non-null float64\n",
      "2000       178 non-null float64\n",
      "2001       178 non-null float64\n",
      "2002       178 non-null float64\n",
      "2003       178 non-null float64\n",
      "2004       178 non-null float64\n",
      "2005       178 non-null float64\n",
      "2006       178 non-null float64\n",
      "2007       178 non-null float64\n",
      "2008       178 non-null float64\n",
      "2009       178 non-null float64\n",
      "2010       178 non-null float64\n",
      "2011       178 non-null float64\n",
      "2012       178 non-null float64\n",
      "2013       178 non-null float64\n",
      "2014       178 non-null float64\n",
      "2015       178 non-null float64\n",
      "2016       178 non-null float64\n",
      "2017       178 non-null float64\n",
      "dtypes: float64(28), object(1)\n",
      "memory usage: 40.4+ KB\n"
     ]
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   "source": [
    "df_flpr.info()"
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   "cell_type": "markdown",
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   "source": [
    "### Gross National Income per Capita (GNI)\n",
    "Source: Gapminder, provided by World Bank \n",
    "\n",
    "**Description**  \n",
    "To evaluate the world economy by country, I'm investigating the Gross National Incomes of countries (GNI), provided by the World Bank. It measures the country's income per capita. The GNI is closely related to the GDP. In contrast to the GDP, it includes all compensations paid to domestic residents by foreign businesses. Subsidies and paid compensations to overseas residents are subtracted. Product and income taxes are also included in the GNI. The GNI is calculated in national currency and converted to U.S. dollars at official exchange rates. The data set uses the 'Atlas method' to smooth out fluctuations in prices and exchange rates. https://data.worldbank.org/indicator/NY.GNP.PCAP.CD\n",
    "\n",
    "**Properties**  \n",
    "We got GNI values by country for the years 1962 to 2017. The data of a country is of type string and the GNI comes as a float, which makes up for 56 years. Many rows contain null values."
   ]
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       "      <td>Afghanistan</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>570.0</td>\n",
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       "      <td>Albania</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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>790.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1010.0</td>\n",
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       "      <td>1410.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>2150.0</td>\n",
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       "      <td>3090.0</td>\n",
       "      <td>3480.0</td>\n",
       "      <td>3950.0</td>\n",
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       "      <td>4360.0</td>\n",
       "      <td>4410.0</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>4540.0</td>\n",
       "      <td>4540.0</td>\n",
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       "      <td>4320.0</td>\n",
       "      <td>4320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>190.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>2260.0</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>2360.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>3090.0</td>\n",
       "      <td>3580.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4270.0</td>\n",
       "      <td>4460.0</td>\n",
       "      <td>4580.0</td>\n",
       "      <td>5140.0</td>\n",
       "      <td>5480.0</td>\n",
       "      <td>5470.0</td>\n",
       "      <td>4830.0</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>3960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>560.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>2760.0</td>\n",
       "      <td>3190.0</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>3390.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>4340.0</td>\n",
       "      <td>4440.0</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>3330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>2770.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>3560.0</td>\n",
       "      <td>4440.0</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6390.0</td>\n",
       "      <td>6810.0</td>\n",
       "      <td>7030.0</td>\n",
       "      <td>7480.0</td>\n",
       "      <td>7920.0</td>\n",
       "      <td>7590.0</td>\n",
       "      <td>8070.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>8770.0</td>\n",
       "      <td>9230.0</td>\n",
       "      <td>8950.0</td>\n",
       "      <td>8770.0</td>\n",
       "      <td>9570.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>11300.0</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>13900.0</td>\n",
       "      <td>14100.0</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>13600.0</td>\n",
       "      <td>14200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country   1962   1963   1964   1965   1966   1967   1968  \\\n",
       "0          Afghanistan    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1              Albania    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2              Algeria  190.0  230.0  240.0  240.0  230.0  250.0  280.0   \n",
       "3               Angola    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4  Antigua and Barbuda    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "    1969   1970   1971   1972   1973   1974   1975    1976    1977    1978  \\\n",
       "0    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN     NaN     NaN   \n",
       "1    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN     NaN     NaN   \n",
       "2  310.0  330.0  310.0  430.0  530.0  710.0  910.0  1050.0  1140.0  1350.0   \n",
       "3    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN     NaN     NaN   \n",
       "4    NaN    NaN    NaN    NaN    NaN    NaN    NaN     NaN     NaN     NaN   \n",
       "\n",
       "     1979    1980    1981    1982    1983    1984    1985    1986    1987  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN     NaN   720.0   760.0   \n",
       "2  1670.0  2010.0  2220.0  2280.0  2210.0  2260.0  2390.0  2590.0  2810.0   \n",
       "3     NaN     NaN     NaN     NaN     NaN     NaN   560.0   640.0   630.0   \n",
       "4  1520.0  1830.0  2060.0  2110.0  2320.0  2770.0  3200.0  3560.0  4440.0   \n",
       "\n",
       "     1988    1989    1990    1991    1992    1993    1994    1995    1996  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1   760.0   790.0   680.0   420.0   300.0   320.0   400.0   660.0   920.0   \n",
       "2  2750.0  2540.0  2360.0  1990.0  1890.0  1720.0  1610.0  1550.0  1510.0   \n",
       "3   630.0   830.0   760.0  1350.0  1140.0     NaN   320.0   430.0   310.0   \n",
       "4  5560.0  6000.0  6390.0  6810.0  7030.0  7480.0  7920.0  7590.0  8070.0   \n",
       "\n",
       "     1997    1998    1999    2000    2001    2002    2003     2004     2005  \\\n",
       "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN    210.0    250.0   \n",
       "1   830.0   900.0  1010.0  1170.0  1330.0  1410.0  1690.0   2150.0   2670.0   \n",
       "2  1500.0  1530.0  1530.0  1590.0  1660.0  1720.0  1920.0   2240.0   2700.0   \n",
       "3   420.0   390.0   330.0   360.0   390.0   540.0   610.0    800.0   1090.0   \n",
       "4  8370.0  8500.0  8770.0  9230.0  8950.0  8770.0  9570.0  10400.0  11300.0   \n",
       "\n",
       "      2006     2007     2008     2009     2010     2011     2012     2013  \\\n",
       "0    270.0    330.0    360.0    460.0    500.0    560.0    670.0    680.0   \n",
       "1   3090.0   3480.0   3950.0   4230.0   4360.0   4410.0   4360.0   4540.0   \n",
       "2   3090.0   3580.0   4200.0   4270.0   4460.0   4580.0   5140.0   5480.0   \n",
       "3   1490.0   2150.0   2760.0   3190.0   3240.0   3390.0   3800.0   4340.0   \n",
       "4  12600.0  13900.0  14100.0  12600.0  11800.0  11500.0  11900.0  12000.0   \n",
       "\n",
       "      2014     2015     2016     2017  \n",
       "0    650.0    600.0    580.0    570.0  \n",
       "1   4540.0   4390.0   4320.0   4320.0  \n",
       "2   5470.0   4830.0   4360.0   3960.0  \n",
       "3   4440.0   4030.0   3450.0   3330.0  \n",
       "4  12500.0  12900.0  13600.0  14200.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gni.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 190 entries, 0 to 189\n",
      "Data columns (total 57 columns):\n",
      "country    190 non-null object\n",
      "1962       73 non-null float64\n",
      "1963       74 non-null float64\n",
      "1964       75 non-null float64\n",
      "1965       75 non-null float64\n",
      "1966       75 non-null float64\n",
      "1967       82 non-null float64\n",
      "1968       87 non-null float64\n",
      "1969       89 non-null float64\n",
      "1970       92 non-null float64\n",
      "1971       93 non-null float64\n",
      "1972       102 non-null float64\n",
      "1973       103 non-null float64\n",
      "1974       103 non-null float64\n",
      "1975       102 non-null float64\n",
      "1976       102 non-null float64\n",
      "1977       105 non-null float64\n",
      "1978       107 non-null float64\n",
      "1979       112 non-null float64\n",
      "1980       113 non-null float64\n",
      "1981       114 non-null float64\n",
      "1982       123 non-null float64\n",
      "1983       126 non-null float64\n",
      "1984       129 non-null float64\n",
      "1985       130 non-null float64\n",
      "1986       132 non-null float64\n",
      "1987       132 non-null float64\n",
      "1988       133 non-null float64\n",
      "1989       137 non-null float64\n",
      "1990       139 non-null float64\n",
      "1991       138 non-null float64\n",
      "1992       150 non-null float64\n",
      "1993       152 non-null float64\n",
      "1994       156 non-null float64\n",
      "1995       162 non-null float64\n",
      "1996       164 non-null float64\n",
      "1997       173 non-null float64\n",
      "1998       175 non-null float64\n",
      "1999       175 non-null float64\n",
      "2000       176 non-null float64\n",
      "2001       178 non-null float64\n",
      "2002       183 non-null float64\n",
      "2003       184 non-null float64\n",
      "2004       186 non-null float64\n",
      "2005       186 non-null float64\n",
      "2006       187 non-null float64\n",
      "2007       187 non-null float64\n",
      "2008       186 non-null float64\n",
      "2009       186 non-null float64\n",
      "2010       185 non-null float64\n",
      "2011       185 non-null float64\n",
      "2012       184 non-null float64\n",
      "2013       185 non-null float64\n",
      "2014       184 non-null float64\n",
      "2015       184 non-null float64\n",
      "2016       184 non-null float64\n",
      "2017       182 non-null float64\n",
      "dtypes: float64(56), object(1)\n",
      "memory usage: 84.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_gni.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Female Population\n",
    "Source: Gapminder, provided by esa\n",
    "\n",
    "**Description**  \n",
    "This datasets includes the total numbers of a country's female population over the years. Values after 2010 are based on medium estimates from the UN population devision.\n",
    "\n",
    "**Properties**  \n",
    "We got the total female population numbers over the years 1950 to 2100 of 184 countries. Countries are of type string and the population numbers are of type integer. No null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>1959</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
       "      <th>1972</th>\n",
       "      <th>1973</th>\n",
       "      <th>1974</th>\n",
       "      <th>1975</th>\n",
       "      <th>1976</th>\n",
       "      <th>1977</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</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",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "      <th>2024</th>\n",
       "      <th>2025</th>\n",
       "      <th>2026</th>\n",
       "      <th>2027</th>\n",
       "      <th>2028</th>\n",
       "      <th>2029</th>\n",
       "      <th>2030</th>\n",
       "      <th>2031</th>\n",
       "      <th>2032</th>\n",
       "      <th>2033</th>\n",
       "      <th>2034</th>\n",
       "      <th>2035</th>\n",
       "      <th>2036</th>\n",
       "      <th>2037</th>\n",
       "      <th>2038</th>\n",
       "      <th>2039</th>\n",
       "      <th>2040</th>\n",
       "      <th>2041</th>\n",
       "      <th>2042</th>\n",
       "      <th>2043</th>\n",
       "      <th>2044</th>\n",
       "      <th>2045</th>\n",
       "      <th>2046</th>\n",
       "      <th>2047</th>\n",
       "      <th>2048</th>\n",
       "      <th>2049</th>\n",
       "      <th>2050</th>\n",
       "      <th>2051</th>\n",
       "      <th>2052</th>\n",
       "      <th>2053</th>\n",
       "      <th>2054</th>\n",
       "      <th>2055</th>\n",
       "      <th>2056</th>\n",
       "      <th>2057</th>\n",
       "      <th>2058</th>\n",
       "      <th>2059</th>\n",
       "      <th>2060</th>\n",
       "      <th>2061</th>\n",
       "      <th>2062</th>\n",
       "      <th>2063</th>\n",
       "      <th>2064</th>\n",
       "      <th>2065</th>\n",
       "      <th>2066</th>\n",
       "      <th>2067</th>\n",
       "      <th>2068</th>\n",
       "      <th>2069</th>\n",
       "      <th>2070</th>\n",
       "      <th>2071</th>\n",
       "      <th>2072</th>\n",
       "      <th>2073</th>\n",
       "      <th>2074</th>\n",
       "      <th>2075</th>\n",
       "      <th>2076</th>\n",
       "      <th>2077</th>\n",
       "      <th>2078</th>\n",
       "      <th>2079</th>\n",
       "      <th>2080</th>\n",
       "      <th>2081</th>\n",
       "      <th>2082</th>\n",
       "      <th>2083</th>\n",
       "      <th>2084</th>\n",
       "      <th>2085</th>\n",
       "      <th>2086</th>\n",
       "      <th>2087</th>\n",
       "      <th>2088</th>\n",
       "      <th>2089</th>\n",
       "      <th>2090</th>\n",
       "      <th>2091</th>\n",
       "      <th>2092</th>\n",
       "      <th>2093</th>\n",
       "      <th>2094</th>\n",
       "      <th>2095</th>\n",
       "      <th>2096</th>\n",
       "      <th>2097</th>\n",
       "      <th>2098</th>\n",
       "      <th>2099</th>\n",
       "      <th>2100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3650000</td>\n",
       "      <td>3710000</td>\n",
       "      <td>3760000</td>\n",
       "      <td>3820000</td>\n",
       "      <td>3880000</td>\n",
       "      <td>3950000</td>\n",
       "      <td>4020000</td>\n",
       "      <td>4100000</td>\n",
       "      <td>4180000</td>\n",
       "      <td>4260000</td>\n",
       "      <td>4350000</td>\n",
       "      <td>4440000</td>\n",
       "      <td>4530000</td>\n",
       "      <td>4630000</td>\n",
       "      <td>4730000</td>\n",
       "      <td>4840000</td>\n",
       "      <td>4950000</td>\n",
       "      <td>5070000</td>\n",
       "      <td>5190000</td>\n",
       "      <td>5310000</td>\n",
       "      <td>5450000</td>\n",
       "      <td>5600000</td>\n",
       "      <td>5750000</td>\n",
       "      <td>5910000</td>\n",
       "      <td>6060000</td>\n",
       "      <td>6190000</td>\n",
       "      <td>6320000</td>\n",
       "      <td>6430000</td>\n",
       "      <td>6510000</td>\n",
       "      <td>6540000</td>\n",
       "      <td>6510000</td>\n",
       "      <td>6410000</td>\n",
       "      <td>6260000</td>\n",
       "      <td>6070000</td>\n",
       "      <td>5900000</td>\n",
       "      <td>5760000</td>\n",
       "      <td>5670000</td>\n",
       "      <td>5630000</td>\n",
       "      <td>5650000</td>\n",
       "      <td>5760000</td>\n",
       "      <td>5980000</td>\n",
       "      <td>6330000</td>\n",
       "      <td>6790000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>7810000</td>\n",
       "      <td>8250000</td>\n",
       "      <td>8600000</td>\n",
       "      <td>8890000</td>\n",
       "      <td>9130000</td>\n",
       "      <td>9410000</td>\n",
       "      <td>9750000</td>\n",
       "      <td>10200000</td>\n",
       "      <td>10600000</td>\n",
       "      <td>11100000</td>\n",
       "      <td>11600000</td>\n",
       "      <td>12100000</td>\n",
       "      <td>12500000</td>\n",
       "      <td>12900000</td>\n",
       "      <td>13200000</td>\n",
       "      <td>13600000</td>\n",
       "      <td>14000000</td>\n",
       "      <td>14400000</td>\n",
       "      <td>14900000</td>\n",
       "      <td>15400000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>16300000</td>\n",
       "      <td>16800000</td>\n",
       "      <td>17200000</td>\n",
       "      <td>17600000</td>\n",
       "      <td>18100000</td>\n",
       "      <td>18500000</td>\n",
       "      <td>18900000</td>\n",
       "      <td>19300000</td>\n",
       "      <td>19800000</td>\n",
       "      <td>20200000</td>\n",
       "      <td>20600000</td>\n",
       "      <td>21000000</td>\n",
       "      <td>21500000</td>\n",
       "      <td>21900000</td>\n",
       "      <td>22300000</td>\n",
       "      <td>22700000</td>\n",
       "      <td>23200000</td>\n",
       "      <td>23600000</td>\n",
       "      <td>24000000</td>\n",
       "      <td>24400000</td>\n",
       "      <td>24800000</td>\n",
       "      <td>25200000</td>\n",
       "      <td>25700000</td>\n",
       "      <td>26100000</td>\n",
       "      <td>26500000</td>\n",
       "      <td>26800000</td>\n",
       "      <td>27200000</td>\n",
       "      <td>27600000</td>\n",
       "      <td>28000000</td>\n",
       "      <td>28400000</td>\n",
       "      <td>28700000</td>\n",
       "      <td>29100000</td>\n",
       "      <td>29400000</td>\n",
       "      <td>29800000</td>\n",
       "      <td>30100000</td>\n",
       "      <td>30400000</td>\n",
       "      <td>30700000</td>\n",
       "      <td>31000000</td>\n",
       "      <td>31300000</td>\n",
       "      <td>31600000</td>\n",
       "      <td>31900000</td>\n",
       "      <td>32200000</td>\n",
       "      <td>32500000</td>\n",
       "      <td>32700000</td>\n",
       "      <td>33000000</td>\n",
       "      <td>33200000</td>\n",
       "      <td>33500000</td>\n",
       "      <td>33700000</td>\n",
       "      <td>33900000</td>\n",
       "      <td>34100000</td>\n",
       "      <td>34300000</td>\n",
       "      <td>34500000</td>\n",
       "      <td>34700000</td>\n",
       "      <td>34900000</td>\n",
       "      <td>35000000</td>\n",
       "      <td>35200000</td>\n",
       "      <td>35300000</td>\n",
       "      <td>35500000</td>\n",
       "      <td>35600000</td>\n",
       "      <td>35700000</td>\n",
       "      <td>35800000</td>\n",
       "      <td>35900000</td>\n",
       "      <td>36000000</td>\n",
       "      <td>36100000</td>\n",
       "      <td>36100000</td>\n",
       "      <td>36200000</td>\n",
       "      <td>36200000</td>\n",
       "      <td>36200000</td>\n",
       "      <td>36300000</td>\n",
       "      <td>36300000</td>\n",
       "      <td>36300000</td>\n",
       "      <td>36300000</td>\n",
       "      <td>36300000</td>\n",
       "      <td>36300000</td>\n",
       "      <td>36200000</td>\n",
       "      <td>36200000</td>\n",
       "      <td>36100000</td>\n",
       "      <td>36100000</td>\n",
       "      <td>36000000</td>\n",
       "      <td>36000000</td>\n",
       "      <td>35900000</td>\n",
       "      <td>35800000</td>\n",
       "      <td>35800000</td>\n",
       "      <td>35700000</td>\n",
       "      <td>35600000</td>\n",
       "      <td>35500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>615000</td>\n",
       "      <td>626000</td>\n",
       "      <td>640000</td>\n",
       "      <td>655000</td>\n",
       "      <td>672000</td>\n",
       "      <td>690000</td>\n",
       "      <td>709000</td>\n",
       "      <td>728000</td>\n",
       "      <td>749000</td>\n",
       "      <td>771000</td>\n",
       "      <td>794000</td>\n",
       "      <td>818000</td>\n",
       "      <td>843000</td>\n",
       "      <td>869000</td>\n",
       "      <td>895000</td>\n",
       "      <td>920000</td>\n",
       "      <td>945000</td>\n",
       "      <td>970000</td>\n",
       "      <td>994000</td>\n",
       "      <td>1020000</td>\n",
       "      <td>1040000</td>\n",
       "      <td>1070000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>1120000</td>\n",
       "      <td>1150000</td>\n",
       "      <td>1170000</td>\n",
       "      <td>1200000</td>\n",
       "      <td>1220000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>1280000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>1330000</td>\n",
       "      <td>1360000</td>\n",
       "      <td>1380000</td>\n",
       "      <td>1410000</td>\n",
       "      <td>1440000</td>\n",
       "      <td>1480000</td>\n",
       "      <td>1520000</td>\n",
       "      <td>1550000</td>\n",
       "      <td>1580000</td>\n",
       "      <td>1600000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>1610000</td>\n",
       "      <td>1600000</td>\n",
       "      <td>1590000</td>\n",
       "      <td>1580000</td>\n",
       "      <td>1570000</td>\n",
       "      <td>1560000</td>\n",
       "      <td>1550000</td>\n",
       "      <td>1550000</td>\n",
       "      <td>1540000</td>\n",
       "      <td>1540000</td>\n",
       "      <td>1540000</td>\n",
       "      <td>1540000</td>\n",
       "      <td>1540000</td>\n",
       "      <td>1540000</td>\n",
       "      <td>1520000</td>\n",
       "      <td>1510000</td>\n",
       "      <td>1490000</td>\n",
       "      <td>1480000</td>\n",
       "      <td>1470000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1470000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1460000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1450000</td>\n",
       "      <td>1440000</td>\n",
       "      <td>1440000</td>\n",
       "      <td>1430000</td>\n",
       "      <td>1420000</td>\n",
       "      <td>1420000</td>\n",
       "      <td>1410000</td>\n",
       "      <td>1400000</td>\n",
       "      <td>1390000</td>\n",
       "      <td>1390000</td>\n",
       "      <td>1380000</td>\n",
       "      <td>1370000</td>\n",
       "      <td>1360000</td>\n",
       "      <td>1350000</td>\n",
       "      <td>1340000</td>\n",
       "      <td>1330000</td>\n",
       "      <td>1320000</td>\n",
       "      <td>1310000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>1290000</td>\n",
       "      <td>1280000</td>\n",
       "      <td>1270000</td>\n",
       "      <td>1260000</td>\n",
       "      <td>1250000</td>\n",
       "      <td>1240000</td>\n",
       "      <td>1220000</td>\n",
       "      <td>1210000</td>\n",
       "      <td>1200000</td>\n",
       "      <td>1190000</td>\n",
       "      <td>1180000</td>\n",
       "      <td>1170000</td>\n",
       "      <td>1160000</td>\n",
       "      <td>1150000</td>\n",
       "      <td>1140000</td>\n",
       "      <td>1130000</td>\n",
       "      <td>1120000</td>\n",
       "      <td>1110000</td>\n",
       "      <td>1090000</td>\n",
       "      <td>1080000</td>\n",
       "      <td>1070000</td>\n",
       "      <td>1060000</td>\n",
       "      <td>1050000</td>\n",
       "      <td>1040000</td>\n",
       "      <td>1030000</td>\n",
       "      <td>1020000</td>\n",
       "      <td>1010000</td>\n",
       "      <td>999000</td>\n",
       "      <td>989000</td>\n",
       "      <td>979000</td>\n",
       "      <td>969000</td>\n",
       "      <td>959000</td>\n",
       "      <td>949000</td>\n",
       "      <td>939000</td>\n",
       "      <td>929000</td>\n",
       "      <td>920000</td>\n",
       "      <td>910000</td>\n",
       "      <td>901000</td>\n",
       "      <td>891000</td>\n",
       "      <td>882000</td>\n",
       "      <td>873000</td>\n",
       "      <td>863000</td>\n",
       "      <td>855000</td>\n",
       "      <td>846000</td>\n",
       "      <td>837000</td>\n",
       "      <td>829000</td>\n",
       "      <td>821000</td>\n",
       "      <td>813000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4360000</td>\n",
       "      <td>4450000</td>\n",
       "      <td>4550000</td>\n",
       "      <td>4650000</td>\n",
       "      <td>4760000</td>\n",
       "      <td>4870000</td>\n",
       "      <td>4990000</td>\n",
       "      <td>5120000</td>\n",
       "      <td>5250000</td>\n",
       "      <td>5390000</td>\n",
       "      <td>5530000</td>\n",
       "      <td>5670000</td>\n",
       "      <td>5810000</td>\n",
