FinalProject_group058.ipynb

{
 "cells": [
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   "source": [
    "<a href=\"https://colab.research.google.com/github/mickremedi/COGS108_Repo/blob/master/FinalProject_group58.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M4P1eeEW6Yh_"
   },
   "source": [
    "# What Causes Suicides Globally? \n",
    "\n",
    "Team #58: \n",
    "\n",
    "1. Wan Hin Choi -  XXXXXXXXX\n",
    "2. Shivani Patel - XXXXXXXXX\n",
    "3. Michael Remediakis - XXXXXXXXX\n",
    "4. Samantha Stone - XXXXXXXXX\n",
    "5. Yingkun Wang - XXXXXXXXX\n",
    "6. Yujie Wang - XXXXXXXXX\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MBZo2nsu6vNI"
   },
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VX2ue4RpO3UE"
   },
   "source": [
    "In this study, we analyze factors that correlate to suicide rates among different cohorts in various countries between 1985 and 2016. We will first explore the data provided and look for any correlation with suicide rates among various factors such as age, gender, and geographical region building upon existing research.  We decide to work on this topic because we are inspired by suicide prevention. Suicide issue has tight relation to both families and individuals. Whenever we saw news related to suicide, we felt really sorry and regretful for those people and their families. We would like to analyze the suicide data, and find out what factors are large likely to cause suicide. Moreover, we desire to use our analyzation and visualization to tell readers and remind them about this issue. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_K_b2pjJLBiN"
   },
   "source": [
    "## 1.1 Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mbSmv5gKLJqm"
   },
   "source": [
    "In this project, we used various factors such as age, gender, and geographical region to find out what has a big influence on suicide rate. To compare different factors, we used regression, plots, and graphs to find out whether the correlation is strong or weak. Throughout the study, we concluded that  suicide rates would be higher for individuals whose age are 35+, males, and GDP has weak correlation with suicide rate. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B14aeIWvUnfn"
   },
   "source": [
    "## 1.2 Research Question\n",
    "\n",
    "Is there any correlation between suicide rates and factors such as gender, age, and gdp? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3T9G1bMPMMQq"
   },
   "source": [
    "## 1.3 Background\n",
    "\n",
    "According to the CDC’s National Vital Statistics System data, from 1999 to 2017, Suicide rates have increased by 33%. Alarmingly, American suicides are at their highest point in 50 years and suicides have now become the second-leading cause of death for Americans under the age of 35. Suicide is often considered a disturbing indicator of underlying social problems and this rise in suicide rates could be an indicator of rising social problems. It seems that suicide rates are not consistent across the entire population. Jamie Ducharme from TIME shares that U.S boys have died by suicide far more frequently than girls, even though girls attempt suicide and report contemplating it more often. Studies also look to see how suicides are being committed. Girls are more likely to attempt suicide by methods such as poisoning while boys have more often used lethal means such as firearms. This could help explain the “gender paradox” of why girls often outpace boys in attempts while the reverse is true for deaths. Additional studies show that the suicide rate increased with decreasing urbanization. Suicide rates for the most rural counties was 1.4 times the rate for urban areas. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wSNDQ6xBMPS8"
   },
   "source": [
    "## 1.4 Sources\n",
    "1. https://psmag.com/news/the-suicide-rate-is-at-its-highest-in-a-half-century \n",
    "2. https://www.cdc.gov/nchs/products/databriefs/db330.htm\n",
    "3. http://time.com/5590344/youth-suicide-rates\n",
    "4. https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y13o7YZtMSL9"
   },
   "source": [
    "## 1.5 Hypothesis\n",
    "Based on what we've seen with suicide trends in the US, we presume that suicide rates would be higher for younger individuals, for males, and those in countries with a weaker economy. We expect that the global trends will follow the trends we see in the US\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c2e8FhrMMT9R"
   },
   "source": [
    "## 1.6 Dataset\n",
    "The data set being used is a compiled dataset pulled from four other datasets linked by time and place, and was built to find signals correlated to increased suicide rates among different cohorts globally, across the socio-economic spectrum. We obtained this dataset from kaggle (link in sources). The data contains many factors in columns, such as “country”, “year”, ”suicide rate”, “age”, and more. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dwcUmonUV3wv"
   },
   "source": [
    "## 1.7 Library Imports and Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y4XmogtbV-1a"
   },
   "outputs": [],
   "source": [
    "#Hides unnecessary output\n",
    "%%capture\n",
    "!pip install scipy==1.2 --upgrade\n",
    "!pip install geopandas\n",
    "!pip install descartes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SgWMWVF1WAjB"
   },
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import geopandas as gpd\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "#import data \n",
    "url = 'https://raw.githubusercontent.com/shivanipatel7/COGS-108-Final-Project/master/master.csv'\n",
    "original_data = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "byj7l06QcJbk"
   },
   "source": [
    "# 2. Data Cleaning and Pre-Processing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kj4Oil43cc0F"
   },
   "source": [
    "We first look at the first few rows of data, and observe columns, types and missingness of dataframe. After we dropped some irrelevent columns corresponding to our research, we select the sub-table from data based on the year after 2010. Then we do some groups and comparisons between various groups in order to help with future analyzing and try to find patterns between columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ddpIF_khkomh"
   },
   "source": [
    "## 2.1 Column Features:\n",
    "\n",
    "*   country:                    Names of the 101 countries where data are collected from\n",
    "*   year:                          Years from 1987 to 2016\n",
    "*   sex:                            Female or male\n",
    "*   age:                           '5-14 years' ,'15-24 years', '35-54 years', '75+ years', '25-34 years', '55-74 years' \n",
    "*   suicides_no:             Numbers of suicides during the given year\n",
    "*   population:               Population of the country in the year\n",
    "*   suicides/100k pop: Numbers of suicides per 100k population\n",
    "*   country-year:            String of  country and year combined\n",
    "*   HDI for year:             Human development index for the given year\n",
    "*   gdp_for_year:           GDP of the country for the given year\n",
    "*   gdp_per_capita:       GDP per capita for the country\n",
    "*   generation:               Generation (based on age grouping average)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "binvwxYycsJf",
    "outputId": "e1a3d8c3-9539-479f-cc48-b2a44bc38420"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country                object\n",
      "year                    int64\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\n"
     ]
    }
   ],
   "source": [
    "# check whether each type of columns as our excepted\n",
    "print(original_data.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SE8c0fD2LfKD"
   },
   "source": [
    "## 2.2 Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UMNnzJPdddoL"
   },
   "source": [
    "Since each columns has the correct data type, we do not need to further set the type manully. We then need to deal with the missingness of data. We dropped the column 'HDI for year' since it contained mostly missing data. And we dropped the 'generation' column since it wouldn't be relevant to our analysis. Finally we dropped 'country-year' since the same information can be taken from other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LLNVDcoZV8qO"
   },
   "outputs": [],
   "source": [
    "original_data.isnull().any()\n",
    "data=original_data.drop(['HDI for year','generation', 'country-year'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-QdCm_ib-HrZ"
   },
   "source": [
