claimants by service type.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a335d439",
   "metadata": {},
   "source": [
    "# Overview\n",
    "The purpose of this analysis is to compare the number and percentage of unique beneficiaries. <br>\n",
    "Visit Data.gov to download the data used for this analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d03834c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-06T18:40:06.060716Z",
     "start_time": "2022-10-06T18:40:06.039721Z"
    }
   },
   "source": [
    "#  Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e3d0312",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T15:33:39.079350Z",
     "start_time": "2023-04-05T15:33:37.423810Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "import matplotlib\n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "507fc841",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5dcf95d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T15:34:13.949520Z",
     "start_time": "2023-04-05T15:33:39.081352Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inpatient shape (58066, 197)\n",
      "outpatient shape(575092, 162)\n",
      "carrier shape(1121004, 96)\n",
      "dme shape (103828, 87)\n",
      "hha shape (6215, 88)\n",
      "hospice shape (12107, 86)\n",
      "snf shape (12548, 160)\n"
     ]
    }
   ],
   "source": [
    "synthetic_bene_df = pd.read_csv('data/beneficiary_2022.csv', sep=\"|\", low_memory=False)\n",
    "\n",
    "synthetic_inpatient_df = pd.read_csv('data/inpatient.csv',  sep=\"|\", low_memory=False)\n",
    "print(f\"inpatient shape {synthetic_inpatient_df.shape}\")\n",
    "\n",
    "synthetic_outpatient_df = pd.read_csv('data/outpatient.csv', sep=\"|\",low_memory=False)\n",
    "print(f\"outpatient shape{synthetic_outpatient_df.shape}\")\n",
    "\n",
    "synthetic_carrier_df = pd.read_csv('data/carrier.csv', sep=\"|\", low_memory=False)\n",
    "print(f\"carrier shape{synthetic_carrier_df.shape}\")\n",
    "\n",
    "synthetic_dme_df = pd.read_csv('data/dme.csv', sep=\"|\",  low_memory=False)\n",
    "print(f\"dme shape {synthetic_dme_df.shape}\")\n",
    "\n",
    "synthetic_hha_df = pd.read_csv('data/hha.csv', sep=\"|\", low_memory=False)\n",
    "print(f\"hha shape {synthetic_hha_df.shape}\")\n",
    "\n",
    "synthetic_hospice_df = pd.read_csv('data/hospice.csv', sep=\"|\", low_memory=False)\n",
    "print(f\"hospice shape {synthetic_hospice_df.shape}\")\n",
    "\n",
    "synthetic_snf_df = pd.read_csv('data/snf.csv', sep=\"|\", low_memory=False)\n",
    "print(f\"snf shape {synthetic_snf_df.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4de2d49",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T15:34:13.979522Z",
     "start_time": "2023-04-05T15:34:13.957522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8671 unique ids and 8671 total rows\n"
     ]
    }
   ],
   "source": [
    "# unique count of BENE_IDs\n",
    "unique_ids = synthetic_bene_df['BENE_ID'].unique()\n",
    "\n",
    "# print results\n",
    "print(f\"{len(unique_ids)} unique ids and {synthetic_bene_df.shape[0]} total rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2ce84b",
   "metadata": {},
   "source": [
    "# Calculate number of unique synthetic benes in 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b933302",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-05T15:34:17.031558Z",
     "start_time": "2023-04-05T15:34:13.981521Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BENE_ID    1684\n",
      "dtype: int64\n",
      "BENE_ID    6053\n",
      "dtype: int64\n",
      "BENE_ID    7581\n",
      "dtype: int64\n",
      "BENE_ID    3653\n",
      "dtype: int64\n",
      "BENE_ID    61\n",
      "dtype: int64\n",
      "BENE_ID    150\n",
      "dtype: int64\n",
      "BENE_ID    235\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# evaluate 2022 only\n",
    "values = [2022]\n",
    "\n",
    "# convert CLM_FROM_DT to datetime\n",
    "synthetic_inpatient_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_inpatient_df['CLM_FROM_DT'])\n",
    "# extract year from CLM_FROM_DT \n",
    "synthetic_inpatient_df['CLM_FROM_YR'] = synthetic_inpatient_df['CLM_FROM_DT'].dt.year\n",
    "# filter CLM_FROM_YR on 2022 only\n",
    "synthetic_inpatient_df = synthetic_inpatient_df[synthetic_inpatient_df.CLM_FROM_YR.isin(values)]\n",
    "\n",
    "synthetic_outpatient_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_outpatient_df['CLM_FROM_DT'])\n",
    "synthetic_outpatient_df['CLM_FROM_YR'] = synthetic_outpatient_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_outpatient_df = synthetic_outpatient_df[synthetic_outpatient_df.CLM_FROM_YR.isin(values)]\n",
    "\n",
    "synthetic_carrier_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_carrier_df['CLM_FROM_DT'])\n",
    "synthetic_carrier_df['CLM_FROM_YR'] = synthetic_carrier_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_carrier_df = synthetic_carrier_df[synthetic_carrier_df.CLM_FROM_YR.isin(values)]\n",
    "\n",
    "synthetic_dme_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_dme_df['CLM_FROM_DT'])\n",
    "synthetic_dme_df['CLM_FROM_YR'] = synthetic_dme_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_dme_df = synthetic_dme_df[synthetic_dme_df.CLM_FROM_YR.isin(values)]\n",
    "\n",
    "synthetic_hha_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_hha_df['CLM_FROM_DT'])\n",
    "synthetic_hha_df['CLM_FROM_YR'] = synthetic_hha_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_hha_df = synthetic_hha_df[synthetic_hha_df.CLM_FROM_YR.isin(values)]\n",
    "\n",
    "synthetic_hospice_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_hospice_df['CLM_FROM_DT'])\n",
    "synthetic_hospice_df['CLM_FROM_YR'] = synthetic_hospice_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_hospice_df = synthetic_hospice_df[synthetic_hospice_df.CLM_FROM_YR.isin(values)]\n",
    "\n",
    "synthetic_snf_df['CLM_FROM_DT'] = pd.to_datetime(synthetic_snf_df['CLM_FROM_DT'])\n",
    "synthetic_snf_df['CLM_FROM_YR'] = synthetic_snf_df['CLM_FROM_DT'].dt.year\n",
    "synthetic_snf_df = synthetic_snf_df[synthetic_snf_df.CLM_FROM_YR.isin(values)]\n",
    "\n",
    "# print count of unique BENE_IDs in each service type\n",
    "print(synthetic_inpatient_df[['BENE_ID']].nunique())\n",
    "print(synthetic_outpatient_df[['BENE_ID']].nunique())\n",
    "print(synthetic_carrier_df[['BENE_ID']].nunique())\n",
    "print(synthetic_dme_df[['BENE_ID']].nunique())\n",
    "print(synthetic_hha_df[['BENE_ID']].nunique())\n",
    "print(synthetic_hospice_df[['BENE_ID']].nunique())\n",
    "print(synthetic_snf_df[['BENE_ID']].nunique())  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}