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Update etl constants to use score field_names and put strings around tract IDs in downloadable CSV (#985)

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* Update etl constants to use score field_names

Put strings around tract IDs in downloadable CSV

No need to modify the xls file creation because the string type is
preserved and interpreted correctly in Excel already.

One note is that this does cause the ID in the CSV to be have quotes
around it, which might be annoying. Maybe we don't want this behavior?

* Update based on PR feedback and lint needs

* Change field we're using in downloadable

This reverts the downloadable csv field list to use
MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD instead of
MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD in order to get the test to pass.
The point of this PR is a refactor (and a small change to the CSV
quotations), not to change the output. That will be a different PR
later.

Co-authored-by: Shelby Switzer <shelby.switzer@cms.hhs.gov>
This commit is contained in:
Shelby Switzer 2021-12-06 13:17:17 -05:00 • committed by GitHub
commit 819f3ff478
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4 changed files with 101 additions and 85 deletions
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8
data/data-pipeline/data_pipeline/score/field_names.py
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@ -1,6 +1,12 @@
# Suffixes
PERCENTILE_FIELD_SUFFIX = " (percentile)"
MIN_MAX_FIELD_SUFFIX = " (min-max normalized)"
TOP_25_PERCENTILE_SUFFIX = " (top 25th percentile)"
# Geographic field names
GEOID_TRACT_FIELD = "GEOID10_TRACT"
STATE_FIELD = "State Name"
COUNTY_FIELD = "County Name"
# Score file field names
SCORE_A = "Score A"
@ -21,6 +27,7 @@ SCORE_I = "Score I"
SCORE_I_COMMUNITIES = "Score I (communities)"
SCORE_K = "NMTC (communities)"
SCORE_K_COMMUNITIES = "Score K (communities)"
SCORE_L = "Definition L"
SCORE_L_COMMUNITIES = "Definition L (communities)"
L_CLIMATE = "Climate Factor (Definition L)"
L_ENERGY = "Energy Factor (Definition L)"
@ -45,7 +52,6 @@ POVERTY_LESS_THAN_150_FPL_FIELD = (
POVERTY_LESS_THAN_100_FPL_FIELD = (
"Percent of individuals < 100% Federal Poverty Line"
)
MEDIAN_INCOME_PERCENT_AMI_FIELD = "Median household income (% of AMI)"
STATE_MEDIAN_INCOME_FIELD = (
"Median household income (State; 2019 inflation-adjusted dollars)"
)

2
data/data-pipeline/data_pipeline/score/score_l.py
View file

@ -528,7 +528,7 @@ class ScoreL(Score):
median_income_threshold = (
self.df[
field_names.MEDIAN_INCOME_PERCENT_AMI_FIELD
field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
# Note: a high median income as a % of AMI is good, so take 1 minus the threshold to invert it.