    "We rename some columns to make the dataset easier to access."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R2upTEw3-KrB"
   },
   "outputs": [],
   "source": [
    "data.rename(index = str, columns = {'suicides/100k pop':'suiciderate'}, inplace=True)\n",
    "data.rename(index = str, columns = {'gdp_per_capita ($)':'gdp'}, inplace=True)\n",
    "data.rename(index = str, columns = {' gdp_for_year ($) ':'yearlygdp'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_eHF0GQGOCWq"
   },
   "source": [
    "In order to avoid misrepresentation of any country, we removed countries that we had too little information on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XPy-d4RTOBts"
   },
   "outputs": [],
   "source": [
    "missing_countries = ['Antigua and Barbuda', 'Aruba','Bahrain','Barbados',\n",
    "                     'Cabo Verde','Grenada','Maldives','Malta','Mauritius',\n",
    "                     'Saint Lucia','Saint Vincent and Grenadines','Seychelles',\n",
    "                     'Singapore']\n",
    "data = data[~data['country'].isin(missing_countries)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XMp5gaOkNuGn"
   },
   "source": [
    "We corrected the name of any country who's name isn't written in the common form used by most geomapping software."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AP6ds08lR_Rd"
   },
   "outputs": [],
   "source": [
    "data.loc[data['country'] == 'United States', 'country'] = 'United States of America'\n",
    "data.loc[data['country'] == 'Republic of Korea', 'country'] = 'South Korea'\n",
    "data.loc[data['country'] == 'Russian Federation', 'country'] = 'Russia'\n",
    "data.loc[data['country'] == 'Bosnia and Herzegovina', 'country'] = 'Bosnia and Herz.'\n",
    "data.loc[data['country'] == 'Czech Republic', 'country'] = 'Czechia'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SWl_kvxKSwQp"
   },
   "source": [
    "To ensure our analysis was relevent to current state of the world, we  dropped any data from before 2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tdQDa7DtSuPh"
   },
   "outputs": [],
   "source": [
    "total_data = data.copy()\n",
    "data = data[data['year'] >= 2010]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BnU3do2pL6Vp"
   },
   "source": [
    "## 2.3 Overview of Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c2vUqsGOo5DP"
   },
   "source": [
    "Here is the overview of data in each column, which will be helpful in our further analysis. We checked the data of the first row to understand its general structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "1q0xAhY9o3Tf",
    "outputId": "a5cd7bd9-27ba-464e-a43d-02fe38b19396"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country               Albania\n",
       "year                     2010\n",
       "sex                      male\n",
       "age               55-74 years\n",
       "suicides_no                20\n",
       "population             241852\n",
       "suiciderate              8.27\n",
       "yearlygdp      11,926,953,259\n",
       "gdp                      4359\n",
       "Name: 252, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dJsDgt0UMYna"
   },
   "source": [
    "There is an even distribution of males and females within the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "IR3csDgLpFcn",
    "outputId": "771419be-9400-47f2-d0bb-37f3bcdbc2d1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      2566\n",
       "female    2566\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sex.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kG2n6rvQMfss"
   },
   "source": [
    "We have information from a total of 80 countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "GHpVf_so4KV3",
    "outputId": "6ebef9f2-a523-4d43-b335-81a300547d69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.country.unique().size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gc5qjtnfOIGM"
   },
   "source": [
    "The gdp of the countries we have observations from ranges from 991 to 126352."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "vHav6BujNPpg",
    "outputId": "c3d4faee-87a4-45fc-ab8a-fe32cc431185"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDP Range: (991, 126352)\n"
     ]
    }
   ],
   "source": [
    "print(\"GDP Range:\", (data.gdp.min(), data.gdp.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZqBcVYHoMnzH"
   },
   "source": [
    "Each observation is placed in one of  6 age groups and there's an about even distribution of observations from each age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "If1QmuZs7Y_5",
    "outputId": "c5358593-b896-4c74-c0fb-92e6904e94e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['55-74 years', '35-54 years', '25-34 years', '75+ years',\n",
       "       '15-24 years', '5-14 years'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "qft9mAeIpFfH",
    "outputId": "3b32bf49-dc70-4ca2-8d52-96700047f7fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25-34 years    860\n",
       "75+ years      860\n",
       "15-24 years    860\n",
       "35-54 years    860\n",
       "55-74 years    860\n",
       "5-14 years     832\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JPFHLDmiM4Eb"
   },
   "source": [
    "For example, the column with the highest suicide rate is the group of males ages 35-54 in the Suriname with a suicide rate of 27.49 people per 100k people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "fuHoBOh6sApm",
    "outputId": "bdf2807c-f693-4c0c-b3d5-b43af5ec7474"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country             Suriname\n",
       "year                    2012\n",
       "sex                     male\n",
       "age                75+ years\n",
       "suicides_no               10\n",
       "population              5346\n",
       "suiciderate           187.06\n",
       "yearlygdp      4,980,000,000\n",
       "gdp                    10227\n",
       "Name: 24332, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the column with the maximum suicide number\n",
    "data.loc[data['suiciderate'].idxmax()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qKYBTZyR2M_m"
   },
   "source": [
    "# 3. Data Analysis\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ddRNzVONa6Fb"
   },
   "source": [
    "## 3.1 Suicide Rates By Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NCLZsn1BIMxH"
   },
   "source": [
    "We first investigated the relationship between young and old people's suicide rate. We combined the ages into two groups, placing people with ages between 5-34 in the first group and ages above 34 in the second group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hJznwi6PCApP"
   },
   "outputs": [],
   "source": [
    "# copy the origional data and change the column\n",
    "agegrp = data[['age','suicides_no','suiciderate']].copy()\n",
    "agegrp['age'] = agegrp['age'].apply(lambda x: '5-34' if x == '15-24 years' or x == '5-14 years' or x == '25-34 years' else '35+')\n",
    "agegrp = agegrp.sort_values(by='suicides_no', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "id": "lCCRsw-hmdKD",
    "outputId": "a0b8c216-c970-45e3-ab9a-80b38267488c"
   },
   "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>suicides_no</th>\n",
       "      <th>suiciderate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35+</th>\n",
       "      <td>382.560853</td>\n",
       "      <td>17.243477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-34</th>\n",
       "      <td>149.124608</td>\n",
       "      <td>6.963209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      suicides_no  suiciderate\n",
       "age                           \n",
       "35+    382.560853    17.243477\n",
       "5-34   149.124608     6.963209"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agegrp.groupby('age').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "A4Ag3IafCAuZ",
    "outputId": "b1bff55e-822e-4fb2-9bf6-1ae68a17993c"
   },
   "outputs": [
    {
     "data": {
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LYoxL6sbWp627JEmSdEqlCc+PA+eEEGaFECqAW4B7uhcIIVwEfJ0kOO/oduiXwFtDCOPK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\nFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "sns.distplot(agegrp[agegrp['age'] == '5-34']['suiciderate'], label = 'Ages 5-34')\n",
    "sns.distplot(agegrp[agegrp['age'] == '35+']['suiciderate'], label = 'Ages 35+')\n",
    "plt.xlim(0, 80)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f4ktnDQypMm5"
   },
   "source": [
    "From the table, we realize that older people are much more likely to commit suicide globally. Moreover from the picture above, we observe that youth group has more shaded area at range 0 to around 5. However, the likelihood of youth suicide then surpasses that of older people as countries get higher suicide rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "colab_type": "code",
