5.5.4 Challenge What test to use.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Necessary imports \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "# Import parametric statistics\n",
    "from scipy.stats import ttest_ind, f_oneway, ttest_rel, shapiro\n",
    "# Import nonparametric statistics\n",
    "from scipy.stats import kruskal, wilcoxon\n",
    "# Import test for normality\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.5.4 Challenge\n",
    "Using selected questions from the 2012 and 2014 editions of the European Social Survey, address the following questions. Keep track of your code and results in a Jupyter notebook or other source that you can share with your mentor. For each question, explain why you chose the approach you did.\n",
    "\n",
    "In this dataset, the same participants answered questions in 2012 and again 2014.\n",
    "\n",
    "1. Did people become less trusting from 2012 to 2014? Compute results for each country in the sample.\n",
    "2. Did people become happier from 2012 to 2014? Compute results for each country in the sample.\n",
    "3. Who reported watching more TV in 2012, men or women?\n",
    "4. Who was more likely to believe people were fair in 2012, people living with a partner or people living alone?\n",
    "5. Pick three or four of the countries in the sample and compare how often people met socially in 2014. Are there differences, and if so, which countries stand out?\n",
    "6. Pick three or four of the countries in the sample and compare how often people took part in social activities, relative to others their age, in 2014. Are there differences, and if so, which countries stand out?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>cntry</th>\n",
       "      <th>idno</th>\n",
       "      <th>year</th>\n",
       "      <th>tvtot</th>\n",
       "      <th>ppltrst</th>\n",
       "      <th>pplfair</th>\n",
       "      <th>pplhlp</th>\n",
       "      <th>happy</th>\n",
       "      <th>sclmeet</th>\n",
       "      <th>sclact</th>\n",
       "      <th>gndr</th>\n",
       "      <th>agea</th>\n",
       "      <th>partner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CH</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CH</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CH</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CH</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CH</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cntry  idno  year  tvtot  ppltrst  pplfair  pplhlp  happy  sclmeet  sclact  \\\n",
       "0    CH   5.0     6    3.0      3.0     10.0     5.0    8.0      5.0     4.0   \n",
       "1    CH  25.0     6    6.0      5.0      7.0     5.0    9.0      3.0     2.0   \n",
       "2    CH  26.0     6    1.0      8.0      8.0     8.0    7.0      6.0     3.0   \n",
       "3    CH  28.0     6    4.0      6.0      6.0     7.0   10.0      6.0     2.0   \n",
       "4    CH  29.0     6    5.0      6.0      7.0     5.0    8.0      7.0     2.0   \n",
       "\n",
       "   gndr  agea  partner  \n",
       "0   2.0  60.0      1.0  \n",
       "1   2.0  59.0      1.0  \n",
       "2   1.0  24.0      2.0  \n",
       "3   2.0  64.0      1.0  \n",
       "4   2.0  55.0      1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in data\n",
    "ess_raw = pd.read_csv('ESSdata_Thinkful.csv')\n",
    "ess_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cntry         6\n",
       "idno       3086\n",
       "year          2\n",
       "tvtot         8\n",
       "ppltrst      11\n",
       "pplfair      11\n",
       "pplhlp       11\n",
       "happy        11\n",
       "sclmeet       7\n",
       "sclact        5\n",
       "gndr          2\n",
       "agea         83\n",
       "partner       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess_raw.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1. Did people become less trusting from 2012 to 2014? Compute results for each country in the sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first plot histograms of trust for each country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t\t\tDistribution of trust\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf72fd8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro Tests for Normality\n",
