NHANES_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ssl\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import graphic\n",
    "import utils\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "pd.set_option('display.max_columns', 40)\n",
    "pd.set_option('display.precision', 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CURRENT_DIR = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### File names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "MENTAL_HEALTH_URL = \"https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DPQ_I.XPT\"\n",
    "DRUG_USE_URL = \"https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DUQ_I.XPT\"\n",
    "ALCOHOL_USE_URL = \"https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/ALQ_I.XPT\"\n",
    "OCCUPATION_URL = \"https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/OCQ_I.XPT\"\n",
    "SEXUAL_BEHAVIOUR_URL = \"https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/SXQ_I.XPT\"\n",
    "DEMOGRAPHIC_URL = \"https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DEMO_I.XPT\"\n",
    "INCOME_URL = \"https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/INQ_I.XPT\"\n",
    "REPRODUCTIVE_URL = \"https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/RHQ_I.XPT\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Columns:\n",
    "<br>\n",
    "<details>\n",
    "  <summary>Column codes vocabulary</summary>\n",
    "  \n",
    "### Mental Health\n",
    "\n",
    "#### NHANES Mental health variable dictionary:\n",
    "\n",
    "This information is taken from [NHANES resourse](https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/DPQ_H.htm)\n",
    "\n",
    "| Survey code | Meaning | Value Description type |\n",
    "| :--- | :--- | :---: |\n",
    "| DPQ010 | Have little interest in doing things | 1 |\n",
    "| DPQ020 | Feeling down, depressed, or hopeless | 1 |\n",
    "| DPQ030 | Trouble sleeping or sleeping too much | 1 |\n",
    "| DPQ040 | Feeling tired or having little energy | 1 |\n",
    "| DPQ050 | Poor appetite or overeating | 1 |\n",
    "| DPQ060 | Feeling bad about yourself | 1 |\n",
    "| DPQ070 | Trouble concentrating on things | 1 |\n",
    "| DPQ080 | Moving or speaking slowly or too fast | 1 |\n",
    "| DPQ090 | Thought you would be better off dead | 1 |\n",
    "| DPQ100 | How difficult have these problems made it for you to do your work, take care of things at home,<br> or get along with people? | 2 |\n",
    "\n",
    "#### Value descriptions \n",
    "#### Value description type 1: \n",
    "\n",
    " Value  Description \n",
    "\n",
    "0 – Not at all \\\n",
    "1 – Several day \\\n",
    "2 – More than half the days \\\n",
    "3 – Nearly every day \\\n",
    "7 – Refused \\\n",
    "9 – Don't know  \\\n",
    ". – Missing\n",
    "\n",
    "#### Value description type 2: \n",
    "\n",
    "0 – Not at all difficult  \n",
    "1 – Somewhat difficult  \n",
    "2 – Very Difficult  \n",
    "3 – Extremely Difficult  \n",
    "7 – Refused  \n",
    "9 – Don't know  \n",
    ". – Missing\n",
    "\n",
    "\n",
    "### Drug use\n",
    "#### NHANES Drug use variable dictionary:\n",
    "\n",
    "| Survey code | Meaning | Value Description type |\n",
    "| :--- | :--- | :---: |\n",
    "| DUQ240 | Ever used cocaine/heroin/methamphetamine | YES/NO |\n",
    "| DUQ430 | Ever been in rehabilitation program | YES/NO |\n",
    "\n",
    "#### Value descriptions \n",
    "\n",
    "#### Value description type YES/NO: \n",
    "1 – Yes\t\\\n",
    "2 – No \\\n",
    "7 – Refused \\\n",
    "9 – Don't know \\\n",
    ". – Missing\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ID = \"SEQN\"\n",
    "\n",
    "# Demographic:\n",
    "GENDER = \"RIAGENDR\"\n",
    "AGE = \"RIDAGEYR\"\n",
    "RACE = \"RIDRETH3\"\n",
    "EDUCATION = \"DMDEDUC2\"\n",
    "MARITAL_STATUS = \"DMDMARTL\"\n",
    "FAMILY_SIZE = \"DMDFMSIZ\"\n",
    "AGE_GROUP = \"RIDAGEYRGR\"\n",
    "ANNUAL_INC = \"INDFMIN2\"\n",
    "DEMOGRAPHIC_CODES = [GENDER, AGE, RACE, EDUCATION, MARITAL_STATUS, FAMILY_SIZE, ANNUAL_INC]\n",
    "# INCOME:\n",
    "POVERTY_INDEX = \"INDFMMPI\"\n",
    "FAMILY_INCOME = \"IND235\"\n",
    "FAMILY_INCOME_GROUP = \"IND235GR\"\n",
    "POVERTY_LEVEL_CATEGORY = \"INDFMMPC\"\n",
    "INCOME_CODES = [POVERTY_INDEX, FAMILY_INCOME, POVERTY_LEVEL_CATEGORY]\n",
    "# Mental health:\n",
    "DEPRESSION_CODES = [f\"DPQ0{i}\" if i < 100 else \"DPQ100\" for i in range(10,101,10)]\n",
    "DEPRESSION_SCORE = \"DPQSC\"\n",
    "DEPRESSION_GROUP = \"DPQGR\"\n",
    "DEPRESSED = \"DPQMDF\"\n",
    "# Drug use:\n",
    "DRUG_USE_CODES = (\"DUQ430\", \"DUQ240\")\n",
    "# Alco use:\n",
    "ALCOHOL_USE_CODES = (\"ALQ120Q\", \"ALQ120U\")\n",
    "ALCOHOL_USE_GROUP = \"ALQGR\"\n",
    "# Sexual Behaviour:\n",
    "ORIENTATION_FEMALE = \"SXQ295\"\n",
    "ORIENTATION_MALE = \"SXQ296\"\n",
    "ORIENTATION_BOTH = \"SXQBOTH\"\n",
    "SEXUAL_BEH_CODES = [ORIENTATION_FEMALE, ORIENTATION_MALE]\n",
    "# Reproductive Health:\n",
    "EVER_BEEN_PREGNANT = \"RHQ131\"\n",
    "TIMES_BEEN_PREGNANT = \"RHQ160\"\n",
    "REPRODUCTIVE_HEALTH_CODES = [EVER_BEEN_PREGNANT, TIMES_BEEN_PREGNANT]\n",
    "\n",
    "ALL_CODES = [\n",
    "    *DEPRESSION_CODES,\n",
    "    *ALCOHOL_USE_CODES,\n",
    "    *DRUG_USE_CODES,\n",
    "    *DEMOGRAPHIC_CODES, \n",
    "    *INCOME_CODES,\n",
    "    *SEXUAL_BEH_CODES,\n",
    "    *REPRODUCTIVE_HEALTH_CODES\n",
    "]\n",
    "PROCESSED_COLUMNS = [ID, *ALL_CODES]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "depr_df = utils.get_sas_file_from_url(MENTAL_HEALTH_URL)\n",
    "alco_df = utils.get_sas_file_from_url(ALCOHOL_USE_URL)\n",
    "drug_df = utils.get_sas_file_from_url(DRUG_USE_URL)\n",
    "demographic_df = utils.get_sas_file_from_url(DEMOGRAPHIC_URL)\n",
    "income_df = utils.get_sas_file_from_url(INCOME_URL)\n",
    "sexual_behaviour_df = utils.get_sas_file_from_url(SEXUAL_BEHAVIOUR_URL)\n",
    "reproductive_health_df = utils.get_sas_file_from_url(REPRODUCTIVE_URL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Create the whole dataframe; \n",
    "* Drop missing rows: serveyed who did not passed all necessary questionaries;\n",
