investigate-gapminder-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate Gapminder Dataset\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> This project explores gapminder dataset to find what parameters affect a country's public peace/security.  \n",
    "Below are the questions aimed to be answered based on the analysis.\n",
    ">\n",
    ">* Q1: What is the relationship between a country's wealth inequality and its murder rate?\n",
    "* Q2: What about suicide rate?\n",
    "* Q3: Any other parameters linked to murder/suicide rate?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> * Loading and taking a look on the data.\n",
    "* Combining data if necessary\n",
    "* Making sure each dataframe have similar foramt and are easy to joint together\n",
    "\n",
    "The following dataset are used:\n",
    "* Murder rate (male/female)\n",
    "* Suicide rate (male/female)\n",
    "* Gini index\n",
    "* Unemployment rate\n",
    "* GDP per capita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Help functions\n",
    "\n",
    "There seem to be many simular operations on different dataset.  \n",
    "Define functions here that would later help reduce duplicate code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def data_for_certain_year(dataframe, year, isDrop=True):\n",
    "    \"\"\"\n",
    "    Extract data of the specified year.\n",
    "    \n",
    "    [Args]\n",
    "      dataframe: target dataframe\n",
    "      year: the year of the data\n",
    "      isDrop: whether to drop NaN\n",
    "      \n",
    "    [Return]\n",
    "      A pandas series for the year's data\n",
    "    \"\"\"\n",
    "\n",
    "    # Extract the data\n",
    "    s_result = dataframe[year]\n",
    "    # Drop NaN\n",
    "    s_result.dropna(inplace=isDrop)\n",
    "    \n",
    "    # Rename the series name\n",
    "    s_result.name = dataframe.index.name\n",
    "    s_result.index.name = ''\n",
    "    \n",
    "    return s_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data for murder and suicide\n",
    "Load the murder and suicide data.  \n",
    "Check data integrity and decide how to process the data for later exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load data about suicide and murder rate\n",
    "df_suicide_f = pd.read_excel('data/Suicide female indicator file UL 20100916.xlsx', sheetname='Data', index_col=0)\n",
    "df_suicide_m = pd.read_excel('data/Suicide male indicator file UL 20100916.xlsx', sheetname='Data', index_col=0)\n",
    "df_murder_f = pd.read_excel('data/murdered female indicator file UL 20100916.xlsx', sheetname='Data', index_col=0)\n",
    "df_murder_m = pd.read_excel('data/murdered male indicator file UL 20100916.xlsx', sheetname='Data', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
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       "      <th>Suicide among men, per 100 000, age adjusted</th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.884804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.330574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.145025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.048293</td>\n",
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       "      <th>Angola</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 55 columns</p>\n",
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       "                                              1950  1951  1952  1953  1954  \\\n",
       "Suicide among men, per 100 000, age adjusted                                 \n",
       "Afghanistan                                    NaN   NaN   NaN   NaN   NaN   \n",
       "Albania                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Algeria                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Andorra                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                                         NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "                                              1955  1956  1957  1958  1959  \\\n",
       "Suicide among men, per 100 000, age adjusted                                 \n",
       "Afghanistan                                    NaN   NaN   NaN   NaN   NaN   \n",
       "Albania                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Algeria                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Andorra                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                                         NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "                                                ...      1995  1996  1997  \\\n",
       "Suicide among men, per 100 000, age adjusted    ...                         \n",
       "Afghanistan                                     ...       NaN   NaN   NaN   \n",