      "CH 6 (0.9517106413841248, 9.510712936601148e-15)\n",
      "CH 7 (0.9547930955886841, 2.465479128363826e-14)\n",
      "\n",
      "CZ 6 (0.9680074453353882, 3.6126571179018185e-10)\n",
      "CZ 7 (0.9715775847434998, 1.8777941424019673e-09)\n",
      "\n",
      "DE 6 (0.8929166197776794, 0.10693283379077911)\n",
      "DE 7 (0.9425466060638428, 0.4519253373146057)\n",
      "\n",
      "ES 6 (0.949491024017334, 1.6942651933374377e-19)\n",
      "ES 7 (0.9663601517677307, 1.5275811452092943e-15)\n",
      "\n",
      "NO 6 (0.9211018085479736, 8.881221102071063e-19)\n",
      "NO 7 (0.9292040467262268, 7.928973676032161e-18)\n",
      "\n",
      "SE 6 (0.9367397427558899, 1.2586250953636158e-18)\n",
      "SE 7 (0.9376322627067566, 1.3852298444033788e-18)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plotting histograms of trust for each country for each year\n",
    "g = sns.FacetGrid(ess_raw, row='year', col='cntry', palette='pastel', dropna=True, sharey=False)\n",
    "g = g.map(plt.hist, 'ppltrst', alpha=0.8)\n",
    "print('\\t\\t\\t\\t\\tDistribution of trust')\n",
    "plt.show()\n",
    "\n",
    "# Testing each country and each year to determine if trust is normally distributed through Shapiro test\n",
    "# Creating lists of countries and years\n",
    "cntrys = list(ess_raw.cntry.unique())\n",
    "years = [6,7]\n",
    "print('Shapiro Tests for Normality')\n",
    "for cnt in cntrys:\n",
    "    # Isolating data from each country\n",
    "    ess_temp = ess_raw[ess_raw.cntry ==cnt].dropna()\n",
    "    for year in years:\n",
    "        # Printing Shapiro test results\n",
    "        print(cnt, year, shapiro(ess_temp[ess_temp.year ==year].ppltrst))\n",
    "    print()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histograms and the Shapiro Test results, we can determine that the values are non-normally distributed. As we are comparing the same group of people, and the distribution is not normally distributed, I will use the Wilcoxon Signed-Rank test to find if there are difference between the distribution over both years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wilcoxon Signed-Rank Test\n",
      "CH WilcoxonResult(statistic=106278.5, pvalue=0.44112865109524768)\n",
      "\n",
      "CZ WilcoxonResult(statistic=82837.0, pvalue=0.29318914263273788)\n",
      "\n",
      "DE WilcoxonResult(statistic=44.0, pvalue=0.91571346008998633)\n",
      "\n",
      "ES WilcoxonResult(statistic=243475.5, pvalue=0.0055708296504396841)\n",
      "\n",
      "NO WilcoxonResult(statistic=85392.0, pvalue=0.61391756699706612)\n",
      "\n",
      "SE WilcoxonResult(statistic=130719.5, pvalue=0.028545290985536906)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mache/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2400: RuntimeWarning: invalid value encountered in greater\n",
      "  r_plus = np.sum((d > 0) * r, axis=0)\n",
      "/home/mache/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2401: RuntimeWarning: invalid value encountered in less\n",
      "  r_minus = np.sum((d < 0) * r, axis=0)\n"
     ]
    }
   ],
   "source": [
    "# Wilcoxon Signed Rank Test for each country between years.\n",
    "print('Wilcoxon Signed-Rank Test')\n",
    "for cnt in cntrys:\n",
    "    # Isolating data from each country\n",
    "    ess_temp = ess_raw[ess_raw.cntry ==cnt]\n",
    "    # Printing Shapiro test results\n",
    "    print(cnt, wilcoxon(ess_temp[ess_temp.year == 6].ppltrst, \n",
    "                        y=ess_temp[ess_temp.year == 7].ppltrst))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the Wilcoxon signed-rank test, Spain and Sweden were the two countries whose trust in people changed from 2012 to 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2. Did people become happier from 2012 to 2014? Compute results for each country in the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t\t\tDistribution of happiness\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfa6bd1048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro Tests for Normality\n",