    "* Fill missing values with -1111;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = (\n",
    "    # Merging:\n",
    "    pd.merge(depr_df, alco_df, how=\"outer\")\n",
    "    .merge(drug_df, how=\"outer\")\n",
    "    .merge(demographic_df)\n",
    "    .merge(income_df)\n",
    "    .merge(sexual_behaviour_df)\n",
    "    .merge(reproductive_health_df, how=\"outer\")\n",
    "    [PROCESSED_COLUMNS]\n",
    "    # Dealing with missing values\n",
    "    .dropna(subset=DEPRESSION_CODES, how=\"all\")\n",
    "    .dropna(subset=ALCOHOL_USE_CODES, how=\"all\")\n",
    "    .dropna(subset=DRUG_USE_CODES, how=\"all\")\n",
    "    .dropna(subset=DEMOGRAPHIC_CODES, how=\"all\")\n",
    "    .dropna(subset=INCOME_CODES, how=\"all\")\n",
    "    .fillna(-1111)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Map Race values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "RACE_MAP = {1:\"Mexican American\", 2:\"Other Hispanic\", 3: \"Non-Hispanic White\", 4:\"Non-Hispanic Black\", 6:\"Non-Hispanic Asian\", 7:\"Other Race - Including Multi-Racial\"}\n",
    "df[RACE] = df[RACE].map(lambda x: RACE_MAP[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Map Marital status values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "MARITAL_MAP = {1:\"Married\", 2:\"Widowed\", 3:\"Divorced\", 4:\"Separated\", 5:\"Never married\", 6:\"Living with partner\"}\n",
    "df[MARITAL_STATUS] = df[MARITAL_STATUS].map(lambda x: MARITAL_MAP[x] if x in [1,2,3,4,5,6] else np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Map Sexual Orientation values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[ORIENTATION_BOTH] = df.apply(utils.orientation_det, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Observation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>SEQN</th>\n",
       "      <th>DPQ010</th>\n",
       "      <th>DPQ020</th>\n",
       "      <th>DPQ030</th>\n",
       "      <th>DPQ040</th>\n",
       "      <th>DPQ050</th>\n",
       "      <th>DPQ060</th>\n",
       "      <th>DPQ070</th>\n",
       "      <th>DPQ080</th>\n",
       "      <th>DPQ090</th>\n",
       "      <th>DPQ100</th>\n",
       "      <th>ALQ120Q</th>\n",
       "      <th>ALQ120U</th>\n",
       "      <th>DUQ430</th>\n",
       "      <th>DUQ240</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>DMDEDUC2</th>\n",
       "      <th>DMDFMSIZ</th>\n",
       "      <th>INDFMIN2</th>\n",
       "      <th>INDFMMPI</th>\n",
       "      <th>IND235</th>\n",
       "      <th>INDFMMPC</th>\n",
       "      <th>SXQ295</th>\n",
       "      <th>SXQ296</th>\n",
       "      <th>RHQ131</th>\n",
       "      <th>RHQ160</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3.3700e+03</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "      <td>3370.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>88694.7045</td>\n",
       "      <td>4.1424e-01</td>\n",
       "      <td>3.7062e-01</td>\n",
       "      <td>6.4659e-01</td>\n",
       "      <td>8.1632e-01</td>\n",
       "      <td>4.3442e-01</td>\n",
       "      <td>2.7596e-01</td>\n",
       "      <td>2.7418e-01</td>\n",
       "      <td>1.6113e-01</td>\n",
       "      <td>6.5875e-02</td>\n",
       "      <td>-3.1495e+02</td>\n",
       "      <td>3.5217e+00</td>\n",
       "      <td>-179.3804</td>\n",
       "      <td>-575.3510</td>\n",
       "      <td>1.8240</td>\n",
       "      <td>1.4828</td>\n",
       "      <td>43.3950</td>\n",
       "      <td>-36.4264</td>\n",
       "      <td>3.1585</td>\n",
       "      <td>9.6792</td>\n",
       "      <td>-108.3311</td>\n",
       "      <td>15.8665</td>\n",
       "      <td>1.9976</td>\n",
       "      <td>-668.3671</td>\n",
       "      <td>-653.4920</td>\n",
       "      <td>-592.8608</td>\n",
       "      <td>-682.8181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2873.9796</td>\n",
       "      <td>8.0251e-01</td>\n",
       "      <td>7.4544e-01</td>\n",
       "      <td>9.3956e-01</td>\n",
       "      <td>9.2447e-01</td>\n",
       "      <td>8.1940e-01</td>\n",
       "      <td>6.8943e-01</td>\n",
       "      <td>6.8453e-01</td>\n",
       "      <td>5.4794e-01</td>\n",
       "      <td>3.8654e-01</td>\n",
       "      <td>5.0103e+02</td>\n",
       "      <td>2.5913e+01</td>\n",
       "      <td>411.0441</td>\n",
       "      <td>556.1476</td>\n",
       "      <td>0.4959</td>\n",
       "      <td>0.4998</td>\n",
       "      <td>14.7655</td>\n",
       "      <td>207.4080</td>\n",
       "      <td>1.7493</td>\n",
       "      <td>10.7492</td>\n",
       "      <td>333.1727</td>\n",
       "      <td>26.6296</td>\n",
       "      <td>19.2252</td>\n",
       "      <td>544.8839</td>\n",
       "      <td>547.7959</td>\n",
       "      <td>554.8778</td>\n",
       "      <td>542.0855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>83732.0000</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>-1.1110e+03</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>86175.2500</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>-1.1110e+03</td>\n",
       "      <td>1.0000e+00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88708.0000</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>1.0000e+00</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>2.0000e+00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>43.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>1.6900</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "      <td>-1111.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>91220.7500</td>\n",
       "      <td>1.0000e+00</td>\n",
       "      <td>1.0000e+00</td>\n",
       "      <td>1.0000e+00</td>\n",
       "      <td>1.0000e+00</td>\n",
       "      <td>1.0000e+00</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>5.3976e-79</td>\n",
       "      <td>3.0000e+00</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>56.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>3.3050</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>93702.0000</td>\n",
       "      <td>7.0000e+00</td>\n",
       "      <td>7.0000e+00</td>\n",
