Add tests for all non-census sources (#1899)

* Refactor CDC life-expectancy (1554)

* Update to new tract list (#1554)

* Adjust for tests (#1848)

* Add tests for cdc_places (#1848)

* Add EJScreen tests (#1848)

* Add tests for HUD housing (#1848)

* Add tests for GeoCorr (#1848)

* Add persistent poverty tests (#1848)

* Update for sources without zips, for new validation (#1848)

* Update tests for new multi-CSV but (#1848)

Lucas updated the CDC life expectancy data to handle a bug where two
states are missing from the US Overall download. Since virtually none of
our other ETL classes download multiple CSVs directly like this, it
required a pretty invasive new mocking strategy.

* Add basic tests for nature deprived (#1848)

* Add wildfire tests (#1848)

* Add flood risk tests (#1848)

* Add DOT travel tests (#1848)

* Add historic redlining tests (#1848)

* Add tests for ME and WI (#1848)

* Update now that validation exists (#1848)

* Adjust for validation (#1848)

* Add health insurance back to cdc places (#1848)

Ooops

* Update tests with new field (#1848)

* Test for blank tract removal (#1848)

* Add tracts for clipping behavior

* Test clipping and zfill behavior (#1848)

* Fix bad test assumption (#1848)

* Simplify class, add test for tract padding (#1848)

* Fix percentage inversion, update tests (#1848)

Looking through the transformations, I noticed that we were subtracting
a percentage that is usually between 0-100 from 1 instead of 100, and so
were endind up with some surprising results. Confirmed with lucasmbrown-usds

* Add note about first street data (#1848)

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

@@ -1,6 +1,7 @@
+import typing
 import pandas as pd
 
-from data_pipeline.etl.base import ExtractTransformLoad
+from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
 from data_pipeline.utils import get_module_logger, download_file_from_url
 from data_pipeline.score import field_names
 
@@ -8,13 +9,27 @@ logger = get_module_logger(__name__)
 
 
 class CDCPlacesETL(ExtractTransformLoad):
+    NAME = "cdc_places"
+    GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
+    PUERTO_RICO_EXPECTED_IN_DATA = False
+
+    CDC_GEOID_FIELD_NAME = "LocationID"
+    CDC_VALUE_FIELD_NAME = "Data_Value"
+    CDC_MEASURE_FIELD_NAME = "Measure"
+
     def __init__(self):
         self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "cdc_places"
 
         self.CDC_PLACES_URL = "https://chronicdata.cdc.gov/api/views/cwsq-ngmh/rows.csv?accessType=DOWNLOAD"
-        self.CDC_GEOID_FIELD_NAME = "LocationID"
-        self.CDC_VALUE_FIELD_NAME = "Data_Value"
-        self.CDC_MEASURE_FIELD_NAME = "Measure"
+        self.COLUMNS_TO_KEEP: typing.List[str] = [
+            self.GEOID_TRACT_FIELD_NAME,
+            field_names.DIABETES_FIELD,
+            field_names.ASTHMA_FIELD,
+            field_names.HEART_DISEASE_FIELD,
+            field_names.CANCER_FIELD,
+            field_names.HEALTH_INSURANCE_FIELD,
+            field_names.PHYS_HEALTH_NOT_GOOD_FIELD,
+        ]
 
         self.df: pd.DataFrame
 
@@ -22,9 +37,7 @@ class CDCPlacesETL(ExtractTransformLoad):
         logger.info("Starting to download 520MB CDC Places file.")
         file_path = download_file_from_url(
             file_url=self.CDC_PLACES_URL,
-            download_file_name=self.get_tmp_path()
-            / "cdc_places"
-            / "census_tract.csv",
+            download_file_name=self.get_tmp_path() / "census_tract.csv",
         )
 
         self.df = pd.read_csv(
@@ -42,7 +55,6 @@ class CDCPlacesETL(ExtractTransformLoad):
             inplace=True,
             errors="raise",
         )
-
         # Note: Puerto Rico not included.
         self.df = self.df.pivot(
             index=self.GEOID_TRACT_FIELD_NAME,
@@ -65,12 +77,4 @@ class CDCPlacesETL(ExtractTransformLoad):
         )
 
         # Make the index (the census tract ID) a column, not the index.
-        self.df.reset_index(inplace=True)
-
-    def load(self) -> None:
-        logger.info("Saving CDC Places Data")
-
-        # mkdir census
-        self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
-
-        self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
+        self.output_df = self.df.reset_index()