sparcs_descriptives.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib2to3.pgen2.pgen import DFAState\n",
    "import math\n",
    "import statistics # for descriptive statistics\n",
    "import numpy as np # for single/multi dimensional arrays\n",
    "from scipy import stats # scientific computing based on numpy\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot # for data visualization\n",
    "from pandas.plotting import scatter_matrix # for plotting data\n",
    "from statsmodels.formula.api import ols \n",
    "import seaborn ## for statistical visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparcs=pd.read_csv('https://health.data.ny.gov/resource/gnzp-ekau.csv') #used csv link from API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>health_service_area</th>\n",
       "      <th>hospital_county</th>\n",
       "      <th>operating_certificate_number</th>\n",
       "      <th>facility_id</th>\n",
       "      <th>facility_name</th>\n",
       "      <th>age_group</th>\n",
       "      <th>zip_code_3_digits</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>...</th>\n",
       "      <th>apr_risk_of_mortality</th>\n",
       "      <th>apr_medical_surgical_description</th>\n",
       "      <th>payment_typology_1</th>\n",
       "      <th>payment_typology_2</th>\n",
       "      <th>payment_typology_3</th>\n",
       "      <th>birth_weight</th>\n",
       "      <th>abortion_edit_indicator</th>\n",
       "      <th>emergency_department_indicator</th>\n",
       "      <th>total_charges</th>\n",
       "      <th>total_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Cuba Memorial Hospital Inc</td>\n",
       "      <td>70 or Older</td>\n",
       "      <td>147</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Span/Hispanic</td>\n",
       "      <td>...</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>3913.23</td>\n",
       "      <td>3466.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Cuba Memorial Hospital Inc</td>\n",
       "      <td>30 to 49</td>\n",
       "      <td>147</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Span/Hispanic</td>\n",
       "      <td>...</td>\n",
       "      <td>Minor</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Self-Pay</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>3597.10</td>\n",
       "      <td>3746.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Cuba Memorial Hospital Inc</td>\n",
       "      <td>50 to 69</td>\n",
       "      <td>147</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Span/Hispanic</td>\n",
       "      <td>...</td>\n",
       "      <td>Minor</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>Federal/State/Local/VA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>9149.22</td>\n",
       "      <td>8322.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Cuba Memorial Hospital Inc</td>\n",
       "      <td>70 or Older</td>\n",
       "      <td>147</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Span/Hispanic</td>\n",
       "      <td>...</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>Medicaid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>5880.61</td>\n",
       "      <td>5828.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Cuba Memorial Hospital Inc</td>\n",
       "      <td>50 to 69</td>\n",
       "      <td>147</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Span/Hispanic</td>\n",
       "      <td>...</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>7019.76</td>\n",
       "      <td>6604.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>228000.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Memorial Hosp of Wm F &amp; Gertrude F Jones A/K/A...</td>\n",
       "      <td>18 to 29</td>\n",
       "      <td>148</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Span/Hispanic</td>\n",
       "      <td>...</td>\n",
       "      <td>Minor</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "      <td>Private Health Insurance</td>\n",
       "      <td>Medicaid</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>9919.75</td>\n",
       "      <td>6485.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>228000.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Memorial Hosp of Wm F &amp; Gertrude F Jones A/K/A...</td>\n",
       "      <td>70 or Older</td>\n",
       "      <td>148</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Span/Hispanic</td>\n",
       "      <td>...</td>\n",
       "      <td>Major</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>Self-Pay</td>\n",
       "      <td>Self-Pay</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>21794.75</td>\n",
       "      <td>13726.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>228000.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Memorial Hosp of Wm F &amp; Gertrude F Jones A/K/A...</td>\n",
       "      <td>30 to 49</td>\n",
       "      <td>148</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Not Span/Hispanic</td>\n",
       "      <td>...</td>\n",
       "      <td>Minor</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Blue Cross/Blue Shield</td>\n",
       "      <td>Self-Pay</td>\n",
       "      <td>Self-Pay</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>7557.50</td>\n",
       "      <td>5175.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Redacted for Confidentiality</td>\n",