       "      <td>7.0000e+00</td>\n",
       "      <td>3.0000e+00</td>\n",
       "      <td>9.0000e+00</td>\n",
       "      <td>9.0000e+00</td>\n",
       "      <td>3.0000e+00</td>\n",
       "      <td>9.0000e+00</td>\n",
       "      <td>9.0000e+00</td>\n",
       "      <td>9.0000e+00</td>\n",
       "      <td>9.9900e+02</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>69.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>99.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>99.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>11.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SEQN      DPQ010      DPQ020      DPQ030      DPQ040      DPQ050  \\\n",
       "count   3370.0000  3.3700e+03  3.3700e+03  3.3700e+03  3.3700e+03  3.3700e+03   \n",
       "mean   88694.7045  4.1424e-01  3.7062e-01  6.4659e-01  8.1632e-01  4.3442e-01   \n",
       "std     2873.9796  8.0251e-01  7.4544e-01  9.3956e-01  9.2447e-01  8.1940e-01   \n",
       "min    83732.0000  5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79   \n",
       "25%    86175.2500  5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79   \n",
       "50%    88708.0000  5.3976e-79  5.3976e-79  5.3976e-79  1.0000e+00  5.3976e-79   \n",
       "75%    91220.7500  1.0000e+00  1.0000e+00  1.0000e+00  1.0000e+00  1.0000e+00   \n",
       "max    93702.0000  7.0000e+00  7.0000e+00  7.0000e+00  3.0000e+00  9.0000e+00   \n",
       "\n",
       "           DPQ060      DPQ070      DPQ080      DPQ090      DPQ100     ALQ120Q  \\\n",
       "count  3.3700e+03  3.3700e+03  3.3700e+03  3.3700e+03  3.3700e+03  3.3700e+03   \n",
       "mean   2.7596e-01  2.7418e-01  1.6113e-01  6.5875e-02 -3.1495e+02  3.5217e+00   \n",
       "std    6.8943e-01  6.8453e-01  5.4794e-01  3.8654e-01  5.0103e+02  2.5913e+01   \n",
       "min    5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79 -1.1110e+03  5.3976e-79   \n",
       "25%    5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79 -1.1110e+03  1.0000e+00   \n",
       "50%    5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79  2.0000e+00   \n",
       "75%    5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79  5.3976e-79  3.0000e+00   \n",
       "max    9.0000e+00  3.0000e+00  9.0000e+00  9.0000e+00  9.0000e+00  9.9900e+02   \n",
       "\n",
       "         ALQ120U     DUQ430     DUQ240   RIAGENDR   RIDAGEYR   DMDEDUC2  \\\n",
       "count  3370.0000  3370.0000  3370.0000  3370.0000  3370.0000  3370.0000   \n",
       "mean   -179.3804  -575.3510     1.8240     1.4828    43.3950   -36.4264   \n",
       "std     411.0441   556.1476     0.4959     0.4998    14.7655   207.4080   \n",
       "min   -1111.0000 -1111.0000     1.0000     1.0000    18.0000 -1111.0000   \n",
       "25%       1.0000 -1111.0000     2.0000     1.0000    31.0000     3.0000   \n",
       "50%       2.0000 -1111.0000     2.0000     1.0000    43.0000     4.0000   \n",
       "75%       3.0000     2.0000     2.0000     2.0000    56.0000     5.0000   \n",
       "max       3.0000     2.0000     9.0000     2.0000    69.0000     5.0000   \n",
       "\n",
       "        DMDFMSIZ   INDFMIN2   INDFMMPI     IND235   INDFMMPC     SXQ295  \\\n",
       "count  3370.0000  3370.0000  3370.0000  3370.0000  3370.0000  3370.0000   \n",
       "mean      3.1585     9.6792  -108.3311    15.8665     1.9976  -668.3671   \n",
       "std       1.7493    10.7492   333.1727    26.6296    19.2252   544.8839   \n",
       "min       1.0000     1.0000 -1111.0000     1.0000 -1111.0000 -1111.0000   \n",
       "25%       2.0000     5.0000     0.8000     5.0000     1.0000 -1111.0000   \n",
       "50%       3.0000     8.0000     1.6900     8.0000     3.0000 -1111.0000   \n",
       "75%       4.0000    14.0000     3.3050    11.0000     3.0000     2.0000   \n",
       "max       7.0000    99.0000     5.0000    99.0000     9.0000     9.0000   \n",
       "\n",
       "          SXQ296     RHQ131     RHQ160  \n",
       "count  3370.0000  3370.0000  3370.0000  \n",
       "mean   -653.4920  -592.8608  -682.8181  \n",
       "std     547.7959   554.8778   542.0855  \n",
       "min   -1111.0000 -1111.0000 -1111.0000  \n",
       "25%   -1111.0000 -1111.0000 -1111.0000  \n",
       "50%   -1111.0000 -1111.0000 -1111.0000  \n",
       "75%       2.0000     1.0000     2.0000  \n",
       "max       9.0000     9.0000    11.0000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mental Health"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore all values that are different from 0, 1, 2, 3 for collumns resposible for Mental Health. Those which different from provided range are accepted as 0 (not to spoil the measures):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:, DEPRESSION_CODES] = df[DEPRESSION_CODES].apply(\n",
    "    lambda row: row.map(lambda value: value if value in [0, 1, 2, 3] else 0),\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Describe depression questionnaire answers (missing values are not counted):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>DPQ010</th>\n",
       "      <th>DPQ020</th>\n",
       "      <th>DPQ030</th>\n",
       "      <th>DPQ040</th>\n",
       "      <th>DPQ050</th>\n",
       "      <th>DPQ060</th>\n",
       "      <th>DPQ070</th>\n",
       "      <th>DPQ080</th>\n",
       "      <th>DPQ090</th>\n",
       "      <th>DPQ100</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANSWER VALUE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>2463</td>\n",
       "      <td>2510</td>\n",
       "      <td>2003</td>\n",
       "      <td>1516</td>\n",
       "      <td>2424</td>\n",
       "      <td>2759</td>\n",
       "      <td>2784</td>\n",
       "      <td>3019</td>\n",
       "      <td>3232</td>\n",
       "      <td>2787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>584</td>\n",
       "      <td>604</td>\n",
       "      <td>838</td>\n",
       "      <td>1247</td>\n",
       "      <td>609</td>\n",
       "      <td>412</td>\n",
       "      <td>364</td>\n",
       "      <td>227</td>\n",
       "      <td>93</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>178</td>\n",