       "Albania                                         ...       NaN   NaN   NaN   \n",
       "Algeria                                         ...       NaN   NaN   NaN   \n",
       "Andorra                                         ...       NaN   NaN   NaN   \n",
       "Angola                                          ...       NaN   NaN   NaN   \n",
       "\n",
       "                                              1998  1999  2000  2001  2002  \\\n",
       "Suicide among men, per 100 000, age adjusted                                 \n",
       "Afghanistan                                    NaN   NaN   NaN   NaN   NaN   \n",
       "Albania                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Algeria                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Andorra                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                                         NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "                                              2003       2004  \n",
       "Suicide among men, per 100 000, age adjusted                   \n",
       "Afghanistan                                    NaN   4.884804  \n",
       "Albania                                        NaN   9.330574  \n",
       "Algeria                                        NaN   5.145025  \n",
       "Andorra                                        NaN   8.048293  \n",
       "Angola                                         NaN  21.472977  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are similar data, check how they look like\n",
    "df_suicide_m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.884804</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.330574</td>\n",
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       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.145025</td>\n",
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       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.048293</td>\n",
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       "      <th>Angola</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "                                              1950  1951  1952  1953  1954  \\\n",
       "Suicide among men, per 100 000, age adjusted                                 \n",
       "Afghanistan                                    NaN   NaN   NaN   NaN   NaN   \n",
       "Albania                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Algeria                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Andorra                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                                         NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "                                              1955  1956  1957  1958  1959  \\\n",
       "Suicide among men, per 100 000, age adjusted                                 \n",
       "Afghanistan                                    NaN   NaN   NaN   NaN   NaN   \n",
       "Albania                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Algeria                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Andorra                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                                         NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "                                                ...      1995  1996  1997  \\\n",
       "Suicide among men, per 100 000, age adjusted    ...                         \n",
       "Afghanistan                                     ...       NaN   NaN   NaN   \n",
       "Albania                                         ...       NaN   NaN   NaN   \n",
       "Algeria                                         ...       NaN   NaN   NaN   \n",
       "Andorra                                         ...       NaN   NaN   NaN   \n",
       "Angola                                          ...       NaN   NaN   NaN   \n",
       "\n",
       "                                              1998  1999  2000  2001  2002  \\\n",
       "Suicide among men, per 100 000, age adjusted                                 \n",
       "Afghanistan                                    NaN   NaN   NaN   NaN   NaN   \n",
       "Albania                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Algeria                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Andorra                                        NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                                         NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "                                              2003       2004  \n",
       "Suicide among men, per 100 000, age adjusted                   \n",
       "Afghanistan                                    NaN   4.884804  \n",
       "Albania                                        NaN   9.330574  \n",
       "Algeria                                        NaN   5.145025  \n",
