Remove unused persistent poverty from score (#1835)

usds · Sep 1, 2022 · b26d359 · b26d359
1 parent 9c0e199
commit b26d359
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diff --git 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
@@ -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 = (