    "id": "qgi-aC3fUIh3",
    "outputId": "4890a512-6faf-4162-a1f1-28a0f1eb4246"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            suiciderate   R-squared:                       0.154\n",
      "Model:                            OLS   Adj. R-squared:                  0.153\n",
      "Method:                 Least Squares   F-statistic:                     186.8\n",
      "Date:                Thu, 13 Jun 2019   Prob (F-statistic):          2.61e-183\n",
      "Time:                        02:05:33   Log-Likelihood:                -21305.\n",
      "No. Observations:                5132   AIC:                         4.262e+04\n",
      "Df Residuals:                    5126   BIC:                         4.266e+04\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept              8.6350      0.524     16.465      0.000       7.607       9.663\n",
      "age[T.25-34 years]     2.7514      0.742      3.710      0.000       1.297       4.205\n",
      "age[T.35-54 years]     5.5608      0.742      7.498      0.000       4.107       7.015\n",
      "age[T.5-14 years]     -7.9719      0.748    -10.659      0.000      -9.438      -6.506\n",
      "age[T.55-74 years]     6.6256      0.742      8.933      0.000       5.172       8.080\n",
      "age[T.75+ years]      13.6390      0.742     18.390      0.000      12.185      15.093\n",
      "==============================================================================\n",
      "Omnibus:                     3200.896   Durbin-Watson:                   0.884\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            49793.391\n",
      "Skew:                           2.727   Prob(JB):                         0.00\n",
      "Kurtosis:                      17.252   Cond. No.                         6.84\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#linear model for age \n",
    "outcome_age, predictors_age = patsy.dmatrices('suiciderate ~ age', data)\n",
    "mod_age = sm.OLS(outcome_age, predictors_age)\n",
    "res_age = mod_age.fit()\n",
    "print(res_age.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TJz0Fzb4UJ-r"
   },
   "source": [
    "From this model, we can see how different cohorts affect the suicide rate. For example, the suicide rate for the 5-14 cohort is 7.9719 lower than the average suicide rate of 14-25 year cohort. The suicide rate for the 25-34 cohort is on average 2.7514 higher than the average suicide rate for the 14-25 cohort. Based on the results, we can see that suicide rates are increasing as age cohorts increase. The p-values for each of our coefficients are = 0 which implies that are results are statistically significant. The R-squared values, however so only a small percentage of the variability in the data can be explained by this model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CemxZ3ZAqVe-"
   },
   "source": [
    "## 3.2 Suicide Rates By Country GDP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WtX1oj5tHvF6"
   },
   "source": [
    "In this section, we analyze the relationship between suicide rates and different countries. Along with this, we take into account the correlation between suicide rates and country GDP. Based on this information, we found that those countries with a higher GDP have a higher suicide rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "LDh6tGKRvwBA",
    "outputId": "a21eede8-d48a-40d8-9b75-290ec0245f81"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suiciderate</th>\n",
       "      <th>gdp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>37.215972</td>\n",
       "      <td>26367.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>33.781463</td>\n",
       "      <td>15358.731707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suriname</th>\n",
       "      <td>31.281000</td>\n",
       "      <td>9913.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guyana</th>\n",
       "      <td>30.770000</td>\n",
       "      <td>3945.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>24.116042</td>\n",
       "      <td>7649.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             suiciderate           gdp\n",
       "country                               \n",
       "South Korea    37.215972  26367.500000\n",
       "Lithuania      33.781463  15358.731707\n",
       "Suriname       31.281000   9913.600000\n",
       "Guyana         30.770000   3945.750000\n",
       "Belarus        24.116042   7649.750000"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country = data[['country', 'suiciderate', 'gdp']].copy()\n",
    "\n",
    "sum_country = country.groupby('country').mean()\n",
    "sum_country.sort_values(by = 'suiciderate', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YmZTf8TSze4-"
   },
   "outputs": [],
   "source": [
    "mean_gdp = country['gdp'].mean()\n",
    "country['gdp'] = country['gdp'].apply(lambda x: 'above average' if x > mean_gdp else 'below average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ojgVWbsUIf2f"
   },
   "source": [
    "In the table below, we can see that higher GDP leads to higher suicide rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "bJ-0igRM0Nr6",
    "outputId": "bb7f5750-7ef1-4d44-8ac0-114eae28a3a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gdp\n",
       "above average    10.970654\n",
       "below average    12.819469\n",
       "Name: suiciderate, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country.groupby('gdp')['suiciderate'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "colab_type": "code",
    "id": "HPkaPaUxisBI",
    "outputId": "f1bfef21-4236-418f-bdde-fe06efde9804"
   },
   "outputs": [
    {
     "data": {
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Fkm4prCIzsyZR6SqIkyos+3Ax5ZiZNY9KY0HMlfQjSRMlDalnUWZmzaDSVRD7A9eQDR15\nq6QZkk6XtFtdKjMza3CVuiA6yMbqvQVA0k5kg6ufJemNwJyI8O3CZma9VHE84FIR8YykacCVwCrg\nbUUVZWbWDDZ6I4akSyVtLWlLYAFwP/D5iJhdeHVmZg2smjvh9oyIF4H3kT0dYwzw0UKrMjNrAtUE\n8CBJg8gC+LqIeIVNfIqFmZm9WjUB/FPgSbJH0d8m6XXAi0UWZbXx2/bHWPT8KhY9v4rftj+Wuhwz\nK7PRL+Ei4jzgvJKmpyQdXFxJZmbNoZov4UZK+pmk6/P5PVn/SCEzM+ularogpgEzgZ3y+YeBM4oq\nyMysWVQTwNtHxOXAOvjnDRqdhVZlZtYEqgng1ZJGkF/50PXE4kKrMjNrAtXcCfc5sgdxvkHSbKAV\nOKbQqszMmkA1V0HcJekgYHeywdgfyq8FNjOzzVDpkUQf6GHRbpKIiKsLqsnMrClUOgN+T/5zB+Dt\nwB/z+YPJHknkADYz2wyVhqM8EUDSjWTjQSzK53ckuzTNzMw2QzVXQezSFb65xcCoguoxM2sa1VwF\ncZOkmcBl+fwk4A/FlWSb66c33lOx/ZPv2rue5ZhZD6q5CuLU/Au5A/OmqRFxTbFlmZk1vqqeiJFf\n8VCTL90k7Q5ML2l6PfANYBvgE8DSvP2rETGjFsc0s9roeOEZBm6z08ZXtKpUugztzxFxgKSVbDj+\nr4CIiK17c8CIeAgYmx+jBVhI9vDPE4EfRsT3e7NfM7P+ptJVEAfkP4cVePxDgcci4ilJBR7GzKzv\nqWY4yvGShpXMD5O0f42Ofxzrv9wDOFXSfEkXS9q2h3omS2qX1L506dLuVjEz6xequQztQrKnIHdZ\nnbdtFkmDgfcCV5Qc5w1k3ROLgHO62y4ipkZEW0S0tba2bm4ZZmbJVBPAioh/9gFHxDo24XH2FRwJ\n3BURi/P9Lo6Iznz/FwHjanAMM7M+q5oAflzSaZIG5a/TgcdrcOzjKel+yO+w6/J+YEENjmFm1mdV\nE8Ank40FsRB4GtgfmLw5B5W0JXA4G17a9t+S7pU0n2y8ic9uzjHMzPq6am7EWEL2ZVnNRMRqYERZ\n20dreQwzq621T94J4OuAa6jSdcBfioj/lvRjNrwOGICIOK3QyszMGlylM+AH8p/t9SjEzKzZVLoR\n47f5z0vqV46ZWfPYaB+wpJvpvgvikEIqMjNrEtVcz/uFkukhwAeBjmLKMTNrHtVcBTG3rGm2pDsK\nqsfMrGlU0wWxXcnsAGBfYHhhFZmZNYlquiDmkvUBi6zr4QngpCKLMjNrBtV0QYypRyFmZs2mmuEo\nP9Q1HKWkMyVdLemtxZdmZtbYqumC+HpEXCHpAOAw4HtkQ0fWakxgq4FvTp/dq3W/NWlCEeWYWRWq\nGYynM//5/8geyPl7YHBxJZmZNYdqAnihpJ+SPY5+hqTXVLmdmZlVUE2QHgvMBI6IiBeA7YAvFlqV\nmVkTqOYqiJcoGbc3IhaRPTLIzMw2g7sSzMwScQCbmSXiADazqnSueIbOFc+w+p5rU5fSMBzAZmaJ\nOIDNzBJxAJuZJeIANjNLxAFsZpaIA9jMLBEHsJlZIg5gM7NEHMBmZok4gM3MEqnmiRiFkPQksJJs\nwPeOiGjLn8A8HRgNPAkcGxHPp6rRzKxIqc+AD46IsRHRls9/BbgpInYFbsrnzcwaUuoALnc0cEk+\nfQnwvoS1mJkVKmUAB3CjpLmSJudtI/MB3wGeBUaWbyRpsqR2Se1Lly6tV61mZjWXrA8YOCAiFkra\nAZgl6cHShRERkqJ8o4iYCkwFaGtre9VyM7P+IlkAR8TC/OcSSdcA44DFknaMiEWSdgSWpKrPzDIr\nbr2gx7bhB32q3uU0lCRdEJK2lDSsaxp4F7AAuA44IV/tBMAjP5tZw0p1BjwSuEZSVw2XRsQNku4E\nLpd0EvAU2ROZDfjEhTcm2e9Fp7yrkOOaWaIAjojHgb27aV8OHFr/iszM6q+vXYZmZtY0HMBmZok4\ngM3MEnEAm5kl4gA2M0vEAWxmlogD2MwsEQewmVkiDmAzs0QcwGZmiTiAzcwScQCbmSWSckB2M+uD\nll97Zq/XHXH0WbUup6H5DNjMLBEHsJlZIg5gM7NEHMBmZok4gM3MEnEAm5kl4gA2M0vEAWxmlogD\n2MwsEUVE6hp6ra2tLdrb21OXsUne/V9Xpy6hLn731Q+kLsEqWHzJiXU/5sgTfl73YyakalbyGbCZ\nWSIOYDOzRBzAZmaJOIDNzBJxAJuZJVL3AJa0i6SbJd0v6T5Jp+ftUyQtlDQvfx1V79rMzOopxYDs\nHcDnI+IuScOAuZJm5ct+GBHfT1CTmVnd1T2AI2IRsCifXinpAeC19a7DzCy1pH3AkkYD+wC3502n\nSpov6WJJ2/awzWRJ7ZLaly5dWqdKzcxqL9kz4SRtBVwFnBERL0q6EPhPIPKf5wAfL98uIqYCUyG7\nE65+FZv1Lc/86IjUJWySzal3p9Nn1rCSviPJGbCkQWTh++uIuBogIhZHRGdErAMuAsalqM3MrF5S\nXAUh4GfAAxHxg5L2HUtWez+woN61mZnVU4ouiAnAR4F7Jc3L274KHC9pLFkXxJPAJxPUZmZWNymu\ngvgz3Y8UNKPetZiZpeQ74czMEnEAm5kl4gA2M0vEAWxmlogD2MwsEQewmVkiDmAzs0SSjQVh1ij+\n9u03