550_Project2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><center>EDA ON SUICIDE RATES OVERVIEW FROM 1996 TO 2016 </center></h1>\n",
    "\n",
    "\n",
    "Compares socio-economic information with suicide rates by year and country\n",
    "\n",
    "Data Source : https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016\n",
    "\n",
    "**Authors:** SOWMYA CHENTHI KUMAR , POOJITHA GOWTHAMAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * *\n",
    "### 1) DESCRIPTION OF THE DATASET\n",
    "\n",
    "The dataset gives suicide rates overview from 1986 to 2016. We are are using a subset of the dataframe from analysis ther statistics from 1996 to 2016. The dataset has a total of 12 columns\n",
    "\n",
    "* **Country** : Lists the countries where the suicide rates have been recorded\n",
    "\n",
    "* **Year** : Each record of suicide recorded in the year from 1986 ti 2016.\n",
    "\n",
    "* **Sex** : Denotes the gender of the person - Male or female\n",
    "\n",
    "* **AGE** : The age is binned into 6 levels : '5-14 years', '15-24 years', '25-34 years', '35-54 years', '55-74 years', '75+years\n",
    "\n",
    "* **Suicide_no** : Number of suicides recorded for a particular category of age bin, sex and year\n",
    "\n",
    "* **Population** : Population of the country at the time of record taken\n",
    "\n",
    "* **suicides/100k pop** : Proportion of suicides per 100,000 of the population\n",
    "\n",
    "* **country- year** : Concated information of country and year\n",
    "\n",
    "* **HDI for year**: Human Development Index for that particular year. A composite index measuring average achievement in three basic dimensions of human development—a long and healthy life, knowledge and a decent standard of living\n",
    "\n",
    "* **gdp_for_year** : Gross Domestic Product for the year recorded \n",
    "\n",
    "* **gdp_per_capita**: Gross Domestic Product per capita of the population\n",
    "\n",
    "* **generation** : Recorded of the generations - 6 levles - G.I. Generation', 'Generation X', 'Boomers', 'Silent', 'Millenials', 'Generation Z\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### 2) LOAD THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "suicide = pd.read_csv(\"master.csv\", parse_dates = ['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * *\n",
    "### 3) EXPLORE THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k pop</th>\n",
       "      <th>country-year</th>\n",
       "      <th>HDI for year</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>21</td>\n",
       "      <td>312900</td>\n",
       "      <td>6.71</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>16</td>\n",
       "      <td>308000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>14</td>\n",
       "      <td>289700</td>\n",
       "      <td>4.83</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>1</td>\n",
       "      <td>21800</td>\n",
       "      <td>4.59</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>9</td>\n",
       "      <td>274300</td>\n",
       "      <td>3.28</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country       year     sex          age  suicides_no  population  \\\n",
       "0  Albania 1987-01-01    male  15-24 years           21      312900   \n",
       "1  Albania 1987-01-01    male  35-54 years           16      308000   \n",
       "2  Albania 1987-01-01  female  15-24 years           14      289700   \n",
       "3  Albania 1987-01-01    male    75+ years            1       21800   \n",
       "4  Albania 1987-01-01    male  25-34 years            9      274300   \n",
       "\n",
       "   suicides/100k pop country-year  HDI for year  gdp_for_year ($)   \\\n",
       "0               6.71  Albania1987           NaN      2,156,624,900   \n",
       "1               5.19  Albania1987           NaN      2,156,624,900   \n",
       "2               4.83  Albania1987           NaN      2,156,624,900   \n",
       "3               4.59  Albania1987           NaN      2,156,624,900   \n",
       "4               3.28  Albania1987           NaN      2,156,624,900   \n",
       "\n",
       "   gdp_per_capita ($)       generation  \n",
       "0                 796     Generation X  \n",
       "1                 796           Silent  \n",
       "2                 796     Generation X  \n",
       "3                 796  G.I. Generation  \n",
       "4                 796          Boomers  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27820 entries, 0 to 27819\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   country             27820 non-null  object        \n",
      " 1   year                27820 non-null  datetime64[ns]\n",
      " 2   sex                 27820 non-null  object        \n",
      " 3   age                 27820 non-null  object        \n",
      " 4   suicides_no         27820 non-null  int64         \n",
      " 5   population          27820 non-null  int64         \n",
      " 6   suicides/100k pop   27820 non-null  float64       \n",
      " 7   country-year        27820 non-null  object        \n",
      " 8   HDI for year        8364 non-null   float64       \n",
      " 9    gdp_for_year ($)   27820 non-null  object        \n",
      " 10  gdp_per_capita ($)  27820 non-null  int64         \n",
      " 11  generation          27820 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(3), object(6)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "suicide.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                   0\n",
       "year                      0\n",
       "sex                       0\n",
       "age                       0\n",
       "suicides_no               0\n",
       "population                0\n",
       "suicides/100k pop         0\n",
       "country-year              0\n",
       "HDI for year          19456\n",
       " gdp_for_year ($)         0\n",
       "gdp_per_capita ($)        0\n",
       "generation                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                       object\n",
       "year                  datetime64[ns]\n",
       "sex                           object\n",
       "age                           object\n",
       "suicides_no                    int64\n",
       "population                     int64\n",
       "suicides/100k pop            float64\n",
       "country-year                  object\n",
       "HDI for year                 float64\n",
       " gdp_for_year ($)             object\n",
       "gdp_per_capita ($)             int64\n",
       "generation                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * *\n",
    "### 4) INITIAL THOUGHTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* At the first look at the dataset was can understand that the data has values about the number of suicides with respect to a person's age,sex, generation and country. \n",
    "\n",
    "* There is a population and GDP providing us opertunities to explore more among them and suicides\n",
    "\n",
    "* The data is provided in such a way that we can create an understanding with the historical happenings(from year and generation) and the suicide rates\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * *\n",
