Open tasks

[donbowen]

Everyone should create and work in new branches, so that you're working based on the current state of the folder. And your code files should be inside the "code" folder, naturally.

• @tws223 will start a new branch, run until bank_tract_clean_WITH_CENSUS.csv is created. Email the file to me.
• Map team: @esd223 @vrg223 @annakharv46 Continue refining the WIP map file (you can copy from main and/or start your own branches)
  1. Add to "Non-White Rates (%) in Arizona" map: redlines around majority minority tracts (In the future, the shading inside the tracts will be things like denial rates.)
  2. Can you zoom that map in around Phoenix? This will be a bit more interesting.
  3. Repeat map # 2 (zoomed in), but change it so that the "redlined" counties are those with median incomes below the 10th percentile.
• @XiaozheZhangLehigh @isaacgrodin @SebastianStoneham @mromano224
  • Create a new file, maybe call it "analysis_by_tract_type"
  • Merge banktract clean and census clean
  • define majorminority
  • KEY Table: groupby(majormin)[['var1','var2']].mean() where the variables are the hdma based variables.
    • groupby + mean might make sense for some of the variables but not others.
  • repeat that and create tables grouped by
    • majority poor
    • median income quintiles
    • high hispanic population share (define a threshold)
    • most common ethnicity (=white non hispanic,black,asian,hispanic)
  • Monday - we will show Matt and go over those stats.
• We need to prepare for convo with Matt next Monday.
  • Questions and materials to present ready to go at 3pm. This means work across various teams can't be done only on Sunday night and Monday. (Some people might be waiting on upstream fixes.)
  • Everyone (@LeDataSciFi/fintech2023) should prepare questions to follow up with - aim to get feedback and guidance about where we can proceed towards. Reply below with any/all. Create a list.

[donbowen]

[vrg223]

https://ffiec.cfpb.gov/documentation/2018/lar-data-fields/#interestrate

[annakharv46]

MinoritiesMapFromCensusAZ

Here is the attempted code for minorities - we just need to change it based off of the variable html noted in the notebook. Also noted what each variable used was in this one. We can most certainly change which ones we want to use, but now we hopefully know how to. @donbowen could I join with zoom tomorrow? Should be able to join at 3:20ish. If not no worries I tried to write a lot of comments at the beginning of the MinoritiesMap notebook.

[donbowen]

I've updated the todo-list with some extra comments.

@annakharv46 Good job re:map code. Vic had similar stuff on her branch and we were able to put together a working file on main.

CC: @esd223 @vrg223 @mromano224 @jum223 @XiaozheZhangLehigh @isaacgrodin @SebastianStoneham @tws223

[XiaozheZhangLehigh]

@donbowen Professor Bowen, in the first step in my section, we need to merge banktract clean and census clean, do you mean the state_AZ_CA_mini and the census_clean? or you already did on as I see one called "bank_tract_clean_WITH_CENSUS"

[donbowen]

@tws223 Did you finish the first task above?

[donbowen]

@donbowen Professor Bowen, in the first step in my section, we need to merge banktract clean and census clean, do you mean the state_AZ_CA_mini and the census_clean? or you already did on as I see one called "bank_tract_clean_WITH_CENSUS"

@XiaozheZhangLehigh You're right, you can just use bank_tract_clean_WITH_CENSUS.csv .

[XiaozheZhangLehigh]

@donbowen Professor Bowen, I am just not sure what do you mean by majority poor to groupby.
Here is what I did for the minority group, I do feel like we are looking for a similar table like this

[donbowen]

Please replace your "bank_tract_clean_WITH_CENSUS.csv " file so that your code uses the latest and greatest. You can download it here.

@XiaozheZhangLehigh @isaacgrodin @SebastianStoneham @mromano224

[donbowen]

data = data.eval('pct_White = Tot.WhitePop / Tot.Pop * 100,
				  pct_Black = Tot.BlackPop / Tot.Pop * 100,
				  pct_AmericIndian = Tot.AmericIndianPop / Tot.Pop * 100,
				  pct_Asian = Tot.AsianPop / Tot.Pop * 100,
				  pct_NativeHawaiianPacific = Tot.NativeHawaiianPacificPop / Tot.Pop * 100,
				  pct_OtherRaceAlone = Tot.OtherRaceAlonePop / Tot.Pop * 100,
				  pct_TwoOrMoreRace = Tot.TwoOrMoreRace / Tot.Pop * 100,
				  pct_TwoOrMoreRace(Some other race) = Tot.`TwoOrMoreRace(Some other race)` / Tot.Pop * 100,
				  pct_Hispanic/Latino = Tot.`Hispanic/Latino` / Tot.Pop * 100,
                  pct_NotHispanic/Latino = Tot.`NotHispanic/Latino` / Tot.Pop * 100
				  )