Exploring World Happiness - YanaStamenova.ipynb

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   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: researchpy in c:\\users\\az\\anaconda3\\lib\\site-packages (0.1.7)\n",
      "Requirement already satisfied: numpy in c:\\users\\az\\anaconda3\\lib\\site-packages (from researchpy) (1.16.2)\n",
      "Requirement already satisfied: pandas in c:\\users\\az\\anaconda3\\lib\\site-packages (from researchpy) (0.24.2)\n",
      "Requirement already satisfied: scipy in c:\\users\\az\\anaconda3\\lib\\site-packages (from researchpy) (1.2.1)\n",
      "Requirement already satisfied: statsmodels in c:\\users\\az\\anaconda3\\lib\\site-packages (from researchpy) (0.9.0)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\az\\anaconda3\\lib\\site-packages (from pandas->researchpy) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\users\\az\\anaconda3\\lib\\site-packages (from pandas->researchpy) (2.8.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\az\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas->researchpy) (1.12.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install researchpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from matplotlib.pyplot import figure\n",
    "#from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from scipy.stats import skew, kurtosis\n",
    "# Import a scoring metric to compare methods\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "#for t test\n",
    "import scipy.stats as st\n",
    "import researchpy as rp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring World Happiness\n",
    "\n",
    "## Is there a link between happiness and population density?\n",
    "\n",
    "\n",
    "#### Author: Yana Stamenova\n",
    "#### Date: 26.07.2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Abstract*\n",
    "\n",
    "The main purpose of the author is to explore the paths for good data cleaning and tyding. Together with this, visualizations are made so the data can be more accessible to the reader. Finally the data is analyzed and t-tests are used for accepting/rejecting of hypothesis/ questions asked about the correlations between the data sets. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Used Abbreviations*\n",
    "\n",
    "- **HLE** - Healthy life expectancy\n",
    "- **SD** - Standard deviation\n",
    "- **WB** - World Bank\n",
    "- **est.** - estimate\n",
    "- **HI** - household income\n",
    "- **GWP** - Gallup World Poll"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Introduction*\n",
    "\n",
    "The basic idea of this project is to explore the world happiness report, as it is calculated based on set of variables. What makes people happy and where do the happiest people live? We will also explore additional data about population density in different countries to see what is, if there is any, correlation between happiness and more space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country name                                                 object\n",
       "Year                                                          int64\n",
       "Life Ladder                                                 float64\n",
       "Log GDP per capita                                          float64\n",
       "Social support                                              float64\n",
       "Healthy life expectancy at birth                            float64\n",
       "Freedom to make life choices                                float64\n",
       "Generosity                                                  float64\n",
       "Perceptions of corruption                                   float64\n",
       "Positive affect                                             float64\n",
       "Negative affect                                             float64\n",
       "Confidence in national government                           float64\n",
       "Democratic Quality                                          float64\n",
       "Delivery Quality                                            float64\n",
       "Standard deviation of ladder by country-year                float64\n",
       "Standard deviation/Mean of ladder by country-year           float64\n",
       "GINI index (World Bank estimate)                            float64\n",
       "GINI index (World Bank estimate), average 2000-16           float64\n",
       "gini of household income reported in Gallup, by wp5-year    float64\n",
       "Most people can be trusted, Gallup                          float64\n",
       "Most people can be trusted, WVS round 1981-1984             float64\n",
       "Most people can be trusted, WVS round 1989-1993             float64\n",
       "Most people can be trusted, WVS round 1994-1998             float64\n",
       "Most people can be trusted, WVS round 1999-2004             float64\n",
       "Most people can be trusted, WVS round 2005-2009             float64\n",
       "Most people can be trusted, WVS round 2010-2014             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#start with reading the data\n",
    "world_happiness = pd.read_excel(\"world_happiness_report_2019.xls\", sheet_name = \"Table2.1\")\n",
    "world_happiness.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1704, 26)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Everythin looks fine\n",
    "world_happiness.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Life Ladder</th>\n",
       "      <th>Log GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy at birth</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Positive affect</th>\n",
       "      <th>...</th>\n",
       "      <th>GINI index (World Bank estimate)</th>\n",
       "      <th>GINI index (World Bank estimate), average 2000-16</th>\n",
       "      <th>gini of household income reported in Gallup, by wp5-year</th>\n",
       "      <th>Most people can be trusted, Gallup</th>\n",
       "      <th>Most people can be trusted, WVS round 1981-1984</th>\n",
       "      <th>Most people can be trusted, WVS round 1989-1993</th>\n",
       "      <th>Most people can be trusted, WVS round 1994-1998</th>\n",
       "      <th>Most people can be trusted, WVS round 1999-2004</th>\n",
       "      <th>Most people can be trusted, WVS round 2005-2009</th>\n",
       "      <th>Most people can be trusted, WVS round 2010-2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>2013</td>\n",
       "      <td>7.158000</td>\n",
       "      <td>9.549330</td>\n",
       "      <td>0.902069</td>\n",
       "      <td>70.300003</td>\n",
       "      <td>0.897879</td>\n",
       "      <td>0.021019</td>\n",
       "      <td>0.812863</td>\n",
       "      <td>0.850213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.490882</td>\n",
       "      <td>0.489893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.233445</td>\n",
       "      <td>10.596948</td>\n",
       "      <td>0.928484</td>\n",
       "      <td>72.300003</td>\n",
       "      <td>0.837508</td>\n",
       "      <td>0.221998</td>\n",
       "      <td>0.404276</td>\n",
       "      <td>0.783172</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.341083</td>\n",
       "      <td>0.417473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.304498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300558</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country name  Year  Life Ladder  Log GDP per capita  Social support  \\\n",
       "357       Costa Rica  2013     7.158000            9.549330        0.902069   \n",
       "1603  United Kingdom  2018     7.233445           10.596948        0.928484   \n",
       "\n",
       "      Healthy life expectancy at birth  Freedom to make life choices  \\\n",
       "357                          70.300003                      0.897879   \n",
       "1603                         72.300003                      0.837508   \n",
       "\n",
       "      Generosity  Perceptions of corruption  Positive affect  ...  \\\n",
       "357     0.021019                   0.812863         0.850213  ...   \n",
       "1603    0.221998                   0.404276         0.783172  ...   \n",
       "\n",
       "      GINI index (World Bank estimate)  \\\n",
       "357                              0.493   \n",
       "1603                               NaN   \n",
       "\n",
       "      GINI index (World Bank estimate), average 2000-16  \\\n",
       "357                                            0.490882   \n",
       "1603                                           0.341083   \n",
       "\n",
       "      gini of household income reported in Gallup, by wp5-year  \\\n",
       "357                                            0.489893          \n",
       "1603                                           0.417473          \n",
       "\n",
       "      Most people can be trusted, Gallup  \\\n",
       "357                                  NaN   \n",
       "1603                                 NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1981-1984  \\\n",
       "357                                               NaN   \n",
       "1603                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1989-1993  \\\n",
       "357                                               NaN   \n",
       "1603                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 1994-1998  \\\n",
       "357                                               NaN   \n",
       "1603                                         0.304498   \n",
       "\n",
       "      Most people can be trusted, WVS round 1999-2004  \\\n",
       "357                                               NaN   \n",
       "1603                                              NaN   \n",
       "\n",
       "      Most people can be trusted, WVS round 2005-2009  \\\n",
       "357                                               NaN   \n",
       "1603                                         0.300558   \n",
       "\n",
       "      Most people can be trusted, WVS round 2010-2014  \n",
       "357                                               NaN  \n",
       "1603                                              NaN  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Explanation of Variables:*\n",
    "\n",
    "According to the source (*add link*), the data set does not make use of any imputed missing values in the happiness rankings. The only place such imputation is used is for decomposing a country’s average ladder score into components explained by six hypothesized underlying determinants **(*GDP per person, healthy life expectancy, social support, perceived freedom to make life choice, generosity and perception of corruption*)**. A small number of countries have missing values in one or more of these factors. More information in the [link](https://s3.amazonaws.com/happiness-report/2019/WHR19_Ch2A_Appendix1.pdf).\n",
    "\n",
    "#### Life ladder\n",
    "\n",
    "**Happiness score** or **subjective well-being** (variable name *ladder*): The survey measure is from the January, 2019 release of the Gallup World Poll (GWP) covering years from 2005 to 2018. Unless stated otherwise, it is the national average response to the question of life evaluations. \n",
    "\n",
    "The English wording of the question is:\n",
    "**“Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”**\n",
    "\n",
    "This measure is also referred to as *Cantril life ladder*, or just *life ladder* as it is in the dataset.\n",
    "\n",
    "1. Log GDP per capita \n",
    "\n",
    "The statistics of **GDP per capita** in purchasing power parity (PPP) at constant 2011 international dollar prices are from the November 14, 2018 update of the World Development Indicators (WDI). GDP per capita in 2018 are not yet available as of December 2018. The data is extended from 2017 to 2018, using countryspecific forecasts of real GDP growth in 2018 from different sources.\n",
    "\n",
    "2. Social support  \n",
    "\n",
    "The national average of Yes/No (1/0) answers to the question *If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?*\n",
    "\n",
    "3. Healthy life expectancy at birth\n",
    "\n",
    "**Healthy life expectancies** (HLE) at birth are based on the data extracted from the World Health Organization’s (WHO) Global Health Observatory data repository.\n",
    "\n",
    "4. Freedom to make life choices\n",
    "\n",
    "The national average of responses to the question *Are you satisfied or dissatisfied with your freedom to choose what\n",
    "you do with your life?*\n",
    "\n",
    "5. Generosity    \n",
    "\n",
    "The residual of regressing national average of response to the question *Have you donated money to a charity in the past month?* on GDP per capita.\n",
    "\n",
    "6. Perceptions of corruption              \n",
    "\n",
    "The measure is the national average of the survey responses to two questions in the GWP: *Is corruption widespread throughout\n",
    "the government or not* and *Is corruption widespread within businesses or not?* The overall perception is just the average of the two 0-or-1 responses.\n",
    "\n",
    "- Positive affect - defined as the average of three positive affect measures in GWP: happiness, laugh and enjoyment.                                          \n",
    "- Negative affect - defined as the average of three negative affect measures in GWP: worry, sadness and anger.                                            \n",
    "- Confidence in national government \n",
    "\n",
    "- Democratic Quality        \n",
    "\n",
    "- Delivery Quality \n",
    "\n",
    "- Standard deviation of ladder by country-year - Alternative measures of inequality in happiness scores.                \n",
    "- Standard deviation/Mean of ladder by country-year - Alternative measures of inequality in happiness scores.\n",
    "\n",
    "- GINI index (World Bank estimate) - GINI index from the World Bank from the World Development Indicators.                     \n",
    "- GINI index (World Bank estimate), average 2000-16           \n",
    "- gini of household income reported in Gallup, by wp5-year - Gini of household income    \n",
    " \n",
    "- Most people can be trusted - Variables in the expanded data set: The question’s English wording is “Generally speaking, would you say that most people can be trusted or that you have to be careful in dealing with people?” This indicator has a limited coverage. \n",
    "\n",
    "More information about the report variables can be found [here.](https://s3.amazonaws.com/happiness-report/2019/WHR19_Ch2A_Appendix1.pdf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a good idea to make our column labels more Python friendly. This could be useful for our work ahead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_happiness.rename(columns={\n",
    "    \"Country name\":\"country_name\",\n",
    "    \"Year\": \"year\",\n",
    "    \"Life Ladder\": \"life_ladder\",\n",
    "    \"Log GDP per capita\" :\"log_GDP_per_capita\",\n",
    "    \"Social support\"  :\"social_support\",\n",
    "    \"Healthy life expectancy at birth\" : \"HLE_at_birth\",\n",
    "    \"Freedom to make life choices\" : \"freedom_to_make_life_choices\",\n",
    "    \"Generosity\" :\"generosity\",\n",
    "    \"Perceptions of corruption\"  : \"perceptions_of_corruption\",\n",
    "    \"Positive affect\"  : \"positive_affect\",\n",
    "    \"Negative affect\"  : \"negative_affect\",\n",
    "    \"Confidence in national government\"  : \"confidence_in_national_government\",\n",
    "    \"Democratic Quality\" : \"democratic_quality\",\n",
    "    \"Delivery Quality\" : \"delivery_quality\",\n",
    "    \"Standard deviation of ladder by country-year\" : \"SD_of_ladder_by_country_year\",\n",
    "    \"Standard deviation/Mean of ladder by country-year\" : \"SD_mean_of_ladder_by_country_year\",\n",
    "    \"GINI index (World Bank estimate)\" : \"GINI_index_WB_est\",\n",
    "    \"GINI index (World Bank estimate), average 2000-16\": \"GINI_index_WB_est_average_2000_16\",\n",
    "    \"gini of household income reported in Gallup, by wp5-year\" : \"GINI_HI_wp5_year\",\n",
    "    \"Most people can be trusted, Gallup\" : \"most_people_can_be_trusted\",\n",
    "    \"Most people can be trusted, WVS round 1981-1984\" : \"most_people_can_be_trusted_1981_1984\",\n",
    "    \"Most people can be trusted, WVS round 1989-1993\" : \"most_people_can_be_trusted_1989_1993\",\n",
    "    \"Most people can be trusted, WVS round 1994-1998\" : \"most_people_can_be_trusted_1994_1998\",\n",
    "    \"Most people can be trusted, WVS round 1999-2004\" : \"most_people_can_be_trusted_1999_2004\",\n",
    "    \"Most people can be trusted, WVS round 2005-2009\" : \"most_people_can_be_trusted_2005_2009\",\n",
    "    \"Most people can be trusted, WVS round 2010-2014\" : \"most_people_can_be_trusted_2010_2014\"\n",
    "\n",
    "}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_name</th>\n",
       "      <th>year</th>\n",
       "      <th>life_ladder</th>\n",
       "      <th>log_GDP_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>HLE_at_birth</th>\n",
       "      <th>freedom_to_make_life_choices</th>\n",
       "      <th>generosity</th>\n",
       "      <th>perceptions_of_corruption</th>\n",
       "      <th>positive_affect</th>\n",
       "      <th>...</th>\n",
       "      <th>GINI_index_WB_est</th>\n",
       "      <th>GINI_index_WB_est_average_2000_16</th>\n",
       "      <th>GINI_HI_wp5_year</th>\n",
       "      <th>most_people_can_be_trusted</th>\n",
       "      <th>most_people_can_be_trusted_1981_1984</th>\n",
       "      <th>most_people_can_be_trusted_1989_1993</th>\n",
       "      <th>most_people_can_be_trusted_1994_1998</th>\n",
       "      <th>most_people_can_be_trusted_1999_2004</th>\n",
       "      <th>most_people_can_be_trusted_2005_2009</th>\n",
       "      <th>most_people_can_be_trusted_2010_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>Niger</td>\n",
       "      <td>2013</td>\n",
       "      <td>3.71633</td>\n",
       "      <td>6.77317</td>\n",
       "      <td>0.695814</td>\n",
       "      <td>50.959999</td>\n",
       "      <td>0.825387</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>0.710963</td>\n",
       "      <td>0.6502</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.36875</td>\n",
       "      <td>0.390307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country_name  year  life_ladder  log_GDP_per_capita  social_support  \\\n",
       "1109        Niger  2013      3.71633             6.77317        0.695814   \n",
       "\n",
       "      HLE_at_birth  freedom_to_make_life_choices  generosity  \\\n",
       "1109     50.959999                      0.825387      -0.064   \n",
       "\n",
       "      perceptions_of_corruption  positive_affect  ...  GINI_index_WB_est  \\\n",
       "1109                   0.710963           0.6502  ...                NaN   \n",
       "\n",
       "      GINI_index_WB_est_average_2000_16  GINI_HI_wp5_year  \\\n",
       "1109                            0.36875          0.390307   \n",
       "\n",
       "      most_people_can_be_trusted  most_people_can_be_trusted_1981_1984  \\\n",
       "1109                         NaN                                   NaN   \n",
       "\n",
       "      most_people_can_be_trusted_1989_1993  \\\n",
       "1109                                   NaN   \n",
       "\n",
       "      most_people_can_be_trusted_1994_1998  \\\n",
       "1109                                   NaN   \n",
       "\n",
       "      most_people_can_be_trusted_1999_2004  \\\n",
       "1109                                   NaN   \n",
       "\n",
       "      most_people_can_be_trusted_2005_2009  \\\n",
       "1109                                   NaN   \n",
       "\n",
       "      most_people_can_be_trusted_2010_2014  \n",
       "1109                                   NaN  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the floating value variables \"fill\" the dataset a little bit too much. We need to fix this, to make it more clear and easy to read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_name</th>\n",
       "      <th>year</th>\n",
       "      <th>life_ladder</th>\n",
       "      <th>log_GDP_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>HLE_at_birth</th>\n",
       "      <th>freedom_to_make_life_choices</th>\n",
       "      <th>generosity</th>\n",
       "      <th>perceptions_of_corruption</th>\n",
       "      <th>positive_affect</th>\n",
       "      <th>...</th>\n",
       "      <th>GINI_index_WB_est</th>\n",
       "      <th>GINI_index_WB_est_average_2000_16</th>\n",
       "      <th>GINI_HI_wp5_year</th>\n",
       "      <th>most_people_can_be_trusted</th>\n",
       "      <th>most_people_can_be_trusted_1981_1984</th>\n",
       "      <th>most_people_can_be_trusted_1989_1993</th>\n",
       "      <th>most_people_can_be_trusted_1994_1998</th>\n",
       "      <th>most_people_can_be_trusted_1999_2004</th>\n",
       "      <th>most_people_can_be_trusted_2005_2009</th>\n",
       "      <th>most_people_can_be_trusted_2010_2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>2018</td>\n",
       "      <td>5.682</td>\n",
       "      <td>8.592</td>\n",
       "      <td>0.892</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.824</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.582</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.176</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country_name  year  life_ladder  log_GDP_per_capita  social_support  \\\n",
       "1003      Moldova  2018        5.682               8.592           0.892   \n",
       "\n",
       "      HLE_at_birth  freedom_to_make_life_choices  generosity  \\\n",
       "1003          65.0                         0.824      -0.034   \n",
       "\n",
       "      perceptions_of_corruption  positive_affect  ...  GINI_index_WB_est  \\\n",
       "1003                      0.929            0.582  ...                NaN   \n",
       "\n",
       "      GINI_index_WB_est_average_2000_16  GINI_HI_wp5_year  \\\n",
       "1003                              0.326             0.401   \n",
       "\n",
       "      most_people_can_be_trusted  most_people_can_be_trusted_1981_1984  \\\n",
       "1003                         NaN                                   NaN   \n",
       "\n",
       "      most_people_can_be_trusted_1989_1993  \\\n",
       "1003                                   NaN   \n",
       "\n",
       "      most_people_can_be_trusted_1994_1998  \\\n",
       "1003                                 0.218   \n",
       "\n",
       "      most_people_can_be_trusted_1999_2004  \\\n",
       "1003                                 0.141   \n",
       "\n",
       "      most_people_can_be_trusted_2005_2009  \\\n",
       "1003                                 0.176   \n",
       "\n",
       "      most_people_can_be_trusted_2010_2014  \n",
       "1003                                   NaN  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness = world_happiness.round(3)\n",
    "world_happiness.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets see how much data we have in terms of Years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country_name</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>114</td>\n",
       "      <td>124</td>\n",
       "      <td>146</td>\n",
       "      <td>142</td>\n",
       "      <td>137</td>\n",
       "      <td>145</td>\n",
       "      <td>143</td>\n",
       "      <td>142</td>\n",
       "      <td>147</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_ladder</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>114</td>\n",
       "      <td>124</td>\n",
       "      <td>146</td>\n",
       "      <td>142</td>\n",
       "      <td>137</td>\n",
       "      <td>145</td>\n",
       "      <td>143</td>\n",
       "      <td>142</td>\n",