py6h4aX4bzzqG/cWfgyfAZuZJdK0Z8D7fvEXqUtoaKn++8793r8lOa5Zb/gM2MwsEQewmVki\nDmAzs0QcwGZmiTiAzcwScQCbmSXiADYzS8QBbGaWiAPYzCyRpr0TzqwW5syZw8NPdqYuwwqw25w5\njB8/vtBj+AzYzCwRnwGbbYbx48ez040tqcuwAowq+OwXmjSA58yZwysL709dhhVgTh3+bDSrFXdB\nmJkl0pRnwOPHj2fQVQ+nLsMK4LNf6098BmxmlogD2MwsEQewmVkifS6AJU2U9JCkRyV9JXU9ZmZF\nUUSkruGfJLUADwOHA08DdwLHR0S314y1tbVFe3t7HSvcfO/+r6tTl1AXv/vqB1KX0PCe+dERqUuo\nm51On5m6hE2lalbqa1dBjAMejYjHAST9L3A00DAX7fY2mD5x4Y01rqQ6F53yriTHNWsGfS2AXwv8\nvWT+aWD/0hUkTQYmA4waNap+lSXmILRy/fCs0Mr0uT7gjYmIqRHRFhFtra2tqcsxM+u1vhbAC4Fd\nSuZ3ztvMzBpOXwvgO4FdJY2RNBg4DrgucU1mZoXoU33AEdEh6VRgJtACXBwR9yUuy8ysEH0qgAEi\nYgYwI3UdZmZF62tdEGZmTcMBbGaWiAPYzCwRB7CZWSIOYDOzRBzAZmaJOIDNzBJxAJuZJdKnxgPe\nVJKWAk+lrqMf2B5YlroIawj+LFVnWURM3NhK/TqArTqS2iOiLXUd1v/5s1Rb7oIwM0vEAWxmlogD\nuDlMTV2ANQx/lmrIfcBmZon4DNjMLBEHsJlZIg7gGpM0WtKCTdxmmqRjiqrJGo+kValrsM3nALaq\nSOpzT0+x/sufp4wDuBgDJf1a0gOSrpS0BYCkfSXdKmmupJmSdizfUNKhku6WdK+kiyW9RtJ+kq7O\nlx8taY2kwZKGSHq8m328R9Lt+X7+IGmkpAGSnpS0Tcl6j+TLWiVdJenO/DUhXz5F0i8lzQZ+mZ/d\n/0nSXfnr7fl6AyRdIOlBSbMkzeg6o6/mPVvPJP0m/293n6TJZct+mLffJKk1bxsraY6k+ZKukbSt\npD0k3VGy3WhJ9+bT1Xwm/XkqSkT4VcMXMBoIYEI+fzHwBWAQ8BegNW+fRPbQUYBpwDHAEODvwG55\n+y+AM8ie3fd43vZ9sqdHTwAOAi7rpoZtWX+Fy78D5+TTPwJOzKf3B/6QT18KHJBPjwIeyKenAHOB\nofn8FsCQfHpXoD2fPobsOX4DgH8Bns/benzPflX9edou/zkUWACMyOcD+Eg+/Q3g/Hx6PnBQPv1t\n4Nx8eh4wJp/+MnBmtb8ff56Ke/nPgGL8PSJm59O/Ak4DbgD2AmZJguypz4vKttsdeCIiHs7nLwE+\nHRHnSnpM0r8C44AfAO/I9/Gnbo6/MzA9PzsYDDyRt08n+8f6c+C4fB7gMGDPvC6ArSVtlU9fFxFr\n8ulBwPmSxgKdwG55+wHAFRGxDnhW0s0l72dj79kqO03S+/PpXciCajmwjvW/v18BV0saDmwTEbfm\n7ZcAV+TTl5MF1tn5z0lU//vx56kgDuBilF9cHYCA+yLibb3c523AkcArwB/IzppbgC92s+6PgR9E\nxHWS3kl25gHwV+CN+Z+r7wPOytsHAOMjYm3pTvIP+eqSps8Ci4G98202WL8bm/uem1r+uzsMeFtE\nvCTpFrK/krqzsQv6pwNX5F1ZERGPSHoz1f1+/HkqiPuAizFKUteH5MPAn4GHgNaudkmDJL2pbLuH\ngNGS3pjPfxToOpv5E1l3xF8jYikwguyMoLsrLoYDC/PpE7oaI/u77RqyM+gHImJ5vuhG4DNd6+Vn\nJN0ZDizKz0w+SvY/AIDZwAfzvruRwDtL3s/G3rP1bDjwfB6+ewDjS5YNIPuzHPLPWESsAJ6XdGDe\n/s/PT0Q8RnaW+XXWn6lW+/vx56kgDuBiPAR8WtIDZP1nF0bEy2T/YL4r6R6yPrm3l26UnzGcSHam\nci/Zn5k/yRffDowkOxOGrK/v3vwfQbkp+T7m8uqhA6cD/5/1/wgh6yJpy7+4uR84uYf3dQFwQl7/\nHqw/m7kKeBq4n+zP4buAFdW8Z6voBrIvdB8g6zqYU7JsNTBO2SWPh5D190IWkN+TNB8YW9IO63/3\nlwNswu9nCv48FcK3IltNSNoqIlZJGgHcQfYl5LOp67L+qVk+T+4Dtlr5XX5J0mDgPxvxH4vVVVN8\nnnwGbGaWiPuAzcwScQCbmSXiADYzS8QBbGaWiAPYklAvhu3sDyR9W9Jh+fQZygdi2sR9nCvpHWVt\nU8rm3yxp2ubUauk5gK2pqOBhECPiGxHxh3z2DLIBZ6qWX/c6PiJuy+cPkHQ7cHI+stih+XHuBXaW\nNKqG5VudOYCt5iR9XdJDkv4s6TJJX8jb95V0T34X06dL1v+YpGsl3aJsSMNvVtj36HyYwqqH+8z3\ne66kduD0HvY7Utnwjffkr66hEbsdDlLSKnU/HOQ0ScdIOg3YCbi5azAZSRdKas+3+VYPb/GDZHfA\ndfkB8CWyOyIPAx4pWfZbskFwrJ9yAFtNSdqPLET2Jhs8qK1k8c+Bz0TE3t1sOi7f7i3AhyS1dbNO\nl92BCyLiX4EXgU9JGkQ2aMwxEbEv2TCg3ynZZnBEtEXEOT3s8zzg1ry2twL35e0fz/fXRjYy2Yi8\nfUuy4RPfRDbewgb/04iI84BngIMj4uC8+WsR0Za/x4MkvaWbOiaQDdnY5WWgNd/nioj4W8myduBA\nrN9yAFutTQCujYi1EbGS7CyN/K6mbbr+tAZ+WbbdrIhYng9VeDXZkIQ9KR/u8wA2HKpwHtl4tzuX\nbDOdyg4BLgSIiM58YBvIQvcesnEYuoaDhFcPB1mp3i7HSroLuBt4E7BnN+vsCCwtmZ9MNr7Daflf\nE6NLli0hO8u2fsq3Iltf0d0Qnpuy7saGKlzdQ3uPVMPhICWNIRuYf7+IeD7/Aq27fa0pbY+I+4H3\nSPoO2YhkPwMOzRcPyde3fspnwFZrs8kCY4iyQbjfDRARLwAvSOo6U/xI2XaHS9pO0lCysWVn07Pe\nDvdZyU3AKfm2LcoGN9+k4SC72edKYFg+vTXZ/wRW5EMsHtlDHQ8AXcORImmvfPIVslHBhpWsuxvd\nD0dq/YQD2GoqIu4EriMbLvN64F6g68/5E4H/ybsIVLbpHWTDEM4HroqI9gqH6dVwnxtxOnBwPgzo\nXLLugd4MB1lqKnCDpJsj4h6yrocHyR7Z09P/YH7P+vFvIevf/gtwEtkXcV8qWXZwvr71Ux6Mx2qu\nZCjBLcjGL54cEXdVWP9jQFtEnFrFvkcDv4uIvTayaqEkrYqIrTa+Zq/2/Wfg3flfDV1tUyJiSsn8\na8i+/DsgIjqKqMOK5z5gK8JUSXuS9VFeUil8rVufJ3uY5QslbbeUrTMK+IrDt3/zGbD1SfnlXjd1\ns+jQkkff9Ga/XwM+VNZ8RUR8p7v1zYrkADYzS8RfwpmZJeIANjNLxAFsZpaIA9jMLJH/A7dzI8fA\nRrteAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.catplot(x=\"gdp_per_capita ($)\", y=\"suicides/100k pop\", kind = 'boxen', data=country);\n",
    "ax = sns.catplot(x=\"gdp\", y=\"suiciderate\", kind = 'boxen', data=country)\n",
    "ax.set_xlabels(\"gdp_per_capita ($)\")\n",
    "ax.set_ylabels(\"suicides/100k pop\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "id": "Ugi0hvJMFgHi",
    "outputId": "65f749d0-3cc4-4ca0-8f47-c47d7d5df598"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            suiciderate   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                 -0.000\n",
      "Method:                 Least Squares   F-statistic:                    0.3735\n",
      "Date:                Thu, 13 Jun 2019   Prob (F-statistic):              0.541\n",
      "Time:                        02:05:33   Log-Likelihood:                -21734.\n",
      "No. Observations:                5132   AIC:                         4.347e+04\n",
      "Df Residuals:                    5130   BIC:                         4.349e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     12.2839      0.342     35.960      0.000      11.614      12.954\n",
      "gdp        -5.615e-06   9.19e-06     -0.611      0.541   -2.36e-05    1.24e-05\n",
      "==============================================================================\n",
      "Omnibus:                     3410.224   Durbin-Watson:                   0.921\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            51535.584\n",
      "Skew:                           3.000   Prob(JB):                         0.00\n",
      "Kurtosis:                      17.318   Cond. No.                     5.44e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.44e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "#linear model for gdp \n",
    "outcome_gdp, predictors_gdp = patsy.dmatrices('suiciderate ~ gdp', data) \n",
    "mod_gdp = sm.OLS(outcome_gdp, predictors_gdp) \n",
    "res_gdp = mod_gdp.fit() \n",
    "print(res_gdp.summary()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0Gi3L8R511a0"
   },
   "source": [
    "From this model, we aim to see how gdp affects suicide rates. Because the p-value, 0.541 is not significant (it is not less than 0.05). We cannot say that GDP is a significant factor affecting suicide rate.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KotR1xybTjgf"
   },
   "source": [
    "## 3.3 Changes of Suicide Rates Over Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nJ-HP95IUvH1"
   },
   "source": [
    "In this analysis, we won't work with just the recent data. Instead we will pull from the entire dataset to analyze how suicide rates change over time.\n",
    "\n",
    "To make a more direct comparison to our initial assumptions in our hypothesis, we create two plots of suicide rates over time, one for the United States population and the second for the global population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zePQqp__V4Ke"
   },
   "outputs": [],
   "source": [
    "#group data by year and calculate suicides/100k each year in the United States\n",
    "US_pop_sum=total_data[total_data['country'] == \"United States of America\"].groupby('year')['population'].sum()\n",
    "US_sui_sum=total_data[total_data['country'] == \"United States of America\"].groupby('year')['suicides_no'].sum()\n",
    "US_per100k=(US_sui_sum / US_pop_sum) * 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "FVIeetqIWRsI",
    "outputId": "63ff7af6-9db6-4209-a0f9-663b3af6603c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Suicide Rate: (2000, 11.189107904701489)\n"
     ]
    },
    {
     "data": {
      "image/png": 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YHjMzC+jZpqlXzzdEhIfxpwl9KNh/lOcXZnvtuMYY79q+9zBPzt3Imd0TParw0NB4MkVr\ntIhcKSL3i8hDxx+1/UBVfUBV2wPTgNur2KyjqqYBVwL/EpEqb60UkclOckkvKiqqbVgAbNtzmB+2\n7fPJD8qQTi24aEBbXlycw9Y9h7x+fGNM3agq989YTZjAny/ua+cbK+FJD2Mm7hPVZcChCo+6mgZM\nrGyFquY7zznAQmBgVQdR1ZdUNU1V0xITE+sU0MyMfAAu7O+bvyz+MLYnkWHCY5+u88nxjTG19+HK\nfL7O2s295/egXQO/Qa8qnhQfTFbVE09e14qIpKrq8ZsSJgAbKtmmOXBYVY+JSAIwHPi7Nz6/OqrK\nxxn5DElp4bMfllZNG/Gbs1N5fM4Gvtqwy+tXX5S7lGU5exjSqQWRtbx/xJiGqOjAMR77dB1pHZvz\nq6EdAx1O0PLkt8q3ItK3pgcWkXeApUB3EckTkUnAEyKyRkRWAaOBKc62aSJyfBa/nkC6iGQCC4An\nVNXnf5Kv21HM5qJDTBjo23HL64d3onNiLH/6ZB3Hyrx7AvyFhdlc9fJ3/HHGGlSD7noCY4LWI5+s\n5UhJOU9M7NdgplutDU96GCOA60RkC+5qtccnUOpX3U6qekUli1+pYtt04Ebn9bdAjRNUXc3KKCAi\nTBjbx7czaEVFhPHIhb255tXvefnrLdx2ZlevHHddQTFPz8+iTbNGvJe+nW6t45g0opNXjm1MfTZv\n7U4+W7WDu0d3o2tSk0CHE9Q8SRjn+zyKAHO5lFmZBYzslkjz2KonSvKWM7olcl7vVjz7VTYXD2xX\n54JmJWUufvd+Bs0aR/HpHSO4f8Zq/vLZOjonxnJm9yQvRW1M/VN8tJQHZ66hR+s4bmrAZcs9VV0t\nqabOywNVPOqN73P3smP/0VpVpq2tP17gnqHvz5+tq/Pw0b+/ymLDzgM8fklfWjaJ5qnLB9C9dVN+\n8/YPZBfWq6/KGK96fPYGig4c4++X9rPzfh6o7l/obed5BZDuPK+o8L7emJlRQExUOOf28l8JgPYt\nYrjjrK7MXr2T5xdurvVxMrfv4/mFm5k4KPm/8cdERfDytWlER4Yz6Y10fjxU4q2wjak3luXs4Z3v\nt3Hj6Z3pl9wwZ9CrqSoThqqOc547qWpn5/n4o97Mh1FS5mL26h2M7tWKmCj/ztF766iuXDSgLU/O\n3cjb322r8f5HS8u5a3omSXHRP5uQvl18Y168ejA79h3llmkrKClzeStsY0Le0dJy7vtwFR1bxnDn\nOTaVsqesDwb8aUJvrhvu/xPEYWHCk5f1Z1T3RP748WrmrN5Ro/3/OW8j2YUH+dvEfjRr/PNqmoM7\nNudvl/ZlWc