       "      <td>5960000</td>\n",
       "      <td>6120000</td>\n",
       "      <td>6290000</td>\n",
       "      <td>6460000</td>\n",
       "      <td>6650000</td>\n",
       "      <td>6840000</td>\n",
       "      <td>7040000</td>\n",
       "      <td>7240000</td>\n",
       "      <td>7440000</td>\n",
       "      <td>7640000</td>\n",
       "      <td>7850000</td>\n",
       "      <td>8070000</td>\n",
       "      <td>8300000</td>\n",
       "      <td>8530000</td>\n",
       "      <td>8780000</td>\n",
       "      <td>9040000</td>\n",
       "      <td>9310000</td>\n",
       "      <td>9590000</td>\n",
       "      <td>9890000</td>\n",
       "      <td>10200000</td>\n",
       "      <td>10500000</td>\n",
       "      <td>10800000</td>\n",
       "      <td>11200000</td>\n",
       "      <td>11500000</td>\n",
       "      <td>11800000</td>\n",
       "      <td>12200000</td>\n",
       "      <td>12500000</td>\n",
       "      <td>12800000</td>\n",
       "      <td>13100000</td>\n",
       "      <td>13400000</td>\n",
       "      <td>13700000</td>\n",
       "      <td>14000000</td>\n",
       "      <td>14300000</td>\n",
       "      <td>14500000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>15000000</td>\n",
       "      <td>15200000</td>\n",
       "      <td>15400000</td>\n",
       "      <td>15600000</td>\n",
       "      <td>15800000</td>\n",
       "      <td>16000000</td>\n",
       "      <td>16200000</td>\n",
       "      <td>16500000</td>\n",
       "      <td>16700000</td>\n",
       "      <td>17000000</td>\n",
       "      <td>17300000</td>\n",
       "      <td>17600000</td>\n",
       "      <td>17900000</td>\n",
       "      <td>18200000</td>\n",
       "      <td>18600000</td>\n",
       "      <td>19000000</td>\n",
       "      <td>19400000</td>\n",
       "      <td>19700000</td>\n",
       "      <td>20100000</td>\n",
       "      <td>20500000</td>\n",
       "      <td>20800000</td>\n",
       "      <td>21100000</td>\n",
       "      <td>21400000</td>\n",
       "      <td>21800000</td>\n",
       "      <td>22100000</td>\n",
       "      <td>22400000</td>\n",
       "      <td>22600000</td>\n",
       "      <td>22900000</td>\n",
       "      <td>23200000</td>\n",
       "      <td>23400000</td>\n",
       "      <td>23700000</td>\n",
       "      <td>23900000</td>\n",
       "      <td>24200000</td>\n",
       "      <td>24400000</td>\n",
       "      <td>24600000</td>\n",
       "      <td>24900000</td>\n",
       "      <td>25100000</td>\n",
       "      <td>25300000</td>\n",
       "      <td>25500000</td>\n",
       "      <td>25700000</td>\n",
       "      <td>26000000</td>\n",
       "      <td>26200000</td>\n",
       "      <td>26400000</td>\n",
       "      <td>26600000</td>\n",
       "      <td>26800000</td>\n",
       "      <td>27000000</td>\n",
       "      <td>27300000</td>\n",
       "      <td>27500000</td>\n",
       "      <td>27700000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>28100000</td>\n",
       "      <td>28300000</td>\n",
       "      <td>28500000</td>\n",
       "      <td>28700000</td>\n",
       "      <td>28800000</td>\n",
       "      <td>29000000</td>\n",
       "      <td>29100000</td>\n",
       "      <td>29300000</td>\n",
       "      <td>29400000</td>\n",
       "      <td>29500000</td>\n",
       "      <td>29700000</td>\n",
       "      <td>29800000</td>\n",
       "      <td>29900000</td>\n",
       "      <td>30000000</td>\n",
       "      <td>30000000</td>\n",
       "      <td>30100000</td>\n",
       "      <td>30200000</td>\n",
       "      <td>30200000</td>\n",
       "      <td>30300000</td>\n",
       "      <td>30300000</td>\n",
       "      <td>30400000</td>\n",
       "      <td>30400000</td>\n",
       "      <td>30500000</td>\n",
       "      <td>30500000</td>\n",
       "      <td>30500000</td>\n",
       "      <td>30600000</td>\n",
       "      <td>30600000</td>\n",
       "      <td>30600000</td>\n",
       "      <td>30700000</td>\n",
       "      <td>30700000</td>\n",
       "      <td>30700000</td>\n",
       "      <td>30700000</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>30800000</td>\n",
       "      <td>30800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2340000</td>\n",
       "      <td>2400000</td>\n",
       "      <td>2460000</td>\n",
       "      <td>2520000</td>\n",
       "      <td>2570000</td>\n",
       "      <td>2630000</td>\n",
       "      <td>2690000</td>\n",
       "      <td>2740000</td>\n",
       "      <td>2790000</td>\n",
       "      <td>2850000</td>\n",
       "      <td>2900000</td>\n",
       "      <td>2960000</td>\n",
       "      <td>3010000</td>\n",
       "      <td>3070000</td>\n",
       "      <td>3130000</td>\n",
       "      <td>3190000</td>\n",
       "      <td>3240000</td>\n",
       "      <td>3290000</td>\n",
       "      <td>3350000</td>\n",
       "      <td>3410000</td>\n",
       "      <td>3480000</td>\n",
       "      <td>3550000</td>\n",
       "      <td>3640000</td>\n",
       "      <td>3730000</td>\n",
       "      <td>3830000</td>\n",
       "      <td>3930000</td>\n",
       "      <td>4050000</td>\n",
       "      <td>4160000</td>\n",
       "      <td>4290000</td>\n",
       "      <td>4420000</td>\n",
       "      <td>4570000</td>\n",
       "      <td>4730000</td>\n",
       "      <td>4900000</td>\n",
       "      <td>5080000</td>\n",
       "      <td>5250000</td>\n",
       "      <td>5420000</td>\n",
       "      <td>5580000</td>\n",
       "      <td>5730000</td>\n",
       "      <td>5880000</td>\n",
       "      <td>6040000</td>\n",
       "      <td>6220000</td>\n",
       "      <td>6420000</td>\n",
       "      <td>6630000</td>\n",
       "      <td>6850000</td>\n",
       "      <td>7080000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>7510000</td>\n",
       "      <td>7720000</td>\n",
       "      <td>7930000</td>\n",
       "      <td>8160000</td>\n",
       "      <td>8410000</td>\n",
       "      <td>8680000</td>\n",
       "      <td>8990000</td>\n",
       "      <td>9310000</td>\n",
       "      <td>9650000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>10400000</td>\n",
       "      <td>10700000</td>\n",
       "      <td>11100000</td>\n",
       "      <td>11500000</td>\n",
       "      <td>11900000</td>\n",
       "      <td>12400000</td>\n",
       "      <td>12800000</td>\n",
       "      <td>13300000</td>\n",
       "      <td>13700000</td>\n",
       "      <td>14200000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>15200000</td>\n",
       "      <td>15700000</td>\n",
       "      <td>16200000</td>\n",
       "      <td>16700000</td>\n",
       "      <td>17300000</td>\n",
       "      <td>17800000</td>\n",
       "      <td>18400000</td>\n",
       "      <td>19000000</td>\n",
       "      <td>19600000</td>\n",
       "      <td>20200000</td>\n",
       "      <td>20800000</td>\n",
       "      <td>21400000</td>\n",
       "      <td>22100000</td>\n",
       "      <td>22700000</td>\n",
       "      <td>23400000</td>\n",
       "      <td>24100000</td>\n",
       "      <td>24800000</td>\n",
       "      <td>25500000</td>\n",
       "      <td>26300000</td>\n",
       "      <td>27000000</td>\n",
       "      <td>27800000</td>\n",
       "      <td>28500000</td>\n",
       "      <td>29300000</td>\n",
       "      <td>30100000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>31700000</td>\n",
       "      <td>32600000</td>\n",
       "      <td>33400000</td>\n",
       "      <td>34200000</td>\n",
       "      <td>35100000</td>\n",
       "      <td>36000000</td>\n",
       "      <td>36900000</td>\n",
       "      <td>37800000</td>\n",
       "      <td>38700000</td>\n",
       "      <td>39600000</td>\n",
       "      <td>40500000</td>\n",
       "      <td>41400000</td>\n",
       "      <td>42300000</td>\n",
       "      <td>43300000</td>\n",
       "      <td>44200000</td>\n",
       "      <td>45200000</td>\n",
       "      <td>46200000</td>\n",
       "      <td>47100000</td>\n",
       "      <td>48100000</td>\n",
       "      <td>49100000</td>\n",
       "      <td>50100000</td>\n",
       "      <td>51100000</td>\n",
       "      <td>52100000</td>\n",
       "      <td>53100000</td>\n",
       "      <td>54100000</td>\n",
       "      <td>55200000</td>\n",
       "      <td>56200000</td>\n",
       "      <td>57200000</td>\n",
       "      <td>58200000</td>\n",
       "      <td>59300000</td>\n",
       "      <td>60300000</td>\n",
       "      <td>61300000</td>\n",
       "      <td>62400000</td>\n",
       "      <td>63400000</td>\n",
       "      <td>64500000</td>\n",
       "      <td>65500000</td>\n",
       "      <td>66500000</td>\n",
       "      <td>67600000</td>\n",
       "      <td>68600000</td>\n",
       "      <td>69700000</td>\n",
       "      <td>70700000</td>\n",
       "      <td>71700000</td>\n",
       "      <td>72800000</td>\n",
       "      <td>73800000</td>\n",
       "      <td>74800000</td>\n",
       "      <td>75800000</td>\n",
       "      <td>76800000</td>\n",
       "      <td>77800000</td>\n",
       "      <td>78800000</td>\n",
       "      <td>79800000</td>\n",
       "      <td>80800000</td>\n",
       "      <td>81800000</td>\n",
       "      <td>82800000</td>\n",
       "      <td>83700000</td>\n",
       "      <td>84700000</td>\n",
       "      <td>85600000</td>\n",
       "      <td>86600000</td>\n",
       "      <td>87500000</td>\n",
       "      <td>88400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>23500</td>\n",
       "      <td>24300</td>\n",
       "      <td>25000</td>\n",
       "      <td>25600</td>\n",
       "      <td>26200</td>\n",
       "      <td>26700</td>\n",
       "      <td>27300</td>\n",
       "      <td>27800</td>\n",
       "      <td>28300</td>\n",
       "      <td>28800</td>\n",
       "      <td>29300</td>\n",
       "      <td>29800</td>\n",
       "      <td>30300</td>\n",
       "      <td>30800</td>\n",
       "      <td>31300</td>\n",
       "      <td>31900</td>\n",
       "      <td>32500</td>\n",
       "      <td>33100</td>\n",
       "      <td>33800</td>\n",
       "      <td>34400</td>\n",
       "      <td>35000</td>\n",
       "      <td>35600</td>\n",
       "      <td>36100</td>\n",
       "      <td>36600</td>\n",
       "      <td>37000</td>\n",
       "      <td>37300</td>\n",
       "      <td>37500</td>\n",
       "      <td>37700</td>\n",
       "      <td>37800</td>\n",
       "      <td>37700</td>\n",
       "      <td>37600</td>\n",
       "      <td>37400</td>\n",
       "      <td>37100</td>\n",
       "      <td>36700</td>\n",
       "      <td>36300</td>\n",
       "      <td>35900</td>\n",
       "      <td>35400</td>\n",
       "      <td>35000</td>\n",
       "      <td>34600</td>\n",
       "      <td>34400</td>\n",
       "      <td>34400</td>\n",
       "      <td>34700</td>\n",
       "      <td>35200</td>\n",
       "      <td>35900</td>\n",
       "      <td>36800</td>\n",
       "      <td>37800</td>\n",
       "      <td>39100</td>\n",
       "      <td>40500</td>\n",
       "      <td>41900</td>\n",
       "      <td>43100</td>\n",
       "      <td>44200</td>\n",
       "      <td>45000</td>\n",
       "      <td>45500</td>\n",
       "      <td>45800</td>\n",
       "      <td>46100</td>\n",
       "      <td>46500</td>\n",
       "      <td>47000</td>\n",
       "      <td>47500</td>\n",
       "      <td>48100</td>\n",
       "      <td>48700</td>\n",
       "      <td>49300</td>\n",
       "      <td>49800</td>\n",
       "      <td>50400</td>\n",
       "      <td>50900</td>\n",
       "      <td>51400</td>\n",
       "      <td>52000</td>\n",
       "      <td>52500</td>\n",
       "      <td>53000</td>\n",
       "      <td>53600</td>\n",
       "      <td>54100</td>\n",
       "      <td>54600</td>\n",
       "      <td>55100</td>\n",
       "      <td>55700</td>\n",
       "      <td>56200</td>\n",
       "      <td>56700</td>\n",
       "      <td>57200</td>\n",
       "      <td>57600</td>\n",
       "      <td>58100</td>\n",
       "      <td>58600</td>\n",
       "      <td>59000</td>\n",
       "      <td>59500</td>\n",
       "      <td>59900</td>\n",
       "      <td>60300</td>\n",
       "      <td>60700</td>\n",
       "      <td>61100</td>\n",
       "      <td>61500</td>\n",
       "      <td>61800</td>\n",
       "      <td>62100</td>\n",
       "      <td>62400</td>\n",
       "      <td>62700</td>\n",
       "      <td>63000</td>\n",
       "      <td>63300</td>\n",
       "      <td>63500</td>\n",
       "      <td>63700</td>\n",
       "      <td>64000</td>\n",
       "      <td>64100</td>\n",
       "      <td>64300</td>\n",
       "      <td>64500</td>\n",
       "      <td>64600</td>\n",
       "      <td>64700</td>\n",
       "      <td>64800</td>\n",
       "      <td>64900</td>\n",
       "      <td>65000</td>\n",
       "      <td>65100</td>\n",
       "      <td>65100</td>\n",
       "      <td>65100</td>\n",
       "      <td>65200</td>\n",
       "      <td>65200</td>\n",
       "      <td>65200</td>\n",
       "      <td>65200</td>\n",
       "      <td>65200</td>\n",
       "      <td>65200</td>\n",
       "      <td>65100</td>\n",
       "      <td>65100</td>\n",
       "      <td>65100</td>\n",
       "      <td>65100</td>\n",
       "      <td>65000</td>\n",
       "      <td>65000</td>\n",
       "      <td>65000</td>\n",
       "      <td>64900</td>\n",
       "      <td>64900</td>\n",
       "      <td>64900</td>\n",
       "      <td>64800</td>\n",
       "      <td>64800</td>\n",
       "      <td>64800</td>\n",
       "      <td>64700</td>\n",
       "      <td>64700</td>\n",
       "      <td>64600</td>\n",
       "      <td>64500</td>\n",
       "      <td>64500</td>\n",
       "      <td>64400</td>\n",
       "      <td>64300</td>\n",
       "      <td>64200</td>\n",
       "      <td>64100</td>\n",
       "      <td>64000</td>\n",
       "      <td>63900</td>\n",
       "      <td>63800</td>\n",
       "      <td>63700</td>\n",
       "      <td>63600</td>\n",
       "      <td>63500</td>\n",
       "      <td>63400</td>\n",
       "      <td>63300</td>\n",
       "      <td>63100</td>\n",
       "      <td>63000</td>\n",
       "      <td>62900</td>\n",
       "      <td>62800</td>\n",
       "      <td>62600</td>\n",
       "      <td>62500</td>\n",
       "      <td>62400</td>\n",
       "      <td>62300</td>\n",
       "      <td>62100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country     1950     1951     1952     1953     1954     1955  \\\n",
       "0          Afghanistan  3650000  3710000  3760000  3820000  3880000  3950000   \n",
       "1              Albania   615000   626000   640000   655000   672000   690000   \n",
       "2              Algeria  4360000  4450000  4550000  4650000  4760000  4870000   \n",
       "3               Angola  2340000  2400000  2460000  2520000  2570000  2630000   \n",
       "4  Antigua and Barbuda    23500    24300    25000    25600    26200    26700   \n",
       "\n",
       "      1956     1957     1958     1959     1960     1961     1962     1963  \\\n",
       "0  4020000  4100000  4180000  4260000  4350000  4440000  4530000  4630000   \n",
       "1   709000   728000   749000   771000   794000   818000   843000   869000   \n",
       "2  4990000  5120000  5250000  5390000  5530000  5670000  5810000  5960000   \n",
       "3  2690000  2740000  2790000  2850000  2900000  2960000  3010000  3070000   \n",
       "4    27300    27800    28300    28800    29300    29800    30300    30800   \n",
       "\n",
       "      1964     1965     1966     1967     1968     1969     1970     1971  \\\n",
       "0  4730000  4840000  4950000  5070000  5190000  5310000  5450000  5600000   \n",
       "1   895000   920000   945000   970000   994000  1020000  1040000  1070000   \n",
       "2  6120000  6290000  6460000  6650000  6840000  7040000  7240000  7440000   \n",
       "3  3130000  3190000  3240000  3290000  3350000  3410000  3480000  3550000   \n",
       "4    31300    31900    32500    33100    33800    34400    35000    35600   \n",
       "\n",
       "      1972     1973     1974     1975     1976     1977     1978     1979  \\\n",
       "0  5750000  5910000  6060000  6190000  6320000  6430000  6510000  6540000   \n",
       "1  1090000  1120000  1150000  1170000  1200000  1220000  1250000  1280000   \n",
       "2  7640000  7850000  8070000  8300000  8530000  8780000  9040000  9310000   \n",
       "3  3640000  3730000  3830000  3930000  4050000  4160000  4290000  4420000   \n",
       "4    36100    36600    37000    37300    37500    37700    37800    37700   \n",
       "\n",
       "      1980     1981      1982      1983      1984      1985      1986  \\\n",
       "0  6510000  6410000   6260000   6070000   5900000   5760000   5670000   \n",
       "1  1300000  1330000   1360000   1380000   1410000   1440000   1480000   \n",
       "2  9590000  9890000  10200000  10500000  10800000  11200000  11500000   \n",
       "3  4570000  4730000   4900000   5080000   5250000   5420000   5580000   \n",
       "4    37600    37400     37100     36700     36300     35900     35400   \n",
       "\n",
       "       1987      1988      1989      1990      1991      1992      1993  \\\n",
       "0   5630000   5650000   5760000   5980000   6330000   6790000   7300000   \n",
       "1   1520000   1550000   1580000   1600000   1610000   1610000   1600000   \n",
       "2  11800000  12200000  12500000  12800000  13100000  13400000  13700000   \n",
       "3   5730000   5880000   6040000   6220000   6420000   6630000   6850000   \n",
       "4     35000     34600     34400     34400     34700     35200     35900   \n",
       "\n",
       "       1994      1995      1996      1997      1998      1999      2000  \\\n",
       "0   7810000   8250000   8600000   8890000   9130000   9410000   9750000   \n",
       "1   1590000   1580000   1570000   1560000   1550000   1550000   1540000   \n",
       "2  14000000  14300000  14500000  14700000  15000000  15200000  15400000   \n",
       "3   7080000   7300000   7510000   7720000   7930000   8160000   8410000   \n",
       "4     36800     37800     39100     40500     41900     43100     44200   \n",
       "\n",
       "       2001      2002      2003      2004      2005      2006      2007  \\\n",
       "0  10200000  10600000  11100000  11600000  12100000  12500000  12900000   \n",
       "1   1540000   1540000   1540000   1540000   1540000   1520000   1510000   \n",
       "2  15600000  15800000  16000000  16200000  16500000  16700000  17000000   \n",
       "3   8680000   8990000   9310000   9650000  10000000  10400000  10700000   \n",
       "4     45000     45500     45800     46100     46500     47000     47500   \n",
       "\n",
       "       2008      2009      2010      2011      2012      2013      2014  \\\n",
       "0  13200000  13600000  14000000  14400000  14900000  15400000  15900000   \n",
       "1   1490000   1480000   1470000   1460000   1450000   1450000   1450000   \n",
       "2  17300000  17600000  17900000  18200000  18600000  19000000  19400000   \n",
       "3  11100000  11500000  11900000  12400000  12800000  13300000  13700000   \n",
       "4     48100     48700     49300     49800     50400     50900     51400   \n",
       "\n",
       "       2015      2016      2017      2018      2019      2020      2021  \\\n",
       "0  16300000  16800000  17200000  17600000  18100000  18500000  18900000   \n",
       "1   1450000   1450000   1450000   1450000   1460000   1460000   1460000   \n",
       "2  19700000  20100000  20500000  20800000  21100000  21400000  21800000   \n",
       "3  14200000  14700000  15200000  15700000  16200000  16700000  17300000   \n",
       "4     52000     52500     53000     53600     54100     54600     55100   \n",
       "\n",
       "       2022      2023      2024      2025      2026      2027      2028  \\\n",
       "0  19300000  19800000  20200000  20600000  21000000  21500000  21900000   \n",
       "1   1460000   1460000   1460000   1470000   1460000   1460000   1460000   \n",
       "2  22100000  22400000  22600000  22900000  23200000  23400000  23700000   \n",
       "3  17800000  18400000  19000000  19600000  20200000  20800000  21400000   \n",
       "4     55700     56200     56700     57200     57600     58100     58600   \n",
       "\n",
       "       2029      2030      2031      2032      2033      2034      2035  \\\n",
       "0  22300000  22700000  23200000  23600000  24000000  24400000  24800000   \n",
       "1   1460000   1460000   1460000   1450000   1450000   1450000   1440000   \n",
       "2  23900000  24200000  24400000  24600000  24900000  25100000  25300000   \n",
       "3  22100000  22700000  23400000  24100000  24800000  25500000  26300000   \n",
       "4     59000     59500     59900     60300     60700     61100     61500   \n",
       "\n",
       "       2036      2037      2038      2039      2040      2041      2042  \\\n",
       "0  25200000  25700000  26100000  26500000  26800000  27200000  27600000   \n",
       "1   1440000   1430000   1420000   1420000   1410000   1400000   1390000   \n",
       "2  25500000  25700000  26000000  26200000  26400000  26600000  26800000   \n",
       "3  27000000  27800000  28500000  29300000  30100000  30900000  31700000   \n",
       "4     61800     62100     62400     62700     63000     63300     63500   \n",
       "\n",
       "       2043      2044      2045      2046      2047      2048      2049  \\\n",
       "0  28000000  28400000  28700000  29100000  29400000  29800000  30100000   \n",
       "1   1390000   1380000   1370000   1360000   1350000   1340000   1330000   \n",
       "2  27000000  27300000  27500000  27700000  27900000  28100000  28300000   \n",
       "3  32600000  33400000  34200000  35100000  36000000  36900000  37800000   \n",
       "4     63700     64000     64100     64300     64500     64600     64700   \n",
       "\n",
       "       2050      2051      2052      2053      2054      2055      2056  \\\n",
       "0  30400000  30700000  31000000  31300000  31600000  31900000  32200000   \n",
       "1   1320000   1310000   1300000   1290000   1280000   1270000   1260000   \n",
       "2  28500000  28700000  28800000  29000000  29100000  29300000  29400000   \n",
       "3  38700000  39600000  40500000  41400000  42300000  43300000  44200000   \n",
       "4     64800     64900     65000     65100     65100     65100     65200   \n",
       "\n",
       "       2057      2058      2059      2060      2061      2062      2063  \\\n",
       "0  32500000  32700000  33000000  33200000  33500000  33700000  33900000   \n",
       "1   1250000   1240000   1220000   1210000   1200000   1190000   1180000   \n",
       "2  29500000  29700000  29800000  29900000  30000000  30000000  30100000   \n",
       "3  45200000  46200000  47100000  48100000  49100000  50100000  51100000   \n",
       "4     65200     65200     65200     65200     65200     65100     65100   \n",
       "\n",
       "       2064      2065      2066      2067      2068      2069      2070  \\\n",
       "0  34100000  34300000  34500000  34700000  34900000  35000000  35200000   \n",
       "1   1170000   1160000   1150000   1140000   1130000   1120000   1110000   \n",
       "2  30200000  30200000  30300000  30300000  30400000  30400000  30500000   \n",
       "3  52100000  53100000  54100000  55200000  56200000  57200000  58200000   \n",
       "4     65100     65100     65000     65000     65000     64900     64900   \n",
       "\n",
       "       2071      2072      2073      2074      2075      2076      2077  \\\n",
       "0  35300000  35500000  35600000  35700000  35800000  35900000  36000000   \n",
       "1   1090000   1080000   1070000   1060000   1050000   1040000   1030000   \n",
       "2  30500000  30500000  30600000  30600000  30600000  30700000  30700000   \n",
       "3  59300000  60300000  61300000  62400000  63400000  64500000  65500000   \n",
       "4     64900     64800     64800     64800     64700     64700     64600   \n",
       "\n",
       "       2078      2079      2080      2081      2082      2083      2084  \\\n",
       "0  36100000  36100000  36200000  36200000  36200000  36300000  36300000   \n",
       "1   1020000   1010000    999000    989000    979000    969000    959000   \n",
       "2  30700000  30700000  30800000  30800000  30800000  30800000  30900000   \n",
       "3  66500000  67600000  68600000  69700000  70700000  71700000  72800000   \n",
       "4     64500     64500     64400     64300     64200     64100     64000   \n",
       "\n",
       "       2085      2086      2087      2088      2089      2090      2091  \\\n",
       "0  36300000  36300000  36300000  36300000  36200000  36200000  36100000   \n",