       "      <td>147</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_GDP_per_capita</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>145</td>\n",
       "      <td>140</td>\n",
       "      <td>136</td>\n",
       "      <td>143</td>\n",
       "      <td>139</td>\n",
       "      <td>139</td>\n",
       "      <td>143</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>social_support</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>100</td>\n",
       "      <td>110</td>\n",
       "      <td>113</td>\n",
       "      <td>120</td>\n",
       "      <td>145</td>\n",
       "      <td>142</td>\n",
       "      <td>137</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>146</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLE_at_birth</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>142</td>\n",
       "      <td>138</td>\n",
       "      <td>134</td>\n",
       "      <td>142</td>\n",
       "      <td>140</td>\n",
       "      <td>139</td>\n",
       "      <td>145</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freedom_to_make_life_choices</th>\n",
       "      <td>25</td>\n",
       "      <td>86</td>\n",
       "      <td>101</td>\n",
       "      <td>107</td>\n",
       "      <td>112</td>\n",
       "      <td>122</td>\n",
       "      <td>145</td>\n",
       "      <td>141</td>\n",
       "      <td>136</td>\n",
       "      <td>138</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>146</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generosity</th>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>102</td>\n",
       "      <td>109</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>144</td>\n",
       "      <td>133</td>\n",
       "      <td>133</td>\n",
       "      <td>139</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "      <td>142</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perceptions_of_corruption</th>\n",
       "      <td>25</td>\n",
       "      <td>83</td>\n",
       "      <td>98</td>\n",
       "      <td>107</td>\n",
       "      <td>111</td>\n",
       "      <td>119</td>\n",
       "      <td>138</td>\n",
       "      <td>132</td>\n",
       "      <td>130</td>\n",
       "      <td>136</td>\n",
       "      <td>133</td>\n",
       "      <td>131</td>\n",
       "      <td>136</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positive_affect</th>\n",
       "      <td>26</td>\n",
       "      <td>89</td>\n",
       "      <td>101</td>\n",
       "      <td>108</td>\n",
       "      <td>113</td>\n",
       "      <td>120</td>\n",
       "      <td>145</td>\n",
       "      <td>142</td>\n",
       "      <td>135</td>\n",
       "      <td>141</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>146</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>negative_affect</th>\n",
       "      <td>26</td>\n",
       "      <td>88</td>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>113</td>\n",
       "      <td>120</td>\n",
       "      <td>146</td>\n",
       "      <td>142</td>\n",
       "      <td>137</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>142</td>\n",
       "      <td>146</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confidence_in_national_government</th>\n",
       "      <td>24</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>103</td>\n",
       "      <td>94</td>\n",
       "      <td>103</td>\n",
       "      <td>122</td>\n",
       "      <td>123</td>\n",
       "      <td>125</td>\n",
       "      <td>132</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>134</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>democratic_quality</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>101</td>\n",
       "      <td>110</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>145</td>\n",
       "      <td>140</td>\n",
       "      <td>136</td>\n",
       "      <td>144</td>\n",
       "      <td>142</td>\n",
       "      <td>141</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delivery_quality</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>113</td>\n",
       "      <td>123</td>\n",
       "      <td>145</td>\n",
       "      <td>140</td>\n",
       "      <td>136</td>\n",
       "      <td>144</td>\n",
       "      <td>142</td>\n",
       "      <td>141</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD_of_ladder_by_country_year</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>114</td>\n",
       "      <td>124</td>\n",
       "      <td>146</td>\n",
       "      <td>142</td>\n",
       "      <td>137</td>\n",
       "      <td>145</td>\n",
       "      <td>143</td>\n",
       "      <td>142</td>\n",
       "      <td>147</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD_mean_of_ladder_by_country_year</th>\n",
       "      <td>27</td>\n",
       "      <td>89</td>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>114</td>\n",
       "      <td>124</td>\n",
       "      <td>146</td>\n",
       "      <td>142</td>\n",
       "      <td>137</td>\n",
       "      <td>145</td>\n",
       "      <td>143</td>\n",
       "      <td>142</td>\n",
       "      <td>147</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GINI_index_WB_est</th>\n",
       "      <td>17</td>\n",
       "      <td>42</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>64</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>67</td>\n",
       "      <td>64</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GINI_index_WB_est_average_2000_16</th>\n",
       "      <td>25</td>\n",
       "      <td>78</td>\n",
       "      <td>92</td>\n",
       "      <td>100</td>\n",
       "      <td>99</td>\n",
       "      <td>108</td>\n",
       "      <td>127</td>\n",
       "      <td>123</td>\n",
       "      <td>121</td>\n",
       "      <td>127</td>\n",
       "      <td>125</td>\n",
       "      <td>124</td>\n",
       "      <td>130</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GINI_HI_wp5_year</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>118</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "      <td>134</td>\n",
       "      <td>142</td>\n",
       "      <td>140</td>\n",
       "      <td>141</td>\n",
       "      <td>146</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>most_people_can_be_trusted</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>62</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>most_people_can_be_trusted_1981_1984</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>most_people_can_be_trusted_1989_1993</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>most_people_can_be_trusted_1994_1998</th>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "      <td>44</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>most_people_can_be_trusted_1999_2004</th>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>most_people_can_be_trusted_2005_2009</th>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "      <td>44</td>\n",
       "      <td>42</td>\n",
       "      <td>46</td>\n",
       "      <td>50</td>\n",
       "      <td>52</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>most_people_can_be_trusted_2010_2014</th>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>54</td>\n",
       "      <td>52</td>\n",
       "      <td>55</td>\n",
       "      <td>54</td>\n",
       "      <td>55</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year                                  2005  2006  2007  2008  2009  2010  \\\n",
       "country_name                            27    89   102   110   114   124   \n",
       "life_ladder                             27    89   102   110   114   124   \n",
       "log_GDP_per_capita                      27    89   102   110   113   123   \n",
       "social_support                          27    89   100   110   113   120   \n",
       "HLE_at_birth                            27    89   102   110   113   123   \n",
       "freedom_to_make_life_choices            25    86   101   107   112   122   \n",
       "generosity                               1    81   102   109   113   123   \n",
       "perceptions_of_corruption               25    83    98   107   111   119   \n",
       "positive_affect                         26    89   101   108   113   120   \n",
       "negative_affect                         26    88   102   110   113   120   \n",
       "confidence_in_national_government       24    87    99   103    94   103   \n",
       "democratic_quality                      27    89   101   110   113   123   \n",
       "delivery_quality                        27    89   102   110   113   123   \n",
       "SD_of_ladder_by_country_year            27    89   102   110   114   124   \n",
       "SD_mean_of_ladder_by_country_year       27    89   102   110   114   124   \n",
       "GINI_index_WB_est                       17    42    52    52    53    64   \n",
       "GINI_index_WB_est_average_2000_16       25    78    92   100    99   108   \n",
       "GINI_HI_wp5_year                         0     0     0     0   105   118   \n",
       "most_people_can_be_trusted               0     0     0     0   109    62   \n",
       "most_people_can_be_trusted_1981_1984     5     5     9     9     8    10   \n",
       "most_people_can_be_trusted_1989_1993     7    11    16    14    16    17   \n",
       "most_people_can_be_trusted_1994_1998    13    32    45    37    44    47   \n",
       "most_people_can_be_trusted_1999_2004    12    21    36    33    37    39   \n",
       "most_people_can_be_trusted_2005_2009    19    31    41    44    42    46   \n",
       "most_people_can_be_trusted_2010_2014    15    35    43    45    50    53   \n",
       "\n",
       "year                                  2011  2012  2013  2014  2015  2016  \\\n",
       "country_name                           146   142   137   145   143   142   \n",
       "life_ladder                            146   142   137   145   143   142   \n",
       "log_GDP_per_capita                     145   140   136   143   139   139   \n",
       "social_support                         145   142   137   142   142   142   \n",
       "HLE_at_birth                           142   138   134   142   140   139   \n",
       "freedom_to_make_life_choices           145   141   136   138   140   140   \n",
       "generosity                             144   133   133   139   138   138   \n",
       "perceptions_of_corruption              138   132   130   136   133   131   \n",
       "positive_affect                        145   142   135   141   142   142   \n",
       "negative_affect                        146   142   137   142   142   142   \n",
       "confidence_in_national_government      122   123   125   132   130   131   \n",
       "democratic_quality                     145   140   136   144   142   141   \n",
       "delivery_quality                       145   140   136   144   142   141   \n",
       "SD_of_ladder_by_country_year           146   142   137   145   143   142   \n",
       "SD_mean_of_ladder_by_country_year      146   142   137   145   143   142   \n",
       "GINI_index_WB_est                       70    72    63    67    64    26   \n",
       "GINI_index_WB_est_average_2000_16      127   123   121   127   125   124   \n",
       "GINI_HI_wp5_year                       138   138   134   142   140   141   \n",
       "most_people_can_be_trusted               8     1     0     0     0     0   \n",
       "most_people_can_be_trusted_1981_1984    10    10    10    10    10    10   \n",
       "most_people_can_be_trusted_1989_1993    16    18    17    17    18    18   \n",
       "most_people_can_be_trusted_1994_1998    48    51    49    50    51    51   \n",
       "most_people_can_be_trusted_1999_2004    39    39    39    38    39    40   \n",
       "most_people_can_be_trusted_2005_2009    50    52    51    51    52    51   \n",
       "most_people_can_be_trusted_2010_2014    55    55    54    52    55    54   \n",
       "\n",
       "year                                  2017  2018  \n",
       "country_name                           147   136  \n",
       "life_ladder                            147   136  \n",
       "log_GDP_per_capita                     143   127  \n",
       "social_support                         146   136  \n",
       "HLE_at_birth                           145   132  \n",
       "freedom_to_make_life_choices           146   136  \n",
       "generosity                             142   126  \n",
       "perceptions_of_corruption              136   129  \n",
       "positive_affect                        146   135  \n",
       "negative_affect                        146   135  \n",
       "confidence_in_national_government      134   123  \n",
       "democratic_quality                     147     0  \n",
       "delivery_quality                       147     0  \n",
       "SD_of_ladder_by_country_year           147   136  \n",
       "SD_mean_of_ladder_by_country_year      147   136  \n",
       "GINI_index_WB_est                        1     0  \n",
       "GINI_index_WB_est_average_2000_16      130   123  \n",
       "GINI_HI_wp5_year                       146   133  \n",
       "most_people_can_be_trusted               0     0  \n",
       "most_people_can_be_trusted_1981_1984    10     9  \n",
       "most_people_can_be_trusted_1989_1993    18    17  \n",
       "most_people_can_be_trusted_1994_1998    51    49  \n",
       "most_people_can_be_trusted_1999_2004    40    39  \n",
       "most_people_can_be_trusted_2005_2009    52    48  \n",
       "most_people_can_be_trusted_2010_2014    55    50  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.groupby(\"year\").count().T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11398"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.isna().values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information above gives us goose bumps since some variables have very few non-null values. But let's see it in more detail. From the table above we can see there are certain variables which contain very few values during the years. We can prove it with the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country_name: 0\n",
      "year: 0\n",
      "life_ladder: 0\n",
      "log_GDP_per_capita: 28\n",
      "social_support: 13\n",
      "HLE_at_birth: 28\n",
      "freedom_to_make_life_choices: 29\n",
      "generosity: 82\n",
      "perceptions_of_corruption: 96\n",
      "positive_affect: 19\n",
      "negative_affect: 13\n",
      "confidence_in_national_government: 174\n",
      "democratic_quality: 146\n",
      "delivery_quality: 145\n",
      "SD_of_ladder_by_country_year: 0\n",
      "SD_mean_of_ladder_by_country_year: 0\n",
      "GINI_index_WB_est: 1061\n",
      "GINI_index_WB_est_average_2000_16: 202\n",
      "GINI_HI_wp5_year: 369\n",
      "most_people_can_be_trusted: 1524\n",
      "most_people_can_be_trusted_1981_1984: 1579\n",
      "most_people_can_be_trusted_1989_1993: 1484\n",
      "most_people_can_be_trusted_1994_1998: 1086\n",
      "most_people_can_be_trusted_1999_2004: 1213\n",
      "most_people_can_be_trusted_2005_2009: 1074\n",
      "most_people_can_be_trusted_2010_2014: 1033\n"
     ]
    }
   ],
   "source": [
    "#create list with the column names\n",
    "column_names = world_happiness.columns\n",
    "\n",
    "#print the missing values in each column\n",
    "for i in column_names:\n",
    "    print(\"{}: {}\".format(i, world_happiness[i].isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name                             0.000000\n",
       "year                                     0.000000\n",
       "life_ladder                              0.000000\n",
       "log_GDP_per_capita                       1.643192\n",
       "social_support                           0.762911\n",
       "HLE_at_birth                             1.643192\n",
       "freedom_to_make_life_choices             1.701878\n",
       "generosity                               4.812207\n",
       "perceptions_of_corruption                5.633803\n",
       "positive_affect                          1.115023\n",
       "negative_affect                          0.762911\n",
       "confidence_in_national_government       10.211268\n",
       "democratic_quality                       8.568075\n",
       "delivery_quality                         8.509390\n",
       "SD_of_ladder_by_country_year             0.000000\n",
       "SD_mean_of_ladder_by_country_year        0.000000\n",
       "GINI_index_WB_est                       62.265258\n",
       "GINI_index_WB_est_average_2000_16       11.854460\n",
       "GINI_HI_wp5_year                        21.654930\n",
       "most_people_can_be_trusted              89.436620\n",
       "most_people_can_be_trusted_1981_1984    92.664319\n",
       "most_people_can_be_trusted_1989_1993    87.089202\n",
       "most_people_can_be_trusted_1994_1998    63.732394\n",
       "most_people_can_be_trusted_1999_2004    71.185446\n",
       "most_people_can_be_trusted_2005_2009    63.028169\n",
       "most_people_can_be_trusted_2010_2014    60.622066\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.isna().sum() / len(world_happiness) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No matter how we look at it, our data set is a __nightmare__!!! \n",
    "\n",
    "We are missing entire rows with year values. This is not looking very good. We have some relatively good stats, based on the six key determinants, composing the **index** but this is not the case with the other variables. \n",
    "\n",
    "Another good news is that most NaN values are located in the variables from the expanded data set - \"Most people can be trusted\". We can read from the documentation for the data that these indicators have limited coverage. \n",
    "\n",
    "Based on this information we can drop the columns from the expanded data set. The *GINI_indexes* will be removed too, since there are too many missing values.    \n",
    "\n",
    "Further, we will choose a method to fill the NaN values in the rest of the variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name                          object\n",
       "year                                   int64\n",
       "life_ladder                          float64\n",
       "log_GDP_per_capita                   float64\n",
       "social_support                       float64\n",
       "HLE_at_birth                         float64\n",
       "freedom_to_make_life_choices         float64\n",
       "generosity                           float64\n",
       "perceptions_of_corruption            float64\n",
       "positive_affect                      float64\n",
       "negative_affect                      float64\n",
       "confidence_in_national_government    float64\n",
       "democratic_quality                   float64\n",
       "delivery_quality                     float64\n",
       "SD_of_ladder_by_country_year         float64\n",
       "SD_mean_of_ladder_by_country_year    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.drop([\n",
    "    \"GINI_index_WB_est\", \n",
    "    \"GINI_index_WB_est_average_2000_16\",\n",
    "    \"GINI_HI_wp5_year\",\n",
    "    \"most_people_can_be_trusted\",\n",
    "    \"most_people_can_be_trusted_1981_1984\",\n",
    "    \"most_people_can_be_trusted_1989_1993\",\n",
    "    \"most_people_can_be_trusted_1994_1998\",\n",
    "    \"most_people_can_be_trusted_1999_2004\",\n",
    "    \"most_people_can_be_trusted_2005_2009\",\n",
    "    \"most_people_can_be_trusted_2010_2014\"], axis = 1, inplace = True)\n",
    "world_happiness.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the happiness distribution for the entire timeframe. \n",
    "Maybe good idea will be to ask:\n",
    "\n",
    "__What is the distribution of the index for the whole period?__\n",
    "\n",
    "- H0 - Nothing interesting, it is normal, most people are neither happy, nor unhappy constantly.\n",
    "- H1 - The distribution is not normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "\n",
    "cm = plt.cm.get_cmap('twilight_shifted')\n",
    "\n",
    "n, bins, patches = plt.hist(world_happiness.life_ladder, bins = 40, color='green', label = \"life ladder\")\n",
    "\n",
    "for i, p in enumerate(patches):\n",
    "    plt.setp(p, 'facecolor', cm(i/40))\n",
    "    \n",
    "plt.xlabel(\"Life ladder\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Happiness Index\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does not seem very Gaussian (suppose I can combine these two words).\n",
    "\n",
    "It looks like it is positive skewed. The dreams about perfect Gaussian distribution are fading.\n",
    "\n",
    "We still cannot make conclusions. We need to make some calculations. We have a __mean__ for the __life ladder__ = __5.432140__ and __std__ = __1.125504__. \n",
    "\n",
    "We are going to write function to calculate and show important metrics about the happiness index and its composing values. Even that we can see some of the values in the table below it would not be a good idea just to copy/paste it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1704.0</td>\n",
       "      <td>2012.332160</td>\n",
       "      <td>3.688072</td>\n",
       "      <td>2005.000</td>\n",
       "      <td>2009.00000</td>\n",
       "      <td>2012.0000</td>\n",
       "      <td>2015.00000</td>\n",
       "      <td>2018.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_ladder</th>\n",
       "      <td>1704.0</td>\n",
       "      <td>5.437157</td>\n",
       "      <td>1.121149</td>\n",
       "      <td>2.662</td>\n",
       "      <td>4.61125</td>\n",
       "      <td>5.3395</td>\n",
       "      <td>6.27350</td>\n",
       "      <td>8.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_GDP_per_capita</th>\n",
       "      <td>1676.0</td>\n",
       "      <td>9.222456</td>\n",
       "      <td>1.185781</td>\n",
       "      <td>6.457</td>\n",
       "      <td>8.30475</td>\n",
       "      <td>9.4065</td>\n",
       "      <td>10.19275</td>\n",
       "      <td>11.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>social_support</th>\n",
       "      <td>1691.0</td>\n",
       "      <td>0.810567</td>\n",
       "      <td>0.119212</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.74750</td>\n",
       "      <td>0.8330</td>\n",
       "      <td>0.90400</td>\n",
       "      <td>0.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HLE_at_birth</th>\n",
       "      <td>1676.0</td>\n",
       "      <td>63.111970</td>\n",
       "      <td>7.583622</td>\n",
       "      <td>32.300</td>\n",
       "      <td>58.30000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>68.30000</td>\n",
       "      <td>76.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freedom_to_make_life_choices</th>\n",
       "      <td>1675.0</td>\n",
       "      <td>0.733823</td>\n",
       "      <td>0.144107</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.63800</td>\n",
       "      <td>0.7530</td>\n",
       "      <td>0.84800</td>\n",
       "      <td>0.985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generosity</th>\n",
       "      <td>1622.0</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.163367</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>-0.11575</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perceptions_of_corruption</th>\n",
       "      <td>1608.0</td>\n",
       "      <td>0.751330</td>\n",
       "      <td>0.186070</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.69575</td>\n",
       "      <td>0.8060</td>\n",
       "      <td>0.87625</td>\n",
       "      <td>0.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positive_affect</th>\n",
       "      <td>1685.0</td>\n",
       "      <td>0.709376</td>\n",
       "      <td>0.107976</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.62200</td>\n",
       "      <td>0.7190</td>\n",
       "      <td>0.80200</td>\n",
       "      <td>0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>negative_affect</th>\n",