       "Andorra                                        NaN   8.048293  \n",
       "Angola                                         NaN  21.472977  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It seems there are more data for 2004\n",
    "# Let's just focus on 2004 data\n",
    "df_suicide_m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Afghanistan    13.514959\n",
       "Albania        15.391449\n",
       "Algeria         9.745790\n",
       "Andorra        10.951090\n",
       "Angola         29.616796\n",
       "Name: Suicide per 100,000, age adjusted, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine suicide numbers for male and female\n",
    "# Focus on 2004 data\n",
    "df_suicide = df_suicide_f + df_suicide_m\n",
    "df_suicide.index.name = 'Suicide per 100,000, age adjusted'\n",
    "s_suicide_2004 = data_for_certain_year(df_suicide, 2004)\n",
    "\n",
    "# Take a look on the data\n",
    "s_suicide_2004.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Afghanistan     7.429596\n",
       "Albania        13.489231\n",
       "Algeria        19.721519\n",
       "Andorra         1.452563\n",
       "Angola         98.177108\n",
       "Name: Murdered per 100,000, age adjusted, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine murder numbers for male and female\n",
    "df_murder = df_murder_f + df_murder_m\n",
    "df_murder.index.name = 'Murdered per 100,000, age adjusted'\n",
    "s_murder_2004 = data_for_certain_year(df_murder, 2004)\n",
    "\n",
    "# Take a look on the data\n",
    "s_murder_2004.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Data regarding inequality : Gini coefficient\n",
    "Wealth Inequaliy can modeled by either income distribution or Gini coefficient.  \n",
    "For simpliciy, only Gini coefficient are used in this analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Albania                   31.09\n",
       "Argentina                 50.18\n",
       "Armenia                   37.79\n",
       "Belarus                   26.22\n",
       "Bosnia and Herzegovina    35.78\n",
       "Name: GINI index, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load and modify data of Gini coefficient, which is used as a gauge of economic inequality\n",
    "df_gini = pd.read_excel('data/indicator SI_POV_GINI.xls.xlsx', sheetname='Data', index_col=0)\n",
    "s_gini_2004 = data_for_certain_year(df_gini, '2004')\n",
    "\n",
    "# Take a look on the data\n",
    "s_gini_2004.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. General data: GDP, and unemployment rate\n",
    "These data are for used to investigate whether there are any factors that affect public peace other than wealth inequality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Albania                 1454.022854\n",
       "Algeria                 2043.135713\n",
       "Andorra                20254.346287\n",
       "Angola                   353.231466\n",
       "Antigua and Barbuda    10599.284668\n",
       "Name: Income per person (fixed 2000 US$), dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load and modify GDP per captita data\n",
    "df_GDP_capita = pd.read_excel('data/GDPpercapitaconstant2000US.xlsx', sheetname='Data', index_col=0)\n",
    "s_GDP_capita_2004 = data_for_certain_year(df_GDP_capita, '2004')\n",
    "\n",
    "# Take a look at the data\n",
    "s_GDP_capita_2004.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Australia     5.5\n",
       "Canada        7.2\n",
       "Czech Rep.    8.3\n",
       "Estonia       9.5\n",
       "Finland       8.8\n",
       "Name: Total 15+ unemployment (%), dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load and modify unemployement rate data\n",
    "df_unemployment = pd.read_excel('data/indicator_t above 15 unemploy.xlsx', sheetname='Data', index_col=0)\n",
    "s_unemployment_2004 = data_for_certain_year(df_unemployment, 2004)\n",
    "\n",
    "# Take a look at the data\n",
    "s_unemployment_2004.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "> Since all the indicators are from different excel files thus imported as separate dataframe/series. It would be easier to examine the relationships of the indicators by combining the data at hands. In the following sections, I will examine the questions posted in the beginning of this notebook, and combine the dataframe only when necessary.\n",
    "\n",
    "\n",
    "### Q1: Is wealth inequality has impact on a country's murder rate?\n",
    "\n",
    "To find out the answers, let's combine the `s_murder_2004` with other indicators such as `s_gini_2004`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Combine s_murder_2004 and s_gini_2004 into a single dataframe\n",
    "# Transpose it so that the indicators appears in columns\n",