    "### 5) DATA WRANGLING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are subsetting the data to analyze the suicide statistics from 1996 and 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "suicide_subset = suicide.query('year >= 1996')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We intent to drop the HDI Column for Visualization purposes - the values need to be imputed and exploded from UN data for each row. This statistic comes handy when we perform predictive modeling, for now to keep visualizations simple, we are temporarily dropping them as they have more than 70% missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "suicide1 = suicide_subset.drop('HDI for year', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Dropping country-year as this is redudant information \n",
    "suicide2 = suicide1.drop('country-year', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "suicide_data = suicide2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19960, 10)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wrangling column names to be more intuitive\n",
    "suicide_data.columns = ['country', 'year', 'sex', 'age', 'suicides_no', 'population',\n",
    "       'suicides_per_100k_pop','gdp_for_year','gdp_per_capita', 'generation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides_per_100k_pop</th>\n",
       "      <th>gdp_for_year</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>2</td>\n",
       "      <td>25400</td>\n",
       "      <td>7.87</td>\n",
       "      <td>3,314,898,292</td>\n",
       "      <td>1127</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>17</td>\n",
       "      <td>243600</td>\n",
       "      <td>6.98</td>\n",
       "      <td>3,314,898,292</td>\n",
       "      <td>1127</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>14</td>\n",
       "      <td>235300</td>\n",
       "      <td>5.95</td>\n",
       "      <td>3,314,898,292</td>\n",
       "      <td>1127</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>16</td>\n",
       "      <td>287700</td>\n",
       "      <td>5.56</td>\n",
       "      <td>3,314,898,292</td>\n",
       "      <td>1127</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>2</td>\n",
       "      <td>41200</td>\n",
       "      <td>4.85</td>\n",
       "      <td>3,314,898,292</td>\n",
       "      <td>1127</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country       year     sex          age  suicides_no  population  \\\n",
       "84  Albania 1996-01-01    male    75+ years            2       25400   \n",
       "85  Albania 1996-01-01    male  15-24 years           17      243600   \n",
       "86  Albania 1996-01-01    male  25-34 years           14      235300   \n",
       "87  Albania 1996-01-01  female  15-24 years           16      287700   \n",
       "88  Albania 1996-01-01  female    75+ years            2       41200   \n",
       "\n",
       "    suicides_per_100k_pop   gdp_for_year  gdp_per_capita       generation  \n",
       "84                   7.87  3,314,898,292            1127  G.I. Generation  \n",
       "85                   6.98  3,314,898,292            1127     Generation X  \n",
       "86                   5.95  3,314,898,292            1127     Generation X  \n",
       "87                   5.56  3,314,898,292            1127     Generation X  \n",
       "88                   4.85  3,314,898,292            1127  G.I. Generation  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* CATEGORICAL VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['75+ years', '15-24 years', '25-34 years', '35-54 years',\n",
       "       '55-74 years', '5-14 years'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.sex.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1996-01-01T00:00:00.000000000', '1997-01-01T00:00:00.000000000',\n",
       "       '1998-01-01T00:00:00.000000000', '1999-01-01T00:00:00.000000000',\n",
       "       '2000-01-01T00:00:00.000000000', '2001-01-01T00:00:00.000000000',\n",
       "       '2002-01-01T00:00:00.000000000', '2003-01-01T00:00:00.000000000',\n",
       "       '2004-01-01T00:00:00.000000000', '2005-01-01T00:00:00.000000000',\n",
       "       '2006-01-01T00:00:00.000000000', '2007-01-01T00:00:00.000000000',\n",
       "       '2008-01-01T00:00:00.000000000', '2009-01-01T00:00:00.000000000',\n",
       "       '2010-01-01T00:00:00.000000000', '2012-01-01T00:00:00.000000000',\n",
       "       '2013-01-01T00:00:00.000000000', '2014-01-01T00:00:00.000000000',\n",
       "       '2015-01-01T00:00:00.000000000', '2011-01-01T00:00:00.000000000',\n",
       "       '2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['G.I. Generation', 'Generation X', 'Boomers', 'Silent',\n",
       "       'Millenials', 'Generation Z'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.generation.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* NUMERICAL COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides_per_100k_pop</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19960.000000</td>\n",
       "      <td>1.996000e+04</td>\n",
       "      <td>19960.000000</td>\n",
       "      <td>19960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>240.658768</td>\n",
       "      <td>1.839285e+06</td>\n",
       "      <td>12.646991</td>\n",
       "      <td>19588.653106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>906.871742</td>\n",
       "      <td>3.972111e+06</td>\n",
       "      <td>18.826476</td>\n",
       "      <td>20747.693028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.780000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>291.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.841575e+04</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>4311.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>4.361375e+05</td>\n",
       "      <td>5.740000</td>\n",
       "      <td>11635.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>125.000000</td>\n",
       "      <td>1.477856e+06</td>\n",
       "      <td>16.210000</td>\n",
       "      <td>29247.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21262.000000</td>\n",
       "      <td>4.380521e+07</td>\n",
       "      <td>204.920000</td>\n",
       "      <td>126352.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        suicides_no    population  suicides_per_100k_pop  gdp_per_capita\n",
       "count  19960.000000  1.996000e+04           19960.000000    19960.000000\n",
       "mean     240.658768  1.839285e+06              12.646991    19588.653106\n",
       "std      906.871742  3.972111e+06              18.826476    20747.693028\n",
       "min        0.000000  2.780000e+02               0.000000      291.000000\n",
       "25%        3.000000  9.841575e+04               0.930000     4311.000000\n",
       "50%       24.000000  4.361375e+05               5.740000    11635.000000\n",
       "75%      125.000000  1.477856e+06              16.210000    29247.000000\n",
       "max    21262.000000  4.380521e+07             204.920000   126352.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                  0\n",
       "year                     0\n",
       "sex                      0\n",
       "age                      0\n",
       "suicides_no              0\n",
       "population               0\n",
       "suicides_per_100k_pop    0\n",
       "gdp_for_year             0\n",
       "gdp_per_capita           0\n",