      "CH 6 (0.8773602843284607, 1.4611401593361174e-23)\n",
      "CH 7 (0.8950787782669067, 4.273768685964887e-22)\n",
      "\n",
      "CZ 6 (0.9462587833404541, 5.939277796787254e-14)\n",
      "CZ 7 (0.9448645710945129, 3.1643033327052525e-14)\n",
      "\n",
      "DE 6 (0.9160983562469482, 0.22210942208766937)\n",
      "DE 7 (0.9213663339614868, 0.23006466031074524)\n",
      "\n",
      "ES 6 (0.9052610397338867, 3.4151276945678624e-26)\n",
      "ES 7 (0.9136462211608887, 6.898695605418019e-25)\n",
      "\n",
      "NO 6 (0.8498518466949463, 2.291937245544563e-25)\n",
      "NO 7 (0.8795287609100342, 4.6808705797415216e-23)\n",
      "\n",
      "SE 6 (0.9025380611419678, 5.918160728712866e-23)\n",
      "SE 7 (0.912103533744812, 5.600742285549146e-22)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plotting histograms of happiness for each country for each year\n",
    "g = sns.FacetGrid(ess_raw, row='year', col='cntry', dropna=True, sharey=False)\n",
    "g = g.map(plt.hist, 'happy', alpha=0.8, color='purple')\n",
    "print('\\t\\t\\t\\t\\tDistribution of happiness')\n",
    "plt.show()\n",
    "\n",
    "# Testing each country and each year to determine if happiness is normally distributed through Shapiro test\n",
    "# Creating lists of countries and years\n",
    "cntrys = list(ess_raw.cntry.unique())\n",
    "years = [6,7]\n",
    "print('Shapiro Tests for Normality')\n",
    "for cnt in cntrys:\n",
    "    \n",
    "    # Isolating data from each country\n",
    "    ess_temp = ess_raw[ess_raw.cntry ==cnt].dropna()\n",
    "    for year in years:\n",
    "        \n",
    "        # Printing Shapiro test results\n",
    "        print(cnt, year, shapiro(ess_temp[ess_temp.year ==year].happy))\n",
    "    print()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the histograms and the Shapiro tests, all values are non-normally distributed. As we are comparing the same group of people, and the distribution is not normally distributed, I will use the Wilcoxon Signed-Rank test to find if there are difference between the distribution over both years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wilcoxon Signed-Rank Test\n",
      "CH WilcoxonResult(statistic=92816.5, pvalue=0.82086881614253426)\n",
      "\n",
      "CZ WilcoxonResult(statistic=73213.0, pvalue=0.01064243454053068)\n",
      "\n",
      "DE WilcoxonResult(statistic=23.5, pvalue=0.393351316918508)\n",
      "\n",
      "ES WilcoxonResult(statistic=231958.5, pvalue=0.049238680843060129)\n",
      "\n",
      "NO WilcoxonResult(statistic=67571.5, pvalue=2.7264367804254175e-06)\n",
      "\n",
      "SE WilcoxonResult(statistic=125481.0, pvalue=0.51364034410050219)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mache/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2400: RuntimeWarning: invalid value encountered in greater\n",
      "  r_plus = np.sum((d > 0) * r, axis=0)\n",
      "/home/mache/anaconda3/lib/python3.6/site-packages/scipy/stats/morestats.py:2401: RuntimeWarning: invalid value encountered in less\n",
      "  r_minus = np.sum((d < 0) * r, axis=0)\n"
     ]
    }
   ],
   "source": [
    "# Wilcoxon Signed Rank Test for each country between years.\n",
    "print('Wilcoxon Signed-Rank Test')\n",
    "for cnt in cntrys:\n",
    "    \n",
    "    # Isolating data from each country\n",
    "    ess_temp = ess_raw[ess_raw.cntry ==cnt]\n",
    "    \n",
    "    # Printing Shapiro test results\n",
    "    print(cnt, wilcoxon(ess_temp[ess_temp.year == 6].happy, y=ess_temp[ess_temp.year == 7].happy))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watching the Wilcoxon Signed-Rank tests, Czech Republic, Norway, and Spain suffered a happiness change between 2012 and 2014 as their p-values are below the threshhold rejecting the null hypothesis that the distributions are the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3. Who reported watching more TV in 2012, men or women?\n",
    "\n",