       "      <td>144</td>\n",
       "      <td>253</td>\n",
       "      <td>317</td>\n",
       "      <td>165</td>\n",
       "      <td>95</td>\n",
       "      <td>106</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>145</td>\n",
       "      <td>112</td>\n",
       "      <td>276</td>\n",
       "      <td>290</td>\n",
       "      <td>172</td>\n",
       "      <td>104</td>\n",
       "      <td>116</td>\n",
       "      <td>59</td>\n",
       "      <td>23</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "      <td>3370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              DPQ010  DPQ020  DPQ030  DPQ040  DPQ050  DPQ060  DPQ070  DPQ080  \\\n",
       "ANSWER VALUE                                                                   \n",
       "0.0             2463    2510    2003    1516    2424    2759    2784    3019   \n",
       "1.0              584     604     838    1247     609     412     364     227   \n",
       "2.0              178     144     253     317     165      95     106      65   \n",
       "3.0              145     112     276     290     172     104     116      59   \n",
       "TOTAL           3370    3370    3370    3370    3370    3370    3370    3370   \n",
       "\n",
       "              DPQ090  DPQ100  \n",
       "ANSWER VALUE                  \n",
       "0.0             3232    2787  \n",
       "1.0               93     483  \n",
       "2.0               22      69  \n",
       "3.0               23      31  \n",
       "TOTAL           3370    3370  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depression_qst_description = df[DEPRESSION_CODES].apply(pd.Series.value_counts)#[1:9]\n",
    "depression_qst_description.index.name = \"ANSWER VALUE\"\n",
    "depression_qst_description.loc[\"TOTAL\", :] = depression_qst_description.apply(np.sum)\n",
    "depression_qst_description.fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate depression score\n",
    "It represents the sum of the values for the rows of Mental Health related features (it consists of 10 questions).\n",
    "\n",
    "Look for *Analytic notes* In [NHANES Mental health data documentation](https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/DPQ_H.htm) for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:, DEPRESSION_SCORE] = df[DEPRESSION_CODES].sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate surveyed on 5 groups:\n",
    "\n",
    "1. Not depressed at all 0-5\n",
    "2. Mild depression 5-10\n",
    "3. Moderate Depression 10-15\n",
    "4. Moderately Severe Depression 15-20\n",
    "5. Severe Depression 20+\n",
    "\n",
    "According to Analytic notes In [NHANES Mental health data documentation](https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/DPQ_H.htm) depression is scored in integer numbers from 0 to 27.\n",
    "However there are some subjects in the table which got 27-30 score. Let them be in *Severe Depression* group.\n",
    "\n",
    "For future analytical mesurements people with depression score 10 and above are accepted as depressed people.\n",
    "\n",
    "Provided groups are defined according to [Validity of a brief depression severity measure](https://www.ncbi.nlm.nih.gov/pubmed/11556941)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "depr_bins = [-1, 5, 10, 15, 20, 31]\n",
    "depr_bin_labels = [\"Not Depressed\", \"Mild\", \"Moderate\", \"Moderately Severe\", \"Severe\"]\n",
    "\n",
    "df[DEPRESSION_GROUP] = pd.cut(df[DEPRESSION_SCORE], depr_bins, labels=depr_bin_labels)\n",
    "df[DEPRESSED] = df[DEPRESSION_SCORE].map(lambda x: x >= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "depression_groups = df.groupby(DEPRESSION_GROUP).size()\n",
    "depression_groups_percentile = depression_groups / depression_groups.sum() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People who thought at least several times over the last two weeks (at the moment when survey was processed) they would be better of dead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "depression_suicidal = df[df[DEPRESSION_GROUP].isin(depr_bin_labels[-3:])][\"DPQ090\"].value_counts()\n",
    "depression_suicidal_perc = utils.percentage(depression_suicidal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mental Health vs Demographic:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate items on 4 age groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [17, 30, 43, 56, 150]\n",
    "labels = [\"17-29\", \"30-42\", \"43-55\", \"56-69\"]\n",
    "df[AGE_GROUP] = pd.cut(df[AGE], bins=bins, labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mental Health vs Age vs Race:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>RIDRETH3</th>\n",
       "      <th>Mexican American</th>\n",
       "      <th>Non-Hispanic Asian</th>\n",
       "      <th>Non-Hispanic Black</th>\n",
       "      <th>Non-Hispanic White</th>\n",
       "      <th>Other Hispanic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RIDAGEYRGR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17-29</th>\n",
       "      <td>8.0247</td>\n",
       "      <td>6.1856</td>\n",
       "      <td>12.2222</td>\n",
       "      <td>8.2090</td>\n",
       "      <td>8.1395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-42</th>\n",
       "      <td>8.6420</td>\n",
       "      <td>3.3708</td>\n",
       "      <td>14.7368</td>\n",
       "      <td>12.5461</td>\n",
       "      <td>9.3458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43-55</th>\n",
       "      <td>5.6000</td>\n",
       "      <td>5.0633</td>\n",
       "      <td>3.8043</td>\n",
       "      <td>12.7036</td>\n",
       "      <td>13.3333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56-69</th>\n",
       "      <td>12.5000</td>\n",
       "      <td>4.2553</td>\n",
       "      <td>7.3529</td>\n",
       "      <td>14.8789</td>\n",
       "      <td>12.8000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RIDRETH3    Mexican American  Non-Hispanic Asian  Non-Hispanic Black  \\\n",
       "RIDAGEYRGR                                                             \n",