    "df_q1 = pd.DataFrame([s_murder_2004, s_gini_2004]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop NaN rows\n",
    "df_q1.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8WlFMZgPZeOAM4IIu5adExA+rD8esM7T6o9d3SBoHjKifJyJ2bEdQZgNVREyQNKJ0HGad\nptVkdBlwNnAu8Hr7wjEbtA6R9DlgEnBERDzTqJKkscBYgLXXXrvC8Mzaq9Wr6eZFxFkR8deIuKv2\n19eVSvq6pOmSpkm6WNKSfV2W2QBwFvAuYCQwCzi5WcWIGBcRoyJi1NChQ6uKz6ztWk1Gv5F0kKRh\nklau/fVlhZKGA4cCoyJiE9JVefv0ZVlmA0FEzI6I1yPiDeAcYMvSMZlVrdVuuv3z/2/UlQWwbj/W\nu5Sk14ClgSf6uByzhZ6kYRExKz/dA5jWXX2zgailZBQR6yyoFUbE45J+CDwGvALcEBE3dK3nvnEb\niCRdDOwADJE0E/gWsIOkkaQveI8ABxYL0KyQlpJRPrE6n4joenlqK8taCdgdWAd4FrhM0mci4hdd\nlj0OGAcwatSo6O16zDpRROzboPi8ygMx6zCtdtNtUfd4SWAnYDLz/1aiFR8GHo6IuQCSriSN8vCL\nbucyM7MBq9Vuuq/WP5e0AnBhH9f5GLC1pKVJ3XQ7kS5nNTOzQaqvt5B4GVi/LzNGxETgclLLamqO\nwbdrNTMbxFo9Z/Qb0slVSJdibwhc2teVRsS3SCduzczMWj5nVD9m1jzg0YiY2YZ4zMxsEGqpmy4P\nmHofacTulYBX2xmUmZkNLq3e6fVTwF+BvYBPARMl+RYSZma2QLTaTXccsEVEzAGQNBT4A+lCBDMz\ns35p9Wq6RWqJKHuqF/OamZl1q9WW0XWSrgcuzs/3Bq5tT0hmZjbYdJuMJK0HrBYR35D0CeADgIDb\ngYsqiM/MzAaBnrraTgVeAIiIKyPi8Ij4OqlVdGq7gzMzs8Ghp2Q0IiLu6VoYEZNItyA3MzPrt56S\nUXd3YF1qQQZiZmaDV0/J6E5JX+paKGkM0OfbjpuZmdXr6Wq6w4CrJO3HW8lnFLAE6Y6UZmZm/dZt\nMoqI2cC2kj4EbJKLfxcRN7c9MjMzGzRavZ/RLcAtbY7FzMwGKY+iYGZmxTkZmZlZcU5GZmZWnJOR\nmZkV52RkZmbFFUlGklaUdLmk+yTNkLRNiTjMzKwztHoLiQXtNOC6iNhT0hLA0oXiMDOzDlB5MpK0\nPLA9cABARLwKvFp1HGZm1jlKdNOtC8wFfibpbknnSlqmayVJYyVNkjRp7ty51UdpZmaVKZGMFgM2\nB86KiM2Al4BjulaKiHERMSoiRg0dOrTqGM3MrEIlktFMYGZETMzPLyclJzMzG6QqT0YR8S/gn5I2\nyEU7AfdWHYdZCZLOlzRH0rS6spUl3Sjp/vx/pZIxmpVQ6ndGXwUuknQPMBL4XqE4zKo2Hti5S9kx\nwE0RsT5wEw26rc0GuiKXdkfEFNJ9kcwGlYiYIGlEl+LdgR3y458DtwJHVxaUWQfwCAxm5a0WEbMA\n8v9Vm1X0VaY2UDkZmS1EfJWpDVRORmblzZY0DCD/n1M4HrPKORmZlXcNsH9+vD9wdcFYzIpwMjKr\nkKSLgduBDSTNlDQGOBEYLel+YHR+bjaolBoo1WxQioh9m0zaqdJAzDqMW0ZmZlack5GZmRXnbrpO\n8cC40hGYmRXjlpGZmRXnZGRmZsU5GZmZWXFORmZmVpyTkZmZFedkZGZmxTkZmZlZcU5GZmZWnJOR\nmZkV52RkZmbFFUtGkhaVdLek35aKwczMOkPJltHXgBkF129mZh2iSDKStCbwUeDcEus3M7POUmrU\n7lOBo4DlmlWQNBYYC7D22mtXFJb120AYfXy9saUjMBt0Km8ZSfoYMCci7uquXkSMi4hRETFq6NCh\nFUVnZmYllGgZbQfsJmlXYElgeUm/iIjPFIjFzProlxMfa/s6Pr2Ve0UGi8pbRhHxzYhYMyJGAPsA\nNzsRmZkNbv6dkZmZFVf0tuMRcStwa8kYzMysPLeMzMysOCcjMzMrzsnIzMyKczIyM7PinIzMzKw4\nJyMzMyvOycjMzIpzMjIzs+KK/ujVzN4i6RHgBeB1YF5EjCobkVl1nIzMOsuHIuLJ0kGYVc3ddGZm\nVpxbRmadI4AbJAXw04iY706Fg+2mk+2+TYVvUdE53DIy6xzbRcTmwC7AwZK271rBN520gcrJyKxD\nRMQT+f8c4Cpgy7IRmVXHycisA0haRtJytcfAR4BpZaMyq47PGZl1htWAqyRBOi5/GRHXlQ3JrDpO\nRmYdICIeAt5XOg6zUtxNZ2ZmxTkZmZlZcZV300laC7gAWB14AxgXEadVHYeZWbt/xwT+LVOrSpwz\nmgccERGT89VDd0m6MSLuLRCLmZl1gMq76SJiVkRMzo9fAGYAw6uOw8zMOkfRc0aSRgCbARMbTBsr\naZKkSXPnzq06NDMzq1CxZCRpWeAK4LCIeL7rdA97YmY2eBRJRpIWJyWiiyLiyhIxmJlZ56g8GSn9\nxPw8YEZE/Kjq9ZuZWecp0TLaDvgssKOkKflv1wJxmJlZh6j80u6I+BOgqtdrZmadyyMwmJlZcU5G\nZmZWnJORmZkV52RkZmbFORmZmVlxTkZmZlac7/RqZtZGvk1Fa9wyMjOz4pyMzMysOCcjMzMrzsnI\nzMyKczIyM7PinIzMzKw4JyMzMyvOvzMyM1vIDYTfMrllZGZmxTkZmZlZcU5GZmZWnJORmZkVVyQZ\nSdpZ0t8lPSDpmBIxmHUaHxc2mFWejCQtCpwJ7AJsBOwraaOq4zDrJD4ubLAr0TLaEnggIh6KiFeB\nXwG7F4jDrJP4uLBBrcTvjIYD/6x7PhPYqmslSWOBsfnpi5L+3oZYhgBPtmG5C4Jj65sFENuBPVV4\nZ/+W31B/j4tO3CaOqTULRUz7dV+/38dEiWSkBmUxX0HEOGBcWwORJkXEqHauo68cW990cmw96Ndx\n0Ymv2zG1xjElJbrpZgJr1T1fE3iiQBxmncTHhQ1qJZLRncD6ktaRtASwD3BNgTjMOomPCxvUKu+m\ni4h5kg4BrgcWBc6PiOlVx5G1tRuwnxxb33RybE0tgOOiE1+3Y2qNYwIUMV+3tJmZWaU8AoOZmRXn\nZGRmZsUNmmQkaUlJf5X0N0nTJX07l68jaaKk+yVdkk8el4hvUUl3S/ptJ8WVY3lE0lRJUyRNymUr\nS7oxx3ejpJUKxbaipMsl3SdphqRtOiW2KnTCEEKS1pJ0S37/p0v6Wi4/QdLjeb+ZImnXArF1zL4r\naYO692KKpOclHVbifZJ0vqQ5kqbVlTV8X5ScnvexeyRt3o6YBk0yAv4D7BgR7wNGAjtL2hr4PnBK\nRKwPPAOMKRTf14AZdc87Ja6aD0XEyLrfHhwD3JTjuyk/L+E04LqIeA/wPtJ72CmxtVUHDSE0Dzgi\nIjYEtgYOrovjlLzfjIyIawvEBh2y70bE32vvBfB+4GXgqjy56vdpPLBzl7Jm78suwPr5byxwVjsC\nGjTJKJIX89PF818AOwKX5/KfAx+vOjZJawIfBc7Nz9UJcfVgd1JcUO59Wx7YHjgPICJejYhnOyG2\ninTEEEIRMSsiJufHL5C+EAyvOo5e6IT9YyfgwYh4tMC6iYgJwNNdipu9L7sDF+TP0DuAFSUNW9Ax\nDZpkBG92hU0B5gA3Ag8Cz0bEvFxlJmUOolOBo4A38vNVOiSumgBukHRXHo4GYLWImAXpwwhYtUBc\n6wJzgZ/lLs5zJS3TIbFVodEQQkWTgKQRwGbAxFx0SO7aOb9Qd2mn7rv7ABfXPS/9PkHz96WS/WxQ\nJaOIeD03kdckfavcsFG1KmOS9DFgTkTcVV/coGrJa/C3i4jNSc31gyVtXzCWeosBmwNnRcRmwEsM\n0C65JjpqP5G0LHAFcFhEPE/qznkXqVt8FnBygbA6bt/N5393Ay7LRZ3wPnWnkv1sUCWjmtyVcyup\nf3tFSbUf/5YYgmU7YDdJj5C6WXYktZRKx/WmiHgi/59D6uPeEphda6rn/3MKhDYTmBkRtW/hl5OS\nUyfEVoWOGUJI0uKkRHRRRFwJEBGz8xfAN4BzSPtNpTp0390FmBwRs3Nsxd+nrNn7Usl+NmiSkaSh\nklbMj5cCPkzq274F2DNX2x+4usq4IuKbEbFmRIwgNd1vjoj9SsdVI2kZScvVHgMfAaaRhqrZv2R8\nEfEv4J+SNshFOwH3dkJsFemIIYTyOc7zgBkR8aO68vrzCnuQ9psq4+rUfXdf6rroSr9PdZq9L9cA\nn8tX1W0NPFfrzlugImJQ/AGbAncD95A29vG5fF3gr8ADpGbzOwrGuAPw206KK8fxt/w3HTgul69C\nuuLm/vx/5ULxjQQm5e36a2ClTomtote/K/AP0vnP4wrF8AFSt809wJT8tytwITA1l18DDKs4ro7b\nd4GlgaeAFerKKn+fSMlwFvAaqeUzptn7QuqmOzPvY1OBUe2IycMBmZlZcYOmm87MzDqXk5GZmRXn\nZGRmZsU5GZmZWXFORmZmVpyTUYeQtJqkX0p6KA9dcrukPfK0HfTWaN4HSHpD0qZ1807Lw7DURike\n0mD519Z+Z9ViPCPqR/S1akkKSRfWPV9M0tzaftCP5R4g6Yz+R9hw2eMl7dlzzX6vZ74Rp3N5v0ad\nVpMR0NVkBH1J78jPH8jTR7TvVfdf/fbJQ2f1elBdSSPVh1HFJd0qaVR3dZyMOkD+0eCvgQkRsW5E\nvJ/0A8Y1m8wyEziuN+uIiF0jjTxhC4eXgE3yD7QBRgOP92YB+UO4X8d43SggRTRZ/3jmH3Ea+jHq\ntLofAb3ZCPpjgGciYj3glFxvoRARX4yIe/sw60jSb8gWOCejzrAj8GpEnF0riIhHI+LHTer/Fti4\nbuSBHtVaTLnFM0PSOUr3nrmh9oEn6f1K93u6HTi4bt5FJZ0k6c78zfLAXL6HpD/kD71hkv4hafW+\nvAHW0O9Jo7nD/L/aP0HSkXXPp+VtW9u+PwEmA2tJ+nzeNreRhp+qzTNU0hV5u94pabu6ZY+TdANw\nQTfbX5LOkHSvpN/RZMDR/K34VEl/yXFumcuXya2cO5UGut09lx8g6TJJvwFu6Lq8aDziNPRv1OmG\nI6DnL4rNRtCvX9/lwE65flOSfq3U8zFdbw3ciqQxeRvdmo/NM3J5w23UZZkjJP1R0uT8t20ub7p9\n6lsqkl6sK99T0vj8eK+8vf4maYJSi/A7wN5K913au5ttuJSkX+X95RJgKXrgZNQZNiZ9cLTqDeAH\nwLF9XN/6wJkRsTHwLPDJXP4z4NCI2KZL/TGkIUC2ALYAviRpnYi4CvgXKXGdA3wr0hA9tmD8CthH\n0pKkEUQm9lC/ZgPSh+9mwKvAt0lJaDTpW3/NaaRv/FuQ9oFz66a9H9g9Ij5Nk+1PGrpmA+C9wJeA\nbbuJaZmI2BY4CDg/lx1HGv5qC+BDwElKw/YAbAPsHxE7tviaoX+jTjer090I+m/Ok6c/l+t35wu5\n52MUcKikVSStAfwPaazM0cB76up3t41q5gCjIw0Iuzdwei7vzfZp5HjgvyLdA263nKSPBy6JdN+l\nS2i+Db8CvBwRmwL/l7Q/datoE9wak3QmaYiVV/NGbuSXwHH5Q6G3Ho6IKfnxXcAISSsAK0bEbbn8\nQlKXBaQxvTbVW+cDViAltIeBr5KGV7ojIuqHxLd+ioh7lM5D7Av05oZrj+YWAMBWwK0RMRcgf0t9\nd572YWCjui/zyyuP5QZcExGv5MfNtv/2wMUR8TrwhKSbu4np4vyaJkhaXun85UdIgwTXWnhLAmvn\nxzdGRKPWT1+0Mup0szrdzduX0awPVT4XTBp8dH1gdeC22uuVdBk9bKNI942qWRw4Q9JI4PW6eXuz\nfRr5MzBe0qXAlU3qNNuG25OTYt6P7+lpZU5GnWE6b7VOiIiDlS5CmNRshoiYJ+lk4Og+rO8/dY9f\nJzWhRfMDScBXI+L6BtOGk1pqq0laJNLIw7bgXAP8kDRuYf237nm8vWdjybrHL3VZRrPtugiwTV3S\nASB/8NUvo+H2VzqR3ep4Yl3r1T7oPxkRf++y3K2Y/zW0YrakYRExS70fdbpZnSfJI+jn1k/9vLV5\nZiqd21qBxt2Htde1Aym5bBMRL0u6lbTduuvaa7iNuvg6MJt0p+NFgH/XTWtl+9TXeXM/iogv523x\nUWBKTnZdNduGra77Te6m6ww3A0tK+kpd2dItzDeetHMP7W8A+eKG5yR9IBftVzf5euArSrcJQNK7\nc1/xYqSuvU+TRkA/vL9x2HzOB74TEVO7lD9Cul0GSleHNWshTwR2yN1BiwN71U27ATik9qTJhw00\n2f7ABFI34qL5w/9D3byOvfNo2MJYAAACTklEQVS8HyB1+T2Xl/vV2nkWSZt1M38rej3qtKT7cp2G\nI6BHGryz2Qj69evbk9RdFZKGS7qpQXwrkC54eFnSe0jdcpAGRP6gpJXyMfXJunla2UYrALPyF8HP\nAovm8la3z2xJGypd7FJrtSHpXRExMSKOJyXltYAXgOXq5m22DSeQP0MkbULqZu6Wk1EHyDv8x0k7\n5MOS/ko6Mdptqyf34Z7OgrtT5eeBM5UuYKj/JnYu6dYMk5Uup/0pqVV9LPDHiPgjKRF9UVKjGxZa\nH0XEzIg4rcGkK4CVle5c/BXSyN2N5p8FnADcDvyBt5+bPBQYlU8y3wt8uUkYzbb/VaQRnqeSrlC7\nrcn8AM9I+gtwNm9djfZdUhfTPXm53+1m/jdJuji/ng0kzZRUW96JwGhJ95POvZyYy68FHiKNgH8O\n6bwVufchfYVPrZ5DSB+uM4BLI2J6nv9o4HBJD5Bap+fl8vOAVXL54bx19d4wUsu1q+uAxXKX1XeB\nO/K6Hwe+R/ri8AfSe/1cnqeVbfQTYH9Jd5C66Gqtyp62T63lcgzpoqibSSN515wkaWreNhNIo5/f\nQuo2nCJpb5pvw7OAZfNrPYqUcLvlUbvNrK1yd9SREdG027kEpbssrxsRp/dYuXfLPQR4LCJavreU\npGUj4sXcMroKOD9fINQWkqaSLkp4uF3r6C2fMzKzQSki+vUD4m6W25cfFZ8g6cOkczY3kH532BaS\nbgSmdlIiAreMzMysA/ickZmZFedkZGZmxTkZmZlZcU5GZmZWnJORmZkV9/8BHXnOlGufyY4AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10df4048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect the distributions\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "sns.distplot(df_q1[s_gini_2004.name], kde=False, ax=axes[0], color='orange');axes[0].set_title('Gini Index Dist.');axes[0].set_ylabel('Count');\n",
    "sns.distplot(df_q1[s_murder_2004.name], kde=False, ax=axes[1]); axes[1].set_title('Murder Rate Dist.');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1921a320>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XI+LXhRUR8WRE/LzM9tcCWxddsdqjQskilQweknSWsjk3bkpXrSNp\nB2VzcfwFOLrosYMl/Yeke9KAa0el9R+X9AdlNpX0d0lvruYAmOXhBGHtaGvg3l5svxo4lewq/WpM\nBU6PiK2BxcAn0/pzgeMiYpdu2x8OvBwRO5IN+HeEpC0j4krgObJkchbw3TTcgllDOEFY25N0ejqT\nv6fCZhcBO0vasopdPF40adBsYLKkjYBREfHHtP63Rdt/EDg4DR9zF9kQ51PTfceSjRa7PCIuriIW\ns9z6+1hMZtV4gDVn8UTE0amhubPcAyJipaTTqG7o7uVFt1cBI8jGaSo3zo2AYyPixhL3bU5Wohkv\naVAasdOsIVyCsHZ0KzBc0heL1r0px+POIxvxdlytAaQG7Jcl7ZpWHVR0943AF9NQ9kh6axr5dAhZ\ntdSBZKMCf7XWOMwqcYKwthPZCJUfA94r6XFJd5NNHVmxdBARrwM/o34ziB0GnJ4aqV8rWn822bDS\n96aur2eQlfZPBP47Iv6bLDl8XlKpCajM6sKjuZqZWUkuQZiZWUlOEGZmVpIThJmZleQEYWZmJTlB\nmJlZSU4QZmZWkhOEmZmV9P8Bt5kEcqzN/0oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10cf1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot gini index vs murder rate\n",
    "title_q1 = 'Scatter plot: Murder rates vs GINI index'\n",
    "df_q1.plot(kind='scatter', x=s_gini_2004.name, y=s_murder_2004.name, title=title_q1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter plot with regression line: Murder rates vs GINI index')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bl9ROZdXyE7mgblrW5cJeSpeA/wy4CWfqyQU4xSDNwDrc9dyMGUmxFSgUo76Y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\ncAghFBIFOIQQCokCHEIIhUQBDiGEQv4Fo0hQCsRvQz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a192d03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn plot: scatter plot with regression line\n",
    "sns_title_q1 = 'Scatter plot with regression line: Murder rates vs GINI index'\n",
    "sns.lmplot(x=s_gini_2004.name, y=s_murder_2004.name, data=df_q1, fit_reg=True)\n",
    "ax = plt.gca()\n",
    "ax.set_title(sns_title_q1, fontdict={'fontsize': 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murdered per 100,000, age adjusted</th>\n",
