Closed td928 closed 2 years ago
To summarize my thoughts:
Design Changes/Cleaning
reorder_mapper
constant can be re-named to be more specific to categoryget_all_boroughs
instead of hard-coding boroughsalright I think looking at the checklist they are all done. Avoid some more intense refactoring to save time. Let me know your thoughts! Thank you! @SashaWeinstein @mbh329
Overview
Six remaining indicattors (#41 #39 #40 #44 #199 #53) from the March 4th POP data. At first glance a massive but hopefully become actually quite managable with explanations.
Methodology
It is going to be similar to previous work to ingest population data with similar way to handle the columns. The work is broken out to smaller pieces of scripts that I thought make sense. There are two median indicators homevalue_median.py and rent_median.py which are single datapoint. Indicators share the same denominators are wrapped under the same functions units_overcrowd.py and units_housing_tenure.py and households_rent_burden.py each has three data points.
Review
The heavy lifting for this work is probably reviewing the final output but there are some patterns I want to share here.
For sanity check on those results, I did some math on the number of columns and they are roughly the following logics:
three datapoint sets (housing tenure, overcrowd, rent burden)
(4 race + 1 total pop) 3 data points 5 measurements * 2 years = 150
median single datapoint (homevalue and rent)
(4race + 1 total pop) 2 years 3 measurements = 30 columns
Column and Token Convention
the new output files should follow all the new column and token conventions that were established.