       "      <td>1691.0</td>\n",
       "      <td>0.265670</td>\n",
       "      <td>0.084700</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.2550</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>0.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confidence_in_national_government</th>\n",
       "      <td>1530.0</td>\n",
       "      <td>0.481979</td>\n",
       "      <td>0.192061</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.33500</td>\n",
       "      <td>0.4640</td>\n",
       "      <td>0.61500</td>\n",
       "      <td>0.994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>democratic_quality</th>\n",
       "      <td>1558.0</td>\n",
       "      <td>-0.136053</td>\n",
       "      <td>0.876062</td>\n",
       "      <td>-2.448</td>\n",
       "      <td>-0.79025</td>\n",
       "      <td>-0.2270</td>\n",
       "      <td>0.65025</td>\n",
       "      <td>1.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delivery_quality</th>\n",
       "      <td>1559.0</td>\n",
       "      <td>-0.001396</td>\n",
       "      <td>0.975838</td>\n",
       "      <td>-2.145</td>\n",
       "      <td>-0.71150</td>\n",
       "      <td>-0.2190</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>2.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD_of_ladder_by_country_year</th>\n",
       "      <td>1704.0</td>\n",
       "      <td>2.026708</td>\n",
       "      <td>0.401487</td>\n",
       "      <td>0.863</td>\n",
       "      <td>1.74375</td>\n",
       "      <td>1.9730</td>\n",
       "      <td>2.24225</td>\n",
       "      <td>3.719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD_mean_of_ladder_by_country_year</th>\n",
       "      <td>1704.0</td>\n",
       "      <td>0.392124</td>\n",
       "      <td>0.124668</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.31000</td>\n",
       "      <td>0.3730</td>\n",
       "      <td>0.45600</td>\n",
       "      <td>1.023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count         mean       std       min  \\\n",
       "year                               1704.0  2012.332160  3.688072  2005.000   \n",
       "life_ladder                        1704.0     5.437157  1.121149     2.662   \n",
       "log_GDP_per_capita                 1676.0     9.222456  1.185781     6.457   \n",
       "social_support                     1691.0     0.810567  0.119212     0.290   \n",
       "HLE_at_birth                       1676.0    63.111970  7.583622    32.300   \n",
       "freedom_to_make_life_choices       1675.0     0.733823  0.144107     0.258   \n",
       "generosity                         1622.0     0.000079  0.163367    -0.336   \n",
       "perceptions_of_corruption          1608.0     0.751330  0.186070     0.035   \n",
       "positive_affect                    1685.0     0.709376  0.107976     0.362   \n",
       "negative_affect                    1691.0     0.265670  0.084700     0.083   \n",
       "confidence_in_national_government  1530.0     0.481979  0.192061     0.069   \n",
       "democratic_quality                 1558.0    -0.136053  0.876062    -2.448   \n",
       "delivery_quality                   1559.0    -0.001396  0.975838    -2.145   \n",
       "SD_of_ladder_by_country_year       1704.0     2.026708  0.401487     0.863   \n",
       "SD_mean_of_ladder_by_country_year  1704.0     0.392124  0.124668     0.134   \n",
       "\n",
       "                                          25%        50%         75%       max  \n",
       "year                               2009.00000  2012.0000  2015.00000  2018.000  \n",
       "life_ladder                           4.61125     5.3395     6.27350     8.019  \n",
       "log_GDP_per_capita                    8.30475     9.4065    10.19275    11.770  \n",
       "social_support                        0.74750     0.8330     0.90400     0.987  \n",
       "HLE_at_birth                         58.30000    65.0000    68.30000    76.800  \n",
       "freedom_to_make_life_choices          0.63800     0.7530     0.84800     0.985  \n",
       "generosity                           -0.11575    -0.0220     0.09375     0.678  \n",
       "perceptions_of_corruption             0.69575     0.8060     0.87625     0.983  \n",
       "positive_affect                       0.62200     0.7190     0.80200     0.944  \n",
       "negative_affect                       0.20500     0.2550     0.31500     0.705  \n",
       "confidence_in_national_government     0.33500     0.4640     0.61500     0.994  \n",
       "democratic_quality                   -0.79025    -0.2270     0.65025     1.575  \n",
       "delivery_quality                     -0.71150    -0.2190     0.70000     2.185  \n",
       "SD_of_ladder_by_country_year          1.74375     1.9730     2.24225     3.719  \n",
       "SD_mean_of_ladder_by_country_year     0.31000     0.3730     0.45600     1.023  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Wanted to see the stats for the key variables but then decided I want to see it all\n",
    "# df = world_happiness.iloc[:, 0:13]\n",
    "# df = df.set_index(\"year\")\n",
    "world_happiness.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def important_metrics(data, title):\n",
    "    std_happiness = np.std(data)\n",
    "    mean_happiness = data.mean() #mean value \n",
    "    median_happiness = data.median() #middle of the ordered list\n",
    "    mode_happiness = data.mode() #most often occuring\n",
    "    diff_max_min = data.max() - data.min()\n",
    "    \n",
    "    sk_r_ew_happiness = skew(data)\n",
    "    #If you are thinking sk_r_ew and screw are not linked by chance  - Yes, I like to play with the words :)\n",
    "    \n",
    "    kurtosis_happiness = kurtosis(world_happiness.life_ladder)\n",
    "    #hashtag - platykurtic. #hashtag ILoveThisWord #hashtag ItIsStupidToWriteHashtags\n",
    "    \n",
    "    print(title)\n",
    "    print(\"Standard deviation = {}\".format(std_happiness))\n",
    "    print(\"Mean = {}\".format(mean_happiness))\n",
    "    print(\"Median = {}\".format(median_happiness))\n",
    "    print(\"Mode = {}\".format(mode_happiness))\n",
    "    print(\"Difference between max and min values = {}\".format(diff_max_min))\n",
    "    print(\"Skewness = {}\".format(sk_r_ew_happiness))\n",
    "    print(\"Kurtosis = {}\".format(kurtosis_happiness))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happiness index (life_ladder)\n",
      "Standard deviation = 1.1208199513565953\n",
      "Mean = 5.437156690140837\n",
      "Median = 5.3395\n",
      "Mode = 0    5.252\n",
      "dtype: float64\n",
      "Difference between max and min values = 5.357\n",
      "Skewness = 0.11442161112098703\n",
      "Kurtosis = -0.724115292080358\n"
     ]
    }
   ],
   "source": [
    "important_metrics(world_happiness.life_ladder, \"Happiness index (life_ladder)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What do we have here? \n",
    "- The standard deviation is a little over 1.1\n",
    "- Mean, Median, Mode and Difference between min and max value difference is not larger than 0.2 between any two\n",
    "- The skewness value is near 0, not so far from the symetrical center\n",
    "- The kurtosis is negative, meaning it is a bit flat, but still in the acceptable range of (-2:2)\n",
    "- We need to take into consideration that our data for the countries is missing values for some years. \n",
    "\n",
    "Together all factors, stated above, are still not enough to reject H0 hypothesis. Let's perform a ttest to make a final statement about it. We will make three calculations - with the median, mode, and difference between min and max values. \n",
    "The critical value above which we will reject the null hypothesis is **0.01** (1%)\n",
    "\n",
    "*Since we have the standard deviation, we can perform Z-test but we decided to stick with the t-test because it is good with small data sets*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.463744029028542e-05\n",
      "1.2838403345132013e-11\n",
      "0.0032077986270270926\n"
     ]
    }
   ],
   "source": [
    "print(st.ttest_1samp(world_happiness.life_ladder, 5.326).pvalue) #median\n",
    "print(st.ttest_1samp(world_happiness.life_ladder, 5.252).pvalue) #mode \n",
    "print(st.ttest_1samp(world_happiness.life_ladder, 5.357).pvalue) #dif_max_min\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-values for the three tests are way below our significance level. \n",
    "\n",
    "Conclusion: Given the results from the last ttests we can **reject the H0 hypothesis**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2005    6.446259\n",
       "2006    5.196899\n",
       "2007    5.418275\n",
       "2008    5.418509\n",
       "2009    5.457667\n",
       "2010    5.496806\n",
       "2011    5.424082\n",
       "2012    5.443754\n",
       "2013    5.394562\n",
       "2014    5.389021\n",
       "2015    5.404035\n",
       "2016    5.399479\n",
       "2017    5.460408\n",
       "2018    5.502125\n",
       "Name: life_ladder, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_over_the_years = world_happiness.groupby(\"year\")[\"life_ladder\"].mean()\n",
    "happiness_over_the_years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will check the  H0 - H1 hypothesis for the key values of the happiness index:\n",
    "- log_GDP_per_capita                   \n",
    "- social_support                       \n",
    "- HLE_at_birth                         \n",
    "- freedom_to_make_life_choices         \n",
    "- generosity                          \n",
    "- perceptions_of_corruption,\n",
    "\n",
    "and also\n",
    "- positive_affect                      \n",
    "- negative_affect                      \n",
    "\n",
    "Also we will see the correlations (if there are any) between these variable and the happiness index. But before that we have process the data more. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# important_metrics(world_happiness., \"\")\n",
    "# print()\n",
    "# print(st.ttest_1samp(world_happiness., ).pvalue) #median\n",
    "# print(st.ttest_1samp(world_happiness., ).pvalue) #mode \n",
    "# print(st.ttest_1samp(world_happiness., ).pvalue) #dif_max_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter(data_x, data_y, title, xl, yl):\n",
    "    figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "    color = np.random.rand(data_x.count())\n",
    "    area = (4 * happiness_over_the_years) **1.5\n",
    "    plt.scatter(range(len(data_x.index)), data_y, \n",
    "               c = color,\n",
    "               alpha = 0.5,\n",
    "               s = area,\n",
    "               label = \"{}\".format(yl) )\n",
    "    plt.xlabel(\"{}\".format(xl), )\n",
    "    plt.ylabel(\"{}\".format(yl))\n",
    "    plt.title(\"{}\".format(title))\n",
    "    plt.legend()\n",
    "    plt.xticks(range(len(data_x.index)),data_x.index, rotation='vertical')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U1tZi7dq1GD16NN58803s3bsXQ4YMAdA4rHfWrFn4n//5H7z88ssQQvjOoJw/4/Db3/4WL774IhYsWIBFixbBYDDg4MGDOHnyZLNzqQQqISEBWq0WL7zwAmbNmoVPPvkE7777Lkwmk1+76upq/P73v8d9992H3bt3Y+PGjdi2bZtfm6effhrJycmIjY3F73//e1x99dUYOHDgT6rrgQcewFVXXYVly5bhtttugxACe/fuRUNDAxYuXIhnnnkGFosFGRkZsNvt+OSTT3zB6NFHH8Xw4cPRv39/VFZW4ssvv8Q111zT7Hb++c9/4vjx4/jlL3+JpKQkVFRU4LHHHkN0dDSuvPJKAMCyZcvwv//7v4iJicHVV1+NM2fO4OzZs5g+fTruvfdePPPMM3jkkUcwZ84cfPLJJ9i8eTN27drV6v794Q9/wIwZM2CxWHDDDTfAZrNh9+7diI2NxcyZM9u1D8nJyXjkkUdw//33w+Vy4aabboLNZsPq1avx/vvvY9u2bX5n3y7151sOqampuPnmmzFr1ixkZmaiT58+OH36NDZt2oTly5cjOjq63et88MEHMWbMGDz66KOYNWsWPB4P9uzZA6fTiXvuuQfJycl499138eWXX8JsNuPhhx8OaL2LFy/GTTfdhOTkZBQUFODIkSOYOnVqu+ujTqAjO8zQ5WHy5Mktdir94x//KAYMGOB77Ha7RVxcnNBoNL6RAOd5PB7xxBNPiKSkJKHVaoXFYhFz5swRJSUlQgj/DosXWrlypYiNjRUmk0nceeed4v777/cbLVNRUSGmTJki9Hq96N27t9i8ebMA4BsdIoQQhw4dEpMmTRJhYWHCZDKJ4cOH+w2T/bFARwOdH0YaGhoqfvWrX4lVq1b5dYo8P3T597//vQgPDxexsbHimWee8S0/34l0w4YNIi0tTeh0OjF27FiRn5/f4jYTExP9hv8KIQQAv07GO3fuFKNHjxYGg0FERESIq6++2jeC6sUXXxRpaWkiJCREREVFienTp/t+BytWrBB9+/YVer1edOnSRdx1113CarU2+x5lZWWJW2+9VSQmJgq9Xi/i4uLETTfdJPbv3+/XLjMzUyQlJfmGJ7/yyiu+ZeeHLmu1WpGcnOzXmfb8e5Odnd1k22+//bYYPHiw0Ol0IiYmRkyaNMn3+27PPpy3bt06MXToUKHX64XJZBLXX3+92LNnT5N2cny+W9LW0OUL/XjdNptNLFmyRHTr1s03ZPw3v/mNX4f45nz22WcCQLPD+nfs2CGuuuoqYTAYRGRkpBg7dqz46KOPhBCNQ6VnzJghjEaj6Natm3juuefEyJEjxWOPPSaE+KGD7YXDmYVo7OB8/rNhsVjEQw89JDweTyBvD3UykhABXvglukx88cUXGDNmDEpLS9u8Ri+3+fPnw+124/XXX292eX5+Pnr27Ins7OxLfpmKiEgpeBmILnt79+5FYWEhBg8ejNOnT2PRokW47rrrOjyoEBFRI4YVuux5vV6sWLEC2dnZMJvNmDhxIv72t791dFlERPRfvAxEREREisZJ4YiIiEjRGFaIiIhI0Tp9nxW9Xo/Y2NiOLoOIiIjaoby8HA6HI6C2nT6sxMbG8n4QREREnYzFYgm4LS8DERERkaIxrBAREZGiMawQERGRonX6PitERESBEEL4fkh+kiT53cjzYjCsEBHRz5rX60VZWRmqq6sZVIJMq9UiISEBOp3uotbDsEJERD9rBQUFUKlUSEpKglar7ehyLhtCCJw7dw6FhYUXfaNVhhUiIvrZ8nq9sNvtSElJgUbDQ16wRUdHo7KyEl6v96IuCfE3R0REP1vnL/tIktSu11VYHdhXUIV9hVVocHoQqlNjSEIkhiRGIsaol6PUn6Xz7/vFXn5jWCEiIvovr1fgsxNl+PBwCYQQMIfooNeoYHd58dGREnx89CxuSIvH+L5doFK1LwDRT8ehy0RERP/12YkyvHfgDGKNeiREhcEcokWoTgNziBYJUWGINerx3oFifHai7KK2I0kSrFYrAGDy5MnIyckBAOTk5GDIkCEYPHgw1qxZE/D61q5di2nTprW7juXLl+P+++9v97Jg45kVIiIiNF76+fBwCeLNITBo1c22MWjViDcb8OHhEqT3iLgkl4S2bt3q+/fmzZtx5ZVX4h//+MdFr7cjuN1uWfoGMaw0o9Zqh83uhEqlQrjRAL2ObxMR0c/dvoIqCCFaDCrnGbRqCCGwv7AKE/t3vejtJiUlYcuWLdi3bx+eeeYZeL1efPnll3jjjTcQFRWF3/3ud8jPz4fdbscvf/lLrFixotX1nT17Frfeeitqa2tht9sxYcIEPPvss5AkCTU1Nbjzzjtx7Ngx9OjRA7GxsejatXEfWlvmcrnw8MMPY8eOHXA6nUhNTcXq1asRERGB+fPnIzw8HCdPnsTp06dx9OjRi35PfoxH4f/yeLzIPV2BrEMFOFVQDqCxQ1CIQYdhaQlI72dBVERYB1dJRERy2VdYBXNIYPOBmEN0+K7g0oSV8379618jNzcXVqsVTz31FABg0qRJ+OMf/4irr74abrcbN954I9555x386le/anE9ERER+OCDD2A0GuHxeDB16lT8+9//xrRp07BixQqEh4fj2LFjqKiowJAhQzBjxgwAaHXZX//6VxiNRuzZswcA8Nhjj+HRRx/Fs88+CwD44osvsGvXLhiNxkv2flyIYQWAy+XB1s+PYP/R09DrNOgSbYJG3didp8HuxO69p/DN/nzccv1g9OnZpYOrJSIiOTQ4PdBrAuvKqVFJaHB6ZK2nvr4eO3bsQGlpqe85q9WK48ePt/o6r9eLpUuX4osvvoAQAmVlZcjIyMC0adPw2Wef4bnnngMAxMTE4Oabb/a9rrVl7777Lmpra7F582YAgNPpRHJysm/5jBkzZAsqAMMKvF6Bj3Yexb4jp9E9zgy12v+DGmrQIdSgQ129HW9tycLcm0cisXt0B1VLRERyCdWpYXd5A2rr9gqE6lq/XHSxvF4vJEnC3r172zWZXWZmJs6dO4dvv/0WBoMBS5Ysgd1uB9D6EOK2lr3wwgu45pprml0uZ1ABOBoIRWersP9o80HlQqYwA3Q6Dbbt+p7TNRMR/QwNSYhEjc0ZUNsamxNDEyNlrcdkMmHMmDH4y1/+4nuuuLgYRUVFrb6uqqoKXbt2hcFgQGlpKTZt2uRbNmHCBN8oo8rKSrzzzjsBLZsyZQoyMzPR0NAAAGhoaJClb0pLLvuwcuBoETQaVatB5bxIcyhKympQdLY6CJUREVEwDUmMhCRJsLtav7xjd3kgSRIGJ8gbVgBg/fr1+P7775GWloa0tDTccsstOHfuXKuv+d3vfoevvvoKGRkZuOOOO3Dttdf6lj388MOoqqpC//79MXv2bEycODGgZQ899BAyMjIwcuRIDBo0CFdccQUOHDhw6Xe4BZLo5KcJLBZLmymzJW63B6te+hQRppCAR/wUl9Vg1LBkXHNl35+0TSIiCh6Px4OTJ0+iT58+UKvbvmyz/ftSvHfgTIvDl+0uD0pq7Jia0Q0T+sXJUfLPSmvvf3uO35d1nxWH0w2XywNdG8PULqRRq1Df4JCxKiIi6ijj+zYOorhwBluNSoLbK1Bjc0KSJEzN6OZrR8FxWYcVjVoFlUqC1yugVgc2bbLXK6DVyNupioiIOoZKJWFCvzik94jA/sIqfFfww72BRvWOx+AE3huoI1zWYUWn06B71wicq7QGPIeKy+1Bj/gomSsjIqJL4afeSC/GqMfE/l0v6Twql7P23kjyxy7rDraSJGFEehIa7K6APsgNNifCQvXonRQbhOqIiOhiqVQqqNVq39BdCi6XywVJki46rFzWZ1YAICWpC2KjTSirtCIu2tRiO7fHi4qqevzi6v6cfp+IqBOJjY3FmTNn0L17dxgMhos+cFJghBAoLS1FREQEw8rF0us0mHHDELzx7h6cKa1GbJQROu0Pb4sQAnX1DlTX2jA8PREjMpI6rlgiImq3yMjGIcbFxcXweOSddZb8GQwGdOly8Z2RL+uhyxeqrm3A7j2ncOj4GXg8XkACzr8zURFhuHJwTwwZmACViomciKiz8nq9nNgzSCRJgkrVcm+T9hy/GVZ+pK7ejtzCCjTYnFCrVYiOCEOiJdp3ryAiIiK6eJxn5SKYwgxI72fp6DKIiIjov2Q/XeBwOLBo0SKkpKRgwIABmDNnTotty8vLERcXh2nTpsldFhEREXUSsp9Zeeihh6BSqXDy5ElIkoSSkpIW2y5cuBCTJ09GXV2d3GURERFRJyFrWKmvr8eaNWtQVFTkG7YUHx/fbNv169cjLi4Ow4YNw5YtW+Qsi4iIiDoRWS8D5eTkIDo6Go8//jiGDRuGMWPGYPv27U3aFRcXIzMz0+822ERERESAzGHF5XIhNzcX/fv3R1ZWFp5//nnMmjUL5eXlfu0WLFiAVatWwWg0trnOzMxMWCwW34/VapWrfCIiIlIAWYcuV1RUIC4uDk6n03dr6BEjRmDVqlUYN26cr11UVBTCw8MBAFarFTabDaNHj8a2bdva3MalHrpMRERE8mvP8VvWMysxMTGYMGGCL3QUFBQgLy8Pffv29WtXWVmJ/Px85Ofn46mnnsL1118fUFAhIiKinz/Zhy6vXr0aq1atQlpaGqZOnYqXXnoJ8fHxmDx5MrKysuTePBEREXVynMGWiIiIgk4xl4GIiIiILhbDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKZrsYcXhcGDRokVISUnBgAEDMGfOnCZtNm7ciMGDB2PgwIFIS0vDc889J3dZRERE1Elo5N7AQw89BJVKhZMnT0KSJJSUlDRpY7FY8NFHH6Fr166oqanB0KFDMWTIEIwaNUru8oiIiEjhZA0r9fX1WLNmDYqKiiBJEgAgPj6+SbsLQ4nZbEZqairy8vIYVoiIiEjey0A5OTmIjo7G448/jmHDhmHMmDHYvn17q685duwYvv76a1xzzTXNLs/MzITFYvH9WK1WOUonIiIihZA1rLhcLuTm5qJ///7IysrC888/j1mzZqG8vLzZ9kVFRZg6dSpWr16Nbt26NdtmyZIlKCoq8v0YjUY5d4GIiIg6mKxhJTExESqVCrNnzwYApKeno2fPnjh69GiTtsXFxbj22muxbNkyTJ8+Xc6yiIiIqBORNazExMRgwoQJ2LZtGwCgoKAAeXl56Nu3r1+7kpISTJgwAUuXLsW8efPkLImIiIg6GdmHLq9evRqrVq1CWloapk6dipdeegnx8fGYPHkysrKyAACPPPIICgsL8eyzzyIjIwMZGRlYs2aN3KURERFRJyAJIURHF3ExLBYLioqKOroMIiIiaof2HL85gy0REREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESkawwoREREpGsMKERERKRrDChERESma7GHF4XBg0aJFSEkqQjaaAAAgAElEQVRJwYABAzBnzpxm2z3++ONITk5GcnIyHn74YbnLIiIiok5CI/cGHnroIahUKpw8eRKSJKGkpKRJm127dmHDhg04dOgQNBoNRo0ahdGjR2PSpElyl0dEREQKJ2tYqa+vx5o1a1BUVARJkgAA8fHxTdpt3LgR8+fPR1hYGADgjjvuwIYNGxhWiIiISN7LQDk5OYiOjsbjjz+OYcOGYcyYMdi+fXuTdoWFhUhMTPQ9TkpKQmFhoZylERERUScha1hxuVzIzc1F//79kZWVheeffx6zZs1CeXl5k7bnz7wAgBCixXVmZmbCYrH4fqxWqyy1ExERkTLIGlYSExOhUqkwe/ZsAEB6ejp69uyJo0eP+rVLSEhAfn6+73FBQQESEhKaXeeSJUtQVFTk+zEajbLVT0RERB1P1rASExODCRMmYNu2bQAaQ0heXh769u3r12769Ol47bXXUF9fD4fDgVdffRWzZs2SszQiIiLqJGQfurx69WqsWrUKaWlpmDp1Kl566SXEx8dj8uTJyMrKAgCMGzcOM2bMQFpaGvr164frrrsOv/jFL+QujYiIiDoBSbTWQaQTsFgsKCoq6ugyiIiIqB3ac/zmDLZERESkaAwrREREpGgMK0RERKRoDCtERESkaAwrREREpGgMK0RERKRoDCtERESkaAwrREREpGgMK0RERKRoDCtERESkaAwrREREpGgMK0RERKRoDCtERESkaAwrREREpGhthhWPx4O0tLRg1EJERETURJthRa1Ww2KxwGazBaMeIiIiIj+aQBr16dMHY8aMwYwZM2A0Gn3PL1y4ULbCiIiIiIAAw0ptbS3S0tLw/fff+56TJEm2ooiIiIjOCyisrFmzRu46iIiIiJoV0Gig2tpaLF68GL/85S8BAMeOHcOGDRtkLYyIiIgICDCs3HPPPYiJicGpU6cAAD179sSTTz4pa2FEREREQIBh5fjx41i2bBm0Wi0AICQkBEIIWQsjIiIiAgIMKzqdzu+xzWZjWCEiIqKgCCisjB8/HitXroTD4cDnn3+OmTNn+vqvEBEREckpoLDy2GOPQZIkmEwmPPjggxgxYgQeeeQRuWsjIiIigiQ6+fUci8WCoqKiji6DiIiI2qE9x+9W51l58MEHW33xqlWrAq+KiIiI6Cdo9TJQWFgYwsLCUFJSgo0bN8LlcsHlcuGtt95CdXV1sGokIiKiy1hAl4Guv/56vP7664iOjgYAVFZWYu7cufjwww9lL7AtvAxERETU+bTn+B1QB9vTp0/7ggoAREVFoaCg4KdVR0RERNQOAd0bqF+/frjrrrtw5513Ami8V1BqaqqshREREREBAZ5ZeeWVV2A2m7Fo0SL89re/hdlsxquvvip3bUREREQcukxERETBx6HLRERE9LPBoctERESkaBy6TEREREHHoctERET0s8Ghy0RERKRoAQ9djoiI4NBlIiIiCjoOXSYiIqKgu2RDly/09ttv48CBA7Db7b7nOHSZiIiI5BbQZaDFixdjzZo1ePnll+HxePDmm2/i3LlzctdGREREFFhY2b59O9577z3Exsbi6aefxt69e1FWViZ3bURERESBhRWDwQCVSgVJkuByuRAXF4czZ84EtIGkpCSkpqYiIyMDGRkZ2LhxY5M2drsd8+fPR1paGgYOHIgpU6agoqKifXtCREREP0sB9VkxmUxoaGjA6NGjMW/ePHTt2hVarTbgjWzevBkDBw5scfmLL74Iq9WKQ4cOQZIkLFiwAKtWrWKfGCIiIgrszMqGDRugVqvx17/+FQMGDIBKpcKmTZsuaSENDQ1wuVxwu92wWq2wWCyXdP1ERETUOck+dDkpKQlmsxlerxcjR47En//8Z8TGxvq1OX8Z6OOPP4ZarcbIkSOxZcsWqFRNs1RmZiYyMzN9j61WK+9TRERE1Mm0Z+hyq2Fl+vTpkCSpxRe/9dZbbW6gsLAQCQkJcLlcWLZsGQ4fPoytW7f6tdmyZQvWr1+PNWvWQKVSYf78+ejTpw+WL1/e5vo5zwoREVHnc8nmWbnxxhsvupiEhAQAgFarxeLFi9GnT58mbVavXo1f//rXMBgMAIDZs2dj1apVAYUVIiIi+nlrNazMmzfvolZeX18Pl8uFiIgIAI19XwYPHtykXa9evbBt2zZMnz4dQOOZltY65BIREdHlI6AOtj9VaWkpxo8fj0GDBiEtLQ07d+7EunXrAACTJ09GVlYWAGD58uWoqanBgAEDMHDgQFRUVOCxxx6TszQiIiLqJHhvICIiIgq69hy/ZT2zQkRERHSxAg4r7733Hp588kkAwJkzZ3D48GHZiiIiIiI6L6Cwsnz5cqxevRqvvPJK44tUKtxzzz2yFkZEREQEBBhW3n33XWzZsgVhYWEAgPj4eNTV1claGBERERHQjhsZqtVquWshIiIiaiKgGxkmJibiiy++gCRJ8Hq9WLlyJdLS0uSujYiIiCiwsPL3v/8d8+bNw5EjRxAaGooxY8Zg/fr1ctdGRERE1HpYee211zBv3jxUVVXh448/RkNDA7xeL4xGY7DqIyIiostcq31W/v73vwMAbrvtNgBAaGgogwoREREFVZuXge677z4UFxfjwQcfbLJs1apVshRFREREdF6rZ1Y2bdqEHj16QJIkhIWFNfkhIiIiklurZ1Z69eqFBx98EPHx8Zg7d26waiIiIiLyaTWs5OXloWfPnhg6dCiOHTvWZHn//v1lK4yIiIgIaCOs3HfffdiyZQtuuOGGJsskSUJubq5shREREREBbYSVLVu2AGg8w0JERETUEVoNKw0NDa2+ODQ09JIWQ0RERPRjrYYVo9EISZIghGiyTJIkeDwe2QojIiIiAtoIK16vN1h1EBERETUroLsuExEREXUUhhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjTZw0pSUhJSU1ORkZGBjIwMbNy4sdl2O3fuxPDhwzFgwACkpqbi66+/lrs0IiIi6gQ0wdjI5s2bMXDgwBaXFxcXY968efjoo4/Qr18/2O122O32YJRGRERECheUsNKWF154AXPmzEG/fv0AAAaDAQaDoYOrIiIiIiUISp+V2bNnIy0tDXfddRfKy8ubLD927BhsNhuuvfZaZGRk4L777kNDQ0MwSiMiIiKFkz2s7Nq1CwcPHsS+ffsQHR2NefPmNWnjcrnw+eefY9OmTcjKykJNTQ2WL1/e7PoyMzNhsVh8P1arVeY9ICIioo4ke1hJSEgAAGi1WixevBi7d+9u0iYxMRE33HADIiMjodFoMGvWLOzZs6fZ9S1ZsgRFRUW+H6PRKGv9RERE1LFkDSv19fWorq72Pd6wYQMGDx7cpN1tt92Gzz77DA6HAwDw8ccfIz09Xc7SiIiIqJOQtYNtaWkpbrnlFng8Hggh0KtXL6xbtw4AMHnyZKxYsQLDhg3DVVddhZtuugkZGRnQaDQYOHAgVq9eLWdpRERE1ElIQgjR0UVcDIvFgqKioo4ug4iIiNqhPcdvzmBLREREisawQkRERIrGsEJERESKxrBCREREisawQkRERIrGsEJERESKxrBCREREisawQkRERIrGsEJERESKxrBCREREisawQkRERIrGsEJERESKxrBCREREiqbp6AKIiIhIWWxWO+qqrIAkwRxtgj5E16H1MKwQERERAKCs6BwOfn4Mh788AbfLAwDQGbTIGNcf6WP6ITLO3CF1MawQdWJer0BBWRX2ZxehpLIOEiQkxkVicO9uiI8OhyRJHV0iEXUSOYcK8d4Ln8Dj9SIqLhw6Q+PZFHu9A3s+PohDu4/jlt9dj+7JcUGvTRJCiKBv9RKyWCwoKirq6DKIgq6uwY5Nuw4hp/gcNGoVwgw6QABWuwNer0B6cjxuunIgDDp+JyGi1pUVncPrj78Dg1EPU2RYs22qymogSRLmPXwLwqNNF73N9hy/2cGWqBOyOVx4ffs+5JVUwhJjRnxUOMJDDQgPM6BbtBnx0eHYf6oYb+8+BLfH29HlEpHCHfz8GDxeb4tBBQAiu5jRUGfHsW+zg1hZI4YVok5o/6kiFJZWo1sLl3rUKhUsMRE4kn8W2WfKO6BCIuosbFY7Dn95AlFx4W22NccY8d32I/C4PUGo7AcMK0SdjNvjxVfHChFpCmm1T4pKJSFEr8U3xwrQya/2EpGM6qqscLs8vj4qrQk1hcBa3QCb1R6Eyn7AsELUyZyrrUeN1QZTiL7NtpHGUOSdrYTDFdxvQUTUibS3I74QkFTBjQ8MK0SdjOe/fVACGemjVknwegXcHoYVImqeOdoEnUELe72jzbbW6gZExJlhCGv7y9KlxLBC1MmE/PdUrSeAjrMOlxt6nQZ6jggiohboQ3TIGNcfVWW1bbatrbRi2LWDoFbzzAoRtSIizIDkbtGorGtos21lXQOGpligVauDUBkRdVYZV/eHIUyPqrKaFttUnKlEZBczUocnB7GyRgwrRJ2MJEm4sn8ibE4XnK30yG9wOCFBwuCU7kGsjoh+quraBhw7VYJDx88gO78MTpc7aNuO6BKOW353PSRJQnFuGeprGyCEgBACdVX1OJNTitDwUNzyu18gLDwkaHWdx0nhiH4Cu82JnBMlyMsug9PphjkyFKkDLejWIyoos8YKIfCffdnYsf8UIk0hMIXofdv1CoFqqw31NiduHpOGoX0sstdDRD9dRZUVO/dk41j2WXiFgCRJEEIgLESHkelJuGJwT2g1wTk7WltpxbFvs/Hdfw7DWt0ACIGIODOGTRyE1GHJlzSotOf4zbBC1A5CCBw9UIjtWw/CbnNBp9dArVbBYXfB6/XCkhiDG24ZhogoY1Bq2Zd9BjsP5eBcbQNUEiDQ+EcuPioc1w7pjdSE4E+LTUSBK62oxevv7UWD3YmYSKNfKGmwOVFRZcWAlHjcPCkjaIEFADxuT2OHW0mCIUwvSx8VhhUimRzal4+t/85CRFQYwowGv2Ver0BFaQ1M5lDMun0MwiNCg1KT2+NFQWklquvtkADEhIfBEhsBlYr3BSJSMrfbgxff/BJ19TbERDY/fb3XK1B0tgoTR6VizPDeQa5QXpxun0gG9XV2/GfLQUTGGJsEFaBxErYu8RGorrTiq8+/D1pdGrUKyd1iMDTFgiEpFiTERTKooPGPfG2DHdX1Njjdwbv2TxSo3KJzKK+sQ3REy2diVSoJMZFGfHswHw7n5fs55nhGogCdOHYGLqcboaGtT0kd3SUcRw6cxugJ/WE0Bb8j2uXO5nThSMFZfHkiH6XVVgCAQavB8N49MDTZgrhWDgxEwXT4+BnotJo2+7mFhuhQVduAguJK9EnqEqTqlIVhhShAJ4+eQWhY29NR63QaeD0eFJ+uRJ/+HIkTTDX1dqzftQ8FFdUwhxpgiTZDkiTYnC58eTwf32QXYtaodPTvwb481PGqam0w6AM7DEtSYx+WyxUvAxEFyOFwQ92ODm4uTnEfVC6PB2/s3o8zlbVIiIlARNgP904K0WnRPdqMUL0Wb+w+gKKK6g6ulgjQ6dTweALsNioaL/leri7fPSdqpzCjAc52XDM2GLQyVkM/lnP2HArKq9Etqvk7UQNAeIgBkgR88X1+cIsjakafpC5osLd9tsTt8QCShG5xEUGoSpkYVogCNDAjAfaGtv+w2BqcMBh0sCTGBKEq5fF4vbC5XXB7274dwKX07cnTMOjavv4fYwrDkdOlqK63Bakyoub17x0PtVoFu8PVaruKSitSe8UhyhycEYZKxD4rRAHq1ScOJnMIqqvqEREZ1mwbIQTOlddh9IR+0F9mZ1ZO19Vg79ki7DlbBKfbDa1ajYwu8RjZtQd6miNlnyzvTGUNjAHc4l6rUUMIgSqrDRFh7ABNHSfcaMC1V/XFRzuPIjbKBIPe/2+GEAKVNfXQ67UYf0VKB1WpDAwr1Ca3y4PC3DJUVtRBCCAiKgyJveOg64Cb4zk95ah3fA+3txZqlR4GbU+EaHpBkuQ/SajTazFlxkhsWvcFKspqERVjhOqC26Q7HC5UlNaiZ0oXjBzdR/Z6lEIIgZ1FeXg/53tIkBBlCIXeEAqX14t9pcX4tqQIExKScUOvvlAFYXZfos5kZHoSAOA/X52Ap7oeoSE6qFUqOJxuOJwuREeEYdovBiM2qvl5WC4XDCvUIiEEju4vwBf/OYaaqnpIkABJQAggzGTAVeNSkXFFst8BWy5ubx3Kre/A6jwIIbyQJDUgvBAQ0GviEWu8BaFa+W+u1SMpBrNuvxqffXwIRYXnAAE0/keCVqfG8FEpGH1Nf+j0l89ZlazSM3gn+yjiwkwI0fyw32qVCt2M4XB6PPgkPxshGg2uTZRvUitLdARyzlYgVN/62RWX2wNJAqKMl+8pdVIOSZJwRUZP9O/dFcdOncXx3FI4XR5YukYgPbU7eiXEBHXmWqXiDLbUor27T2D7hwdhjgxDmMngdxq/wepA1bk6XDmuH66eNFDWU/werxVnal+C3V0EvbobJOmHjC2EgMt7DkI40T18AUJ1wTlVKoRAaXE1zp6pgtvtRUiYDknJXZqdLO7nzOX1YOU3n0MAMOtb3neb24Uahx2PXHkNwrRtX6r5KU6cKceaHXvRIyai1c9jSWUt0hK7YsaodFnqIKLAcAZbumhni6rw+ceHERNnhjE8pMkf/1CjHl26ReCbncdRcKpM1lrONfwHdlch9OoefkEFaPxWolPHQCUZcNb6BrzCIWstF263a/dIZIzohWFX9caA9ITLLqgAQHbVOVQ5bAjX6VttF6LRwuFx40hFqWy1JHeNRs+4aBSdq0FL38FqG+wAgFH9kmSrg4guPV4GomYd2ZcPSFKrnUS1Wg20Og32f5uDpBR5JtnyeOtRa/8GWnVcq9+Wteoo2F0FqHceh0nPb8zBUtZghRAI6MyaVqVGUV0tRsbLU4tGrcKtY9Lxxq4DyCuthClUj4jQEEhS46y2lXU2aLVqzB03BN2jzPIUQW0SQqCooRL7qwpQYK2AFwLdQyIxJCoJicYYqIPQ/4w6H4YVasLj8eLI/gJERDU/4uVCkTFGnDpegoZ6B0LDWv92/VPYXHnwwgmdqu1RGypJD6vjIMNKMAmgPRcAvZB3OHN4iAG3XzMMR0+X4svj+SiurIEAEKrXYtzAXhjSqztiwtv+XJM87B4X3jv9HQ5Vn4ZGkmDUGABIOFJThH1V+Ug2xmFG4giYtMEZpSWEwJnaWhw6exZVdjv0ajX6xMSgb0wM9BoeHpWEvw1qwuV0w+X0QBvAaB+NRg3hFXDaXbKEFa9wNHbsDYAk6eAR9Ze8BmpZVEgoAu305vJ6EB/W+n2VLgW9VoMhvbpjcM9uaHC44BFehOi00Ko7ppOix+tFXk0Vah0OqCUJ8aZwdAm9/AKT2+vB24V7caS6CJbQSKguOINi0hoghECetQxv5H+Neb3GwKCWt5N6tc2GjUeO4ER5OSRJgl6thkcIfFlYCLPBgFv690da166y1kCBY1ihJrRaNTQaFdxuD9RtTO/s8TR+Uw4k2PwUKpUBIsDDoRBOqKXL7yDQkfpGxcCk08PqdMDYSr8Vh8cNjaRCWkzw7skjSRLCAph3RS5CCHxbXIQdBTkoq6/3u1Q2IKYLfpGcAoupYy5HNY6oC+7lllxrWbNB5TxJktA9JBL51gocqTqNYTG9ZKul1m7HS1lZKLNaYQkPh/qCEY1CCNQ4HFizbx/mDx6MQfEyXbekdpH905qUlITU1FRkZGQgIyMDGzdubLFteXk54uLiMG3aNLnLolaoNWr0HWhBTaW1zbbV56xI6t0FocZLf1YFAEI0SVBBB4+39dlGhRDwCjtM+gxZ6qDm6dUaTEzqjTJbPZye5u+F5PZ6UWytxRhLz1ZHDP2cCCHwwanjWH/0IBxuNxLCzb6fbkYTjp8rx/NZ3yC3ujJo9QhPMby29+GtWQ5R80d4ax+H1/4JhKc8KDV8W5ELg1rTbFA5T5IkmLUh+KoiG14h3yXD7bm5KKmraxJUztcQYTAg0mDAW0eOwOZqfXZZCo6gROvNmzfjwIEDOHDgAGbOnNliu4ULF2Ly5MnBKInakDY8CR63Fy5Xy/fC8bg9cNhcGHxlb9mGLqtVYTAbroTTU9riCA8AcHuroFFHIlTXV5Y6qGVjuidhYkJvlNTX4mx9nW+afY/woqzBitN11bgivgcm97p8Jso7VHYW/8nPQQ9TOCIM/qPpNCoVupvCoVGpsPbQftkPhkIICMcOiLq/AY4vAOgBVTQgJMD+KUTd0/A6smStwSO8yLWWIkLX9plPszYEZfZa1LrsstRidTrx7enT6Go0tvp3K9xgQL3LhaOl8o1go8Apptv1+vXrERcXh7Fjx3Z0KQTAkhiDkVenovRMNezN3Jbc6XChpKgKg0b0RHJfea/rRoVeixBtEhzuQniF/x92IQScnjJ4hQNdTbOhkuQ5w0MtU0kSbkpOxW8GjUBKZDRK6mtRWFuNM3W1sBjNuCttOGb2HQSt6vKY2EoIgc8L82DU6lrtJxMdEooahx1HyuU9GArnV4DtQ0AVC6gtgCoMkHSAygSoewCSGbBtgNd5VLYavMILrxBQBdD/TJIae6nJdWbldE0NXF4vDAF0oA3VanGkTN6pGSgwQemzMnv2bHi9XowcORJ//vOfERsb67e8uLgYmZmZ2LlzJzZv3tzqujIzM5GZmel7bLW2famC2k+SJIy5bgAMoVp8veM4KsvroNE2/uF1uzzQaDUYdU1/jJrQT/YZbNWqMHQPvwvl9e+jzrEPQngASQUILyABenV3dDHejBCtfNe4qXWSJKFfdBf0i+6COqcDNrcLerUG4Tq97PcEUpqyhnrk11QF1B/FqNXim+LTGN7NIkstQtgB+7bGoNJSkFeFAV4HYN8KoU1tnB36EtNIaoRrQ2DzOGFqY2Sfw+OCRqVGqEaeLx5Od+B3TteoVLC1oz3JR/awsmvXLiQkJMDlcmHZsmWYN28etm7d6tdmwYIFWLVqFYxGY5vrW7JkCZYsWeJ7bLHI8z85ASqVCiOvTsWgYT1x6ngJyoqrIYRATFw4Uvp1R5gpeP0P1CojuppuQ3ToJNQ7j8PtrYNK0iNEmwiDJinonQWpZSadHqY2Jon7Oat3OSFBCug+SAaNFtUOeS53AABc3wOiHlC3cQdwKRLwngY8eYDm0t8SQZIkXBHTGx8VH2xzWHKlsx7DonvKNhooRKsFhIAQos0g7XC7YdZfvp9lJZE9rCQkJAAAtFotFi9ejD59ml63/vrrr3HnnXcCaDxTYrPZMGnSJGzbtk3u8igAIaF6pA1JAoZ0dCWAVh2NiJBRHV0GUYu0KhW8QEAHQ4/wIlQt34gl4S5CQH/mz9fpKZclrADAwAgLdpYdR7WzvsW+K/Xuxhmoh0XLd5Y0KTISJr0eVqcTplaCSOMlZg8Gd+smWy0UOFm/jtbX16O6utr3eMOGDRg8eHCTdpWVlcjPz0d+fj6eeuopXH/99QwqRNQpxYWZYNbrYXU17ev1YzUOB9Lj5Ozz5UH7pu2T71ZxZl0obk26Ek7hQbGtGk7vD5dX3F4PSm01qHY2YFrCCMSHRMhWh06txtVJSShvaIC3lU775fX16GI0ondUlGy1UOBkDSulpaUYP348Bg0ahLS0NOzcuRPr1q0DAEyePBlZWfL2QCciCjadWo2reyShoqGh1RFsdrcbkiRhcFcZv7mr4wAEMNpICAACUMkXEgCgpzEWd/cej4zIBJxzWHHGVoUzDVUos9ciJbwr7uo9DmmRPWStAQBGJyUhvWtXFFRXo+FHo7HcXi+K6+qgUqkwJz29wyYTJH+86zIR0SXW4HLixf17UVhbg+5GU5O5PBpcLpQ2WDG1dz9M6JksWx3CWwtRu7JxqHJrI+W8dYDkhWRaCkkKzkR6tS4bzjmsEBCI1IYhUh/cCR2dHg925OTgi4ICNLjdfuef+sfGYnLfvog3mYJa0+WmPcdvhhUiIhnUOhx489ghHKsog0olwaDWQAgBm9sNrVqNG5L7YmxCkuyjpby2LYBjB6DqATQ30kc4Ae8ZIGQaVPrLrz+YzeVC9rlzaHC5oFGp0MNsRlwAgz3o4rXn+M3p9omIZBCu12NBxjAU1dVi/9lilDfUQ61SISUqGoO6dA3aiCnJMAnCWwe49gIIA1RRjaFFuAFvBQAHoJ8ISXdlUOpRmhCtFoN4DyDFY1ghIpKJJEnoEW5Gj/COuQdQYw1aIHQG4B4A4fgCcOedXwJo+kHSXwlo+l528+FQ58KwQkT0MydJakA7CNCkAaK28dKPZICkYp8M6hwYVoiILhOSJDVOr0/UyXDaTyIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNIYVIiIiUjSGFSIiIlI0hhUiIiJSNE1HF0AtczpcKM0vg8vhhiFMj7ikWKjV6o4ui4iIKKgYVhTIaXci65OD+O6Tg2iotUGSAK8QiIqLwPDrB///9u4/KOp63+P4awEB2RWPgoS6/NIkIiAWhUKdQdTU8d60W4M/BkBWqtEAAA/TSURBVMsfOTVmc6dbUznmOOiMNtOko2OjTY6Tv+Z4MLzWpOKdLomgo/krkCJvNiSIJsKipJAbLO/7R7iH5Yd5zmm/33fyeszsDOx3Z79Pkj77Zvf73UVKViKHFiIi6jN8/jJQbGwsEhISkJqaitTUVOTn53e7TX5+PhwOB5KSkpCcnIxNmzb5Okst1y8u7NtwEEf+dhz9Avth6IiHEBn3EIbGPYRfXW04tPV/8T/bi+F2u81OJSIiMoQhz6wUFBQgKSmp1+12ux2FhYWIjIxEU1MTRo8ejbS0NIwbN86IPFVKCk7iUkUN7KOGwmKxeK63WCwIHWyDNbQ/yooqMDQuAo6JySaWEhERGUPFAbbjxo1DZGQkAGDgwIFISEjAjz/+aHKV8ZqbmlFe/C2GRIV5DSqd+Qf4Y2B4KE4dOsdnV4iIqE8wZFjJzc1FcnIyFi9ejPr6+nvetrKyEidOnMDEiRONSFOlurIWbb+2ITA48J63sw2y4sb1JvxUdd2gMiIiIvP4fFgpKSlBeXk5zp07h7CwMMyfP7/X29bW1mLmzJn48MMPMWzYsB5vs379etjtds/l9u3bvko33J1mF4Cen1HpzGKxwGLxg6vF5fsoIiIik/l8WImOjgYA9OvXD6+99hpKS0t7vN3Vq1cxefJkrFixAjk5Ob3e3+uvv47a2lrPxWaz+aTbDAGBAQDkvm4r7e0dtyciInqw+XRYaW5uxs2bNz3f79mzBw6Ho9vtfvrpJ0yaNAlvv/32PZ95edBFPTIMFj8L2lrvfSxKy61fEBLaH0PjIgwqIyIiMo9Ph5W6ujpkZ2cjJSUFycnJOHr0KHbu3AkAmD59Os6cOQMAWLlyJWpqarBx40bPKc4ff/yxL9NUGvTQXxA/ZiQaap293kZE0PjTDaQ9lfK7x7YQERE9CCwicn+vOyhlt9tRW1trdsYf5mfnLfz13f/Gz/W3EG4PQ0C/v7/526+uVtRfdiImcTie+69/R1D/IBNLiYiI/nn/yOM3D3pQJjRsAOYu+w8U/bUUF89WQdoFIoDFAvj384djUjKyZmVyUCEioj6Dw4pCA8ND8ex//hsar93A5f+76vlsoNjHomD7i9XsPCIiIkNxWFFscOQgDI4cZHYGERGRqVS8gy0RERFRbzisEBERkWocVoiIiEg1DitERESkGocVIiIiUo3DChEREanGYYWIiIhU47BCREREqnFYISIiItU4rBAREZFqHFaIiIhINQ4rREREpBqHFSIiIlLNIiJidsS/IigoCEOGDPHJfd++fRs2m80n9/1n7ADYorkDYIvmDkBPi5YOgC2aOwDfttTX18Plct3Xbf/0w4ov2e121NbWmp2hpgNgi+YOgC2aOwA9LVo6ALZo7gD0tPBlICIiIlKNwwoRERGp5p+Xl5dndoRmmZmZZicA0NMBsKUnWjoAtvRESwegp0VLB8CWnmjpAHS08JgVIiIiUo0vAxEREZFqHFaIiIhINQ4rREREpBqHFSIiIlKNZwPdw40bN9C/f39TGyorK1FYWAgRQWRkpKH7/uabbxAREWHoPu/lwoULaG9vh9VqxcWLF3HgwAG43W4MGzbM0A6Xy4UDBw7g8OHDOHv2LJqbmxEXF2dow13t7e0oLS1FaWkpzp8/j5s3byIqKgoWi8WUnq6cTidCQkLMzgBg/v/PbW1tKC8vR0hICIKDg03rAIBt27YhLS3N1Ia7zP53ucustZbr7H0SEhGRDRs2eL6uqqqSxMRECQ4OltjYWDl//rxhHdnZ2XLt2jUREcnPz5fhw4fLrFmzJDY2VrZu3WpYh4iIxWKRlJQU2bhxozidTkP33dV7770nEREREhUVJbt375bo6GjJycmRqKgo2bRpk2EdX375pURFRUlycrIEBQXJ5MmT5dFHH5X09HSpra01rENE5NixYxITEyNPPPGEzJo1S3JyciQjI0NiYmKktLTU0JbeREVFGbq/srIyiY+Pl+DgYHn22Welvr7es83hcBjaUlRUJOHh4RIRESFHjx6V9PR0SUhIkLCwMCkuLjas4+DBg90ukZGRnq+NpGWdFdGz1nKdvT8cVjp0XsjmzJkjH3zwgYiIFBQUyOTJkw3rSEpK8nydmZkpNTU1IiLS2NgoycnJhnXcbSkoKJDp06eL1WqV2bNnyxdffGFow12JiYnS2NgoNTU1EhISIlVVVSIiUl9fL4899phhHQ6HQy5cuCAiIidPnpQFCxaIiMhHH30kM2fONKxDRCQ5OVlOnz7d7fpTp055/R75Wk8PhncvQ4YMMaxDRCQrK0sOHDggDQ0NsmLFCklISPAMkampqYa2ZGRkSFlZmRw5ckTCwsKkqKhIRES++uorGTdunGEdFotFxo4dKxMmTPBcgoODZcKECZKdnW1Yh4iedVZEz1rLdfb+cFjp0HkhS0lJ8dr2+OOPG9YRHx8vbW1tIiLy5JNPem0z8gFIxHthqa2tlTVr1sjDDz8sMTExsmrVKtNaoqOjvbYZ+SDU9Xdj9OjRnq/j4+MN6xARGTVq1D+17Y/m5+cn2dnZXg+GnR8UjdT12ZNdu3ZJfHy81NTUGP7MSuffy5EjR3ptM3JN2b59u4wdO1ZOnTrluS42Ntaw/XemZZ0V0bPWcp29PzzAtsPPP/+MwsJCHDx4EG1tbV7bxMD3zZs7dy7mzJmDqqoqPPfcc1izZg0uXbqELVu2mHZcBAAMHz4cy5cvx8WLF7F9+3b88MMPhu4/ODgYBw8exO7du2GxWLBv3z4AQElJCfz9/Q3rGDBgAEpKSgAA+/fvN/W15pEjR2L16tVwOp2e65xOJ1atWmXo78qoUaOwbds2HDlypNslPDzcsA4AaGlpQXt7u+f7efPmYfXq1Zg0aZLXfycjdO7IycnpdZuvzZ8/H3v37kVeXh6WLVsGl8tl2jFNWtZZQOday3X2HkwdlRTp+hfh3aeO6+rqZMyYMYa2bNiwQex2uwQFBYnFYpHQ0FB5+eWXpaGhwdCOsWPHGrq/ezlz5ow4HA5JS0uTsrIyyc3NlZCQEK+n141w6tQpsdvtYrVaZcSIEVJRUSEiIteuXTP8r6Dr16/LokWLxGazidVqFZvNJjabTRYuXCh1dXWGdaxdu9brr/bO3n33XcM6REQWLVokn3/+ebfr8/PzpV+/foa2LFy4UJqamrpdf/HiRUNfBupsy5YtkpaWJkOHDjVl/5rWWREday3X2fvDt9v/HW63Gy6Xy5QzGm7duoXW1lYMHjzY8H3/GTidTgwaNAh+fsY/Qeh0OhEWFmb4fnvT2NgIAPxd+RNwu91wu90IDAw0Zf+XLl3CiRMnMHfuXFP23xMz11mAa+29mLnOdsZhpZP29nYcO3YMNTU1AIDo6GiMHz/e8H8kLR1s0d1xL1qGKS0dAFs0dwBs0dwBmN/CYaXD8ePHkZubi8jISMTExEBEUF1djbq6OuzevRvjx4/vUx1s0d3xe6Kjoz3DFDt+wxa9HQBbNHcA5rcEmLZnZZYsWYKCggKMGTPG6/rTp09j0aJFqKio6FMdbNHdAQCHDh3qddudO3f6XAfAFs0dAFs0dwC6WrriMysd4uPj8f333//D2x7UDrbo7gAAf39/ZGVl9XgWxcmTJ/HLL7/0qQ626O5gi+4ObS3dGHk0r2bTpk2TVatWeR0F3tDQIHl5eTJlypQ+18EW3R0iIo888ojnTZu6stvtfa6DLbo72KK7Q1tLV3qOCDTZzp07UV1djdjYWNhsNgwYMACxsbGorq7Grl27+lwHW3R3AL+9f0ZDQ0OP25YuXdrnOtiiu4Mtuju0tXTFl4F6oOU0UC0dAFs0dxARPeh4gG0nWk5J1dLBFt0dmlq0dLBFdwdbdHdoa+mMw0oHLaekaulgi+4OTS1aOtiiu4Mtuju0tXRj/GEyOmn5BFstHWzR3aGpRUsHW3R3sEV3h7aWrniAbYc7d+50e+8MAEhPT4fL5epzHWzR3aGpRUsHW3R3sEV3h7aWrjisdNDyCbZaOtiiu0NTi5YOtujuYIvuDm0t3Zj6vI4i169fl4ULF5r+CbZaOtiiu0NTi5YOtujuYIvuDm0tXfHU5R5oOSVVSwfAFs0dgJ4WLR0AWzR3AGzR3AHoagH4MpBHdXU1pk6divj4eKxdu9bro8ozMzP7XAdbdHdoatHSwRbdHWzR3aGtpSsOKx2WLFmCGTNmYM+ePaivr8ekSZNw69YtAMZ+gJOWDrbo7tDUoqWDLbo72KK7Q1tLN6a+CKWIw+Hw+n7NmjWSnp4uN2/e7LatL3SwRXeHphYtHWzR3cEW3R3aWrrim8J1aGlp8fp++fLlCAwM9Jos+1IHW3R3aGrR0sEW3R1s0d2hraUbU0clRZ555hkpLCzsdv26devEYrH0uQ626O7Q1KKlgy26O9iiu0NbS1c8G6jD3Te8CQoK6rbtypUrGD58eJ/qYIvuDk0tWjrYoruDLbo7tLV0xWGFiIiIVOPZQERERKQahxUiIiJSjcMKERERqcZhhYh8Jjc3F++8847XdVOnTsW6detMKiKiPyMeYEtEPnPjxg2kpqaioKAA6enp2Lp1K3bt2oXi4mL4+f1rfyu1tbUhIIBvFUXUF3BYISKfOnz4MN544w3s378fEydORGlpKeLi4rBjxw5s3rwZra2tCA0NxebNm5GYmIiysjK8+uqraGlpwZ07d7BgwQK89dZbAIB58+YhPDwcFy5cwNWrV3Hy5EnMnz8f3377LQICAmC323Ho0CGTf2Ii+qPxzxIi8qlp06bh008/RUZGBt5//33ExcXh6NGj2LdvH0pLSxEYGIgjR47g+eefx9mzZzFixAgUFRUhKCgILS0tyMzMxFNPPQWHwwEAOH78OIqLi2G1WvHJJ5+gubkZlZWVAP7+SbFE9GDhsEJEPvfmm29i7969WLx4MQDgs88+w7lz55CRkeG5TX19PdxuN1paWvDKK6+gvLwcfn5+uHz5MsrLyz3DyuzZs2G1WgEAqampqKiowNKlS5GVlYXp06cb/8MRkc/xAFsi8jl/f3+vY1REBC+99BLKyso8lytXrsDf3x/Lli2D3W7H119/jfLycowfP97rE19tNpvn61GjRuG7777DlClTUFJSgqSkJDQ1NRn6sxGR73FYISLDzZgxAzt27MCVK1cAAO3t7Thz5gyA3w7KtdvtCAgIQGVlJYqKinq9n8uXL8NisWDmzJlYv349WltbPfdJRA8OvgxERIbLzs5GXl4enn76abjdbrS2tmLGjBkYM2YMVq5ciRdeeAE7duzAiBEjMHHixF7vp7y83HNqtNvtxosvvojExESjfgwiMgjPBiIiIiLV+DIQERERqcZhhYiIiFTjsEJERESqcVghIiIi1TisEBERkWocVoiIiEg1DitERESkGocVIiIiUu3/AeWtCQapNYGcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Average Happiness Scores Over The Years\"\n",
    "xl = \"Years\"\n",
    "yl = \"life ladder\"\n",
    "plot_scatter(happiness_over_the_years, happiness_over_the_years, title, xl, yl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we have an outlier. We can plot the happiness distribution over the years with a barchart. Seems that 2005 is \"shining diamond\" in a stone river."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_barh(data, title, xl, yl, cmap):\n",
    "    plt.figure(figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "    cm = plt.cm.get_cmap(\"{}\".format(cmap))\n",
    "    patches = plt.barh(range(len(data.index)), \n",
    "                       data, align='center', alpha=0.7)\n",
    "\n",
    "    for i, p in enumerate(patches):\n",
    "        plt.setp(p, 'facecolor', cm(i**2))\n",
    "\n",
    "\n",
    "    plt.yticks(range(len(data.index)),\n",
    "               data.index)\n",
    "    plt.xlabel(\"{}\".format(xl))\n",
    "    plt.ylabel(\"{}\".format(yl))\n",
    "    plt.title(\"{}\".format(title))\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_barh(happiness_over_the_years, \"Average Total Happiness Index Over The Years\", \"Years\", \"Life Ladder\", \"pink\")\n",
    "\n",
    "# figure(num=None, figsize=(8, 6), dpi=80)\n",
    "# plt.bar(range(len(happiness_over_the_years.index)), happiness_over_the_years, \n",
    "#            alpha = 0.5,\n",
    "#            label = \"life ladder\" ) \n",
    "\n",
    "# plt.xlabel(\"Years\")\n",
    "# plt.ylabel(\"Life Ladder\")\n",
    "# plt.title(\"Average Total Happiness Index Over The Years\")\n",
    "# plt.legend()\n",
    "# plt.xticks(range(len(happiness_over_the_years.index)),happiness_over_the_years.index)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2006    5.196899\n",
       "2014    5.389021\n",
       "2013    5.394562\n",
       "2016    5.399479\n",
       "2015    5.404035\n",
       "2007    5.418275\n",
       "2008    5.418509\n",
       "2011    5.424082\n",
       "2012    5.443754\n",
       "2009    5.457667\n",
       "2017    5.460408\n",
       "2010    5.496806\n",
       "2018    5.502125\n",
       "2005    6.446259\n",
       "Name: life_ladder, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort to see the happiest and unhappiest year\n",
    "happiness_over_the_years_sorted = happiness_over_the_years.sort_values(ascending = True)\n",
    "happiness_over_the_years_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can notice that in 2005 people were significantly happier compared to the rest of the years. \n",
    "\n",
    "### Why is 2005 the happiest year?\n",
    "\n",
    "Were we really that happy in 2005? Well, as we can see from the data, life ladder is reported for only 27 countries, compared to 89 in 2006 and over 100 during the following years. We can see who these countries are?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27 entries, 59 to 1642\n",
      "Data columns (total 16 columns):\n",
      "country_name                         27 non-null object\n",
      "year                                 27 non-null int64\n",
      "life_ladder                          27 non-null float64\n",
      "log_GDP_per_capita                   27 non-null float64\n",
      "social_support                       27 non-null float64\n",
      "HLE_at_birth                         27 non-null float64\n",
      "freedom_to_make_life_choices         25 non-null float64\n",
      "generosity                           1 non-null float64\n",
      "perceptions_of_corruption            25 non-null float64\n",
      "positive_affect                      26 non-null float64\n",
      "negative_affect                      26 non-null float64\n",
      "confidence_in_national_government    24 non-null float64\n",
      "democratic_quality                   27 non-null float64\n",
      "delivery_quality                     27 non-null float64\n",
      "SD_of_ladder_by_country_year         27 non-null float64\n",
      "SD_mean_of_ladder_by_country_year    27 non-null float64\n",
      "dtypes: float64(14), int64(1), object(1)\n",
      "memory usage: 3.6+ KB\n"
     ]
    }
   ],
   "source": [
    "happiest_year = 2005\n",
    "group_years = world_happiness.groupby(\"year\")\n",
    "happiest_year_countries = group_years.get_group(happiest_year)\n",
    "happiest_year_countries.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name\n",
       "Turkey            4.719\n",
       "Romania           5.049\n",
       "Egypt             5.168\n",
       "Hungary           5.194\n",
       "Pakistan          5.225\n",
       "Iran              5.308\n",
       "Lebanon           5.491\n",
       "Poland            5.587\n",
       "Greece            6.006\n",
       "Jordan            6.295\n",
       "Czech Republic    6.439\n",
       "Japan             6.516\n",
       "Mexico            6.581\n",
       "Germany           6.620\n",
       "Brazil            6.637\n",
       "Italy             6.854\n",
       "United Kingdom    6.984\n",
       "Saudi Arabia      7.080\n",
       "France            7.093\n",
       "Spain             7.153\n",
       "Venezuela         7.170\n",
       "Belgium           7.262\n",
       "Australia         7.341\n",
       "Sweden            7.376\n",
       "Canada            7.418\n",
       "Netherlands       7.464\n",
       "Denmark           8.019\n",
       "Name: life_ladder, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The happy group\n",
    "happy_2005 = happiest_year_countries.groupby(\"country_name\")[\"life_ladder\"].mean().sort_values(ascending = True)\n",
    "happy_2005"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see who are the 27 happiest countries on average for the whole period. Then we can make a comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_happy_index = world_happiness.groupby(\"country_name\")[\"life_ladder\"].mean().sort_values(ascending = False)\n",
    "group_happy_index = group_happy_index.head(27)\n",
    "group_happy_index = group_happy_index.groupby(\"country_name\").mean().sort_values(ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XL1eItbKzBY969H26d+8eLl68KP378qR9/ns/qvtMdezYEXK5XCWOEydO1Lh/eogJJFEt8/f3x759+xASEgIAuHXrFm7dulXhx2Tr1q1o164dPD09a23bHTt2xLZt25CWloZff/0Vn332WYV1dHR0MHXqVCQnJyMyMhILFy6sMErx22+/YcOGDUhNTcWsWbOQk5NT6UN6ZTIZ5s2bhwULFmDz5s1IT0/HyZMnsXTpUunuzlmzZiEiIgKZmZmIiYnB6dOnpR8LU1NTpKam4sKFC7h7926lI2d9+/ZFjx49MGzYMMTGxiI9PR0hISFSEvjuu+9i0aJFOHDgAM6ePQs/Pz+YmppKIxhubm44evQodu/ejQsXLsDf31/lrvOaqEmclbG0tMTQoUPh7e2N3377DRkZGTh69CjeeecdZGVlISMjAx999BESEhJw5coV7Ny5EwUFBejUqZNa8dWUTCbDiy++iMOHD+Pvv/+u8eny6dOnIz09HZMmTUJycjJSU1OxY8cOLF68uMo2HTp0wAsvvICffvpJGm12dXXFjz/+CHt7e2lU3tLSEgMGDMD48ePx559/IjY2FjNnzsS4cePUejTL1KlTERYWJn1u3333XeTm5ta4PQAMGDAAHTp0gJ+fH86cOYODBw9i4cKFeO+999Tqpz4e55qYOXMmdu/ejbVr1+LixYv4+OOPkZiYiGnTplW6/rx58/D1119j5cqVuHjxIpKTk7F27Vr89NNP1W5ny5YtCAsLw/nz5zFp0iS0adNGerTZk/b5qMd9pgwNDfH2229j1qxZSEhIQHx8PBYsWFDj/un/q7OrL4kaKQCVvsrvZCzXpUsXMXfu3Br1WdO7sI8fPy5sbW2Ftra2cHR0FGFhYSrbLl9/8+bN4oUXXhB6enpi/Pjxori4WOrD1NRUfP7552LgwIFCW1tbdO7cWURGRla5TSGEWL9+vbC0tBRNmjQRbdu2FUOGDJHukp4+fbro0KGDdMPP+++/L93Uk5+fL1577TWhr69f6TEqd/v2beHt7S0MDQ2Fvr6+6N27t0hPTxdCPLwR4f333xdGRkZCR0dHeHh4iIsXL0pty8rKxAcffCBat24tjI2NxcqVKyu9iebQoUPS9L9v0Kgszn/fUFDu3zftFBUViYCAAPHCCy8ILS0t0aFDBzFlyhRRWFgobt26JQYPHizatGkjtLW1RdeuXcWWLVsqPQZCVH4TzaP7IcTD9++7776rso+ffvpJmJqaCg0NDekGk5r0c/r0aeHl5SWaNm0qDAwMRI8ePaqNVQghvL29hY6OjnSX761btwQAMWvWLJX1bt26JYYOHSqaNm0qmjVrJsaPHy8KCgqq3c9/v0dCCLFq1SphZGQkDAwMxLvvvivefvtttW6iEUKIixcvCk9PT6GjoyOMjIzEnDlzVN7jxx3fcvXxOFcW+78/+1u2bBEdO3YUTZo0EdbW1uLAgQPSsso+87t37xb29vZCS0tLtG7dWnh5eT32Luz169cLFxcXoaWlJezt7UVSUpLKOtX1WdPv3eM+U9nZ2eLNN98U2trawtzcXOzdu5c30ahJJsR/vMCIiBqM4OBgzJ8/H9evX69yHTMzM8yfPx8TJ058hpER0fNAJpPh0KFDtXrmheoGT2ETERERkVqYQBIRERGRWngKm4iIiIjUwhFIIiIiIlILE0giIiIiUgsr0VCt09bWhpGRUV2HQURERGq4c+cO7t+/X6N1mUBSrTMyMqr2MTFERERU/5iYmNR4XZ7CJiIiIiK1MIEkIiIiIrUwgSQiIiIitTCBJCIiIiK1MIEkIiIiIrUwgSQiIiIitTCBJCIiIiK1MIEkIiIiIrUwgSQiIiIitTCBJCIiIiK1MIEkIiIiIrUwgSQiIiIitTCBJCIiIiK1MIEkIiIiIrUwgSQiIiIitcjrOgBqhMryIHLm1HUUREREjYqsxfK6DkHCEchnyMzMDJaWlrCzs0OnTp3wxhtvIC4urq7DqlRwcDCGDx9e12EQERFRPcQE8hkLCwtDcnIy0tLSMH78eLz66qtISEio67BUKJXKug6BiIiI6jEmkHXojTfewLRp07B8+XKUlpZi7ty5cHJygkKhgLe3N3JzcwEAfn5+mDZtGjw9PdG5c2cMHToUJSUlAIBPPvkEo0aNwqBBg2BhYYERI0YgMTER/fr1g7m5OQICAqTtBQUFoUePHrC3t4eTk5NK4iqTybBixQq4u7vjww8/VInzxo0b6NGjBzZv3vwMjgoRERHVd0wg61iPHj1w9uxZfPnll9DX18fx48eRlJQEKysrLFy4UFovKSkJ+/btw/nz53H79m3s2rVLWnby5EmEhIQgNTUVqampmDt3Lg4ePIiUlBRs374dFy9eBAD4+vrixIkTSExMxJo1azBhwgSVWO7fv4/o6Gh8+eWX0rzTp0/Dy8sLn332GcaNG/eUjwYRERE1BLyJpo4JIQAA4eHhuHfvHsLCwgAAJSUl6Nixo7Te0KFDoaurCwBwcnJCenq6tMzLywvNmjUDANja2sLOzg7a2trQ1tZGly5dcPnyZXTu3BmJiYlYsmQJsrKyIJfLce7cOZSUlEBLSwsAMH78eJXYUlJS8Oabb2LPnj2wsbGpch+CgoIQFBQkTRf8U/JfDgkRERHVc0wg69iJEydgbW2NjIwMrFu3Dv369at0PR0dHelvTU1NlesU/72ssnVLSkowbNgwREdHo3v37rh37x6aNWumkkDq6+urbLN9+/YoKChAVFRUtQlkQECAyqlykxcMarj3RERE1BDxFHYd2rNnD7755hsEBARg8ODBCAoKQmFhIQCgsLAQZ8+erbVtFRcXo7S0FC+++CIA4Kuvvnpsm5YtWyIiIgJhYWFYsmRJrcVCREREDRsTyGds+PDhsLOzg4WFBTZt2oQDBw6gZ8+emDt3LhQKBZydnWFra4uePXsiKSmp1rZraGiIwMBAODk5wc3NDdra2jVu9+uvvyImJgZz586ttXiIiIio4ZKJ8ovwiGqJyQsGuHZ2Sl2HQURE1Kg87QeJm5iY4Pr16zVal9dAUu3TaFavnpZPREREtYunsImIiIhILRyBpNrHWthERERqa0hn75hA1lNmZmbQ0dGRHsnTs2dPGBsbw8rKCiNHjqy2bXBwMPbv3y89U7I2PI0+iYiIqGFiAlmPhYWFwdrauq7DICIiIlLBBLIB8fPzg6OjI2bMmIFPPvkEFy9eRH5+PtLT09G2bVuEhYWhZcuWKm1u3bqFUaNG4d69eyguLoaHhwdWr14NmUxWbR8lJSV45513EBUVhfbt28PS0rKO9pqIiIjqG95EU48NHz4cCoUCCoUCP//8c4XlCQkJ2LJlC86dOwdjY2OsX7++wjrNmzfHvn378Oeff+L06dO4fPmySh3tqvpYv349MjIycPbsWfzyyy84ceLE09tRIiIialCYQNZjYWFhSEpKQlJSEoYMGVJh+SuvvCKNOLq4uKjUxy5XVlaG//3vf7Czs4O9vT1Onjyp8oDyqvqIiorC2LFj0aRJE+jp6WH06NFVxhkUFAQTExPpxVrYREREjRsTyAasuvrY5YKCgpCVlYWEhAScPn0ab7/9NoqLix/bhzrPlw8ICMD169ell35TrSfZHSIiImogmEA2cjk5OWjbti10dHRw+/Zt7Ny5s0btPDw8sG3bNiiVShQVFSE0NPQpR0pEREQNBW+iaeRmzpyJt956CwqFAu3bt4enp2eN2k2ePBmnT59Gt27dYGJiAldXV1y5cuUpR0tEREQNAWthU61jLWwiIiL11fWDxFkLm+oWa2ETERE1arwGkoiIiIjUwhFIqn2shU1ERKS2hnT2jiOQRERERKQWJpD1jJmZGSwtLVWe6ejo6Ijo6Oi6C4qIiIjoEUwg66H79+9j06ZNT9y+sgeKExEREdUWJpD10KJFi/Dpp5+isLBQZf7t27cxZMgQ2NjYwNraGhs2bJCWmZmZYcmSJejbty/Gjh2LUaNG4YcffgAArFmzBtra2vjnn38AAK6uroiJiYFSqYSXlxccHR1hZWUFHx8faZs2NjY4duyY1P/69esxcuTIp73rRERE1AAwgayHHBwc4ObmhpUrV6rMnzlzJiwtLZGSkoLDhw/j008/xfHjx6XlV69exeHDhxESEgJPT08cOnQIABAZGYnu3bsjJiYGBQUFOHv2LHr27AlNTU2Ehobi5MmTOHPmDAwNDbFu3TppW19//bXU99dff40ZM2ZUGi9rYRMRET1fmEDWU4sXL8aqVauQlZUlzYuIiMD06dMBAMbGxhg6dCgiIyOl5ePGjYNMJgMA9O/fHxEREXjw4AHOnz+PgIAARERE4MiRI3BxcUGTJk0ghMDKlSthb28PW1tb/PLLL0hKSgIAjB49GocPH8bff/+NI0eOQCaTwdXVtdJYWQubiIjo+cLH+NRT5ubmGDVqFBYvXqwyvzxBrGxaX19f+vull16CtrY2tm/fDkdHR3h4eGDp0qV48OCBVM4wNDQUR44cwdGjR2FgYIA1a9bg6NGjAABdXV2MHTsWGzduRGJiYpWjj0RERPT84QhkPbZgwQJs374df/31FwDA09NTuu7xzp07+Pnnn9GvX78q23t6emLhwoXw9PREixYtoKmpid27d0sJZE5ODlq1agUDAwPk5+cjODhYpf306dPxzTff4MiRI/Dx8Xk6O0lEREQNDhPIeszIyAgzZ87EzZs3ATy8Geb06dOwtbVF37598dFHH8HJyanK9v3798eVK1ekhNHDwwPFxcWwsbEBAIwZMwYFBQXo1q0bhg4dWuEUtYmJCRQKBXx9faGnp/eU9pKIiIgaGpkQQtR1EFQ/FRQUwNLSEjExMejQoUON26lTjJ2IiIjqB3V+vzkCSZX69ttvYWlpiWnTpqmVPBIREVHjxxFIqnUcgSQiImp41Pn95l3YVPvK8iBy5tR1FERERA2GrMXyug5BLTyF/YR2796N7t27Q6FQoGvXrvDw8EBZWVmN2k6cOBExMTEAgPDwcJWHgdcWMzMznDlzpt73SURERA0PRyCfwK1bt+Dv748TJ07A1NQUAHDq1KkKz2isysaNG6W/w8PD4ejoWO3d1OqoaRJLRERE9KQ4AvkEbt68CblcjlatWknzHBwc8Pvvv8PLywsAkJubC01NTXz33XcAgE2bNmHChAkAAHd3d+zfvx8HDhzA3r17sXTpUigUCmzcuBGBgYFQKBRQKBSwsbGBTCbDlStXAADLly+Hk5MTHBwc8Oqrr+LatWsAgE8++QS+vr4YOnQoFAqF9NifckFBQejRowfs7e3h5OSEhIQEaZlMJsOyZcvg7OyMDh06YPPmzdKymJgY2NjYwMnJCTNmzAAvlyUiIiKAI5BPxM7ODi4uLnjppZfQp08f9OrVC2+//Tbc3Nzg7e2N+/fvIyoqCs7OzoiMjMSkSZMQERGBwYMHq/Tz6quvYvDgwXB0dFSp9PLxxx8DACZPngwnJyeYmpoiNDQUFy9exLFjx6CpqYlt27ZhxowZ2LNnDwAgKioKp06dgrGxcYV4fX19ERAQAACIj4/HhAkTVE5F6+joICEhAefPn4eTkxN8fX3x4MEDeHt7IyQkBO7u7tixY4dKbWwiIiJ6fnEE8gloaGhg165diIuLw8CBAxEbGwsrKyvcuHEDCoUCsbGxiIiIwIcffohTp06hrKwMUVFR8PDwqPE2Fi9ejKtXr2L9+vUAHp7qjoiIkK67/OKLL6SRSQAYNGhQpckjACQmJqJPnz6wtraGv78/zp07h5KSEml5eZWZrl27Qi6X49atW0hNTYWenh7c3d0BACNGjECzZs0q7T8oKAgmJibSq+CfkkrXIyIiosaBI5D/gaWlJSwtLTFlyhQMHDgQe/fuhaenJyIiIhATE4Nly5bBysoK27ZtQ5s2bapM8P5t27Zt+Pnnn3HkyBHI5Q/fIiEE5s+fj/Hjx1fa5tE62I8qKSnBsGHDEB0dje7du+PevXto1qwZSkpKoKWlBeDhCGQ5TU1NKJVKtU5XBwQESCOcAGDygkGN2xIREVHDwxHIJ3Djxg3ExsZK0zk5OcjIyEDHjh3h6emJ0NBQtGjRAvr6+ir1qCtjaGiIvLw8aToyMhKBgYH7DR+WAAAgAElEQVTYv3+/SlI4ePBgrFu3DtnZ2QCA0tJSJCYmPjbW4uJilJaW4sUXXwQAfPXVVzXaR0tLSxQVFeHo0aMAgLCwMJU4iYiI6PnFEcgnoFQqERgYiIyMDOjp6UGpVGLs2LF444038ODBA+Tl5Umnq/v3748ZM2ZUmUD6+vrCz88PO3fuxIwZMxASEoLCwkK88sor0joHDhyAr68vsrKy4O7uDplMBqVSiQkTJsDe3r7aWA0NDREYGAgnJye89NJLFa7DrIq2tjZ++OEHTJs2Dbq6unB3d8dLL71UwyNEREREjRkr0VCtM3nBANfOTqnrMIiIiBqM+vAgcVaiobql0axefBGIiIjo6eA1kERERESkFo5AUu1jLWwiIqJqNfQzdRyBrCEzMzNYWlpCqVRK8xwdHREdHV1tu+DgYFy8eFFlevjw4bUSU3lFm9r0NPokIiKixoUJpBru37+PTZs2qdXm3wlkbXnw4EGt90lERERUE0wg1bBo0SJ8+umnKCwsVJmfn5+PSZMmwcnJCba2tvD390dpaSk2btyIkydPYubMmVAoFDhw4IC0/qhRo2BjYwNHR0dcvnxZ6mvbtm1wdnaGg4MD+vTpI5UcDA4OxsCBAzFmzBg4Ojri+PHjKjGEhobC2dkZ9vb2KtsCHo6eLlq0CL169UKHDh2wePFiadm5c+ek7fn4+KC4uFhatnjxYnTt2lWqzf1o5RsiIiJ6fvEaSDU4ODjAzc0NK1euxEcffSTNnz17Ntzc3PDdd99BCIFJkyZh7dq1mDVrFrZv3445c+Zg0KBBAB4mggkJCUhOToapqSnmzp2LZcuWYf369YiNjcWPP/6Io0ePQltbGzExMfDx8UFycjIA4I8//kBiYiI6depUITYvLy+MGjUKMpkMmZmZ6NWrF65cuYImTZoAAHJzcxEXF4c7d+7AwsIC48aNQ/v27eHr64uZM2di7NixiI+Px8svvwzg4cPRly9fjps3b0JXVxeFhYXQ0OD/N4iIiIgJpNoWL14MZ2dn+Pv7S/PCw8MRHx+PFStWAACKioqkMoGV6d27N0xNTQEALi4uUnWYPXv2IDk5Gc7OztK6d+7ckepW9+7du9LkEQAyMjLg4+OD69evQy6X4+7du7hy5QosLCwA/F+9ayMjI5ibmyMjIwMGBgY4c+YMfH19AQA9e/aEjY0NgIcPIO/UqRNGjx6NAQMG4LXXXoOJiUml2w4KCkJQUJA0zVrYREREjRsTSDWZm5tj1KhRKqeBhRAIDw+Hubl5jfqorPZ0eT/jx49HYGBgpe2qqncNAN7e3li+fDnefPNNAEDLli1VTkdXtU2ZTFZpf5qamoiPj0dcXByio6PRs2dP/PDDD3B1da2wLmthExERPV94TvIJLFiwANu3b8dff/0F4GGd6qVLl0pJWU5ODi5dugSgYq3r6rz++uvYunUrrl27BgAoKyvDyZMna9Q2JycHZmZmAIDt27cjJyfnsW0MDQ1hbW2NkJAQAMDx48eRkpIC4OF1mrdv34arqysWLFiA3r1716j2NhERETV+TCCfgJGREWbOnImbN28CAFatWgW5XA6FQgFbW1t4enoiMzMTADB58mQEBgZWuLGlMm5ubvjss8/wxhtvwM7ODtbW1vjpp59qFNPq1asxZMgQ9O7dG8nJyTWuW71161asXbsWDg4O2LBhg3T6PC8vD0OHDoWNjQ1sbW1RWlqKsWPH1qhPIiIiatxYC5tqHWthExERVa8+PkictbCpbrEWNhERUaPGU9hEREREpBaOQFLtYy1sIiKiChrT2TmOQBIRERGRWphA1kNKpRKBgYGwtLSElZUVLC0tMXnyZOTm5j71bfv5+WHt2rVPfTtERETUcPEUdj00YcIEZGdn49ixY2jRogXKysqwa9cuZGdno3nz5nUdHhERET3nmEDWM5cuXcLOnTtx9epVtGjRAgCgoaGBt956C7du3ULfvn1x7949FBcXw8PDA6tXr4ZMJsMnn3yCixcvIj8/H+np6Wjbti3CwsLQsmVLpKSkYNq0afjnn39QXFwMX19ffPjhhwCAGzduYMyYMbhz5w46dOiABw8eSLGEhoZi9erVKCkpgRACn332GV599dU6OS5ERERUf/AUdj1z6tQpdOrUCa1bt66wrHnz5ti3bx/+/PNPnD59GpcvX8auXbuk5QkJCdiyZQvOnTsHY2NjrF+/HgBgZmaGiIgInDp1Cn/++Sd27NghVbiZOXMm3NzccPr0aaxcuRJHjhyR+vPy8kJ8fDwSExMRHh6OiRMnorS0tEJcQUFBMDExkV6shU1ERNS4MYFsQMrKyvC///0PdnZ2sLe3x8mTJ5GUlCQtf+WVV9CyZUsAgIuLC9LT0wEARUVFmDhxImxsbNCzZ09cuXJFahcVFYWJEycCeFjn28PDQ+ovIyMDr7zyCqytrfHmm2/i7t27uHLlSoW4AgICcP36deml31TrqR0DIiIiqns8hV3PODg4IC0tDVlZWWjVqpXKsqCgIGRlZSEhIQE6OjoICAhAcXGxtFxHR0f6W1NTU6rNPW/ePLRp0waJiYmQy+UYOnSoSruqeHt7Y/ny5XjzzTcBAC1btqxROyIiImrcOAJZz1hYWGDYsGGYMGGCdNe1EAJbt27Fn3/+ibZt20JHRwe3b9/Gzp07a9RnTk4OTExMIJfLkZqaikOHDknL+vXrh++//x4AkJmZicjISJV2ZmZmAIDt27cjJyenlvaSiIiIGjKOQNZD33//PRYvXgxnZ2fI5XIIIeDm5oZVq1bhrbfegkKhQPv27eHp6Vmj/ubPnw9fX1+EhITAzMwM/fr1k5atXr0aY8aMwc6dO9G5c2eVPlevXo0hQ4agffv2cHFxwUsvvVTr+0pEREQNj0wIIeo6CGpc1CnGTkRERPWDOr/fPIVNRERERGphAklEREREauE1kFT7yvIgcubUdRRERET1hqzF8roOoVZxBLKeMjMzw5kzZ56orbu7O/bv31+r8TyNPomIiKhhYgJJRERERGphAlnP3bhxA8OHD4etrS1sbW2xYMECAEB+fj4mTZoEJycn2Nrawt/fv9Iyg6GhoXB2doa9vT0UCgUOHDggLTMzM8OiRYvQq1cvdOjQAYsXL5aWnTt3Ds7OznBwcICPjw8fIE5EREQSJpD13OjRo+Hs7IzTp0/j9OnTmDlzJgBg9uzZcHNzw/Hjx5GcnAylUom1a9dWaP+4eta5ubmIi4vD8ePH8eWXX+LGjRsAAF9fX0ybNg2nTp3CO++8gxMnTjybHSYiIqJ6jzfR1HNxcXEqlWOMjIwAAOHh4YiPj8eKFSsAPKx3raVVsQZ1RkYGfHx8cP36dcjlcqmetYWFBQDAx8dH6tfc3BwZGRkwMDDAmTNn4OvrCwDo2bMnbGxsqowxKCgIQUFB0nTBPyX/ca+JiIioPmMC2UAJIRAeHg5zc/Nq13tcPeuq6mfLZLIaxxIQEICAgABp2uQFgxq3JSIiooaHp7Drud69e2PlypXS9J07dwAAgwcPxtKlS6WELycnB5cuXarQ/knqWRsaGsLa2hohISEAgOPHjyMlJeW/7goRERE1Ekwg67lt27YhPj4eVlZWsLOzk65zXLVqFeRyORQKBWxtbeHp6YnMzMwK7cvrWffu3RvJyck1rme9detWrF27Fg4ODtiwYQOcnZ1rc7eIiIioAWMtbKp1rIVNRETU8LAWNhERERE9NUwgiYiIiEgtvAubah9rYRMREQFofDWwy3EEsh7ZvXs3unfvDoVCga5du8LDwwNlZWW1vp3/UmebiIiIiCOQ9cStW7fg7++PEydOwNTUFABw6tQptZ7HSERERPQscASynrh58ybkcjlatWolzXNwcMDvv/8OLy8vAA/LDmpqauK7774DAGzatAkTJkwA8DABHTFihFQb++OPP5b6iYmJgY2NDZycnDBjxgw8euN9WloaXnvtNfTo0QN2dnZYt26dtEwmk2HZsmVwdnZGhw4dsHnz5qd6DIiIiKhhYAJZT9jZ2cHFxQUvvfQShgwZItWlLq93ff/+fURFRcHZ2RmRkZEAgIiICHh6egIAxo4dixkzZuD48eM4deoUjh8/jp9//hn379+Ht7c3vvrqKxw/fhxubm64evUqAODBgwd4++23sWLFCpw4cQLHjh3Dt99+i1OnTklx6ejoICEhAQcOHMDMmTOlB5cTERHR84sJZD2hoaGBXbt2IS4uDgMHDkRsbCysrKxw48YNKBQKxMbGIiIiAh9++CFOnTqFsrIyREVFwcPDA//88w8OHz6MmTNnQqFQwNHREZcuXcKFCxeQmpoKPT09uLu7AwBGjBiBZs2aAQBSU1Nx9uxZeHt7Q6FQoFevXsjPz8e5c+ekuMprZXft2hVyuRy3bt2qEHtQUBBMTEykF2thExERNW68BrKesbS0hKWlJaZMmYKBAwdi79698PT0REREBGJiYrBs2TJYWVlh27ZtaNOmDYyNjZGfnw+ZTIYTJ06gSZMmKv0lJydXuS0hBFq3bo2kpKQq16mqVvajWAubiIjo+cIRyHrixo0biI2NlaZzcnKQkZGBjh07wtPTE6GhoWjRogX09fXh6emJhQsXSqevDQwM4OrqiqVLl0rt//rrL1y/fh2WlpYoKirC0aNHAQBhYWHIy8sDAHTp0gV6enrYunWr1O7SpUvIzs5+FrtMREREDRQTyHpCqVQiMDAQnTt3hkKhgKurK8aOHYs33ngDjo6OyMvLg4eHBwCgf//+uHLlipRAAkBISAjOnz8PGxsb2NjYYNiwYcjKyoK2tjZ++OEHTJ8+HU5OTjh+/LhUD1sul2Pfvn3YsWMHbG1tYWVlhYkTJ6KoqKhOjgERERE1DKyFTbXO5AUDXDs7pa7DICIiqnMN6UHi6tTC5jWQVPs0mjWoLwwRERGph6ewiYiIiEgtHIGk2sda2ERE9Bx7Hs7CcQSyEmZmZrC0tFR5ZI2joyOio6NrfVvffvstFAqFysvIyAiWlpb/qd+nUe+aNbSJiIgIYAJZpfv372PTpk1PfTv+/v5ISkqSXnv27IGGhgbWrFnz1LdNRERE9CSYQFZh0aJF+PTTT1FYWFhhWX5+PiZNmiTVnfb390dpaSlSU1PRpUsXAP/3kO6PPvoIABAZGSk9hqcqRUVFGDJkCGbNmoUBAwYAAEpLSzF37lw4OTlBoVDA29sbubm5AIDQ0FA4OzvD3t4eCoUCBw4cqLTfoKAg9OjRA/b29nByckJCQoK0rLp619XV0CYiIqLnFxPIKjg4OMDNzQ0rV66ssGz27NlSjerk5GQolUqsXbsWXbp0QVFREa5evYqkpCR07twZhw8fBqBat7oqU6ZMgbm5OebOnSvN+/LLL6Gvr4/jx48jKSkJVlZWWLhwIQDAy8sL8fHxSExMRHh4OCZOnIjS0tIK/fr6+uLEiRNITEzEmjVrMGHCBJXlldW7rq6GNhERET3feBNNNRYvXgxnZ2f4+/urzA8PD0d8fDxWrFgB4OHIoZaWFgDAw8MDERERyMrKwujRo7Fhwwbk5eUhIiIC69atq3Jba9aswZ9//qkyOli+rXv37iEsLAwAUFJSgo4dOwIAMjIy4OPjg+vXr0Mul+Pu3bu4cuUKLCwsVPpITEzEkiVLkJWVBblcjnPnzqGkpESKubJ619nZ2RVqaE+ePLnS2IOCghAUFCRNsxY2ERFR48YEshrm5uYYNWoUFi9erDJfCIHw8HCYm5tXaOPp6YlffvkF2dnZWLNmDdLS0rB7925kZGSge/fulW4nJiYGn376KWJjY6Gvr19hW+vWrUO/fv0qtPP29sby5cvx5ptvAgBatmyJ4uJilXVKSkowbNgwREdHo3v37rh37x6aNWumkkBWVu9andPVrIVNRET0fOEp7MdYsGABtm/fjr/++kuaN3jwYCxdulS6SzsnJweXLl0C8DCBjIyMxJUrV9C5c2d4enpi0aJF6NOnDzQ0Kh7uGzduYMSIEfj+++/RuXPnCssHDx6MoKAg6VrMwsJCnD17VtqumZkZAGD79u3Iycmp0L64uBilpaV48cUXAQBfffVVjfa7uhraRERE9HxjAvkYRkZGmDlzJm7evCnNW7VqFeRyORQKBWxtbeHp6YnMzEwAQJs2bdCmTRu4uLgAAPr06YO//vqryusfP/30U+Tm5mLBggUVHucDAHPnzoVCoYCzszNsbW3Rs2dPJCUlAQBWr16NIUOGoHfv3khOTpZqXD/K0NAQgYGBcHJygpubG7S1tWu039XV0CYiIqLnG2thU61jLWwiInqeNdQHibMWNtUt1sImIiJq1HgKm4iIiIjUwhFIqn2shU1ERM+h5+nsG0cgiYiIiEgtTCDrSH5+PvT19TFx4sT/1E90dDR+//33J27v7u6O/fv3AwA+/vhj/PTTT/8pHiIiImr8mEDWkR9//BEODg7YtWsXCgoKnrifxyWQ5c+qrInAwECMHDnyiWMhIiKi5wMTyDqyadMm/O9//4Orqyt27NgBAAgODsbw4cOldfbv3y+VEkxLS8PLL78MOzs72NjYYP78+UhKSsK3336LrVu3QqFQIDAwEJmZmWjdujUCAwPh6uqKr776CpGRkXBxcYG9vT2sra2xefPmSmPy8/PD2rVrAaDGbYiIiOj5w5to6sDZs2dx7do1DBw4EEqlEl988QXGjx9fbZu1a9fitddew7x58wAA2dnZaNmyJfz9/VFQUIDlyx9euJuZmYmsrCxYWFjg448/BvCwYs0ff/wBTU1NZGdnw8HBAQMHDkS7du2q3J6Dg0ON27AWNhER0fOFI5B1YNOmTRgzZgw0NTXx2muv4fLlyzh//ny1bdzc3LBx40Z89NFH+P3339G8efMq19XR0cGoUaOk6aysLLz11luwtrZGv379cPfuXakcYlXUaRMQEIDr169LL/2mWtX2TURERA0bE8hnrLS0FNu3b8fWrVthZmYGCwsLFBYW4vvvv4dcLseDBw+kdYuLi6W/hw0bhtjYWHTp0gVr167FoEGDqtxG06ZNIZPJpGl/f3/06dMHKSkpSEpKQufOnVX6rsyTtCEiIqLnA09hP2N79uyBubk54uPjpXlnzpyBh4cHwsLCkJycjOLiYsjlcoSGhkrrpKWlwdzcHGPGjIGTkxN69eoF4GGt6xs3blS7zZycHJiamkImk+Ho0aNITk5+bJxP0oaIiIieD0wgn7FNmzbBx8dHZZ61tTVeeOEF3LlzB15eXrC2toaZmRns7e2RnZ0NANi5cydCQkKgpaUFIQS+/fZbAMCQIUOwbds2KBQKDB06FGPGjKmwzaVLl2LatGlYunQpunXrBmdn58fG+SRtiIiI6PkgE0KIug6CGhd1irETERFR/aDO7zevgSQiIiIitTCBJCIiIiK18BpIqn1leRA5c+o6CiIioqdO1mJ5XYdQJzgCWc89y5rZ3377LVauXPmftkNERESNHxPIeu5Z1sz29/fHrFmznngbRERE9HxgAlnPPcua2Z988gnmzHl46jklJQWurq5wcHBAt27d8Pnnnz/zfSciIqL6iddA1mPPumb2J598IvVjZmaGiIgIaGtro6ioCL169UL//v3h6Oj4dHaWiIiIGgyOQNZjz7pm9qOKioowceJE2NjYoGfPnrhy5QqSkpIqXTcoKAgmJibSq+CfkprvJBERETU4TCDrqbqomf2oefPmoU2bNkhMTERycjLc3d2rrIUdEBCA69evSy/9plpPuNdERETUEDCBrKfKa2bfuHEDmZmZyMzMRGxsLLZu3QpTU1OpZrZSqaxQM9vY2BhjxozBF198IdXcNjQ0RF5eXo23n5OTAxMTE8jlcqSmpuLQoUO1vo9ERETUMPEayHqqLmpmP2r+/Pnw9fVFSEgIzMzM0K9fv6ezo0RERNTgsBY21TqTFwxw7eyUug6DiIjoqWtMDxJXpxY2RyCp9mk0a1RfKCIiIlLFayCJiIiISC0cgaRaV6bMx+2LX9R1GERERE9Nm84f1HUIdYojkA2cmZkZzpw589S34+7ujv379z/17RAREVH9xwTyOaJUKus6BCIiImoEmEA2EosXL0bXrl2hUCigUChw5coVAIBMJsOKFSvg7u6ODz/8sNoa1/n5+Zg0aRKcnJxga2sLf39/lJaW1tUuERERUT3FayAbgZycHCxfvhw3b96Erq4uCgsLoaHxf/83uH//PqKjowE8TBKrqnE9e/ZsuLm54bvvvoMQApMmTcLatWsxa9asOtozIiIiqo+YQDYChoaG6NSpE0aPHo0BAwbgtddeg4mJibR8/Pjx0t9FRUWYNm0akpKSoKGhgWvXriEpKQmOjo4IDw9HfHw8VqxYIa2rpfX4soRBQUEICgqSpgsKWQubiIioMWMC2QhoamoiPj4ecXFxiI6ORs+ePfHDDz/A1dUVAKCvry+t+2iNa7lcjqFDh0o1roUQCA8Ph7m5uVrbDwgIQEBAgDT9QttmtbBXREREVF/xGshGQAiB27dvw9XVFQsWLEDv3r2RmJhY6brV1bgePHgwli5dKt1sk5OTg0uXLj2TfSAiIqKGgwlkI5CXl4ehQ4fCxsYGtra2KC0txdixYytdd/78+di4cSN69OiB+fPnq9S4XrVqFeRyORQKBWxtbeHp6YnMzMxntBdERETUULAWNtW6F9o2Q+LRj+o6DCIioqemMT5InLWwqU5pyA0a5ReLiIiIHuIpbCIiIiJSC0cgqdaVlvyDlIRv6joMIiKip8LGeWpdh1DnOAJJRERERGphAlnLlEolAgMDYWlpCSsrK1haWmLy5MnIzc19ov6Cg4Nx8eLFJ2ork8lga2sLOzs7dOvWDZs3b5aW7d27F++///4T9UtERETPN57CrmUTJkxAdnY2jh07hhYtWqCsrAy7du1CdnY2mjdvrnZ/wcHBaN26NTp37vxE8cTFxUFfXx8pKSlwdHTEwIED0a5dOwwePBiDBw9+oj6JiIjo+cYRyFp06dIl7Ny5E5s3b0aLFi0AABoaGnjrrbek6i5ffPEFrKysYGNjAx8fH+Tl5QEA9u3bB1tbWygUClhbW2PPnj3YuHEjTp48iZkzZ0KhUODAgQNISUmBq6srHBwc0K1bN3z++ec1is3GxgbNmzeXbs8PDg7G8OHDpeWbN2+GQqGAnZ0dHB0dkZmZCaVSCS8vLzg6OsLKygo+Pj4oLCyszUNGREREDRBHIGvRqVOn0KlTJ7Ru3brS5QcPHsTmzZtx7NgxNG/eHJMnT8a8efPw9ddfY/78+fj222/Rq1cvlJWV4d69e2jevDm2b9+OOXPmYNCgQQCA/Px8REREQFtbG0VFRejVqxf69+8PR0fHamM7cuQIWrduDTs7uwrLoqOjsWTJEsTExKBdu3ZSkqipqYnQ0FC0atUKQghMmzYN69atw5w5c1Ta/7sWdmHhfbWOGxERETUsTCCfoYiICPj4+EinsqdOnQpvb28AgIeHB9577z0MHz4cAwYMgEKhqLSPoqIiTJs2DUlJSdDQ0MC1a9eQlJRUZQLZq1cvFBYWIiMjAzt37oSWllaFdX755ReMGTMG7dq1AwDo6ekBAMrKyrBy5Ur88ssvUCqVyMvLg5ubW4X2/66F3ca4hRpHhYiIiBoansKuRQ4ODkhLS0NWVlaly4UQkMlkKvPKp4OCgrB582bo6elh7Nix+OKLLyrtY968eWjTpg0SExORnJwMd3d3FBcXVxlTXFwcLl26hE2bNmHcuHG4fft2jfcnNDQUR44cwdGjR5GSkoI5c+ZUuy0iIiJ6PjCBrEUWFhYYNmwYJkyYIN11LYTA1q1bkZ6ejv79++PHH39Efn4+AGDDhg3w9PQEAFy4cAFWVlaYMWMGpk6divj4eACAoaGhdJ0kAOTk5MDExARyuRypqak4dOhQjWLz8/ODh4cHPvvsswrLXn/9dWzduhW3bt0CABQWFqKwsBA5OTlo1aoVDAwMkJ+fj+Dg4Cc+NkRERNR48BR2Lfv++++xePFiODs7Qy6XQwgBNzc3DB48GK+88gpSUlLg4uIiPWJn3bp1AIAPP/wQFy9ehJaWFvT09PDNNw8fxD158mTMnj0bX375JT777DPMnz8fvr6+CAkJgZmZGfr161fj2JYtW4bu3bvjgw9Uywy6ublh/vz5GDBgAGQyGbS0tBAWFoYxY8Zgz5496NatG9q3bw9XV1fcuHGj9g4WERERNUgyIYSo6yCocVGnGDsRERHVD+r8fvMUNhERERGphQkkEREREamF10BSrbt/vxDRv31f12EQERH9J+5e4+s6hHqLI5CPkMlkKCgoUJlnZmaGM2fOPLbtxIkTERMTAwAIDw/H8ePHnyiGf1eIedTRo0fh4uIChUKBbt264eWXX5Yey6NOzez/Eh8RERERRyBrycaNG6W/w8PD4ejoCCcnp1rrX6lUYsiQIYiIiIC9vT0AIDU1FU2bNgWgXs3spxEfERERPT84AqkGMzMzLFq0CL169UKHDh2wePFiaZm7uzv279+PAwcOYO/evVi6dCkUCoWUWG7btg3Ozs5wcHBAnz59pFHNkpISTJkyBZ07d0bfvn2RkJBQ6bbz8/ORn58vVYsBgC5dukBfX1+tmtnqxhcfH4/u3btLNbrLHy9EREREzy+OQKopNzcXcXFxuHPnDiwsLDBu3Di0b99eWv7qq69i8ODBcHR0xIwZMwAAsbGx+PHHH3H06FFoa2sjJiYGPj4+SE5Oxvr165GRkYGzZ8+itLQUbm5uMDMzq7DdFi1aYNq0aejUqRNcXV3h4uKCkSNHonPnzpg4cWKNa2arG9/nn3+O2bNn4+233wbw8EHmRERE9HxjAlkDj5Yf9PHxAQAYGRnB3NwcGRkZKglkZfbs2YPk5GQ4OztL8+7cuYOSkhJERUVh7NixaNKkCZo0aYLRo0fjjz/+qLSfVatWYdasWYiKikJkZJT00noAACAASURBVCTs7e3x22+/oXfv3hXWVadmdnXx9e3bF4sXL8alS5fQr1+/SrcVFBSEoKCgR7bNcodERESNGRPIRxgZGeHu3bvQ19eX5t29exfGxsbStI6OjvS3pqYmlErlY/sVQmD8+PEIDAysdJk6TE1N4efnBz8/PzRt2hQ7duyoNKl7tGa2XC7H0KFDq6xjXV187733HgYPHozIyEjMmzcP1tbWUvWccgEBAQgICJCmjYxaqrVPRERE1LDwGshHeHl5qVzjt3XrVnTu3BlGRkZq9fPv+tXltaavXbsGACgrK8PJkycBAB4eHti2bRuUSiWKiooQGhpaaZ8FBQU4ePCglHAWFRXh/Pnz6NixY6XbrK5mtjrxpaamwtzcHJMmTcK8efOkGt1ERET0/OII5CNWrVqF9957D7a2ttDQ0Ph/7d15WFXl+v/xN4JJTh0gy5J0OyGBwGYIQ4UcUBGHLLWcJTXymFl59KvnW3nMPGXflEo9Hu0ckxxoUI+zaakQmYqiAqKnHHHIckBEQZRp//7wcv3EeRuwGT6v69rXtdde63nW/WxFbp9nrXXz2GOP8fXXX1vdz8CBA4mIiGDx4sWMHDmSYcOG8f777/Pss89SUFBAXl4eXbp0ISAggMjISFJSUvDw8MDV1ZXg4GCOHj16U58Wi4XZs2fz+uuv8+CDD5KXl0dYWBivvvoqYF3NbGvimzFjBrGxsTzwwAPY29szbdq0+/+CRUREpEJQLWwpdqqFLSIiUv6oFraIiIiIlBglkCIiIiJiFV0DKcUuJ+cSy5Z8YeswRERErPZcr8G2DqFc0AxkGXevtbivFxcXd8vnPf4RJdGniIiIlE9KIMuhe3n2pIiIiEhJ0RJ2OdGmTRtatWplPIdx48aNrF+/nvfee4+cnBwcHBz46KOPCAkJKdIuPz+fLl26kJ6eTk5ODmazmX/9619Ur16d6OhovvzyS5ydnUlNTaVatWp88803NGrUCIC3336br776inr16vHUU0+V+phFRESkbNIMZDmSlJTEunXr2LhxI4cPH+bdd99l7dq17Ny5k0WLFtG3b1/y8vKKtLG3tycmJobExERSU1OpXbt2kUoyCQkJTJkyhT179hAaGsqHH34IwKpVq1i5ciVJSUls2rSJ/fv3l+pYRUREpOxSAlmODBw4kKpVqwKwbt06Dh48SEhICGazmV69egEY1WSusVgsfPzxx/j6+uLt7c2aNWtISkoy9rdu3ZoGDRoAEBQUxKFDhwCIjY3lxRdfpGbNmtjb2zNkyJDbxhUVFYWrq6vxul3JRBEREakYtIRdjlxfo9tisRAWFsb8+fNvOu7YsWPG+5iYGH744Qfi4+OpVasW06dPJz4+3th/u9re1jxf/sZa2C4uqoUtIiJSkWkGspzq2LEj69atK3KH9vbt2286LiMjAxcXF2rVqsXFixeJjo6+p/7bt2/PN998Q3Z2NgUFBffcTkRERCo+zUCWU02bNmXhwoUMGzaMnJwccnNz8fPzY9GiRUWOGzRoECtWrMDDw4N69eoRHBzMr7/+etf+u3btytatW/Hx8aFevXo888wzKk8oIiIigGphSwlwcXHm33M+tnUYIiIiVqvMDxK3pha2ZiCl2D34YPVK/QMoIiJS0ekaSBERERGximYgpdhdunRJN92IiEi5EhERYesQyhXNQN7F/dSiLkkTJ05kzJgxtg5DREREKjElkFKE6myLiIjI3SiBvA83zkoGBAQQFxdHTk4OPj4+LFmyBICtW7fSsGFDzp49C8D69etp3bo1/v7+tGjRwnigd1xcHGazmeHDh+Pl5YWfnx+pqam8+OKLeHh40KFDB7KysozzHTt2jPDwcJo3b0737t3JyMgAICsriyFDhtC8eXOaN2/Ou+++a7Rp06YNq1evNrZ79eplLDNHREQwatQowsLC8PHxAWDp0qW4u7vj6+vL5MmTsbOzKxKDiIiIVF5KIIvRgw8+yOLFi3nzzTfZsWMHAwYMYMGCBTz88MN3rV29d+9ehg8fzp49ewgKCiIsLIxp06axb98+qlatSkxMjHGeH3/8kXnz5pGamoqrqytvvfUWAO+99x65ubmkpKSQkJDA8uXLWbx48T3FvnnzZpYsWcLevXs5ffo0kZGRrFq1it27dxepgCMiIiKiBLKYubm58eGHHxIUFMSwYcNo3bo1cPfa1c2aNcNsNgPg5+eH2WzG1dUVAH9/fw4fPmyco2vXrjz66KMAREZGsmHDBgA2bNjA8OHDqVKlCjVq1GDQoEHGvrt54YUXjERx27Zt+Pn50bRpUwBeeumlO7ZVLWwREZHKRXdh3wcHBwcKCgqM7RsTpl27dlGnTp0iNanvVrv6xprUN27n5OTcNh47OzvjHNfe37jvbjHfWGf7xn7u5MZa2M7OqoUtIiJSkWkG8j40btyYhIQE4Gr96V9++cXYt3r1atavX8/evXvZtm0bX3/9NXDvtavvxZo1azh9+jQAc+fOJTQ0FIAOHTrwr3/9C4vFQnZ2NgsXLjT2XR/zkSNH2Lx58237f/rpp9m5cycHDx4E4IsvvrivOEVERKRi0gzkPQgNDcXB4f9/VV999RWvvPIKc+fOxc/PD09PT+DqTOKIESNYt24dzs7OfPPNN7Rt29ZYDr6X2tX3on379gwdOpQjR47QqFEjI8F75513eO211/Dy8gKgd+/exlL5uHHjePHFF1m/fj3NmjWjRYsWt+3/0UcfZfbs2XTp0gUXFxe6detG1apVqV69utWxioiISMWjWthySxcvXqRWrVoAzJs3j7lz595x1vJ6zs7OREVFlWR4IiIixUoPElctbCkG06dPZ/HixeTn5+Ps7My//vWve25bvXp1/SCKiIhUYJqBlGJnzf9gREREpGyw5ve3bqIREREREatoCVuKXXZ2NjNn3fuSt4iIiK2NHPGyrUMoVzQDWQaYTCbc3d0xm808+eST9OvXj+zs7Du2mThxImPGjLlr3ytXrmTs2LHFFaqIiIiIEsiyYsmSJSQlJbFv3z4uXLhg1Kn+o7p3785HH31ULH2JiIiIgBLIMufKlStkZ2fj5OQEwNSpUwkMDMTPz4/w8HCj9OH1cnNziYyMxM3NjVatWjFixAjj+Y/R0dHG+7i4OAICAox2qampmEwmANLS0nj44Yd5++238fX1xd3dncTERCIjI/H29iYwMJCTJ0+W8OhFRESkPFACWUb06tULs9nMo48+ip2dHS+88AIxMTHs37+frVu3smvXLvr27cvIkSNvajtnzhyOHTvGvn372LhxI7t27bqvGNLT0wkKCmL37t0MHTqU0NBQRowYQUpKCgEBAcycOfOW7W6uhX3lvs4vIiIi5YNuoikjlixZQvPmzcnPz+eVV15h3LhxHD9+nMTERPz9/QEoKCjA3t7+praxsbEMHDgQBwcHHBwc6Nu3Lz/++KPVMdSsWZMuXboA4Ofnh6urK2azGQB/f3++//77W7a7sRb2tdlTERERqZiUQJYxDg4O9OzZk7Fjx+Lh4cHbb7/NkCFD7tjGYrFgZ2d3T30XFBQY25cvXy6yv1q1asZ7e3t7HB0di2zn5+ff6zBERESkAtMSdhm0adMmmjVrRvfu3Zk1axbnzp0DIC8vj927d990fNu2bVm4cCH5+flcvnyZr7/++pb9NmzYkCNHjpCeng7AggULSm4QIiIiUmFpBrKM6NWrF46OjuTl5WEymZg9ezZPPPEE6enptGnTBjs7O/Lz8xk6dCi+vr5F2g4fPpzk5GQ8PT1xdXXFz8+PnJycm85Rr149xowZQ0BAACaTiZCQkNIanoiIiFQgKmVYQVy8eJFatWpx5coVunfvTu/evRk2bJhNYlEpQxERkfLHmt/fmoGsIEJDQ7ly5QqXL18mNDSUiIgIW4ckIiIiFZQSyAoiISHB1iGIiIhIJaEEUordxaxspkydY+swREREihg/5hVbh1BhVMi7sO3s7MjKyirymclkIjU19a5thw0bZjxDcfny5Wzfvv2+Yri+AsyN2rRpw+rVqwEoLCzkz3/+M23atOHixYtMmDDhtndR3687xSIiIiJiLc1A3uDf//638X758uUEBAQQGBhYIufKy8tj4MCBXLp0iXXr1uHo6MikSZNK5FwiIiIixaVCzkDejclk4t1336Vly5Y0bNiQyZMnG/uuzQ6uXbuWlStXMmXKFMxms5FYLliwgBYtWuDn58czzzxjzGrm5ubyyiuv4ObmRtu2be96TeKlS5fo3r07VatW5T//+Y/x0O6IiAijZODEiRPp168f3bp1w8PDg3bt2hnPhLxT/es7xVJQUMCYMWNo3rw5zZs357XXXiM3N9c49/Dhw2nfvj0NGjTg9ddfJzY2lpCQEEwmE1FRUcXx9YuIiEg5V2lnIM+fP8+WLVs4c+YMTZo04aWXXqJevXrG/vDwcLp3705AQIBRf/qnn37iq6++Ij4+nmrVqvHjjz/Sv39/kpOTmTNnDkeOHGHv3r3k5eUZSdftjBgxgk6dOjF//vw7VpFJSEhgx44dODs706dPH+bMmcNf//rXIvWv8/PzadOmDa6urgB3jOWzzz5j586d7Ny5E3t7e7p3786nn37K2LFjAUhNTWXjxo0UFBRgMpm4ePEicXFx/PbbbzRr1ozIyEhq1qz5B799ERERKc8q1Qzk9Yla//79AahTpw6NGjXiyJEjd22/YsUKkpOTadGiBWazmddee40zZ86Qm5tLbGwsgwcPpmrVqlSvXp0BAwbcsa+wsDA2bdrEnj177nhc586dcXZ2BiAoKIhDhw4BRetfOzo60rdvX6PNnWLZsGEDQ4cOpVq1ajg4OPDyyy+zYcMGY3+PHj2oVq0a1atXp1mzZoSHh1OlShXq1auHk5PTLZ8PFRUVhaurq/HKvXLlrt+liIiIlF8VMoGsU6cOZ8+eLfLZ2bNneeSRR4zt+6nzbLFYGDJkCElJScbr5MmTPPDAA1j7PPY+ffoQFRVFx44dSUpKuu1xt4vzTvWv7xTLrdpdv33j+e7lexo9ejQnTpwwXg9cV1NbREREKp4KmUB26tSJf/7zn8b2/PnzcXNzo06dOlb1U7t2bTIzM43tbt26MX/+fI4fPw5cvYM6MTERgPbt27NgwQLy8/PJyckhJibmrv337duXTz/9lE6dOt2yxvWd3Kn+9Z1i6dChA9HR0eTm5pKfn8/cuXMJDQ216twiIiJSuVXIayA/+eQT3njjDby9valSpQqPPfbYfT0aZ+DAgURERLB48WJGjhzJsGHDeP/993n22WcpKCggLy+PLl26EBAQQGRkJCkpKXh4eODq6kpwcDBHjx696zlefPFF7Ozs6NSpE99+++09x3an+td3iiUyMpJDhw7h5+cHXL1paNSoUVZ/NyIiIlJ5qRZ2OVaW6l9f76E/OfHXt6fYOgwREZEi9CDxO7OmFrYSyHKsRYsWRepff/LJJzg42H5S2Zq/gCIiIlI2WPP72/bZhtw31b8WERERW1ACKcXuwsVs3p6sWtgiImJ7k9/WsnVJqJB3YVcGubm5jBs3jiZNmvDkk0/SvHlz5s2bZ+uwREREpBLQDGQ5FRERwZUrV0hOTqZGjRqkpaXRuXNn8vLyiIyMtHV4IiIiUoFpBrIcOnjwIMuXL+ezzz6jRo0awNX63tOmTeO9994jLi4Os9nM8OHD8fLyws/Pj9TUVF588UU8PDzo0KEDWVlZAGzcuJGgoCB8fX1vmsVs06YN48aNIzg4mMaNGzN8+HCbjFdERETKFiWQ5dCuXbto2rQpLi4uRT4PCgrixIkTnDlzhr179zJ8+HD27NlDUFAQYWFhTJs2jX379lG1alXj4eJ+fn5s3ryZ3bt3Ex8fz7vvvstvv/1m9Hno0CHi4uJITU1l/fr1bN26tVTHKiIiImWPEshy6nZlDK9p1qwZZrMZuJokms1mXF1dAfD39+fw4cMApKen07t3b5o3b067du04e/Yse/fuNfrp06cP9vb2PPjgg5jNZqMW9/VUC1tERKRyUQJZDvn6+rJ//37S09OLfL5161ZcXV2pU6fOPde0Hj58OM888wx79uwhKSkJNzc3Ll++bByrWtgiIiJyIyWQ5VDTpk3p1q0bkZGRXLp0CYC0tDT+8pe/8NZbb1nVV0ZGBg0aNMDOzo74+HiSk5NLImQRERGpQJRAllPz58+nUaNGeHl58eSTT9K1a1f+8pe/WH2jy5QpUxg7dixPP/000dHRtGjRooQiFhERkYpCpQyl2NV+yIlRY1ULW0REbE8PEr93qoUtNqVa2CIiIuWPNb+/tYQtIiIiIlZRJRopdpkXsnnzHdXCFhER2/r4PS1flxTNQIqIiIiIVUo0gbSzszNK5l1jMplITU29a9thw4bx448/ArB8+XK2b99+XzFER0fTq1ev2+63WCyYTCZCQ0Pvq/9r7nVccXFxVK9eHbPZbLzuFN/tTJgwga+//trqdufPn+f//u//rG4nIiIick2ZXcL+97//bbxfvnw5AQEBBAYGFvt5NmzYwEMPPURSUhJHjhyhYcOGtzwuPz8fB4fi+bo8PDxITEz8Q31MmjTplp8XFhYCUKXKrf9vcC2B/J//+Z8/dH4RERGpvGy6hG0ymXj33Xdp2bIlDRs2ZPLkyca+Nm3asHr1atauXcvKlSuZMmUKZrPZSCwXLFhAixYt8PPz45lnnjFm/3Jzc3nllVdwc3Ojbdu2JCQk3DGGuXPnEhkZSb9+/Zg3b57xeVxcHGazmVGjRhEUFMSyZcuIiYmhRYsW+Pr6YjabWbt2bZG+Fi1aREhICE2aNCEqKsrq7yM6OpqOHTvSp08f3N3dadeuHXv37qVLly64ubnRp08fI0GMiIhg5syZAEycOJGBAwfy/PPPYzab+e233xg7dixPPfUUZrOZZ555hgMHDgBXK8+cP38es9lMQEAAAL///jsvvPACgYGBeHt7M2HCBOBqMjpy5Ejc3d3x8fHB39+/SJUaERERqZxsPgN5/vx5tmzZwpkzZ2jSpAkvvfQS9erVM/aHh4fTvXt3AgICGDlyJAA//fQTX331FfHx8VSrVo0ff/yR/v37k5yczJw5czhy5Ah79+4lLy+PkJAQTCbTLc997tw51q1bxz//+U+OHTtG165dmThxojF7l5KSwsyZM5k+fTpwtW503759sbOzIy0tjZYtW3L06FGqVq0KwKlTp4iPj+fs2bP4+/vTqlWrWz6Ye9++fUadaoCwsDCmTLn63MQdO3awZ88eXF1d6dq1K/369SM+Pp4aNWrg5+fHhg0b6Nix4019xsbGsmvXLh555BEAxo0bx0cffQTAV199xZtvvsnq1auZPXs2AQEBJCUlGW0HDx7MW2+9RUhICPn5+XTt2pVly5ZhMpnYuHEj+/bto0qVKmRmZvLAAw/cdO6oqKgiCXNurmphi4iIVGQ2SSDt7OyM9/379wegTp06NGrUiCNHjhRJIG9lxYoVJCcnF0nOzpw5Q25uLrGxsQwePJiqVatStWpVBgwYwObNm2/Zz8KFC+ncuTNOTk44OTnx6KOP8t133xEWFgaAm5sbrVu3No4/cuQI/fv358SJEzg4OHD27FmOHj1KkyZNABg6dCgADz/8MM899xwbN268ZQJ5pyXsVq1a4erqClyteW0ymXjooYcA8PHx4fDhw7ds17VrVyN5BPjuu++YMWMGFy9epLCwkAsXLtyyXXZ2Nps2beLUqVPGZ1lZWfz888+0a9eOvLw8hgwZQtu2benSpcstl8ZHjx7N6NGjje1atZ1ueS4RERGpGEo0gaxTpw5nz56lZs2axmdnz54tkug4Ojoa7+3t7cnPz79rvxaLhSFDhtzyOkBrnov++eef8/vvvxszlBcvXmTu3LlGAnl93AB9+vRh6tSp9OjRAwBnZ+c7Lulenyjfqxu/j3v9fq6P9dixY4waNYrt27fTqFEjUlJSaNeu3S3bFRYWYmdnx44dO4yZ1Ovt3buXH374gdjYWP76178SHx9vJMwiIiJSOZXoNZCdOnXin//8p7E9f/583NzcqFOnjlX91K5dm8zMTGO7W7duzJ8/n+PHjwNXk6BrM3rt27dnwYIF5Ofnk5OTQ0xMzC37TExM5MyZM5w8eZK0tDTS0tI4cOAA69at48yZM7dsk5GRYSSbCxcuJCMjo8j+a9dQnjt3juXLl9O+fXurxllcri01161bF4vFYlwrCVe/y0uXLhmJaK1atQgODjaW0AFOnjzJiRMnOHPmDNnZ2XTs2JH3338fk8nEvn37Sn08IiIiUraU6AzkJ598whtvvIG3tzdVqlThscceu69HzwwcOJCIiAgWL17MyJEjGTZsGO+//z7PPvssBQUF5OXl0aVLFwICAoiMjCQlJQUPDw9cXV0JDg7m6NGjN/U5d+5c+vTpU2RJ1tnZmdDQUBYsWICfn99NbT799FOee+456tWrR1BQEPXr1y+yv0GDBgQHB/Pbb78xatSo2941fuM1kE5OTsTGxlr9vdyOl5cXvXv3xtPTk/r169OhQwdjn7OzM/3798fLy4saNWqQmJjIokWLGD16NF5eXsDV2czZs2dTUFDAyy+/TF5eHoWFhbRs2ZLOnTsXW5wiIiJSPqkWthQ71cIWEREpf1QLW0RERERKjBJIEREREbGKzZ8DKRVPRmY2w/7nM1uHISIildC//y/S1iFUCpqBLGV5eXm8++67uLu74+npia+vLz169CjyYG8RERGRskwzkKXspZdeIisri61bt+LkdPWB26tWrWLv3r1F7sy+k7vVuxYREREpScpAStGBAwdYtmwZn3/+uZE8wtXnWl6ryDN16lQCAwPx8/MjPDzceNblrepdm0wmJkyYQMuWLalfvz4LFy7k008/JTAwkMaNGxMXFwdAfn4+nTp1IiAgAE9PT/r378+lS5eAq/W3O3XqRN++ffHy8iIgIMCodtOlSxe+/PJLI87169ffsrKOiIiIVC5KIEvR7t27adKkCc7OzrfcHxMTw/79+9m6dSu7du2ib9++Rv1vuFrvevbs2aSkpBjlHnNyctiyZQtLly4lMjKSqlWrsn37dj744AP+93//F7hawSYmJobExERSU1OpXbs2s2bNMvpNSEhgypQp7Nmzh9DQUD788EMA3njjDf7xj38Yx82cObNIPCIiIlI5aQm7lF1f3vDQoUP07NmTnJwcQkJCyMzMJDExEX9/fwAKCgqwt7c3jr+x3jXAiy++CICfnx85OTm88MILAPj7+xsziRaLhY8//pg1a9aQn59PZmYmISEhRh+tW7emQYMGAAQFBTFjxgwAOnTowBtvvEFycjK1a9cmMTGRJUuW3DSmqKgooqKijO283Cv3/wWJiIhImacEshT5+vpy4MABMjIycHJyonHjxiQlJREdHc3q1auxs7Pj7bffZsiQIbdsf2Ntbvj/tbOvJZrXb18rVxgTE8MPP/xAfHw8tWrVYvr06cTHx9/Ux43tAEaNGsU//vEPHnroIYYMGUK1atVuimH06NGMHj3a2K5Ry+mmY0RERKTi0BJ2KWratCnPPvssQ4cO5fz588bn2dnZAHTv3p1Zs2Zx7tw54Ood27t37/7D583IyMDFxYVatWpx8eJFoqOj77ntwIED+fbbb/niiy8YPnz4H45FREREyj/NQJay6Oho/v73v9OiRQvs7e1xcnLikUceYfz48bRo0YL09HTatGmDnZ0d+fn5DB06FF9f3z90zkGDBrFixQo8PDyoV68ewcHB/Prrr/fUtnr16vTo0YOTJ0/yxBNP/KE4REREpGJQLWy5o4KCAvz8/Jg5cybBwcH31KZGLSf6/vnDEo5MRETkZnqQ+P2zpha2ZiDltlauXMlrr71GeHj4PSePAE4P1dAPsIiISAWmGUgpdtb8D0ZERETKBs1Aik2lZ2bT881/2ToMERGpZJZ+/LKtQ6g0dBd2GWYymXjkkUfIy8szPtu0aRN2dnaMGTPmvvsNDw/n0KFDxRGiiIiIVEJKIMu4+vXrs3LlSmP7888/JyAg4A/1uXbtWho3bvxHQxMREZFKSglkGTdkyBA+//xzADIzM9m2bRthYWHG/tvVzn755Zd57bXXADh37hyNGzdm8+bNwNWZzdTUVAB+/fVXevXqhbe3N97e3rzzzjsAnDp1iueeew4vLy+aN2/OZ599VmpjFhERkbJN10CWcSEhIcyYMYNff/2VVatW0bt3b6PqzPW1s+3t7VmwYAEjR45kxYoVzJgxg6CgIBYvXsz8+fN5+eWXad269U39DxgwgPDwcKNE4ZkzZ4CrFWjc3d1ZtmwZp0+fxt/fH7PZTGBgYOkNXkRERMokJZDlwMCBA/niiy9Yvnw5ixYtYtGiRQAsX778trWzHR0dWbx4Mf7+/gQFBTFu3Lib+s3KymLLli18//33xmd16tQBYMOGDSQnJwPwyCOP8Pzzz7Nx48ZbJpA31sLOVy1sERGRCk0JZDkQERGBn58fbm5uNG3a1PjcYrHcsXb2zz//TI0aNTh9+jS5ubm3rGN9J3Z2dnfcvubGWtgPqha2iIhIhaZrIMuBxx9/nA8++IAPPyxa3eVOtbOPHTvGiBEj2LBhA4GBgbzxxhs39VuzZk1at27Nxx9/bHx2bQk7NDTUuO7xzJkzLFu2jHbt2pXI+ERERKR8UQJZTrz00ksEBQUV+WzgwIEMGDCANm3a4OPjg9lsJjY2lvz8fPr06cN7772Hh4cHn3zyCdu2bePrr7++qd8FCxawbds2PD098fHxYebMmQBMnz6dlJQUvL29adu2LW+99ZaufxQRERFAlWikBDxYy4nwYf9n6zBERKSS0YPE/xhrKtEogZRip1KGIiIi5Y81v7+1hC0iIiIiVtFd2FLs0s9n0+21f9o6DBERqQRWzfizrUOolDQDKSIiIiJW0QxkGWEymXB0dMTR0ZHc3FxeffVVSf8OOgAAGdZJREFUXn31VVuHJSIiInITJZBlyJIlS2jevDnHjx/Hy8uL4OBgvL29bR2WiIiISBFawi6DnnjiCdzc3EhJSaFt27b4+/vj6enJqFGjuHbT/MSJE+nXrx/dunXDw8ODdu3aGQ8U37NnD8HBwfj5+eHh4cEHH3xg9B0REcHw4cNp3749DRo04PXXXyc2NpaQkBBMJlORkoRjx47lqaeewmw288wzz3DgwIHS/SJERESkTFICWQbt2bOHn3/+GR8fH1atWsXOnTtJSUnh8OHDLF261DguISGBL774gn379vHII48wZ84c4Opy+IYNG9i1axc7d+7km2++ITEx0WiXmprK2rVr+e9//8uXX37JggULiIuL46effmLChAlkZWUBMG7cOHbs2EFSUhJ//vOfefPNN28Zb1RUFK6ursYrP0+1sEVERCoyLWGXIb169cLR0ZHq1avz+eef07hxY8aOHcvmzZuxWCycPn0as9lMr169AOjcuTPOzs4ABAUFsWfPHgBycnIYMWIESUlJVKlShePHj5OUlERAQAAAPXr0MOpiN2vWjPDwcKpUqUK9evVwcnLixIkTuLu789133zFjxgwuXrxIYWEhFy5cuGXcN9XCrqla2CIiIhWZEsgy5No1kNdMnjyZ9PR0EhIScHR0ZPTo0Vy+fNnY7+joaLy3t7cnPz8fgP/93//l0UcfZffu3Tg4OPD888/fsd2t+jl27BijRo1i+/btNGrUiJSUFNXCFhEREUBL2GVaRkYGdevWxdHRkVOnTrF48eJ7bufq6oqDgwO//PIL33//vdXnzszM5IEHHqBu3bpYLBajRraIiIiIZiDLsFGjRtG7d2/MZjP16tUjNDT0ntq9/fbbDBw4kEWLFmEyme5r5tDLy4vevXvj6elJ/fr16dChg9V9iIiISMWkWthS7FQLW0REpPxRLWwRERERKTFKIEVERETEKroGUopd+vksuo2IuvuBIiIi92HVrNF3P0hKlGYgyzGTyYS7uztms5knn3ySfv36kZ2dXSx9nzx5krZt2xrbdnZ2xgPGRUREpHJTAlnOLVmyhKSkJPbt28eFCxeIjo6+6ZiCggKr+3388ceJjY0thghFRESkolECWUFcuXKF7OxsnJyciI6OJiwsjEGDBhEQEMD27duJioriqaeewtfXl8DAQBISEgBYu3YtZrPZeDk6OvLFF1+QlpbGww8/bONRiYiISFmkayDLuWvlD48cOYK/vz8vvPACCxcuZPPmzezevZumTZsC0KRJE6Pc4LZt2xg6dCipqamEh4cTHh4OwMKFC5k2bRrPP/886enpNhuTiIiIlG2agSznri1hp6en07BhQ8aNGwdA69atjeQRYPfu3TzzzDM0b96c4cOHs2/fPnJzc439sbGxTJw4kTVr1lCrVi2rYoiKisLV1dV45eddKZ7BiYiISJmkBLKCcHBwoGfPnqxbtw6AmjVrGvtyc3Pp2bMnUVFRpKamEh8fj8ViMRLI1NRUXnrpJVasWMHjjz9u9blHjx7NiRMnjJdD1WrFMygREREpk5RAViCbNm2iWbNmN31++fJl8vLyeOKJJwCYMWOGse/XX3+lR48ezJs3D09Pz1KLVURERMovJZDlXK9evTCbzXh6evLf//6XTz/99KZjateuzaRJkwgMDCQkJIRq1f7/DOG///1vzpw5w5tvvmncSLNy5crSHIKIiIiUM6qFLcVOtbBFRETKH9XCFhEREZESowRSRERERKyi50BKsUs/f5Fur0yxdRgiIlIBrZoz3tYhCEogywWTyYSjoyOOjo4APP3008yePdvGUYmIiEhlpQSynFiyZAnNmze/5b78/HwcHPRHKSIiIqVD10CWUxEREYwaNYqwsDB8fHwAGDBgAAEBAXh7e9O1a1dOnz4NQFxcHGazmREjRuDj44OnpyeJiYlGX2vWrOGpp57Cx8cHs9ls1MnesWMH7dq1IyAgAD8/P5YuXVr6AxUREZEyR4/xKQduXML+29/+xooVK0hJSSE+Pt6oOnP27FkefvhhAKZMmcKJEyeYOXMmcXFxdOjQga1btxIQEMDs2bNZtmwZ69evZ//+/YSEhBAfH4+bmxt5eXlcunQJi8VCu3btWLNmDY899hhnz57F39+fhIQE6tate8d4H6z5EKH9/1qyX4qIiFRKugay5FjzGB+te5YTNy5hr1ixghdeeKFIycJFixaxYMECrly5Qk5OTpFEr1mzZgQEBAAQFBTE1KlTAfj+++8JDw/Hzc0NgKpVq/LQQw+xdu1aDh8+TOfOnY0+LBYLv/zyy00JZFRUFFFRUcZ2fl4uIiIiUnEpgSzHrk8eN2/ezMyZM9myZQt16tRh5cqVTJo0ydh/bfYSwN7envz8/Dv2bbFY8Pb2Jj4+/q5xjB49mtGjRxvbD9Z8yJphiIiISDmjayAriIyMDGrXro2zszO5ubnMmTPnntp16tSJb7/9lv379wOQl5dHZmYmLVu25MCBA2zatMk4NikpidxczS6KiIhUdkogK4jOnTvTpEkT3N3d6dSpE2az+Z7aNWnShLlz59K3b1+8vb0JDAzkl19+wcnJiVWrVvHee+/h4+ODh4cH48ePp7CwsIRHIiIiImWdbqKRYqebaEREpKToJpqSY81NNEogpdhZ8xdQREREygZrfn9rCVtERERErKK7sKXYpWdcpNvLk20dhoiIlHOr/vW2rUOQ29AMZDmXm5vLuHHjaNKkCU8++SSenp589tlnd20XFxfHd999VwoRioiISEWjGchyLiIigitXrpCcnEyNGjVIS0ujc+fO5ObmMnLkyNu2i4uLIysri44dO1p9zoKCAuzt7f9I2CIiIlKOaQayHDt48CDLly/ns88+o0aNGsDVsofTpk3j73//O7///jtt27bF398fT09PRo0ahcViISkpidmzZzN//nzMZjOTJk0iPz+fTp06ERAQgKenJ/379+fSpUsAREdHExYWxqBBgwgICGD79u22HLaIiIjYmGYgy7Fdu3bRtGlTXFxcinweFBTE77//zpUrV1i1ahU1a9akoKCAZ599lqVLl9KrVy+GDx9OVlaWUdLQYrEQExODi4sLFouFESNGMGvWLMaMGQNcrXSze/dumjZtWurjFBERkbJFCWQ5Z2dnd9t9Tk5OjBs3js2bN2OxWDh9+jRms5levXrddKzFYuHjjz9mzZo15Ofnk5mZSUhIiLG/devWt00eb66FfeUPjEhERETKOi1hl2O+vr7s37+f9PT0Ip9v3boVT09Ppk+fTnp6OgkJCaSkpNCvXz8uX758y75iYmL44YcfiI+PZ8+ePYwZM6bIsdfX3b7R6NGjOXHihPFyqFqteAYoIiIiZZISyHKsadOmdOvWjcjISON6xbS0NMaNG8e0adPIyMigbt26ODo6curUKRYvXmy0rV27NpmZmcZ2RkYGLi4u1KpVi4sXLxIdHV3awxEREZFyQkvY5dz8+fN555138PLyokqVKhw5coTVq1fTqVMn3N3d6d27N2azmXr16hEaGmq0e+6551iwYAFms5nnn3+e119/nRUrVuDh4UG9evUIDg7m119/teHIREREpKxSKcMKpLCwkPHjx/P999+zceNGnJ2dbRLHgzUeIrTfWJucW0REKg49SLx0qRa22JRqYYuIiJQ/qoUtIiIiIiVG10BKsVMtbBER+aO0fF22aQZSRERERKxSKRLI/Px8Jk2ahLu7O56enri7uxMZGcn58+eL9TzR0dG3fEj3rURERODq6orZbMbd3Z2BAwcaj+IpKXZ2dmRlZd11X3h4OIcOHSrRWERERKT8qhQJ5NChQ9mxYwdbt25l79697Nu3jw4dOnDu3DmbxjV+/HiSkpJISUkhLS2NmTNn2jSea9auXUvjxo1tHYaIiIiUURU+gTx48CCLFy9m3rx5ODk5AVClShV69+5No0aNmDRpEmazGbPZjJeXF3Z2dhw9ehSAqVOnEhgYiJ+fH+Hh4Rw/fhyA3Nxcxo4di5eXFz4+PoSFhRnnu3jxIn379sXLy4uAgAAOHz581xgfeOABWrZsaZwXYMeOHbRr146AgAD8/PxYunQpcPVB4Q8//DBjxoyhRYsWeHp6smnTpiL7rsnKyrqp1OHUqVNp1aoVbm5ufPnll7eMx2QykZqaCsCvv/5Kr1698Pb2xtvbm3feeeeu4xEREZGKrcLfRLNr1y6aNm1aJLG63oQJE5gwYQIAkZGRBAYG0qBBA2JiYti/fz9bt27F3t6eBQsWMHLkSFasWMEHH3zAoUOHSExMpFq1apw5c8boLyEhgeTkZBo0aMD48eP58MMPmTNnzh1jzMzMJDY2lilTpgBw/vx5XnnlFdasWcNjjz3G2bNn8ff3p1WrVgCkp6fj5eXF1KlT2bZtGz169LjnJWc7Ozt++uknDh8+TGBgIK1bt+aJJ5647fEDBgwgPDycJUuWABQZ6zWqhS0iIlK5VPgE8l5NnjyZY8eOsXr1agCWL19OYmIi/v7+ABQUFGBvbw/A6tWrmTZtGtWqXa35XKdOHaOf1q1b06BBAwCCgoKYMWPGbc85ZcoUPvvsM/bv3094eDht27YFYMuWLRw+fJjOnTsbx1osFn755RcaNGjAAw88wMCBAwF4+umnqVu3LsnJyTz++ON3HeewYcMAaNSoEa1bt+bHH3+kX79+tzw2KyuLLVu28P333xufXT/Wa0aPHs3o0aON7QdrPHTXOERERKT8qvAJpJ+fHwcOHCA9PR0XF5dbHrNgwQKWLVvGDz/8gIPD1a/EYrHw9ttvM2TIEKvO5+joaLy3t7cnPz//tseOHz+ekSNHcuzYMYKDg5k9ezZ//vOfsVgseHt7Ex8ff1ObtLS0W/ZlZ2eHg4MDBQUFxmeXL1++a7w3LnGLiIiI3E2FvwaySZMm9OzZk6FDhxp3XVssFubPn8+hQ4fYuHEjkyZNYvXq1dSsWdNo1717d2bNmmXcaJOXl8fu3buNfZ988glXrlxdqr3Vsq416tevz4wZM5g0aRI5OTm0bNmSAwcOGNc2AiQlJZGbmwtcvQZz0aJFAGzfvp3ff/8db29v6tatS35+Pr/88gtwtU72jT7//HPgaiK6efNmWrdufdu4atasSevWrfn444+Nz/7oWEVERKT8q/AJJFxNmnx8fIybTjw9PdmyZQsuLi5MnjyZS5cu0blzZ+NmmpMnTzJw4EAGDBhAmzZt8PHxwWw2ExsbC8C4ceNo3Lgxvr6+mM1mBg8e/Idj7N69O+7u7syaNQsnJydWrVrFe++9h4+PDx4eHowfP57CwkIAXFxcOHjwIC1atOCll14iJiaGGjVq4ODgwPTp0+ncuTMhISFGgnu9atWq0apVKzp27MiMGTPueP0jXJ2d3bZtG56envj4+JSZO8VFRETEdlQLu5xJS0sjICCAs2fP2jqU21ItbBERkfJHtbBFREREpMQogSxnTCZTmZ59FBERkYpPCaSIiIiIWEUJpIiIiIhYRQmkiIiIiFhFCaSIiIiIWEUJpIiIiIhYRQmkiIiIiFhFCaSIiIiIWEUJpIiIiIhYRQmkiIiIiFhFCaSIiIiIWEUJpIiIiIhYRQmkiIiIiFhFCaSIiIiIWEUJpIiIiIhYxc5isVhsHYRULA4ODtStW9fWYZS6rKwsatasaeswSpXGXHlUxnFXxjFD5Ry3xnzVmTNnuHLlyj21dyiJoKRyq1u3LidOnLB1GKXO1dW10o1bY648KuO4K+OYoXKOW2O2npawRURERMQqSiBFRERExCr2EydOnGjrIKTiCQoKsnUINlEZx60xVx6VcdyVccxQOcetMVtHN9GIiIiIiFW0hC0iIiIiVlECKSIiIiJWUQIpxebAgQO0bNkSNzc3AgMD2bdvn61DKnGjRo3CZDJhZ2dHamqqrcMpFZcvX6ZHjx64ublhNpsJCwsjLS3N1mGVio4dO+Lt7Y3ZbCY4OJikpCRbh1Rq3n333Urz99xkMuHu7o7ZbMZsNvP111/bOqRSceXKFUaOHEnTpk3x9PRkwIABtg6pRJ0/f974Mzabzbi5ueHg4MC5c+dsHVqJW79+Pf7+/vj6+tK8eXO++OIL6zuxiBSTtm3bWubNm2exWCyWxYsXW55++mnbBlQKfvjhB8vx48ctDRo0sOzZs8fW4ZSKnJwcy5o1ayyFhYUWi8VimTFjhqVDhw42jqp0ZGRkGO+XLVtm8fX1tWE0pWfnzp2WsLAwS/369SvF3/PK9PN8vTfeeMPy2muvGT/bJ0+etHFEpeujjz6ydO3a1dZhlLjCwkKLs7OzJTk52WKxWCxHjhyxVKtWzXLhwgWr+tEMpBSL06dPs2vXLuN/rD179uTIkSMVfmYqJCQEV1dXW4dRqhwdHQkPD8fOzg6Ap59+msOHD9s4qtLxpz/9yXifmZlJlSoV/5/QK1eu8OqrrzJr1izjz1wqnuzsbObNm8f7779v/Dk/9thjNo6qdM2bN4+hQ4faOoxSc/78eQAuXLiAi4sL1apVs6q9KtFIsTh+/DiPP/44Dg5X/0rZ2dlRv359jh07hslksm1wUqKmT59Ot27dbB1GqRk0aBCxsbEArFu3zsbRlLwJEyYwYMAAGjZsaOtQSlX//v0pLCykRYsWfPDBB9SpU8fWIZWoQ4cO4eLiwuTJk9mwYQMPPvggEydOpH379rYOrVRs3bqV9PR0unbtautQSpydnR3ffPMNzz//PDVq1CAjI4P//Oc/PPDAA1b1U/H/+yyl5sbZCYueEFXhvf/++xw4cIC///3vtg6l1MyfP5/jx48zefJkxo4da+twStTWrVvZsWMHI0aMsHUopSo+Pp7k5GR27dqFi4sLgwcPtnVIJS4vL4/Dhw/j4eFBYmIiM2fOpE+fPpw5c8bWoZWKzz//nEGDBhmTIBVZfn4+H3zwAStWrODo0aNs3LiRwYMHW33tpxJIKRZPPPEEJ06cID8/H7iaPB4/fpz69evbODIpKVOnTuU///kP3377LdWrV7d1OKVu8ODBxMbGkp6ebutQSswPP/zAzz//TMOGDTGZTJw4cYJOnTrx7bff2jq0EnXt362qVavyxhtv8OOPP9o4opLXoEEDqlSpQv/+/QHw8fGhYcOG7N2718aRlbzs7Gy+/vprhgwZYutQSkVSUhInT56kVatWADz11FM8/vjjJCcnW9WPEkgpFo888gi+vr4sXLgQgKVLl2IymbR8XUFFRUXx5Zdf8v333xe5LrAiu3DhAidPnjS2ly1bhouLC87OzjaMqmSNHz+ekydPkpaWRlpaGq6urqxfv57OnTvbOrQSk52dbVwbBvDll1/i6+trw4hKx8MPP0z79u1Zv349AEePHuXIkSM0a9bMxpGVvMWLF+Pt7Y27u7utQykV1yZ8fvnlFwAOHjzIoUOHcHNzs6qfij9XK6Vmzpw5RERE8P7771O7du37eyxAOfPqq6+yYsUKfv/9d0JDQ6lZsyYHDx60dVgl6sSJE/zlL3+hUaNGtG3bFoBq1aqRkJBg48hKVmZmJj179iQnJ4cqVapQp04dVq9erRtLKphTp07Rs2dPCgoKsFgsNGrUiPnz59s6rFIxe/ZshgwZwrhx47C3t+ezzz6rFDfSzJ07t1LdPPPoo48yZ84cevXqRZUqVbBYLMyaNYt69epZ1Y9KGYqIiIiIVbSELSIiIiJWUQIpIiIiIlZRAikiIiIiVlECKSIiIiJWUQIpIiIiIlZRAikiUg6ZTCZSU1Nv+jw8PJxDhw4BV8vT+fn54evry7x58+6577i4OAICAv5QfBMnTmTMmDFW7xOR8kHPgRQRqUDWrl1rvF+yZAlBQUH84x//sGFExSM/P79SlJkTKS/00ygiUoGYTCZWr17Nrl27+PjjjyksLOSnn34iJiYGZ2dnRo0aRVpaGpcvX6ZHjx5MmjTpnvv+/fff6du3LxcuXODy5cu0b9+eTz/9FDs7OzIzMxk6dCj79u3jiSeeoE6dOtStWxfgjvvy8vJ455132LRpE7m5ubi7uzN79mz+9Kc/ERERQe3atdm/fz/Hjx+vFGX1RMoLLWGLiFRAgwYNYvjw4QwaNIikpCQ8PDwYPHgwI0eOZPv27ezatYvt27ezbNmye+7zT3/6E6tWrWLnzp2kpKRw+PBhli5dCsCkSZOoXbs2+/btY9GiRcTHxxvt7rTvo48+ombNmmzfvp2kpCQ8PT3529/+ZuzfvHkzS5YsUfIoUsZoBlJEpBLIzs5m06ZNnDp1yvgsKyuLn3/++Z77KCwsZNy4cWzevBmLxcLp06cxm8306tWL2NhYZsyYAVytq/z8888b7e60b/ny5Vy4cIElS5YAkJubS+PGjY39L7zwAjVr1ry/QYtIiVECKSJSCRQWFmJnZ8eOHTuoWrXqffURFRVFeno6CQkJODo6Mnr0aC5fvgzAnari3m3frFmzaNeu3S33K3kUKZu0hC0iUgnUqlWL4OBgpkyZYnx28uRJTpw4cc99ZGRkULduXRwdHTl16hSLFy829rVv39640/vcuXNFlsbvtK979+5ERUVx6dIlAC5duqTlapFyQAmkiEg5FRoaiqurq/G6WzK4aNEi/vvf/+Ll5YWXlxc9e/YkPT39lsempKQU6bt3796MGjWKLVu2YDabGTJkCKGhocbx77zzDhkZGXh4eNC/f386dOhwT/vGjx+P2WymRYsWeHt78/TTT5OUlPQHvxkRKWl2ljutLYiIiIiI3EAzkCIiIiJiFSWQIiIiImIVJZAiIiIiYhUlkCIiIiJiFSWQIiIiImIVJZAiIiIiYhUlkCIiIiJiFSWQIiIiImIVJZAiIiIiYpX/B95gz7xelrKFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot happiest in 2005\n",
