mirror of
https://github.com/DOI-DO/j40-cejst-2.git
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6f39033dde
* Add a rough prototype allowing a developer to pre-download data sources for all ETLs * Update code to be more production-ish * Move fetch to Extract part of ETL * Create a downloader to house all downloading operations * Remove unnecessary "name" in data source * Format source files with black * Fix issues from pylint and get the tests working with the new folder structure * Clean up files with black * Fix unzip test * Add caching notes to README * Fix tests (linting and case sensitivity bug) * Address PR comments and add API keys for census where missing * Merging comparator changes from main into this branch for the sake of the PR * Add note on using cache (-u) during pipeline
104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
import typing
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import pandas as pd
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from data_pipeline.etl.base import ExtractTransformLoad
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from data_pipeline.etl.base import ValidGeoLevel
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from data_pipeline.score import field_names
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from data_pipeline.utils import get_module_logger
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from data_pipeline.config import settings
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from data_pipeline.etl.datasource import DataSource
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from data_pipeline.etl.datasource import FileDataSource
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logger = get_module_logger(__name__)
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class CDCPlacesETL(ExtractTransformLoad):
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"""#TODO: Need description"""
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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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# fetch
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if settings.DATASOURCE_RETRIEVAL_FROM_AWS:
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self.cdc_places_url = (
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f"{settings.AWS_JUSTICE40_DATASOURCES_URL}/raw-data-sources/"
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"cdc_places/PLACES__Local_Data_for_Better_Health__Census_Tract_Data_2021_release.csv"
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)
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else:
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self.cdc_places_url = "https://chronicdata.cdc.gov/api/views/cwsq-ngmh/rows.csv?accessType=DOWNLOAD"
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# input
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self.places_source = self.get_sources_path() / "census_tract.csv"
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# output
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self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "cdc_places"
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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 get_data_sources(self) -> [DataSource]:
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return [
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FileDataSource(
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source=self.cdc_places_url, destination=self.places_source
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)
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]
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def extract(self, use_cached_data_sources: bool = False) -> None:
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super().extract(
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use_cached_data_sources
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) # download and extract data sources
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self.df = pd.read_csv(
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filepath_or_buffer=self.places_source,
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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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# 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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