5eHp611q6cMsYxdWkuuXsO8/jFfWs0o2ZD1+ATRlREGBMGtGNAgCZ1jwwP4/mrBjGg\nfTxT3s3g2827Pdpvee5eXl6yhauGduCMblXfsHjxwGRuGdWFd77fxuvf5nopamNC17Gycl7+egvD\nurRkWC0nSGuoGnzCCAYxURG8et0pdGwZw+SpK1iTv7/a7Q+XlHH39EySmzfm/rE9T3r834/uzrm9\nWvHYp+tYtKlu5VOMCXUf/5BP4YFj3DLKroqqKU9qSU2qZNkTvgmn4YqPiWLqpCE0axzJta9+z5bd\nVVdfeWLOBrbtPcyTl/YnNvrk513CwoR/XT6Abq3iuP3tlWQXHvRm6MaEjHKX8uKiHPq0a1rr6Zcb\nMk96GBNF5Krjb0TkOaBuRZtMpdo0a8zUSUNQ4OpXvmNX8dGfbfNN9m6mLt3K9cM6cWrnlh4fOzba\nfeVUVHgYN76xnH2H7cop0/DMW7uTnN2HuGVkVysuWAseJQzcd3pfISJvAGWq+rNeh/GOLolNeP36\nU/jxUAnXvPI9+w+X/ndd8dFS7vlgFZ0TY7lnTPcaHzu5eQwvXj2Ygn1HuXXaSkrL7cop03CoKi8s\n2kxKyxjG9Gkd6HBCUnU37rUQkRZAY9xlO+7BfcPeo85y4yP9kuN58eo0cnYfZNIby/876dKfP13H\njv1H+Odl/WkUWbsrO9JSWvDXS/ry7eY9PP1l1sl3MKae+HbzHlbl7eemkV0It3pRtVJdD6PiDXsL\ngXjgAurhjXvBaERqAk9dPoAV237ktrdXMm/tTt5Pz+PmkV0Y2KF5nY596eBkLhnUjv8s2sz6HcUn\n38GYeuCFhZtJiovmkkHem02zoanyjKmqWuW6ABvXry0/Hi7lwY/XsHBjIT1axzHlnFSvHPvBC3qx\naGMR9364io9uGUaElUUw9diqvH0syd7Nfef3IDrC7ruorWp/S4hIDxG5V0SecR73ikgPfwVn4OpT\nO3L36G7ERkfwj8v6e+2HvXlsFA+P782qvP12f4ap9/6zaDNxjSK4amiHQIcS0qo7h3Ev8C7ucubf\nOw8B3hWR+/wTngG4/axUfnjwXPq0a+bV417Yrw1n90jiH/M2sm3PYa8e25hgkVN0kDlrdnL1qR2J\na/TzigjGc9X1MCYBp6jqE6r6lvN4AhjirDN+5IshIxHhzxf3ISIsjPtnrLbSIaZeemlxDpHhYVwf\ngPI/9U11v4VcQGX1vts460w90KZZY+49vwdLsnczfUVeoMMxxqt2FR/lo5X5/CItmcS46ECHE/Kq\nu034t8B8EckCtjvLOgBdgdt9HZjxn6uGdOCTjAL+/Ok6RnVPJCmuUaBDMsYrXl2yhTKXi8mnWxkQ\nb6iuvPnnQDfgUWCu83gE6O6sM/VEWJjw+MS+HC1z8cistbU6xuGSMo6WeneOcmPqYv/hUt5atpVx\n/drSoWVMoMOpF042MK6VPDwejhKRV0WkUETWVFj2mIisEpEMEZknIpVOcyci14pIlvO41tPPNLXT\nJbEJU85OZfbqncxdu9Pj/VSVT1cVMOJvCzjrHwtZbMUNTZB467utHCop56aR9Wb6noCr7iqp0UAW\n7l7FWOfxKJDlrPPE68CYE5Y9qar9VHUA8CnwUCWf3QJ4GBiK+yT7wyJSt7vVzElNPqMzPVrH8eDH\na9h/pPSk2xcdOMat01Zy+9s/0C6+MTHREVzz6vfc9+Eqio+efH9jfOVoaTmvLtnCyG6J9G7r3asL\nG7LqzmE8DZyjqrkVF4pIJ2A2cNK62qq6WERSTlhW8dbiWNy9lhOdB3yhqnudz/wCd+J552SfaWov\nMjyMv1/aj4ue+4Yn5mzg8Uv6VrqdqjIrs4BHZq3l0LFy7hnTncmnd6bMpTw9P4sXF21m0aYiHr+k\nL6O6J/m5FcbA9PTt7DlUYiXMvay6IakIoLLLZvKBOl3MLCJ/EZHtwFVU0sMA2vG/E+04cdj9/H7Q\nLzmeSSM68c7321iWs+dn6wuLjzL5zRVMeTeDji1jmT1lBLeO6kpEeBiNIsO5d0wPPrp1OE2iI7ju\nteXc80GmR70VY7ylrNzFi4tzGNghnqGdrOydN1WXMF4Fljt3d1/pPO4FvgNeqcuHquoDqtoemEYd\nr7gSkckiki4i6UVFNn7uDb87tzsdWsRw34er/nsiW1X5aGUe5z61mMWbirh/bA8+vGUYXZPifrb/\ngPbxfHLHCG4d1YUPV+Zz3lOLWbCh0N/NMA3UZ6t3kPfjEW4Z2cVKmHtZdVdJPQ5cifvu7tOchwBX\nOeu8YRru8uknygfaV3if7CyrLM6XVDVNVdMSE22aDm9oHBXO45f0JXfPYf71ZRa7io9y4xvp/O79\nTLomNWH2lNOZfEb1FT8bRYZzz5gezLh1GE0bR3D968u5e3rmT8q1G+NtqsoLCzfTNakJ5/RsFehw\n6p1qp2tT1fXAem9+oIikqurxutoTgA2VbDYX+GuFE92jgT94Mw5TveFdE/hFWjL/93UO077bSmm5\niwfH9eK6YSk1Kg3dL9nd2/j3/GxeWLSZr7OKeGhcb87r3coKHhqvm7NmJxt2HuAfl/UnzEqYe93J\n5/eshIjMUdXzPdjuHWAUkCAiebivfBorIt1xX567FbjZ2TYNuFlVb1TVvSLyGLDcOdSfjp8AN/7z\nwNhefJO9h3bNG/P3if1ISYit1XGiI8K5+7zunNe7NXdPz+S2t1fStlkjrhjSgcuHtLcbBY1XLN5U\nxJ3vZdCzTVPG96/0an1TR1JV/SARGVTVPsCnqtrGZ1HVUlpamqan21Qd3lTuUq9ONlNW7uLL9bt4\na9k2lmTvJiJMGN27Fb8a2pHTurS0MWdTKws3FjL5zRV0SWzCtBuH0iI2KtAhhQwRWaGqaZ5sW10P\nYzmwCHeCOFF8bQIzocfbM5NFhIcxpk8bxvRpw5bdh3j7u61MX5HH7NU76ZwYy1VDO3LpoGSaxVhV\nUeOZBRsKuenNFaS2asJbk4bS3JKFz1TXw1gDXFzhfEPFddudq5yCivUwQtPR0nI+W7WDt77byg/b\n9tEoMowL+7Xl+uGd6NW2aaDDM0Fs/vpd3PLWSrq1dieL+BhLFjXlrR7GI1R9FdUdNQ3KmKo0igxn\n4uBkJg5OZk3+fqZ9t42ZGfnMzChg7p1n0KmW505M/TZv7U5ue3slPds05c0bhlqv1A+qu6z2A1Xd\nWMW6j30XkmnI+rRrxuOX9OWru0YRFRHGnz6pXTFEU799vmYnt05bSa+2zXhzkiULf7HrGk1Qat2s\nEVPOTmXBxiLmr98V6HBMEJmzege3v72SvsnNeHPSEJo1tmThL5YwTNC6dlgKXRJj+dOn66x0ugHg\n01UF3P7OD/RvH8/UG4bQ1KZc9atqE4aIhInIMH8FY0xFURFhPDK+N1v3HOaVJVsCHY4JsFmZBUx5\nN4NBHeJ544YhNj93AFSbMFTVBTznp1iM+ZnTUxM5r3crnv0qm4J9RwIdjgmQuWt38tt3f2Bwx+a8\nfv0QmkTX6p5jU0eeDEnNF5GJYndUmQD54wW9cKny+JzKqsiY+s7lUv42ZwPdWsXx+vWnEGvJImA8\nSRg3AdOBEhEpFpEDIlJ8sp2M8Zb2LWK4eWQXPsksqLTkuqnfvtm8m5zdh7hpZGdioixZBNJJE4aq\nxqlqmKpGqmpT573dTWX86pZRXWgX35hHZq2lrNzjWYJNPTB16VZaxkYxtm/QVSNqcE6aMMTtVyLy\noPO+vYgM8X1oxvxPo8hwHhzXkw07DzDtu22BDsf4Sd6Ph5m/fheXn9Ke6IjwQIfT4HkyJPU87rkw\nrnTeH8ROhJsAOK93a0Z0TeCf8zay5+CxQIdj/OD4HwdXndoxwJEY8CxhDFXV24CjAKr6I2AFW4zf\niQgPX9iLwyXl/GNepUUITD1ytLSc95Zv5+yerWgX3zjQ4Rg8SxilIhIOKICIJOKey8IYv0ttFce1\nw1J4d/l2VuXtC3Q4xodmr97B3kMlXHOa9S6ChScJ4xlgBtBKRP4CLAH+6tOojKnGlHNSaRkbzcOz\n1uJyVV5t2YS+qUu30jkhluFdEgIdinF4cpXUNOAe3EmiALhIVaf7OjBjqtK0UST3junOD9v28dEP\nlU71bkLc6rz9ZGzfx69O7WhTrQYRT2tJxQDhzvY2mGgCbuKgZAZ2iOeJORs4cLQ00OEYL5u6NJeY\nKHfZexM8PLms9iHgDaAFkAC8JiJ/9HVgxlQnLEx4dHxv9hw6xjPzfzbHlwlhPx4qYVZmARcNbGeV\naIOMJz2Mq4BTVPURVX0YOBW4+mQ7icirIlLozNx3fNmTIrJBRFaJyAwRqXSqVxHJFZHVIpIhIjaF\nnqlUv+R4Lk9rz2vf5LJ+hxUfqC/eT9/OsTKXnewOQp4kjAKgUYX30YAnA8evA2NOWPYF0EdV+wGb\ngD9Us/+ZqjrA06kDTcN075gexMdEcvf0TErtDvCQV+5S3vpuK0NSWtCjtRWUCDaeJIz9wFoReV1E\nXgPWAPtE5BkReaaqnVR1MbD3hGXzVLXMebsMsAFKUyfNY6P480V9WVtQzPMLNgc6HFNHizYVsn3v\nEa623kVQ8qSS1wzncdxCL332DcB7VaxTYJ6IKPCiqr7kpc809dCYPq0Z378t//4qi3N7taJXW/vL\nNFRNXbqVxLhozuvdOtChmEqcNGGo6hve/lAReQAoA6ZVsckIVc0XkSTgCxHZ4PRYKjvWZGAyQIcO\nHbwdqgkRj47vzbeb93D39Exm3j6cyHCbTDLUbN1ziEWbirjjrFSiIuz7C0Z+/1ZE5DpgHHCVqlZ6\n15Wq5jvPhbh7N1UWO1TVl1Q1TVXTEhMTfRCxCQXNY6P468V9WLejmOcWZAc6HFMLby3bSpgIVw6x\nP/yClV8ThoiMwX0T4HhVPVx5it5eAAAYHElEQVTFNrEiEnf8NTAa93kTY6o1undrLhrQlme/ymZt\nwf5Ah2Nq4EhJOe+n5zGmd2taN2t08h1MQNQoYThzfHs0QCwi7wBLge4ikicik4BngTjcw0wZIvIf\nZ9u2IjLb2bUVsEREMoHvgc9U9fOaxGkarkfG96Z5bBR3vZ9JSZldNRUqPsksYP+RUjvZHeROeg5D\nRN4GbgbKgeVAUxF5WlWfrG4/Vb2iksWvVLFtATDWeZ0D9D9ZXMZUJj4mir9e3JdfT03n2QXZ/O7c\nboEOyZyEqvLG0ly6tWrC0E4tAh2OqYYnPYxeqloMXATMATrhwY17xgTKub1accnAdjy3IJs1+TY0\nFexWbtvH2oJirj4tBRGrGxXMPEkYkSISiTthzFLVUpxS58YEq4cv7E3L2Cjunm5DU8HuzaW5NImO\n4OKB7QIdijkJTxLGi0AuEAssFpGOgNVhMEGtWUwkj1/Slw07D/Dvr6zWVLDaffAYs1fvZOKgdjSJ\n9uS2MBNInpQ3f0ZV26nqWHXbCpzph9iMqZOze7bikkHteH7hZlbn2dBUMHpv+XZKyl12sjtEeFKt\ntpWIvCIic5z3vYBrfR6ZMV7w8LjeJDSJ4q7pGRwrKw90OKaCkjIXby7dyvCuLemaFBfocIwHPBmS\neh2YC7R13m8CfuurgIzxpuNDU5t2HbQy6EFmZkY+O4uP8uvTOwc6FOMhTxJGgqq+jzOPt1M80P5U\nMyHjrB6tuHRwMv9ZlMP7y7dTRYEB40cul/LS4hx6tI5jZDer0BAqPEkYh0SkJc6VUSJyKu4KtsaE\njAfH9SKtY3Pu+XAVN76RTmHx0UCH1KAt2FhIVuFBbh7ZxS6lDSGeJIzfAbOALiLyDTAVuMOnURnj\nZc0aR/LOr0/lwXG9WJK9m3OfWszMjHzrbQTIfxZtpl18Yy7o1ybQoZga8OQqqZXASGAYcBPQW1VX\n+TowY7wtLEyYNKITs6ecTufEWKa8m8Gt01ay5+CxQIfWoKzYupfluT8yaUQnqyocYqq88FlELqli\nVTcRQVU/8lFMxvhUl8QmfHDzMF5anMNTX2zi+y2L+cvFfRjTx/7a9YcXF+XQrHEkl5/SPtChmBqq\n7k6ZC53nJNy9i6+c92cC3wKWMEzICg8TbhnVhbN6JHHX9AxufmslEwa05dHxvYmPiQp0ePVWduFB\nvli/izvO7Eqs3agXcqrsD6rq9ap6PRCJu57URFWdCPR2lhkT8rq3jmPGrcO585xufLZqB6OfWsxX\nG3YFOqx66+Wvc4gKD+OaYSmBDsXUgicDiO1VdUeF97sAm+HE1BuR4WFMOSeVj28bTvOYKG54PZ3Z\nq3ecfEdTI4XFR/loZT6XpSWT0CQ60OGYWvAkYcwXkbkicp0zW95nwJe+DcsY/+vTrhmz7hhOm2aN\n+HRVQaDDqXde+zaXMpeLG0fYjXqhypM5vW93ToCf7ix6SVVn+DYsYwIjOiKcM1ITmbNmB2XlLiLs\nKh6vOHC0lLeWbeX8Pm1ISYgNdDimljz636CqH6nqnc7DkoWp107vlkDx0TIyrWCh17zz/TYOHC3j\nppHWuwhlVSYMEVniPB8QkeIKjwMiYuXNTb01omsCIrB4U1GgQ6kXSspcvLJkC8O6tKRfcnygwzF1\nUN1VUiOc5zhVbVrhEaeqHs3rbUwoio+Jol9yPF9nWcLwhpkZ+ewqPsZNI7sEOhRTR56UNz9VROIq\nvI8TkaEe7PeqiBSKyJoKy54UkQ0iskpEZohIpX9uiMgYEdkoItkicp+njTHGW0amJpCxfR/7D5cG\nOpSQVrHI4BmpCYEOx9SRJ+cwXgAOVnh/yFl2Mq8DY05Y9gXQR1X74S6T/ocTdxKRcOA54HygF3CF\nMweHMX5zRrdEXArfbN4d6FBCmhUZrF88SRiiFSq0qaoLz66uWgzsPWHZPKc8OsAyILmSXYcA2aqa\no6olwLvABA/iNMZr+rePJy46woal6ujFRTlWZLAe8SRh5IjIb0Qk0nlMAXK88Nk3AHMqWd4O2F7h\nfZ6zzBi/iQwPY1jXlizetNsq2tbSiq0/8n3uXisyWI948i3ejLuWVD7uX95Dgcl1+VAReQAoA6bV\n5TjOsSaLSLqIpBcV2V+DxnvO6JZI/r4jbC46FOhQQtKLizYTHxPJL4dYkcH6wpOhpULgl976QOdu\n8XHA2Vr5n275QMWfsGRnWVXxvQS8BJCWlmZ/ChqvOSPVPRPc11lFdE1qEuBoQkvFIoMxUVZksL6o\nrrz5Par6dxH5N85sexWp6m9q+mEiMga4Bxipqoer2Gw5kCoinXAnil8CV9b0s4ypq/YtYuiUEMvi\nTUVcP7xToMMJGarK8wuzrchgPVRd6l/vPKfX5sAi8g4wCkgQkTzgYdxXRUUDXzhXTCxT1ZtFpC3w\nsqqOVdUyEbkdmAuEA6+q6traxGBMXZ2RmsD76XkcKysnOiI80OEEve17D/PAx2tYvKmIG4Z3siKD\n9UyVCUNVP3Ge36jNgVX1ikoWv1LFtgXA2ArvZwOza/O5xnjT6amJvLF0Kytyf2RYV7uPoCrlLuW1\nb7bwz3mbCBN4dHxvfnVqx0CHZbzspIOLIrKAyoekzvJJRMYEkdO6tCQyXFiUVWQJowrrCoq576NV\nrMrbz9k9knjsoj60jW8c6LCMD3hyNuruCq8bARNxX+FkTL0XGx3B4I7NWbxpN384P9DRBJejpeU8\nPT+Llxbn0Dwmkn9fMZBx/drYDXr1mCdXSa04YdE3IvK9j+IxJuicnprIk3M3UnjgKElxjQIdjtet\nLdjPTW+uoFXTRvRp25Te7ZrRp20zUls1qfL+iW837+b+j1aTu+cwlw1O5oELetrUtg2AJ0NSLSq8\nDQMGA818FpExQWZkN3fCWJK1m0sGVVacIHQVHy3ltmkrOVrqIjxM+HBlPm8s3QpAVHgYPdrE0btt\nM/q0a0qfts1o06wR/5y3iffSt9OxZQzTbhzKcBuqazA8GZJagfschuAeitoCTPJlUMYEk15tmtIy\nNorFm4rqVcJQVe77cBXbfzzCe5NPJS2lBS6XsnXvYdbk72dNwX7W5hcze/UO3vl+23/3Cw8Tbh7Z\nhSlnp9I4yq4ca0g8GZKyC9BNgxYWJoxITWBJ9m5cLiUsrH6M0b/xbS6zV+/kD+f3IC3FPZAQFiZ0\nSoilU0IsF/ZvC7gTS96PR1hbsJ/swoOc2SOJ3m1tkKEh8qS8+WXHy5uLyB9F5CMRGeT70IwJHmek\nJrL7YAnrdgR+7rDc3YeYujSXsnJXrY+RsX0ff5m9nnN6JvHr06ufBU9EaN8ihjF92nD7WamWLBow\nT2pJPaiqB0RkBHAO7nspPClvbky9cbozl8PiAFevPXisjOtfX85DM9dy81srOFxS8wsW9x0u4bZp\nK0mKa8Q/Lutfb3pMxvc8SRjlzvMFwEuq+hlgl0OYBiWpaSN6tI7j602Bmx9DVXlgxmq27jnEdcNS\n+GpDIVf+33fsOXisRse4e3omhQeO8txVg+zKJlMjniSMfBF5EbgcmC0i0R7uZ0y9MrJbIulb93Lo\nWGBuQ3pv+XZmZhRw5zndeGR8b/7zq8Gs31HMxBe+Zesezyrq/t/XOXy5vpAHxvZkQHubX9vUjCe/\n+H+Bu67Teaq