    "plot_barh(happy_2005,\"Happiest countries in the world in 2005\", \"Life Ladder\", \" \", \"copper\")\n",
    "\n",
    "#happiest 27 for the period as a whole\n",
    "plot_barh(group_happy_index, \"27 Happiest countries in the world for the whole period\", \"Life Ladder\", \" \", \"cividis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['Turkey', 'Romania', 'Egypt', 'Hungary', 'Pakistan', 'Iran', 'Lebanon', 'Poland', 'Greece', 'Jordan', 'Czech Republic', 'Japan', 'Mexico', 'Germany', 'Brazil', 'Italy', 'United Kingdom', 'Saudi Arabia', 'France', 'Spain', 'Venezuela', 'Belgium', 'Australia', 'Sweden', 'Canada', 'Netherlands', 'Denmark'])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking for a way to compare the two data sets and extract the duplicate countries\n",
    "#First - create a dictionary with countries as values\n",
    "'''\n",
    "Converting a list to dictionary with list elements as values in dictionary\n",
    "and keys are enumerated index starting from 0 i.e. index position of element in list\n",
    "''' \n",
    "dict_happy = { i : happy_2005.index[i] for i in range(0, len(happy_2005.index) ) }\n",
    "dict_happy.values()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['Czech Republic', 'Qatar', 'France', 'Brazil', 'Panama', 'Mexico', 'Germany', 'United Arab Emirates', 'Oman', 'United Kingdom', 'Belgium', 'Luxembourg', 'Ireland', 'United States', 'Costa Rica', 'Israel', 'Austria', 'Australia', 'New Zealand', 'Sweden', 'Iceland', 'Canada', 'Netherlands', 'Switzerland', 'Norway', 'Finland', 'Denmark'])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_most_happy = { i : group_happy_index.index[i] for i in range(0, len(group_happy_index.index) ) }\n",
    "dict_most_happy.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of happiest countries: 12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Czech Republic',\n",
       " 'Mexico',\n",
       " 'Germany',\n",
       " 'Brazil',\n",
       " 'United Kingdom',\n",
       " 'France',\n",
       " 'Belgium',\n",
       " 'Australia',\n",
       " 'Sweden',\n",
       " 'Canada',\n",
       " 'Netherlands',\n",
       " 'Denmark']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Second - make the comparison\n",
    "common_countries = [] \n",
    "for k in dict_happy.values():\n",
    "    if k in dict_most_happy.values():\n",
    "        common_countries.append(k)\n",
    "\n",
    "print(\"Count of happiest countries: {}\".format(len(common_countries)))\n",
    "common_countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearly half of the countries which were included in the 2005 rating are still top rated. Denmark and Netherlands ocupy positions in top 5. Well, these very happy countries (ok, the people in the countries, countries cannot be happy, I know that) are doing ok. But, we are thinking now, will they do ok on average if we skip 2005? Also, what will be the distribution of the life_ladder in this case? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,\n",
       "       2007, 2006], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are going to delete the rows with year 2005\n",
    "to_del_year = world_happiness[world_happiness.year.isin([2005])].index.tolist()\n",
    "\n",
    "#drop it and save in another dataframe\n",
    "world_happiness_no_2005 = world_happiness.drop(to_del_year)\n",
    "\n",
    "#done\n",
    "world_happiness_no_2005.year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check again the distribution\n",
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "\n",
    "plt.hist(world_happiness.life_ladder, bins = 40, color='purple', label = \"life ladder 05 - 18\", alpha = 0.5)\n",
    "plt.hist(world_happiness_no_2005.life_ladder, bins = 40, color='green', label = \"life ladder 06 - 18\", alpha = 0.5)\n",
    "\n",
    "  \n",
    "plt.xlabel(\"Life ladder\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Happiness Index\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears to be overlapping, with green moving a little to the left. Time to explore the metrics: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happiness index data ex. 2005\n",
      "Standard deviation = 1.116560245572919\n",
      "Mean = 5.420909958258789\n",
      "Median = 5.326\n",
      "Mode = 0    5.252\n",
      "dtype: float64\n",
      "Difference between max and min values = 5.309\n",
      "Skewness = 0.12405763736970989\n",
      "Kurtosis = -0.724115292080358\n"
     ]
    }
   ],
   "source": [
    "important_metrics(world_happiness_no_2005.life_ladder, \"Happiness index data ex. 2005\")\n",
    "\n",
    "#For comparison\n",
    "# Happiness index (life_ladder)\n",
    "# Standard deviation = 1.1208199513565953\n",
    "# Mean = 5.437156690140837\n",
    "# Median = 5.3395\n",
    "# Mode = 0    5.252\n",
    "# dtype: float64\n",
    "# Difference between max and min values = 5.357\n",
    "# Skewness = 0.11442161112098703\n",
    "# Kurtosis = -0.724115292080358"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0005144458125723629\n",
      "7.403653906822444e-10\n",
      "4.270245066566228e-05\n"
     ]
    }
   ],
   "source": [
    "print(st.ttest_1samp(world_happiness_no_2005.life_ladder, 5.326).pvalue) #median\n",
    "print(st.ttest_1samp(world_happiness_no_2005.life_ladder, 5.252).pvalue) #mode \n",
    "print(st.ttest_1samp(world_happiness_no_2005.life_ladder, 5.309).pvalue) #dif_max_min\n",
    "\n",
    "# For comparison\n",
    "# 4.463744029028542e-05\n",
    "# 1.2838403345132013e-11\n",
    "# 0.0032077986270270926"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are clear again - We reject the H0 hypothesis... again\n",
    "\n",
    "Actually we can continue working with our main data set which includes 2005 results. But before that we need to fill the NaN values for our variables (and the percentage of Nan values). We will use linear interpolation for this process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log_GDP_per_capita\n",
    "# social_support\n",
    "# HLE_at_birth\n",
    "# freedom_to_make_life_choices\n",
    "# generosity\n",
    "# perceptions_of_corruption\n",
    "\n",
    "#function to plot the annualized data for the variables\n",
    "def plot_func(data, to_group, variable, color, style):\n",
    "    plt.figure(figsize=(6, 4), dpi=80, facecolor='w', edgecolor='k')\n",
    "    data.groupby(to_group)[variable].mean().plot(color = color, style = style, markersize = 12)\n",
    "    data.groupby(to_group)[variable].mean().plot(color = \"grey\", style = '--')\n",
    "    plt.xlabel(\"{}\".format(to_group))\n",
    "    plt.ylabel(\"{}\".format(variable))\n",
    "    plt.title(\"Average annual {}\".format(variable))\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#before filling the na values\n",
    "plot_func(world_happiness, \"year\", \"log_GDP_per_capita\", \"red\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"social_support\", \"green\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"HLE_at_birth\", \"purple\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"freedom_to_make_life_choices\", \"orange\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"generosity\", \"blue\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"perceptions_of_corruption\", \"cyan\", \"*\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# positive_affect\n",
    "# negative_affect\n",
    "plot_func(world_happiness, \"year\", \"positive_affect\", \"magenta\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"negative_affect\", \"turquoise\", \"*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Interpolation, I guess a function will make it look better. \n",
    "\n",
    "# % NaN values - log_GDP_per_capita -> 1.643192\n",
    "world_happiness.log_GDP_per_capita = world_happiness.log_GDP_per_capita.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - social_support  -> 0.762911\n",
    "world_happiness.social_support = world_happiness.social_support.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - HLE_at_birth  -> 1.643192\n",
    "world_happiness.HLE_at_birth = world_happiness.HLE_at_birth.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - freedom_to_make_life_choices  -> 1.701878\n",
    "world_happiness.freedom_to_make_life_choices = world_happiness.freedom_to_make_life_choices.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - generosity  -> 4.812207\n",
    "world_happiness.generosity = world_happiness.generosity.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - perceptions_of_corruption -> 5.633803\n",
    "world_happiness.perceptions_of_corruption = world_happiness.perceptions_of_corruption.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - positive_affect -> 1.115023\n",
    "world_happiness.positive_affect = world_happiness.positive_affect.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - negative_affect -> 0.762911\n",
    "world_happiness.negative_affect = world_happiness.negative_affect.interpolate(method ='linear', limit_direction ='both') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below - Variables that we will not use further since we are not sure linear interpolation is good method for them, due to bigger amount of missing vallues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# % NaN values - confidence_in_national_government -> 10.211268\n",
    "world_happiness.confidence_in_national_government = world_happiness.confidence_in_national_government.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - democratic_quality -> 8.568075\n",
    "world_happiness.democratic_quality = world_happiness.democratic_quality.interpolate(method ='linear', limit_direction ='both') \n",
    "\n",
    "# % NaN values - delivery_quality -> 8.509390\n",
    "world_happiness.delivery_quality = world_happiness.delivery_quality.interpolate(method ='linear', limit_direction ='both') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if Nan are filled\n",
    "\n",
    "world_happiness.isna().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Interpolated results. Good idea to plot it together with the  original data\n",
    "\n",
    "plot_func(world_happiness, \"year\", \"log_GDP_per_capita\", \"red\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"social_support\", \"green\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"HLE_at_birth\", \"purple\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"freedom_to_make_life_choices\", \"orange\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"generosity\", \"blue\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"perceptions_of_corruption\", \"cyan\", \"*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# positive_affect\n",
    "# negative_affect\n",
    "plot_func(world_happiness, \"year\", \"positive_affect\", \"magenta\", \"*\")\n",
    "plot_func(world_happiness, \"year\", \"negative_affect\", \"turquoise\", \"*\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The biggest change we notice is in the *generosity* variable. This could be due to the big number of missing values in the column and inapropriately chosen interpolation method (*Next time compare different interpolation methods in order to pic the best for every variable*)\n",
    "\n",
    "**After examining the plots it will be interesting to see if there is significant difference between any two key variables.** \n",
    "In our case we will compare first *perception of corruption and freedom to make life choices*. From the plots looks like they are moving in an opposite directions(possible explanation - choices are limited by rules. rules limit corruption).\n",
    "The second comparison will be between gdp per capita and positive affects. \n",
    "\n",
    "For both hypothesis that we will state we will use the [Paired Two-Sample t-test](https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/t-test/). This test is applied to compare whether two population means are equal. Also, the groups are part of the same data set and are linked to the same index.\n",
    "\n",
    "#### First Test: Does the Freedom to make life choices is more significant than the perceptions of corruption?\n",
    "- H0: Nothing interesting. It is not\n",
    "- H1: It is more significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01821416047375123"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.ttest_rel(world_happiness.freedom_to_make_life_choices, world_happiness.perceptions_of_corruption).pvalue / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a significance level, equal to 0.01 and p value = 0.026, we cannot reject the null hypothesis.\n",
    "With a significance level, equal to 0.05 and p value = 0.026, we reject the null hupothesis.\n",
    "In the first case we cannot reject the possibility that freedom to make life choices is not linked to perception of corruption. On the other hand, in the second  case we reject the null hypothesis. This does not mean that we automatically will accept the H1 hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First Test: Does GDP affects the positive affect?\n",
    "- H0: Nothing interesting. It does not\n",
    "- H1: It is more significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.ttest_rel(world_happiness.log_GDP_per_capita, world_happiness.positive_affect).pvalue / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one is interesting. Obviously, we can reject the H0 hypothesis both at significance levels 0.01 and 0.05. There is a correlation between both.\n",
    "Lets see it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3047814799158787"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.log_GDP_per_capita.corr(world_happiness.positive_affect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is positive correlation between both variables, meaning they move in the same direction. In other words, increasing gdp has positive effect on happiness, laugh and enjoyment (*positive effect* is the average of these three measures). But we have to keep in mind that the correlation is low, so higher increse in the gdp will lead to not so big increase in the possitive affect and vise versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the variables that we have already plotted\n",
    "df_cut = world_happiness.iloc[:, 0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Not working properly\n",
    "# f = plt.figure(figsize=(14, 10))\n",
    "# plt.matshow(df_cut.corr(), fignum=f.number)\n",
    "# plt.xticks(range(df_cut.shape[1]), df_cut.columns, fontsize=14, rotation=90)\n",
    "# plt.yticks(range(df_cut.shape[1]), df_cut.columns, fontsize=14)\n",
    "# cb = plt.colorbar()\n",
    "# cb.ax.tick_params(labelsize=14)\n",
    "#plt.title('Correlation Matrix', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore population density data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 222 entries, 0 to 221\n",
      "Data columns (total 17 columns):\n",
      "Country Name     219 non-null object\n",
      "Country Code     217 non-null object\n",
      "Series Name      217 non-null object\n",
      "Series Code      217 non-null object\n",
      "2005 [YR2005]    217 non-null object\n",
      "2006 [YR2006]    217 non-null object\n",
      "2007 [YR2007]    217 non-null object\n",
      "2008 [YR2008]    217 non-null object\n",
      "2009 [YR2009]    217 non-null object\n",
      "2010 [YR2010]    217 non-null object\n",
      "2011 [YR2011]    217 non-null object\n",
      "2012 [YR2012]    217 non-null object\n",
      "2013 [YR2013]    217 non-null object\n",
      "2014 [YR2014]    217 non-null object\n",
      "2015 [YR2015]    217 non-null object\n",
      "2016 [YR2016]    217 non-null object\n",
      "2017 [YR2017]    217 non-null object\n",
      "dtypes: object(17)\n",
      "memory usage: 29.6+ KB\n"
     ]
    }
   ],
   "source": [
    "population_density = pd.read_csv(\"population_density.csv\")\n",
    "population_density.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>Series Code</th>\n",
       "      <th>2005 [YR2005]</th>\n",
       "      <th>2006 [YR2006]</th>\n",
       "      <th>2007 [YR2007]</th>\n",
       "      <th>2008 [YR2008]</th>\n",
       "      <th>2009 [YR2009]</th>\n",
       "      <th>2010 [YR2010]</th>\n",
       "      <th>2011 [YR2011]</th>\n",
       "      <th>2012 [YR2012]</th>\n",
       "      <th>2013 [YR2013]</th>\n",
       "      <th>2014 [YR2014]</th>\n",
       "      <th>2015 [YR2015]</th>\n",
       "      <th>2016 [YR2016]</th>\n",
       "      <th>2017 [YR2017]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>EN.POP.DNST</td>\n",
       "      <td>39.2952194957571</td>\n",
       "      <td>40.488081671415</td>\n",
       "      <td>41.5104861685507</td>\n",
       "      <td>42.4628189810986</td>\n",
       "      <td>43.4929586741415</td>\n",
       "      <td>44.7040820390283</td>\n",
       "      <td>46.1315029255889</td>\n",
       "      <td>47.7305639800263</td>\n",
       "      <td>49.4280381705113</td>\n",
       "      <td>51.1147780534877</td>\n",
       "      <td>52.7120715007812</td>\n",
       "      <td>54.1971142358239</td>\n",
       "      <td>55.5959930153479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>EN.POP.DNST</td>\n",
       "      <td>109.908284671533</td>\n",
       "      <td>109.21704379562</td>\n",
       "      <td>108.394781021898</td>\n",
       "      <td>107.566204379562</td>\n",
       "      <td>106.843759124088</td>\n",
       "      <td>106.314635036496</td>\n",
       "      <td>106.02901459854</td>\n",
       "      <td>105.854051094891</td>\n",
       "      <td>105.660291970803</td>\n",
       "      <td>105.441751824818</td>\n",
       "      <td>105.135145985401</td>\n",
       "      <td>104.967189781022</td>\n",
       "      <td>104.870693430657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country Name Country Code  \\\n",
       "0  Afghanistan          AFG   \n",
       "1      Albania          ALB   \n",
       "\n",
       "                                         Series Name  Series Code  \\\n",
       "0  Population density (people per sq. km of land ...  EN.POP.DNST   \n",
       "1  Population density (people per sq. km of land ...  EN.POP.DNST   \n",
       "\n",
       "      2005 [YR2005]    2006 [YR2006]     2007 [YR2007]     2008 [YR2008]  \\\n",
       "0  39.2952194957571  40.488081671415  41.5104861685507  42.4628189810986   \n",
       "1  109.908284671533  109.21704379562  108.394781021898  107.566204379562   \n",
       "\n",
       "      2009 [YR2009]     2010 [YR2010]     2011 [YR2011]     2012 [YR2012]  \\\n",
       "0  43.4929586741415  44.7040820390283  46.1315029255889  47.7305639800263   \n",
       "1  106.843759124088  106.314635036496   106.02901459854  105.854051094891   \n",
       "\n",
       "      2013 [YR2013]     2014 [YR2014]     2015 [YR2015]     2016 [YR2016]  \\\n",
       "0  49.4280381705113  51.1147780534877  52.7120715007812  54.1971142358239   \n",
       "1  105.660291970803  105.441751824818  105.135145985401  104.967189781022   \n",
       "\n",
       "      2017 [YR2017]  \n",
       "0  55.5959930153479  \n",
       "1  104.870693430657  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will have some cleaning to do here:\n",
    "- First, we will start with making the year columns looking better\n",
    "- Then, removing unnecessary column\n",
    "- Followed by dropping unnecessary rows\n",
    "- Melting columns\n",
    "- and more. \n",
    "\n",
    "Let's do this first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove unnecessary labeling, cleaning the columns \n",
    "population_density = population_density.rename(columns=lambda x: re.sub(r'\\[(.*?)\\]','',x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Column \"Series Code contains the same data, which is not interesting for us\"\n",
    "population_density.drop([\"Series Code\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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 Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Series Name</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>102</th>\n",
       "      <td>Kiribati</td>\n",
       "      <td>KIR</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>113.981481481481</td>\n",
       "      <td>116.472839506173</td>\n",
       "      <td>119.169135802469</td>\n",
       "      <td>121.927160493827</td>\n",
       "      <td>124.604938271605</td>\n",
       "      <td>127.07037037037</td>\n",
       "      <td>129.293827160494</td>\n",
       "      <td>131.320987654321</td>\n",
       "      <td>133.197530864198</td>\n",
       "      <td>135.050617283951</td>\n",
       "      <td>136.950617283951</td>\n",
       "      <td>138.918518518519</td>\n",
       "      <td>140.935802469136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country Name Country Code  \\\n",
       "102     Kiribati          KIR   \n",
       "\n",
       "                                           Series Name             2005   \\\n",