       "1    949000    939000    929000    920000    910000    901000    891000   \n",
       "2  30900000  30900000  30900000  30900000  30900000  30900000  30900000   \n",
       "3  73800000  74800000  75800000  76800000  77800000  78800000  79800000   \n",
       "4     63900     63800     63700     63600     63500     63400     63300   \n",
       "\n",
       "       2092      2093      2094      2095      2096      2097      2098  \\\n",
       "0  36100000  36000000  36000000  35900000  35800000  35800000  35700000   \n",
       "1    882000    873000    863000    855000    846000    837000    829000   \n",
       "2  30900000  30900000  30900000  30900000  30900000  30900000  30900000   \n",
       "3  80800000  81800000  82800000  83700000  84700000  85600000  86600000   \n",
       "4     63100     63000     62900     62800     62600     62500     62400   \n",
       "\n",
       "       2099      2100  \n",
       "0  35600000  35500000  \n",
       "1    821000    813000  \n",
       "2  30800000  30800000  \n",
       "3  87500000  88400000  \n",
       "4     62300     62100  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 184 entries, 0 to 183\n",
      "Columns: 152 entries, country to 2100\n",
      "dtypes: int64(151), object(1)\n",
      "memory usage: 218.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_pop.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countries by Category\n",
    "Source: Wikimedia, https://meta.wikimedia.org/wiki/List_of_countries_by_regional_classification  \n",
    "\n",
    "***Description***  \n",
    "To evaluate the datasets by a world's region, I parsed countries and their corresponding region and development status from Wikimedia into a csv file. In the process of data cleaning, I matched differing country names to names used in the other datasets.\n",
    "\n",
    "***Properties***    \n",
    "The dataset consists of 247 entries for country's with their corresponding world region and development status. All data is provided in the data format of a string. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 247 entries, 0 to 246\n",
      "Data columns (total 3 columns):\n",
      "Country        247 non-null object\n",
      "Region         247 non-null object\n",
      "Development    247 non-null object\n",
      "dtypes: object(3)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_countries.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Female Employment by Sector (% Female Employment)\n",
    "Source: Gapminder, provided by ilostat  \n",
    "\n",
    "***Female Agriculture Workers (% Female Employment)***  \n",
    "Percentage of women working in the agricultural sector.\n",
    "\n",
    "***Female Industry Workers (% Female Employment)***   \n",
    "Percentage of women working in the industrial sector.\n",
    "\n",
    "***Female Service Workers (% Female Employment)***    \n",
    "Percentage of women working in the service sector.\n",
    "\n",
    "***Properties***  \n",
    "All employment sector datasets consist of 179 countries covering the years 1991 to 2022. Countries come in the data format of a string. Every year contains a percentage value of a float. No null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179 entries, 0 to 178\n",
      "Data columns (total 33 columns):\n",
      "country    179 non-null object\n",
      "1991       179 non-null float64\n",
      "1992       179 non-null float64\n",
      "1993       179 non-null float64\n",
      "1994       179 non-null float64\n",
      "1995       179 non-null float64\n",
      "1996       179 non-null float64\n",
      "1997       179 non-null float64\n",
      "1998       179 non-null float64\n",
      "1999       179 non-null float64\n",
      "2000       179 non-null float64\n",
      "2001       179 non-null float64\n",
      "2002       179 non-null float64\n",
      "2003       179 non-null float64\n",
      "2004       179 non-null float64\n",
      "2005       179 non-null float64\n",
      "2006       179 non-null float64\n",
      "2007       179 non-null float64\n",
      "2008       179 non-null float64\n",
      "2009       179 non-null float64\n",
      "2010       179 non-null float64\n",
      "2011       179 non-null float64\n",
      "2012       179 non-null float64\n",
      "2013       179 non-null float64\n",
      "2014       179 non-null float64\n",
      "2015       179 non-null float64\n",
      "2016       179 non-null float64\n",
      "2017       179 non-null float64\n",
      "2018       179 non-null float64\n",
      "2019       179 non-null float64\n",
      "2020       179 non-null float64\n",
      "2021       179 non-null float64\n",
      "2022       179 non-null float64\n",
      "dtypes: float64(32), object(1)\n",
      "memory usage: 46.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_agr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179 entries, 0 to 178\n",
      "Data columns (total 33 columns):\n",
      "country    179 non-null object\n",
      "1991       179 non-null float64\n",
      "1992       179 non-null float64\n",
      "1993       179 non-null float64\n",
      "1994       179 non-null float64\n",
      "1995       179 non-null float64\n",
      "1996       179 non-null float64\n",
      "1997       179 non-null float64\n",
      "1998       179 non-null float64\n",
      "1999       179 non-null float64\n",
      "2000       179 non-null float64\n",
      "2001       179 non-null float64\n",
      "2002       179 non-null float64\n",
      "2003       179 non-null float64\n",
      "2004       179 non-null float64\n",
      "2005       179 non-null float64\n",
      "2006       179 non-null float64\n",
      "2007       179 non-null float64\n",
      "2008       179 non-null float64\n",
      "2009       179 non-null float64\n",
      "2010       179 non-null float64\n",
      "2011       179 non-null float64\n",
      "2012       179 non-null float64\n",
      "2013       179 non-null float64\n",
      "2014       179 non-null float64\n",
      "2015       179 non-null float64\n",
      "2016       179 non-null float64\n",
      "2017       179 non-null float64\n",
      "2018       179 non-null float64\n",
      "2019       179 non-null float64\n",
      "2020       179 non-null float64\n",
      "2021       179 non-null float64\n",
      "2022       179 non-null float64\n",
      "dtypes: float64(32), object(1)\n",
      "memory usage: 46.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_ind.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179 entries, 0 to 178\n",
      "Data columns (total 33 columns):\n",
      "country    179 non-null object\n",
      "1991       179 non-null float64\n",
      "1992       179 non-null float64\n",
      "1993       179 non-null float64\n",
      "1994       179 non-null float64\n",
      "1995       179 non-null float64\n",
      "1996       179 non-null float64\n",
      "1997       179 non-null float64\n",
      "1998       179 non-null float64\n",
      "1999       179 non-null float64\n",
      "2000       179 non-null float64\n",
      "2001       179 non-null float64\n",
      "2002       179 non-null float64\n",
      "2003       179 non-null float64\n",
      "2004       179 non-null float64\n",
      "2005       179 non-null float64\n",
      "2006       179 non-null float64\n",
      "2007       179 non-null float64\n",
      "2008       179 non-null float64\n",
      "2009       179 non-null float64\n",
      "2010       179 non-null float64\n",
      "2011       179 non-null float64\n",
      "2012       179 non-null float64\n",
      "2013       179 non-null float64\n",
      "2014       179 non-null float64\n",
      "2015       179 non-null float64\n",
      "2016       179 non-null float64\n",
      "2017       179 non-null float64\n",
      "2018       179 non-null float64\n",
      "2019       179 non-null float64\n",
      "2020       179 non-null float64\n",
      "2021       179 non-null float64\n",
      "2022       179 non-null float64\n",
      "dtypes: float64(32), object(1)\n",
      "memory usage: 46.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_serv.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data wrangling showed the data comes already in appropriate data formats. We got recorded yearly data without gaps. The smallest number of recorded countries is the FLPR data containing 178 countries. Since there are currently 195 countries in the world, our dataset covers the great majority of the globe. Nevertheless, there are four key findings, I have to pay attention to throughout the following data cleaning process.  \n",
    "  \n",
    "**Time frame**  \n",
    "The data wrangling uncovered the years 1991 to 2017 to be in common across all datasets. It makes sense to focus on the time frame of common data, so I'm going to drop values of earlier or later years.  \n",
    "\n",
    "**Countries**  \n",
    "I noticed that some of the datasets include different countries than others. Throughout the analysis I want to make sure to compare a set of matching countries. Throughout the cleaning, I will drop rows containing non-matching countries.\n",
    "The naming/spelling of some countries differs to some extend in the df_countries containing data from Wikimedia and the Gampminder dataframes. Throughout the cleaning/analysis, I renamed country names in the countries_by_category.csv file directly. \n",
    "\n",
    "**Null values in GNI data**  \n",
    "The GNI dataset contains quite a significant amount of null values. Especially the early years seem to be quite incomplete. The possible reasons for missing data: countries may have not existed, were re-formed or annexed. Also, economic data may have not been recorded or shared with the world bank. However, since the 1990s the dataset's null values seem to be less missing at random, but for geographical or political reasons. \n",
    "\n",
    "Analysis of the GNI development over the years shows a linear change for most of the countries. Since previously existing countries are not part of the list, yet the geographic area existed, I decided to fill null values using linear interpolation. For example, Bosnia Herzegovina shows recorded data only since the mid '90s. The previously existing Yugoslavia is not part of the data set anymore. Not counting in the geographic area would have underestimated the GNI significantly. \n",
    "\n",
    "**Merging and table format**  \n",
    "The data currently comes in separate files. To analyse variables and their possible dependencies, we can make our lives easier by combining the data into a common dataframe. The current formatting of our data tables follows the pattern of having the years as column labels. Since, we are analysing the same time frame and are interested in different variables, it makes sense to shift years into rows. Instead of year labels, we will now have variable names (e.g. FLPR, GNI) as columns.      \n",
    "\n",
    "## Data Cleaning\n",
    "\n",
    "\n",
    "### Cleaning GNI  \n",
    "The GNI data contains values over the period of 1962 to 2017. Since, we are only interested in the years 1991 to 2017, we drop data of earlier years. Even after removing, we find many null values. As explained above, we are going to perform linear interpolation to fill in missing values.   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(190, 28)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop all years ealier than 1991\n",
    "df_gni = df_gni.drop([str(i) for i in range(1962,1991)], axis=1)\n",
    "# Since we will cover 28 years, we expect 28 columns (1 country, 27x years)\n",
    "df_gni.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country     0\n",
       "1991       52\n",
       "1992       40\n",
       "1993       38\n",
       "1994       34\n",
       "1995       28\n",
       "1996       26\n",
       "1997       17\n",
       "1998       15\n",
       "1999       15\n",
       "2000       14\n",
       "2001       12\n",
       "2002        7\n",
       "2003        6\n",
       "2004        4\n",
       "2005        4\n",
       "2006        3\n",
       "2007        3\n",
       "2008        4\n",
       "2009        4\n",
       "2010        5\n",
       "2011        5\n",
       "2012        6\n",
       "2013        5\n",
       "2014        6\n",
       "2015        6\n",
       "2016        6\n",
       "2017        8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the GNI for null values shows shows many NaN cells\n",
    "df_gni.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interpolate GNI in both directions\n",
    "df_gni.loc[:, '1991':'2017'] = df_gni.loc[:,'1991':'2017'].interpolate(axis=1, limit_direction ='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double check if any null values left\n",
    "df_gni.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</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",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>190.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5659.894737</td>\n",
       "      <td>6068.368421</td>\n",
       "      <td>6088.394737</td>\n",
       "      <td>6270.736842</td>\n",
       "      <td>6630.578947</td>\n",
       "      <td>7009.315789</td>\n",
       "      <td>7128.368421</td>\n",
       "      <td>6906.526316</td>\n",
       "      <td>6881.105263</td>\n",
       "      <td>6996.842105</td>\n",
       "      <td>6865.000000</td>\n",
       "      <td>6818.105263</td>\n",
       "      <td>7466.052632</td>\n",
       "      <td>8800.842105</td>\n",
       "      <td>10086.473684</td>\n",
       "      <td>10993.315789</td>\n",
       "      <td>12094.684211</td>\n",
       "      <td>13125.105263</td>\n",
       "      <td>12891.052632</td>\n",
       "      <td>13169.894737</td>\n",
       "      <td>13514.157895</td>\n",
       "      <td>14199.736842</td>\n",
       "      <td>14740.263158</td>\n",
       "      <td>14792.578947</td>\n",
       "      <td>14256.473684</td>\n",
       "      <td>13673.789474</td>\n",
       "      <td>13533.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10295.293679</td>\n",
       "      <td>11215.881832</td>\n",
       "      <td>11201.203364</td>\n",
       "      <td>11636.056055</td>\n",
       "      <td>12446.716623</td>\n",
       "      <td>13191.988632</td>\n",
       "      <td>13257.531591</td>\n",
       "      <td>12682.984040</td>\n",
       "      <td>12586.755126</td>\n",
       "      <td>12648.884575</td>\n",
       "      <td>12064.613776</td>\n",
       "      <td>11801.232721</td>\n",
       "      <td>12947.727332</td>\n",
       "      <td>15420.226438</td>\n",
       "      <td>17650.756318</td>\n",
       "      <td>18964.322506</td>\n",
       "      <td>21000.554809</td>\n",
       "      <td>22776.553867</td>\n",
       "      <td>22109.142832</td>\n",
       "      <td>22388.095712</td>\n",
       "      <td>22627.501740</td>\n",
       "      <td>23171.802455</td>\n",
       "      <td>23681.904025</td>\n",
       "      <td>23665.886264</td>\n",
       "      <td>22952.195586</td>\n",
       "      <td>22276.251581</td>\n",
       "      <td>22056.693290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>290.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>502.500000</td>\n",
       "      <td>492.500000</td>\n",
       "      <td>517.500000</td>\n",
       "      <td>552.500000</td>\n",
       "      <td>572.500000</td>\n",
       "      <td>562.500000</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>572.500000</td>\n",
       "      <td>562.500000</td>\n",
       "      <td>532.500000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>685.000000</td>\n",
       "      <td>807.500000</td>\n",
       "      <td>947.500000</td>\n",
       "      <td>1010.000000</td>\n",
       "      <td>1160.000000</td>\n",
       "      <td>1215.000000</td>\n",
       "      <td>1332.500000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1682.500000</td>\n",
       "      <td>1732.500000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>1927.500000</td>\n",
       "      <td>1850.000000</td>\n",
       "      <td>1850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1570.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>1595.000000</td>\n",
       "      <td>1705.000000</td>\n",
       "      <td>1755.000000</td>\n",
       "      <td>1855.000000</td>\n",
       "      <td>1875.000000</td>\n",
       "      <td>1935.000000</td>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1740.000000</td>\n",
       "      <td>1775.000000</td>\n",
       "      <td>1875.000000</td>\n",
       "      <td>2120.000000</td>\n",
       "      <td>2510.000000</td>\n",
       "      <td>2910.000000</td>\n",
       "      <td>3375.000000</td>\n",
       "      <td>3660.000000</td>\n",
       "      <td>3995.000000</td>\n",
       "      <td>4190.000000</td>\n",
       "      <td>4405.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>5460.000000</td>\n",
       "      <td>5760.000000</td>\n",
       "      <td>5750.000000</td>\n",
       "      <td>5415.000000</td>\n",
       "      <td>5050.000000</td>\n",
       "      <td>4955.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4860.000000</td>\n",
       "      <td>5155.000000</td>\n",
       "      <td>5032.500000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>5150.000000</td>\n",
       "      <td>5395.000000</td>\n",
       "      <td>5582.500000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5352.500000</td>\n",
       "      <td>5752.500000</td>\n",
       "      <td>6227.500000</td>\n",
       "      <td>5867.500000</td>\n",
       "      <td>6867.500000</td>\n",
       "      <td>8337.500000</td>\n",
       "      <td>9830.000000</td>\n",
       "      <td>11225.000000</td>\n",
       "      <td>12100.000000</td>\n",
       "      <td>13775.000000</td>\n",
       "      <td>13125.000000</td>\n",
       "      <td>12750.000000</td>\n",
       "      <td>13100.000000</td>\n",
       "      <td>13800.000000</td>\n",
       "      <td>15125.000000</td>\n",
       "      <td>15100.000000</td>\n",
       "      <td>14825.000000</td>\n",
       "      <td>14475.000000</td>\n",
       "      <td>14350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82500.000000</td>\n",
       "      <td>91300.000000</td>\n",
       "      <td>89600.000000</td>\n",
       "      <td>92200.000000</td>\n",
       "      <td>97100.000000</td>\n",
       "      <td>101000.000000</td>\n",
       "      <td>99600.000000</td>\n",
       "      <td>95000.000000</td>\n",
       "      <td>92400.000000</td>\n",
       "      <td>90800.000000</td>\n",
       "      <td>86400.000000</td>\n",
       "      <td>84100.000000</td>\n",
       "      <td>94200.000000</td>\n",
       "      <td>113000.000000</td>\n",
       "      <td>128000.000000</td>\n",
       "      <td>137000.000000</td>\n",
       "      <td>161000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "      <td>186000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1991          1992          1993          1994          1995  \\\n",
       "count    190.000000    190.000000    190.000000    190.000000    190.000000   \n",
       "mean    5659.894737   6068.368421   6088.394737   6270.736842   6630.578947   \n",
       "std    10295.293679  11215.881832  11201.203364  11636.056055  12446.716623   \n",
       "min      110.000000    120.000000    120.000000    120.000000    120.000000   \n",
       "25%      500.000000    550.000000    502.500000    492.500000    517.500000   \n",
       "50%     1570.000000   1600.000000   1595.000000   1705.000000   1755.000000   \n",
       "75%     4860.000000   5155.000000   5032.500000   5200.000000   5150.000000   \n",
       "max    82500.000000  91300.000000  89600.000000  92200.000000  97100.000000   \n",
       "\n",
       "                1996          1997          1998          1999          2000  \\\n",
       "count     190.000000    190.000000    190.000000    190.000000    190.000000   \n",
       "mean     7009.315789   7128.368421   6906.526316   6881.105263   6996.842105   \n",
       "std     13191.988632  13257.531591  12682.984040  12586.755126  12648.884575   \n",
       "min       120.000000    120.000000    120.000000    110.000000    120.000000   \n",
       "25%       552.500000    572.500000    562.500000    570.000000    572.500000   \n",
       "50%      1855.000000   1875.000000   1935.000000   1810.000000   1740.000000   \n",
       "75%      5395.000000   5582.500000   5500.000000   5352.500000   5752.500000   \n",
       "max    101000.000000  99600.000000  95000.000000  92400.000000  90800.000000   \n",
       "\n",
       "               2001          2002          2003           2004           2005  \\\n",
       "count    190.000000    190.000000    190.000000     190.000000     190.000000   \n",
       "mean    6865.000000   6818.105263   7466.052632    8800.842105   10086.473684   \n",
       "std    12064.613776  11801.232721  12947.727332   15420.226438   17650.756318   \n",
       "min      130.000000    120.000000     80.000000      90.000000     120.000000   \n",
       "25%      562.500000    532.500000    580.000000     685.000000     807.500000   \n",
       "50%     1775.000000   1875.000000   2120.000000    2510.000000    2910.000000   \n",
       "75%     6227.500000   5867.500000   6867.500000    8337.500000    9830.000000   \n",
       "max    86400.000000  84100.000000  94200.000000  113000.000000  128000.000000   \n",
       "\n",
       "                2006           2007           2008           2009  \\\n",
       "count     190.000000     190.000000     190.000000     190.000000   \n",
       "mean    10993.315789   12094.684211   13125.105263   12891.052632   \n",
       "std     18964.322506   21000.554809   22776.553867   22109.142832   \n",
       "min       130.000000     160.000000     180.000000     190.000000   \n",
       "25%       947.500000    1010.000000    1160.000000    1215.000000   \n",
       "50%      3375.000000    3660.000000    3995.000000    4190.000000   \n",
       "75%     11225.000000   12100.000000   13775.000000   13125.000000   \n",
       "max    137000.000000  161000.000000  186000.000000  186000.000000   \n",
       "\n",
       "                2010           2011           2012           2013  \\\n",
       "count     190.000000     190.000000     190.000000     190.000000   \n",
       "mean    13169.894737   13514.157895   14199.736842   14740.263158   \n",
       "std     22388.095712   22627.501740   23171.802455   23681.904025   \n",
       "min       210.000000     230.000000     260.000000     280.000000   \n",
       "25%      1332.500000    1435.000000    1682.500000    1732.500000   \n",
       "50%      4405.000000    4810.000000    5460.000000    5760.000000   \n",
       "75%     12750.000000   13100.000000   13800.000000   15125.000000   \n",
       "max    186000.000000  186000.000000  186000.000000  186000.000000   \n",
       "\n",
       "                2014           2015           2016           2017  \n",
       "count     190.000000     190.000000     190.000000     190.000000  \n",
       "mean    14792.578947   14256.473684   13673.789474   13533.105263  \n",
       "std     23665.886264   22952.195586   22276.251581   22056.693290  \n",
       "min       290.000000     280.000000     280.000000     290.000000  \n",
       "25%      1840.000000    1927.500000    1850.000000    1850.000000  \n",
       "50%      5750.000000    5415.000000    5050.000000    4955.000000  \n",
       "75%     15100.000000   14825.000000   14475.000000   14350.000000  \n",
       "max    186000.000000  186000.000000  186000.000000  186000.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To finish cleaning, I'm checking min, max, mean values for outliers. \n",
    "df_gni.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Countries by Category\n",
    "When checking for unique values in the df_countries dataset, we find unnecessary tab characters in the columns 'Region' and 'Development'. The original dataset contained countries with a different spelling/names compared to other datasets. It became obvious when I merged the country data with other datasets at a later stage. To not loose any country's data, I adapted the spelling in the file directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Europe' '\\tArab States' 'Asia & Pacific\\t' 'South/Latin America'\n",
      " 'Africa' 'South/Latin America\\t' 'Asia & Pacific' 'Europe\\t'\n",
      " 'Arab States' 'North America' '\\tAsia & Pacific' '\\tAfrica']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Global North', 'Global South', '\\tGlobal South', '\\tGlobal North'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show unique values\n",
    "print(df_countries['Region'].unique())\n",
    "df_countries['Development'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove all tabs '\\t' from country dataset \n",