       "generation               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) DATA ANALYSIS & VISUALIZATIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportion of suicides among age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>proportion_of_suicides</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>30365</td>\n",
       "      <td>285894005</td>\n",
       "      <td>0.010621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>42593</td>\n",
       "      <td>278429626</td>\n",
       "      <td>0.015298</td>\n",
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      "text/plain": [
       "        year          age  suicides_no  population  proportion_of_suicides\n",
       "0 1996-01-01  15-24 years        30365   285894005                0.010621\n",
       "1 1996-01-01  25-34 years        42593   278429626                0.015298"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_age = suicide_data.groupby(['year','age'])[['suicides_no', 'population']].sum().reset_index()\n",
    "suicide_age['proportion_of_suicides'] = (suicide_age.suicides_no)*100/suicide_age.population\n",
    "suicide_age.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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98693, \"population\": 531132194, \"proportion_of_suicides\": 0.018581626403915558}, {\"year\": \"2003-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1873, \"population\": 299034887, \"proportion_of_suicides\": 0.0006263483230302824}, {\"year\": \"2003-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 60929, \"population\": 304473586, \"proportion_of_suicides\": 0.0200112596959396}, {\"year\": \"2003-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 22973, \"population\": 95024117, \"proportion_of_suicides\": 0.024175967875607832}, {\"year\": \"2004-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 28702, \"population\": 290415721, \"proportion_of_suicides\": 0.009883073788557059}, {\"year\": \"2004-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 38221, \"population\": 279976620, \"proportion_of_suicides\": 0.01365149704286022}, {\"year\": \"2004-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 92531, \"population\": 509520667, \"proportion_of_suicides\": 0.018160401725176734}, {\"year\": \"2004-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1839, \"population\": 270982469, \"proportion_of_suicides\": 0.0006786416873336555}, {\"year\": \"2004-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 57103, \"population\": 298895614, \"proportion_of_suicides\": 0.019104663074781685}, {\"year\": \"2004-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 22465, \"population\": 95455522, \"proportion_of_suicides\": 0.023534521135403774}, {\"year\": \"2005-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 27624, \"population\": 286690446, \"proportion_of_suicides\": 0.009635479795514358}, {\"year\": \"2005-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 37442, \"population\": 278296962, \"proportion_of_suicides\": 0.013453973672914187}, {\"year\": \"2005-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 88498, \"population\": 507445700, \"proportion_of_suicides\": 0.017439895539562163}, {\"year\": \"2005-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1774, \"population\": 264397401, \"proportion_of_suicides\": 0.0006709596967634338}, {\"year\": \"2005-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 56581, \"population\": 301001533, \"proportion_of_suicides\": 0.01879757868209927}, {\"year\": \"2005-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 22456, \"population\": 97077603, \"proportion_of_suicides\": 0.023132009141181617}, {\"year\": \"2006-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 27937, \"population\": 305127668, \"proportion_of_suicides\": 0.009155839646767135}, {\"year\": \"2006-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 37221, \"population\": 295325866, \"proportion_of_suicides\": 0.0126033660729196}, {\"year\": \"2006-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 87032, \"population\": 536511821, \"proportion_of_suicides\": 0.016221823377121823}, {\"year\": \"2006-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1755, \"population\": 283329957, \"proportion_of_suicides\": 0.0006194191459959174}, {\"year\": \"2006-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 56613, \"population\": 318224726, \"proportion_of_suicides\": 0.017790258070644077}, {\"year\": \"2006-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 22803, \"population\": 102388799, \"proportion_of_suicides\": 0.022270990794608304}, {\"year\": \"2007-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 27670, \"population\": 305436926, \"proportion_of_suicides\": 0.009059153509160187}, {\"year\": \"2007-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 37936, \"population\": 298222521, \"proportion_of_suicides\": 0.012720702605823656}, {\"year\": \"2007-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 85749, \"population\": 542880362, \"proportion_of_suicides\": 0.015795192827402365}, {\"year\": \"2007-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1671, \"population\": 281095405, \"proportion_of_suicides\": 0.0005944600908719942}, {\"year\": \"2007-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 56857, \"population\": 326135725, \"proportion_of_suicides\": 0.01743353936463109}, {\"year\": \"2007-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 23525, \"population\": 105793414, \"proportion_of_suicides\": 0.022236733942625198}, {\"year\": \"2008-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 28145, \"population\": 302200881, \"proportion_of_suicides\": 0.009313341478974708}, {\"year\": \"2008-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 37756, \"population\": 297532003, \"proportion_of_suicides\": 0.012689727363546838}, {\"year\": \"2008-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 86664, \"population\": 542789284, \"proportion_of_suicides\": 0.01596641690516499}, {\"year\": \"2008-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1640, \"population\": 278903896, \"proportion_of_suicides\": 0.0005880161674041297}, {\"year\": \"2008-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 57687, \"population\": 332062421, \"proportion_of_suicides\": 0.01737233614881101}, {\"year\": \"2008-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 23555, \"population\": 107132366, \"proportion_of_suicides\": 0.02198681955740621}, {\"year\": \"2009-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 28487, \"population\": 321540733, \"proportion_of_suicides\": 0.008859530714573571}, {\"year\": \"2009-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 39389, \"population\": 318594904, \"proportion_of_suicides\": 0.012363349038376333}, {\"year\": \"2009-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 90390, \"population\": 575243831, \"proportion_of_suicides\": 0.015713336698086208}, {\"year\": \"2009-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1835, \"population\": 297254525, \"proportion_of_suicides\": 0.0006173160862732031}, {\"year\": \"2009-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 59902, \"population\": 351397333, \"proportion_of_suicides\": 0.01704679984011148}, {\"year\": \"2009-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 23484, \"population\": 