       "17-29                 8.0247              6.1856             12.2222   \n",
       "30-42                 8.6420              3.3708             14.7368   \n",
       "43-55                 5.6000              5.0633              3.8043   \n",
       "56-69                12.5000              4.2553              7.3529   \n",
       "\n",
       "RIDRETH3    Non-Hispanic White  Other Hispanic  \n",
       "RIDAGEYRGR                                      \n",
       "17-29                   8.2090          8.1395  \n",
       "30-42                  12.5461          9.3458  \n",
       "43-55                  12.7036         13.3333  \n",
       "56-69                  14.8789         12.8000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "races_age = df.groupby([RACE, AGE_GROUP, DEPRESSED]).size().unstack().T.apply(utils.percentage).T[True].unstack()[:5]\n",
    "races = df.groupby([RACE ,DEPRESSED]).size().unstack().T.apply(utils.percentage).T[:5]\n",
    "races_age.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mental Health vs Age vs Gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>RIDAGEYRGR</th>\n",
       "      <th colspan=\"2\" halign=\"left\">17-29</th>\n",
       "      <th colspan=\"2\" halign=\"left\">30-42</th>\n",
       "      <th colspan=\"2\" halign=\"left\">43-55</th>\n",
       "      <th colspan=\"2\" halign=\"left\">56-69</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPQMDF</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>387</td>\n",
       "      <td>30</td>\n",
       "      <td>430</td>\n",
       "      <td>35</td>\n",
       "      <td>365</td>\n",
       "      <td>29</td>\n",
       "      <td>417</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>373</td>\n",
       "      <td>46</td>\n",
       "      <td>345</td>\n",
       "      <td>59</td>\n",
       "      <td>387</td>\n",
       "      <td>49</td>\n",
       "      <td>322</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RIDAGEYRGR 17-29       30-42       43-55       56-69      \n",
       "DPQMDF     False True  False True  False True  False True \n",
       "RIAGENDR                                                  \n",
       "1.0          387    30   430    35   365    29   417    50\n",
       "2.0          373    46   345    59   387    49   322    46"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_depr_age = df.groupby([AGE_GROUP, DEPRESSED, GENDER]).size().unstack()\n",
    "male_age_depr = gender_depr_age[1].unstack().apply(utils.percentage, axis=1)\n",
    "female_age_depr = gender_depr_age[2].unstack().apply(utils.percentage, axis=1)\n",
    "depr_age = df.groupby([AGE_GROUP, DEPRESSED]).size().unstack().apply(utils.percentage, axis=1)\n",
    "gender_depr_age.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mental Health vs Marital Status:"
   ]
  },
  {
   "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>DMDMARTL</th>\n",
       "      <th>Living with partner</th>\n",
       "      <th>Married</th>\n",
       "      <th>Never married</th>\n",
       "      <th>Separated</th>\n",
       "      <th>Widowed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPQMDF</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>89.8438</td>\n",
       "      <td>93.7808</td>\n",
       "      <td>86.0465</td>\n",
       "      <td>79.4872</td>\n",
       "      <td>86.2069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>10.1562</td>\n",
       "      <td>6.2192</td>\n",
       "      <td>13.9535</td>\n",
       "      <td>20.5128</td>\n",
       "      <td>13.7931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DMDMARTL  Living with partner  Married  Never married  Separated  Widowed\n",
       "DPQMDF                                                                   \n",
       "False                 89.8438  93.7808        86.0465    79.4872  86.2069\n",
       "True                  10.1562   6.2192        13.9535    20.5128  13.7931"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marital_gender = df.groupby([MARITAL_STATUS, DEPRESSED, GENDER]).size().unstack()\n",
    "marital_male = marital_gender[1.].unstack().apply(utils.percentage, axis=1)[1:10]\n",
    "marital_female = marital_gender[2.].unstack().apply(utils.percentage, axis=1)[1:10]\n",
    "marital_total = df.groupby([MARITAL_STATUS, DEPRESSED]).size().unstack().apply(utils.percentage, axis=1)[1:10]\n",
    "marital_total.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mental Health vs Income:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate people on 4 groups accordings to the Family monthly income:\n",
    "\n",
    "1. less than \\\\$ 1650   \n",
    "2. \\\\$ 1650 - \\\\$ 4599\n",
    "3. \\\\$ 4600 and above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[FAMILY_INCOME_GROUP] = pd.cut(df[FAMILY_INCOME], bins=[0, 4, 8, 13], labels=[\"$1649 and less\", \"\\\\$1650-\\\\$4599\", \"$4600 and above\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>DPQMDF</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND235GR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1649 and less</th>\n",
       "      <td>608</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\\$1650-\\$4599</th>\n",
       "      <td>1103</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$4600 and above</th>\n",
       "      <td>1017</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DPQMDF           False  True \n",
       "IND235GR                     \n",
       "$1649 and less     608    144\n",
       "\\$1650-\\$4599     1103    113\n",
       "$4600 and above   1017     50"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income = df.groupby([FAMILY_INCOME_GROUP, DEPRESSED]).size().unstack()\n",
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_race = df.groupby([FAMILY_INCOME_GROUP, RACE, DEPRESSED]).size().unstack().T.apply(utils.percentage).T[True].unstack().iloc[:,:-1]\n",
    "income_total = df.groupby([FAMILY_INCOME_GROUP, DEPRESSED]).size().unstack().T.apply(utils.percentage).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mental Health vs Education level vs Income: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>DPQMDF</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DMDEDUC2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>85.7741</td>\n",
