diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index ad6941d0d..62a5006d2 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -42,7 +42,6 @@ def __init__(self): self.doe_energy_burden_df: pd.DataFrame self.national_risk_index_df: pd.DataFrame self.geocorr_urban_rural_df: pd.DataFrame - self.persistent_poverty_df: pd.DataFrame self.census_decennial_df: pd.DataFrame self.census_2010_df: pd.DataFrame self.national_tract_df: pd.DataFrame @@ -159,16 +158,6 @@ def extract(self) -> None: low_memory=False, ) - # Load persistent poverty - persistent_poverty_csv = ( - constants.DATA_PATH / "dataset" / "persistent_poverty" / "usa.csv" - ) - self.persistent_poverty_df = pd.read_csv( - persistent_poverty_csv, - dtype={self.GEOID_TRACT_FIELD_NAME: "string"}, - low_memory=False, - ) - # Load decennial census data census_decennial_csv = ( constants.DATA_PATH @@ -359,7 +348,6 @@ def _prepare_initial_df(self) -> pd.DataFrame: self.doe_energy_burden_df, self.ejscreen_df, self.geocorr_urban_rural_df, - self.persistent_poverty_df, self.national_risk_index_df, self.census_acs_median_incomes_df, self.census_decennial_df, @@ -484,7 +472,6 @@ def _prepare_initial_df(self) -> pd.DataFrame: non_numeric_columns = [ self.GEOID_TRACT_FIELD_NAME, - field_names.PERSISTENT_POVERTY_FIELD, field_names.TRACT_ELIGIBLE_FOR_NONNATURAL_THRESHOLD, field_names.AGRICULTURAL_VALUE_BOOL_FIELD, ] diff --git a/data/data-pipeline/data_pipeline/tests/score/test_calculation.py b/data/data-pipeline/data_pipeline/tests/score/test_calculation.py index 783474e4a..d241918cd 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_calculation.py +++ b/data/data-pipeline/data_pipeline/tests/score/test_calculation.py @@ -28,7 +28,6 @@ def full_percentile_column_name(self): return self.percentile_column_name -### TODO: we need to blow this out for all eight categories def _check_percentile_against_threshold(df, config: PercentileTestConfig): """Note - for the purpose of testing, this fills with False""" is_minimum_flagged_ok = (