       "102  Population density (people per sq. km of land ...  113.981481481481   \n",
       "\n",
       "                2006              2007              2008              2009   \\\n",
       "102  116.472839506173  119.169135802469  121.927160493827  124.604938271605   \n",
       "\n",
       "               2010              2011              2012              2013   \\\n",
       "102  127.07037037037  129.293827160494  131.320987654321  133.197530864198   \n",
       "\n",
       "                2014              2015              2016              2017   \n",
       "102  135.050617283951  136.950617283951  138.918518518519  140.935802469136  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.sample()\n",
    "#looks better now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212                               Virgin Islands (U.S.)\n",
       "213                                  West Bank and Gaza\n",
       "214                                         Yemen, Rep.\n",
       "215                                              Zambia\n",
       "216                                            Zimbabwe\n",
       "217                                                 NaN\n",
       "218                                                 NaN\n",
       "219                                                 NaN\n",
       "220    Data from database: World Development Indicators\n",
       "221                            Last Updated: 07/10/2019\n",
       "Name: Country Name, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Time to deal with some rows that we don't need\n",
    "population_density[\"Country Name\"].tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove the last five rows - they don't bring information, will write reference in the end :)\n",
    "population_density.drop(population_density.index[217:222], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212    Virgin Islands (U.S.)\n",
       "213       West Bank and Gaza\n",
       "214              Yemen, Rep.\n",
       "215                   Zambia\n",
       "216                 Zimbabwe\n",
       "Name: Country Name, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#double check\n",
    "population_density[\"Country Name\"].tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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 Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2639</th>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.01827985013488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country Name Country Code  \\\n",
       "2639       Canada          CAN   \n",
       "\n",
       "                                            Series Name   Year  \\\n",
       "2639  Population density (people per sq. km of land ...  2017    \n",
       "\n",
       "                 Value  \n",
       "2639  4.01827985013488  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Years should be in one column. \n",
    "population_density = pd.melt(population_density, id_vars = [\"Country Name\", \"Country Code\", \"Series Name\"], var_name=\"Year\", value_name=\"Value\" )\n",
    "population_density.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2821, 5)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Population density (people per sq. km of land area)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density[\"Series Name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Python friendly labeling\n",
    "population_density.rename(columns = {\n",
    "    \"Value\": \"population_density_(people per sq. km)\",\n",
    "    \"Country Name\": \"country_name\",\n",
    "    \"Country Code\": \"country_code\",\n",
    "    \"Year\": \"year\"\n",
    "},\n",
    "                          inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>year</th>\n",
       "      <th>population_density_(people per sq. km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>ATG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2009</td>\n",
       "      <td>197.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country_name country_code  \\\n",
       "874  Antigua and Barbuda          ATG   \n",
       "\n",
       "                                           Series Name   year  \\\n",
       "874  Population density (people per sq. km of land ...  2009    \n",
       "\n",
       "    population_density_(people per sq. km)  \n",
       "874                                 197.15  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>year</th>\n",
       "      <th>population_density_(people per sq. km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2005</td>\n",
       "      <td>39.2952194957571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>40.488081671415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2007</td>\n",
       "      <td>41.5104861685507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2008</td>\n",
       "      <td>42.4628189810986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2009</td>\n",
       "      <td>43.4929586741415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_name country_code  \\\n",
       "0    Afghanistan          AFG   \n",
       "217  Afghanistan          AFG   \n",
       "434  Afghanistan          AFG   \n",
       "651  Afghanistan          AFG   \n",
       "868  Afghanistan          AFG   \n",
       "\n",
       "                                           Series Name   year  \\\n",
       "0    Population density (people per sq. km of land ...  2005    \n",
       "217  Population density (people per sq. km of land ...  2006    \n",
       "434  Population density (people per sq. km of land ...  2007    \n",
       "651  Population density (people per sq. km of land ...  2008    \n",
       "868  Population density (people per sq. km of land ...  2009    \n",
       "\n",
       "    population_density_(people per sq. km)  \n",
       "0                         39.2952194957571  \n",
       "217                        40.488081671415  \n",
       "434                       41.5104861685507  \n",
       "651                       42.4628189810986  \n",
       "868                       43.4929586741415  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It is good to sort things out \n",
    "population_density.sort_values(by = [\"country_name\", \"year\"], inplace = True)\n",
    "population_density.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Our next steps will be to fixt the types of the columns, if needed. \n",
    "- Values need some makeover too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name                              object\n",
       "country_code                              object\n",
       "Series Name                               object\n",
       "year                                      object\n",
       "population_density_(people per sq. km)    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['39.2952194957571', '40.488081671415', '41.5104861685507', ...,\n",
       "       '35.7105570634613', '36.2682952048598', '36.8017190125372'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density[\"population_density_(people per sq. km)\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I know because i've seen it\n",
    "population_density[\"population_density_(people per sq. km)\"].replace(\"..\", np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make float\n",
    "population_density[\"population_density_(people per sq. km)\"] = population_density[\"population_density_(people per sq. km)\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#round up to two decimal\n",
    "population_density[\"population_density_(people per sq. km)\"] = population_density[\"population_density_(people per sq. km)\"].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's have some categories\n",
    "population_density.year = population_density.year.astype(\"category\")\n",
    "population_density.country_name = population_density.country_name.astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>year</th>\n",
       "      <th>population_density_(people per sq. km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>SEN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>71.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country_name country_code  \\\n",
       "1903      Senegal          SEN   \n",
       "\n",
       "                                            Series Name   year  \\\n",
       "1903  Population density (people per sq. km of land ...  2013    \n",
       "\n",
       "      population_density_(people per sq. km)  \n",
       "1903                                   71.59  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name                              category\n",
       "country_code                                object\n",
       "Series Name                                 object\n",
       "year                                      category\n",
       "population_density_(people per sq. km)     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.dtypes\n",
    "#looks good now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.country_name.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.country_name.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to make comparisons between the world happiness index and the population density we need to have the same number of countries and the same countries. Since it is there is a slight difference, we will need to drop some countries and regions from the population density data set, so we can compare the same. Also we will rename some of the countries in the world happiness index to make everything beautiful.\n",
    "\n",
    "The following cleaning of data was possible after 15 minutes of work off the notebook and with excel :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove the countries that are not present in the world_happiness index\n",
    "to_del_pop = population_density[population_density[\"country_name\"].isin([\"American Samoa\",\n",
    "\"Andorra\", \"Antigua and Barbuda\", \"Aruba\", \"Bahamas, The\", \"Barbados\", \"Bermuda\", \"British Virgin Islands\",\n",
    "\"Brunei Darussalam\", \"Cabo Verde\", \"Cayman Islands\", \"Channel Islands\", \"Curacao\", \"Dominica\", \"Equatorial Guinea\",\n",
    "\"Eritrea\", \"Faroe Islands\", \"Fiji\", \"French Polynesia\", \"Gibraltar\", \"Greenland\", \"Grenada\", \"Guam\", \"Guinea-Bissau\",\n",
    "\"Isle of Man\", \"Kiribati\", \"Korea, Dem. People’s Rep.\", \"Liechtenstein\", \"Macao SAR, China\", \"Maldives\", \"Marshall Islands\",\n",
    "\"Micronesia, Fed. Sts.\", \"Monaco\", \"Nauru\", \"New Caledonia\", \"Northern Mariana Islands\", \"Palau\", \"Papua New Guinea\",\n",
    "\"Puerto Rico\", \"Samoa\", \"San Marino\", \"Sao Tome and Principe\", \"Seychelles\", \"Sint Maarten (Dutch part)\", \"Solomon Islands\",\n",
    "\"St. Kitts and Nevis\", \"St. Lucia\", \"St. Martin (French part)\", \"St. Vincent and the Grenadines\", \"Timor-Leste\",\n",
    "\"Tonga\", \"Turks and Caicos Islands\", \"Tuvalu\", \"Vanuatu\", \"Virgin Islands (U.S.)\"])].index.tolist()\n",
    "population_density = population_density.drop(to_del_pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density[\"country_name\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename countries to match the same names in all data sets\n",
    "world_happiness[\"country_name\"] = world_happiness[\"country_name\"].replace({\n",
    "    \"Ivory Coast\": \"Cote d'Ivoire\",\n",
    "    \"Swaziland\": \"Eswatini\",\n",
    "    \"Hong Kong S.A.R. of China\": \"Hong Kong SAR, China\",\n",
    "    \"South Korea\": \"Korea, Rep.\",\n",
    "    \"Kyrgyzstan\": \"Kyrgyz Republic\",\n",
    "    \"Laos\": \"Lao PDR\",\n",
    "    \"Macedonia\": \"North Macedonia\",\n",
    "    \"Russia\": \"Russian Federation\",\n",
    "    \"Slovakia\": \"Slovak Republic\",\n",
    "    \"Syria\": \"Syrian Arab Republic\",\n",
    "    \"Palestinian Territories\": \"West Bank and Gaza\",\n",
    "    \"Congo (Brazzaville)\": \"Congo, Rep.\",\n",
    "    'Congo (Kinshasa)': \"Congo, Dem. Rep.\",\n",
    "    'Egypt': 'Egypt, Arab Rep.',\n",
    "    'Gambia': 'Gambia, The', \n",
    "    'Iran': 'Iran, Islamic Rep.',\n",
    "    'Venezuela': 'Venezuela, RB',\n",
    "    'Yemen': 'Yemen, Rep.',\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_happiness.sort_values(by = [\"country_name\", \"year\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_del = world_happiness[world_happiness[\"country_name\"].isin([\"North Cyprus\", \"Somaliland region\", \"Taiwan Province of China\"])].index.tolist()\n",
    "world_happiness = world_happiness.drop(to_del)\n",
    "world_happiness[\"country_name\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will check for NaN values and make further processing of the population density data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2005, 2006, 2007, 2008, 2009, ..., 2013, 2014, 2015, 2016, 2017]\n",
       "Length: 13\n",
       "Categories (13, object): [2005, 2006, 2007, 2008, ..., 2014, 2015, 2016, 2017]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.year.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset includes only the period from 2005 to 2018. We will drop 2018 from the world_happiness data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2007,\n",
       "       2006, 2005], dtype=int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_del_2018 = world_happiness[world_happiness.year.isin([2018])].index.tolist()\n",
    "\n",
    "#drop it \n",
    "world_happiness = world_happiness.drop(to_del_2018)\n",
    "\n",
    "#done\n",
    "world_happiness.year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.isna().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>year</th>\n",
       "      <th>population_density_(people per sq. km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1046</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2782</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2572</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country_name country_code  \\\n",
       "178   South Sudan          SSD   \n",
       "395   South Sudan          SSD   \n",
       "612   South Sudan          SSD   \n",
       "829   South Sudan          SSD   \n",
       "1046  South Sudan          SSD   \n",
       "1263  South Sudan          SSD   \n",
       "1480  South Sudan          SSD   \n",
       "1697  South Sudan          SSD   \n",
       "1914  South Sudan          SSD   \n",
       "2131  South Sudan          SSD   \n",
       "2348  South Sudan          SSD   \n",
       "2565  South Sudan          SSD   \n",
       "2782  South Sudan          SSD   \n",
       "185         Sudan          SDN   \n",
       "402         Sudan          SDN   \n",
       "619         Sudan          SDN   \n",
       "836         Sudan          SDN   \n",
       "1053        Sudan          SDN   \n",
       "1270        Sudan          SDN   \n",
       "1487        Sudan          SDN   \n",
       "1704        Sudan          SDN   \n",
       "1921        Sudan          SDN   \n",
       "2138        Sudan          SDN   \n",
       "2355        Sudan          SDN   \n",
       "2572        Sudan          SDN   \n",
       "2789        Sudan          SDN   \n",
       "\n",
       "                                            Series Name   year  \\\n",
       "178   Population density (people per sq. km of land ...  2005    \n",
       "395   Population density (people per sq. km of land ...  2006    \n",
       "612   Population density (people per sq. km of land ...  2007    \n",
       "829   Population density (people per sq. km of land ...  2008    \n",
       "1046  Population density (people per sq. km of land ...  2009    \n",
       "1263  Population density (people per sq. km of land ...  2010    \n",
       "1480  Population density (people per sq. km of land ...  2011    \n",
       "1697  Population density (people per sq. km of land ...  2012    \n",
       "1914  Population density (people per sq. km of land ...  2013    \n",
       "2131  Population density (people per sq. km of land ...  2014    \n",
       "2348  Population density (people per sq. km of land ...  2015    \n",
       "2565  Population density (people per sq. km of land ...  2016    \n",
       "2782  Population density (people per sq. km of land ...  2017    \n",
       "185   Population density (people per sq. km of land ...  2005    \n",
       "402   Population density (people per sq. km of land ...  2006    \n",
       "619   Population density (people per sq. km of land ...  2007    \n",
       "836   Population density (people per sq. km of land ...  2008    \n",
       "1053  Population density (people per sq. km of land ...  2009    \n",
       "1270  Population density (people per sq. km of land ...  2010    \n",
       "1487  Population density (people per sq. km of land ...  2011    \n",
       "1704  Population density (people per sq. km of land ...  2012    \n",
       "1921  Population density (people per sq. km of land ...  2013    \n",
       "2138  Population density (people per sq. km of land ...  2014    \n",
       "2355  Population density (people per sq. km of land ...  2015    \n",
       "2572  Population density (people per sq. km of land ...  2016    \n",
       "2789  Population density (people per sq. km of land ...  2017    \n",
       "\n",
       "      population_density_(people per sq. km)  \n",
       "178                                      NaN  \n",
       "395                                      NaN  \n",
       "612                                      NaN  \n",
       "829                                      NaN  \n",
       "1046                                     NaN  \n",
       "1263                                     NaN  \n",
       "1480                                     NaN  \n",
       "1697                                     NaN  \n",
       "1914                                     NaN  \n",
       "2131                                     NaN  \n",
       "2348                                     NaN  \n",
       "2565                                     NaN  \n",
       "2782                                     NaN  \n",
       "185                                      NaN  \n",
       "402                                      NaN  \n",
       "619                                      NaN  \n",
       "836                                      NaN  \n",
       "1053                                     NaN  \n",
       "1270                                     NaN  \n",
       "1487                                     NaN  \n",
       "1704                                     NaN  \n",
       "1921                                     NaN  \n",
       "2138                                     NaN  \n",
       "2355                                     NaN  \n",
       "2572                                     NaN  \n",
       "2789                                     NaN  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density[population_density.isna().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no information about these two countries for the whole period. \n",
    "- South Sudan declared [independance from Republic of the Sudan in 2011](https://en.wikipedia.org/wiki/South_Sudan). This make it impossible to get real past data for the whole period we examining. We will drop this country from our data sets.\n",
    "- For Sudan we will add information from [indexmundi](https://www.indexmundi.com/facts/sudan/population-density) - for the period from 2005 to 2016 and add data for 2017 we will estimate based on the average change in population density over the years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove South Sudan\n",
    "to_del_country_no_info = population_density[population_density[\"country_name\"].isin([\"South Sudan\"])].index.tolist()\n",
    "population_density.drop(to_del_country_no_info, inplace = True)\n",
    "to_del_country_no_info = world_happiness[world_happiness[\"country_name\"].isin([\"South Sudan\"])].index.tolist()\n",
    "world_happiness.drop(to_del_country_no_info, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13 entries, 0 to 12\n",
      "Data columns (total 2 columns):\n",
      "year                            13 non-null int64\n",
      "population_density_per_sq_km    12 non-null float64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 288.0 bytes\n"
     ]
    }
   ],
   "source": [
    "#add values in Sudan\n",
    "# initialize list of lists \n",
    "data_population = [\n",
    "    [2005, 16.87],\n",
    "    [2006, 17.36],\n",
    "    [2007, 17.87],\n",
    "    [2008, 18.38],\n",
    "    [2009, 18.91],\n",
    "    [2010, 19.43],\n",
    "    [2011, 19.96],\n",
    "    [2012, 20.49],\n",
    "    [2013, 21.03],\n",
    "    [2014, 21.57],\n",
    "    [2015, 22.13],\n",
    "    [2016, 22.69],\n",
    "    [2017, ]] \n",
    "  \n",
    "# Create the pandas DataFrame \n",
    "df_sudan = pd.DataFrame(data_population, columns = ['year', 'population_density_per_sq_km'], )\n",
    "df_sudan.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sudan[\"population_density_per_sq_km\"].diff().plot(figsize=(8,6), linewidth=5, fontsize=10, label = \"diff between every two periods (period = 1 year)\")\n",
    "plt.title(\"Change in population density in Sudan\")\n",
    "plt.xlabel('year', fontsize=10)\n",
    "plt.ylabel('change for one period', fontsize = 10)\n",
    "plt.legend(loc=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5290909090909092"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_diff = df_sudan[\"population_density_per_sq_km\"].diff().mean()\n",
    "mean_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>population_density_per_sq_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>16.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>17.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>18.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>18.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>19.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011</td>\n",
       "      <td>19.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012</td>\n",
       "      <td>20.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>21.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014</td>\n",
       "      <td>21.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015</td>\n",
       "      <td>22.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016</td>\n",
       "      <td>22.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017</td>\n",
       "      <td>23.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  population_density_per_sq_km\n",
       "0   2005                         16.87\n",
       "1   2006                         17.36\n",
       "2   2007                         17.87\n",
       "3   2008                         18.38\n",
       "4   2009                         18.91\n",
       "5   2010                         19.43\n",
       "6   2011                         19.96\n",
       "7   2012                         20.49\n",
       "8   2013                         21.03\n",
       "9   2014                         21.57\n",
       "10  2015                         22.13\n",
       "11  2016                         22.69\n",
       "12  2017                         23.22"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's get the data\n",
    "df_sudan.loc[df_sudan[\"year\"] == 2017, \"population_density_per_sq_km\"] = 22.69 + mean_diff\n",
    "df_sudan = df_sudan.round(2)\n",
    "df_sudan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add to the main data set/ fill na\n",
    "for row, j in zip(population_density.loc[population_density[\"population_density_(people per sq. km)\"].isnull(), 'population_density_(people per sq. km)'].index, df_sudan.population_density_per_sq_km):\n",
    "    population_density.at[row, 'population_density_(people per sq. km)'] = j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>year</th>\n",
