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updates to PR review

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This commit is contained in:
Saran Ahluwalia 2022-01-13 14:26:24 -05:00
commit 0176c770c0
2 changed files with 4 additions and 12 deletions
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11
data/data-pipeline/data_pipeline/etl/sources/maryland_ejscreen/etl.py
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@ -32,8 +32,6 @@ class MarylandEJScreenETL(ExtractTransformLoad):
field_names.MARYLAND_EJSCREEN_TRACT_90_PERCENTILE_FIELD,
field_names.MARYLAND_EJSCREEN_SCORE_FIELD,
field_names.MARYLAND_EJSCREEN_BURDENED_THRESHOLD_FIELD,
field_names.MARYLAND_EJSCREEN_SCORE_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX,
]
self.df: pd.DataFrame
@ -93,11 +91,8 @@ class MarylandEJScreenETL(ExtractTransformLoad):
inplace=True,
)
# Interpretation: An EJ score (reported as a percentile)
# has a percentile rank of N for some N between 0 - 100"
# Please see the README and the following linksfor details and context
# 1. https://github.com/usds/justice40-tool/issues/239#issuecomment-1003448074
# 2. https://github.com/usds/justice40-tool/issues/239#issuecomment-995798345
# This computational step will be used to establish a
# threshold for burden (line 104)
self.df[
field_names.MARYLAND_EJSCREEN_SCORE_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
@ -111,7 +106,7 @@ class MarylandEJScreenETL(ExtractTransformLoad):
field_names.MARYLAND_EJSCREEN_SCORE_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
> 0.75
>= 0.75
)
# Baseline Comparisons with some quartiles and the 90th percent OF EJ Score