    "In this case we are just comparing men from women, so these values are independent from one another. We can use t-test or Wilcoxon test to describe the distribution differences. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ess_2012 = ess_raw[ess_raw.year == 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\tDistribution of total TV Watching in 2012 for men and women\n"
     ]
    },
    {
     "data": {
      "image/png": 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qrQBJ/h64rarOTHIKcFn3+n8EHq2qv5vNtyLNjH3UE57i66/PMliU8gjgNODGqvrhkHkvBy4HqKrrgGOTPGvlypSaZh81zIDqqar6EXAD8JsM3gFecZCpvfxTC9JKsI/aZkD1yyPAMxY8voLBAo+vYLAQ6LA5NwJvhMEVR8CD3d/WOXCetFbYRz3hRRI9k+SfgBMYnJp4J/AdYGdVndc9fwyDJjuc//9w91Lg+XQf7lbVniS/wOBvwPwUP9zVGmMf9YMBJUlqkqf4JElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElN+l/sIxoex5NO0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf6ae18f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Males (1.0): (0.9434293508529663, 5.994494507539187e-28)\n",
      "Females (2.0): (0.9390434622764587, 8.488309331265169e-29)\n"
     ]
    }
   ],
   "source": [
    "g = sns.FacetGrid(ess_2012, col='gndr', dropna=True, sharey=True)\n",
    "g = g.map(plt.hist, 'tvtot', alpha=0.8, color='red', bins=8)\n",
    "\n",
    "print('\\t\\tDistribution of total TV Watching in 2012 for men and women')\n",
    "plt.show()\n",
    "\n",
    "print('Males (1.0):', shapiro(ess_2012[ess_2012.gndr ==1].tvtot.dropna()))\n",
    "print('Females (2.0):', shapiro(ess_2012[ess_2012.gndr ==2].tvtot.dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watching the histograms and the Shapiro-Wilks test results we can affirm that the distribution of men and women watching tv time is not normally distributed, therefore we will use the Wilcoxon Signed-Rank test to determine if the distributions are different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we determine what's the count for each gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2151"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess_2012[ess_2012.gndr == 1].tvtot.dropna().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2140"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess_2012[ess_2012.gndr == 2].tvtot.dropna().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wilcoxon Test: WilcoxonResult(statistic=782530.0, pvalue=0.50335077823729635)\n"
     ]
    }
   ],
   "source": [
    "# Printing Wilcoxon Test for TV watching across genders\n",
    "print('Wilcoxon Test:', wilcoxon(ess_2012[ess_2012.gndr == 1].tvtot.dropna()[:2140], \n",
    "                                 y=ess_2012[ess_2012.gndr == 2].tvtot.dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the p-value is 0.5, above 0.05, we can affirm that there is not significant difference between the tv habits of men and women in this study."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 4. Who was more likely to believe people were fair in 2012, people living with a partner or people living alone?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\tDistribution of Fairness in 2012 for People who Live with and without a Partner\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf6a9dc780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lives with Partner (1.0): (0.94523686170578, 2.068099309311279e-30)\n",
      "Lives Alone (2.0): (0.9332861304283142, 2.8958550733254737e-26)\n"
     ]
    }
   ],
   "source": [
    "g = sns.FacetGrid(ess_2012, col='partner', dropna=True, sharey=True)\n",
    "g = g.map(plt.hist, 'pplfair', alpha=0.8, color='green', bins=8, normed=True)\n",
    "\n",
    "print('\\t\\tDistribution of Fairness in 2012 for People who Live with and without a Partner')\n",
    "plt.show()\n",
    "\n",
    "print('Lives with Partner (1.0):', shapiro(ess_2012[ess_2012.partner ==1].tvtot.dropna()))\n",