       "      <td>14.2259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>86.5753</td>\n",
       "      <td>13.4247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>88.9488</td>\n",
       "      <td>11.0512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>89.1892</td>\n",
       "      <td>10.8108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>93.8870</td>\n",
       "      <td>6.1130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DPQMDF      False    True \n",
       "DMDEDUC2                  \n",
       "1.0       85.7741  14.2259\n",
       "2.0       86.5753  13.4247\n",
       "3.0       88.9488  11.0512\n",
       "4.0       89.1892  10.8108\n",
       "5.0       93.8870   6.1130"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([DEPRESSED, EDUCATION]).size().unstack().iloc[:, 1:].T.apply(utils.percentage, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_school_ed = df[(df[EDUCATION]==3) & (df[FAMILY_INCOME] < 15)][FAMILY_INCOME]\n",
    "college_ed = df[(df[EDUCATION]==4) & (df[FAMILY_INCOME] < 15)][FAMILY_INCOME]\n",
    "univers_ed = df[(df[EDUCATION]==5) & (df[FAMILY_INCOME] < 15)][ FAMILY_INCOME]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mental Health vs Sexual Behaviour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>DPQMDF</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SXQBOTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bisexual</th>\n",
       "      <td>80.7339</td>\n",
       "      <td>19.2661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gay</th>\n",
       "      <td>83.3333</td>\n",
       "      <td>16.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Straight</th>\n",
       "      <td>90.5350</td>\n",
       "      <td>9.4650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DPQMDF      False    True \n",
       "SXQBOTH                   \n",
       "Bisexual  80.7339  19.2661\n",
       "Gay       83.3333  16.6667\n",
       "Straight  90.5350   9.4650"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_orientation_gender = df.groupby([ORIENTATION_BOTH, DEPRESSED, GENDER]).size().unstack()\n",
    "sex_orientation_male = sex_orientation_gender[1].unstack().apply(utils.percentage, axis=1)[1:]\n",
    "sex_orientation_female =sex_orientation_gender[2].unstack().apply(utils.percentage, axis=1)[1:]\n",
    "sex_orientation_total = df.groupby([ORIENTATION_BOTH, DEPRESSED]).size().unstack().apply(utils.percentage, axis=1)[1:]\n",
    "sex_orientation_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mental Health vs Drug use:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### People who were on rehabilitation grouped with depression group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>DPQGR</th>\n",
       "      <th>Not Depressed</th>\n",
       "      <th>Mild</th>\n",
       "      <th>Moderate</th>\n",
       "      <th>Moderately Severe</th>\n",
       "      <th>Severe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUQ430</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>62.4161</td>\n",
       "      <td>20.8054</td>\n",
       "      <td>7.3826</td>\n",
       "      <td>4.698</td>\n",
       "      <td>4.6980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>74.2702</td>\n",
       "      <td>15.6823</td>\n",
       "      <td>5.4990</td>\n",
       "      <td>2.444</td>\n",
       "      <td>2.1045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DPQGR   Not Depressed     Mild  Moderate  Moderately Severe  Severe\n",
       "DUQ430                                                             \n",
       "1.0           62.4161  20.8054    7.3826              4.698  4.6980\n",
       "2.0           74.2702  15.6823    5.4990              2.444  2.1045"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rehab = pd.crosstab(df[DEPRESSION_GROUP], df[\"DUQ430\"]).T[1:]\n",
    "rehab_perc = rehab.apply(utils.percentage, axis=1)\n",
    "rehab_perc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### People who were on rehabilitation separated on depressed and not:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rehab_2 = pd.crosstab(df[DEPRESSED], df[\"DUQ430\"]).T[1:]\n",
    "rehab_2_perc = rehab_2.apply(utils.percentage, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### People who tried cocaine/heroine/methamphitamine groupped with depression level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>DPQGR</th>\n",
       "      <th>Not Depressed</th>\n",
       "      <th>Mild</th>\n",
       "      <th>Moderate</th>\n",
       "      <th>Moderately Severe</th>\n",
       "      <th>Severe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUQ240</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>70.2194</td>\n",
       "      <td>17.8683</td>\n",
       "      <td>6.2696</td>\n",
       "      <td>2.8213</td>\n",
       "      <td>2.8213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>79.0459</td>\n",
       "      <td>13.2477</td>\n",
       "      <td>4.4771</td>\n",
       "      <td>1.9450</td>\n",
       "      <td>1.2844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DPQGR   Not Depressed     Mild  Moderate  Moderately Severe  Severe\n",
       "DUQ240                                                             \n",
       "1.0           70.2194  17.8683    6.2696             2.8213  2.8213\n",
       "2.0           79.0459  13.2477    4.4771             1.9450  1.2844"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tried_drugs = df.groupby([\"DUQ240\", DEPRESSION_GROUP]).size().unstack()[0:2]\n",
    "tried_drugs_perc = tried_drugs.apply(utils.percentage, axis=1)\n",
    "tried_drugs_perc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### People who tried cocaine/heroine/methamphitamine separated on depressed and not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "tried_drugs_2 = df.groupby([\"DUQ240\", DEPRESSED]).size().unstack()[0:2]\n",