       "      <td>50 to 69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Minor</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Medicare</td>\n",
       "      <td>Medicaid</td>\n",
       "      <td>Self-Pay</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>17047.25</td>\n",
       "      <td>18075.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Western NY</td>\n",
       "      <td>Allegany</td>\n",
       "      <td>228000.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Memorial Hosp of Wm F &amp; Gertrude F Jones A/K/A...</td>\n",
       "      <td>0 to 17</td>\n",
       "      <td>148</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Minor</td>\n",
       "      <td>Medical</td>\n",
       "      <td>Medicaid</td>\n",
       "      <td>Self-Pay</td>\n",
       "      <td>Self-Pay</td>\n",
       "      <td>3300</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>3256.25</td>\n",
       "      <td>1394.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    health_service_area hospital_county  operating_certificate_number  \\\n",
       "0            Western NY        Allegany                      226700.0   \n",
       "1            Western NY        Allegany                      226700.0   \n",
       "2            Western NY        Allegany                      226700.0   \n",
       "3            Western NY        Allegany                      226700.0   \n",
       "4            Western NY        Allegany                      226700.0   \n",
       "..                  ...             ...                           ...   \n",
       "995          Western NY        Allegany                      228000.0   \n",
       "996          Western NY        Allegany                      228000.0   \n",
       "997          Western NY        Allegany                      228000.0   \n",
       "998                 NaN             NaN                           NaN   \n",
       "999          Western NY        Allegany                      228000.0   \n",
       "\n",
       "     facility_id                                      facility_name  \\\n",
       "0           37.0                         Cuba Memorial Hospital Inc   \n",
       "1           37.0                         Cuba Memorial Hospital Inc   \n",
       "2           37.0                         Cuba Memorial Hospital Inc   \n",
       "3           37.0                         Cuba Memorial Hospital Inc   \n",
       "4           37.0                         Cuba Memorial Hospital Inc   \n",
       "..           ...                                                ...   \n",
       "995         39.0  Memorial Hosp of Wm F & Gertrude F Jones A/K/A...   \n",
       "996         39.0  Memorial Hosp of Wm F & Gertrude F Jones A/K/A...   \n",
       "997         39.0  Memorial Hosp of Wm F & Gertrude F Jones A/K/A...   \n",
       "998          NaN                       Redacted for Confidentiality   \n",
       "999         39.0  Memorial Hosp of Wm F & Gertrude F Jones A/K/A...   \n",
       "\n",
       "       age_group zip_code_3_digits gender   race          ethnicity  ...  \\\n",
       "0    70 or Older               147      F  White  Not Span/Hispanic  ...   \n",
       "1       30 to 49               147      M  White  Not Span/Hispanic  ...   \n",
       "2       50 to 69               147      F  White  Not Span/Hispanic  ...   \n",
       "3    70 or Older               147      M  White  Not Span/Hispanic  ...   \n",
       "4       50 to 69               147      M  White  Not Span/Hispanic  ...   \n",
       "..           ...               ...    ...    ...                ...  ...   \n",
       "995     18 to 29               148      F  White  Not Span/Hispanic  ...   \n",
       "996  70 or Older               148      M  White  Not Span/Hispanic  ...   \n",
       "997     30 to 49               148      F  White  Not Span/Hispanic  ...   \n",
       "998     50 to 69               NaN      M  White            Unknown  ...   \n",
       "999      0 to 17               148      F  White            Unknown  ...   \n",
       "\n",
       "     apr_risk_of_mortality apr_medical_surgical_description  \\\n",
       "0                 Moderate                          Medical   \n",
       "1                    Minor                          Medical   \n",
       "2                    Minor                          Medical   \n",
       "3                 Moderate                          Medical   \n",
       "4                 Moderate                          Medical   \n",
       "..                     ...                              ...   \n",
       "995                  Minor                          Medical   \n",
       "996                  Major                          Medical   \n",
       "997                  Minor                          Medical   \n",
       "998                  Minor                          Medical   \n",
       "999                  Minor                          Medical   \n",
       "\n",
       "         payment_typology_1        payment_typology_2  payment_typology_3  \\\n",
       "0                  Medicare  Private Health Insurance                 NaN   \n",
       "1                  Self-Pay                       NaN                 NaN   \n",
       "2                  Medicare    Federal/State/Local/VA                 NaN   \n",
       "3                  Medicare                  Medicaid                 NaN   \n",