    "print('Lives Alone (2.0):', shapiro(ess_2012[ess_2012.partner ==2].tvtot.dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watching the histograms and the Shapiro-Wilks test results we can affirm that the distribution the values is not normal, therefore we will use the Wilcoxon Signed-Rank test to determine if the distributions are different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now find the count for each case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2663"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess_2012[ess_2012.partner == 1].pplfair.dropna().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1608"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ess_2012[ess_2012.partner == 2].pplfair.dropna().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wilcoxon Test: WilcoxonResult(statistic=426739.0, pvalue=0.0043401582762541717)\n"
     ]
    }
   ],
   "source": [
    "# Printing Wilcoxon Test for fairness across living with and without partners.  \n",
    "# Using only first 1608 entries of living with partners to even counts.\n",
    "print('Wilcoxon Test:', wilcoxon(ess_2012[ess_2012.partner == 1].pplfair.dropna()[:1608], \n",
    "                                 y=ess_2012[ess_2012.partner == 2].pplfair.dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the Wilcoxon test, there is a significant difference between the two distributions. Therefore we will calculate the median value to determine which has a higher median value of belief in fariness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Fairness for People living with Partners: 7.0\n",
      "Median Fairness for People living Alone: 6.0\n"
     ]
    }
   ],
   "source": [
    "print('Median Fairness for People living with Partners:', ess_2012[ess_2012.partner == 1].pplfair.median())\n",
    "print('Median Fairness for People living Alone:', ess_2012[ess_2012.partner == 2].pplfair.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, people who lived with partners had a greater belief that people were fair, compared to those living alone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 5. Pick three or four of the countries in the sample and compare how often people met socially in 2014. Are there differences, and if so, which countries stand out?\n",
    "I will be using Czech Republic, Spain, Norway, and Sweden as our countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Pulling out the countries in 2014\n",
    "country_list = ['CZ','ES','NO','SE']\n",
    "ess_c = ess_raw[(ess_raw.cntry.isin(country_list)) & (ess_raw.year == 7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\tDistribution of Social Meetings from 2014 for Four European Countries\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf72fd8978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro Tests for Normality\n",
      "CZ (0.9451602101325989, 7.663770300607355e-15)\n",
      "\n",
      "ES (0.8880351781845093, 9.848293928138278e-29)\n",
      "\n",
      "NO (0.8881818056106567, 1.422473605641294e-22)\n",
      "\n",
      "SE (0.8798282146453857, 6.682967662559975e-26)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plotting histograms of happiness for each country for each year\n",
    "g = sns.FacetGrid(ess_c, col='cntry', dropna=True, sharey=False)\n",
    "g = g.map(plt.hist, 'sclmeet', alpha=0.8, color='orange', bins=7)\n",
    "\n",
    "print('\\t\\tDistribution of Social Meetings from 2014 for Four European Countries')\n",
    "plt.show()\n",
    "\n",
    "# Testing each country and each year to determine if happiness is normally distributed through Shapiro test\n",
    "# Creating lists of countries and years\n",
    "cntrys = list(ess_c.cntry.unique())\n",
    "print('Shapiro Tests for Normality')\n",
    "for cnt in cntrys:\n",
    "    # Isolating data from each country\n",
    "    print(cnt, shapiro(ess_c[ess_c.cntry ==cnt].sclmeet.dropna()))\n",
    "    # Printing Shapiro test results\n",
    "    print()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watching the histograms and the Shapiro-Wilks test results we can affirm that the distribution of the values are not normal. I will choose the Kruskal-Wallis, a non-parametric version of ANOVA test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=199.91509305978323, pvalue=4.4005635362919113e-43)\n"
     ]
    }
   ],
   "source": [
    "# Printing Kruskal test statistic\n",