112197040, \"proportion_of_suicides\": 0.0209310334746799}, {\"year\": \"2010-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 27684, \"population\": 321498365, \"proportion_of_suicides\": 0.008610930260873955}, {\"year\": \"2010-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 38317, \"population\": 321277547, \"proportion_of_suicides\": 0.011926448131154338}, {\"year\": \"2010-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 87364, \"population\": 581133742, \"proportion_of_suicides\": 0.015033372472803343}, {\"year\": \"2010-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1760, \"population\": 297317505, \"proportion_of_suicides\": 0.0005919597636876442}, {\"year\": \"2010-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 59743, \"population\": 361189135, \"proportion_of_suicides\": 0.016540641511821778}, {\"year\": \"2010-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 23834, \"population\": 114881035, \"proportion_of_suicides\": 0.0207466793801083}, {\"year\": \"2011-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 27937, \"population\": 317262371, \"proportion_of_suicides\": 0.008805645596086148}, {\"year\": \"2011-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 37936, \"population\": 318111842, \"proportion_of_suicides\": 0.011925365544863935}, {\"year\": \"2011-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 85345, \"population\": 578742563, \"proportion_of_suicides\": 0.014746625780830984}, {\"year\": \"2011-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1879, \"population\": 294238144, \"proportion_of_suicides\": 0.0006385983728880508}, {\"year\": \"2011-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 59178, \"population\": 367532048, \"proportion_of_suicides\": 0.016101453008527843}, {\"year\": \"2011-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 24209, \"population\": 117475364, \"proportion_of_suicides\": 0.020607725037566173}, {\"year\": \"2012-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 26280, \"population\": 295020664, \"proportion_of_suicides\": 0.008907850603983455}, {\"year\": \"2012-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 36625, \"population\": 303401688, \"proportion_of_suicides\": 0.012071455581354577}, {\"year\": \"2012-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 82482, \"population\": 558344819, \"proportion_of_suicides\": 0.014772591630334444}, {\"year\": \"2012-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1752, \"population\": 271680873, \"proportion_of_suicides\": 0.0006448742528886087}, {\"year\": \"2012-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 58443, \"population\": 365281729, \"proportion_of_suicides\": 0.015999431496339638}, {\"year\": \"2012-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 24578, \"population\": 119082315, \"proportion_of_suicides\": 0.02063950469891352}, {\"year\": \"2013-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 24249, \"population\": 288206852, \"proportion_of_suicides\": 0.00841374860858617}, {\"year\": \"2013-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 35068, \"population\": 298709101, \"proportion_of_suicides\": 0.011739849868183293}, {\"year\": \"2013-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 80227, \"population\": 550420191, \"proportion_of_suicides\": 0.01457559175913298}, {\"year\": \"2013-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1806, \"population\": 268175176, \"proportion_of_suicides\": 0.0006734404082203344}, {\"year\": \"2013-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 57696, \"population\": 364931201, \"proportion_of_suicides\": 0.015810103340547197}, {\"year\": \"2013-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 24153, \"population\": 119719189, \"proportion_of_suicides\": 0.02017471067232171}, {\"year\": \"2014-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 24028, \"population\": 287809150, \"proportion_of_suicides\": 0.008348587944476401}, {\"year\": \"2014-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 35381, \"population\": 302734620, \"proportion_of_suicides\": 0.011687133767522196}, {\"year\": \"2014-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 79132, \"population\": 556105537, \"proportion_of_suicides\": 0.014229673098903167}, {\"year\": \"2014-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1882, \"population\": 268239985, \"proportion_of_suicides\": 0.000701610537295549}, {\"year\": \"2014-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 57696, \"population\": 374038522, \"proportion_of_suicides\": 0.015425149177549152}, {\"year\": \"2014-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 24865, \"population\": 123129495, \"proportion_of_suicides\": 0.020194186616293682}, {\"year\": \"2015-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 21841, \"population\": 261674147, \"proportion_of_suicides\": 0.008346640373303672}, {\"year\": \"2015-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 32118, \"population\": 279250191, \"proportion_of_suicides\": 0.011501514067003808}, {\"year\": \"2015-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 71298, \"population\": 515972039, \"proportion_of_suicides\": 0.013818190640365302}, {\"year\": \"2015-01-01T00:00:00\", \"age\": \"5-14 years\", \"suicides_no\": 1681, \"population\": 246636333, \"proportion_of_suicides\": 0.0006815703021338709}, {\"year\": \"2015-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 53588, \"population\": 354566127, \"proportion_of_suicides\": 0.01511368287021958}, {\"year\": \"2015-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 23114, \"population\": 116559095, \"proportion_of_suicides\": 0.01983028437206037}, {\"year\": \"2016-01-01T00:00:00\", \"age\": \"15-24 years\", \"suicides_no\": 1195, \"population\": 20296338, \"proportion_of_suicides\": 0.005887761624781771}, {\"year\": \"2016-01-01T00:00:00\", \"age\": \"25-34 years\", \"suicides_no\": 2070, \"population\": 22641704, \"proportion_of_suicides\": 0.009142421436125125}, {\"year\": \"2016-01-01T00:00:00\", \"age\": \"35-54 years\", \"suicides_no\": 5766, \"population\": 47314975, \"proportion_of_suicides\": 0.01218641666829582}, {\"year\": \"2016-01-01T00:00:00\", \"age\": \"55-74 years\", \"suicides_no\": 4715, \"population\": 32084580, \"proportion_of_suicides\": 0.014695532869683817}, {\"year\": \"2016-01-01T00:00:00\", \"age\": \"75+ years\", \"suicides_no\": 1857, \"population\": 9764299, \"proportion_of_suicides\": 0.019018262345305074}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "prop_of_suicides = alt.Chart(suicide_age, title = 'Proportion of Suicides among age groups').mark_line().encode(\n",
    "    alt.X('year', title='Year'),\n",
    "    alt.Y('proportion_of_suicides', title='Proportion of suicides'),\n",
    "    color = 'age').properties(\n",
    "    width = 500, height = 500)\n",
    "prop_of_suicides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is satisfying to note that the proportion of suicides has fallen steadily over time!\n",
    "We see the suicide rates suddenly drop in 2014 to 2016.\n",
    "\n",
    "There seems to be a bit of an increasing trend in 1997 - 1998 in all age groups but the 5 -14 years. We are lucky to see no suicides in the 5 - 14 years category. After 1998 we observe a general downward trend up until 2015. There is sudden decrease from 2015 to 2016 and no recorded suicides in in the 5 - 14 years category. \n",