       "      <th>GINI index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Murdered per 100,000, age adjusted</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GINI index</th>\n",
       "      <td>0.537432</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Murdered per 100,000, age adjusted  \\\n",
       "Murdered per 100,000, age adjusted                            1.000000   \n",
       "GINI index                                                    0.537432   \n",
       "\n",
       "                                    GINI index  \n",
       "Murdered per 100,000, age adjusted    0.537432  \n",
       "GINI index                            1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Pearson correlation\n",
    "df_q1.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "* Histograms: The murder rate distribution among countries is right-skewed. A low murder rate is more typical in the available data.\n",
    "* Scatter plots : The plots and the regression line show that there is a positive relationship (positive slope) between murder rate and Gini index.\n",
    "* Correlation coefficient: Pandas's corr() function gives us positive Pearson coefficient between these two indicators as well.  \n",
    "\n",
    "\n",
    "Clearly, wealth inequality has positive relationship with the murder rate among the countries in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2: What about suicide rate?\n",
    "\n",
    "Similar to murder rate data, let's combine the `s_suicide_2004` with `s_gini_2004`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Combine s_suicide_2004 and s_gini_2004 into a single dataframe\n",
    "# Transpose it so that the indicators appears in columns\n",
    "df_q2 = pd.DataFrame([s_suicide_2004, s_gini_2004]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop NaN rows\n",
    "df_q2.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZmRXnZGRmZsU5GZmZWXFORmZmVlylIzBI2hf4OjAMOCciVhnKRNIhwKlAALdHxHuqjMnM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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a0f9668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect the distributions\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "sns.distplot(df_q2[s_gini_2004.name], hist=False, kde=False, ax=axes[0], color='orange'); axes[0].set_title('Gini Index Dist.');axes[0].set_ylabel('Count');\n",
    "sns.distplot(df_q2[s_suicide_2004.name], kde=False, ax=axes[1], color='purple'); axes[1].set_title('Suicide Rate Dist.');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yedo85i5c0uiQzGwQqvU++h7gvcCOkuaWfLQFsLzWgVlzKm2au4ZUIX/i7EXM3HmCcwZm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\nCMzMOpwTAjOzDueEwMysw/1/+RKV5jNkOi8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10caaa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot gini index vs suicide rate\n",
    "title_q2 = 'Scatter plot: Suicide rates vs GINI index'\n",
    "df_q2.plot(kind='scatter', x=s_gini_2004.name, y=s_suicide_2004.name, title=title_q2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter plot with regression line: Suicide rates vs GINI index')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jo+kFYBFZAfw7UAO0uZubgWPAH6rq1iw0MiOFXBEjkVSe3evkiV9qHcoT15X7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1966f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn plot: scatter plot with regression line\n",
    "sns_title_q2 = 'Scatter plot with regression line: Suicide rates vs GINI index'\n",
    "sns.lmplot(x=s_gini_2004.name, y=s_suicide_2004.name, data=df_q2, fit_reg=True)\n",
    "ax = plt.gca()\n",
    "ax.set_title(sns_title_q2, fontdict={'fontsize': 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suicide per 100,000, age adjusted</th>\n",
       "      <th>GINI index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Suicide per 100,000, age adjusted</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.536617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GINI index</th>\n",
       "      <td>-0.536617</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Suicide per 100,000, age adjusted  \\\n",
       "Suicide per 100,000, age adjusted                           1.000000   \n",
       "GINI index                                                 -0.536617   \n",
       "\n",
       "                                   GINI index  \n",
       "Suicide per 100,000, age adjusted   -0.536617  \n",
       "GINI index                           1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Pearson correlation\n",
    "df_q2.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "* Histograms: The suicide rate distribution is also right-skewed. More countries have suicide rates lower than average.\n",
    "* Scatter plots : The plots and the regression line show that there is a negative relationship (negative slope) between suicide rate and Gini index.\n",