       "4    Blue Cross/Blue Shield                       NaN                 NaN   \n",
       "..                      ...                       ...                 ...   \n",
       "995  Blue Cross/Blue Shield  Private Health Insurance            Medicaid   \n",
       "996                Medicare                  Self-Pay            Self-Pay   \n",
       "997  Blue Cross/Blue Shield                  Self-Pay            Self-Pay   \n",
       "998                Medicare                  Medicaid            Self-Pay   \n",
       "999                Medicaid                  Self-Pay            Self-Pay   \n",
       "\n",
       "    birth_weight  abortion_edit_indicator emergency_department_indicator  \\\n",
       "0              0                        N                              N   \n",
       "1              0                        N                              Y   \n",
       "2              0                        N                              Y   \n",
       "3              0                        N                              Y   \n",
       "4              0                        N                              Y   \n",
       "..           ...                      ...                            ...   \n",
       "995            0                        N                              N   \n",
       "996            0                        N                              Y   \n",
       "997            0                        N                              N   \n",
       "998            0                        N                              Y   \n",
       "999         3300                        N                              N   \n",
       "\n",
       "     total_charges total_costs  \n",
       "0          3913.23     3466.83  \n",
       "1          3597.10     3746.80  \n",
       "2          9149.22     8322.10  \n",
       "3          5880.61     5828.87  \n",
       "4          7019.76     6604.00  \n",
       "..             ...         ...  \n",
       "995        9919.75     6485.86  \n",
       "996       21794.75    13726.59  \n",
       "997        7557.50     5175.34  \n",
       "998       17047.25    18075.81  \n",
       "999        3256.25     1394.61  \n",
       "\n",
       "[1000 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparcs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 34)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sparcs.shape # 1000 rows x 34 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['health_service_area', 'hospital_county',\n",
       "       'operating_certificate_number', 'facility_id', 'facility_name',\n",
       "       'age_group', 'zip_code_3_digits', 'gender', 'race', 'ethnicity',\n",
       "       'length_of_stay', 'type_of_admission', 'patient_disposition',\n",
       "       'discharge_year', 'ccs_diagnosis_code', 'ccs_diagnosis_description',\n",
       "       'ccs_procedure_code', 'ccs_procedure_description', 'apr_drg_code',\n",
       "       'apr_drg_description', 'apr_mdc_code', 'apr_mdc_description',\n",
       "       'apr_severity_of_illness_code', 'apr_severity_of_illness_description',\n",
       "       'apr_risk_of_mortality', 'apr_medical_surgical_description',\n",
       "       'payment_typology_1', 'payment_typology_2', 'payment_typology_3',\n",
       "       'birth_weight', 'abortion_edit_indicator',\n",
       "       'emergency_department_indicator', 'total_charges', 'total_costs'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparcs.columns # view column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "health_service_area                     object\n",
       "hospital_county                         object\n",
       "operating_certificate_number           float64\n",
       "facility_id                            float64\n",
       "facility_name                           object\n",
       "age_group                               object\n",
       "zip_code_3_digits                       object\n",
       "gender                                  object\n",
       "race                                    object\n",
       "ethnicity                               object\n",
       "length_of_stay                           int64\n",
       "type_of_admission                       object\n",
       "patient_disposition                     object\n",
       "discharge_year                           int64\n",
       "ccs_diagnosis_code                       int64\n",
       "ccs_diagnosis_description               object\n",
       "ccs_procedure_code                       int64\n",
       "ccs_procedure_description               object\n",
       "apr_drg_code                             int64\n",
       "apr_drg_description                     object\n",
       "apr_mdc_code                             int64\n",
       "apr_mdc_description                     object\n",
       "apr_severity_of_illness_code             int64\n",
       "apr_severity_of_illness_description     object\n",
       "apr_risk_of_mortality                   object\n",
       "apr_medical_surgical_description        object\n",
       "payment_typology_1                      object\n",
       "payment_typology_2                      object\n",
       "payment_typology_3                      object\n",
       "birth_weight                             int64\n",
       "abortion_edit_indicator                 object\n",
       "emergency_department_indicator          object\n",