    "df_countries['Region'] = df_countries['Region'].str.replace('\\t','')\n",
    "df_countries['Development'] = df_countries['Development'].str.replace('\\t','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Europe' 'Arab States' 'Asia & Pacific' 'South/Latin America' 'Africa'\n",
      " 'North America']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Global North', 'Global South'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Doublecheck if unneccessary characters were removed. \n",
    "print(df_countries['Region'].unique())\n",
    "df_countries['Development'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Female Population dataset\n",
    "In our research, we are interested in the latest population numbers, which is available for the year 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>17200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1450000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country      2017\n",
       "0  Afghanistan  17200000\n",
       "1      Albania   1450000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a mask of columns 'country' and year '2017'.\n",
    "df_pop = df_pop.loc[:, ['country', '2017']]\n",
    "df_pop.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The dataset doesn't have any null values \n",
    "df_pop.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Female Employment by Sector.\n",
    "Since the datasets already come in a clean format, we only have to pick the years we are interested in. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</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",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>80.3</td>\n",
       "      <td>83.9</td>\n",
       "      <td>87.2</td>\n",
       "      <td>89.6</td>\n",
       "      <td>88.6</td>\n",
       "      <td>88.2</td>\n",
       "      <td>88.1</td>\n",
       "      <td>87.8</td>\n",
       "      <td>87.2</td>\n",
       "      <td>86.3</td>\n",
       "      <td>85.2</td>\n",
       "      <td>83.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>80.9</td>\n",
       "      <td>81.4</td>\n",
       "      <td>80.4</td>\n",
       "      <td>78.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>78.2</td>\n",
       "      <td>75.6</td>\n",
       "      <td>73.5</td>\n",
       "      <td>72.8</td>\n",
       "      <td>72.7</td>\n",
       "      <td>72.6</td>\n",
       "      <td>72.4</td>\n",
       "      <td>72.6</td>\n",
       "      <td>73.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>73.8</td>\n",
       "      <td>78.6</td>\n",
       "      <td>80.1</td>\n",
       "      <td>79.5</td>\n",
       "      <td>79.2</td>\n",
       "      <td>76.6</td>\n",
       "      <td>73.6</td>\n",
       "      <td>75.8</td>\n",
       "      <td>75.2</td>\n",
       "      <td>73.6</td>\n",
       "      <td>73.0</td>\n",
       "      <td>71.8</td>\n",
       "      <td>70.8</td>\n",
       "      <td>69.3</td>\n",
       "      <td>67.4</td>\n",
       "      <td>66.0</td>\n",
       "      <td>61.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>54.1</td>\n",
       "      <td>54.3</td>\n",
       "      <td>58.6</td>\n",
       "      <td>53.9</td>\n",
       "      <td>50.5</td>\n",
       "      <td>47.8</td>\n",
       "      <td>47.3</td>\n",
       "      <td>46.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  1991  1992  1993  1994  1995  1996  1997  1998  1999  2000  \\\n",
       "0  Afghanistan  80.3  83.9  87.2  89.6  88.6  88.2  88.1  87.8  87.2  86.3   \n",
       "1      Albania  73.8  78.6  80.1  79.5  79.2  76.6  73.6  75.8  75.2  73.6   \n",
       "\n",
       "   2001  2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  \\\n",
       "0  85.2  83.5  83.0  80.9  81.4  80.4  78.9  76.8  78.2  75.6  73.5  72.8   \n",
       "1  73.0  71.8  70.8  69.3  67.4  66.0  61.8  61.0  57.2  54.1  54.3  58.6   \n",
       "\n",
       "   2013  2014  2015  2016  2017  \n",
       "0  72.7  72.6  72.4  72.6  73.7  \n",
       "1  53.9  50.5  47.8  47.3  46.9  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = [df_serv, df_ind, df_agr]\n",
    "\n",
    "# select country and drop columns younger than 2017\n",
    "for dataset in datasets:\n",
    "    dataset = dataset.drop([str(i) for i in range(2018,2023)], axis=1, inplace=True)\n",
    "\n",
    "df_agr.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for duplicates in any of the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Checking for duplicates shows no duplicated rows in both datasets results in no duplicates\n",
    "datasets = [df_flpr, df_gni, df_pop, df_countries, df_serv, df_ind, df_agr]\n",
    "\n",
    "for set in datasets:\n",
    "    print(sum(set.duplicated()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create combined dataframes\n",
    "To analysis and plot data, it makes sense to change the table formatting and combine data in common tables. To keep the dataframes neat, I will create two dataframes: the first dataframe will combine FLPR and GNI data. To help us analyse geographic data, I'm assigning a world region and their development status to every country. I can simply do this by merging df_countries with our data. The second dataframe will comprise employment data by sector.\n",
    "\n",
    "All datasets are currently formatted with the recorded years as separate columns. However, this format turns out to be unhandy, if we want to plot our data. Instead, we want all years in one column and the variables we are analysing in the form of columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pivot the tables from wide format to long format\n",
    "df_serv = pd.melt(df_serv, id_vars=[\"country\"], var_name='year', value_name=\"service\")\n",
    "df_ind = pd.melt(df_ind, id_vars=[\"country\"], var_name='year', value_name=\"industry\")\n",
    "df_agr = pd.melt(df_agr, id_vars=[\"country\"], var_name='year', value_name=\"agriculture\")\n",
    "df_flpr = pd.melt(df_flpr, id_vars=[\"country\"], var_name='year', value_name=\"flpr\")\n",
    "df_gni = pd.melt(df_gni, id_vars=[\"country\"], var_name='year', value_name=\"gni\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should have 3 columns now: 'country', 'year' and the respective variable's name. Country and year shall remain a string, while the investigated variable comes in form of a number (float or int)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4833 entries, 0 to 4832\n",
      "Data columns (total 3 columns):\n",
      "country    4833 non-null object\n",
      "year       4833 non-null object\n",
      "service    4833 non-null float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 113.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#doublecheck if the below melting worked as intended\n",
    "df_serv.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2017</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4980</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2017</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4982</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2017</td>\n",
       "      <td>70.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4983</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>78.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  flpr\n",
       "4979  Venezuela  2017  50.2\n",
       "4980    Vietnam  2017  73.2\n",
       "4981      Yemen  2017   6.0\n",
       "4982     Zambia  2017  70.1\n",
       "4983   Zimbabwe  2017  78.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flpr.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I'm combining datasets. I'm merging FLPR and GNI data and merging employment sector data thereafter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1991</td>\n",
       "      <td>14.9</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  flpr    gni\n",
       "0  Afghanistan  1991  14.9  210.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine FLPR and GNI data.\n",
    "df_comb = pd.merge(df_flpr, df_gni, how='inner', left_on=['country', 'year'], right_on=['country', 'year'])\n",
    "df_comb.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, I'm merging countries by category into the combined dataset and printing any non-matching country names. I could have combined it using the merge function. \n",
    "\n",
    "Since the source of countries and their regions is different from the rest of the data, we expect differences in spelling/naming of countries. The below cell assigns regions/development status to our combined dataset and prints differing country names. This gives us the chance to correct spelling/naming of countries in the file directly.\n",
    "Running this cell won't print any country names now, since the csv-file was cleaned directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1991</td>\n",
       "      <td>14.9</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1991</td>\n",
       "      <td>55.8</td>\n",
       "      <td>420.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1991</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1991</td>\n",
       "      <td>75.1</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1991</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3950.0</td>\n",
       "      <td>South/Latin America</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  flpr     gni               region    dev_status\n",
       "0  Afghanistan  1991  14.9   210.0       Asia & Pacific  Global South\n",
       "1      Albania  1991  55.8   420.0               Europe  Global North\n",
       "2      Algeria  1991  11.5  1990.0          Arab States  Global South\n",
       "3       Angola  1991  75.1  1350.0               Africa  Global South\n",
       "4    Argentina  1991  45.0  3950.0  South/Latin America  Global South"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add two new empty columns to our data set\n",
    "df_comb['region'] = np.empty(len(df_comb.index), dtype=object)\n",
    "df_comb['dev_status'] = np.empty(len(df_comb.index), dtype=object)\n",
    "\n",
    "# loop through every column and match corresponding region and development status\n",
    "for i, country in enumerate(df_comb['country'].values):\n",
    "    if country in df_countries['Country'].values:\n",
    "        df_comb.at[i, 'region'] = df_countries[df_countries['Country'] == country]['Region'].values[0]\n",
    "        df_comb.at[i, 'dev_status'] = df_countries[df_countries['Country'] == country]['Development'].values[0]\n",
    "    else:\n",
    "        # Print any non-existing country to adapt spelling of country names in the countries-by_category.csv directly\n",
    "        print(country)\n",
    "        \n",
    "df_comb.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [country, year, flpr, gni, region, dev_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print remaining cells with empty values. \n",
    "df_comb[df_comb.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are mo empty cells left. My combined dataset is ready for analysis. Now I'm merging the employment sectors into one dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>service</th>\n",
       "      <th>industry</th>\n",
       "      <th>agriculture</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1991</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.76</td>\n",
       "      <td>80.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  service  industry  agriculture\n",
       "0  Afghanistan  1991     13.0      6.76         80.3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the employment sector variables into one dataset.\n",
    "sectors = [df_serv, df_ind, df_agr]\n",
    "df_sectors = reduce(lambda left, right:  pd.merge(left, right, how='inner', left_on=['country', 'year'], right_on=['country', 'year']), sectors)\n",
    "df_sectors.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doublecheck if the merging of the sector data into one dataframe worked out. We should now have 6 columns: country, year, service, industry, agriculture and self_empl. The sector data should come in the format of a float."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "###   Does any region's female labour force participation outperform other regions? \n",
    "\n",
    "\n",
    "I am going to analyse to what extend women participate in employed work around the globe. To get an impression how female employment participation is distributed, I am going to explore the Female Labour Force Participation Rate geographically. How is female employment participation distributed across the world's regions? I am going to investigate where we typically find countries with high FLPRs and where in the world do women engage less in the labour market. Part of the analysis is to find out how female labour participation changed over time. Are there world regions which were faced with bigger changes compared to other regions? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of countries observed:  176\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    4752.000000\n",
       "mean       50.388119\n",
       "std        16.343913\n",
       "min         6.000000\n",
       "25%        41.600000\n",
       "50%        50.900000\n",
       "75%        61.000000\n",
       "max        90.300000\n",
       "Name: flpr, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look on the FLPR \n",
    "print(\"Number of countries observed: \",df_comb.query('year == \"1991\"')['country'].count())\n",
    "df_comb['flpr'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over the period of 1991 to 2017, we observed Labour Force Participation Rates in 176 countries ranging from 6% to 90.3%. Meaning, there were countries with a minmum of 6% of all adult women engaging in the labour market and other countries with up to 90% of all adult women engaging in employment. The global average female labour participation rate over the years 1991 to 2017 was 50.4%.\n",
    "\n",
    "Let's visualize how the global FLPR changed over time. I am going to plot line graphs and box plots of the average female labour participation over the time. By grouping our data geographically, we can observe patterns across continental regions of the globe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot line graphs of mean FLPRs \n",
    "#parameters\n",
    "plt.rcParams['figure.figsize']= [10,8]\n",
    "\n",
    "# plot global average FLPR over time \n",
    "plt.plot([str(i) for i in range(1991,2018)], df_comb.groupby('year')['flpr'].mean(), label=\" Global ØFLPR\", linewidth=4)\n",
    "\n",
    "# plot regional average FLPR rates\n",
    "regions = df_comb['region'].unique()\n",
    "for region in regions:\n",
    "    plt.plot([str(i) for i in range(1991,2018)], df_comb.query(\"region == @region\").groupby('year')['flpr'].mean(), label= region + \" ØFLPR\")\n",
    "\n",
    "# Marker year 1991\n",
    "plt.plot(['1991'], df_comb.query('year == \"1991\"')['flpr'].mean(), marker='o', label=\"ØFLPR 1991: \" + \n",
    "         str(df_comb.query('year == \"1991\"')['flpr'].mean().round(4)))\n",
    "\n",
    "# Marker highest FLPR\n",
    "#calculate local max\n",
    "flpr_max = max(df_comb.groupby('year')['flpr'].mean())\n",
    "flpr_max_year = df_comb.groupby('year')['flpr'].mean().idxmax()\n",
    "plt.plot(flpr_max_year, flpr_max, marker='o', label=\"MAX ØFLPR \" + flpr_max_year +\": \" + str(round(flpr_max,4)))\n",
    "\n",
    "# Marker year 2017\n",
    "plt.plot(['2017'], df_comb.query('year == \"2017\"')['flpr'].mean(), marker='o', label=\"ØFLPR 2017: \" + str(df_comb.query('year == \"2017\"')['flpr'].mean().round(4)))\n",
    "\n",
    "#titles\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Female Labour Force Particiation Rate %', fontsize=12)\n",
    "plt.title('Mean Female Labour Participation over the years 1990-2017', fontsize=18).set_position([.5, 1.05])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above line graphs picture mean female labour participation rates over the period of 1991 to 2017. The highlighted graph describes the global mean, the other graphs show the mean FLPRs of the Asia & Pacific, Europe, Arab States, Africa, South/Latin America and North America. \n",
    "\n",
    "We observe a modest increase of the global average FLPR over the time period of 1991 to 2017. In 1991 the mean FLPR was at 48.46%. By 2017 it had risen to 52.06% with a slight max. of 52.06% in the year 2016. \n",
    "Close to the global mean FLPR are Europe and Asian & Pacific States. The plot of South/Latin America shows a stronger increase than the average. In the early 90s South Americas FLPRs were at a bit more than 40%. By the mid 2000s, the FLPRs had risen above the levels of the global mean and are in 2017 at roughly 50%.  \n",
    "\n",
    "The far lowest mean FLPRs occur in Arab States. As a matter of fact, over the years we observe a relatively strong growth. Yet the Arab States average below 30%.  \n",
    "\n",
    "North America's female employment participation is above the global average. However, since 2008/2009 we also observe the strongest recession of all regions.  \n",
    "\n",
    "African women's employment participation outweighs by far other world regions with a rate at above 60%.  \n",
    "\n",
    "While the mean labour participation rates are interesting to observe global patterns, they don't tell us much about how divers the FLP rates across each world regions are. To visualize their diversity, we are going to use boxplot they year 2017.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2017</td>\n",
       "      <td>50.2</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>South/Latin America</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2017</td>\n",
       "      <td>73.2</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2017</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>78.5</td>\n",
       "      <td>910.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  flpr      gni               region    dev_status\n",
       "171  Venezuela  2017  50.2  12800.0  South/Latin America  Global South\n",
       "172    Vietnam  2017  73.2   2170.0       Asia & Pacific  Global South\n",
       "173      Yemen  2017   6.0   1030.0          Arab States  Global South\n",
       "174     Zambia  2017  70.1   1300.0               Africa  Global South\n",
       "175   Zimbabwe  2017  78.5    910.0               Africa  Global South"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create separate dataframes for the years 1991 and 2017 and reindex\n",
    "df_comb_1991 = df_comb.query('year == \"1991\"').reset_index(drop=True)\n",
    "df_comb_2017 = df_comb.query('year == \"2017\"').reset_index(drop=True)\n",
    "\n",
    "# Check the end of the dataframe. We shoul now see 175 entries. \n",
    "df_comb_2017.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameters\n",
    "plt.rcParams['figure.figsize']= [12,10]\n",
    "\n",
    "# Boxplot FLPR over world region\n",
    "p = sns.catplot(x=\"region\", y=\"flpr\", kind=\"box\", data=df_comb_2017)\n",
    "\n",
    "# labels and titles\n",
    "plt.title(\"FLPR by World Region in 2017\", fontsize=18, pad=15)\n",
    "plt.xlabel('', fontsize=12)\n",
    "plt.ylabel('Female Labour Force Particiation Rate %', fontsize=12)\n",
    "plt.xticks(rotation='vertical', fontsize=12)\n",
    "p.set_xticklabels(rotation=90)\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot of year 2017 FLPRs affirms our finding above. The graph of Arab States is comparatively low to other regions. Arab States tend to have lower female labour participation rates. The African region has a comparatively high boxplot. African women seem to engage at a higher rate in employment. Other region's FLPR medians average at a similar level, with North America having a bit higher median FLPR.   \n",
    "\n",
    "In addition to the line graphs above, we get an impression of the ranges of individual country's FLPR rates within a region. North America is comparatively the shortest, which indicates homogeneous FLPRs across states. Europe and South America show a moderate length of boxplots. Meaning, the countries tend to have relatively moderate homogenous FLPRs. The boxplots of Asian & Pacific have the longest tails. African and Arab States have also quite long tails, with Arab States being skewed to the right. The tails are an indication for a wide range of countries with very different FLPRs in respective regions. \n",
    "\n",
    "**What were countries with the highest/lowest female labour participation rates in 1991 and 2017?**\n",
    "\n",
    "I'm curious about finding out more details of FLPRs. Below, I'm investigating the countries with the highest and lowest female labour participation of the years 1991 and 2017. \n",
    "\n",
    "***Countries of the highest female labour participation rates in 1991 and 2017***  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Countries in 1991 with the highest female labour participation rates.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>1991</td>\n",
       "      <td>90.3</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>1991</td>\n",
       "      <td>87.1</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>1991</td>\n",
       "      <td>86.8</td>\n",
       "      <td>320.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>1991</td>\n",
       "      <td>84.2</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>1991</td>\n",
       "      <td>82.1</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Togo</td>\n",
       "      <td>1991</td>\n",
       "      <td>80.8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>1991</td>\n",
       "      <td>80.7</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Lao</td>\n",
       "      <td>1991</td>\n",
       "      <td>79.9</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>1991</td>\n",
       "      <td>77.1</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>1991</td>\n",
       "      <td>76.3</td>\n",
       "      <td>360.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  flpr    gni          region    dev_status\n",
       "25        Burundi  1991  90.3  220.0          Africa  Global South\n",
       "107    Mozambique  1991  87.1  200.0          Africa  Global South\n",
       "130        Rwanda  1991  86.8  320.0          Africa  Global South\n",
       "94     Madagascar  1991  84.2  220.0          Africa  Global South\n",
       "154      Tanzania  1991  82.1  180.0          Africa  Global South\n",
       "157          Togo  1991  80.8  400.0          Africa  Global South\n",
       "110         Nepal  1991  80.7  210.0  Asia & Pacific  Global South\n",
       "85            Lao  1991  79.9  210.0  Asia & Pacific  Global South\n",
       "26       Cambodia  1991  77.1  250.0  Asia & Pacific  Global South\n",
       "24   Burkina Faso  1991  76.3  360.0          Africa  Global South"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the 10 countries with the highest female participation rates in the year 1991.\n",
    "print(\"Countries in 1991 with the highest female labour participation rates.\")\n",
    "df_comb_1991.nlargest(10, 'flpr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Countries in 2017 with the highest female labour participation rates.\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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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>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>2017</td>\n",
       "      <td>86.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>2017</td>\n",
       "      <td>83.6</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>2017</td>\n",
       "      <td>82.7</td>\n",
       "      <td>790.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>2017</td>\n",
       "      <td>82.5</td>\n",
       "      <td>420.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.9</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.2</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>2017</td>\n",
       "      <td>79.5</td>\n",
       "      <td>910.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>78.5</td>\n",
       "      <td>910.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>2017</td>\n",
       "      <td>77.2</td>\n",
       "      <td>740.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Lao</td>\n",
       "      <td>2017</td>\n",
       "      <td>76.9</td>\n",
       "      <td>2270.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  flpr     gni          region    dev_status\n",
       "130      Rwanda  2017  86.0   720.0          Africa  Global South\n",
       "94   Madagascar  2017  83.6   400.0          Africa  Global South\n",
       "110       Nepal  2017  82.7   790.0  Asia & Pacific  Global South\n",
       "107  Mozambique  2017  82.5   420.0          Africa  Global South\n",
       "26     Cambodia  2017  80.9  1230.0  Asia & Pacific  Global South\n",
       "25      Burundi  2017  80.2   290.0          Africa  Global South\n",
