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198 lines
7.4 KiB
Python
198 lines
7.4 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 = (
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self.DATA_PATH / "dataset" / f"census_acs_{self.ACS_YEAR}"
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)
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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 = (
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"Linguistic isolation (total)"
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)
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self.LINGUISTIC_ISOLATION_FIELDS = [
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"C16002_001E", # Estimate!!Total
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"C16002_004E", # Estimate!!Total!!Spanish!!Limited English speaking household
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"C16002_007E", # Estimate!!Total!!Other Indo-European languages!!Limited English speaking household
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"C16002_010E", # Estimate!!Total!!Asian and Pacific Island languages!!Limited English speaking household
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"C16002_013E", # Estimate!!Total!!Other languages!!Limited English speaking household
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]
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self.MEDIAN_INCOME_FIELD = "B19013_001E"
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self.MEDIAN_INCOME_FIELD_NAME = (
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"Median household income in the past 12 months"
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)
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self.POVERTY_FIELDS = [
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"C17002_001E", # Estimate!!Total,
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"C17002_002E", # Estimate!!Total!!Under .50
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"C17002_003E", # Estimate!!Total!!.50 to .99
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"C17002_004E", # Estimate!!Total!!1.00 to 1.24
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"C17002_005E", # Estimate!!Total!!1.25 to 1.49
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"C17002_006E", # Estimate!!Total!!1.50 to 1.84
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"C17002_007E", # Estimate!!Total!!1.85 to 1.99
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]
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self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME = (
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"Percent of individuals < 100% Federal Poverty Line"
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)
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self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME = (
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"Percent of individuals < 150% Federal Poverty Line"
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)
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self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
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"Percent of individuals < 200% Federal Poverty Line"
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)
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self.MEDIAN_HOUSE_VALUE_FIELD = "B25077_001E"
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self.MEDIAN_HOUSE_VALUE_FIELD_NAME = (
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"Median value ($) of owner-occupied housing units"
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)
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self.STATE_GEOID_FIELD_NAME = "GEOID2"
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self.df: pd.DataFrame
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def _fips_from_censusdata_censusgeo(
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self, censusgeo: censusdata.censusgeo
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) -> 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(
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f"Downloading data for state/territory with FIPS code {fips}"
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)
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try:
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response = 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", "*"), ("tract", "*")]
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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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# Income field
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self.MEDIAN_INCOME_FIELD,
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# House value
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self.MEDIAN_HOUSE_VALUE_FIELD,
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]
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+ self.LINGUISTIC_ISOLATION_FIELDS
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+ self.POVERTY_FIELDS,
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)
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dfs.append(response)
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except ValueError:
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logger.error(
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f"Could not download data for state/territory with FIPS code {fips}"
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)
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self.df = pd.concat(dfs)
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self.df[self.GEOID_TRACT_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 two fields.
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self.df = self.df.rename(
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columns={
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self.MEDIAN_HOUSE_VALUE_FIELD: self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
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self.MEDIAN_INCOME_FIELD: self.MEDIAN_INCOME_FIELD_NAME,
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}
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)
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# Handle null values for various fields, which are `-666666666`.
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for field in [
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self.MEDIAN_INCOME_FIELD_NAME,
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self.MEDIAN_HOUSE_VALUE_FIELD_NAME,
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]:
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missing_value_count = sum(self.df[field] == -666666666)
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logger.info(
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f"There are {missing_value_count} ({int(100*missing_value_count/self.df[field].count())}%) values of "
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+ f"`{field}` being marked as null values."
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)
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self.df[field] = self.df[field].replace(
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to_replace=-666666666, value=None
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)
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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] = (
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self.df.B23025_005E / self.df.B23025_003E
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)
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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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# Calculate percent at different poverty thresholds
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self.df[self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME] = (
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self.df["C17002_002E"] + self.df["C17002_003E"]
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) / self.df["C17002_001E"]
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self.df[self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME] = (
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self.df["C17002_002E"]
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+ self.df["C17002_003E"]
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+ self.df["C17002_004E"]
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+ self.df["C17002_005E"]
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) / self.df["C17002_001E"]
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self.df[self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME] = (
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self.df["C17002_002E"]
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+ self.df["C17002_003E"]
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+ self.df["C17002_004E"]
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+ self.df["C17002_005E"]
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+ self.df["C17002_006E"]
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+ self.df["C17002_007E"]
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) / self.df["C17002_001E"]
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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_TRACT_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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self.POVERTY_LESS_THAN_100_PERCENT_FPL_FIELD_NAME,
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self.POVERTY_LESS_THAN_150_PERCENT_FPL_FIELD_NAME,
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self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
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self.MEDIAN_HOUSE_VALUE_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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