       "      <th>population_density_(people per sq. km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2005</td>\n",
       "      <td>16.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>17.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2007</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2008</td>\n",
       "      <td>18.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2009</td>\n",
       "      <td>18.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2010</td>\n",
       "      <td>19.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2011</td>\n",
       "      <td>19.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2012</td>\n",
       "      <td>20.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>21.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2014</td>\n",
       "      <td>21.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2015</td>\n",
       "      <td>22.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2572</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2016</td>\n",
       "      <td>22.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2017</td>\n",
       "      <td>23.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country_name country_code  \\\n",
       "185         Sudan          SDN   \n",
       "402         Sudan          SDN   \n",
       "619         Sudan          SDN   \n",
       "836         Sudan          SDN   \n",
       "1053        Sudan          SDN   \n",
       "1270        Sudan          SDN   \n",
       "1487        Sudan          SDN   \n",
       "1704        Sudan          SDN   \n",
       "1921        Sudan          SDN   \n",
       "2138        Sudan          SDN   \n",
       "2355        Sudan          SDN   \n",
       "2572        Sudan          SDN   \n",
       "2789        Sudan          SDN   \n",
       "\n",
       "                                            Series Name   year  \\\n",
       "185   Population density (people per sq. km of land ...  2005    \n",
       "402   Population density (people per sq. km of land ...  2006    \n",
       "619   Population density (people per sq. km of land ...  2007    \n",
       "836   Population density (people per sq. km of land ...  2008    \n",
       "1053  Population density (people per sq. km of land ...  2009    \n",
       "1270  Population density (people per sq. km of land ...  2010    \n",
       "1487  Population density (people per sq. km of land ...  2011    \n",
       "1704  Population density (people per sq. km of land ...  2012    \n",
       "1921  Population density (people per sq. km of land ...  2013    \n",
       "2138  Population density (people per sq. km of land ...  2014    \n",
       "2355  Population density (people per sq. km of land ...  2015    \n",
       "2572  Population density (people per sq. km of land ...  2016    \n",
       "2789  Population density (people per sq. km of land ...  2017    \n",
       "\n",
       "      population_density_(people per sq. km)  \n",
       "185                                    16.87  \n",
       "402                                    17.36  \n",
       "619                                    17.87  \n",
       "836                                    18.38  \n",
       "1053                                   18.91  \n",
       "1270                                   19.43  \n",
       "1487                                   19.96  \n",
       "1704                                   20.49  \n",
       "1921                                   21.03  \n",
       "2138                                   21.57  \n",
       "2355                                   22.13  \n",
       "2572                                   22.69  \n",
       "2789                                   23.22  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density[population_density[\"country_name\"] == \"Sudan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name                              0\n",
       "country_code                              0\n",
       "Series Name                               0\n",
       "year                                      0\n",
       "population_density_(people per sq. km)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finally got rid of the NaNs\n",
    "population_density.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161, (2093, 5))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.country_name.nunique(), population_density.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161, (1545, 16))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.country_name.nunique(), world_happiness.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Seems we have different size, we will have to deal with this later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2093,)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "figure(num=None, figsize=(10, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "#Return array with rows, equal to the population_density\n",
    "color = np.random.rand(2093)\n",
    "#area = (30 * np.random.rand(2821))**2\n",
    "plt.scatter(population_density[\"year\"], population_density[\"population_density_(people per sq. km)\"], \n",
    "           c = color,\n",
    "           alpha = 0.5,\n",
    "           label = \"country population density per sq. km\")\n",
    "\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"People per sq. km.\")\n",
    "plt.title(\"Population density over the years\")\n",
    "plt.legend(loc = 2)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#either put it in new row or remove it later\n",
    "#embed plot\n",
    "color.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7915.73, 1.63)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prove the scatter should look like this\n",
    "population_density[\"population_density_(people per sq. km)\"].max(), population_density[\"population_density_(people per sq. km)\"].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Some people live really close. There is clear enormous distinction between the most populated areas and the ones with    less than two people per sq. km. (yes, person and a half is fine here :)). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name                                                                      Singapore\n",
       "country_code                                                                            SGP\n",
       "Series Name                               Population density (people per sq. km of land ...\n",
       "year                                                                                  2017 \n",
       "population_density_(people per sq. km)                                              7915.73\n",
       "Name: 2775, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.loc[population_density[\"population_density_(people per sq. km)\"].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name                                                                       Mongolia\n",
       "country_code                                                                            MNG\n",
       "Series Name                               Population density (people per sq. km of land ...\n",
       "year                                                                                  2005 \n",
       "population_density_(people per sq. km)                                                 1.63\n",
       "Name: 131, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_density.loc[population_density[\"population_density_(people per sq. km)\"].idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name\n",
       "Singapore               7252.032308\n",
       "Hong Kong SAR, China    6753.926923\n",
       "Bahrain                 1603.390769\n",
       "Malta                   1326.313077\n",
       "Bangladesh              1147.542308\n",
       "West Bank and Gaza       645.202308\n",
       "Mauritius                616.233846\n",
       "Lebanon                  539.365385\n",
       "Korea, Rep.              512.973846\n",
       "Netherlands              494.676154\n",
       "India                    419.513846\n",
       "Rwanda                   418.638462\n",
       "Comoros                  381.023077\n",
       "Haiti                    366.328462\n",
       "Belgium                  362.130769\n",
       "Name: population_density_(people per sq. km), dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_densed = population_density.groupby(\"country_name\")[\"population_density_(people per sq. km)\"].mean().sort_values(ascending = False)\n",
    "most_densed = most_densed.head(15)\n",
    "most_densed = most_densed.groupby(\"country_name\").mean().sort_values(ascending = False)\n",
    "most_densed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name\n",
       "Bolivia                     9.426154\n",
       "Russian Federation          8.754615\n",
       "Central African Republic    7.006154\n",
       "Gabon                       6.614615\n",
       "Kazakhstan                  6.128462\n",
       "Guyana                      3.840769\n",
       "Canada                      3.779231\n",
       "Botswana                    3.537692\n",
       "Libya                       3.531538\n",
       "Mauritania                  3.513077\n",
       "Suriname                    3.430000\n",
       "Iceland                     3.198462\n",
       "Australia                   2.914615\n",
       "Namibia                     2.623846\n",
       "Mongolia                    1.796154\n",
       "Name: population_density_(people per sq. km), dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least_densed = population_density.groupby(\"country_name\")[\"population_density_(people per sq. km)\"].mean().sort_values(ascending = True)\n",
    "least_densed = least_densed.head(15)\n",
    "least_densed = least_densed.groupby(\"country_name\").mean().sort_values(ascending = False)\n",
    "least_densed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It could be seen that some of the most developed countries - Canada, Australia, and Iceland have some space to share. \n",
    "Further we will ask the question stated in the begining of this work: Is happiness dependent on the \"free space\"?\n",
    "Do people feel more happy in less populated areas? First, we need to deal with the data sizes. \n",
    "Easiest way will be to merge the tables and to get only the values that match in the smaller table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_happiness=world_happiness.merge(population_density, on='country_name', how='left') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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_name</th>\n",
       "      <th>year_x</th>\n",
       "      <th>life_ladder</th>\n",
       "      <th>log_GDP_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>HLE_at_birth</th>\n",
       "      <th>freedom_to_make_life_choices</th>\n",
       "      <th>generosity</th>\n",
       "      <th>perceptions_of_corruption</th>\n",
       "      <th>positive_affect</th>\n",
       "      <th>negative_affect</th>\n",
       "      <th>confidence_in_national_government</th>\n",
       "      <th>democratic_quality</th>\n",
       "      <th>delivery_quality</th>\n",
       "      <th>SD_of_ladder_by_country_year</th>\n",
       "      <th>SD_mean_of_ladder_by_country_year</th>\n",
       "      <th>country_code</th>\n",
       "      <th>Series Name</th>\n",
       "      <th>year_y</th>\n",
       "      <th>population_density_(people per sq. km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.724</td>\n",
       "      <td>7.169</td>\n",
       "      <td>0.451</td>\n",
       "      <td>50.8</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.612</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.655</td>\n",
       "      <td>1.775</td>\n",
       "      <td>0.477</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2005</td>\n",
       "      <td>39.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.724</td>\n",
       "      <td>7.169</td>\n",
       "      <td>0.451</td>\n",
       "      <td>50.8</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.612</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.655</td>\n",
       "      <td>1.775</td>\n",
       "      <td>0.477</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>40.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.724</td>\n",
       "      <td>7.169</td>\n",
       "      <td>0.451</td>\n",
       "      <td>50.8</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.612</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.655</td>\n",
       "      <td>1.775</td>\n",
       "      <td>0.477</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2007</td>\n",
       "      <td>41.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.724</td>\n",
       "      <td>7.169</td>\n",
       "      <td>0.451</td>\n",
       "      <td>50.8</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.612</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.655</td>\n",
       "      <td>1.775</td>\n",
       "      <td>0.477</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2008</td>\n",
       "      <td>42.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.724</td>\n",
       "      <td>7.169</td>\n",
       "      <td>0.451</td>\n",
       "      <td>50.8</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.612</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.655</td>\n",
       "      <td>1.775</td>\n",
       "      <td>0.477</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Population density (people per sq. km of land ...</td>\n",
       "      <td>2009</td>\n",
       "      <td>43.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_name  year_x  life_ladder  log_GDP_per_capita  social_support  \\\n",
       "0  Afghanistan    2008        3.724               7.169           0.451   \n",
       "1  Afghanistan    2008        3.724               7.169           0.451   \n",
       "2  Afghanistan    2008        3.724               7.169           0.451   \n",
       "3  Afghanistan    2008        3.724               7.169           0.451   \n",
       "4  Afghanistan    2008        3.724               7.169           0.451   \n",
       "\n",
       "   HLE_at_birth  freedom_to_make_life_choices  generosity  \\\n",
       "0          50.8                         0.718       0.178   \n",
       "1          50.8                         0.718       0.178   \n",
       "2          50.8                         0.718       0.178   \n",
       "3          50.8                         0.718       0.178   \n",
       "4          50.8                         0.718       0.178   \n",
       "\n",
       "   perceptions_of_corruption  positive_affect  negative_affect  \\\n",
       "0                      0.882            0.518            0.258   \n",
       "1                      0.882            0.518            0.258   \n",
       "2                      0.882            0.518            0.258   \n",
       "3                      0.882            0.518            0.258   \n",
       "4                      0.882            0.518            0.258   \n",
       "\n",
       "   confidence_in_national_government  democratic_quality  delivery_quality  \\\n",
       "0                              0.612               -1.93            -1.655   \n",
       "1                              0.612               -1.93            -1.655   \n",
       "2                              0.612               -1.93            -1.655   \n",
       "3                              0.612               -1.93            -1.655   \n",
       "4                              0.612               -1.93            -1.655   \n",
       "\n",
       "   SD_of_ladder_by_country_year  SD_mean_of_ladder_by_country_year  \\\n",
       "0                         1.775                              0.477   \n",
       "1                         1.775                              0.477   \n",
       "2                         1.775                              0.477   \n",
       "3                         1.775                              0.477   \n",
       "4                         1.775                              0.477   \n",
       "\n",
       "  country_code                                        Series Name year_y  \\\n",
       "0          AFG  Population density (people per sq. km of land ...  2005    \n",
       "1          AFG  Population density (people per sq. km of land ...  2006    \n",
       "2          AFG  Population density (people per sq. km of land ...  2007    \n",
       "3          AFG  Population density (people per sq. km of land ...  2008    \n",
       "4          AFG  Population density (people per sq. km of land ...  2009    \n",
       "\n",
       "   population_density_(people per sq. km)  \n",
       "0                                   39.30  \n",
       "1                                   40.49  \n",
       "2                                   41.51  \n",
       "3                                   42.46  \n",
       "4                                   43.49  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do people feel more happy in less populated areas? \n",
    "- H0: nothing interesting. \n",
    "- H1: there is a relationship\n",
    "\n",
    "significance level = 0.05\n",
    "\n",
    "We will perform [**independent t test**](https://pythonfordatascience.org/independent-t-test-python/) since the sources for the variables are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.ttest_ind(world_happiness.life_ladder, world_happiness[\"population_density_(people per sq. km)\"]).pvalue / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second zero for today (or at least the second so small number that we cannot see it). According to the test, we can reject the null hypothesys. This gives us ground for further research and digging in the issue. But let's plot another test by [**researchpy**](https://researchpy.readthedocs.io/en/latest/ttest_documentation.html) to see more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Variable</th>\n",
       "      <th>N</th>\n",
       "      <th>Mean</th>\n",
       "      <th>SD</th>\n",
       "      <th>SE</th>\n",
       "      <th>95% Conf.</th>\n",
       "      <th>Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>life_ladder</td>\n",
       "      <td>20085.0</td>\n",
       "      <td>5.432019</td>\n",
       "      <td>1.123194</td>\n",
       "      <td>0.007925</td>\n",
       "      <td>5.416485</td>\n",
       "      <td>5.447554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>population_density_(people per sq. km)</td>\n",
       "      <td>20085.0</td>\n",
       "      <td>228.513161</td>\n",
       "      <td>809.074811</td>\n",
       "      <td>5.708904</td>\n",
       "      <td>217.323240</td>\n",
       "      <td>239.703082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>combined</td>\n",
       "      <td>40170.0</td>\n",
       "      <td>116.972590</td>\n",
       "      <td>582.868009</td>\n",
       "      <td>2.908167</td>\n",
       "      <td>111.272516</td>\n",
       "      <td>122.672664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Variable        N        Mean          SD  \\\n",
       "0                             life_ladder  20085.0    5.432019    1.123194   \n",
       "1  population_density_(people per sq. km)  20085.0  228.513161  809.074811   \n",
       "2                                combined  40170.0  116.972590  582.868009   \n",
       "\n",
       "         SE   95% Conf.    Interval  \n",
       "0  0.007925    5.416485    5.447554  \n",
       "1  5.708904  217.323240  239.703082  \n",
       "2  2.908167  111.272516  122.672664  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptives, results = rp.ttest(world_happiness.life_ladder, world_happiness[\"population_density_(people per sq. km)\"])\n",
    "descriptives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>Independent t-test</th>\n",
       "      <th>results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Difference (life_ladder - population_density_(...</td>\n",
       "      <td>-223.0811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Degrees of freedom =</td>\n",
       "      <td>40168.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t =</td>\n",
       "      <td>-39.0760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Two side test p value =</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Difference &gt; 0 p value =</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Difference &lt; 0 p value =</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cohen's d =</td>\n",
       "      <td>-0.3899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hedge's g =</td>\n",
       "      <td>-0.3899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Glass's delta =</td>\n",
       "      <td>-198.6132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>r =</td>\n",
       "      <td>0.1914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Independent t-test     results\n",
       "0  Difference (life_ladder - population_density_(...   -223.0811\n",
       "1                              Degrees of freedom =   40168.0000\n",
       "2                                               t =     -39.0760\n",
       "3                           Two side test p value =       0.0000\n",
       "4                          Difference > 0 p value =       0.0000\n",
       "5                          Difference < 0 p value =       1.0000\n",
       "6                                       Cohen's d =      -0.3899\n",
       "7                                       Hedge's g =      -0.3899\n",
       "8                                   Glass's delta =    -198.6132\n",
       "9                                               r =       0.1914"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result in this test is confirming the other but it gives more information about the information we compare, incl. mean, standard deviation, confidence intervals, and degrees of freedom. More information and documentation can be found [here.](https://researchpy.readthedocs.io/en/latest/ttest_documentation.html)\n",
    "\n",
    "Finally, we will check the correlation between the happiness index and the population density: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0714813324915958"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_happiness.life_ladder.corr(world_happiness[\"population_density_(people per sq. km)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The link between these two variables is positive but very weak, it could be ignored. As we saw above the highest gdp has more influence on the positive side.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitations\n",
    "\n",
    "The analysis that we made in this work are based on relatively small data sets. The further cleaning of the data and the filling of missing values additionaly made our work harder. We can assume that the conclusions could be significantly revised if we had more data to work with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "\n",
    "It would be much more satisfying if we had found stronger correlations between the variables we examined. The only big player we had in this analysis is the GDP per capita. A further research in this area may include comparisons of the happiness index with alcohol consumption and education, as well as deeper analysis of the key variables of the index and separate countries which stand out as most happy or least happy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "1. [World Happiness Report 2019](https://worldhappiness.report/ed/2019/)\n",
    "2. [Population density data from World Development Indicators](https://databank.worldbank.org/reports.aspx?source=world-development-indicators#selectedDimension_WDI_Time) - The World Bank"
   ]
  }
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