       "total_charges                          float64\n",
       "total_costs                            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparcs.dtypes # view data types "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      F\n",
      "1      M\n",
      "2      F\n",
      "3      M\n",
      "4      M\n",
      "      ..\n",
      "995    F\n",
      "996    M\n",
      "997    F\n",
      "998    M\n",
      "999    F\n",
      "Name: gender, Length: 1000, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(sparcs['gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2769726247987117"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#simple selector: female and avg about of time they stay \n",
    "sparcs[sparcs['gender'] == 'F']['length_of_stay'].mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>operating_certificate_number</th>\n",
       "      <th>facility_id</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>discharge_year</th>\n",
       "      <th>ccs_diagnosis_code</th>\n",
       "      <th>ccs_procedure_code</th>\n",
       "      <th>apr_drg_code</th>\n",
       "      <th>apr_mdc_code</th>\n",
       "      <th>apr_severity_of_illness_code</th>\n",
       "      <th>birth_weight</th>\n",
       "      <th>total_charges</th>\n",
       "      <th>total_costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>992.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>228305.546371</td>\n",
       "      <td>38.987903</td>\n",
       "      <td>3.446000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>162.346000</td>\n",
       "      <td>83.419000</td>\n",
       "      <td>466.211000</td>\n",
       "      <td>11.490000</td>\n",
       "      <td>1.827000</td>\n",
       "      <td>703.600000</td>\n",
       "      <td>12954.426590</td>\n",
       "      <td>10103.517850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7769.500403</td>\n",
       "      <td>1.266607</td>\n",
       "      <td>2.917121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.533056</td>\n",
       "      <td>83.052436</td>\n",
       "      <td>214.849307</td>\n",
       "      <td>5.134224</td>\n",
       "      <td>0.871683</td>\n",
       "      <td>1388.046726</td>\n",
       "      <td>12228.596473</td>\n",
       "      <td>12149.476518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>226700.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1527.250000</td>\n",
       "      <td>660.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>228000.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>248.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5419.187500</td>\n",
       "      <td>2996.962500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>228000.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9829.625000</td>\n",
       "      <td>6134.855000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>228000.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15681.875000</td>\n",
       "      <td>12881.465000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>401001.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>951.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4900.000000</td>\n",
       "      <td>121678.110000</td>\n",
       "      <td>109008.010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       operating_certificate_number  facility_id  length_of_stay  \\\n",
       "count                    992.000000   992.000000     1000.000000   \n",
       "mean                  228305.546371    38.987903        3.446000   \n",
       "std                     7769.500403     1.266607        2.917121   \n",
       "min                   226700.000000    37.000000        1.000000   \n",
       "25%                   228000.000000    39.000000        2.000000   \n",
       "50%                   228000.000000    39.000000        3.000000   \n",
       "75%                   228000.000000    39.000000        4.000000   \n",
       "max                   401001.000000    66.000000       27.000000   \n",
       "\n",
       "       discharge_year  ccs_diagnosis_code  ccs_procedure_code  apr_drg_code  \\\n",
       "count          1000.0         1000.000000         1000.000000   1000.000000   \n",
       "mean           2016.0          162.346000           83.419000    466.211000   \n",
       "std               0.0           95.533056           83.052436    214.849307   \n",
       "min            2016.0            2.000000            0.000000     43.000000   \n",
       "25%            2016.0          122.000000            0.000000    248.500000   \n",
       "50%            2016.0          187.000000           92.000000    540.000000   \n",
       "75%            2016.0          218.000000          137.000000    640.000000   \n",
       "max            2016.0          662.000000          231.000000    951.000000   \n",
       "\n",
       "       apr_mdc_code  apr_severity_of_illness_code  birth_weight  \\\n",
       "count   1000.000000                   1000.000000   1000.000000   \n",
       "mean      11.490000                      1.827000    703.600000   \n",
       "std        5.134224                      0.871683   1388.046726   \n",
       "min        1.000000                      1.000000      0.000000   \n",
       "25%        6.000000                      1.000000      0.000000   \n",
       "50%       14.000000                      2.000000      0.000000   \n",