6D2gB/N6nURkThE5PTaS0XFmWs8fvn71hZzEPz1rLiK4J3HpmVwBG927N278+lf1H\nSrnk+W/J3L6v2mOk5+7lb59vZGzf1lxrRQFNLZw0YajqYVX9SFWznPc7VHWe70MzJrikpTSnUWQY\nX2f5d1jq0LEybpu2kqaNI3nq8gGEVzjnMLhjcz64ZRiNo8L55UvLWLChsNJj7Dl4jNvf/oH2zRvz\nxMR+dje2qRUbWjLGQ40iwzm1c0sWb/Lvie8HZ64hZ/chnr58AIlxP6/+2iWxCR/dOowuSbHcODWd\n95Zv+8l6l0u58/1M9h4u4bmrBtG0UaS/Qjf1jCUMY2rg9NREcnYfYvveqqZz8a7p6dv5aGU+vzkr\ntdrih0lxjXh38mkM75rAvR+u5qkvNv13atnnF2azeFMRj47vbZfEmjqxhGFMDYzs5v6l7Y9hqaxd\nB3ho5lpO69yS35ydetLtm0RH8Mq1aUwclMzT87O478PVfJ1VxP/7YhMXDWjLL0+xqVJN3djcicbU\nQJfEJrRt1ojFm4q4cmgHn33OkZJybnt7JbHR4Tz9y5+et6hOZHgY/7isH23jG/Hvr7J5f8V2OifE\n8peL+9p5C1NnljCMqQER4fTURGav2UFZuYuIKqq51tXDs9aQVXiQqTcMIalpzSrkigh3je5Om2aN\neWVJDi/8ajCx0fZf3dSdDUkZU0NndEvkwNEyMvOqv4y1tmb8kMf76XncNqorp6cm1vo4Vw7twPy7\nRtGtVZwXozMNmSUMY2poeNeWhAks8sFd39mFB3lgxhqGpLTgt+ec/LyFMf7ks4QhIq+KSKGIrKmw\n7DIRWSsiLhFJq2bfXBFZLSIZIpLuqxiNqY34mCj6Jcd7/fLao6Xl3P72ShpFhvPMFQN9NtxlTG35\n8ifydWDMCcvWAJcAiz3Y/0xVHaCqVSYWYwLljG6JrMrbx77DJV475t8/38iGnQf45y/607pZ/ZvZ\nz4Q+nyUMVV0M7D1h2XpV3eirzzTGX85ITcCl8E22d8qEbNtzmDeX5fLLU9pzZvckrxzTGG8L1j6v\nAvNEZIWITA50MMacaED7eJo1juSDFdu9crynvtxEmAi/PaebV45njC8Ea8IYoaqDgPOB20TkjKo2\nFJHJIpIuIulFRYGdq8A0HBHhYdwyqgsLNhaxcGPl9Zs8tWFnMR9n5HPd8BQbijJBLSgThqrmO8+F\nwAxgSDXbvqSqaaqalphY+0sQjamp64enkNIyhj99uo6SstrPfvePuRtpEh3BLSO7eDE6Y7wv6BKG\niMRWmBI2FhiN+2S5MUElOiKchy7sRU6Re8rU2kjP3cuX6wu5eWQXm8zIBD1fXlb7DrAU6C4ieSIy\nSUQuFpE84DTgMxGZ62zbVkRmO7u2ApaISCbwPfCZqn7uqziNqYuzerRiVPdEnv4yi6IDns98B+7Z\n7/72+QYS46K5fniKbwI0xot8Vi9AVa+oYtWMSrYtAMY6r3OA/r6Kyxhve3BcL857ajFPzt3A3y/1\n/Ed34cYiluf+yGMTehMTZaU7TPALuiEpY0JNl8QmXD88hekr8ljlYbkQl0v5+9yNdGgRw+Wn+K6I\noTHeZAnDGC+44+xUWsZG8cistf+dh6I6n6wqYP2OYu4a3Y2oCPtvaEKD/aQa4wVNG0Vyz3k9WLlt\nHx9n5Fe7bUmZi3/O20SP1nFc2K+tnyI0pu4sYRjjJZcOTqZfcjOemLOBQ8fKqtzuvfTtbNt7mHvH\n9CDMw3kujAkGljCM8ZKwMOGR8b3ZVXyM5xZkV7rN4ZIynpmfxZCUFozqbvcNmdBiCcMYLxrUoTmX\nDGzHy19vYeueQz9b/9o3uRQdOMY9Y7rbDHgm5FjCMMbL7j2/BxHhwp8/W/+T5fsPl/Lios2c0zOJ\ntJQWAYrOmNqzhGGMl7Vq2ojbz+rKF+t28XXW/+qbvbBoMweOlXH3ed0DGJ0xtWcJwxgfmDSiEx1b\nxvDoJ+soLXexc/9RXvtmCxcNaEeP1k0DHZ4xtWIJwxgfiI4I548X9CK78CBTl27lma+ycKlyp5Uv\nNyHM6hEY4yPn9EzijG6J/OuLTRwuLedXQzvQoWVMoMMyptash2GMj4gID43rxZHScqIjwrj9rNRA\nh2RMnVgPwxgf6prUhH/+oj/REeEkxkUHOhxj6sQShjE+NmFAu0CHYIxX2JCUMcYYj1jCMMYY4xFL\nGMYYYzxiCcMYY4xHLGEYY4zxiCUMY4wxHrGEYYwxxiOWMIwxxnhEPJmwPlSISBGwtZa7JwC7vRhO\nINWXttSXdoC1JRjVl3ZA3drSUVU9mv6xXiWMuhCRdFVNC3Qc3lBf2lJf2gHWlmBUX9oB/muLDUkZ\nY4zxiCUMY4wxHrGE8T8vBToAL6ovbakv7QBrSzCqL+0AP7XFzmEYY4zxiPUwjDHGeKTeJgwReVVE\nCkVkTYVl/UVkqYisFpFPRKSpszxSRN5wlq8XkT9U2CfXWZ4hIukh0JYoEXnNWZ4pIqMq7DPYWZ4t\nIs+IiIRwWxaKyEbne8kQkSQ/t6O9iCwQkXUislZEpjjLW4jIFyKS5Tw3d5aL82+eLSKrRGRQhWNd\n62yfJSLX+rMdPmhLeYXvZFYItKWH87N3TETuPuFYY5yfsWwRuS+E2+G932GqWi8fwBnAIGBNhWXL\ngZHO6xuAx5zXVwLvOq9jgFwgxXmfCySEUFtuA15zXicBK4Aw5/33wKmAAHOA80O4LQuBtAB+J22A\nQc7rOGAT0Av4O3Cfs/w+4G/O67HOv7k438F3zvIWQI7z3Nx53TwU2+KsOxio76SWbUkCTgH+Atxd\n4TjhwGagMxAFZAK9Qq0dzrpcvPQ7rN72MFR1MbD3hMXdgMXO6y+Aicc3B2JFJAJoDJQAxf6I0xM1\nbEsv4Ctnv0JgH5AmIm2Apqq6TN0/RVOBi3wd+4m80RY/hHlSqrpDVVc6rw8A64F2wATgDWezN/jf\nv/EEYKq6LQPine/kPOALVd2rqj/ibv8YPzbFm20JuJq2RVULVXU5UHrCoYYA2aqao6olwLvOMfzC\ni+3wqnqbMKqwlv996ZcB7Z3XHwCHgB3ANuAfqnr8l5oC80RkhYhM9mewJ1FVWzKB8SISISKdgMHO\nunZAXoX985xlwaCmbTnuNaeb/WAghteOE5EUYCDwHdBKVXc4q3YCrZzX7YDtFXY7/u9f1fKAqGNb\nABqJSLqILBMRv/9BUpGHbalK0HwvdWwHePF3WENLGDcAt4rICtzdvBJn+RCgHGgLdALuEpHOzroR\nqjoIOB+4TUTO8HPMVamqLa/i/uFOB/4FfIu7bcGsNm25SlX7Aqc7j6v9GrFDRJoAHwK/VdWf9Eqd\nnlzIXIbopbZ0VPcdx1cC/xKRLt6P9OTqy/fipXZ47XdYg0oYqrpBVUer6mDgHdxjlOD+4f5cVUud\noY9vcIY+VDXfeS4EZuBOLgFXVVtUtUxV71TVAao6AYjHPf6ZDyRXOESysyzgatGWit/LAeBtAvC9\niEgk7v/M01T1I2fxruPDM85zobM8n5/2jo7/+1e13K+81JaK30sO7vNMA30e/Alq2JaqBPx78VI7\nvPo7rEEljONX0ohIGPBH4D/Oqm3AWc66WNwn8jaISKyIxFVYPhpYc+JxA6GqtohIjBMrInIuUKaq\n65xubLGInOoM31wDzAxM9D9V07Y4Q1QJzvJIYBx+/l6cf8NXgPWq+v8qrJoFHL/S6Vr+9288C7jG\nucLoVGC/853MBUaLSHPnipfRzjK/8VZbnDZEO8dMAIYD6/zSCEct2lKV5UCqiHQSkSjgl84x/MJb\n7fD67zBvnDkPxgfuv1R34D4JlAdMAqbg/gt1E/AE/7txsQkwHfdY+jrg987yzrjH0TOddQ+EQFtS\ngI24T5J9iXuI4Phx0pwfls3As8f3CbW2ALG4r5ha5XwvTwPhfm7HCNzDAauADOcxFmgJzAeynJhb\nONsL8Jzzb7+aCld44R6Sy3Ye1wfgO/FKW4BhzvtM53lSCLSltfNzWIz7ooo83BeH4Oy3yWmnX//v\ne6sdePl3mN3pbYwxxiMNakjKGGNM7VnCMMYY4xFLGMYYYzxiCcMYY4xHLGEYY4zxiCUMY4wxHrGE\nYUwQEZHwQMdgTFUsYRhTSyLyJxH5bYX3fxGRKSLyexFZLu65Ih6tsP5jpwDc2opF4ETkoIj8U0Qy\ngdP83AxjPGYJw5jaexV3iZXjZU1+ibuCaCruej0DgMEVir3doO56WWnAb0SkpbM8FvecEv1VdYk/\nG2BMTUQEOgBjQpWq5orIHhEZiLvM9A+4J7EZ7bwGd9mZVNzzffxGRC52lrd3lu/BXYH3Q3/Gbkxt\nWMIwpm5eBq7DXcvnVeBs4HFVfbHiRuKeXvYc4DRVPSwiC4FGzuqjqhrsJeiNsSEpY+poBu4Z8k7B\nXWV2LnCDM48BItLOqcbbDPjRSRY9cFdENiakWA/DmDpQ1RIRWQDsc3oJ80SkJ7DUmQTwIPAr4HPg\nZhFZj7sC77JAxWxMbVm1WmPqwDnZvRK4TFWzAh2PMb5kQ1LG1JKI9MI9h8V8SxamIbAehjHGGI9Y\nD8MYY4xHLGEYY4zxiCUMY4wxHrGEYYwxxiOWMIwxxnjEEoYxxhiP/H+DO9bFITOWAAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#draw a plot to see the trend during years\n",
    "plt.plot(US_per100k)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('suicides per 100k in the US');\n",
    "print(\"Minimum Suicide Rate:\", (US_per100k.idxmin(), US_per100k.min()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "90Mh3ypKmCeq"
   },
   "source": [
    "In the plot we create for US suicide rates we notice that suicide rates had a downwards trend starting from the mid 80's. However after the turn of the century, suicide rates began to shoot up, with our most recent data being the highest it's ever been."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uHP8ztU6YYj9"
   },
   "outputs": [],
   "source": [
    "#group data by year and calculate suicides/100k each year globally\n",
    "pop_sum=total_data.groupby('year')['population'].sum()\n",
    "sui_sum=total_data.groupby('year')['suicides_no'].sum()\n",
    "per100k=(sui_sum / pop_sum) * 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "dblK-utYAVkt",
    "outputId": "85545051-5734-48af-ad84-a2b23a25cfe8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peak Suicide Rate: (1995, 15.312412627818665)\n"
     ]
    },
    {
     "data": {
      "image/png": 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lc0uxjd9p5zxJGAdEpBugACIyDtcMtsaPtu0p454PspmbvZN+CR157ZqxTOgf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\nFm6uwEQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#draw a plot to see the trend during years\n",
    "plt.plot(per100k)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('suicides per 100k');\n",
    "print(\"Peak Suicide Rate:\", (per100k.idxmax(), per100k.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OMh5D3kTksMf"
   },
   "source": [
    "Completely contrasting the US, in the plot we create for global suicide rates we notice that suicide rates had shot up from the late 80s to the mid 90s with a peak suicide rate in 1995 of about 15.3 deaths per 100k people. However, while rates in the US had risen, rates for most of the world began to slowly drop after this peak."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OXykQvLpT1L3"
   },
   "source": [
    "## 3.4 Suicides By Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wufx7dqiHgnf"
   },
   "source": [
    "In this section, we analyzed our data set by seeing how suicide rates correlate to gender. We begin by comparing the rates of suicide between males and females of different age groups. Globally, the likelihood of suicide seems to increase as age increases. Overall though, the rate of suicide is higher for men than it is for women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "_nHm0Ca9cgKU",
    "outputId": "37373f7b-6540-47db-a966-755f09654964"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    0.210160\n",
       "male      0.881009\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group data by sex and calculate suicides/100k\n",
    "sui_sum_sex=data.groupby('sex')['suiciderate'].sum()\n",
    "pop_sum_sex=data.groupby('sex')['population'].sum()\n",
    "per100k_sex=(sui_sum_sex / pop_sum_sex) * 100000\n",
    "per100k_sex\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "CDhwwver3nLw",
    "outputId": "e6300025-113c-4066-e6ae-e0294ed736e0"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nm6/cue0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#draw a bar/histograph\n",
    "names=['female','male']\n",
    "plt.bar(names,per100k_sex,align='center', alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1aGvgYHGv32N"
   },
   "source": [
    "From the bar graph and table above, we can see that the suicide rate for men is much higher than that for women. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "-4vyABqRPwNA",
    "outputId": "3d049770-10f0-48c6-87e2-3d0ee4459639"
   },
   "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>sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5-14 years</th>\n",
