margaretblue/consumer-complaints

W4D1 Homework

Analyze Consumer Complaint Data

Description

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.

Objectives

Learning Objectives

After completing this assignment, you should understand:

• the joy and power of Pandas

Performance Objectives

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

Details

Deliverables

• A Git repo called consumer-complaints containing at least:
  • README.md file explaining how to run your project
  • a requirements.txt file
  • an IPython notebook and/or a Python package called consumer_complaints

Requirements

• No PEP8 or Pyflakes warnings or errors

Normal Mode

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.

Hard Mode

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.)

Nightmare Mode

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.

Additional Resources

• CFPB Consumer Complaint Database data and visualizations

Credit

The Consumer Financial Protection Bureau is pretty great! Thanks for making your data public.