Analysis by region (#385)

* Adding regional comparisons

* Small ETL fixes

data/data-pipeline/etl/sources/calenviroscreen/etl.py

@@ -8,7 +8,7 @@ logger = get_module_logger(__name__)
 
 class CalEnviroScreenETL(ExtractTransformLoad):
     def __init__(self):
-        self.CALENVIROSCREEN_FTP_URL = "https://justice40-data.s3.amazonaws.com/CalEnviroScreen/CalEnviroScreen_4.0_2021.zip"
+        self.CALENVIROSCREEN_FTP_URL = "https://justice40-data.s3.amazonaws.com/data-sources/CalEnviroScreen_4.0_2021.zip"
         self.CALENVIROSCREEN_CSV = self.TMP_PATH / "CalEnviroScreen_4.0_2021.csv"
         self.CSV_PATH = self.DATA_PATH / "dataset" / "calenviroscreen4"
 

data/data-pipeline/etl/sources/census/etl_utils.py

@@ -1,5 +1,6 @@
 from pathlib import Path
 import csv
+import pandas as pd
 import os
 from config import settings
 
@@ -53,3 +54,18 @@ def get_state_fips_codes(data_path: Path) -> list:
                 fips = row[0].strip()
                 fips_state_list.append(fips)
     return fips_state_list
+
+
+def get_state_information(data_path: Path) -> pd.DataFrame:
+    """Load the full state file as a dataframe.
+
+    Useful because of the state regional information.
+    """
+    fips_csv_path = data_path / "census" / "csv" / "fips_states_2010.csv"
+
+    df = pd.read_csv(fips_csv_path)
+
+    # Left pad the FIPS codes with 0s
+    df["fips"] = df["fips"].astype(str).apply(lambda x: x.zfill(2))
+
+    return df

data/data-pipeline/etl/sources/housing_and_transportation/etl.py

@@ -44,8 +44,6 @@ class HousingTransportationETL(ExtractTransformLoad):
 
         self.df = pd.concat(dfs)
 
-        self.df.head()
-
     def transform(self) -> None:
         logger.info(f"Transforming Housing and Transportation Data")
 

data/data-pipeline/etl/sources/hud_recap/etl.py

@@ -33,7 +33,7 @@ class HudRecapETL(ExtractTransformLoad):
         logger.info(f"Transforming HUD Recap Data")
 
         # Load comparison index (CalEnviroScreen 4)
-        self.df = pd.read_csv(self.HUD_RECAP_CSV, dtype={"Census Tract": "string"})
+        self.df = pd.read_csv(self.HUD_RECAP_CSV, dtype={"GEOID": "string"})
 
         self.df.rename(
             columns={