    "tried_drugs_2_perc = tried_drugs_2.apply(lambda row: row/row.sum() * 100, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mental Health vs Pregnancy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>DPQMDF</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RHQ160</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>85.4749</td>\n",
       "      <td>14.5251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>90.2913</td>\n",
       "      <td>9.7087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>89.3805</td>\n",
       "      <td>10.6195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>84.7291</td>\n",
       "      <td>15.2709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>83.4586</td>\n",
       "      <td>16.5414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>85.4839</td>\n",
       "      <td>14.5161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>88.5714</td>\n",
       "      <td>11.4286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>80.0000</td>\n",
       "      <td>20.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DPQMDF    False    True \n",
       "RHQ160                  \n",
       "1.0     85.4749  14.5251\n",
       "2.0     90.2913   9.7087\n",
       "3.0     89.3805  10.6195\n",
       "4.0     84.7291  15.2709\n",
       "5.0     83.4586  16.5414\n",
       "6.0     85.4839  14.5161\n",
       "7.0     88.5714  11.4286\n",
       "8.0     80.0000  20.0000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ever_been_pregnant = df[df[GENDER] == 2].groupby([EVER_BEEN_PREGNANT, DEPRESSED]).size().unstack()[1:3].apply(utils.percentage, axis=1)\n",
    "df[df[GENDER] == 2].groupby([TIMES_BEEN_PREGNANT, DEPRESSED]).size().unstack()[1:8].apply(utils.percentage, axis=1)[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mental Health vs Alcohol Use:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets separate alcogol use people on groups:\n",
    "1. 1-3 times per year\n",
    "2. Less than 1 time per month\n",
    "3. 1 time per month\n",
    "4. 2-3 times per month\n",
    "5. 1 time per week\n",
    "6. 2-3 times per week\n",
    "7. More than 3 times per week\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[ALCOHOL_USE_GROUP] = df.apply(utils.alcohol_use_group_det, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>ALQGR</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPQMDF</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>88.3002</td>\n",
       "      <td>88.2736</td>\n",
       "      <td>89.8649</td>\n",
       "      <td>92.2667</td>\n",
       "      <td>92.2078</td>\n",
       "      <td>92.2246</td>\n",
       "      <td>87.4126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>11.6998</td>\n",
       "      <td>11.7264</td>\n",
       "      <td>10.1351</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>7.7922</td>\n",
       "      <td>7.7754</td>\n",
       "      <td>12.5874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ALQGR         1        2        3        4        5        6        7\n",
       "DPQMDF                                                               \n",
       "False   88.3002  88.2736  89.8649  92.2667  92.2078  92.2246  87.4126\n",
       "True    11.6998  11.7264  10.1351   7.7333   7.7922   7.7754  12.5874"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alco_depression_crosstable = pd.crosstab(df[ALCOHOL_USE_GROUP], df[DEPRESSED])[1:]\n",
    "alco_depression_crosstable_perc = alco_depression_crosstable.apply(utils.percentage, axis=1)\n",
    "alco_depression_crosstable_perc.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation and Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x540 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_bars_for_depression_groups(depression_groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### During 2015–2026, ~8.55% of Americans aged 17 and over had depression (moderate or severe depressive symptoms in the past 2 weeks). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_grouped_bars(depr_age, female_age_depr, male_age_depr, bar_width=.25, labels=(\"Total\", \"Female\", \"Male\"), xlabel=\"Age group\", ylabel=\"Percentage of Depressed people\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Males have significantly lower rates than females overall and in every age group.**\n",
    "* **The highest rate of depression, 14.6%, was found in women aged 30-42.**\n",
    "* **Depression was more prevalent among females and persons aged 56-69.** \n",
    "\n",
    "---\n",
    "NOTES: Depression is defined as having moderate to severe depressive symptoms. \\\n",
    "SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_grouped_bars(marital_total, marital_female, marital_male, bar_width=0.25, xlabel=\"Marital Status\", ylabel=\"Percentage of depressed people\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Males have significantly lower rates than females overall and in every Marital status group.**\n",
    "* **Significant difference occurred between females and males Living with partner.**\n",
    "* **Major Depression was more prevalent among females and Separated persons.**\n",
    "* **Major Depression was less prevailed among married people.**\n",
    "* **The most prevalent group of depressed people, 22.9%, is separated females.**\n",
    "---\n",
    "NOTES: Depression is defined as having moderate to severe depressive symptoms.  \\\n",
    "SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_grouped_bars(sex_orientation_total, sex_orientation_female, sex_orientation_male, bar_width=0.25, xlabel=\"Sexual orientation\", ylabel=\"Percentage of depressed people\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Straight people have significantly lower rates than Bisexuals and gays among either males or females and overall.**\n",
    "* **Significant difference occurred between females and males living with partner.**\n",
    "* **Major Depression was more prevalent among Bisexual people.**\n",
    "* **Major Depression was less prevalent among Straight people**\n",
    "* **Major Depression was equally rated among bisexual females and males.**\n",
    "---\n",
