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5 changed files with 61 additions and 37 deletions
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@ -129,7 +129,7 @@ class PostScoreETL(ExtractTransformLoad):
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new_df = initial_states_df.rename(
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columns={
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"fips": "State Code",
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"state_name": "State Name",
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"state_name": field_names.STATE_FIELD,
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"state_abbreviation": "State Abbreviation",
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}
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)
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@ -242,7 +242,19 @@ class PostScoreETL(ExtractTransformLoad):
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) -> pd.DataFrame:
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df = score_county_state_merged_df[constants.DOWNLOADABLE_SCORE_COLUMNS]
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# rename fields
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float_columns = df.select_dtypes(include=["float64"])
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# score_tiles[constants.TILES_SCORE_FLOAT_COLUMNS] = score_tiles[
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# constants.TILES_SCORE_FLOAT_COLUMNS
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# ].apply(
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# func=lambda series: floor_series(
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# series=series,
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# number_of_decimals=constants.TILES_ROUND_NUM_DECIMALS,
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# ),
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# axis=0,
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# )
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# [x for x in df.columns if field_names.PERCENTILE_FIELD_SUFFIX in x]
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return df
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@ -301,7 +313,7 @@ class PostScoreETL(ExtractTransformLoad):
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# Rename score column
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downloadable_df_copy = downloadable_df.rename(
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columns={
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field_names.SCORE_L_COMMUNITIES: "Community of focus (v0.1)"
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field_names.SCORE_L_COMMUNITIES: "Identified as disadvantaged (v0.1)"
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},
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inplace=False,
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)
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@ -23,7 +23,7 @@ class CensusACSETL(ExtractTransformLoad):
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self.TOTAL_UNEMPLOYED_FIELD,
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self.TOTAL_IN_LABOR_FORCE,
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]
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self.UNEMPLOYED_FIELD_NAME = "Unemployed civilians (percent)"
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self.UNEMPLOYED_FIELD_NAME = "Unemployment (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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@ -74,7 +74,7 @@ class CensusACS2010ETL(ExtractTransformLoad):
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self.EMPLOYMENT_COLLEGE_IN_LABOR_FORCE,
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]
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self.UNEMPLOYED_FIELD_NAME = "Unemployed civilians (percent)"
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self.UNEMPLOYED_FIELD_NAME = "Unemployment (percent)"
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self.POVERTY_FIELDS = [
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"C17002_001E", # Estimate!!Total,
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