    "print(kruskal(ess_c[ess_c.cntry == 'CZ'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'ES'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'NO'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'SE'].sclmeet.dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the Kruskal test, we can see that the distributions of social meetings are different from one another.  We need to run t-tests for all distributions against each other to determine which ones are the different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CZ vs ES: Ttest_indResult(statistic=-11.400026538179093, pvalue=3.7676844407353374e-29)\n",
      "CZ vs NO: Ttest_indResult(statistic=-11.269186128577815, pvalue=3.0334022155191707e-28)\n",
      "CZ vs SE: Ttest_indResult(statistic=-13.471687937687198, pvalue=3.2525939341481719e-39)\n",
      "ES vs NO: Ttest_indResult(statistic=-0.63291639587000703, pvalue=0.5268628350318294)\n",
      "ES vs SE: Ttest_indResult(statistic=-2.6495472685800778, pvalue=0.0081199227965129493)\n",
      "NO vs SE: Ttest_indResult(statistic=-1.8305856428076237, pvalue=0.067344124325977833)\n"
     ]
    }
   ],
   "source": [
    "# Printing all combinations of t-tests\n",
    "print('CZ vs ES:', ttest_ind(ess_c[ess_c.cntry == 'CZ'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'ES'].sclmeet.dropna()))\n",
    "print('CZ vs NO:', ttest_ind(ess_c[ess_c.cntry == 'CZ'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'NO'].sclmeet.dropna()))\n",
    "print('CZ vs SE:', ttest_ind(ess_c[ess_c.cntry == 'CZ'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'SE'].sclmeet.dropna()))\n",
    "print('ES vs NO:', ttest_ind(ess_c[ess_c.cntry == 'ES'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'NO'].sclmeet.dropna()))\n",
    "print('ES vs SE:', ttest_ind(ess_c[ess_c.cntry == 'ES'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'SE'].sclmeet.dropna()))\n",
    "print('NO vs SE:', ttest_ind(ess_c[ess_c.cntry == 'NO'].sclmeet.dropna(),\n",
    "             ess_c[ess_c.cntry == 'SE'].sclmeet.dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a result from the t-tests we can affirm that:\n",
    "- Spain and Norway are not significantly different\n",
    "- Norway and Sweden are slightly different\n",
    "- Czech Republic is significantly different from Spain, Norway, and Sweden\n",
    "- Spain and Sweden are significantly different from one another\n",
    "\n",
    "Therefore, Czech Republic is the standout country, with fewer social meetings than most other countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 6. Pick three or four of the countries in the sample and compare how often people took part in social activities, relative to others their age, in 2014. Are there differences, and if so, which countries stand out?\n",
    "\n",
    "I will be using Czech Republic, Spain, Norway, and Sweden as our countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\tDistribution of Social Activities from 2014 for Four European Countries\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf6a93e898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro Tests for Normality\n",
      "CZ (0.8517600893974304, 3.2631608025549396e-24)\n",
      "\n",
      "ES (0.892457902431488, 5.746904176126056e-28)\n",
      "\n",
      "NO (0.8678725957870483, 2.398206966061988e-24)\n",
      "\n",
      "SE (0.8822488784790039, 1.283228571387991e-25)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Plotting histograms of happiness for each country for each year\n",
    "g = sns.FacetGrid(ess_c, col='cntry', dropna=True, sharey=False)\n",
    "g = g.map(plt.hist, 'sclact', alpha=0.8, color='teal', bins=5)\n",
    "print('\\t\\tDistribution of Social Activities from 2014 for Four European Countries')\n",
    "plt.show()\n",
    "\n",
    "# Testing each country and each year to determine if happiness is normally distributed through Shapiro test\n",
    "# Creating lists of countries and years\n",
    "cntrys = list(ess_c.cntry.unique())\n",
    "print('Shapiro Tests for Normality')\n",
    "for cnt in cntrys:\n",
    "    # Printing Shapiro test results\n",
    "    print(cnt, shapiro(ess_c[ess_c.cntry ==cnt].sclact.dropna()))\n",
