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876655d2b2
* 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)
80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
import typing
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import pandas as pd
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from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel
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from data_pipeline.utils import get_module_logger, download_file_from_url
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from data_pipeline.score import field_names
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logger = get_module_logger(__name__)
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class CDCPlacesETL(ExtractTransformLoad):
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NAME = "cdc_places"
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GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT
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PUERTO_RICO_EXPECTED_IN_DATA = False
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CDC_GEOID_FIELD_NAME = "LocationID"
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CDC_VALUE_FIELD_NAME = "Data_Value"
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CDC_MEASURE_FIELD_NAME = "Measure"
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def __init__(self):
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self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "cdc_places"
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self.CDC_PLACES_URL = "https://chronicdata.cdc.gov/api/views/cwsq-ngmh/rows.csv?accessType=DOWNLOAD"
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self.COLUMNS_TO_KEEP: typing.List[str] = [
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self.GEOID_TRACT_FIELD_NAME,
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field_names.DIABETES_FIELD,
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field_names.ASTHMA_FIELD,
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field_names.HEART_DISEASE_FIELD,
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field_names.CANCER_FIELD,
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field_names.HEALTH_INSURANCE_FIELD,
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field_names.PHYS_HEALTH_NOT_GOOD_FIELD,
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]
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self.df: pd.DataFrame
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def extract(self) -> None:
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logger.info("Starting to download 520MB CDC Places file.")
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file_path = download_file_from_url(
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file_url=self.CDC_PLACES_URL,
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download_file_name=self.get_tmp_path() / "census_tract.csv",
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)
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self.df = pd.read_csv(
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filepath_or_buffer=file_path,
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dtype={self.CDC_GEOID_FIELD_NAME: "string"},
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low_memory=False,
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)
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def transform(self) -> None:
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logger.info("Starting CDC Places transform")
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# Rename GEOID field
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self.df.rename(
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columns={self.CDC_GEOID_FIELD_NAME: self.GEOID_TRACT_FIELD_NAME},
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inplace=True,
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errors="raise",
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)
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# Note: Puerto Rico not included.
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self.df = self.df.pivot(
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index=self.GEOID_TRACT_FIELD_NAME,
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columns=self.CDC_MEASURE_FIELD_NAME,
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values=self.CDC_VALUE_FIELD_NAME,
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)
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# rename columns to be used in score
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rename_fields = {
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"Current asthma among adults aged >=18 years": field_names.ASTHMA_FIELD,
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"Coronary heart disease among adults aged >=18 years": field_names.HEART_DISEASE_FIELD,
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"Cancer (excluding skin cancer) among adults aged >=18 years": field_names.CANCER_FIELD,
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"Diagnosed diabetes among adults aged >=18 years": field_names.DIABETES_FIELD,
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"Physical health not good for >=14 days among adults aged >=18 years": field_names.PHYS_HEALTH_NOT_GOOD_FIELD,
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}
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self.df.rename(
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columns=rename_fields,
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inplace=True,
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errors="raise",
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)
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# Make the index (the census tract ID) a column, not the index.
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self.output_df = self.df.reset_index()
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