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110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
import pandas as pd
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import censusdata
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from data_pipeline.etl.base import ExtractTransformLoad
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from data_pipeline.etl.sources.census.etl_utils import get_state_fips_codes
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from data_pipeline.utils import get_module_logger
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logger = get_module_logger(__name__)
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class CensusACSETL(ExtractTransformLoad):
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def __init__(self):
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self.ACS_YEAR = 2019
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self.OUTPUT_PATH = self.DATA_PATH / "dataset" / f"census_acs_{self.ACS_YEAR}"
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self.UNEMPLOYED_FIELD_NAME = "Unemployed civilians (percent)"
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self.LINGUISTIC_ISOLATION_FIELD_NAME = "Linguistic isolation (percent)"
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self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME = "Linguistic isolation (total)"
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self.LINGUISTIC_ISOLATION_FIELDS = [
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"C16002_001E",
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"C16002_004E",
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"C16002_007E",
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"C16002_010E",
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"C16002_013E",
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]
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self.MEDIAN_INCOME_FIELD = "B19013_001E"
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self.MEDIAN_INCOME_FIELD_NAME = "Median household income in the past 12 months"
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self.df: pd.DataFrame
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def _fips_from_censusdata_censusgeo(self, censusgeo: censusdata.censusgeo) -> str:
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"""Create a FIPS code from the proprietary censusgeo index."""
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fips = "".join([value for (key, value) in censusgeo.params()])
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return fips
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def extract(self) -> None:
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dfs = []
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for fips in get_state_fips_codes(self.DATA_PATH):
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logger.info(f"Downloading data for state/territory with FIPS code {fips}")
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dfs.append(
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censusdata.download(
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src="acs5",
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year=self.ACS_YEAR,
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geo=censusdata.censusgeo(
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[("state", fips), ("county", "*"), ("block group", "*")]
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),
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var=[
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# Emploment fields
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"B23025_005E",
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"B23025_003E",
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self.MEDIAN_INCOME_FIELD,
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]
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+ self.LINGUISTIC_ISOLATION_FIELDS,
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)
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)
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self.df = pd.concat(dfs)
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self.df[self.GEOID_FIELD_NAME] = self.df.index.to_series().apply(
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func=self._fips_from_censusdata_censusgeo
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)
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def transform(self) -> None:
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logger.info("Starting Census ACS Transform")
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# Rename median income
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self.df[self.MEDIAN_INCOME_FIELD_NAME] = self.df[self.MEDIAN_INCOME_FIELD]
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# Calculate percent unemployment.
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# TODO: remove small-sample data that should be `None` instead of a high-variance fraction.
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self.df[self.UNEMPLOYED_FIELD_NAME] = self.df.B23025_005E / self.df.B23025_003E
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# Calculate linguistic isolation.
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individual_limited_english_fields = [
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"C16002_004E",
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"C16002_007E",
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"C16002_010E",
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"C16002_013E",
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]
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self.df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME] = self.df[
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individual_limited_english_fields
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].sum(axis=1, skipna=True)
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self.df[self.LINGUISTIC_ISOLATION_FIELD_NAME] = (
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self.df[self.LINGUISTIC_ISOLATION_TOTAL_FIELD_NAME].astype(float)
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/ self.df["C16002_001E"]
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)
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self.df[self.LINGUISTIC_ISOLATION_FIELD_NAME].describe()
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def load(self) -> None:
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logger.info("Saving Census ACS Data")
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# mkdir census
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self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
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columns_to_include = [
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self.GEOID_FIELD_NAME,
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self.UNEMPLOYED_FIELD_NAME,
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self.LINGUISTIC_ISOLATION_FIELD_NAME,
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self.MEDIAN_INCOME_FIELD_NAME,
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]
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self.df[columns_to_include].to_csv(
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path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
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)
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def validate(self) -> None:
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logger.info("Validating Census ACS Data")
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pass
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