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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
86 lines
3 KiB
Python
86 lines
3 KiB
Python
import pandas as pd
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from data_pipeline.config import settings
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from data_pipeline.etl.base import ExtractTransformLoad
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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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from data_pipeline.utils import get_module_logger
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logger = get_module_logger(__name__)
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class HudRecapETL(ExtractTransformLoad):
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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.hud_recap_csv_url = (
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f"{settings.AWS_JUSTICE40_DATASOURCES_URL}/raw-data-sources/"
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"hud_recap/Racially_or_Ethnically_Concentrated_Areas_of_Poverty__R_ECAPs_.csv"
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)
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else:
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self.hud_recap_csv_url = (
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"https://opendata.arcgis.com/api/v3/datasets/"
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"56de4edea8264fe5a344da9811ef5d6e_0/downloads/data?format=csv&spatialRefId=4326"
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)
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# input
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self.hud_recap_source = (
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self.get_sources_path()
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/ "Racially_or_Ethnically_Concentrated_Areas_of_Poverty__R_ECAPs_.csv"
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)
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# output
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self.CSV_PATH = self.DATA_PATH / "dataset" / "hud_recap"
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# Defining some variable names
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self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME = (
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"hud_recap_priority_community"
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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.hud_recap_csv_url, destination=self.hud_recap_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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# Load comparison index (CalEnviroScreen 4)
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self.df = pd.read_csv(self.hud_recap_source, dtype={"GEOID": "string"})
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def transform(self) -> None:
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self.df.rename(
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columns={
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"GEOID": self.GEOID_TRACT_FIELD_NAME,
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# Interestingly, there's no data dictionary for the RECAP data that I could find.
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# However, this site (http://www.schousing.com/library/Tax%20Credit/2020/QAP%20Instructions%20(2).pdf)
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# suggests:
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# "If RCAP_Current for the tract in which the site is located is 1, the tract is an R/ECAP. If RCAP_Current is 0, it is not."
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"RCAP_Current": self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME,
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},
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inplace=True,
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)
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# Convert to boolean
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self.df[self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME] = self.df[
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self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME
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].astype("bool")
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self.df[self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME].value_counts()
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self.df.sort_values(by=self.GEOID_TRACT_FIELD_NAME, inplace=True)
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def load(self) -> None:
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# write nationwide csv
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self.CSV_PATH.mkdir(parents=True, exist_ok=True)
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self.df.to_csv(self.CSV_PATH / "usa.csv", index=False)
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