    "\n",
    "The most alarming category that needs to looked into is the age group of `35-45 years`. There seems to be a sudden increase from 1998 to 2004. This is a distributing statistics as we lost a significant number of the mid-life age group. This potentially can be attributed to the famines and recessions that the world for turmoiled into. Loosing this age group of the population puts an increasing pressure on the world economy to bridge the gap between the young workforce and driving workforce of the economic statuses in the country.\n",
    "\n",
    "\n",
    "One of the disturbing category of age group that can be looked at is the `75+ years`. There are by far the highest in proportion of suicides. This is bare truth, yet painful. We have a significant portion of the world's elderly population. The reasons can be enormous, old age, health, retirement homes. Whatever be the reason death by suicide after a long hard life is unjust and we have to address this to get some wise experience in the younger population too!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top countries in the highest recorded year of median suicide numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>suicides_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  suicides_no\n",
       "0 1996-01-01         30.0\n",
       "1 1997-01-01         32.0\n",
       "2 1998-01-01         31.5\n",
       "3 1999-01-01         29.0\n",
       "4 2000-01-01         24.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.groupby(['year'])['suicides_no'].median().reset_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>suicides_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year  suicides_no\n",
       "19 2015-01-01         34.0\n",
       "1  1997-01-01         32.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.sort_values(['year'])\n",
    "suicide_median = suicide_data.groupby('year')['suicides_no'].median().reset_index()\n",
    "median_suicides = suicide_median.sort_values('suicides_no', ascending = False)\n",
    "median_suicides.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Year with highest number of median suicides recorded is 2015. Lets analyze this on a country level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_highest_suicide = suicide_data[suicide_data['year'] == '2015-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_countries = year_highest_suicide.nlargest(200, 'suicides_no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
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       "      loadScript(\"vega\")\n",
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\"female\", \"age\": \"15-24 years\", \"suicides_no\": 256, \"population\": 3211732, \"suicides_per_100k_pop\": 7.97, \"gdp_for_year\": \"1,382,764,027,114\", \"gdp_per_capita\": 28410, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"5-14 years\", \"suicides_no\": 255, \"population\": 21273987, \"suicides_per_100k_pop\": 1.2, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Generation Z\"}, {\"country\": \"Poland\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"55-74 years\", \"suicides_no\": 251, \"population\": 4778252, \"suicides_per_100k_pop\": 5.25, \"gdp_for_year\": \"477,355,617,456\", \"gdp_per_capita\": 13228, \"generation\": \"Boomers\"}, {\"country\": \"Thailand\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"55-74 years\", \"suicides_no\": 248, \"population\": 5795996, \"suicides_per_100k_pop\": 4.28, 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4.39, \"gdp_for_year\": \"859,796,872,794\", \"gdp_per_capita\": 11961, \"generation\": \"Boomers\"}, {\"country\": \"Belgium\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"35-54 years\", \"suicides_no\": 221, \"population\": 1534727, \"suicides_per_100k_pop\": 14.4, \"gdp_for_year\": \"455,039,563,133\", \"gdp_per_capita\": 42830, \"generation\": \"Generation X\"}, {\"country\": \"Hungary\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"75+ years\", \"suicides_no\": 221, \"population\": 241730, \"suicides_per_100k_pop\": 91.42, \"gdp_for_year\": \"122,879,042,002\", \"gdp_per_capita\": 13087, \"generation\": \"Silent\"}, {\"country\": \"Netherlands\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"55-74 years\", \"suicides_no\": 220, \"population\": 1998082, \"suicides_per_100k_pop\": 11.01, \"gdp_for_year\": \"757,999,453,314\", \"gdp_per_capita\": 47206, \"generation\": \"Boomers\"}, {\"country\": \"Serbia\", \"year\": 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8.97, \"gdp_for_year\": \"757,999,453,314\", \"gdp_per_capita\": 47206, \"generation\": \"Generation X\"}, {\"country\": \"Croatia\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"55-74 years\", \"suicides_no\": 207, \"population\": 476580, \"suicides_per_100k_pop\": 43.43, \"gdp_for_year\": \"49,425,513,611\", \"gdp_per_capita\": 12351, \"generation\": \"Boomers\"}, {\"country\": \"Turkey\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"15-24 years\", \"suicides_no\": 207, \"population\": 6573627, \"suicides_per_100k_pop\": 3.15, \"gdp_for_year\": \"859,796,872,794\", \"gdp_per_capita\": 11961, \"generation\": \"Millenials\"}, {\"country\": \"Ecuador\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"35-54 years\", \"suicides_no\": 205, \"population\": 1833181, \"suicides_per_100k_pop\": 11.18, \"gdp_for_year\": \"99,290,381,000\", \"gdp_per_capita\": 6832, \"generation\": \"Generation X\"}, {\"country\": \"Romania\", \"year\": 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{\"country\": \"Spain\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"75+ years\", \"suicides_no\": 168, \"population\": 2624796, \"suicides_per_100k_pop\": 6.4, \"gdp_for_year\": \"1,197,789,902,774\", \"gdp_per_capita\": 27108, \"generation\": \"Silent\"}, {\"country\": \"Ukraine\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"25-34 years\", \"suicides_no\": 168, \"population\": 3421535, \"suicides_per_100k_pop\": 4.91, \"gdp_for_year\": \"91,030,959,455\", \"gdp_per_capita\": 2256, \"generation\": \"Millenials\"}, {\"country\": \"Argentina\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"35-54 years\", \"suicides_no\": 166, \"population\": 5301368, \"suicides_per_100k_pop\": 3.13, \"gdp_for_year\": \"594,749,285,413\", \"gdp_per_capita\": 14981, \"generation\": \"Generation X\"}, {\"country\": \"Mexico\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"75+ years\", \"suicides_no\": 163, \"population\": 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{\"country\": \"Finland\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"55-74 years\", \"suicides_no\": 159, \"population\": 665885, \"suicides_per_100k_pop\": 23.88, \"gdp_for_year\": \"232,464,833,066\", \"gdp_per_capita\": 44862, \"generation\": \"Boomers\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"5-14 years\", \"suicides_no\": 158, \"population\": 20342901, \"suicides_per_100k_pop\": 0.78, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Generation Z\"}, {\"country\": \"Italy\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"75+ years\", \"suicides_no\": 156, \"population\": 4115435, \"suicides_per_100k_pop\": 3.79, \"gdp_for_year\": \"1,832,868,490,534\", \"gdp_per_capita\": 