j40-cejst-2/data/data-pipeline/data_pipeline/ipython/county_lookup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "from pathlib import Path\n",
    "import os\n",
    "import sys"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "module_path = os.path.abspath(os.path.join(\"..\"))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "    \n",
    "from data_pipeline.utils import unzip_file_from_url\n",
    "from data_pipeline.etl.sources.census.etl_utils import get_state_fips_codes"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "DATA_PATH = Path.cwd().parent / \"data\"\n",
    "TMP_PATH: Path = DATA_PATH / \"tmp\"\n",
    "STATE_CSV = DATA_PATH / \"census\" / \"csv\" / \"fips_states_2010.csv\"\n",
    "SCORE_CSV = DATA_PATH / \"score\" / \"csv\" / \"usa.csv\"\n",
    "COUNTY_SCORE_CSV = DATA_PATH / \"score\" / \"csv\" / \"usa-county.csv\"\n",
    "CENSUS_COUNTIES_ZIP_URL = \"https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2020_Gazetteer/2020_Gaz_counties_national.zip\"\n",
    "CENSUS_COUNTIES_TXT = TMP_PATH / \"2020_Gaz_counties_national.txt\""
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "unzip_file_from_url(CENSUS_COUNTIES_ZIP_URL, TMP_PATH, TMP_PATH)"
   ],
   "outputs": [],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "counties_df = pd.read_csv(CENSUS_COUNTIES_TXT, sep=\"\\t\", dtype={\"GEOID\": \"string\", \"USPS\": \"string\"}, low_memory=False)\n",
    "counties_df = counties_df[['USPS', 'GEOID', 'NAME']]\n",
    "counties_df.rename(columns={\"USPS\": \"State Abbreviation\", \"NAME\": \"County Name\"}, inplace=True)\n",
    "counties_df.head()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "states_df = pd.read_csv(STATE_CSV, dtype={\"fips\": \"string\", \"state_abbreviation\": \"string\"})\n",
    "states_df.rename(columns={\"fips\": \"State Code\", \"state_name\": \"State Name\", \"state_abbreviation\": \"State Abbreviation\"}, inplace=True)\n",
    "states_df.head()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "county_state_merged = counties_df.join(states_df, rsuffix=' Other')\n",
    "del county_state_merged[\"State Abbreviation Other\"]\n",
    "county_state_merged.head()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "score_df = pd.read_csv(SCORE_CSV, dtype={\"GEOID10\": \"string\"})\n",
    "score_df[\"GEOID\"] = score_df.GEOID10.str[:5]\n",
    "score_df.head()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "score_county_state_merged = score_df.join(county_state_merged, rsuffix='_OTHER')\n",
    "del score_county_state_merged[\"GEOID_OTHER\"]\n",
    "score_county_state_merged.head()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "score_county_state_merged.to_csv(COUNTY_SCORE_CSV, index=False)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
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