Data directory should adopt standard Poetry-suggested python package structure (#457)

* Fixes #456 - Our data directory should adopt standard python package structure
* a few missed references
* updating readme
* updating requirements
* Running Black
* Fixes for flake8
* updating pylint

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

@@ -0,0 +1,72 @@
+import pandas as pd
+
+from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.utils import get_module_logger
+from data_pipeline.config import settings
+
+logger = get_module_logger(__name__)
+
+
+class CalEnviroScreenETL(ExtractTransformLoad):
+    def __init__(self):
+        self.CALENVIROSCREEN_FTP_URL = (
+            settings.AWS_JUSTICE40_DATASOURCES_URL + "/CalEnviroScreen_4.0_2021.zip"
+        )
+        self.CALENVIROSCREEN_CSV = self.TMP_PATH / "CalEnviroScreen_4.0_2021.csv"
+        self.CSV_PATH = self.DATA_PATH / "dataset" / "calenviroscreen4"
+
+        # Definining some variable names
+        self.CALENVIROSCREEN_SCORE_FIELD_NAME = "calenviroscreen_score"
+        self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME = "calenviroscreen_percentile"
+        self.CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME = (
+            "calenviroscreen_priority_community"
+        )
+
+        # Choosing constants.
+        # None of these numbers are final, but just for the purposes of comparison.
+        self.CALENVIROSCREEN_PRIORITY_COMMUNITY_THRESHOLD = 75
+
+        self.df: pd.DataFrame
+
+    def extract(self) -> None:
+        logger.info("Downloading CalEnviroScreen Data")
+        super().extract(
+            self.CALENVIROSCREEN_FTP_URL,
+            self.TMP_PATH,
+        )
+
+    def transform(self) -> None:
+        logger.info("Transforming CalEnviroScreen Data")
+
+        # Data from https://calenviroscreen-oehha.hub.arcgis.com/#Data, specifically:
+        # https://oehha.ca.gov/media/downloads/calenviroscreen/document/calenviroscreen40resultsdatadictionaryd12021.zip
+        # Load comparison index (CalEnviroScreen 4)
+        self.df = pd.read_csv(
+            self.CALENVIROSCREEN_CSV, dtype={"Census Tract": "string"}
+        )
+
+        self.df.rename(
+            columns={
+                "Census Tract": self.GEOID_TRACT_FIELD_NAME,
+                "DRAFT CES 4.0 Score": self.CALENVIROSCREEN_SCORE_FIELD_NAME,
+                "DRAFT CES 4.0 Percentile": self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME,
+            },
+            inplace=True,
+        )
+
+        # Add a leading "0" to the Census Tract to match our format in other data frames.
+        self.df[self.GEOID_TRACT_FIELD_NAME] = (
+            "0" + self.df[self.GEOID_TRACT_FIELD_NAME]
+        )
+
+        # Calculate the top K% of prioritized communities
+        self.df[self.CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME] = (
+            self.df[self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME]
+            >= self.CALENVIROSCREEN_PRIORITY_COMMUNITY_THRESHOLD
+        )
+
+    def load(self) -> None:
+        logger.info("Saving CalEnviroScreen CSV")
+        # write nationwide csv
+        self.CSV_PATH.mkdir(parents=True, exist_ok=True)
+        self.df.to_csv(self.CSV_PATH / "data06.csv", index=False)