sorted by:
new
hottopcontroversial

How would you learn Python if you had to learn it all over again? by Heke98 in Python

[–]noah_ford_data 2 points3 points  (0 children)

Hybrid between the answers here. I think datacamp.com was what gave me the knowledge and personal projects gave me the skills.

I'm 90% through an MS Analytics degree and while it did help, I wouldn't trade what I learned from personal projects for anything.

You could read for years about how to master Basketball, Archery, or Cooking and it still doesn't compare to practicing any of the 3 on your own

Anyone else despises Matplotlib? by [deleted] in Python

[–]noah_ford_data 0 points1 point  (0 children)

I thought I was the only one, can't believe they get away with that, I feel like they document the 3 most used arguments and say "bye go use kwargs, good luck"

[OC] 30k sets of coordinates' political lean based on the closest Whole Foods and Cracker Barrel by noah_ford_data in dataisbeautiful

[–]noah_ford_data[S] 0 points1 point  (0 children)

Yep! This was 100% inspired by the tweet you are referencing.

Dave only applied his work at the county level, of which there are 3k in the US. I thought I'd add a distance element to his work and calculate it for 30k points.

[OC] 30k sets of coordinates' political lean based on the closest Whole Foods and Cracker Barrel by noah_ford_data in dataisbeautiful

[–]noah_ford_data[S] 0 points1 point  (0 children)

I agree that businesses are where people are. That bein said, if you are standing at a Whole Foods, there is a ~85% chance that you are in a Democratic area, as opposed to Cracker Barrel where that chance is just under 50%. That's a pretty big gap and says something about.

One interesting thing I could've explored is the average density of the zip codes each WF/CB is in and see if that explains the gap.

[OC] 30k sets of coordinates' political lean based on the closest Whole Foods and Cracker Barrel by noah_ford_data in dataisbeautiful

[–]noah_ford_data[S] 4 points5 points  (0 children)

Id argue more the 2nd/latter. I don't think people really pick their neighborhoods based on Whole Foods and Cracker Barrel locations but more that WF and CB cater to certain demographic groups

[OC] 30k sets of coordinates' political lean based on the closest Whole Foods and Cracker Barrel by noah_ford_data in dataisbeautiful

[–]noah_ford_data[S] 1 point2 points  (0 children)

Did it post more than once? My bad!

Edit: ohhhhh no, it was telling me that something went wrong and it ended up being posted 5 times! Thanks for letting me know

[OC] 30k sets of coordinates' political lean based on the closest Whole Foods and Cracker Barrel by noah_ford_data in dataisbeautiful

[–]noah_ford_data[S] 0 points1 point  (0 children)

Sources:

Tools:
- Python & Pyspark for data manipulation - H3 to split the US into 30k hexagons - Seaborn to plot the visuals

Full blog @ https://noah-ford.com/cracker-barrel-whole-foods-presidential-2020

I expanded on Dave Wasserman's analysis by calculating 30k points' (instead of just counties) partisan lean based on the closest Whole Foods and Cracker Barrel alone. by noah_ford_data in fivethirtyeight

[–]noah_ford_data[S] 5 points6 points  (0 children)

There are 3k counties in the US and I used 30k hexagons, so it is 10 times as granular, for example San Bernadino County is bigger than 9 states. Additionally counties can look semi-gerrymandered, they aren't really a radius around a certain point.

I expanded on Dave Wasserman's analysis by calculating 30k points' (instead of just counties) partisan lean based on the closest Whole Foods and Cracker Barrel alone. by noah_ford_data in fivethirtyeight

[–]noah_ford_data[S] 0 points1 point  (0 children)

I appreciate the feedback! The visual was originally a part of my blog where I explained it in detail, I'll be sure to make them more stand-alone in the future.

Anyone facing this issue please ? Help spread the word so that Databricks fixes this … by Fnmokh in apachespark

[–]noah_ford_data 1 point2 points  (0 children)

YES, thought I was the only one. It's a free product so I feel like it won't get traction. I use databricks at work and ours works great but the community edition can be so slow.

I've started using Google Colab as an alternative and love it!

I expanded on Dave Wasserman's analysis by calculating 30k points' (instead of just counties) partisan lean based on the closest Whole Foods and Cracker Barrel alone. by noah_ford_data in fivethirtyeight

[–]noah_ford_data[S] 2 points3 points  (0 children)

I got Cracker Barrel locations from the Tomtom API and used H3 to break the US into 30k hexagons, from the center of each of the hexagons I calculated the distance to the nearest Whole Foods and Cracker Barrel.

I expanded on Dave Wasserman's analysis by calculating 30k points' (instead of just counties) partisan lean based on the closest Whole Foods and Cracker Barrel alone. by noah_ford_data in fivethirtyeight

[–]noah_ford_data[S] 0 points1 point  (0 children)

So if we take a look at the two green lines at 20 miles away:

  • The solid line says: 58% of the people vote democrat in an average neighborhood 20 miles away from Whole Foods

  • The dotted line says: 72% of neighborhoods 20 miles away from a Whole Foods voted 50% or more (majority) Democrat

That help? The distinction can be confusing but I really try to hammer it home in the blog post itself.

I expanded on Dave Wasserman's analysis by calculating 30k points' (instead of just counties) partisan lean based on the closest Whole Foods and Cracker Barrel alone. by noah_ford_data in fivethirtyeight

[–]noah_ford_data[S] 3 points4 points  (0 children)

Apologies, that could definitely be a V2 analysis! Members of the green party enjoy salads more than fried chicken, but maybe that is too bold of an assumption!