       "      <td>0.559495</td>\n",
       "      <td>0.766707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-24 years</th>\n",
       "      <td>4.231605</td>\n",
       "      <td>13.038395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-34 years</th>\n",
       "      <td>4.362907</td>\n",
       "      <td>18.409907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-54 years</th>\n",
       "      <td>5.632860</td>\n",
       "      <td>22.758791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-74 years</th>\n",
       "      <td>6.281674</td>\n",
       "      <td>24.239558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75+ years</th>\n",
       "      <td>7.848047</td>\n",
       "      <td>36.699930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex            female       male\n",
       "age                             \n",
       "5-14 years   0.559495   0.766707\n",
       "15-24 years  4.231605  13.038395\n",
       "25-34 years  4.362907  18.409907\n",
       "35-54 years  5.632860  22.758791\n",
       "55-74 years  6.281674  24.239558\n",
       "75+ years    7.848047  36.699930"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_age = data.pivot_table(index = 'age', columns = 'sex', values = 'suiciderate')\n",
    "reorderlist = ['5-14 years', '15-24 years', '25-34 years', '35-54 years', '55-74 years', '75+ years']\n",
    "sex_age = sex_age.reindex(reorderlist)\n",
    "sex_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y7tJjMRnwFSl"
   },
   "source": [
    "The table above shows the number of suicides per 100,000 people in each age group. We can see that as age increases, there are more suicides per age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "WGiFEHiTP2eu",
    "outputId": "ee9b4cda-59c7-46f3-d7b6-8d6c93a7fbef"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tWzds2DA+++wzUlJSqFGjBiNHjgRg27ZtxMfHEx8fz9SpUxkzZoxv+ejRo9mO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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sex_age.T.plot(kind='bar', rot=0)\n",
    "ylab = ax.set_ylabel('suicides/100k pop')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_IU9v-bmwPJp"
   },
   "source": [
    "Above is a bar graph representation of the number of suicides per 100,000 people in each age group, split up by males and females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "1HBhmun8xd3f",
    "outputId": "ab16ed0b-7dbd-4b9c-eb87-28f2a19e4f30"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            suiciderate   R-squared:                       0.190\n",
      "Model:                            OLS   Adj. R-squared:                  0.190\n",
      "Method:                 Least Squares   F-statistic:                     1205.\n",
      "Date:                Thu, 13 Jun 2019   Prob (F-statistic):          2.48e-237\n",
      "Time:                        02:05:34   Log-Likelihood:                -21193.\n",
      "No. Observations:                5132   AIC:                         4.239e+04\n",
      "Df Residuals:                    5130   BIC:                         4.240e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept       4.8427      0.297     16.308      0.000       4.261       5.425\n",
      "sex[T.male]    14.5774      0.420     34.712      0.000      13.754      15.401\n",
      "==============================================================================\n",
      "Omnibus:                     3398.829   Durbin-Watson:                   1.142\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            67193.993\n",
      "Skew:                           2.875   Prob(JB):                         0.00\n",
      "Kurtosis:                      19.768   Cond. No.                         2.62\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#linear model for gender\n",
    "outcome_gender, predictors_gender = patsy.dmatrices('suiciderate ~ sex', data) \n",
    "mod_gender = sm.OLS(outcome_gender,predictors_gender) \n",
    "res_gender = mod_gender.fit()\n",
    "print(res_gender.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GHL1dR6ywyMx"
   },
   "source": [
    "From this model, we can see how gender affects suicide rate. Males have a suicide rate that is 14.5774 percent larger than females. The p-value is = 0  which implies that are results are statistically significant. The R-squared value, however, is 0.190 so only a small percentage of the variability in the data can be explained by this model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fXW22DDxM8RU"
   },
   "source": [
    "## 3.5 Geographic Analysis on Suicide Rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7gaWIHBqGs7Z"
   },
   "source": [
    "We aggregated the suicide rate data and the number of suicides for each country to build an overall map of suicide rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L7d8w2OGNJEG"
   },
   "outputs": [],
   "source": [
    "suicide_data = data.groupby(data['country'])['suiciderate','suicides_no'].sum()\n",
    "\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "\n",
    "suicide_countries = pd.Series(suicide_data.index)\n",
    "total_countries = pd.Series(world['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jmFkVTtUHjsE"
   },
   "source": [
    "Overall, we're still missing data on 97 of the countries that will be mapped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "fSy5aP9RHe8t",
    "outputId": "749775ff-66a3-4b3c-a0f1-b9e7b02dbd4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_countries = total_countries[~total_countries.isin(suicide_countries)].sort_values()\n",
    "len(missing_countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Mdzo1TPRNcVf"
   },
   "outputs": [],
   "source": [
    "suicide_world = pd.merge(world, suicide_data, left_on='name', right_on='country', how='left')\n",
    "suicide_world['suicides_no'].fillna(0, inplace=True)\n",
    "suicide_world['suicides_per_pop'] = suicide_world['suicides_no'] / suicide_world['pop_est']\n",
    "\n",
    "base_layer = suicide_world[~suicide_world['name'].isin(missing_countries)]\n",
    "missing_layer = suicide_world[suicide_world['name'].isin(missing_countries)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "colab_type": "code",
    "id": "2oW3SoiuNgcG",
    "outputId": "fbed25c3-7a58-4198-e466-4e398deaf56c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vwD1bKRYFAc3CEl69RhR4hL6P9NpIt03YbtJaWaA3amNEEYYMMKMQMwoAmHHy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2djc11ULFvH1P5tdNSH8DllQj4H2aJGz3CWbUcn2debZPOaGloTQdsrfAczMpXnhfd6cQQiyC43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6FA9Z5H8Nc2lrWeSilqtz9APFTB3x38hH+914/PpWFZNof687xzsYg3WkEmny1rubcikS1x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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1224x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(17,10))\n",
    "divider = make_axes_locatable(ax)\n",
    "\n",
    "ax.set_facecolor('lightblue')\n",
    "cax = divider.append_axes('right', size='5%', pad=0.2)\n",
    "\n",
    "base = base_layer.plot(ax=ax, \n",
    "                       column='suiciderate', \n",
    "                       cmap='OrRd', \n",
    "                       legend=True, \n",
    "                       cax=cax, \n",
    "                       edgecolor='grey')\n",
    "total = missing_layer.plot(ax=base, \n",
    "                           color='lightgrey', \n",
    "                           edgecolor='grey');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zck3pN83nIY7"
   },
   "source": [
    "In the map above, there are two things that become clear. The first is that there is a large amount of countries that seem to be omitted from the dataset. This can lead to skewed data towards certain regions that are more open to offering information on their suicide rates. Second, if we look at the data, countries on the eastern side of the world seem to have relatively higher suicide rates than those in the western part, with North America and South America having some of the lowest suicide rates (bar a few outliers in Asia and South Africa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "34KQxhuNEsHR"
   },
   "outputs": [],
   "source": [
    "gdp_world = data.groupby(data['country'])['gdp'].mean()\n",
    "suicide_world = pd.merge(suicide_world, gdp_world, left_on='name', right_on='country', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rDcKFIDkxZsn"
   },
   "outputs": [],
   "source": [
    "suicide_order = suicide_world[suicide_world[\"suicides_no\"] != 0].sort_values(by = ['suiciderate','name'],ascending=[True, False])\n",
    "colors = []\n",
    "for continent in suicide_order['continent']:    \n",
    "    if continent == 'Africa':\n",