    "    print()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watching the histograms and the Shapiro-Wilks test results we can affirm that the distribution of the values are not normal. I will choose the Kruskal-Wallis, a non-parametric version of ANOVA test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KruskalResult(statistic=53.809701709377876, pvalue=1.2319673584021775e-11)\n"
     ]
    }
   ],
   "source": [
    "# Printing Kruskal test statistic\n",
    "print(kruskal(ess_c[ess_c.cntry == 'CZ'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'ES'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'NO'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'SE'].sclact.dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the Kruskal test, we can see that the distributions of social activities are different from one another.  We need to run t-tests for all distributions against each other to determine which ones are the different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CZ vs ES: Ttest_indResult(statistic=1.9231457887987455, pvalue=0.054616497855173432)\n",
      "CZ vs NO: Ttest_indResult(statistic=-3.4467356116942098, pvalue=0.00058446345406456233)\n",
      "CZ vs SE: Ttest_indResult(statistic=-4.0018841917465595, pvalue=6.5802143027997247e-05)\n",
      "ES vs NO: Ttest_indResult(statistic=-5.6711491296635321, pvalue=1.6345881270312057e-08)\n",
      "ES vs SE: Ttest_indResult(statistic=-6.5002573458665971, pvalue=1.0000807299873861e-10)\n",
      "NO vs SE: Ttest_indResult(statistic=-0.48232162702341058, pvalue=0.62964212720174506)\n"
     ]
    }
   ],
   "source": [
    "# Printing all combinations of t-tests\n",
    "print('CZ vs ES:', ttest_ind(ess_c[ess_c.cntry == 'CZ'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'ES'].sclact.dropna()))\n",
    "print('CZ vs NO:', ttest_ind(ess_c[ess_c.cntry == 'CZ'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'NO'].sclact.dropna()))\n",
    "print('CZ vs SE:', ttest_ind(ess_c[ess_c.cntry == 'CZ'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'SE'].sclact.dropna()))\n",
    "print('ES vs NO:', ttest_ind(ess_c[ess_c.cntry == 'ES'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'NO'].sclact.dropna()))\n",
    "print('ES vs SE:', ttest_ind(ess_c[ess_c.cntry == 'ES'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'SE'].sclact.dropna()))\n",
    "print('NO vs SE:', ttest_ind(ess_c[ess_c.cntry == 'NO'].sclact.dropna(),\n",
    "             ess_c[ess_c.cntry == 'SE'].sclact.dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a result from the t-tests we can affirm that:\n",
    "\n",
    "- Norway and Sweden are not significantly different\n",
    "- Czech Republic and Spain are slightly different\n",
    "- Czech Republic is significantly different from Sweden and Norway,\n",
    "- Spain is significantly different from Sweden, and Norway.\n",
    "\n",
    "Therefore, Czech Republic and Spain standout as for social activities, with both having less social activities than the other countries. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can validate this by calculating the mean of social activities for each country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ES_act = ess_raw['sclact'][(ess_raw['cntry'] == 'ES') & (ess_raw['year'] == 7)].dropna()\n",
    "NO_act = ess_raw['sclact'][(ess_raw['cntry'] == 'NO') & (ess_raw['year'] == 7)].dropna()\n",
    "SE_act = ess_raw['sclact'][(ess_raw['cntry'] == 'SE') & (ess_raw['year'] == 7)].dropna()\n",
    "CZ_act = ess_raw['sclact'][(ess_raw['cntry'] == 'CZ') & (ess_raw['year'] == 7)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean social activities for Spain: 2.6168776371308016\n",
      "Mean social activities for Czech Republic: 2.703076923076923\n",
      "Mean social activities for Norway: 2.859097127222982\n",
      "Mean social activities for Sweden: 2.879424778761062\n"
     ]
    }
   ],
   "source": [
    "print('Mean social activities for Spain:', ES_act.mean())\n",
    "print('Mean social activities for Czech Republic:', CZ_act.mean())\n",
    "print('Mean social activities for Norway:', NO_act.mean())\n",
    "print('Mean social activities for Sweden:', SE_act.mean())"
   ]
  }
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