       "75%       15.000000                      2.000000      0.000000   \n",
       "max       23.000000                      4.000000   4900.000000   \n",
       "\n",
       "       total_charges    total_costs  \n",
       "count    1000.000000    1000.000000  \n",
       "mean    12954.426590   10103.517850  \n",
       "std     12228.596473   12149.476518  \n",
       "min      1527.250000     660.030000  \n",
       "25%      5419.187500    2996.962500  \n",
       "50%      9829.625000    6134.855000  \n",
       "75%     15681.875000   12881.465000  \n",
       "max    121678.110000  109008.010000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparcs.describe() # quick view a large dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting dataframe on values of categorical variables \n",
    "groupby_gender = sparcs.groupby('gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('F', 3.2769726247987117)\n",
      "('M', 3.7229551451187337)\n"
     ]
    }
   ],
   "source": [
    "for gender, value in groupby_gender['length_of_stay']: print((gender, value.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>health_service_area</th>\n",
       "      <th>hospital_county</th>\n",
       "      <th>operating_certificate_number</th>\n",
       "      <th>facility_id</th>\n",
       "      <th>facility_name</th>\n",
       "      <th>age_group</th>\n",
       "      <th>zip_code_3_digits</th>\n",
       "      <th>race</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>...</th>\n",
       "      <th>apr_risk_of_mortality</th>\n",
       "      <th>apr_medical_surgical_description</th>\n",
       "      <th>payment_typology_1</th>\n",
       "      <th>payment_typology_2</th>\n",
       "      <th>payment_typology_3</th>\n",
       "      <th>birth_weight</th>\n",
       "      <th>abortion_edit_indicator</th>\n",
       "      <th>emergency_department_indicator</th>\n",
       "      <th>total_charges</th>\n",
       "      <th>total_costs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>618</td>\n",
       "      <td>618</td>\n",
       "      <td>618</td>\n",
       "      <td>618</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>610</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>...</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>613</td>\n",
       "      <td>604</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>374</td>\n",
       "      <td>374</td>\n",
       "      <td>374</td>\n",
       "      <td>374</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>368</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>...</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>364</td>\n",
       "      <td>361</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        health_service_area  hospital_county  operating_certificate_number  \\\n",
       "gender                                                                       \n",
       "F                       618              618                           618   \n",
       "M                       374              374                           374   \n",
       "\n",
       "        facility_id  facility_name  age_group  zip_code_3_digits  race  \\\n",
       "gender                                                                   \n",
       "F               618            621        621                610   621   \n",
       "M               374            379        379                368   379   \n",
       "\n",
       "        ethnicity  length_of_stay  ...  apr_risk_of_mortality  \\\n",
       "gender                             ...                          \n",
       "F             621             621  ...                    621   \n",
       "M             379             379  ...                    379   \n",
       "\n",
       "        apr_medical_surgical_description  payment_typology_1  \\\n",
       "gender                                                         \n",
       "F                                    621                 621   \n",
       "M                                    379                 379   \n",
       "\n",
       "        payment_typology_2  payment_typology_3  birth_weight  \\\n",
       "gender                                                         \n",
       "F                      613                 604           621   \n",
       "M                      364                 361           379   \n",
       "\n",
       "        abortion_edit_indicator  emergency_department_indicator  \\\n",
       "gender                                                            \n",
       "F                           621                             621   \n",
       "M                           379                             379   \n",
       "\n",
       "        total_charges  total_costs  \n",
       "gender                              \n",
       "F                 621          621  \n",
       "M                 379          379  \n",
       "\n",
       "[2 rows x 33 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many males/females were included in this study?\n",
    "groupby_gender.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['70 or Older', '30 to 49', '50 to 69', '18 to 29', '0 to 17'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparcs.age_group.unique() # use .unique() to return the sample space leemnts of a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70 or Older    282\n",