31537, \"generation\": \"Silent\"}, {\"country\": \"Croatia\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"35-54 years\", \"suicides_no\": 155, 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years\", \"suicides_no\": 143, \"population\": 275569, \"suicides_per_100k_pop\": 51.89, \"gdp_for_year\": \"679,289,166,858\", \"gdp_per_capita\": 86068, \"generation\": \"Silent\"}, {\"country\": \"Norway\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"35-54 years\", \"suicides_no\": 140, \"population\": 736827, \"suicides_per_100k_pop\": 19.0, \"gdp_for_year\": \"386,663,139,403\", \"gdp_per_capita\": 79187, \"generation\": \"Generation X\"}, {\"country\": \"Greece\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"55-74 years\", \"suicides_no\": 139, \"population\": 1157869, \"suicides_per_100k_pop\": 12.0, \"gdp_for_year\": \"195,541,761,243\", \"gdp_per_capita\": 18927, \"generation\": \"Boomers\"}, {\"country\": \"Mexico\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"5-14 years\", \"suicides_no\": 137, \"population\": 11790898, \"suicides_per_100k_pop\": 1.16, \"gdp_for_year\": \"1,169,622,672,463\", \"gdp_per_capita\": 10228, \"generation\": \"Generation Z\"}, {\"country\": \"Chile\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"35-54 years\", \"suicides_no\": 136, \"population\": 2431495, \"suicides_per_100k_pop\": 5.59, \"gdp_for_year\": \"243,999,477,738\", \"gdp_per_capita\": 14729, \"generation\": \"Generation X\"}, {\"country\": \"Germany\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"15-24 years\", \"suicides_no\": 136, \"population\": 4195185, \"suicides_per_100k_pop\": 3.24, \"gdp_for_year\": \"3,375,611,100,742\", \"gdp_per_capita\": 43201, \"generation\": \"Millenials\"}, {\"country\": \"Uruguay\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"35-54 years\", \"suicides_no\": 135, \"population\": 423963, \"suicides_per_100k_pop\": 31.84, \"gdp_for_year\": \"53,274,304,222\", \"gdp_per_capita\": 16696, \"generation\": \"Generation X\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(top_countries, title = 'Top countries in 2015 with the highest suicide numbers').mark_bar(size = 13).encode(\n",
    "    x = alt.X('suicides_no', title='Suicide Numbers') , \n",
    "    y=alt.Y('country',sort= '-x', title='Countries'), \n",
    "    color=alt.Color('gdp_per_capita', scale=alt.Scale(scheme='greenblue')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The year with the highest median number of suicides in the 20 year period of 1996 to 2016 turns out to be 2015. And, the second highest was way back in 1997. This is a wide eye opener given, for the past 20 years, a very recent 2015 turns out to the highest.\n",
    "\n",
    "\n",
    "Analyzing what happened in 2015 on a country level, surprising or actually not, United States tops the list, followed by Russian Federation, Japan, Republic of Korea and Brazil respectively. The one common factor among all these countries can be thought of as the ever paced never ending work horse life that we all play a temple run in. However, when we look at the GDP per capita, these 5 countries do not seem to hold the highest! What was the point of all the running? The highest GDP-per-capita looks like Switzerland in the tiny corner with the lowest suicide numbers. A rich and happy place!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis of suicides with generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "suicide_data.year = pd.to_datetime(suicide_data.year, format= '%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataset with the the aggregated values for country and year\n",
    "df= suicide.groupby(['country','year']).mean()\n",
    "df= df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Russian Federation', 'United States', 'Japan']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the top three countries from\n",
    "# our dataset who have the highest number of suicides and filtering from the original dataset\n",
    "df_new = df.groupby(['country']).mean().reset_index().sort_values(by=['suicides_no'],ascending= False).iloc[0:3,:]\n",
    "df_new['country'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides_per_100k_pop</th>\n",
       "      <th>gdp_for_year</th>\n",
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       "      <th>generation</th>\n",
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       "    <tr>\n",
       "      <th>13496</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>1339</td>\n",
       "      <td>2633000</td>\n",
       "      <td>50.85</td>\n",
       "      <td>4,833,712,542,207</td>\n",
       "      <td>40693</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13497</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>4870</td>\n",
       "      <td>12820000</td>\n",
       "      <td>37.99</td>\n",
       "      <td>4,833,712,542,207</td>\n",
       "      <td>40693</td>\n",
       "      <td>Silent</td>\n",
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       "      country       year   sex          age  suicides_no  population  \\\n",
       "13496   Japan 1996-01-01  male    75+ years         1339     2633000   \n",
       "13497   Japan 1996-01-01  male  55-74 years         4870    12820000   \n",
       "\n",
       "       suicides_per_100k_pop       gdp_for_year  gdp_per_capita  \\\n",
       "13496                  50.85  4,833,712,542,207           40693   \n",
       "13497                  37.99  4,833,712,542,207           40693   \n",
       "\n",
       "            generation  \n",
       "13496  G.I. Generation  \n",
       "13497           Silent  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter from the original dataset:\n",
    "highest_df_top= suicide_data[suicide_data['country'].isin(df_new['country'].to_list())]\n",
    "highest_df_top.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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    "alt.Chart(highest_df_top).properties(width=500).mark_boxplot().encode(\n",
    "    alt.X('suicides_per_100k_pop', scale= alt.Scale(zero=False), title= 'suicides per 100k population'),\n",
    "    alt.Y('generation',scale= alt.Scale(zero=False)), \n",
    "    fill= alt.Color('generation',scale= alt.Scale(scheme='viridis'))).facet('sex', title='Distribution of suicides per 100K population').properties(columns=1)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph we can see that the male population is mostly involved in suicides and also the most is from the GI Generation also known as the greatest generation or the World War II generation. The inference from this graph is pretty self explanatory. Men during the great war were more obligated to take care of their nation and family under the then circumstances. Hence they can be more susceptible to depression which explains the suicide rates.\n",
    "\n",
    "The Silent Generation represents the set of people after the GI Generation and this is the period. They were named like that because they were a counterculture of a rebellious generation, distrustful of the establishment and keen to find their own voice. They fall under the circumstances of the economic depression, which now explains why they have the highest suicides rates next to the GI Generation.\n",
    "\n",
    "The Millenials are the second lowest in suicides as they are a pretty settled generation and not succeptible to any of the major concerns as their previous generations. \n",