    "* Correlation coefficient: Pandas's corr() function gives us negative Pearson coefficient between these two indicators as well.  \n",
    "\n",
    "\n",
    "The analysis shows that countries with lower wealth inequality tend to have higher suicide rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3: Any other parameters linked to murder/suicide rate?\n",
    "\n",
    "This time, let's examine the relationship between unemployment rate and suicide/murder rate.\n",
    "\n",
    "#### Unemployment Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Combine unemployment data with murder/suicide data\n",
    "df_q3_1 = pd.DataFrame([s_unemployment_2004, s_suicide_2004, s_murder_2004]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop NaN rows\n",
    "df_q3_1.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a191f8128>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vN0F8jXblgwtZW7dhk2Fr6zZw5YMLKxSRc85Vn7zJINYRdInFRM3O66vXFjTc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\nGt4DqXMN8/sZuPKpsi43una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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a193b84e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plots of both suicide/murder rates with unemployment rate\n",
    "plt.scatter(df_q3_1[s_unemployment_2004.name], df_q3_1[s_suicide_2004.name], label='suicide rate')\n",
    "plt.scatter(df_q3_1[s_unemployment_2004.name], df_q3_1[s_murder_2004.name], label='murder rate')\n",
    "plt.xlabel(s_unemployment_2004.name)\n",
    "plt.ylabel('suicide/murder rate(%)')\n",
    "plt.title('Scatter plot: Murder/Suicide rates vs unemployment rate')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter plot with regression line: Murder rates vs UNEF. rates')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FpWQ8dwIvlLQviWQ8yYEZM4knzWl+HnjdVDPmlEQx4O4OupKWAtvMbLCeBWs2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\njHf/9VL68C5e+3se3dZPm2D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3x9UXAIfEGuoFkuZJukHSnTHv7evG2q+Z3UT5bF21Kvem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\nS8n/BwyN8zz+hjZEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19229588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn plot: unemployment rate vs suicide rate\n",
    "sns_title_q3 = 'Scatter plot with regression line: Murder rates vs UNEF. rates'\n",
    "sns.lmplot(x=s_unemployment_2004.name, y=s_suicide_2004.name, data=df_q3_1, fit_reg=True)\n",
    "ax = plt.gca()\n",
    "ax.set_title(sns_title_q3, fontdict={'fontsize': 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scatter plot with regression line: Suicide rates vs UNEF. rates')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qyF6QNCBpgZn1TFdQzjWbfKHILp+Prekk6YLIELoRbgb6SwvN7PyaReVckzAz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1963af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn plot: unemployment rate vs murder rate\n",
    "sns_title_q3 = 'Scatter plot with regression line: Suicide rates vs UNEF. rates'\n",
    "sns.lmplot(x=s_unemployment_2004.name, y=s_murder_2004.name, data=df_q3_1, fit_reg=True)\n",
    "ax = plt.gca()\n",
    "ax.set_title(sns_title_q3, fontdict={'fontsize': 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total 15+ unemployment (%)</th>\n",
       "      <th>Suicide per 100,000, age adjusted</th>\n",
       "      <th>Murdered per 100,000, age adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total 15+ unemployment (%)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091997</td>\n",
       "      <td>0.171745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suicide per 100,000, age adjusted</th>\n",
       "      <td>0.091997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Murdered per 100,000, age adjusted</th>\n",
       "      <td>0.171745</td>\n",
       "      <td>-0.002459</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Total 15+ unemployment (%)  \\\n",
       "Total 15+ unemployment (%)                            1.000000   \n",
       "Suicide per 100,000, age adjusted                     0.091997   \n",
       "Murdered per 100,000, age adjusted                    0.171745   \n",
       "\n",
       "                                    Suicide per 100,000, age adjusted  \\\n",
       "Total 15+ unemployment (%)                                   0.091997   \n",
       "Suicide per 100,000, age adjusted                            1.000000   \n",
       "Murdered per 100,000, age adjusted                          -0.002459   \n",
       "\n",
       "                                    Murdered per 100,000, age adjusted  \n",
       "Total 15+ unemployment (%)                                    0.171745  \n",
       "Suicide per 100,000, age adjusted                            -0.002459  \n",
       "Murdered per 100,000, age adjusted                            1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Pearson correlation\n",