    "        colors.append('r')\n",
    "    elif continent == 'Asia':\n",
    "        colors.append('b')\n",
    "    elif continent == 'Europe':\n",
    "        colors.append('g')\n",
    "    elif continent == 'North America':\n",
    "        colors.append('y')\n",
    "    elif continent == 'South America':\n",
    "        colors.append('c')\n",
    "    elif continent == 'Oceania':\n",
    "        colors.append('m')\n",
    "    else:\n",
    "        colors.append('w')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-m6lnBRdkyT_"
   },
   "source": [
    "In the below bar graph, we showed the suicide rate in different countries, and we can see each continent has their own colors. With the given data we have, Europe seems to have the highest suicide rate overall, but this can't be said with certainty because of the amount of missing data we have on other countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1158
    },
    "colab_type": "code",
    "id": "3LMKTi2Kx8FT",
    "outputId": "ff3673f5-e7b6-4afd-a9a2-8a7d22fc6841"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fBx4cp+3n11G/b5LvJlkJHAfsM1ajJPOTDCUZGvcskiRJkiTATHvPSfIU4CXA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0j72uF6YN449Ptw2/FjbeeP8cQvdKpfu/7nnwVObkbzDwemMYCqWlhVXVXVxcyZqx61JEmSJE0R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\nSfLZJD9N0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o9Jcmq7PjnJ8RM7O0mSJEma/My0a7Q2T3JVu76+qg4fawdJFgILAeYwZzzn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\nowztkiRJkiR11P8D0IGmFb9kppcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,20))\n",
    "plt.barh(suicide_order['name'],suicide_order['suiciderate'], color = colors)\n",
    "plt.xlabel('Suicide Rate', fontsize=15)\n",
    "plt.ylabel('Country', fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jpGQGRiqyMGN"
   },
   "outputs": [],
   "source": [
    "suicide_world_order = suicide_world[suicide_world[\"suicides_no\"] != 0].sort_values(by = ['gdp','name'],ascending=[True, False])\n",
    "colors = []\n",
    "for continent in suicide_world_order['continent']:    \n",
    "    if continent == 'Africa':\n",
    "        colors.append('r')\n",
    "    elif continent == 'Asia':\n",
    "        colors.append('b')\n",
    "    elif continent == 'Europe':\n",
    "        colors.append('g')\n",
    "    elif continent == 'North America':\n",
    "        colors.append('y')\n",
    "    elif continent == 'South America':\n",
    "        colors.append('c')\n",
    "    elif continent == 'Oceania':\n",
    "        colors.append('m')\n",
    "    else:\n",
    "        colors.append('w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_hShXBuklpFN"
   },
   "source": [
    "In the graph below, we show the correlation between GDP and suicide rate. We can see that there doesn't seem to be a trend for suicide rates between countries with low GDPs and c those with high GDPs. So, there is not much correlation between two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1175
    },
    "colab_type": "code",
    "id": "DZ2LV6Nj0ORK",
    "outputId": "c7a44928-0a12-421d-bf09-f94d5559d6ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Country Sorted by GDP')"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YJEmSJElrz5n26eEw4FdV9cGRgqpaClyf5JIk1yVZluRwgCTzktyS5IwkNyW5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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,20))\n",
    "plt.barh(suicide_world_order['name'],suicide_world_order['suiciderate'], color = colors)\n",
    "plt.xlabel('Suicide Rate Per Population', fontsize=15)\n",
    "plt.ylabel('Country Sorted by GDP', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "swFnIg4nCkUc"
   },
   "source": [
    "# 4. Ethics and Privacy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NG9l5Cy_CwjH"
   },
   "source": [
    "With suicide being a very important and touchy subject in today's society, the ethics surrounding suicide research and databases are extremely important. Data science can expose certain factors that cause events in society, however if not done with the ethics of the data gathered in mind can do more harm than good. The intent of our research was to find more information on what causes suicides globally. \n",
    "\n",
    "To determine the ethicality of our project, we will examine the question, implications of the project, data, informed consent, privacy, evaluation, analysis, transparency & appeal, and monitoring. \n",
    "\n",
    "First off, our question asks what causes suicides globally, the scope of our question covering dozens of countries over the last two decades. Our research could not be used for nefarious purposes, as the implications of our project are to gather information on what is causing suicides throughout the world, which could be used to help lower these rates. We got the data from a set on Kaggle called 'Suicide Rates Overview 1985 - 2016'. There are over 1000 values from dozens of countries over the years, making it reliable data. The data is organized by number of suicides per year, per country, male or female and in age groups. There are no names or any personal information listed, keeping identities of the people entirely private. \n",
    "\n",
    "Our project is simply to gather possible reasonings for suicide globally, which will be how we evaluate our data, resulting in nothing negative happening to anyone because of it. We have a variety of analytical elements to see correlations between county, age, gender, year and GDP to get the most non-biased analysis possible. Through our exploration of the data, a reader can tell that our research was not simply a black box and that there is unbiased reasoning for all of our conclusions. This transparency shows the reliability of our statements and that we can prove our reasoning for everything said. Finally, our data covers the span of almost two decades, so we are taking into account the changes of the world over time. If we were to continue this project, we would keep taking data from more recently and keep continuously monitoring, as the world is always changing. \n",
    "\n",
    "In regards to privacy, as said above, there are no names or personal information besides age and gender for anonymous people. Our dataset was kept completely anonymous to respect those who are a part of the list and to give the most accurate results with no biases or exposure. "
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    "# 5. Conclusion and Discussion"
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    "With the rise of suicide rates by 33% in the last two decades, we decided to see if there was a correlation between these rates and any other factors - such as gender, age and country GDP. We hypothesized that suicide rates would be higher for younger individuals, males, and those in countries with a weaker economy (lower GDP). First, we analyzed suicide rates by age, splitting our dataset into two groups: 5-34 and 35+. We discovered that those in the 35+ group had a higher rate of suicide (17.24 per 100K people), in comparison to the 5-34 group (6.96 per 100K people). When looking at a larger number of age groups (5-14, 15-24, 25-34, 35-54, 55-74, 75+), we saw that the rate of suicide slowly increased in each group, meaning the older the group, the higher the suicide rate. These observations directly contradict with our hypothesis, which we had stated that we thought younger individuals would have a greater suicide rate. Next we looked at suicides by gender, where we discovered that males have a greater rate than females (.88 in comparison to .21 per 100K people). We looked at suicide rate per gender per age group (listed above) and saw that suicide rates get larger as the age groups get older, with male suicide rates always being greater than females. This data matches our hypothesis, since we said we thought males would have a higher suicide rate than females. Finally, we analyzed geographically, to see if there was a correlation between country GDP and suicide rates. We discovered that there was not much of a correlation between country GDP and suicide rates, as from our graphs we saw there were countries with low GDP and countries with high GDP that had the same rates of suicide. The countries with the highest suicide rates (south Korea and Lithuania) were both in the middle for GDP. This does not prove our hypothesis correct, as we said that countries with lower GDP would have a higher rate of suicide. Overall in our project, through graphs, charts, and tables, we discovered that higher suicide rates come from males and older individuals. "
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