    "\n",
    "The trend is similar in case of the females. This can be also visualized as proof that women are mentally stronger than men. However we might need more data on that to prove this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Population distribution based on the generation and age groups "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/poojithagowthaman/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py:5303: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "highest_df_top.age= highest_df_top.age.replace('5-14 years','05-14 years')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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\"2011-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"05-14 years\", \"suicides_no\": 86, \"population\": 20251453, \"suicides_per_100k_pop\": 0.42, \"gdp_for_year\": \"15,517,926,000,000\", \"gdp_per_capita\": 53452, \"generation\": \"Generation Z\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"75+ years\", \"suicides_no\": 2853, \"population\": 7633574, \"suicides_per_100k_pop\": 37.37, \"gdp_for_year\": \"16,155,255,000,000\", \"gdp_per_capita\": 55170, \"generation\": \"Silent\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"35-54 years\", \"suicides_no\": 11763, \"population\": 42326226, \"suicides_per_100k_pop\": 27.79, \"gdp_for_year\": \"16,155,255,000,000\", \"gdp_per_capita\": 55170, \"generation\": \"Generation X\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"55-74 years\", \"suicides_no\": 8032, \"population\": 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55170, \"generation\": \"Generation X\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"55-74 years\", \"suicides_no\": 2264, \"population\": 32244829, \"suicides_per_100k_pop\": 7.02, \"gdp_for_year\": \"16,155,255,000,000\", \"gdp_per_capita\": 55170, \"generation\": \"Boomers\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"25-34 years\", \"suicides_no\": 1231, \"population\": 20667924, \"suicides_per_100k_pop\": 5.96, \"gdp_for_year\": \"16,155,255,000,000\", \"gdp_per_capita\": 55170, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"15-24 years\", \"suicides_no\": 954, \"population\": 21653970, \"suicides_per_100k_pop\": 4.41, \"gdp_for_year\": \"16,155,255,000,000\", \"gdp_per_capita\": 55170, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"75+ years\", \"suicides_no\": 428, \"population\": 11375645, \"suicides_per_100k_pop\": 3.76, \"gdp_for_year\": \"16,155,255,000,000\", \"gdp_per_capita\": 55170, \"generation\": \"Silent\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"05-14 years\", \"suicides_no\": 226, \"population\": 21198150, \"suicides_per_100k_pop\": 1.07, \"gdp_for_year\": \"16,155,255,000,000\", \"gdp_per_capita\": 55170, \"generation\": \"Generation Z\"}, {\"country\": \"United States\", \"year\": \"2012-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"05-14 years\", \"suicides_no\": 85, \"population\": 20273917, \"suicides_per_100k_pop\": 0.42, \"gdp_for_year\": \"16,155,255,000,000\", \"gdp_per_capita\": 55170, \"generation\": \"Generation Z\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"75+ years\", \"suicides_no\": 2990, \"population\": 7816638, \"suicides_per_100k_pop\": 38.25, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Silent\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"55-74 years\", \"suicides_no\": 8445, \"population\": 30405404, \"suicides_per_100k_pop\": 27.77, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Boomers\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"35-54 years\", \"suicides_no\": 11396, \"population\": 42085688, \"suicides_per_100k_pop\": 27.08, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Generation X\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"25-34 years\", \"suicides_no\": 5063, \"population\": 21386891, \"suicides_per_100k_pop\": 23.67, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"15-24 years\", \"suicides_no\": 3903, \"population\": 22735631, \"suicides_per_100k_pop\": 17.17, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"35-54 years\", \"suicides_no\": 3776, \"population\": 41983845, \"suicides_per_100k_pop\": 8.99, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Generation X\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"55-74 years\", \"suicides_no\": 2484, \"population\": 33229667, \"suicides_per_100k_pop\": 7.48, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Boomers\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"25-34 years\", \"suicides_no\": 1285, \"population\": 20951958, \"suicides_per_100k_pop\": 6.13, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"15-24 years\", \"suicides_no\": 975, \"population\": 21698877, \"suicides_per_100k_pop\": 4.49, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"75+ years\", \"suicides_no\": 431, \"population\": 11486590, \"suicides_per_100k_pop\": 3.75, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Silent\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"05-14 years\", \"suicides_no\": 252, \"population\": 21233001, \"suicides_per_100k_pop\": 1.19, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Generation Z\"}, {\"country\": \"United States\", \"year\": \"2013-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"05-14 years\", \"suicides_no\": 143, \"population\": 20308672, \"suicides_per_100k_pop\": 0.7, \"gdp_for_year\": \"16,691,517,000,000\", \"gdp_per_capita\": 56520, \"generation\": \"Generation Z\"}, {\"country\": \"United States\", \"year\": \"2014-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"75+ years\", \"suicides_no\": 3106, \"population\": 7994948, \"suicides_per_100k_pop\": 38.85, \"gdp_for_year\": \"17,427,609,000,000\", \"gdp_per_capita\": 58531, \"generation\": \"Silent\"}, {\"country\": \"United States\", \"year\": \"2014-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"55-74 years\", \"suicides_no\": 8961, \"population\": 31339875, \"suicides_per_100k_pop\": 28.59, \"gdp_for_year\": \"17,427,609,000,000\", \"gdp_per_capita\": 58531, \"generation\": \"Boomers\"}, {\"country\": \"United States\", \"year\": \"2014-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"35-54 years\", \"suicides_no\": 11455, \"population\": 41858354, \"suicides_per_100k_pop\": 27.37, \"gdp_for_year\": \"17,427,609,000,000\", \"gdp_per_capita\": 58531, \"generation\": \"Generation X\"}, {\"country\": \"United States\", \"year\": \"2014-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"25-34 years\", \"suicides_no\": 5222, \"population\": 21760455, \"suicides_per_100k_pop\": 24.0, \"gdp_for_year\": \"17,427,609,000,000\", \"gdp_per_capita\": 58531, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2014-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"15-24 years\", \"suicides_no\": 4089, \"population\": 22700273, \"suicides_per_100k_pop\": 18.01, \"gdp_for_year\": \"17,427,609,000,000\", \"gdp_per_capita\": 58531, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2014-01-01T00:00:00\", 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\"male\", \"age\": \"25-34 years\", \"suicides_no\": 5503, \"population\": 22137097, \"suicides_per_100k_pop\": 24.86, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"15-24 years\", \"suicides_no\": 4359, \"population\": 