    "df_q3_1.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation\n",
    "* Scatter plots : There are very small positive relationship between unemployment rate and murder/suicide rate. The slope of the best fitting line are near 0.\n",
    "* Correlation coefficient: Pandas's corr() function gives us very small positive Pearson coefficient between unemployment rate and murder/suicide rate.  \n",
    "\n",
    "\n",
    "There might be a very small correlation between unemployment rate and suicide/murder rate but not apparent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GDP per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Combine GDP data with murder/suicide data\n",
    "df_q3_2 = pd.DataFrame([s_GDP_capita_2004, s_suicide_2004, s_murder_2004]).T\n",
    "df_q3_2.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a10daf0b8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OnMlvfvMb7ruvfuTU999/n+eee478/Hy++uorXnzxRQoKCnjuuee4+uqreeyxx7jhhhso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\n/lEcEQcFF6LBLMQZEUSzceUDQkBEfIOMkEBEgvh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10cf1320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plots of both suicide/murder rates with unemployment rate\n",
    "plt.scatter(df_q3_2[s_GDP_capita_2004.name], df_q3_2[s_suicide_2004.name], label='suicide rate')\n",
    "plt.scatter(df_q3_2[s_GDP_capita_2004.name], df_q3_2[s_murder_2004.name], label='murder rate')\n",
    "plt.xlabel(s_GDP_capita_2004.name)\n",
    "plt.ylabel('suicide/murder rate(%)')\n",
    "plt.title('Scatter plot: Murder/Suicide rates vs GDP per capita')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income per person (fixed 2000 US$)</th>\n",
       "      <th>Suicide per 100,000, age adjusted</th>\n",
       "      <th>Murdered per 100,000, age adjusted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Income per person (fixed 2000 US$)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027270</td>\n",
       "      <td>-0.387617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suicide per 100,000, age adjusted</th>\n",
       "      <td>-0.027270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.164673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Murdered per 100,000, age adjusted</th>\n",
       "      <td>-0.387617</td>\n",
       "      <td>0.164673</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Income per person (fixed 2000 US$)  \\\n",
       "Income per person (fixed 2000 US$)                            1.000000   \n",
       "Suicide per 100,000, age adjusted                            -0.027270   \n",
       "Murdered per 100,000, age adjusted                           -0.387617   \n",
       "\n",
       "                                    Suicide per 100,000, age adjusted  \\\n",
       "Income per person (fixed 2000 US$)                          -0.027270   \n",
       "Suicide per 100,000, age adjusted                            1.000000   \n",
       "Murdered per 100,000, age adjusted                           0.164673   \n",
       "\n",
       "                                    Murdered per 100,000, age adjusted  \n",
       "Income per person (fixed 2000 US$)                           -0.387617  \n",
       "Suicide per 100,000, age adjusted                             0.164673  \n",
       "Murdered per 100,000, age adjusted                            1.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Pearson correlation\n",
    "df_q3_2.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Observation\n",
    "* Scatter plots : Both plots show that extreme high suicide/murder rates only exist in low GDP countries. Suicide/murder rates of higher-GDP countries show no apparent trend.\n",
    "* Correlation coefficient: Pandas's corr() function gives us very small negative Pearson coefficient between suicide rate and GDP per capita, and siginificant negative Pearson coefficient between murder rate and GDP per capita.\n",
    "\n",
    "\n",
    "Extreme murder/suicide rates are only found in low GDP countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> Through the explorations of 2004's data, the following relationships are observed. \n",
    "* The Gini coeffificients (a metric for inequality) has a positive relationship with murder rates but a negative relationship with suicide rate.\n",
    "* Extreme high murder/suicide rates occurred only in low GDP countries.\n",
    "\n",
    "> Limitations of this analysis\n",
    "* Only one year's data (2004) are used to perform the analysis. The result might be different based on data of different time.\n",
    "* There might be other factors that affect the murder/suicide rate of a country. (e.g. political factors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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