tonight's talk

• Waffle House: The Legend

• Peppered + Smothered: Pandas + GeoPandas

• Covered: API exploration with jupyter notebook
• Capped: Turning the jupyter notebook into a minimal web app

Waffle House

• 1967 locations across the U.S. (mostly in the South)
• FEMA revealed in early 2010s that their disaster mobilization used the Waffle House Index as a guide
  • Green - Waffle House serving full menu, open (most usually 24/7, 365)
  • Yellow - Waffle House serving limited menu
  • Red - Waffle House closed

: locATIONS

country

• You probably already know about pandas if you do data manipulation in python
• Highly recommend revisiting their docs! A beautiful change coinciding with v1.0!
  • 0.25 to 1.0 is not a monumental shift, mostly a better reflection that pandas is pretty stable
  • one of the best changes:
    • if you have a missing value in a column of integers when reading in to pandas, the column no longer gets cast to a float!!
  • more info:

https://towardsdatascience.com/whats-new-in-pandas-1-0-ffa99bd43a58

• Powerful tool in python that wraps a lot of lower level python libraries to quickly work with data that has a spatial component, think Pandas + a column for geometry
• Not only can do you data joins, but you can also do geographical transformations (thanks to the underlying libraries), that can make it a useful part of your GIS stack
• I won't cover the bigger geography tools, for a better dive with an actual geographer, check out KnoxData's talk with Dr. Paulo Raposo from Sept 2018 ->
• docs: https://geopandas.org/

: JUpyter Notebook

• Let's explore the context of the api using geopandas and requests

covered

• Let's explore the context of the api using geopandas and requests

📸 https://www.ajc.com/entertainment/dining/your-guide-waffle-house-hash-browns/u6FvGwLoPboYIaz6hFukGP/