       "154    Tanzania  2017  79.5   910.0          Africa  Global South\n",
       "175    Zimbabwe  2017  78.5   910.0          Africa  Global South\n",
       "53     Ethiopia  2017  77.2   740.0          Africa  Global South\n",
       "85          Lao  2017  76.9  2270.0  Asia & Pacific  Global South"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the 10 countries with the highest female participation rates in the year 2017?\n",
    "print(\"Countries in 2017 with the highest female labour participation rates.\")\n",
    "df_comb_2017.nlargest(10, 'flpr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 1991 and 2017, the highest FLPRs can be observed in African and Asian/Pacific countries of the developing world. The top 3 in 1991 were Burundi (90.3%), followed by Mozambique (87.1%) and Rwanda (86.6%). By 2017 Rwanda reached the top with (86.0%), followed by Madagascar (83.6%) and Nepal (82.7%). Amongst the top 10 of both years we find mostly the same countries. By 2017 Togo and Burkina Faso left the top 10 list. Instead, we find Zimbabwe and Ethiopia amongst the top 10 of the highest FLP rated countries.\n",
    "\n",
    "***Countries of the lowest female labour participation rates in 1991 and 2017***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Countries in 1991 with the lowest female labour participation rates.\n"
     ]
    },
    {
     "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>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.18</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Iran</td>\n",
       "      <td>1991</td>\n",
       "      <td>9.85</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Palestine</td>\n",
       "      <td>1991</td>\n",
       "      <td>10.20</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>1991</td>\n",
       "      <td>10.90</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>1991</td>\n",
       "      <td>11.50</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>1991</td>\n",
       "      <td>13.80</td>\n",
       "      <td>420.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1991</td>\n",
       "      <td>14.20</td>\n",
       "      <td>8270.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1991</td>\n",
       "      <td>14.90</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>1991</td>\n",
       "      <td>16.80</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>1991</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year   flpr     gni          region    dev_status\n",
       "74           Iraq  1991   8.18  2020.0     Arab States  Global South\n",
       "73           Iran  1991   9.85  1330.0  Asia & Pacific  Global South\n",
       "119     Palestine  1991  10.20  1520.0     Arab States  Global South\n",
       "80         Jordan  1991  10.90  1100.0     Arab States  Global South\n",
       "2         Algeria  1991  11.50  1990.0     Arab States  Global South\n",
       "118      Pakistan  1991  13.80   420.0  Asia & Pacific  Global South\n",
       "133  Saudi Arabia  1991  14.20  8270.0     Arab States  Global South\n",
       "0     Afghanistan  1991  14.90   210.0  Asia & Pacific  Global South\n",
       "173         Yemen  1991  16.80   500.0     Arab States  Global South\n",
       "87        Lebanon  1991  19.20  1780.0     Arab States  Global South"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the 10 countries with the lowest female participation rates in the year 1991.\n",
    "print(\"Countries in 1991 with the lowest female labour participation rates.\")\n",
    "df_comb_1991.nsmallest(10, 'flpr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Countries in 2017 with the lowest female labour participation rates.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Syria</td>\n",
       "      <td>2017</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>2017</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2017</td>\n",
       "      <td>15.2</td>\n",
       "      <td>3960.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Iran</td>\n",
       "      <td>2017</td>\n",
       "      <td>16.8</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>2017</td>\n",
       "      <td>18.7</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2017</td>\n",
       "      <td>19.5</td>\n",
       "      <td>570.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Palestine</td>\n",
       "      <td>2017</td>\n",
       "      <td>19.5</td>\n",
       "      <td>3180.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>2017</td>\n",
       "      <td>22.2</td>\n",
       "      <td>3010.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2017</td>\n",
       "      <td>22.3</td>\n",
       "      <td>20100.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  flpr      gni          region    dev_status\n",
       "173         Yemen  2017   6.0   1030.0     Arab States  Global South\n",
       "152         Syria  2017  11.9   1840.0     Arab States  Global South\n",
       "80         Jordan  2017  14.0   3980.0     Arab States  Global South\n",
       "2         Algeria  2017  15.2   3960.0     Arab States  Global South\n",
       "73           Iran  2017  16.8   5400.0  Asia & Pacific  Global South\n",
       "74           Iraq  2017  18.7   4770.0     Arab States  Global South\n",
       "0     Afghanistan  2017  19.5    570.0  Asia & Pacific  Global South\n",
       "119     Palestine  2017  19.5   3180.0     Arab States  Global South\n",
       "48          Egypt  2017  22.2   3010.0     Arab States  Global South\n",
       "133  Saudi Arabia  2017  22.3  20100.0     Arab States  Global South"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the 10 countries with the lowest female participation rates in the year 2017.\n",
    "print(\"Countries in 2017 with the lowest female labour participation rates.\")\n",
    "df_comb_2017.nsmallest(10, 'flpr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find mostly Arab States and a few Asian/Pacific states amongst the top 10 lists of countries with the lowest FLPRs. Women hardly participated in the labour market in 1991 in Iraq (8.18%), followed by Iran (9.85%) and Palestine (10.20%). By 2017, we observe Yemen on the top with a rate of 6%, followed by Syria (11.9%) and Jordan (14%).   \n",
    "Like in the list of the highest rated countries, we only have a few new members among the top 10 of 2017. Syria and Egypt entered the lowest ranks. Lebanon and Pakistan left the lowest rated female employment participation ranks.\n",
    "\n",
    "What caught my eye here, is that among the Asian & Pacific States in the list of the lowest FLPR rates, are Iran and Afghanistan. Even though they belong geographically to Asia, we see mostly Islamic/Arab culture countries among the lowest FLPRs.   \n",
    "\n",
    "Let's visualize how the countries with the highest/lowest FLPR performed against the global mean. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create line plots of countries with lowest/highest rates in comparison to the global mean FLPR.\n",
    "countries = ['Burundi', 'Yemen', 'Rwanda', 'Iraq']\n",
    "plt.rcParams['figure.figsize']= [10,6]\n",
    "for country in countries:\n",
    "    plt.plot(df_comb.query('country == @country')['year'],df_comb.query('country == @country')['flpr'], label=country)\n",
    "    \n",
    "# plot global mean\n",
    "plt.plot([str(year) for year in range(1991,2018)], df_comb.groupby('year')['flpr'].mean(), label=\"Global\")\n",
    "\n",
    "# titles and labels\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Female Labour Force Particiation Rate %', fontsize=14)\n",
    "plt.title(\"Female Labour Participation over the years 1991-2017\", fontsize=18).set_position([.5, 1.05])\n",
    "plt.rcParams['figure.figsize']= [10,6]\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph illustrates the wide range of levels of women's employment participation rates around the globe. While Burundi and Rwanda show FLP rates near 90 %, Yemen goes down to 6% in the year 2017.  \n",
    "\n",
    "The global mean shows only a modest tendency to rise, Yemen, Iraq and Burundi experienced more severe changes over the years 1991 to 2017. Let's find out what countries experienced the biggest changes compared to 1991.\n",
    "\n",
    "***In what regions do we encounter the biggest changes of labour participation?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "      <th>flpr_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2017</td>\n",
       "      <td>50.2</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>South/Latin America</td>\n",
       "      <td>Global South</td>\n",
       "      <td>15.139442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2017</td>\n",
       "      <td>73.2</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2017</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-5.278174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>78.5</td>\n",
       "      <td>910.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "      <td>13.630573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  flpr      gni               region    dev_status  \\\n",
       "171  Venezuela  2017  50.2  12800.0  South/Latin America  Global South   \n",
       "172    Vietnam  2017  73.2   2170.0       Asia & Pacific  Global South   \n",
       "173      Yemen  2017   6.0   1030.0          Arab States  Global South   \n",
       "174     Zambia  2017  70.1   1300.0               Africa  Global South   \n",
       "175   Zimbabwe  2017  78.5    910.0               Africa  Global South   \n",
       "\n",
       "     flpr_change  \n",
       "171    15.139442  \n",
       "172     0.000000  \n",
       "173  -180.000000  \n",
       "174    -5.278174  \n",
       "175    13.630573  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Formula to calculate growth rates of FLPR compared to 1991. \n",
    "def calc_growth(row, col):\n",
    "    val_latest = row[col]\n",
    "    val_1991 = df_comb.query('(country ==@row.country) & (year == \"1991\")').iloc[0][col]\n",
    "    change_rate = ((val_latest - val_1991) / val_latest) * 100\n",
    "    return change_rate\n",
    "\n",
    "# Create new column 'flpr_change' with each cell containing the growth compared to 1991 \n",
    "df_comb_2017['flpr_change'] = df_comb_2017.apply(lambda row: calc_growth(row, 'flpr'), axis=1)\n",
    "df_comb_2017.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "      <th>flpr_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>2017</td>\n",
       "      <td>18.7</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "      <td>56.256684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>2017</td>\n",
       "      <td>42.9</td>\n",
       "      <td>9570.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>49.417249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2017</td>\n",
       "      <td>58.8</td>\n",
       "      <td>5830.0</td>\n",
       "      <td>South/Latin America</td>\n",
       "      <td>Global South</td>\n",
       "      <td>48.299320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Palestine</td>\n",
       "      <td>2017</td>\n",
       "      <td>19.5</td>\n",
       "      <td>3180.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "      <td>47.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2017</td>\n",
       "      <td>24.9</td>\n",
       "      <td>1580.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>44.578313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Mali</td>\n",
       "      <td>2017</td>\n",
       "      <td>60.8</td>\n",
       "      <td>770.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "      <td>41.776316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Iran</td>\n",
       "      <td>2017</td>\n",
       "      <td>16.8</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>41.369048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2017</td>\n",
       "      <td>50.6</td>\n",
       "      <td>13600.0</td>\n",
       "      <td>South/Latin America</td>\n",
       "      <td>Global South</td>\n",
       "      <td>36.956522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Peru</td>\n",
       "      <td>2017</td>\n",
       "      <td>69.0</td>\n",
       "      <td>5970.0</td>\n",
       "      <td>South/Latin America</td>\n",
       "      <td>Global South</td>\n",
       "      <td>36.956522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2017</td>\n",
       "      <td>22.3</td>\n",
       "      <td>20100.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "      <td>36.322870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  flpr      gni               region    dev_status  \\\n",
       "74           Iraq  2017  18.7   4770.0          Arab States  Global South   \n",
       "97       Maldives  2017  42.9   9570.0       Asia & Pacific  Global South   \n",
       "34       Colombia  2017  58.8   5830.0  South/Latin America  Global South   \n",
       "119     Palestine  2017  19.5   3180.0          Arab States  Global South   \n",
       "118      Pakistan  2017  24.9   1580.0       Asia & Pacific  Global South   \n",
       "98           Mali  2017  60.8    770.0               Africa  Global South   \n",
       "73           Iran  2017  16.8   5400.0       Asia & Pacific  Global South   \n",
       "32          Chile  2017  50.6  13600.0  South/Latin America  Global South   \n",
       "123          Peru  2017  69.0   5970.0  South/Latin America  Global South   \n",
       "133  Saudi Arabia  2017  22.3  20100.0          Arab States  Global South   \n",
       "\n",
       "     flpr_change  \n",
       "74     56.256684  \n",
       "97     49.417249  \n",
       "34     48.299320  \n",
       "119    47.692308  \n",
       "118    44.578313  \n",
       "98     41.776316  \n",
       "73     41.369048  \n",
       "32     36.956522  \n",
       "123    36.956522  \n",
       "133    36.322870  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What countries had the greatest positive FLPR growth from 1991 to 2017?\n",
    "df_comb_2017.nlargest(10, 'flpr_change')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "      <th>flpr_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Syria</td>\n",
       "      <td>2017</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>Arab States</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-89.915966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2017</td>\n",
       "      <td>24.9</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-76.305221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Romania</td>\n",
       "      <td>2017</td>\n",
       "      <td>44.1</td>\n",
       "      <td>9970.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "      <td>-41.043084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>2017</td>\n",
       "      <td>58.1</td>\n",
       "      <td>610.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-31.325301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>India</td>\n",
       "      <td>2017</td>\n",
       "      <td>27.2</td>\n",
       "      <td>1820.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-29.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>2017</td>\n",
       "      <td>39.4</td>\n",
       "      <td>2180.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Global North</td>\n",
       "      <td>-20.304569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>2017</td>\n",
       "      <td>51.3</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-19.493177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>China</td>\n",
       "      <td>2017</td>\n",
       "      <td>61.5</td>\n",
       "      <td>8690.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-18.861789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>2017</td>\n",
       "      <td>35.1</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-18.518519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  flpr     gni          region    dev_status  \\\n",
       "173         Yemen  2017   6.0  1030.0     Arab States  Global South   \n",
       "152         Syria  2017  11.9  1840.0     Arab States  Global South   \n",
       "156   Timor-Leste  2017  24.9  1790.0  Asia & Pacific  Global South   \n",
       "128       Romania  2017  44.1  9970.0          Europe  Global North   \n",
       "24   Burkina Faso  2017  58.1   610.0          Africa  Global South   \n",
       "71          India  2017  27.2  1820.0  Asia & Pacific  Global South   \n",
       "103       Moldova  2017  39.4  2180.0          Europe  Global North   \n",
       "108       Myanmar  2017  51.3  1190.0  Asia & Pacific  Global South   \n",
       "33          China  2017  61.5  8690.0  Asia & Pacific  Global South   \n",
       "145     Sri Lanka  2017  35.1  3840.0  Asia & Pacific  Global South   \n",
       "\n",
       "     flpr_change  \n",
       "173  -180.000000  \n",
       "152   -89.915966  \n",
       "156   -76.305221  \n",
       "128   -41.043084  \n",
       "24    -31.325301  \n",
       "71    -29.411765  \n",
       "103   -20.304569  \n",
       "108   -19.493177  \n",
       "33    -18.861789  \n",
       "145   -18.518519  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What countries experienced the biggest decrease of female labour participation?\n",
    "df_comb_2017.nsmallest(10, 'flpr_change')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iraq made the biggest leap in women participating in labour. In 2017 56.26 % more Iraqi women participated in employment compared to 1991. The Maldives (42.9%) and Colombia (58.8%) follow on rank 2 and 3. Yemen (-180%), Syria (89.91%) and Timor-Leste (-76.30 %) were the leading countries of decreased female labour participation. Next, I'm going to investigate if we find growth patterns across regions the world.\n",
    "\n",
    "**Did any world region's FLPR growth overperform or underperform over the period of 1991-2017?** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot change rates by world region\n",
    "# parameters\n",
    "plt.rcParams['figure.figsize']= [12,10]\n",
    "\n",
    "# boxplot positive growth rates\n",
    "p1 = sns.catplot(x=\"region\", y=\"flpr_change\", kind=\"box\", data=df_comb_2017[df_comb_2017['flpr_change'] > 0])\n",
    "\n",
    "# labels and titles\n",
    "plt.title(\"Positive Growth of FLPR, 1991-2017\", fontsize=18, pad=15)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Growth of Female Labour Force Particiation Rate %', fontsize=14)\n",
    "plt.xticks(rotation='vertical', fontsize=14)\n",
    "p1.set_xticklabels(rotation=90)\n",
    "\n",
    "# boxplot negative growt rates\n",
    "p2 = sns.catplot(x=\"region\", y=\"flpr_change\", kind=\"box\", data=df_comb_2017[df_comb_2017['flpr_change'] <= 0])\n",
    "\n",
    "# labels and titles\n",
    "plt.title(\"Negative Growth of FLPR, 1991-2017\", fontsize=18, pad=15)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Growth of Female Labour Force Particiation Rate %', fontsize=14)\n",
    "plt.xticks(rotation='vertical', fontsize=14)\n",
    "p1.set_xticklabels(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigating growth rates of female labour force participation, we find one outstanding area: Arab States. They show the longest tails and highest interquartile ranges for both: positive and negative development. The difference towards other regions is especially significant at decreasing growth rates. Whereas all other regions show relatively minimal interquartile ranges of above -25%, Arab States' lower whisker is at below -175%.  \n",
    "Positive growth is much stronger across all regions, except in Arab States. After the Arab States, South America shows the second highest growth rates. North America's median growth was the lowest at about 5%.\n",
    "\n",
    "\n",
    "***Is the FLPR different in the Global North and Global South?***  \n",
    "We noticed the level of female labour participation rates is roughly connected to world regions. Taking a closer look at specific world regions may result in very diverse female labour participation rates. This is especially true for Asian & Pacific states. One factor that might influence the FLPR is the political and socio-economic region of a country. We can divide the world in Global North for the developed world and Global South for the developing world. To visualize the connection, we are going to use a boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot FLPR according to their development status\n",
    "p = sns.catplot(x=\"dev_status\", y=\"flpr\", kind=\"box\", data=df_comb_2017)\n",
    "\n",
    "# titles and labels\n",
    "plt.title(\"FLPR by Development Status\", fontsize=18, pad=15)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Female Labour Force Particiation Rate %', fontsize=12)\n",
    "plt.rcParams['figure.figsize']= [10,8]\n",
    "plt.xticks(fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both groups Global North and Global South have their median on one level at slightly above 50%. Compared to the Global North, the Global South has a longer interquartile range and longer tails with a few outliers in the low percentage FLPR.\n",
    "It shows extremes to occur rather in the Global South. The developed world tends to have rather mediocre female labour participation rates. \n",
    "\n",
    "As Global North and Global South are a political and socio-economic description of the world, I suspect the economic situation of a country could drive female employment participation. Below, I am going to further investigate the relationship.    \n",
    "\n",
    "\n",
    "### Does a country's economic situation drive female employment participation?\n",
    "\n",
    "To find out the relationship between female employment participation and the economic situation of a country, I will analyse two factors. First, the Gross National Income (GNI) of a country and second the labour market's industry distribution. \n",
    "\n",
    "The GNI describes the total domestic and foreign output of all residents of a country. It is a measure to helps us compare the economic activities across regions. Using the GNI, I am going to investigate whether there is a pattern of poor or rich countries having higher or lower female participation rates.  \n",
    "\n",
    "I am going to describe a country's industry by analysing the distribution of its economic sectors: service, agriculture and industry. The aim is to examine if there a correlation between female employment participation and a dominant economic sector? Is the type of work women engage in connected to a regions overall female labour participation? \n",
    "\n",
    "**Does poor and rich country's female labour participation rates perform differently?** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      4752.000000\n",
       "mean       8993.902567\n",
       "std       14450.303994\n",
       "min          80.000000\n",
       "25%         760.000000\n",
       "50%        2610.000000\n",
       "75%        9747.500000\n",
       "max      105000.000000\n",
       "Name: gni, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look at the GNI.\n",
    "df_comb['gni'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The global average GNI over the years 1991 to 2017 is 8993.9 USD per capita, ranging from 80 USD to 105000 USD.\n",
    "Let's find out how the global GNI developed over the past period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameters\n",
    "plt.rcParams['figure.figsize']= [12,5]\n",
    "\n",
    "# plot the global GNI by year\n",
    "plt.plot([str(i) for i in range(1991,2018)], df_comb.groupby('year')['gni'].mean())\n",
    "\n",
    "# Marker year 1991\n",
    "plt.plot(['1991'], df_comb.query('year == \"1991\"')['gni'].mean(), marker='o', label=\"ØGNI 1991: \" + \n",
    "         str(df_comb.query('year == \"1991\"')['gni'].mean().round(2)))\n",
    "\n",
    "# Marker highest FLPR\n",
    "#calculate local max\n",
    "gni_max = max(df_comb.groupby('year')['gni'].mean())\n",
    "gni_max_year = df_comb.groupby('year')['gni'].mean().idxmax()\n",
    "plt.plot(gni_max_year, gni_max, marker='o', label=\"MAX ØGNI \" + gni_max_year +\": \" + str(round(gni_max,2)))\n",
    "\n",
    "# Marker year 2017\n",
    "plt.plot(['2017'], df_comb.query('year == \"2017\"')['gni'].mean(), marker='o', label=\"ØFGNI 2017: \" + str(df_comb.query('year == \"2017\"')['gni'].mean().round(2)))\n",
    "\n",
    "# titles and labels\n",
    "plt.title('Global Average GNI over the years 1991-2017', fontsize=18, pad=15)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('GNI', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to the global FLPR, the GNI shows a more significant increase from 1991 to 2017. The global GNI more than doubled in 27 years. In 1991 the global GNI was at 5117.6 USD. By 2017 it had risen to 12309.4 USD. We observe a steap increase since the early 2000s. It peaks in the year 2014 at 13681.25 USD and declines thereafter. \n",
