Data folder restructuring in preparation for 361 (#376)

* initial checkin

* gitignore and docker-compose update

* readme update and error on hud

* encoding issue

* one more small README change

* data roadmap re-strcuture

* pyproject sort

* small update to score output folders

* checkpoint

* couple of last fixes

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

@@ -0,0 +1,108 @@
+import pandas as pd
+import censusdata
+
+from etl.base import ExtractTransformLoad
+from etl.sources.census.etl_utils import get_state_fips_codes
+from utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+class CensusACSETL(ExtractTransformLoad):
+    def __init__(self):
+        self.ACS_YEAR = 2019
+        self.OUTPUT_PATH = (
+            self.DATA_PATH / "dataset" / f"census_acs_{self.ACS_YEAR}"
+        )
+        self.UNEMPLOYED_FIELD_NAME = "Unemployed civilians (percent)"
+        self.LINGUISTIC_ISOLATION_FIELD_NAME = "Linguistic isolation (percent)"
+        self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME = (
+            "Linguistic isolation (total)"
+        )
+        self.LINGUISTIC_ISOLATION_FIELDS = [
+            "C16002_001E",
+            "C16002_004E",
+            "C16002_007E",
+            "C16002_010E",
+            "C16002_013E",
+        ]
+        self.df: pd.DataFrame
+
+    def _fips_from_censusdata_censusgeo(
+        self, censusgeo: censusdata.censusgeo
+    ) -> str:
+        """Create a FIPS code from the proprietary censusgeo index."""
+        fips = "".join([value for (key, value) in censusgeo.params()])
+        return fips
+
+    def extract(self) -> None:
+        dfs = []
+        for fips in get_state_fips_codes(self.DATA_PATH):
+            logger.info(
+                f"Downloading data for state/territory with FIPS code {fips}"
+            )
+
+            dfs.append(
+                censusdata.download(
+                    src="acs5",
+                    year=self.ACS_YEAR,
+                    geo=censusdata.censusgeo(
+                        [("state", fips), ("county", "*"), ("block group", "*")]
+                    ),
+                    var=[
+                        # Emploment fields
+                        "B23025_005E",
+                        "B23025_003E",
+                    ]
+                    + self.LINGUISTIC_ISOLATION_FIELDS,
+                )
+            )
+
+        self.df = pd.concat(dfs)
+
+        self.df[self.GEOID_FIELD_NAME] = self.df.index.to_series().apply(
+            func=self._fips_from_censusdata_censusgeo
+        )
+
+    def transform(self) -> None:
+        logger.info(f"Starting Census ACS Transform")
+
+        # Calculate percent unemployment.
+        # TODO: remove small-sample data that should be `None` instead of a high-variance fraction.
+        self.df[self.UNEMPLOYED_FIELD_NAME] = (
+            self.df.B23025_005E / self.df.B23025_003E
+        )
+
+        # Calculate linguistic isolation.
+        individual_limited_english_fields = [
+            "C16002_004E",
+            "C16002_007E",
+            "C16002_010E",
+            "C16002_013E",
+        ]
+
+        self.df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME] = self.df[
+            individual_limited_english_fields
+        ].sum(axis=1, skipna=True)
+        self.df[self.LINGUISTIC_ISOLATION_FIELD_NAME] = (
+            self.df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME].astype(float)
+            / self.df["C16002_001E"]
+        )
+
+        self.df[self.LINGUISTIC_ISOLATION_FIELD_NAME].describe()
+
+    def load(self) -> None:
+        logger.info(f"Saving Census ACS Data")
+
+        # mkdir census
+        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
+
+        columns_to_include = [
+            self.GEOID_FIELD_NAME,
+            self.UNEMPLOYED_FIELD_NAME,
+            self.LINGUISTIC_ISOLATION_FIELD_NAME,
+        ]
+
+        self.df[columns_to_include].to_csv(
+            path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
+        )