    "NOTES: Depression is defined as having moderate to severe depressive symptoms. Sexual orientation is categorized as a result of female orientation and male orientation categories: Gay (Gay or Lesbian for females and Gay for males), Bisexual (Bisexual for both females and males), Straight (Straight for both females and males)  \\\n",
    "SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_race_bars(races_age, races)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Non-Hispanic White and Other Hispanic groups have significantly greater rates than others.**\n",
    "* **The lowest rates occurred over Non-Hispanic Asian race.**\n",
    "* **Significant difference occurred over Non-Hispanic Black betwen 30-42 and 43-55.**\n",
    "---\n",
    "NOTES: Depression is defined as having moderate to severe depressive symptoms. \\\n",
    "SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_income_bars(income_race, income_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Representatives with high level of Monthly income showed the lowest rates of Major Depression overall and among different races, as opposed to persons with low level of Monthly income.**\n",
    "* **Significant difference occurred over different Income group in Non-Hispanic White race.**\n",
    "---\n",
    "NOTES: Depression is defined as having moderate to severe depressive symptoms.  \\\n",
    "SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_edu_distplots(high_school_ed, college_ed, univers_ed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### During the analysis significant correlation occurred between Depression Score and level of Education. However including Education code as a feature in predictive model raised mean absolute error, thus correlation between Education level and Depression Score is accepted as consequence of impact of level of Family Income on Depression Score due to the fact that Education level affects monthly Family Income Index.\n",
    "\n",
    "* **Representatives with high level of Education showed the highest rates of Monthly Family Income as opposed to persons with low level of Education prevailing on rates of lowest Family Income.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_drugs_bars(rehab_2_perc, tried_drugs_2_perc)\n",
    "graphic.create_drugs_waffles(rehab, rehab_perc, tried_drugs_perc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **People who were on rehabilitation program and who tried cocaine/methamphetamine/heroin had significant higher rates of Major Depression.**\n",
    "\n",
    "---\n",
    "NOTES: Depression is defined as having moderate to severe depressive symptoms. \\\n",
    "SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphic.create_alco_bars(alco_depression_crosstable_perc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Major depression rate remains stable over people who drink alcohol 1-3 times per month, 1 time per week and 2-3 times per week.**\n",
    "* **Persons drinking 3 and more times per week occurred the most prevailed rate of depression among different alcohol consuming groups accounting for more than 12%.**\n",
    "* **The second prevailed rate of depression occurred in groups consuming alcohol less than 1 time per month accounting for roughly 12%.** \n",
    "\n",
    "---\n",
    "NOTES: Depression is defined as having moderate to severe depressive symptoms. \\\n",
    "SOURCE: CDC/NCHS, National Health and Nutrition Examination Survey."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation for prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_df = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Map categorical features back to numerical values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "RACE_MAP_BACK = {\"Mexican American\":1, \"Other Hispanic\":2, \"Non-Hispanic White\":3,\"Non-Hispanic Black\":4, \"Non-Hispanic Asian\":6, \"Other Race - Including Multi-Racial\":7}\n",
    "MARITAL_MAP_BACK = {\"Married\":1, \"Widowed\":2, \"Divorced\":3, \"Separated\":4, \"Never married\":5, \"Living with partner\":6}\n",
    "ORIENTATION_MAP_BACK = {\"Straight\":1, \"Gay\":2, \"Bisexual\":3}\n",
    "\n",
    "prediction_df[RACE] = prediction_df[RACE].map(RACE_MAP_BACK)\n",
    "prediction_df[MARITAL_STATUS] = prediction_df[MARITAL_STATUS].map(MARITAL_MAP_BACK)\n",
    "prediction_df[ORIENTATION_BOTH] = prediction_df[ORIENTATION_BOTH].map(ORIENTATION_MAP_BACK)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean up all non-necessary instances (the ones with missing data):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_df = prediction_df[\n",
    "    (prediction_df[POVERTY_LEVEL_CATEGORY] != -1111) & \n",
    "    (prediction_df[MARITAL_STATUS].isna() == False) & \n",
    "    (prediction_df[ORIENTATION_BOTH].isna() == False)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select predictive features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "FEATURES = [ORIENTATION_BOTH, POVERTY_LEVEL_CATEGORY, ALCOHOL_USE_GROUP, *DRUG_USE_CODES, *DEMOGRAPHIC_CODES]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate prediction target from features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = prediction_df[DEPRESSION_SCORE] # We are going to predict Depression score, right!\n",
    "X = prediction_df[FEATURES]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separate data on 2 subsets:\n",
    "1. Training set\n",
    "2. Value set for model validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit Random Forest model to training values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_model = RandomForestRegressor(random_state=1)\n",
    "forest_model.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict values to non-training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = forest_model.predict(val_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Output mean absoulute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.166549003769521"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(val_y, prediction)"
   ]
  }
 ],
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   "display_name": "Python 3",
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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