    "\n",
    "To find out if there is a relationship between the global FLPR and GNI we visualize the 2017 data in a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatterplot FLPR against global average GNI\n",
    "df_comb.query(\"year == '2017'\").plot(x='gni', y='flpr',kind='scatter')\n",
    "\n",
    "#titles and labels\n",
    "plt.title('Female Labour Participation and National Income 2017', fontsize=18, pad=15)\n",
    "plt.xlabel('Global Average GNI in 2017', fontsize=14)\n",
    "plt.ylabel('Female Labour participation rate %', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure shows that extreme high or low FLPRs occur mainly at a low Gross National Income. There is a bulk of countries with low incomes. We also notice that most FLPR are roughly between 40-65%. \n",
    "\n",
    "To remove noise and express the relationship between GNI and FLPR clearer, it makes sense to look on low income and high income countries separately. We are using the percentiles 25%, 50%, 75% and the maximum value to bucket FLPR rates and GNI in very low, low, moderately high and high categories.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      176.000000\n",
       "mean     12309.375000\n",
       "std      17052.404947\n",
       "min        290.000000\n",
       "25%       1722.500000\n",
       "50%       4760.000000\n",
       "75%      14475.000000\n",
       "max      80600.000000\n",
       "Name: gni, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find quartiles of GNI\n",
    "df_comb_2017['gni'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    176.000000\n",
       "mean      52.061364\n",
       "std       15.662366\n",
       "min        6.000000\n",
       "25%       45.100000\n",
       "50%       53.100000\n",
       "75%       60.900000\n",
       "max       86.000000\n",
       "Name: flpr, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find quartiles of FLPR\n",
    "df_comb_2017['flpr'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create bins for GNI's and FLPR using percentiles\n",
    "bins_gni = [290.000000, 1722.500000, 4760.000000, 14475.000000, 80600.000000]\n",
    "bins_flpr = [ 6.000000, 45.100000, 53.100000, 60.900000, 86.000000]\n",
    "bin_labels = ['very low', 'low', 'moderately high', 'high']\n",
    "\n",
    "# Create categorial variable from continuous variables and save it to new columns\n",
    "df_comb_2017['gni_level'] = pd.cut(df_comb_2017['gni'], bins_gni, labels=bin_labels, include_lowest=True)\n",
    "df_comb_2017['flpr_level'] = pd.cut(df_comb_2017['flpr'], bins_flpr, labels=bin_labels,include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot FLPR to their GNI level\n",
    "sns.catplot(x=\"gni_level\", y=\"flpr\", kind=\"box\", data=df_comb_2017)\n",
    "\n",
    "# titles and labels\n",
    "plt.title(\"FLPR and GNI 2017\", fontsize=18, pad=15)\n",
    "plt.xlabel('GNI per capita', fontsize=14)\n",
    "plt.ylabel('Female Labour participation rate %', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2017, the FLPR in relation to the country's GNI per capita describes a u-form. Countries of very low national income have the highest median and interquartile range of female labour participation. The interquartile range drops for low and moderately high income countries and ascends the higher the income. The boxplots of very low and low income countries are relatively long and become shorter for higher income countries. There is a great variety of female labour participation rates in very high and very low income states. Very low income countries have more occurrences towards high female labour participation compared to low income countries.\n",
    "\n",
    "Let's find out the details of how female participation labour rates vary according to country's of very low, low, moderately high and high national incomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>very low GNI</th>\n",
       "      <th>low GNI</th>\n",
       "      <th>moderately high GNI</th>\n",
       "      <th>high GNI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>very low FLPR</th>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low FLPR</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moderately high FLPR</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high FLPR</th>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      very low GNI  low GNI  moderately high GNI  high GNI\n",
       "very low FLPR                    6       17                   17         5\n",
       "low FLPR                         6       12                   12        13\n",
       "moderately high FLPR             5        8                   10        21\n",
       "high FLPR                       27        7                    5         5"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count occurences of very low, low, moderately high or high FLPRs at a given GNI level.\n",
    "flpr_counts = []\n",
    "for level in bin_labels:\n",
    "    levels = [len(df_comb_2017.query('(gni_level == @level) & (flpr_level == @label)').index) for label in bin_labels]    \n",
    "    flpr_counts.append(levels)\n",
    "    \n",
    "# turn into a dictionary\n",
    "categories = dict(zip(bin_labels, flpr_counts))\n",
    "\n",
    "# turn into a dataframe\n",
    "df_cat = pd.DataFrame(categories, index=['very low FLPR', 'low FLPR', 'moderately high FLPR', 'high FLPR'])\n",
    "\n",
    "# rename columns\n",
    "new_cols = df_cat.columns\n",
    "new_cols = list(map(lambda x: x[:15] + \" GNI\", new_cols))\n",
    "df_cat.columns = new_cols\n",
    "\n",
    "df_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create pie plots, two on each axes.\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(10,10))\n",
    "\n",
    "# use green tone for low FLPR rates, use blue tone for high FLPR rates\n",
    "colors = ['#3bb62b', '#79cf6e', '#719ad0', '#33619e']\n",
    "\n",
    "# create pie plots\n",
    "ax1.pie(df_cat['very low GNI'], labels=df_cat.index, colors=colors, autopct='%.2f' )\n",
    "ax2.pie(df_cat['low GNI'], labels=df_cat.index, colors=colors,autopct='%.2f')\n",
    "ax3.pie(df_cat['moderately high GNI'], labels=df_cat.index, colors=colors, autopct='%.2f')\n",
    "ax4.pie(df_cat['high GNI'], labels=df_cat.index, colors=colors, autopct='%.2f')\n",
    "\n",
    "# titles\n",
    "ax1.set_title(\"Very Low Income Countries\", fontsize=14)\n",
    "ax2.set_title(\"Low Income Countries\", fontsize=14)\n",
    "ax3.set_title(\"Moderately High Income Countries\", fontsize=14)\n",
    "ax4.set_title(\"High Income Countries\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pie charts above illustrate how FLPR rates are distributed according to their economic income.  \n",
    "Over 61% of very poor countries tend to have very high female participation rates. Low income and moderately high income countries have a similar distribution of FLPR rates. About 38% of the latter have very low female labour participation rates, another 27% have low labour participation rates. Only about one third of those countries have moderately high to high FLPRs. The picture changes significantly in rich countries. We predominantly find countries at moderately high FLPRs (47.73 %). In comparison to moderately high income countries, there are fewer countries of very low FLPRs (11.4 % compared to 38.6%). \n",
    "\n",
    "We now know how the employment rates are distributed of poor and rich countries. To find out what the situation is like for the majority of women in the world, we have to count in the population size of a country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "      <th>flpr_change</th>\n",
       "      <th>gni_level</th>\n",
       "      <th>flpr_level</th>\n",
       "      <th>f_population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2017</td>\n",
       "      <td>19.5</td>\n",
       "      <td>570.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>23.589744</td>\n",
       "      <td>very low</td>\n",
       "      <td>very low</td>\n",
       "      <td>17200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  flpr    gni          region    dev_status  flpr_change  \\\n",
       "0  Afghanistan  2017  19.5  570.0  Asia & Pacific  Global South    23.589744   \n",
       "\n",
       "  gni_level flpr_level  f_population  \n",
       "0  very low   very low      17200000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge population into 2017 combined dataframe\n",
    "df_comb_2017 = pd.merge(df_comb_2017, df_pop, how='inner', left_on=['country'], right_on=['country'])\n",
    "\n",
    "# rename column \n",
    "df_comb_2017 = df_comb_2017.rename(index=str, columns={'2017': 'f_population'})\n",
    "df_comb_2017.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VK3bu3GnWTaVdu3bY2tqafs6+XmT/TTds2JADBw5gY2Nj2sZgMJi69RTme1qQe8Wxd+9e0tPTefbZZ1Gr1abtevfuTb169ahZsybff/99oc69TZs2Zufl6+vLsWPHzK5x2d+37EEGtmzZgkqlol27dmbXqnr16uHm5sbOnTsZNmxYkZSJEMVJ2gzSZihvbYZsffr04amnnsq1vFq1ag98zG+//ZZvv/021/Lg4GBmzJiBnZ3dfW0fEBDAt99+S9OmTR84FlF4j1zSFxAQUOBIXFevXgWyHvfn7OqWU2RkpOkCkLMBCpgaWs7OzrmW52y0W1tbs2HDBnbv3s3ly5e5cuWK6WJ0d+P+7theeeWVfOOPi4szXVTzs2TJErO4nJycqFWrltmFP7uLV15DVWd3l7tx44bpsx577LFc2/n6+rJjxw4SEhJylUdhPEhZ5eX69evAf5VsTjnPKZuTk1Ouisfa2vqe/fOtrKw4cOAAmzZt4uLFi1y9epVbt24B4O3tfd/xZssu49jYWHx8fEzLGzZsaPa7/Pnnn826A2XL+R2Nj48nNTW1wDLILqcpU6YwZswYPvjgAz788EMCAgLo2LEjAwcOxM3NDYBx48YRHh7OvHnzmDdvHo899hgdO3ZkwIABZrHej3feeYdevXrx7rvvmt61uJter+ezzz7jxIkTXL16lWvXrpm6mt7PexPZsr/f+ZXD7t27zbpM3v39zW7U5PxMS0tLVq1axf79+7ly5QpXr15Fp9MBhfueFuRecWT/7qpXr262nUajMTWwCnvud1/jsq8TOZdn/51kn+fVq1dRFMWsAZTT3Q0DIcoqaTNIm6G8tRmyVatWjZYtWz7w/nnJmUiqVCpsbGyoVq1arldL7t5eURQuX77MN998g42NDR988EGeN1NE8Xrkkr57yf7jnDBhAkFBQXluk/Oilt8dsoLmSsvIyGDkyJEcOnSIZs2a0aJFC4YPH07Tpk3zbSTljG369OlmT3JycnR0zHf/bA97Eci+aFpZWZmW5fz/bNmN8YLu2OV8ATwvD1pWBcWdl+yyzXkeD3qn8ZNPPuGbb76hXr16BAUF0adPH4KDg5k+fXquQVDuR3BwMGvWrCEsLMwskXJ2djb7XR46dCjP/XM+8bmfMshOJOrUqcMff/zBrl272LFjB7t27WLu3LksXbqUVatWUatWLTw9Pfnll18ICwtj27Zt7Nq1i2+++YYlS5awePHiQt3F8/X15cUXX2T+/PmmiWNzOn78OCEhIdjY2NCyZUv69etHvXr1uHr1Kv/3f/93359zv+VgZWVlevfsXt+F27dvM3jwYCIiImjVqhUdO3akTp06eHt7M2DAgELFVpB7xZEde0HbFfbcH+QaZzQasbOz44svvshzvUajyXdfIcoTaTPcm7QZClbUbYbiVNhEMuf2rVq1ol27dvTr149hw4axevVqfH19iylSkRdJ+u6SfVfF1tY21xf76NGjJCYmmnV1ehCbN29m//79zJgxg/79+5uW37x5875iu7uxDxAWFobRaMx3dMfCyv6sixcv5robk/2idc7uDtl3FHO6cuUKTk5OODk5AVkXxOyRqLLdq9vDg5ZVXnKe092yX2jPeU4P4vr163zzzTf06dMn113fe51rfrp06cL06dP5/vvv6du37z274hTE2dkZW1vbe5aBwWDg9OnT2Nvb06lTJzp16gRk/T5eeeUV1q5dy9SpUzlz5gyQ1f0nuwvQoUOHGDZsGMuXLy90141Ro0axadMm5s+fT4MGDczWffzxx1hbW/Prr7+a3QX+6quvCvUZ8F93xIsXL9KwYUOzdZcuXcLW1hZHR8f7Hmly2bJlXLhwgaVLl5p1hfr3338LHdvDqFKlCpD1t5ezMs3IyGDy5Mn06tWryM89L97e3uzevZv69etTqVIls3V//PGH6ZogRHknbQbzz5I2Q+EUR5uhLKtatSozZsxg3LhxTJo0iTVr1jxUm0YUziM3eue91K9fHzc3N5YvX27W9zs5OZmJEyfyxhtvmD05eRDZc9rc3b1h2bJlQP5D3rZs2RKNRsOiRYvMRg67efMmY8eOZfbs2QXeLSyM7M9asmSJ2UU3KiqKjRs3EhgYaNZNZfv27aauEJA1itXu3bvp2rWraZmrqyunT582u3t29zDvdytMWWXfZcuvK4Wbmxv169dnw4YNREVFmZZnZGSwZMkSrK2tH3pi1ux3Ou6O96+//uLy5cuFijebg4MDU6ZM4fTp07z11lu5KkGAY8eOsWHDhnvGp1aradOmDXv27OHEiROm5YqisHDhQlQqlWnuvKFDh/LBBx+Y7Z+dJGTHPmHCBKZMmWJ297VevXpYWVk90F1PjUbDtGnTSE1NJSwszGxddpefnAlfUlKSqStozhgsLCwKLNcOHToAWSNd5vw+njhxgr1799KuXbtC/S3l9T1VFIXvv/8eKNww1g+jZcuWWFlZsWbNGrPz+v333/n999+Boj/3vGQPJ75gwQKz5du3b+fll19m48aND3V8IcoKaTOYf5a0GQqnONoMZV3nzp3p2bMnJ06cYPHixaUdziNF0uu7WFlZ8fbbbzNx4kSefvpp+vfvj0ajYe3atdy4cYPZs2c/9F2Jli1bYmlpyZQpU3j22WextLRkx44d7N69Gysrq3xfNHZ2dmbSpEl8+OGHDBo0iN69e5OZmcmKFStIT0/n9ddff6i4cqpcubLps4YMGUKvXr1ISUlh5cqVGI1GQkNDzbZXqVQ888wzPPfcc+j1er777jucnZ0ZP368aZuePXuyePFiXnrpJdq3b8+JEyf47bffCuy7X5iyyj7O3LlzTd067hYaGsqwYcPo378/Q4YMwc7Ojg0bNnDixAlCQ0NzPZUorMceewwvLy+++uor0tPT8fT05OjRo/z0009oNJo84125ciWxsbH06tUr3+MOGTKEhIQE5s6dS1hYGD169MDX15eUlBR2797Nnj17sLS0ZPz48aanPfl57bXXCAsLIyQkhJCQENzc3Pjzzz/Zt28fI0aMMFU+ISEhLFiwgHHjxtGmTRt0Oh2rV69Gq9XSr18/AEaOHEloaCjDhw+ne/fuKIrCL7/8Qnp6Os8888wDlWHr1q3p0aNHrsq9bdu2LFy4kAkTJtC6dWtiYmJYt26d6W7o3WW7f/9+1qxZYzaQTbbatWsTEhLC8uXLGTFiBJ07dyYmJobly5dTqVIlXn311ULF3LZtW5YvX86oUaPo378/er2e3377jePHj2NhYVFkA7nci4uLC+PGjWPOnDk8//zzdO7cmaioKL7//nuaNWtGhw4dUKvVRXrueWnXrh2dOnVi8eLFXLt2jZYtW3L9+nV++OEHvLy8GDlyZBGcrRClT9oMWaTN8GCKq81QWD/99BPh4eG5ltetW5chQ4YU2edke+ONN9i1axfz58+ne/fuhR4DQDwYSfry0K1bNxYvXsyCBQv48ssvsbCwoHbt2ixYsMB0l/xh+Pn5MXfuXL744gs+/fRT7OzsqF27NkuWLGHFihXs378fvV6fZ5/34cOH4+HhwZIlS/jss8+wsbEhICCAWbNm5Tni4cMYPnw47u7uLF68mE8//RStVkvTpk156aWXzEYfA3jiiSeoVq0aixYtwmg00qpVKyZPnmz2gviECRPIzMzk119/Zffu3TRs2JDvvvuO1157Ld8YClNWQ4YMYd++fSxatIhjx47leQEPDg5m5cqVzJ07l8WLF2M0GqlTpw7z58+nc+fOD11m1tbWfPPNN8ycOZNly5ahKAo+Pj68+eabZGZmMmPGDI4fP079+vVp0aIFTzzxBDt27GDfvn107dq1wHedxowZQ/v27Vm5ciXbt28nKioKtVqNr68vY8aMYfDgwXh4eNwzRh8fH9asWcOcOXNYtWoVOp2OWrVq5eoO8/LLL+Pk5MT69ev56KOPUKvVPP7448yaNcv0EvuAAQOwsrJi2bJlfPrppxiNRurXr8/ChQsLHPzgXt544w3+/vtvsy6G48ePx2AwsHnzZnbs2IG7uzstW7bk+eef58knn2Tfvn2mEfBee+01PvnkE6ZPn8706dNN8zHl9NZbb1GjRg1WrVrFzJkzcXR0pEuXLrz88suFfnm+bdu2vP/++yxevNh0rICAAFavXs3bb7+d66llcRozZgxubm4sW7aMmTNn4ubmxsCBAxk/frzpiUNRnnteVCoVn3/+OYsWLeLnn39mx44dODs707VrVyZMmJDvS/9ClEfSZvjvs6TNUDjF2WYojP379+c532OnTp2KJelzdXVl8uTJhIaG8vbbb/Pdd98V+WeI3FRKUQ0rJx5Z/v7+9O3bl5kzZ5Z2KEIIIYQow6TNIETpkHf6hBBCCCGEEKICk6RPCCGEEEIIISowSfqEEEIIIYQQogKTd/qEEEIIIYQQogIr90/6jhw5QkhISIHbvP/++zz99NOEhIRw5MiREopMCCGEKF1SRwohhIByPmXDwoUL2bBhA1qtNt9tduzYwaVLl1i3bh0JCQm88MIL/PjjjyUYpRBCCFHypI4UQgiRrVw/6fPx8WHevHmmn8+cOWOacHr8+PEkJSVx/vx52rRpg4WFBc7OzqjVamJiYkoxaiGEEKL4SR0phBAiW7lO+rp164al5X8PK99++23eeecdli9fTtu2bVm0aBF169Zl165d6PV6IiIiOH/+PGlpaaUYtRBCCFH8pI4UQgiRrVx377zbhQsXeO+99wDQ6/XUqFGD1q1bc+zYMYYNG0adOnUICAjAycmplCMVQgghSpbUkUII8eiqUElfjRo1+Oijj/Dy8uLQoUPExMRw6dIlXFxcWLFiBZGRkUyZMoVKlSqVdqhCCCFEiZI6UgghHl0VKul79913ef311zEYDADMmDEDLy8vdu3axbp169BoNEybNq2UoxRCCCFKntSRQgjx6JJ5+oQQQgghhBCiAivXA7kIIYQQQgghhCiYJH1CCCGEEEIIUYGV23f6wsPD0Wg0pR1GkUtPT6+Q51UWSVmXHCnrklNRyzo9PZ2goKDSDqPcqIh1ZEX9bpdFUtYlR8q65FTUsr7f+rHcJn0ajYa6deuWdhhF7tSpUxXyvMoiKeuSI2VdcipqWZ86daq0QyhXKmIdWVG/22WRlHXJkbIuORW1rO+3fpTunUIIIYQQQghRgUnSJ4QQQgghhBAVmCR9QgghhBBCCFGBSdInhBBCCCGEEBWYJH1CCCGEEEIIUYFJ0ieEEEIIIYQQFZgkfUIIIYQQQghRgUnSJ4QQQgghhBAVmCR9QgghhBBCCFGBSdInhBBCCCGEEBWYJH1CCCGEEEIIUYFZlnYAQoiil5CagU5vKO0wTLTOVYhKTCvtMExsrNQ42VqXdhhClGnphvTSDsGM72O+ZS4mAI1aU9ohCCHEPUnSJ0QFpNMbeOun46UdhklScjIO9valHYbJjL71SzsEIcqFT0+9X9ohmGRmZmJpWbaaLZPqhpZ2CEIIcV+ke6cQQgghhBBCVGCS9AkhhBBCCCFEBSZJnxBCCCGEEEJUYJL0CSGEEEIIIUQFJkmfEEIIIYQQQlRgkvQJIYQQZcCRI0cICQnJtXzTpk0MGDCAwYMHM23aNIxGYylEJ4QQojyTpE8IIYQoZQsXLiQ0NJT0dPN56HQ6HXPmzGHZsmWsWrWK5ORkduzYUUpRCiGEKK/K1oQ3QohS5+2k5ZnmPtRwtSM+JYPfj0ex61wsAE8Fe9OroZfZ9vsv3eLrvy7mezwLFcx9rhkuDjaMXn7ItNzG0oJBTX0IquYEwF9nY/j58HXT+kFNqtG6titJuky+23uZM1FJAFSysWRm/0AW/n2Rw1cTiuy8hShNPj4+zJs3jylTppgtt7a2ZtWqVWi1WiBrrjqNRiYDF0IIUTiS9AkhTCzVKiZ28cPJ1oqzN5PwcbZleKsapGYYOHQlnqqVsxqeh6/Gm/a5FJuS7/FUKhjawhdPJ1v0BvMuaSNa16CxrzOXY1OopLWiV0MvMg1GNh2NpIarHV0DPPli+zla1HTh2WbVmfZL1mTz3ep7EpuULgmfqFC6devGtWvXci23sLDA1dUVgOXLl5OamkqrVq3uebz09HROnTr1UDH5PuZLZmbmA+3rbuNBt6pySYzNAAAgAElEQVS98batyu2MRPZG/0143EEA6jgG0N6zC47WlYnR3WTL9U1cS72az5FUtPXoSEOXRmjVWm6kXuP3axuITY8x20qjtuHlelNI1iex4PRnpuXN3drQyqM9mUY9f974lZMJxwBQq9SMrzeFf6L/JixmzwOdI4DRaOTU2Ycr57JIp9M99PdH3B8p65LzqJe1JH1CCJOarnbYadTsOB3NirCrNPF1ZnT7WjSqXplDV+LxdtKSmKbni+3n73ksbyctIS2qU9vDIdc6BxtLGvs6c+VWCtM3ncROo2Zmv0C6Bnjy27Eo3ByynmRcj08jMlFHg6pZTwPtrNW093dn2d7LRXreQpRlRqORWbNmcenSJebNm4dKpbrnPhqNhrp16z7U56Yb0rG0LHwzQa2yZEitEThYOnA19QoeNp708umHngwSMuLp5zsEvTGTG2kR+NjV4NnHRvLNuc9J0t/Odaxg5ya0q9KZtMxUItNu4Gtfk8G1hvPNubkYFQMAlipLnvYZgo1aS0pmsilmO0t7Onl1Z1vUbzhaVaZntac5l3waBYXHnZuiVqk5mvDvA51jNgsLi4cu57Lo1KlTFfK8yiIp65JTUcv6fhNZeadPCGFy9mYy4374l3WHsp44VNJmNYZS0jOxtrTA1UFDRqaB55pXZ0QrX/w87PM9ViPfytRyt+eX8Ou51rnaZyV1kYm6O8c3cD0+DTuNJVWcbIhJynqvybuyFk9HG2KSsrbrEuBJYpqe/Zfjiu6khSjjpk2bRnp6Ol9++aWpm2dZ5qX1RqvW8m/cflZcWszv1zcAUKdSADXsHwPg1+s/sfLyUsLjDmJtYU0te788j1XTvjYA66+uZPn5hRxLCKeytTOuGjcAqtpWZ3it0dRyyL2/o5UTFioLYnXRxKZHo1HboFXbYoEFzV3bsP/WXvSKvjiKQAghyhx50ieEMKMokJFpZHS7WjxevTIxSelsPhaFt5MWC5UKNwcbOtSxAaBFLVc++/MMpyKTch3nQnQy0zee5GpcKn2CvM3WxadmAFCtshYVoFarTE/3nO2sOXotkS0noni+dQ1S0jNZsucyNlYWdKzjzpqDEShK8ZaBEKVt48aNpKamUr9+fdatW0fjxo0ZNmwYAEOHDqVLly6lHGH+IlKv8MnJ97FU/ffEDSDNkMa+2F0cvPUPRsVotk5nSMvzWGmGVAAUzLuHZxiybgw1rPw4la1d2BO9k1bu7c22SdQnYFSMuNq442jlhM6gI82QSoPKwWjUGv69FVY0JyyEEOWAJH1CiFxUQLCPE2oLFUk6PTZWFqhUcPx6IpdjU/jjRBTNa7rwbPPq9G9UjembTuY6xokbubtqZUtI1XPseiINvB15t08AlhYWONlaA2ClzuqAsPpABKsPRJj2eTKwCjq9gQOX4nihTU0CqzpyITqZb3ZdJC3DULQFIEQpqFq1KmvWrAGgV69epuWnT58urZAemIKCXtHzVLVB+FeqS0JGPP/E/g1AppKJnaU9A6uH4Kn14kLSWc7ezrt70t7ov6hm68vTPkOISbtJdfsaHLq1jwR91nvF55POsDfmL4yKMVfSl5KZzI6oP2jl1h6DYuD3G1lPHFu4tuXQrTDSjel3f5wQQlRYkvQJIXJRgAmrDuPn4cD4TrUZ1/ExQn86zmd/njVts/10NH0f96aasy1qCxUGY+Eevy36+yLDWvpSp4oD528mE31bR2A1JzIyc89BZm1pQZd6HmwIv0Fj38o0q+nMF9vO8WLbWnSu68HGIzce9pSFEEVOhZ9DHSxUalIzU7C2+G/U0crWLnhqs0YC1hl0WFpYkmHMyHUEC1XWTSA7S3vsHOwxKgZSMv8bPOrM7awbTo5WTnlGsP/WXvbf2mv6OcAxEHtLew7c2ktnzycIcGrIrfRYfrm2Js93CoUQoqKQpE+UmITUDHT6svNERutchajEvLsUlQYbK7XpaVdpstdYYjAqpOkNHL2WSHxKBlUctTjYWFLZ1pq4lAyS07NG9DMYFVSqrGkZCvubTU7PZP6O/waEmdg5692d2OTcd9/b+7thMCr8fS6GAY2qkaTL5Mi1RG4kplHdxfaBz1UIUZwU5pyeSTXb6gyo/iz9fIbwzbm5AESlXWf2yem0cG1LK/d2ZBjTTU/icurq1QtnjQubr//Mibgj9K/xLG09OnErPYbTt08UOqIWbm05HH8ANxsPmri2ZNO1H2nl3p427h3ZfP3nhz5jIYQoq0o06Tty5AizZ89m+fLlZss3bdrEd999h1qtxs/Pj3fffRcLCxljpqLR6Q289dPx0g7DJCk5GQf7/AciKWkz+tYv7RBo6+fGsJa+/HkyilX7I3DSWlFJa4VOb6C9vztPBXuz5UQUqw9E4ONsi4ONFdfiU9EbCv+S3VtP1sVOY0noT8fQWqmp6WZPYpqeqDuDu2SztFDRLcCTLSduknnnc4x3nirKu31ClE1atS1GxUC6MZ0LyWe5rb+Ni8YNW7Udags1yfpkFCWTo/GHaOXejqq2Pnkep6qtD4pi5Gj8v+gNek4kHKWG/WP42NUodNLnX6keTtbOhMXuoZ5jIAAnE49R0742njbe99hbCCHKtxJL+hYuXMiGDRtyjTym0+mYM2cOGzduRKvVMmnSJHbs2EGnTp1KKjQhxB2Hr8bzVLA3Xep5Ut3ZDvdKGqzUFvx27Dp7zsfSNcCDrgGe+Lra4e2U9be86UgkAE1rONO0hjN7z9/i3xzz+OUnJimdmm72vN0rADtrNXYaS1aEXeHuPK6NnxtqCxU7z0QDEBGXSoc67rg5aHBz0HDiRmKRloEQ4uEEVW7ME959OBC7l61Rv2Fv6YCdpT0ZhnQ6V+lBgFMg666s4FzSKTzudPG8rc/77zjdoMPKygF3G0+uJ0fgZuMBZL2vV1gt3NpyNP6Q2b6KYkTJddURQoiKp8Qep/n4+DBv3rxcy62trVm1apUpGczMzESj0eTaTghR/JJ0mXy65QwnbyRSzdmWjEwj6w9dY8ORG8SlZDDnz7Ocu5lEdWdbUjMMLNt7mQN3pk/wdLQh2KcyHo739/e7Iuwqh6/G4+6gQaVSseZABNtORZtto1apeKK+J1tP3iT9zrt++y7e4tCVeN7pFcC1uFS2nrxZtIUghHgoZ2+fIiUzmSauLXm2xkhG1BqDpYUl+2/t5Uj8QYyKkT7V+jPYdzi9qj6NohgJi82aIL2uYwP6+TyDX6V6AByJPwTAEN/hhDz2Ik1dWqA3ZnAi8UihYqpl74e7xoN9sbsBiNZl3ayqovXGRePGTZ28FyyEqNhK7Elft27duHbtWq7lFhYWuLq6ArB8+XJSU1Np1arVPY+Xnp5+35MRlic6na5CnhdkvUOXlFz4u7PFxWg0lKl4dLp0Tt24XCTHepiyPpWczLsRMXmuC09OJvxS3knWD7vP8sPus3mu6z93KxYWarNlScnw4Ybwe8YzZuneXMtm/1q4Bt/dirKsy5qKfA0R5UOqIYWVl5bSqcoTeGm9STWksjPqT/bF7kJB4aeI1bR264C3bTXi0m/xd/R2rqRcBMBF44pfpbpcT80auffv6O1kGDMIcm6Ml9abG2nX2R71BwkZ9+5NkFNL93YcTzxieqJ4JeUSh27tY6DvUOLSY9kVvb1oC0EIIcqYMjGQi9FoZNasWVy6dIl58+ahUqnuuY9Go6Fu3bolEF3JOnXqVIU8L4CoxLQy9Q5dWXunz8ZGg28R/e6lrAtWlGVd1lTUa4gksuVLTPpNVl1emue6s7dPcvZ27mleAHZH72B39I4cSxT2xe5iX+wuMjMzsbTMu9mSqE/gw+Nv5xvP8osLcy3bEvkrWyJ/zXcfIYSoSMpE0jdt2jSsra358ssvZQAXIYQQQgghhChCpZb0bdy4kdTUVOrXr8+6deto3Lgxw4YNA2Do0KF06dKltEITQgghhBBCiAqjRJO+qlWrsmbNGgB69eplWn769OmSDEMIIYQQQghRjNINuefdLU2+j/mWuZgANOqSGcCyTHTvFOJevJ20PNPchxqudsSnZPD78Sh2nYsFoFH1yvQJ8sbV3prrCWms2h/BhZj8BzFp/ZgrvYO8cLCx5GRkEt/tucRtXabZNlprNR/3DyQxTU9ojrkFuwV40qNBFfQGI6sPRJhGrrS0UPFR/0B+Px7FnzKapBBCCCEEn556v7RDMCnoveDSMqluaIl9lrxAJ8o8S7WKiV388PNw4FJsCpW0VgxvVYNG1Svj42zLqHa1cLG35mJMCr4udrza1Y/KttZ5Husxd3tGtK6B1lrNtbhUgqo58ULbmmbbWKlVjGpbE1tr8wuDo9aK/o2qsvHIDQ5cjmNYS1+yxxzKnkvur7N5j3ophBBCCCFEaZGkT5R5NV3tsNOo2XE6mlm/n2HZ3itA1hO+AO9KACzefYnZW87w19kYNFZqAqs65nms9v5uAHz910WmrjnIpdgUArwccXPIerRe292et3vWo0FVp1z7uthbY2Gh4npCGjcS0tBaq3HQWJrmktty4iYZd+aSE0IIIYQQoqwoW884hcjD2ZvJjPvhX6zUWfcoKmmzvrYp6Zn8diyKrSdvYryTa1XSWpnW5aWmW9a0AReis7p/nruZRA1XO2q42hGTlE4bPzfcK9mw6cgNejb0Mtv3VnIGRqOCt5MWF3trUjMySUrPpNVjrmits5JSIYQQQgghyhpJ+kS5oCiQkWlkdLtaPF69MjFJ6Ww+FgWA3qBQSWvJxM5+VHex49i1BA5fTcjzOE5aKwxGhTS9AfgvOczuDnokIoFNR25gUJRcSV9imp61hyLo2bAKBqPC8n+ynjj2aFCFbaeiTccUQgghhBCiLJGkT5QbKiDYxwm1hYoknR4bq/96J3s42FDdxQ6AlAwDVmoVhkwl1zGs1BYYlP+WG5Xs5Vkv5x26Eg9kdeXMy5YTN9ly4r+BWprVdMZRa8WfJ6MY3LQazWu6EJWo4+u/LhKfmvFQ5yuEEEIIIURRkHf6RLmhABNWHebzrWfxdbVjXMfHTOsu30ph7PeH2HTkBs1rujCgSbU8j6E3GLHIHn0FUFuo7izPnSDejycbVOGvMzFUrWxLl3qerD0YgaPWiqeCve69sxBCCCGEECVAkj5RLthrLNFaqdHpjRy9lkh8SgZVHLU42FhS2dYag1EhPdNomsahtrt9nsdJTNOjtlChscz66ttaqwEe6Klco+qVcXOw4fcTkVR3tgUg7FIcl2+lmJ46CiGEEEIIUdok6RNlXls/Nz4fEkyfO0/PnLRWVNJaodMbGNzUh9kDG9KwWtZom9VdspKvWyl5J3FXbqUCUNvDAciawgHgYkxKoePq0aAKu87FcDvtv0FjFON/XUaFEEIIIYQoC+SdPlHmHb4az1PB3nSp50l1ZzvcK2mwUlvw27HrnIlKoqmvMy+2rcmF6GRqudtjVBS2nMga5KVpDWea1nBm7/lb/Hs1nr/ORtOkhjOj2tWkT8Mq1HSz58T1RGKT0wsVU2BVR6pW1jJ/x3kArsZlJZO+rrZ4OdqYfhZCCCGEEIXjpvGgi9eTeGm9ua2/TVjsbo7EHwLA2dqFJ7yfurMuka2Rv3Eh+Wy+x3KyrkyXKk9S3a4GqZmp7I35i/D4gwCosKCNewfqOwVho7YhShfJ1sjNROvutCNdWtHSrS2ZSibbIn/j1O3jAKhVasb4TSIsdjcHbv1TzKVRNORJnyjzknSZfLrlDCdvJFLN2ZaMTCPrD11jw5EbnI5KYsHOC0TfTqeWmz03b6fzxfbznIpMAsDT0YZgn8p4OGbNw3cqMonFuy+RlmGgqrMtRyISWLTrYqFjejLQi38u3CLuzhPF01FJbDt1k4ld/Mg0Kvxy+HrRFYAQQgghxCNCrbJkoG8IPrbVuZF2HTtLO3p4P4V/pXqosKCfzzP42PkSrYvCwaoST/sMwcmqcp7Hsraw5hnf56llX5sbqdfQqDU84d0HHztfANq4d6CVe3tUKhXRuiiq29VgsO8wbNRa7Czt6eDZld0xOziVeJwnvPugImssiIaVG6FWqQmPO1hSxfLQ5EmfKBeuxafxyZa87+L8ezWef6/G57luQ/gNNoTfMFu253wse87HkpScjIN93u/+3UrOYOTSA/nG8+HmU7mWrQi7yoqwq/nuI4QQQgghCual9Uar1vJv3H62RP5K3Ur1ecpnEHUqBaAz6HC1cedk4jF+iVhDI+dmdPXqSWDlx/k7eluuYzWs3AhHayd2RW9n540/qeccyBNevfG29eFqymXqOwVhMGay5PwCUg0p9K46gACnQGrZ1yY+Iw4LlQWxumj0Rj0atQ1atS06QxrNXduw/9Ze9Iq+FErowdxX0nfz5k2ioqLIyMjAxsYGd3d3PDw8ijs2IYQQosyTOlIIIYpOROoVPjn5PpaqrDTFzjLrBn2aIQ1v26zR2a+nXjVtC1BF653nsarb1QTg3O3TAJy9fZKzt0/eWaviz8jN2KhtSDVkje2QkpkMgJWFNYn6BIyKEVcbdxytnNAZdKQZUmlQORiNWsO/t8KK+MyLV75Jn6IoLFq0iBUrVhAVFYWSY24zlUqFu7s7w4YN4/nnny+RQIUQQoiyQupIIYQoPgoKekXPU9UG4V+pLgkZ8fwT+zctXNsCoDOkAZBmyBpDwcGqUp7HcbTO6vbpV6kug32Hk2FMJyx2N//G7QcUziX913PLxsKGuo71AbipiyQlM5kdUX/Qyq09BsXA7zc2ANDCtS2HboWRbizceBClLd+k7+OPP+bXX3/ltddeo1GjRri6umJtbU1GRgYxMTEcPHiQzz77jLi4OF577bWSjFkIIYQoVVJHCiFEcVPh51AHC5Wa1MwUrC00WFpkpS4GxQiA8c5/LS2s8jyC1Z3lLdzacjX5ElVsq9LNqxcpmcmcMT3xy3qP8Onqz+BgVYmIlCtEpmWNzbD/1l7239pr2i7AMRB7S3sO3NpLZ88nCHBqyK30WH65toYk/e2iL4IilO9ALj/++COffPIJvXv3xtvbG41Gg0qlQqPRULVqVZ566ilmzZrF+vXrSzJeIYQQotRJHSmEEMVNYc7pmay5vJwqWi/6+Qwh05g1TZbFnQFV1Kqs+ZYzjXm/W5e9/bbI3/j+wresvfI9AEGVG5u2scCCvtUG3RndM4VN1/O/brdwa8vh+AO42XjQxLUl26P+wN7KgTbuHR/+dItZvkmfWq1GpVIVuLPRaMRoNBZ5UEIIIURZJnWkEEIUH63aFo2FhgxjOheSz3JbfxsXjZvpnTsbtdbsv/k9ZUvSJwJw884UDNlP8BysHE3bPOHdh9qV6qAz6Fh9eRkJGXkPDuhfqR5O1s6Exe7BwyZr7uiTiceITL2Op03e7xSWJfkmfQMGDODVV19l/fr1XLlyhbS0NAwGA2lpaURERPDzzz8zefJknn766ZKMVwghhCh1UkcKIUTxCKrcmIl13zA9PbO3dMDO0p4MQzpRd5K2qnbVs/5r6wPAjbRreR4re6AXX/usAV1cNW4AJN5J7AKdHiew8uMYjJmsvfI9UbobeR4Hsp7yHY0/ZEo8ARTFiIKS7z5lSb7v9L3yyis4OTnxxRdfEBkZaXZHU1EUvLy8CAkJ4YUXXiiRQIUQQoiyQupIIYQoHmdvn6KtRyeauLbEQ+uFs7ULlhaW7I7exaWUi8Sn36KeYwMcrZxwt/Eg06jnaPy/AFS3q0FjlxZcTDrH4fgDhMcdpKlLS1q7tcfH1hc3m6yRlQ/FhaFCRWv3DgDojDqaubaiGa0AOJYQnmOUT6hl74e7xoMfr64EIFoXCWSNGuqiceNmAcliWVHglA0jRoxgxIgRREdHExMTQ1paGhqNBg8PD9zd3UsqRiGEEKLMkTpSCCGKXqohhZWXltKpyhN4ab1JNaSyM+pP9sXuQkFhzZXldPfug7e2Grf1CWyL+o1EfQIAlayc8KtUl+TMJIjPOtaKy0voVqUnXrbVSMyIZ2vkb1xMPoeHTRUcrZ2ArGkh/CrVNcVwPTXCLKaW7u04nniE23e6i15JucShW/sY6DuUuPRYdkVvL6HSeXD3nKcvIiKCw4cPm+Yg0mq1uLm5ERwcTLVq1UoiRiGEEKJMkjpSCCGKXkz6TVZdXprnuriMW6y4tDjPdccSDnMs4bDZsmhdFMsvLSIzMxNLy/9Sn5u6SD48/vZ9xbP84sJcy7ZE/sqWyF/va/+yIN+kLy4ujilTprB79268vLzMhqOOjY3lxo0btG/fng8//JDKlSuXZMxCCCFEqZI6UgghRHmSb9IXGhpKRkYGO3fuxNPTM9f6yMhIpk6dyjvvvMPcuXOLNUghhBCiLJE6UgghRHmS7+ide/fuJTQ0NM/KDKBKlSq8+eab7N69u9iCE0IIIcqi4qgjjxw5QkhISK7l27dvp1+/fgwaNIg1a9Y8cMxCCCEeXfkmfe7u7pw8eTK/1QAcPXoUJyenIg9KCCGEKMuKuo5cuHAhoaGhpKenmy3X6/V8+OGHLF68mOXLl7N69WpiYmIeOG4hhBCPpny7d06cOJE33niDvXv30qRJE9zd3U3vK8TExHDo0CE2bdrE9OnTSzJeIYQQotQVdR3p4+PDvHnzmDJlitnyCxcu4OPjg6Nj1kTCjRo14uDBgzzxxBNFfk5CCCEqrnyTvh49elC1alV++OEHFi1aRExMDDqdDo1Gg7u7O0FBQSxfvpygoKCSjFcIIYQodUVdR3br1o1r13JPLpycnIyDg4PpZzs7O5KTk3NtJ4QQQhSkwCkbAgMDCQwMLKlYhBBCiHKjJOpIe3t7UlJSTD+npKSYJYH5SU9P59SpUw/12b6P+ZKZmflQxyhKiqKUqXgAjEYjp84+XDmXRTqd7qG/P+L+VOSylmvIvZXkNaTApC81NZXNmzcTHh6eaw6ioKAgevToga2tbYkEKoQQQpQlJVFH1qpViytXrpCQkICtrS0HDx5k5MiR99xPo9FQt27de25XkHRDutmcVqXt7jm2ygILC4uHLuey6NSpUxXyvMqiilzWcg25t6K4htzvTYN8B3I5duwYnTt3ZuHChRgMBurUqUPjxo2pXbs2mZmZLFy4kK5du1bYuxNCCCFEfoq7jty4cSOrV6/GysqKqVOnMnLkSAYPHky/fv3w8PAo4rMRQghR0eWb7r777rv07NmTN998M9+dP/jgA9555x0ZQloIIcQjpTjqyKpVq5q27dWrl2l5x44d6dix48MFLIQQ4pGW75O+CxcuMHjw4AJ3HjRoEGfOnCnyoIQQQoiyTOpIIYQQ5Um+SV+dOnVYu3ZtgTuvXLmSWrVq3feHycSzQgghKoLiqCOFEEKI4lJg984XX3yRP/74g8cff9xsDqLY2FgOHz5MamoqX3/99X190MKFC9mwYQNardZsefbEs+vWrUOr1TJkyBA6dOiAm5vbw52ZEEIIUUyKuo4UQgghilOBT/q2bNnC2LFjsbW15eLFi4SHh3PhwgVsbGwYPXo0f/zxB/Xr17+vD8qeePZuOSeetba2Nk08K4QQQpRVRV1HCiGEEMWpwHFLtVot/fv3p3///g/9QUU98WxRzEFUFlXk+Vq0zlVIKkOTChuNhjIVj06Xzqkbl4vkWFLWBSvKsi5rKvI1pKwpyjpSCCGEKE4PNVlFeno6v/32G9bW1jRv3hxnZ+dCH+NBJ54tijmIyqKKPF9LVGIaDvb2pR2GSVJycpmKx8ZGg28R/e6lrAtWlGVd1lTUa0h5TGSLoo4UQgghikK+3TvvR1JSElOnTkWn0zFhwgQMBkOhj5Fz4tmMjAwOHjxIcHDww4QlhBBClLqiqCOFEEKIovBQT/pcXV05ffo0AH379kWlUt33vhs3biQ1NZVBgwaZJp5VFEUmnhVCCFEhPEwdKYQQQhSl+0r6jEYjcXFxZGRkYGNjg5OTExYW5g8J76cyk4lnhRBCVDRFVUcKIYQQxaXApG/z5s388MMPHDt2DL1eb1pubW1NQEAAQ4cOpXv37sUepBBCCFHWSB0phBCivMg36Vu0aBELFizghRdeYMKECbi4uJjNQXTw4EFCQ0OJiopi+PDhJRiyEEIIUbqkjhRCCFGe5Jv0LV26lFmzZuXZ7bJWrVo0a9aMOnXq8N5770mFJoQQ4pEidaQQQojyJN/ROzMyMqhSpUqBO7u5ud3XnHpCCCFERSJ1pBBCiPIk36SvW7duTJkyhX379pGRkWG2LjMzkwMHDvDmm2/SrVu3Yg9SCCGEKEukjhRCCFGe5Nu98+233+bjjz9m1KhR6PV6HB0dTe8r3L59GysrK/r06cMbb7xRkvEKIYQQpU7qSCGEEOVJvkmftbU1oaGhvPrqq5w+fZqYmBjS0tLQaDR4eHhQt25dbGxsSjJWIYQQokyQOlIIIUR5km/3zpyMRiOZmZkoimL6Ofv/hRBCiEeZ1JFCCCHKunyf9Ol0OmbOnMmPP/6IwWDAycnJ1HUlISEBS0tL+vfvz+uvv461tXVJxiyEEEKUKqkjhRBClCf5Jn3vvfcex48fZ8mSJQQFBaFWq03rDAYDhw8f5r333uO9995jxowZJRKsEEIIURZIHSmEEKI8ybd75x9//MHMmTNp1KiRWWUGoFarady4MR988AF//vlnsQcphBBClCVSRwohhChP8k36HBwciImJKXDnqKgorKysijwoIYQQoiyTOlIIIUR5km/3zhdeeIHJkycTEhJC48aNcXd3N72vEBMTw6FDh1iyZAljxowpyXiFEEKIUid1pBBCiPIk36QvJCQEDw8Pli1bxrfffkt6erppnUajoUGDBkyfPp0ePXqUSKBCCCFEWSF1pBBCiPIk36QPoGvXrnTt2hWj0cjt27fR6XRYW1tTuXJlVCpVScUohBBClDlSRwohhCgvCkz6AP755x/Cw8OJiooiIyMDrVaLm5sbQUFBtGjRoiRiFEIIIcokqSOFEEKUB/kmfVevXmXs2CfFhvEAACAASURBVLFERkZSr149XF1dsba2Jj4+nnPnzrFo0SK8vLz48ssvqVatWknGLIQQQpQqqSOFEEKUJ/kmfaGhofj5+bFu3TpsbGxyrU9LS+Ott95i2rRpLFmypFiDFEIIIcoSqSOFEEKUJ/lO2XD06FHGjRuXZ2UGoNVqGTduHOHh4cUWnBBCCFEWSR0phBCiPMk36fPx8eGvv/4qcOetW7fi6elZ5EEJIYQQZZnUkUIIIcqTfLt3vvnmm4wdO5Zt27blmoMoNjaWQ4cOcfToUebNm1eS8QohhBClTupIIYQQ5Um+SV/z5s3ZvHkza9as4ciRI0RHR6PT6dBoNHh4eNC4cWM+/PBDvL29SzJeIYQQotRJHSmEEKI8KXDKBk9PT15++eWSikUIIYQoN6SOFEIIUV7cc56+guj1esLDw9FoNNStWxcrK6uiiksIIYQo16SOFEIIUVbkO5DL/UhMTCQkJIS//vqL4cOHYzAYiiouIYQQolyTOlIIIURZ8VBP+pydndm2bRve3t6MGDECtVpdVHEJIYQQ5dr91pFGo5F3332XM2fOYG1tzfvvv0/16tVN67/99lt+/fVXVCoVo0ePpkuXLiV1CkIIISqIh0r6LCwsTC+p29vbF0lAQgghREVwv3Xk1q1bycjIYPXq1YSHhzNz5kwWLFgAwO3bt1m+fDlbtmwhLS2Np556SpI+IYQQhVZg0hceHs6KFSsIDw8nKioKvV6PjY0Nbm5uNGzYkJCQEAIDA0sqViGEEKLMKKo68tChQ7Rp0waAoKAgjh8/blqn1Wrx8vIiLS2NtLQ0VCpVsZ2PEEKIiivfpO/nn39m2rRp9O7dmzFjxuDi4pJrDqKQkBBmzJhBz549SzJmIYQQolQVZR35/+zdeVhU5dsH8O8MAwOyiAiIiICioEiKuCbiAlqGP3tBjVyzMm3TzDSX0tzNtDQVo8UlF0o03CpTc8tcMjfEBTVREUU22RyWgZk57x/kyZHFUYZhGL+f6+qKOc85z9zPI8w991kVCoXWkUAzMzOoVCrIZKUpumHDhujbty/UajXefPNNneJTKpVISEh48gEC8GzmCZVKVaU+9EkQBKOKByg9NTfhStXm2RgVFRVV+feHdGPKc83PkEcz5GdIhUXf8uXLMWPGDAwYMKDc9v79+yMgIABffvkliz4iInqq6DNH2tjYID8/X3yt0WjEgu/QoUNIT0/Hvn37AAAjR45EQEDAI48g3r9jaFUo1UoxDmPwYCFsLKRSaZXn2RglJCSY5LiMkSnPNT9DHk0fnyG67jSo8O6dOTk5aNOmTaUb+/n5ITMz8/EiIyIiquX0mSMDAgJw6NAhAKWnjHp7e4ttdevWhaWlJSwsLCCXy2Fra4u8vLyqBU9ERE+dCou+wMBAzJ07F8nJyeW2p6SkYO7cuejSpYtOb6TRaPDJJ5/g5ZdfxvDhw5GUlKTVvmrVKvTv3x8DBgzA77///hhDICIiMix95sjevXvDwsICgwYNwqeffoqpU6dizZo12LdvH9q3b49nnnkGERERePnll+Hp6YnAwEB9D4eIiExchcc458yZg8mTJ6N3795wdnaGs7Oz1vUKaWlp6Nq1K+bOnavTG/HuZEREZCr0mSOlUilmz56ttczLy0v8+b333sN7772n9zEQEdHTo8Kir27duvj6669x8+ZNxMfHIyMjA4WFhZDL5WjQoAHatGmDxo0b6/xGvDsZERGZCn3nSCIiour0yKsZ3d3dIZfLkZqaiuLiYlhaWsLZ2RkNGjR4rDeqjruTERER1SR95UgiIqLqVGHRJwgCVq5ciR9++AGpqakQBEFsk0gkcHZ2xogRI/D666/r9Eb6vjuZPm5HbYxM+da9Vg4NcU+hqOkwRBqN2qjiKSpSIiHlhl764lxXTp9zbWxM+TPEmOg7RxIREVWnCou+hQsX4tdff8XEiRPRrl07ODo6itcrZGRk4OTJk1iyZAmysrIwceLER75RQEAADhw4gNDQ0ErvTiaRSHS6O5k+bkdtjEz51r2puYWwfeBob027p1AYVTyWlnJ46unfnnNdOX3OtbEx1c8QYytk9Z0jiYiIqlOFRd+WLVsQGRmJDh06aC2Xy+Vwc3ODm5sbXF1dMW7cOJ0SWu/evXHkyBEMGjQIgiBg/vz5WLNmDdzd3RESEoKjR48iIiICUqkUAQEBvDsZEREZLX3nSCIioupUYdFnZmb2yBuqaDQaaDQand6IdycjIiJToe8cSUREVJ0qfE7fSy+9hAkTJiA2NhZJSUkoLCyEWq1GYWEhkpOTsW3bNnz44Yfo37+/IeMlIiKqccyRRERUm1R4pG/8+PGwt7dHZGQk7ty5o7VHUxAEuLq6Yvjw4XjjjTcMEigREZGxYI4kIqLapNJHNrz22mt47bXXkJ6eXuYZRM7OzoaKkYiIyOgwRxIRUW3xyOf0JScn48yZM+IziKysrODk5IS2bdvywbNERPRUY44kIqLaoMKiLysrC5MmTcLhw4fh6uqqdTvqzMxMpKSkoEePHvj0009Rr149Q8ZMRERUo5gjiYioNqmw6Js2bRqKi4tx8OBBuLi4lGm/c+cOpkyZghkzZmDZsmXVGiQREZExYY4kIqLapMKi7+jRo9i0aVO5yQwAGjZsiI8++giDBw+utuCIiIiMEXMkERHVJhU+ssHZ2RkXL16sdOP4+HjY29vrPSgiIiJjxhxJRES1SYVH+t5//31MnToVR48eRYcOHeDs7Cxer5CRkYFTp07hl19+wZw5cwwZLxERUY1jjiQiotqkwqIvNDQUbm5uiI6OxsqVK5GRkYGioiLI5XI4OzvD398f69evh7+/vyHjJSIiqnHMkUREVJtU+siG1q1bo3Xr1oaKhYiIqNZgjiQiotqiwmv6dKHRaJCSkoK7d+/qKx4iIiKTwBxJRETG4pEPZ69MVlYWgoODER4ejqysLERFRUEqrVIdSUREZBKYI4mIyFhUqeirW7cu1q1bh44dOyI5OZnJjIiI6F/MkUREZCyqVPSZm5ujY8eOAIDGjRvrJSAiIiJTwBxJulKqlTUdghbPZp5GFxMAyM3kNR0CUa1VpaKPiIiIiKpuccLcmg5BpFKpIJMZ11fED1pOq+kQiGo1nmtCRERERERkwircjdOzZ08IgqBTJwcPHtRXPEREREaPOZKIiGqTCou+RYsW4f3334ejoyNGjBhhyJiIiIiMGnMkERHVJhUWfe3bt8eqVaswePBg2NnZISQkxJBxERERGS3mSCIiqk0qvabPx8cHkyZNwo4dOwwVDxERUa3AHElERLXFI2/NNGjQIAwaNMgQsRAREdUqzJFERFQb8O6dREREREREJsy4HsJCRERERFRNjO2h857NPI0uJgCQm8lrOgTSMxZ9RERERLVc4zqeGNZ0JE5n/Y3dKT8DAJzkDdC7YShcrBqhUF2AuOyTOJZxqMI+2jp0QCfHrqhjZo07hbex584vuKvMKLNec9uWGOgxBPHZp/Hr7a0AAN+6rRHs8jzMJGY4mvEHTtw9Jq7/qtdbuJV/E3tTd+p51E9mccLcmg5BpFKpIJMZ19fxD1pOq+kQqBrw9E4iIiKiWszOvC7+59Zfa5mZxAwveQyDh01TZCrTIZVI0aNBb7SuF1BuH81sfdDH9UXIpXKkFabA06YpBnu+CnOphdZ65lILPOfaV2uZBBL0cX0RCbnncSTjIIJdnoe1zAYA4GXjDSd5AxzPPKzHERPR42LRR0RERFRLtanXDq97vQN7i3payx3lzqhrYY8kxTWsu/Yt1l/7DkBpcVceLxtvCIIGMTfWYe3Vb5F47wpsze3gVsdda73uzr1gZ15Xa1kdmTXkZnJkKtORWZQOqcQMdc3tAQBdnLvjXM4Z3FPl6WvIRPQEdDqenJOTg1WrVuHcuXNQqVQQBEGrPTo6ulqCIyIiMnZVzZEajQYzZ87E5cuXYWFhgblz58LDw0Ns/+OPP7BixQoAgK+vL2bMmAGJRKL/gVCt1MWpO1SCCqfv/o2A+h3F5YXqAgCABtq/j8UVXD+2+87POJC2G8WaYgAS1JFZAwCK1IXiOi6WrmhXvyPSClPRwMpFXF6gyodSrYSj3AnmUnNoBDVyS3LgYd0UrlaN8POtn/Q1XCJ6QjoVfVOnTkV8fDz69esHGxub6o6JiIio1qhqjty7dy+Ki4sRExODuLg4LFiwAFFRUQAAhUKBRYsWYd26dXBwcMB3332H7OxsODg46HsYVEsdyziEhNzz8LZriQD8V/TlleTiz/T9CHTqgeFNR8HOvC7yVQocy/yzwr6KNcVwkjdAWLMIOFo6Iy7rJO4U3gbw7ymcjV5EgaoAf6bvx0CPIeJ2AgTsStmBYJfnIZPIsD91N/JVCvyf20u4mHsOOcXZ1TcBRKQTnYq+EydO4Ouvv0b79u2rOx6DyikoRlGJuqbD0GLl0BCpuYWPXtFALM3NYF/H4tErEhE9paqaI0+dOoWgoCAAgL+/P86fPy+2nTlzBt7e3vjss8+QnJyMl156iQUfaYnLPllhm/Tfq3jun6KZXpRW5kj0wxpYucDR0hkaQYMSTQmkkEIDDdrX74yGVo2wI3kzlJqy31Mu5sbjYm68+LqRVWO4W3viu6uR6OTYFZ0cA5GvUmBH8k/IUKY9yVCJqAp0KvocHBwgl5verVuLStT4eOv5R69oQPcUCtga0dHUeeF+NR0CEZFRq2qOVCgUWkcIzczMxDv6ZWdn4/jx49i2bRvq1KmDoUOHwt/fH02aNNFH6GTC3Oq4I9C5B9KLUvHj9e/R0MoVL3kMQ3/3QVh5NbLC7S7nXsSFrBno2zgcHRyfRb5KgfM5cQhyDsENxTVcyI2Hu7XnI98/0LkHLuclQK1RoUeD3vgjbS+a2/qgV8NQ/HhjjR5HSkS60KnomzhxImbNmoWxY8fCzc0NFhbaR34aN25cLcEREREZu6rmSBsbG+Tn54uvNRqNeAt3e3t7PPPMM3BycgIAtG/fHgkJCY8s+pRKJRISEp5kOCLPZp5QqVRV6kOfBEEwqniA0n+rhCtVm2dAP3Ot1pSeuSRoSufJ1dINAHAp5wLylLnIU+Yi1zUXTpYNYCHIUaDO19peAinsLOyQW5wDQRAQd/cEnqnnD1erxsgtzoHcTA5Pm6aY6jdH3KZ1vQC4WzfB8osLtfpysXJFU5tmWHklEo4WzpBKpEjIPg8LiQXaOXZ+orEa01zrE3+vDceU51oXOhV97733HgDgzTffBADxAnJBECCRSHRKLLxQnYiITFFVc2RAQAAOHDiA0NBQxMXFwdvbW2zz8/PDlStXkJWVBTs7O5w9exYRERGPjEkul6Nly5ZPOiQApQ+xNqbnhxnj88ykUmmV5xnQz1ybSc0AABKpBDKZDMVCMQDApY4rZDIZrMzqwFpmA5VGhRJJSZn3G950FNzquGPV1RVIUdyCq3XpzgqFKhf56nu4kvff77GVWR00tvZAbnEOriuulumrW8MQJN77B5klGXCwKt1hITWTQCIt/dt4krEa01zrE3+vDcdU51rXHXw6jXzfvn1VCgbghepERGSaqpoje/fujSNHjmDQoEEQBAHz58/HmjVr4O7ujpCQEEyYMAFvvPEGAKBPnz5aRSFRRS7nXUSPBr3hbdcSI5qOho25Hcyl5jiTdQICNHCUO6N7g15ILUzBkYyDiMs6Abc67hji+RpSC1PgYdMEKk0JTmYdx11lBpLyr4t9u1t7YmiTkUjKv4ZdKTu03tdR7gxv2xZYf20lACCjKBWCoEEDS1c4yRsgreiOQeehOjSu44lhTUfidNbf2J3yM7o690SQc3C56351+QvkluSUWe5btzWedeoGe/N6yCnJxuH0A7icd6HMes1tW2KgxxDEZ5/Gr7e3itsGuzwPM4kZjmb8gRN3j4nrv+r1Fm7l38Te1J16Gi2ZigqLPo1GA6m09ALghg0bVvmNeKE6ERGZCn3mSKlUitmzZ2st8/LyEn/u27cv+vbt+/BmRJUqVBdgw/VVCHZ5Ho3quKNYo8TfmUfxR9rvAIA6sjrwtmsJmaT0q+C5nDjIJObo4NgFjeo0RkrhbexP3Y27yozHet8uTt2RlH8dtwuTAQDZxVn4I20f+rj2Q75Kge23Nut3oAZmZ14X/3Prr7XsrjJT60iohdQCnjZeuFeSh4J/H53xIE/rpnjRbQCKNSW4VZCExtaeCG8cgejrq5FckCSuZy61wHOu2n/7EkjQx/VFnM0+hdySbIS49MHF3HPIVyngZeMNJ3kDxCb9oOdRkymosOhr1aoVDh8+jPr168PX17fSUy11Oayo7wvV9XG9gpVDQ9xTKKrUh75pNGqjiqmoSImElBt66cvY5ptzbTimPNfGpqioqMqfjfRo+s6RRFV1LucMzuWc0VqWqUzHpqT15a5/M/8GPj0/XWvZmewTOJN94pGnwZW37X07yinqjmUewrHMQ48agtFrU68dejZ4DlayOlrLE3LPISH3nPi6l8sL8LTxwq+3t6JEU1ymn2fqtYVEIsUvt2JxMfsc2jt1xguN/g9+9v5aRV93516wM6+rtW0dmTXkZnJkKtORW5wNqcQMdc3tka9SoItzd5zLOYN7qjw9j5xMQYV/0WvXrkXduqW/aOvWravyG+n7QnV9XK+QmltoVHfKBIzv7p2WlnJ46uG8bsD45ptzbTimPNfGJiEhQS/XYhgbYyuc9J0jicj4dXHqDpWgwum7fyOgfsdy13GUOyOgfidcvXcZ1xVXy13ndNYJJOcn4ea/p8zmq0p3ippL/7sJlIulK9rV74i0wlQ0sHIRlxeo8qFUK+Eod4K51BwaQY3ckhx4WDeFq1Uj/HzrJ30Nl0xMhUVfx44dy/35SVXHhepEREQ1Qd85koiM37GMQ0jIPQ9vu5YIQPl/950cA/+91q7iI5u3C27idsFN8XVbhw4AgLSiFAD/nsLZ6EUUqArwZ/p+DPQYIq4rQMCulB0IdnkeMokM+1N3I1+lwP+5vYSLueeQU5ytj6GSCTLYLWxM9UJ17wa2mPxCCxy8nI71x5Lwor8r/s+/UbnrTvrpLO4qyh7mv+/1rk0Q2MwR8369iGsZpUdF3epZYVS3pnC0kePMzRysOnwN95+r+nKHxmjZ0A4zd5S98JeIiIiI9Ccu+2Sl7ZZSS7Ss64f0olStoq4ywQ2fh5etNwrVhYjPLj09t339zmho1Qg7kjdDqSkss83F3HhczI0XXzeyagx3a098dzUSnRy7opNjIPJVCuxI/gkZyrTHGCGZMoMVfaZ4oXp9awuMDNI+BTU1twhnbv63l8VSZoaWrnbIKSiGoqjiZ4MENnPEs171yywf0M4NSpUGi3ZdxvR+voi7mY2TSdmwkcvQ3dsJq49cL6c3IiIiIjIk77q+MJda4HLeRZ3W7+wYhMAGPaARNNh5exsK1QWwldkhyDkENxTXcCE3Hu7Wno/sJ9C5By7nJUCtUaFHg974I20vmtv6oFfDUPx4Y00VR0WmwrgeVlGLBDV3xMB2jWFjqT2Ff1/Pwt/Xs8TXgzo2RktXO6w+fB1KlaZMP9YWZojo0BhdmzuV+z5OtnLcyCzAzax88TUAPNfKBVkFxTh1g4fxiYiIiGqah3VTAMC1e/88ct1n7Nuip8tzAIBdKTtw5d9C0dPGC3IzOTxtmmKq3xxx/db1AuBu3QRRVxZr9dPAsiGa2jTDmsSv4WzVEFKJFFfyEmBpZom2Djz1nP4jfdwNBEGARqPR+u9p9L82rlBpNDhwKb3CdVztLdGzhTPik3NwIaX8Oym5OdRB1+ZOOHMzGzcy88u0Z9xTwtlODncHawBA+j0lrCzM0LOFE3bG34Ggn+EQEZEeMEcSPb0a1/GARlAjrSi10vXqy53Qx7UfAGBfyi6czT4ltuWV5OBKXoL4X3J+6d08c4tzyr0xTKBzDyTe+0fr+YcCNBD4DZEeotORvvPnz2POnDk4f/58uQnM2O6qZgg74+/g7xtZCHC3R084l7vO861cIJNK8eu5ih9EmlNQgsj9/+DMzRx82MenTPuW07fwRlBTfNjHB38l3sXpm9no19oV+UoV/rp2V2/jISKiJ8McSUSABLbmtsgtzoFa0L6cx1HujO4NeiG1MAVHMg4i0KkHZFJzaAQN3Kzd0djGAwDE9qT8/y7dcbf2xNAmI5GUfw27UnaU6dfbtgXWX1sJAMgoSoUgaNDA0hVO8gZahSCRTkXfrFmzIJfLsXz5cq1n7T3N/rhS+cNK61iYoUMTByRnFeBqesXPJ0vLK0JaXlGF7clZhZix/b8btchlUoS0bIAtp2/Bx8UOw5/1gIWZFJtOJmudVkpERIbBHElEVmZWkErMyn0Yex1ZHXjbtYRMUvq128u2OQBAKpHCp66vuN79dl11ceqOpPzruF2YDADILs7CH2n70Me1H/JVCmwv55mJ9PTS6bfrn3/+waZNm2rNHTWNQYBHPchlZjh9U7/X3AW3cEaJWoMjVzMxr/8zuJGZj5yCErzyrCfOJueUe90gERFVH+ZIoqfLuZwzOJdzRmtZobqgwgfWP/ww+yUJ88WfVSqV+NxqXbZ90I5yirpjmYdwLLPix0XQ00una/patGiBW7duVXcsJqWFix0A4PztXL31aW4mQe9WLth1IRVymRSONnJcvJOHc7dzYGVhhgZ2lnp7LyIi0g1zJBERGTudjvT169cP06ZNQ1hYGBo3bgxzc3Ot9oEDB1ZLcLWZdwMbqDUCbmaVPcz/pLp7O0MC4NDlDJibSQAAgkYQn9tHRESGxxxJRETGTqeib9WqVbC0tMSuXbvKtEkkEia0h0gA2NexQKZCCZVauyJrZG+F8IBGSLpbgJ/Ppujcp5lUguf9XPD7xTQUqzUoVgN3FUo0dqgDSwszFJWokV7JtYFEVD1yCopRVKKu6TBEVg4NkZpb9mG+NcXS3Az2dSxqOoxqxRxJRETGTqeib//+/dUdh0mxlstgJpVAoSz7MHYbSxnauteDzOzxnpYR2MwRcpkU+y+licvWHr2BEV08ITOTYsOxJBTxej4igysqUePjredrOgzRPYUCtkZ0M5F54X41HUK1Y44kIiJjp/NtglJTU7F+/XokJiZCo9GgadOmeOmll+Dl5VWd8Rm9I1fv4shV7UcnKJQqjPz+RLnrX069V2Hbol2XK/zCduhKBg49dMfQCyl5mPRT/BNGTkRE+sIcSURExkynw01///03+vTpg1OnTsHT0xMeHh44ffo0wsPDcerUqUd3QEREZKKYI4mIyNjpdKTvs88+wyuvvIIPPvhAa/kXX3yBRYsWYePGjdUSHBERkbFjjiQiImOn05G+q1evYsCAAWWWDxgwAAkJCXoPioiIqLZgjiQiImOnU9Hn5uaGs2fPllkeFxeH+vXr6z0oIiKi2oI5koiIjJ1Op3eOHDkSM2bMwNWrV9G6dWsAwNmzZxEdHY0JEyZUa4BERETGjDmSiIiMnU5FX//+/QEAGzZswNq1a2FpaYkmTZpgwYIFeO6556o1QCIiImPGHElERMZO50c29O/fX0xsRESkH94NbDH5hRY4eDkd648labVJJMC0vr7wdLTGpJ/O4q6iuMz2H/bxQQsXuzLLMxVKTP4pHm71rDCqW1M42shx5mYOVh2+BkEoXeflDo3RsqEdZu64UC1je5owRxIRkTGrsOhbunQpRo8eDSsrKyxdurTSTsaNG6f3wIiITF19awuMDGpSYXuvlg3g6WhdaR9X0xQoLFaLr+vVMYenow1uZRUAAAa0c4NSpcGiXZcxvZ8v4m5m42RSNmzkMnT3dsLqI9f1M5inDHMkERHVJhUWfSdPnsRrr70GKysrnDx5ssIOJBJJtQRGRGTKgpo7YmC7xrCxLP9juF4dc4S1bfTIfraeuS3+LAHwUWgL5CtVWHv0BgDAyVaOG5kFuJmVL74GgOdauSCroBinbmRXbSBPKeZIIiKqTSos+tavXy/+/Nlnn8HFxQVSqfbNPtVqNS5fvlx90RERmaj/tXGFSqPBgUvp6NnCuUz70M4ekABIyytCAztLnfrs5u2Eps622HQiGXlFKgBAxj0lnO3kcHcoPWKYfk8JKwsz9GzhhB+P34SgtxE9XZgjiYioNtHpkQ0hISHIyckps/zWrVsYPHiw3oMiIjJ1O+PvYNq287ieqSjT1tbdHm3d6+Hn+BRkF5S9jq8iffxckK8swYHL6eKyLadvQS6T4sM+Pvgr8S5O38xG75YNkK9U4a9rd/UylqcdcyQRERm7Co/0xcTEICoqCgAgCALCwsLK7MW8d+8emjdvXr0REhGZoD+uZJS7XC6TYkgnD6TkFGLP+TT4NaqrU39+jerC2c4Sv529hWKVRlyenFWIGdv/u1GLXCZFSMsG2HL6Fnxc7DD8WQ9YmEmx6WQy/r6eVbVBPUWYI4mIqDapsOjr378/5HI5NBoNPvroI7zxxhuwtbUV2yUSCerUqYPOnTsbJFAioqdBeEAjOFhb4LPfLkEt6H7yZeemDgCA49fKLybvC27hjBK1BkeuZmJe/2dwIzMfOQUleOVZT5xNzoHygYKRKsYcSUREtUmFRZ+5uTnCwsIAAG5ubggICIBMpvMTHoiI6Am0da8HAJj8Qgut5QsHtsHqw9dw5Gr5p2T6uNihqESNSym5sLYu/46f5mYS9G7lgp3n7kAuk8LRRo6d5+7grkKJXr4N0MDOEjf/vesnVY45koiIahOdMlT79u2xZ88eXL16FWp16a3BBUFAcXExEhISsGbNmmoNkojoaXHhdi7srMzF182cbWBraY4Lt3PLfU4fADjaWMDB2gL/pN2DppKjg929nSEBcOhyBszNSu8qKWgEPMYBRSoHcyQRERk7nYq+2bNnY8uWLfD19UV8fDzatm2LmzdvIjMzE0OHDq3uGImInhrrHnpAe+nD182x9tgN3FUUo5G9FcIDGiHpbgF+A3eWfQAAIABJREFUPpsCAHCwLn0Mw53cogr7NZNK8LyfC36/mIZitQbFauCuQonGDnVgaWGGohI10vMq3p4qxhxJRETGTqe7d+7atQuff/45Nm7cCHd3d8ycORMHDx5E3759UVhYWN0xEhHRv2wsZWjrXg9ezjbiMrt/n/WnUKoq3C6wmSPkMin2X0oTl609egNtGtujj19DbDiWhCJez/dEmCOJiMjY6XSkT6FQ4JlnngEAeHt74+zZs2jevDnefPNNvP7669UaIBGRKTty9W6F1+kBwKJd2s95u5x6DyO/P6G17GRSdpllDzt0JQOHHrpj6IWUPEz6Kf4xI6aHMUcSEZGx0+lIn7u7Oy5cKL3ld/PmzREfX/olQaPRQKEo+4wpIiKipwVzJBERGTudjvSNHDkSEyZMwPz58xEaGorw8HBIJBLExcWhXbt21R0jERGR0apqjtRoNJg5cyYuX74MCwsLzJ07Fx4eHmXWGT16NEJCQvjAdyIiemw6FX0DBgyAp6cnLC0t4eXlhRUrVmDz5s1o06YNxo4dW90xEhERGa2q5si9e/eiuLgYMTExiIuLw4IFC8QHv9/35ZdfIjc3t7qGQEREJk7nhwo9uLcyKCgIQUFB1RIQERFRbVOVHHnq1ClxfX9/f5w/f16rfdeuXZBIJOjWrZt+giUioqdOhUXfkCFDIJFIdOokOjr6kevw9BUiIjIV+syRCoUCNjb/3Y3VzMwMKpUKMpkMV65cwS+//IJly5ZhxYoVOsenVCqRkJCg8/rl8WzmCZWq4jvCGpogCEYVD1D6vSXhStXmGeBc64JzbTica8PR11zrosKir0uXLnp9I56+QkREpkKfOdLGxgb5+fnia41GA5msND1v27YNaWlpGDFiBG7fvg1zc3M0atTokUf95HI5WrZsWaW4lGqlGIcxuF8IGxOpVFrleQY417rgXBsO59pw9DHXuu7gq3DkY8aMqVIAD+PpK0REZCr0mSMDAgJw4MABhIaGIi4uDt7e3mLbpEmTxJ+XL18OR0dH5kkiInpsOpW7Dyad8ixcuPCRfej79BV9nLpi5dAQ94zsdtoajdqoYioqUiIh5YZe+jK2+eZcGw7n2nBMea6NVVVzZO/evXHkyBEMGjQIgiBg/vz5WLNmDdzd3RESEqLPUImI6CmlU9FnZmam9VqlUiE5ORkJCQkYMWKETm+k79NX9HHqSmpuIWwfKESNwT2FwqhisrSUw1MPh/gB45tvzrXhcK4Nx1Tnuqo7+apTVXOkVCrF7NmztZZ5eXmVWY93yyYioielU9H36aeflrt8zZo1uHjxok5vxNNXiIjIFOkjRxIREVUnaVU27t27N/bu3avzuhYWFhg0aBA+/fRTTJ06FWvWrMG+ffuqEgIREZFRepwcSUREVJ10OtKn0WjKLFMoFFi7di3q1aun0xvx9BUiIjJF+siRRERE1Umnos/X17fc5xHJ5XLMnTtX70ERERHVFsyRRERk7HQq+tatW6f1WiKRwNzcHM2aNdO6IycREdHThjmSiIiMnU5FX8eOHQEAeXl5uH79OiQSCZo2bcpkRkRETz3mSCIiMnY6FX35+fmYPn06du/eDbVaDQAwNzdHWFgYpk+fDgsLi2oNkoiIyFgxRxIRkbHT6e6dn3zyCf755x+sXr0ap06dwokTJ/DNN9/gzJkzWLBgQXXHSEREZLSYI4mIyNjpVPQdOHAACxYsQKdOnWBtbQ1bW1t06dIF8+bNw88//1zdMRIRERkt5kgiIjJ2OhV9jo6OyMzMLLNcqVTCzs5O70ERERHVFsyRRERk7HS6pm/UqFGYNm0aRo0aBX9/f8hkMiQkJCAyMhLh4eE4duyYuO6zzz5bbcESEREZG+ZIIiIydjoVfdOnTwcAzJ8/v0zbV199Jf4skUiQkJCgp9CIiIiMH3MkEREZO52KvkuXLlV3HERERLUScyQRERk7nYo+ACgoKMC2bdtw7do1qNVqNG3aFH379oWDg0N1xkdERGT0mCOJiMiY6XQjl0uXLuG5557Dd999h7S0NKSlpWHlypUIDQ3F1atXqztGIiIio8UcSURExk6nI33z5s1DUFAQ5syZA5msdBOVSoXp06dj3rx5WLNmTbUGSUREZKyYI4mIyNjpdKQvPj4eo0aNEpMZAMhkMowaNQpxcXHVFhwREZGxY44kIiJjp1PR5+zsjJs3b5ZZfuPGDdjY2Og9KCIiotqCOZKIiIydTqd3vvzyy5g2bRrGjh2L1q1bAwDOnj2LyMhIDBo0qFoDJCIiMmbMkUREZOx0KvpGjhyJwsJCLF68GLm5uQAAJycnjBw5Eq+++mp1xkdERGTUmCOJiMjY6VT0SSQSjB07FmPHjsXdu3chl8t5ygoRERGYI4mIyPhVWvTt2LEDe/bsgYWFBUJCQtC3b1/Ur1/fULEREREZLeZIIiKqLSq8kcu3336LqVOnoqioCAUFBZg8eTIWL15syNiIiIiMEnMkERHVJhUe6du0aRPmzZuHsLAwAMCePXswdepUjB8/HhKJxGABEhERGRvmSCIiqk0qPNKXmpqKZ599VnwdHByMwsJCpKenGyQwIiIiY8UcSUREtUmFRZ9KpSrzoFm5XI7i4mKDBEZERGSsmCOJiKg20enh7ERERERERFQ7VXr3zl9++QXW1tbia41Gg99++w0ODg5a6w0cOLB6oiMiIjJSzJFERFRbVFj0ubq6Yu3atVrL6tevj40bN2otk0gkTGhERPRUYY4kIqLapMKib//+/YaMg4iIqNZgjiQiotqE1/QRERERERGZMBZ9REREREREJoxFHxERERERkQmr9O6dREREVL00Gg1mzpyJy5cvw8LCAnPnzoWHh4fY/v333+PXX38FAHTv3h1jxoypqVCJiKiW4pE+IiKiGrR3714UFxcjJiYGEyZMwIIFC8S25ORk7NixAxs3bkRMTAwOHz6MS5cu1WC0RERUGxnsSB/3ZBIREZV16tQpBAUFAQD8/f1x/vx5sc3FxQUrV66EmZkZAEClUkEul9dInEREVHsZrOh7cE9mXFwcFixYgKioKAD/7cncvHkzJBIJhgwZgl69eqFFixaGCo+IiKhGKBQK2NjYiK/NzMygUqkgk8lgbm4OBwcHCIKAhQsXwtfXF02aNHlkn0qlEgkJCVWKy7OZJ1QqVZX60CdBEIwqHqB0h3bClarNM8C51gXn2nA414ajr7nWhcGKPu7JJCIiKsvGxgb5+fnia41GA5nsv/SsVCrx0UcfwdraGjNmzNCpT7lcjpYtW1YpLqVaqRVHTbtfCBsTqVRa5XkGONe64FwbDufacPQx17ru4DPYyPW9J1MfezGtHBrinkJRpT70TaNRG1VMRUVKJKTc0EtfxjbfnGvD4VwbjinPtakKCAjAgQMHEBoairi4OHh7e4ttgiDgnXfeQadOnTB69OgajJKIiGozgxV9+t6TqY+9mKm5hbB9oBA1BvcUCqOKydJSDk897O0BjG++OdeGw7k2HFOd66ru5DNmvXv3xpEjRzBo0CAIgoD58+djzZo1cHd3h0ajwd9//43i4mL8+eefAIAPPvgAbdu2reGoiYioNjFY0cc9mURERGVJpVLMnj1ba5mXl5f487lz5wwdEhERmRiDFX3ck0lERERERGR4Biv6uCeTiIiIiIjI8PhwdiIiIiIiIhPGoo+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITBiLPiIiIiIiIhPGoo+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITBiLPiIiIiIiIhPGoo+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITBiLPiIiIiIiIhPGoo+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITBiLPiIiIiIiIhPGoo+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITBiLPiIiIiIiIhPGoo+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITBiLPiIiIiIiIhPGoo+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITBiLPiIiIiIiIhPGoo+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITJjBij6NRoNPPvkEL7/8MoYPH46kpCSt9k2bNqF///6IiIjAgQMHDBUWERFRjWJ+JCKi6iYz1Bvt3bsXxcXFiImJQVxcHBYsWICoqCgAQEZGBtavX4/Y2FgolUoMGTIEgYGBsLCwMFR4RERENYL5kYiIqpvBjvSdOnUKQUFBAAB/f3+cP39ebIuPj0fbtm1hYWEBW1tbuLu749KlS4YKjYiIqMYwPxIRUXUz2JE+hUIBGxsb8bWZmRlUKhVkMhkUCgVsbW3FNmtraygUikr7UyqVSEhIqHJcEzvZPHolgzKueLJTbiA7RX/9Gdd8G1MsnGtD4lwbjr7mWqlUVr0TI6Xv/AjoL0eGmQ2pch96Y1bTAZR17co1vfXFua4c59pwONeGo4+51jU/Gqzos7GxQX5+vvhao9FAJpOV25afn6+V5Mrj7+9fPYESEREZkL7zI8AcSURE2gx2emdAQAAOHToEAIiLi4O3t7fY1rp1a5w6dQpKpRL37t1DYmKiVjsREZGpYn4kIqLqJhEEQTDEG2k0GsycORNXrlyBIAiYP38+Dh06BHd3d4SEhGDTpk2IiYmBIAh488038fzzzxsiLCIiohrF/EhERNXNYEUfERERERERGR4fzk5ERERERGTCWPQRERERERGZMBZ9Rm748OFITEys6TBqrS1btuDzzz+v6TBqJaVSieDg4Cr3s2HDhkrbg4ODdbrdcHnrHTp0CDExMRVus3z5cvz444+6BVoLlPf7PH78eBQXF1e4TWBgYHWHRVQjmB+rjjnyyTFHGh/myMoZ7JENRPR0ioqKwrBhw6ql727dulVLv7XJkiVLajoEIiJ6QsyR1Ys58j8s+vRkzJgxeOWVV9CxY0fEx8cjKioKy5Ytw4wZM5CUlASNRoP3338fnTp1wv/+9z94enrCwsICKSkpmDNnDpo3b44//vgDBw8exIwZM8r0n5eXhw8//BAKhQJqtRrjxo1Dfn4+jh49ik8++QTffPMN4uLiEBUVhe3bt+POnTt46623amAmjNPq1avx66+/QiaToX379vjggw/wwgsvYOfOncjKykL37t1x9OhRWFtb4+WXX8bWrVtrOuQq2bJlCw4cOICioiJkZGTglVdewb59+/DPP/9g0qRJ6NWrF3bs2IG1a9fCwsICnp6emD17NoqLizFx4kTk5eXB3d1d7O/y5cuYO3cuAMDe3h7z58/HxYsX8fnnn8Pc3BwRERGwtLREdHS0uM3SpUsRExOD3NxczJw5Ex9//HG5fw9A6d0Le/fujc2bN8Pe3h4//PADCgoK8MYbb2iNa+bMmbh16xYAIDIyEvv27cO1a9cwceJErFixAnv37oWDgwMKCwsxbtw4AMC+ffuwa9cu5OTkYNy4cXrZM1uTzp49i9dffx1ZWVkYPHgwvvnmG/z2229ITU3FlClTIJPJ0KhRI9y+fRvr169HcXExJkyYgJSUFNjb22PZsmUwNzev6WHQU4T50fgxRzJHMkeafo7k6Z168tJLL4kfglu3bkVERAQ2b96MevXqITo6Gl999RVmz54NACgoKMA777yDxYsXa20XGxuLgQMHltt/VFQUunTpgujoaCxduhQff/wxunTpghMnTgAATp48idTUVKhUKhw4cAC9e/c2wKhrh6SkJPz222/YuHEjNm7ciKSkJBw6dAjt2rVDXFwc/vzzTzRv3hzHjh3DsWPHTOZQf35+Pr777juMGjUKP/74IyIjIzF79mxs2bIF2dnZWL58OdauXYsff/wRtra2iImJwdatW+Ht7Y3o6GgMGjRI7Gv69OmYMWMG1q9fj27dumHlypUASk9v+eGHHxAWFoYbN27g22+/xfr169GkSRMcPnwYb7/9NurWrYuZM2dW+PcAAFKpFP369cOvv/4KANixYwfCwsLKjGnAgAFYv349GjVqhCNHjojLL126hD///BM//fQTVqxYgYyMDLGtQYMGWLt2LT766COTOI1FJpNh1apViIyMxNq1a8XlCxcuxFtvvYX169cjICBAXF5QUIDx48fjxx9/hEKhQEJCQk2ETU8x5kfjxhzJHMkc+XTkSB7p05OgoCAsWrQIOTk5OHnyJKZNm4Y5c+bg1KlTiI+PBwCoVCpkZ2cDAJo0aQIACA0NRXh4OEaOHInU1FS0atWq3P4TExPRr18/AKV/oDY2NigoKECTJk0QHx8PmUwGf39/nDhxAnfu3IGXl5cBRl07JCQkoEePHuKem/bt2+Off/7Bc889hz/++AO3bt3C+PHjsW/fPkil0gq/WNQ2LVu2BADY2trCy8sLEokEdevWhVKpRHJyMpo1awYbGxsAQIcOHXD48GEApb/LANCmTRvIZKUfEYmJiZg1axYAoKSkRPz9vf9/AKhfvz4mT54Ma2trXLt2Df7+/lrxXLlypcK/BwAYOHAgxo8fjw4dOsDR0RGOjo5lxuTn5wcAcHR0RFFRkbg8MTERzzzzDMzMzGBmZiauB0D8m3p4m9rK19cXEokETk5OZeagbdu2AIB27drh559/BgDUrVsXbm5uAErnoLCw0PBB01ON+dG4MUcyR5a3TW3FHFkxFn16IpVK0adPH8ycORO9evWCmZkZmjZtChcXF7z11lsoKipCVFQU6tatK64PAFZWVujUqRPmzZuH//u//6uwfy8vL5w8eRK+vr5IS0tDXl4e7O3t0atXLyxatAghISFo3LgxlixZgi5duhhkzLVFy5YtER8fD5VKBTMzM5w4cQJhYWEIDAzEN998A0tLS3Tv3l08pN+6deuaDlkvJBJJhW1ubm5ITExEQUEB6tSpg7///htNmjSBRCJBXFwcevXqhYsXL0KlUgEoTVyfffYZXF1dcerUKXEv4f3f43v37mHZsmU4ePAgAOC1117D/UeA3v9/ZX8PAODq6gpbW1t8/fXXFX6pqGhMzZo1w/r166HRaKBSqXDx4kWd5qE2qmg83t7eOHPmDLp3746zZ88+cn0iQ2F+NG7MkWUxR9ZezJEV4+mdejRgwAD8/vvvGDBgAABg0KBBuHbtGoYNG4ZBgwahUaNG4gfAgyIiIrB3715xT2V53nzzTfz1118YOnQo3nnnHcyePRsymQw9e/bEmTNn0LVrV3Tq1AkXL17Ec889V21jrI08PDzwwgsvYPDgwRg4cCAaNWqEXr16wcLCAi4uLvD19YVUKkWTJk1MJpk9ioODA8aOHYtXXnkFERERyM7OxuDBgzF06FCkpaVh8ODBiI6OFvf8zpw5E5MnT8aQIUPwxRdfwMfHR6s/GxsbBAQEIDw8HEOHDoWlpSXS09MBlH4hmzhxok5/DxERETh58qS4J1VXPj4+6N69OyIiIvDuu+/C3Nxc3AP7tJg4cSK+++47jBgxAvv373/qxk/GjfnReDFHlsUcaXqYIwGJcH8XA9WY+Ph4bNiwAQsXLqzpUIhq1M6dO/HPP/+IF5jr6u7du9i1axeGDh2K4uJi9O3bF2vXroWrq2s1RWp8duzYgTZt2sDDwwObN2/G6dOn8emnn9Z0WERVwvxI9B/myCfHHMnTO2vchg0bEBsbi2XLltV0KEQ1avHixTh58iS++uqrx962Xr16OH/+PAYMGACJRIKXXnrpqUpmANCwYUOMHz8eVlZWkEqlmD9/fk2HRFQlzI9E/2GOrBrmSB7pIyIiIiIiMmm8po+IiIiIiMiEsegjIiIiIiIyYSz6iIiIiIiITBiLPjJqhYWFiIyMRN++fdGmTRt07NgRb775ptYzVmrS8OHDERwcXOaBprdu3YKPjw+SkpJ06ichIQEnT54EABw/fhw+Pj7i83+qU3BwMDZv3lxu2/LlyzF48OBqj4GIyNgFBwfDx8enzH//+9//DB7LlClTMHHixCfadvjw4ViyZMkTbbtlyxZ069btibbVhy1btpT7b+Dj44Ndu3bVWFz3Pfw70qpVK4SEhODbb7+tUr+7du0Sn/una17W579Vfn4+tmzZIr6u7HsDGTfevZOMVmFhIYYOHYrc3FxMnjwZvr6+UCgU2LJlC4YNG4bo6GijeGbQ7du3ERUVhfHjxz9xH++++y7efvtttG/fHm3btsXhw4efymfIEBEZqylTppQp8vg5bVhOTk7YunVrmeUPPsS8Jj34O6JSqfDXX3/h448/hrOzM8LCwh67v9u3b2PcuHHYs2cPAOD111/H8OHDH7ldaGgoevTo8djvV541a9bgyJEj6N+/PwDgp59+Qp06dfTSNxkWP63IaEVFRSE1NRU7d+6Evb29uPyjjz5CTk4OoqKiEBUVVYMRlmrUqBFWrVqFF198EV5eXlXuz8LCAk5OTnqIjIiI9MXGxoafzTVMKpUa9b/Bw78j4eHh+OWXX7Bnz54nKvoevsG+tbW1TttZWlrC0tLysd9PlxgcHBz00i8ZHk/vJKOk0WgQGxuLV199Vavgu2/KlClYtGgRgNLTIbt164bZs2ejXbt2WL58OYDS0xtCQ0PRunVr9O/fH8ePHxe3P378OPr374/WrVujR48e+Oabb3RqK0+/fv3g6+uL2bNnV7hOeno63nvvPXTo0AF+fn4ICwvDiRMnAJSebnP79m1MmzYNU6ZMKXN6Z2pqKsaNG4eOHTuiU6dOmD17NpRKpTjGwYMHIzIyEp07d0bXrl0xb948aDQaAEBJSQk+++wzdOvWDa1atULPnj3xww8/PHL+H3b/dNXdu3ejd+/eaNeuHd566y1kZWWJ6xw9ehT9+/dHmzZt0LdvX+zfv19sO3PmDAYPHgx/f38EBwcjOjpabJsyZQoWLFiA8ePHi9teunQJS5YsQfv27dG9e3dxL+f9+XjnnXfg7++PHj164PPPP0dxcfFjj4mISJ9iYmIQEhKCtm3bYvDgwYiPjxfbgoODERMTgwEDBqB169YYOXIkbt++jTFjxqBNmzYICwtDYmKiuH5sbCxeeOEF+Pn5oVOnTpgxY0aFp/zv3btXvAQiPDwchw4deuIxnDlzBkOGDEGbNm3g7++PkSNHIi0tTWudJUuWICAgAF27di1zml9leffh0wIfzHX3c8yKFSvQoUMHTJ069Ynif1S+jIiIwHvvvYd27dph8+bNUKvVWLZsGYKCghAQEIC3334b6enpAEqLna+++gpBQUFo164dRo4ciRs3bjx2TDKZDBYWFgAAhUKBjz/+GM8++yz8/Pzw/PPPY/fu3eK6Pj4++PLLL9G5c2e8+uqrCAkJAQA899xz2LJlS5nTOyvKuw+e3nn8+HEEBgYiOjoanTp1wrPPPovIyEitGL/99luEhITAz88PXbt2xdKlS8V+IiMjcfr0afj4+ADQ/nfUaDRYuXIlevXqhdatW2PYsGG4dOmS1ni2bduGfv36oW3bthg+fDhu3rz52HNI+sGij4xScnIyMjMz0bFjx3LbHRwcYGNjI75OS0uDQqHA1q1bER4eji1btmD27NkYPXo0tm/fjsDAQIwePRopKSlQq9V477330LNnT+zcuROffPIJVqxYgT///LPStsrMnDkTJ06cwM8//1xu+6RJk6BSqbBx40Zs27YNLi4umDFjBoDSc/RdXFwwZcoUfPzxx1rbFRcXY8SIESgoKMC6deuwdOlSHDp0CAsWLBDXOXfuHBITExEdHY33338fGzZsEOP97rvvsH//fixbtgy7du1CeHg45s6dWyaJ6+qbb77B559/jq+//hrx8fFYtWoVACAxMRGjR49GcHAwtm/fjoiICIwbNw7JyclITEzEiBEj0KFDB2zduhVjx47FokWL8Ntvv4n9btiwAe3atcP27dtha2uL4cOHIzs7GzExMQgMDMT06dMhCAIEQcC7776LunXrIjY2Fp9//jkOHjyIxYsXP9F4iIj0Yf/+/Vi6dCmmTp2KrVu3olu3bhgxYoRYQADAsmXLMH78eERHR+P8+fMIDw9HUFAQNm/eDKlUii+//BIAcPLkScyaNQvjx4/H7t27MWvWLGzZskVr59d9ly5dwocffohRo0bh559/RkREBMaMGYOEhITHHoNCocCbb76JLl264JdffsGqVatw69YtrTNq0tLScOnSJcTExGDChAmYNWsWjh49CgCV5l1dnTx5ErGxsRg9evRjx69Lvjx79iw8PDywefNm9OzZE8uXL8emTZswd+5cbN68GUqlEpMnTwZQmpe2b9+OhQsXYtOmTfDw8MCrr76KwsJCneIpKSnBnj17cOTIEQQHBwMAPv30UyQmJmL16tX45Zdf0KFDB0yfPl1rx+W+ffvwww8/4OOPPxaLq5iYGISGhmr1X1nefVhOTg5iY2OxevVqzJkzB2vWrBF3AG/fvh2rV6/G3LlzsWvXLrz77rv46quvEB8fj9DQULz++uto3bo1Dh8+XKbfFStWYPXq1eLvvZubG9544w0oFApxncjISHz00UdYt24dMjMzma9rkkBkhE6fPi14e3sL169fF5edPXtW8Pf31/pPEAThr7/+Ery9vYXLly+L64aFhQkLFy7U6jMiIkJYsGCBkJ2dLXh7ewvR0dFi26lTp4T09PRK28ozbNgwYfHixYIgCMLs2bOFwMBA4d69e0JycrLg7e0t3LhxQxAEQfj++++FlJQUcbtDhw4J3t7egkajEQRBEHr27Cls2rRJazwlJSXC3r17hdatWwvZ2dnitn/88YfQsmVLIS8vT4iNjRV8fHyEvLw8rbEvX75cEARB+P3334UTJ06IbUqlUvD29haOHTtW5n0ftmzZMmHQoEGCIAjiePbt2ye2z58/Xxg+fLggCIKwYMECcd37VqxYISQkJAjz588XBg4cqNW2aNEioX///oIgCMLkyZOFAQMGiG3r1q0TWrVqJRQVFQmCIAgXLlwQvL29hbtlfMuPAAAMoklEQVR37wpHjx4VOnbsKKhUKnH948ePC61atRJKSkrKHQcRUVX17NlT8PPzK5ODMjMzBUEQhMGDBwtr1qzR2mbYsGFCZGSkuP1nn30mto0ZM0YYOnSo+HrNmjVCaGioIAiCcO7cOWHHjh1afUVERAhLly4VBKH0M3PChAmCIAjCxIkThTlz5mitO2XKFGHq1KnljuPBnPWw9PR0YeXKlWJeEgRB+Pzzz8U4Y2NjhVatWgl3797Veq+xY8cKglB53r0/Bw/mmwdz3f0cs3///nJju//+Pj4+Zf4N5s6dKwiCoFO+9Pb2Fu7duycIgiBoNBqhc+fOWjElJSUJX3zxhaBWq4Vu3boJe/bsEds0Go0QHBwsbN26tdz4Hv4dadGiheDv7681J7GxscKlS5fE14mJiYK3t7dw8+ZNQRAEwdvbW1i/fr3Y/vB3iQfzcmV5NzY2VggKCtKa5wsXLojrLV26VHjxxRcFQRCEY8eOlZn3wMBA4aeffirznvfHuWnTJkGj0QgdO3bU+r5UXFwsdO/eXdiwYYM4nnXr1onta9euFYKDg8udP6p+vKaPjJKdnR0AIC8vT1zWokULbNu2DUDpKSj398bd16hRI/HnxMREvP3221rt/v7+uHbtGuzt7TF69GjMmjULUVFR6NmzJ1588UXxPPzK2irz/vvvY/fu3fjyyy/x6quvarUNHjwYO3fuxOnTp3H9+nWcP38eAKBWqyu9EUBiYiLc3d21TnENCAiAWq0WTzOpV68ebG1txXYbGxvxNKBevXrhyJEjWLBgAa5du4aLFy8CgHj65+Nyd3cv930SExPRqlUrrXXfeecdsa1NmzZabW3bttU6xbNx48biz5aWlnB0dIRcLgcA8f/FxcVITExEXl4e2rdvL64vCAJKSkqQkpKiFR8RkT6NGTMGffr00Vp2/7M5MTERixcvFk+LA0o/s1xcXMTXD37OyeVyuLq6ar2+f7THz88PlpaWWLZsGa5evYrLly8jKSkJnTt3LhNTYmIirly5gtjYWHFZSUnJE93kzMnJCeHh4fj++++RkJAgvveDfbm5uWld0+Xr64uNGzeKsVSUd3X1YB4vj6Ojo1buACDmP13ypb29vXiWUHZ2NrKysrRyl7u7Oz744APk5+cjNTUVEydOhFT630lxSqWy0lM8H/wdkcvlcHJygpmZmdgeFhaGvXv3YvPmzbh27RouXLgAQDsnP2oO7qss797P9ffJ5XL4+vqKr/38/MS7inbu3Blnz57FF198gcTERCQkJCAjI+OR3xPu3r2LnJwcrfxubm4OPz8/rVOVK/reQIbHoo+MkoeHB+zt7XHmzBkx4VhYWMDDwwNA6R2tHna/OABQ7gXMarUaarUaADBhwgSEh4dj3759OHDgAIYPH465c+diwIABlbZVxtbWFpMmTcKUKVPQqVMncblGo8Hrr7+O3NxchIaGIjg4GCUlJRgzZswj5+HBMT04jvv9AqUfsg8T/r3wesmSJeJ1JP/3f/+HGTNmiKeZPImH3+v++5QXw33ljUGj0YjjAKCVFAFoJdkHqVQqeHh4lHud5YNfroiI9M3BwUHMQQ9Tq9WYPHkyunbtqrX8wbscPryDr6LPuT///BPvvPMOwsLCEBQUhHfffRezZs2q8H1Hjhwp3lnx/9u725Cm3j4O4N9mZmrWVpnhA2tawRIhyDeJgb3QSMt8yKktTA3BKFIxn9LUUleW2oNTSlEzS0cSGVj5FEUkoWampa6V2tyC8iCU2hMD//cL8dDUTefd/75j/D6wF+7ynOvaOeP67Trn+p1r2nQOmSE+f/6MwMBACIVCuLu7QyQS4cmTJ+js7NTZ5snJSbb/ny/uzlU201zx4nccDkfnOVhIvPz9f/TFrentCgoKsHHjRq2y3y+yzqTvOwJMpXq8fPkS+/btQ2hoKKytrREcHDzv55iLvvbPNDPGTk5OsueytrYWEokE+/fvh5eXF5KSkhAWFjbvPnW1c+Y51/W7gfzvUU4f+SstXboUgYGBqKysxPj4+Kzy+XLSHB0dZ63l193dDYFAAIZhkJmZCTs7O0RFRaG6uhoBAQF4+PCh3rKF8PX1haurK86ePcu+9/79e3R0dKCsrAxHjhyBh4eHVqK4Pk5OThgeHsaXL1/Y9169egUTE5MF3dWSyWRIS0tDQkICfHx82FyEP93p8vn8WTkkERERuH37NpycnGadi66uLggEAoPrEQgE+PTpE7hcLvh8Pvh8PhiGQX5+PgUSQsj/zXTfNN0v8fl8lJeXo7293eB91dbWwt/fH1lZWQgKCmLjgK56VSqVVr337t1Dc3OzwfU2NzfD0tISpaWlOHToEFxdXaFSqbT6VrVajW/fvrF/9/T0sE+t1hd3gakf/7/nes2Ve/bfMDReWllZYfXq1Vp3xT58+AA3NzdMTk5izZo1YBiGPa729vYoKCjA27dvF9W+iYkJ1NfXIz8/HzExMfD09MTXr18B6I7JS5Ys0bk/fXF3pu/fv2t9h16/fs0+mKWmpgbR0dFITU2Fn58feDweRkdH2TbpaoOVlRWsra21zrlGo0Fvb++i4jv599Ggj/y1jh8/DhsbG4hEIty/fx8qlQp9fX04d+4cTp06hW3btuncNiIiAtXV1airq8PQ0BDy8/PR398PkUiEVatWoaWlBTk5OVAqlejp6cGLFy/g7Oyst2yhMjIytJL3V65cCQ6HgwcPHuDjx49oaGhgnzA6PZ3H0tISg4ODWsEKANzc3LBhwwYkJiZCLpejra0N2dnZ8Pb2Bo/Hm7ctXC4Xjx8/hkqlQmdnJxITE7Xq/VNCQ0PR3d2NkpISKJVKVFZWoqurC9u3b8eBAwegUChQUFCAoaEh1NXVobq6GgcPHjS4Hnd3dzg4OODEiROQy+Xo6upCWloaOBzOgq+OEkLInxYREYGqqircvXsXw8PDkEqluHPnDhwdHQ3e1/QsF7lcjnfv3iE5ORkMw8zZb4eHh6OhoQHXr1+HUqlETU0Nrl69qveioFqtxtOnT7VearUaXC4XIyMjaG1thUqlQklJCZqamrTq1Wg0SE5OhkKhgEwmQ2NjI5vOoC/uAoCLiwtkMhkUCgXa29tRUVFh8LHRZzHxMiwsDIWFhWhtbcXAwADOnDmDLVu2gMvlIjw8HJcvX0ZLSwuUSiX70JrFnFNg6s6Yubk5mpqaoFar8ezZM/ap37pi8vSdYrlcrjXYBvTH3bmkpaVBoVCgsbERVVVVEIvFAKZSRJ4/f47BwUG8efMGcXFx0Gg0bJssLCzAMMycg/TIyEhIpVI8evQIAwMDSE9Px69fv2atZ0n+DjS9k/y1li9fjhs3buDmzZsoLS2FUqkEh8OBs7MzsrOz4evrq3PbXbt2gWEYXLlyBQzDQCgUoqKiAps2bQIwtQagRCKBn58fzMzM4O3tjaNHj2LZsmU6yxbKyckJkZGR7BTE9evXIzMzE8XFxbh06RIEAgG7PEN/fz9cXV0hFouRm5sLtVqtNRjicDgoKipCVlYWgoODYWFhgb179yI+Pn5BbZFIJMjMzISPjw/WrVsHkUgEU1NT9PX1YefOnQv+TPNxcHBAUVER8vLyUFhYCEdHRxQVFbE5LNeuXcP58+dRXl4OW1tbJCUlISgoyOB6TExMUFxcjJycHISEhMDMzAyenp5ITk7+Y5+FEEIM5e3tjdHRUUilUoyMjLB9oFAoNHhfx44dQ0pKCkJCQrBixQrs2LEDYrF4Vp4WMJUzl5eXB6lUiry8PNjZ2UEikehdmLu+vh719fVa7yUmJiI8PBwdHR2IjY0FMDVIS0lJwcWLF/Hz508AgFAohK2tLYKDg8HlciGRSODi4gJg/rgbGxuLlJQUBAQEQCAQIDY2FjExMQYfH10WEy+joqIwNjaG+Ph4aDQauLu7Iz09HQBw+PBh/PjxA6dPn8bY2BiEQiHKyspgY2OzqPaZmpriwoULyM3Nxa1bt2Bvb4/o6GgUFhair68PmzdvnrUNj8dDQEAA4uPjkZCQoFWmL+5OLwn1Ow8PD4jFYpibmyMuLo5dN/DkyZNITU2Fv78/eDwedu/eDUtLS/b75uXlBZlMhj179mgtxQRMXXSYmJhARkYGxsfHsXXrVlRVVWHt2rWLOkbk37XkH5oTRQghhBBCiNFpa2tDWFgYent79T44jhg/mt5JCCGEEEIIIUaMBn2EEEIIIYQQYsRoeichhBBCCCGEGDG600cIIYQQQgghRowGfYQQQgghhBBixGjQRwghhBBCCCFGjAZ9hBBCCCGEEGLEaNBHCCGEEEIIIUaMBn2EEEIIIYQQYsT+A7++YnrXuosOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize absolute numbers of women living in countries of a certain GNI and FLPR\n",