       "0 to 17        223\n",
       "50 to 69       208\n",
       "18 to 29       169\n",
       "30 to 49       118\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparcs.age_group.value_counts() # counting the number of times the unique values appear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70 or Older    0.282\n",
       "0 to 17        0.223\n",
       "50 to 69       0.208\n",
       "18 to 29       0.169\n",
       "30 to 49       0.118\n",
       "Name: age_group, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparcs.age_group.value_counts(normalize=True) #expressing the relative frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70 or Older    28.2\n",
       "0 to 17        22.3\n",
       "50 to 69       20.8\n",
       "18 to 29       16.9\n",
       "30 to 49       11.8\n",
       "Name: age_group, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparcs.age_group.value_counts(normalize=True) * 100 # expressing the relative frequency as a percentage "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouped Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>age_group</th>\n",
       "      <th>0 to 17</th>\n",
       "      <th>18 to 29</th>\n",
       "      <th>30 to 49</th>\n",
       "      <th>50 to 69</th>\n",
       "      <th>70 or Older</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emergency_department_indicator</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>N</th>\n",
       "      <td>212</td>\n",
       "      <td>145</td>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>164</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age_group                       0 to 17  18 to 29  30 to 49  50 to 69  \\\n",
       "emergency_department_indicator                                          \n",
       "N                                   212       145        84        44   \n",
       "Y                                    11        24        34       164   \n",
       "\n",
       "age_group                       70 or Older  \n",
       "emergency_department_indicator               \n",
       "N                                        42  \n",
       "Y                                       240  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(sparcs.emergency_department_indicator, sparcs.age_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='length_of_stay', ylabel='length_of_stay'>,\n",
       "        <AxesSubplot:xlabel='birth_weight', ylabel='length_of_stay'>,\n",
       "        <AxesSubplot:xlabel='total_charges', ylabel='length_of_stay'>,\n",
       "        <AxesSubplot:xlabel='total_costs', ylabel='length_of_stay'>],\n",
       "       [<AxesSubplot:xlabel='length_of_stay', ylabel='birth_weight'>,\n",
       "        <AxesSubplot:xlabel='birth_weight', ylabel='birth_weight'>,\n",
       "        <AxesSubplot:xlabel='total_charges', ylabel='birth_weight'>,\n",
       "        <AxesSubplot:xlabel='total_costs', ylabel='birth_weight'>],\n",
       "       [<AxesSubplot:xlabel='length_of_stay', ylabel='total_charges'>,\n",
       "        <AxesSubplot:xlabel='birth_weight', ylabel='total_charges'>,\n",
       "        <AxesSubplot:xlabel='total_charges', ylabel='total_charges'>,\n",
       "        <AxesSubplot:xlabel='total_costs', ylabel='total_charges'>],\n",
       "       [<AxesSubplot:xlabel='length_of_stay', ylabel='total_costs'>,\n",
       "        <AxesSubplot:xlabel='birth_weight', ylabel='total_costs'>,\n",
       "        <AxesSubplot:xlabel='total_charges', ylabel='total_costs'>,\n",
       "        <AxesSubplot:xlabel='total_costs', ylabel='total_costs'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Data\n",
    "scatter_matrix(sparcs[['length_of_stay', 'birth_weight', 'total_charges', 'total_costs']], c=(sparcs['gender'] == 'F'), figsize=(10,10), alpha=1, marker='o', cmap='winter')\n",
    "#alpha = # for transparency\n",
    "#figsize = (20,20) for wxh size\n",
    "#cmap = color map\n",
    "#marker = 'shape' ('o', 'x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis Testing: comparing 2 groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=16.029565287165426, pvalue=1.2352039218252227e-51)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare difference between birth weight in males and females \n",
    "# tests if population of mean of data is likely to be equal to a given value \n",
    "stats.ttest_1samp(sparcs['birth_weight'], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_bw = sparcs[sparcs['gender'] == 'M']['birth_weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_bw = sparcs[sparcs['gender'] == 'F']['birth_weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=5.730804104532916, pvalue=1.3230646988456851e-08)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(male_bw, female_bw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Testing the difference between total_costs in males and females using non parametric statistics\n",
    "female_total_costs = sparcs[sparcs['gender'] == 'F']['total_costs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_total_costs = sparcs[sparcs['gender'] == 'M']['total_costs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=116925.5, pvalue=0.43248295025479616)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(female_total_costs, male_total_costs)"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
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