22615073, \"suicides_per_100k_pop\": 19.27, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"35-54 years\", \"suicides_no\": 4053, \"population\": 41531809, \"suicides_per_100k_pop\": 9.76, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Generation X\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"55-74 years\", \"suicides_no\": 2872, \"population\": 35115610, \"suicides_per_100k_pop\": 8.18, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Boomers\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"25-34 years\", \"suicides_no\": 1444, \"population\": 21555712, \"suicides_per_100k_pop\": 6.7, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"15-24 years\", \"suicides_no\": 1132, \"population\": 21633813, \"suicides_per_100k_pop\": 5.23, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Millenials\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"75+ years\", \"suicides_no\": 540, \"population\": 11778666, \"suicides_per_100k_pop\": 4.58, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Silent\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"male\", \"age\": \"05-14 years\", \"suicides_no\": 255, \"population\": 21273987, \"suicides_per_100k_pop\": 1.2, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Generation Z\"}, {\"country\": \"United States\", \"year\": \"2015-01-01T00:00:00\", \"sex\": \"female\", \"age\": \"05-14 years\", \"suicides_no\": 158, \"population\": 20342901, \"suicides_per_100k_pop\": 0.78, \"gdp_for_year\": \"18,120,714,000,000\", \"gdp_per_capita\": 60387, \"generation\": \"Generation Z\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(alt.Chart(highest_df_top)\n",
    " .transform_density(\n",
    "    'population',\n",
    "     groupby=['age','generation'],\n",
    "    as_=['population', 'density'],\n",
    "     counts= True)  \n",
    " .mark_bar(opacity=0.9).encode(\n",
    "    x=alt.X('population',scale= alt.Scale(zero=False)),\n",
    "    y=alt.Y('density:Q', stack= True,scale= alt.Scale(zero= False)), \n",
    "     color= alt.Color('age', scale= alt.Scale(scheme='viridis', reverse= True)),\n",
    "     tooltip= [alt.Tooltip('age')])).properties(height=100,width= 300).facet('generation',title='Distribution of population based on age and Generation', columns= 2).interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above population distribution we can get an idea about how various generation are distributed among the population. The stronger colour corresponds to the older population. We saw in the previous graph that the Generation I is the heighest with respect to the suicide rates. Here we can see that the genaration GI mostly consists of the age group above 75 years ie. they are born around the time of the Great War.\n",
    "\n",
    "The Boomers consists of two age groups ie. 35-54 years and 55-75 years. However they are more populated with the younger age group of 35-54 and few from the older age group.\n",
    "\n",
    "The silent age group corresponds to the age groups over 55 years. This can be shown by the two distributions in age groups. This is the population which have survived the great war but had to endure the repurcussions of the war and live through the great depression.\n",
    "\n",
    "Finally we have the millenials who are from 5 to 34 years. But the maximum age group among them is the ages between 15-24 years. Hence anyone born from the early 1990's fall under this category.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) RESEARCH QUESTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1**\n",
    "\n",
    "When analyzing the top countries with highest suicide numbers 2015, we see a negative or no correlation with GDP_per_capita and suicide numbers. It is common to relate GDP growth with high income and money matters. This leads to various socio-economic problems. However, that has not proven to be the case here and we have clashing GDP data among the top 5 countries. We can research future into the other reasons that are contributing to the suicides and arrive at a deeper understanding how improving a country's economic standing could help prevent the suicide numbers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2**\n",
    "\n",
    "\n",
    "\n",
    "When analysing the graphs we found that women generally tend to be more stronger mentally than men as their suicide rates substantially less as compared to men across various generations. However this data alone is not substantial to understand if this claim is true. We might need more psycological data rel.ated to why they are involved in such tragic decisions. Also this difference in male and female suicide rates are constant and consistent among all the different generations. Hence there should be an underlying reason why men tend to be more sensitive consistently among all generations (through facing the same war and economic depressions like men) while they are seen as the stronger sex.This can be supported by the fact that men at that generation were considered more responsible to bring food to the family and save the nation while women stayed home and took care of household. However we cannot come to a complete concreate conclusion about this and would require more information to support our claim."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8)SUMMARY AND CONCLUSIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suicide statistics is probably an untouched area in the World Economic forum, but these statistics contribute to the future trend of the population of countries. They can help determine the proportion of population in different ages that gives sense of what the respective Governments can anticipate while planning their economic trends and investments. It can can provide insights on areas of improvement, specifically alluding to the 35 - 45 age group that we analyzed in the graphs above. Loosing that chunk of population can be put he economy out of balance. In reference to the top countries in 2015 with highest suicide numbers, United States tops the list followed by the Russian Federation. It is ironic to say how dictatorship beats democracy in determining the mental state of the population.From our analysis we also see suicide rates higher among males than females and the highest number of suicides occurred among the GI Generation. A sense of this data tells us where exactly in those 20 years did we lose a people and also throws some light on the time period. The distribution of population faceted by generations also gives us an idea of what we are looking at. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### References\n",
    "United Nations Development Program. (2018). Human development index (HDI). Retrieved from http://hdr.undp.org/en/indicators/137506\n",
    "\n",
    "World Bank. (2018). World development indicators: GDP (current US$) by country:1985 to 2016. Retrieved from http://databank.worldbank.org/data/source/world-development-indicators#\n",
    "\n",
    "[Szamil]. (2017). Suicide in the Twenty-First Century [dataset]. Retrieved from https://www.kaggle.com/szamil/suicide-in-the-twenty-first-century/notebook\n",
    "\n",
    "World Health Organization. (2018). Suicide prevention. Retrieved from http://www.who.int/mental_health/suicide-prevention/en/"
   ]
  }
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