    "\n",
    "# Create  figure with two subplots in one row\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(15,5))\n",
    "\n",
    "# Create bar plots\n",
    "ax1.bar(bin_labels, df_comb_2017.groupby('gni_level')['f_population'].sum(axis=1), alpha=0.7)\n",
    "ax2.bar(bin_labels, df_comb_2017.groupby('flpr_level')['f_population'].sum(axis=1), alpha=0.9, color='#79cf6e')\n",
    "\n",
    "# Create titels, name axis\n",
    "ax1.set_title('Female Population at Gross National Income', fontsize=18, pad=15)\n",
    "ax1.set_xlabel('Gross National Income', fontsize=14)\n",
    "ax1.set_ylabel(\"Population in 1,000,000,000\", fontsize=14)\n",
    "ax2.set_title('Female Population at FLPR', fontsize=18, pad=15)\n",
    "ax2.set_xlabel('Female Labour Force Participation',  fontsize=14)\n",
    "ax2.set_ylabel(\"Population in 1,000,000,000\", fontsize=14)\n",
    "\n",
    "# Annotate total numbers\n",
    " \n",
    "# count totals \n",
    "fpop_total = sum(df_comb_2017['f_population'])\n",
    "\n",
    "for i in ax1.patches:\n",
    "    ax1.text(i.get_x()+.15, i.get_height()-100000000, str(round((i.get_height()/fpop_total)*100,2)) + '%', fontsize=14, color='white', weight='bold')\n",
    "for i in ax2.patches:\n",
    "    ax2.text(i.get_x()+.15, i.get_height()-100000000, str(round((i.get_height()/fpop_total)*100,2)) + '%', fontsize=14, color='white', weight='bold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2017 most women lived in states of moderately high (35.19%) or low (33.01%) income. At the same time, most women (33.81%) lived in countries of very low employment participation rates under 45.1%. 30.62% of all women lived in countries of high FLPR, meaning they live at employment participation rates between 60.9 to 86%.  \n",
    "\n",
    "This result comes with a surprise. Since we previously found that countries of very low/high FLPRs are mostly very low income countries.   \n",
    "There is simple explanation to it, the populations of China and India are proportionally much higher compared to the rest of the world. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considered global female population of this report: 3698561700.\n"
     ]
    }
   ],
   "source": [
    "print(\"Considered global female population of this report: {}.\".format(fpop_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>flpr</th>\n",
       "      <th>gni</th>\n",
       "      <th>region</th>\n",
       "      <th>dev_status</th>\n",
       "      <th>flpr_change</th>\n",
       "      <th>gni_level</th>\n",
       "      <th>flpr_level</th>\n",
       "      <th>f_population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>China</td>\n",
       "      <td>2017</td>\n",
       "      <td>61.5</td>\n",
       "      <td>8690.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-18.861789</td>\n",
       "      <td>moderately high</td>\n",
       "      <td>high</td>\n",
       "      <td>683000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>India</td>\n",
       "      <td>2017</td>\n",
       "      <td>27.2</td>\n",
       "      <td>1820.0</td>\n",
       "      <td>Asia &amp; Pacific</td>\n",
       "      <td>Global South</td>\n",
       "      <td>-29.411765</td>\n",
       "      <td>low</td>\n",
       "      <td>very low</td>\n",
       "      <td>645000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year  flpr     gni          region    dev_status  flpr_change  \\\n",
       "33   China  2017  61.5  8690.0  Asia & Pacific  Global South   -18.861789   \n",
       "71   India  2017  27.2  1820.0  Asia & Pacific  Global South   -29.411765   \n",
       "\n",
       "          gni_level flpr_level  f_population  \n",
       "33  moderately high       high     683000000  \n",
       "71              low   very low     645000000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comb_2017.query('(country == \"China\") | (country == \"India\")')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "China's female population is about 18.5%, India's 17.4% of the total global female population considered in this report.  \n",
    "China has a moderately high income and also a high number of female employment. India on the other hand has a very low FLPR level at a low national income. The extraordinary size of both countries compared to the rest of the world may bias the findings of the FLPR rates compared to the GNI. While the findings may show a relationship, our findings may not explain the situation of the majority of women in the world.\n",
    "\n",
    "To visualize the amount if women living under the investigated conditions, we are going to consider the population size in our scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create scatterplot considering population sizes.\n",
    "# calculate markersizes for each region\n",
    "def get_markersize(population_series):\n",
    "    # reduce markersize for large populations (China and India) to avoid overplotting\n",
    "    m = [30*3**(n*100 / fpop_total) if n < 600000000 else 80*1.3**(n*100 / fpop_total) for n in population_series]\n",
    "    return m\n",
    "\n",
    "# figure size \n",
    "plt.rcParams['figure.figsize']= [15,10]\n",
    "\n",
    "# List of all regions of our dataset\n",
    "regions = df_comb_2017['region'].unique()\n",
    "\n",
    "# Highlight remarkable countries: start by adding 10 largest populated nations  \n",
    "countries = ['China', 'India', 'Indonesia','Brazil', 'United States','Nigeria', 'Pakistan', 'Russia', 'Mexico', 'Bangladesh']\n",
    "\n",
    "# plots for every country, assigning one color for each world region\n",
    "for region in regions:\n",
    "    # calculate markersize for each country\n",
    "    m = get_markersize(df_comb_2017.query(\"region == @region\")['f_population'])\n",
    "    plt.scatter(df_comb_2017.query(\"region == @region\")['gni'], df_comb_2017.query(\"region == @region\")['flpr'], label=region,alpha=.7,s=m)\n",
    "    # add extreme-value countries of every region to our country highlighting list\n",
    "    countries.append(df_comb_2017.query('region == @region').nlargest(1, 'flpr').iloc[0]['country'])\n",
    "    countries.append(df_comb_2017.query('region == @region').nsmallest(1, 'flpr').iloc[0]['country'])\n",
    "    countries.append(df_comb_2017.query('region == @region').nlargest(1, 'gni').iloc[0]['country'])\n",
    "    countries.append(df_comb_2017.query('region == @region').nsmallest(1, 'gni').iloc[0]['country'])\n",
    "    \n",
    "# titles and labels    \n",
    "plt.title('Female Population Participating in Employment and National Income', fontsize=18).set_position([.5, 1.05])\n",
    "plt.xlabel('Gross National Income (USD)', fontsize=14)\n",
    "plt.ylabel('Female Labour Participation Rate %', fontsize=14)\n",
    "legend = plt.legend(fontsize=12)\n",
    "\n",
    "# Reduce legend marker sizes of Asia & Pacific and North America \n",
    "for i in range(6): \n",
    "    legend.legendHandles[i]._sizes = [150]\n",
    "\n",
    "# Remove duplicates in the country highlight list while keeping the order\n",
    "u_countries = []\n",
    "list(map(lambda country: u_countries.append(country) if country not in u_countries else None, countries))\n",
    "\n",
    "# create a list of coordinates of highlight countries \n",
    "x_coords = [df_comb_2017.query('country == @country').iloc[0]['gni'] for country in u_countries]\n",
    "y_coords = [df_comb_2017.query('country == @country').iloc[0]['flpr'] for country in u_countries]\n",
    "\n",
    "# annotate highlight countries\n",
    "for i,country in enumerate(u_countries):\n",
    "    x = x_coords[i]\n",
    "    y = y_coords[i]\n",
    "    plt.scatter(x, y, marker='.', color='black')\n",
    "    plt.text(x+500, y+1.5, country, fontsize=12, rotation=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please be aware that the population sizes of China and India are severely underestimated. To avoid overlap their size was drastically reduced.   \n",
    "\n",
    "The graph above illustrates that the overwhelming majorty of women live in countries of comparitively low income rates. There is a clear trend of African countries of very low income having comparatively high employment participation rates. On the lower end, we mostly find Arab States and some Southwest Asian States (e.g. Iran, Afghanistan, Pakistan, India). \n",
    "\n",
    "European and North American countries have much higher income rates an score mostly moderately at the FLPR scale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Does female employment participation depend on the branches of trade of a country?**\n",
    "\n",
    "This part of the research is looking into the sectors of female employment around the globe. Do women typically work in the industrial, agriculture or service sector? Are FLPRs typically higher or lower, if there is a dominant economic branch in a region?\n",
    "\n",
    "We are going to start by looking at the global means of each sector and how they developed over time. First, we will line plot each sector over the period 1991-2017. We will add pie charts of the years 1991 and 2017 to visualize the composition of the female labour market 1991 and 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot line graphs of mean FLPRs \n",
    "#parameters\n",
    "plt.rcParams['figure.figsize']= [10,8]\n",
    "\n",
    "# plot global development of service sector\n",
    "plt.plot([str(i) for i in range(1991,2018)], df_sectors.groupby('year')['service'].mean(), label=\" Service\")\n",
    "# plot global development of service sector\n",
    "plt.plot([str(i) for i in range(1991,2018)], df_sectors.groupby('year')['industry'].mean(), label=\"Industry\")\n",
    "plt.plot([str(i) for i in range(1991,2018)], df_sectors.groupby('year')['agriculture'].mean(), label=\"Agriculture\")\n",
    "\n",
    "#titles\n",
    "plt.xlabel('')\n",
    "plt.ylabel('Female Employment Participation Rate %', fontsize=14)\n",
    "plt.title('Female Labour Participation by Economic Sector, 1991-2017', fontsize=18).set_position([.5, 1.05])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create pie plots, two on each axes.\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(10,10))\n",
    "\n",
    "# calculate totals of industry, service, agriculture\n",
    "industries_1991 = df_sectors.query('year == \"1991\"').loc[:, ['service', 'industry', 'agriculture']].sum()\n",
    "industries_2017 = df_sectors.query('year == \"2017\"').loc[:, ['service', 'industry', 'agriculture']].sum()\n",
    "\n",
    "\n",
    "# pie plots \n",
    "ax1.pie(industries_1991, labels=industries_1991.index, autopct='%.2f' )\n",
    "ax2.pie(industries_2017, labels=industries_2017.index, autopct='%.2f' )\n",
    "\n",
    "# titles\n",
    "ax1.set_title(\"1991 Female Workers Sectos\", fontsize=14)\n",
    "ax2.set_title(\"2017 Female Workers Sectors\", fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The global average female labour market changed in the period of 1991 to 2017. By 2017, the service sector is globally the most meaningful economic sector of female employment (60.63%). It's importance has grown significantly. In comparison to 1991 the percentage of women working in the service sector is 12% higher. The growth mostly happened at the cost of the agricultural sector, which is about 8% less compared to 1991. The industrial sector is comparatively small in female employment. Since 1991 it has decreased from 15.4% to 12% in the year 2017.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine sectors with our combined dataframe\n",
    "df_comb_2017 = pd.merge(df_comb_2017, df_sectors, how='inner', left_on=['country', 'year'], right_on=['country', 'year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flpr_level</th>\n",
       "      <th>f_population</th>\n",
       "      <th>service</th>\n",
       "      <th>industry</th>\n",
       "      <th>agriculture</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>very low</td>\n",
       "      <td>1250325900</td>\n",
       "      <td>60.277778</td>\n",
       "      <td>15.592000</td>\n",
       "      <td>24.131867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>low</td>\n",
       "      <td>678256000</td>\n",
       "      <td>70.695349</td>\n",
       "      <td>12.888837</td>\n",
       "      <td>16.414605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>moderately high</td>\n",
       "      <td>637488500</td>\n",
       "      <td>75.461364</td>\n",
       "      <td>11.230091</td>\n",
       "      <td>13.315864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high</td>\n",
       "      <td>1132491300</td>\n",
       "      <td>39.788409</td>\n",
       "      <td>8.524477</td>\n",
       "      <td>51.683409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        flpr_level  f_population    service   industry  agriculture\n",
       "0         very low    1250325900  60.277778  15.592000    24.131867\n",
       "1              low     678256000  70.695349  12.888837    16.414605\n",
       "2  moderately high     637488500  75.461364  11.230091    13.315864\n",
       "3             high    1132491300  39.788409   8.524477    51.683409"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# analyse sectors\n",
    "sectors = ['service', 'industry', 'agriculture']\n",
    "\n",
    "# get population for each FLPR level\n",
    "sorted_sectors = df_comb_2017.groupby('flpr_level', as_index=False)['f_population'].sum()\n",
    "\n",
    "# add mean percentages of sectors of very low, low, moderately high and high FLPR countries\n",
    "for sector in sectors:\n",
    "    right = df_comb_2017.groupby('flpr_level', as_index=False)[sector].mean()\n",
    "    sorted_sectors = pd.merge(sorted_sectors, right)\n",
    "\n",
    "sorted_sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameters\n",
    "width = 0.75\n",
    "plt.rcParams['figure.figsize']= [8,8]\n",
    "\n",
    "# Create stacked bar plots\n",
    "p1 = plt.bar(bin_labels, sorted_sectors['service'], width)\n",
    "p2 = plt.bar(bin_labels, sorted_sectors['agriculture'], width, bottom=sorted_sectors['service'])\n",
    "p3 = plt.bar(bin_labels, sorted_sectors['industry'], width, bottom=sorted_sectors['service']+sorted_sectors['agriculture'])\n",
    "\n",
    "# titles\n",
    "plt.xlabel('Female Labour Participation Rated Countries', fontsize=14)\n",
    "plt.ylabel('Employment Sectors of Women %', fontsize=14)\n",
    "plt.title('Employment Sectors and FLPRs', fontsize=18)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.legend((p1[0], p2[0], p3[0]), ('service', 'agriculture', 'industry'), prop={'size': 14}, loc=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With increasing female labour participation rates we observe a change of the composition of the labour market. From countries of very low to moderately high FLPRs, the service sector participation grows at the cost of the agricultural and industrial sector. The three categories show a dominant service sector, a smaller agricultural sector and an even smaller industrial sector. In countries of moderately high female labour participation a mean of 75.5% of women engage in the service industry, 13.3% in agriculture and 11.2% in industrial work.\n",
    "\n",
    "If we look at countries of highest participation rates, the trend turns around. Following the trend, we would expect an even higher employment in service. Instead, women are mostly employed in agricultural work (51.7%) and are outweighing the service sector (39.8%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(176, 13)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comb_2017.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "### Does any region's female labour force participation outperform other regions?\n",
    "\n",
    "Over the period of 1991 to 2017, we observed female labour force participation rates in 176 countries ranging from 6% to 90.3%. To observe patterns across regions, we segmented our country data into the world regions Asia & Pacific, South/Latin America, North America, Europe, Africa and Arab States. We observe a slow increase of the global average FLPRs. In 1991, the mean FLPR was at 48.5%. By 2017, it had risen to 52.1%. \n",
    "\n",
    "We observed trends of female participation rates that are particular to a world region. However, the variation of female labour participation rates may vary strongly within one world region.      \n",
    "\n",
    "African countries tend to have the highest mean female labour force participation. Among the most extreme FLPRs, we mostly find African states and a few Asian & Pacific States. In 2017, Rwanda reached the top FLPR of 86%, followed by Madagascar (83.6%) and Nepal (82.7%).\n",
    "\n",
    "Female labour particpation rates of Arab States underperform significantly. We find the lowest rates mostly in Arab States. In 2017, Yemen had the lowest rate at 6%, followed by Syria (11.9%) and Jordan (14%). Among the lowest rates is also a noticeable number of Southwest-Asian states like Iran, Afghanistan and Pakistan.  \n",
    "\n",
    "The strongest growth of FLPR was observed in Iraq. By 2017, it had increased by 56.3%. On the other hand, Yemen (-180%) and Syria (-89.91%) experienced dramatic decreased female labour participation rates.\n",
    "\n",
    "The growth rates of Arab States outperformed other regions in both directions. They have significant occurrences of both, extremely positive and negative growth rates. North America was significant in the respect of its median growth. As a region, it was comparatively low at about 5%. We even observe a slightly negative trend since the mid 2000s. \n",
    "\n",
    "If we divide the world by their developmental status of Global North and Global South, we find the Global South to be more heterogeneous in terms of their FLPRs. Extremes tend to occur rather in the Global South. The developed world tends to have rather mediocre female labour participation rates.\n",
    "\n",
    "### Does a country's economic situation drive female employment participation?\n",
    "\n",
    "To investigate the economic situation of a country and its female labour participation, we explored the country's gross national incomes and their economic sectors. \n",
    "\n",
    "Compared to the global FLPR, the GNI shows a more significant growth from 1991 to 2017. The global GNI more than doubled in 27 years. We observed a strong increase in the early 2000s until the year 2014, thereafter it declines. The global FLPR however shows a constant, relatively slow positive trend. The global relationship of these factors seems to be rather weak if any.   \n",
    "\n",
    "Comparing female labour participation rates against a country's economic output directly, results in u-shaped curve. In very poor countries women tend to  participate at high levels in employment. The average FLPR drops for low income and moderately high income countries to mostly very low and low rates. However, the mean FLPRs are slightly increasing at moderately high GNI rates. In rich countries the trend changes towards reversed direction. Country's of moderately high female labour participation outweigh lower rated countries. Yet, rich country's FLPRs don't reach high rates like the poorest countries.      \n",
    "\n",
    "We observe strong variations of FLP rates within different income groups. Especially in very low and low income countries the FLPRs are not consistent at country level. For example, Afghanistan and Cambodia are both very poor countries. Yet, Afghanistan has a rate of 19.5%, while Cambodia a rate of 80.9%. The data suggests the possibility of additional socio-economic, political or cultural factors that drive the degree to which women participate in employment.\n",
    "\n",
    "One other factor could be the type of work that is in demand in an economy. We found out that country's of high female labour participation typically have an unusually dominant agriculture sector (51.7%) compared to service and industry sectors. In all other country's from low to moderately high female labour participation, agricultural employment has a relatively low importance. With rates at 60.3 to 75.5%  the service sector outweighs by far the agricultural sector. \n",
    "\n",
    "In this report we found out that female labour participation varies across the globe significantly. There are patterns of the percentages of female labour participation across regions and according to a country's economy. However, the correlation with the strength of an economy is not straightforward in any case. It is especially insignificant for low to moderately high income countries. There are divergent FLPRs within regions and across different economic outputs. In this respect, we are inspired to ask what other socio-economic, political or cultural factors may drive female labour participation.\n",
    "\n",
    "We have to handle the above results with caution. According to ilostat, female labour participation has been poorly measured and widely underestimated. On the one hand, female work is often not considered as employment. On the other hand, often men report on behalf of women when responding to surveys. In this respect, we expect the data to be biased towards a male view on employment. \n",
    "Another fact that may bias our findings are divergent population numbers of countries. China with 683 million women has the same weight as Montenegro with a population of only 319,000 women. We may expect that the FLPR within a large country like China may vary internally. As we've seen in our research above, this reports findings hold true on a country level, yet may change if we want to describe the situation of women in absolute population numbers.\n"
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    "### Resources\n",
    "\n",
    "The datasets were downloaded from Gapminder.\n",
    "https://www.gapminder.org/data/\n",
    "\n",
    "Further research about the data's details was done at the data provider's websites of ilostat and World Bank.\n",
    "https://data.worldbank.org/indicator/NY.GNP.PCAP.CD\n",
    "https://www.ilo.org/ilostat-files/Documents/description_LFPR_EN.pdf\n",
    "\n",
    "The list of countries by regional classification was taken from Wikimedia.\n",
    "Wikimedia, https://meta.wikimedia.org/wiki/List_of_countries_by_regional_classification\n",
    "\n",
    "Articles used that inspired this report/\n",
    "https://wol.iza.org/articles/female-labor-force-participation-in-developing-countries/long\n",
    "https://ourworldindata.org/female-labor-force-participation-key-facts\n",
    "https://howwegettonext.com/gdp-sucks-here-are-some-better-alternatives-88cf2bfec017\n",
    "\n"
   ]
  }
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