Use pandas to read and analyze data from the CFPB Consumer Complaint Database. This database is a collection of all complaints made by American consumers to the Consumer Financial Protection Bureau.
After completing this assignment, you should understand:
- the joy and power of Pandas
After completing this assignment, you should be able to:
- read in CSV files using Pandas
- pull out and summarize data
- turn Pandas data frames into charts
- A Git repo called consumer-complaints containing at least:
README.mdfile explaining how to run your project- a
requirements.txtfile - an IPython notebook and/or a Python package called consumer_complaints
- No PEP8 or Pyflakes warnings or errors
Go to the Consumer Financial Protection Bureau Consumer Complaint Database and click "Export > Download > CSV" to download the data.
Use Pandas to read in the downloaded file.
Calculate and chart:
- Number of complaints by month (leave off the current month)
- Number of complaints by product
- Number of complaints by company (top 10 companies only)
- Number of complaints by company response
- Mean number of complaints by day of week
- Any other insights you find interesting
Write up a summary of what you found from each chart you made.
In addition to the requirements from Normal Mode:
- Combine the complaints data with US population by state data (find this yourself) and then chart the frequency of complaints by state per capita.
- Find statistically significant outliers of complaints by ZIP code. Look these up to see if there's a possible reason (military bases are often surrounded by predatory lending companies, for example.)
In addition to the requirements from Hard Mode:
- Take the frequency of complaints by state per capita and then make a chart of the US, with frequency of complaints matched to a scale of lighter color (low frequency) to darker color (high frequency), with a legend.
The Consumer Financial Protection Bureau is pretty great! Thanks for making your data public.