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added indoor plumbing to score housing burden
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3 changed files with 41 additions and 18 deletions
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@ -366,6 +366,7 @@ class ScoreETL(ExtractTransformLoad):
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numeric_columns = [
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field_names.HOUSING_BURDEN_FIELD,
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field_names.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD,
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field_names.TOTAL_POP_FIELD,
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field_names.MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD,
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field_names.ASTHMA_FIELD,
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@ -416,7 +417,6 @@ class ScoreETL(ExtractTransformLoad):
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field_names.HEALTHY_FOOD_FIELD,
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field_names.IMPENETRABLE_SURFACES_FIELD,
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field_names.UST_FIELD,
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# We have to pass this boolean here in order to include it in ag value loss percentiles.
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field_names.AGRICULTURAL_VALUE_BOOL_FIELD,
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field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
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]
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@ -473,9 +473,7 @@ class ScoreETL(ExtractTransformLoad):
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set(numeric_columns)
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), "You have a double-entered column in the numeric columns list"
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df_copy[numeric_columns] = df_copy[numeric_columns].apply(
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pd.to_numeric
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)
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df_copy[numeric_columns] = df_copy[numeric_columns].apply(pd.to_numeric)
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# Convert all columns to numeric and do math
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# Note that we have a few special conditions here, that we handle explicitly.
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