13 -- Consumer-Complaints -- Bob Amand

[BobAmand]

• [x] Blank slate
  • [x] Create a GitHub repo called consumer-complaints
  • [x] Put README.md and requirements.txt files in it
  • [x] Make sure any necessary libraries are in requirements.txt
  • [x] Make sure your consumer-complaints folder has its own virtual environment (.envrc), but do not commit .envrc or .direnv
  • [x] Install the requirements using pip install -r requirements.txt
• [x] Normal mode
  • [x] Read in the data file
  • [x] Calculate and chart:
  • [x] Number of complaints by product
  • [x] Number of complaints by company (top 10)
  • [x] Number of complaints by company response
  • [ ] Mean number of complaints by day of the week
  • [ ] Any other insights you find interesting
  • [x] Summary text for each chart
• [ ] Hard mode
  • [ ] Combine the complaints data with the population of each state and chart the frequency of complaints by state per capita
  • [ ] Find statistically significant outliers of # of complaints by ZIP code and try to find a possible reason for the outliers
• [ ] Nightmare mode
  • [ ] Make an infographic of a map of the US, showing the frequency of complaints per capita using shading of each state, with lighter colors representing low frequency and darker colors representing high frequency

[BobAmand]

https://github.com/BobAmand/consumer-complaints @powder-river @jamesmallen

Could not calculate the mean complaints per day using panda.

[powder-river]

@BobAmand hey Bob good submission. Hopefully you enjoy the power of pandas! If you can get the actual mean of the data vs the count we can get you up to green!