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Goals

  • Learn how to use a variety of Python libraries and work with remote files
  • Apply a growing set of practical Python skills to the FDIC failed banks data.

Level up on key Python skills

The skills covered in the tutorials below are needed to complete the Failed Banks assignment. Be sure to carefully work through these before attempting the coding assignment.

Failed CA Banks python script

Please make sure to have carefully worked through the Python tutorials above before attempting this portion of the assignment.

Requirements

The deliverables for this assignment are largely the same as our earlier Bash script assignment:

  • Create a CSV containing failed banks in CA based on the FDIC Failed Banks list.
  • Print the number of failed banks in CA to the shell

This time, however, you must create a Python script to perform the work. We've also thrown in an extra challenge that involves reformatting a date (something that would have been tricky to do in the shell script using standard Unix tools).

For this assignment, we've created an empty script called failed_banks_ca.py. It includes details on the required steps.

Please read the script carefully before starting to code.

Your job is to download and flesh out failed_banks_ca.py in all the spots where there is a TODO.

The steps are detailed in the script, but here's an overview with links to relevant documentation on each task:

  • Download and save a local copy of the FDIC failed banks CSV using the requests library. You don't need to use the CSV module for this step. It's preferable to just use standard file writing techniques.
  • Read the local banklist.csv and generate a new CSV called failed_banks_ca.csv. You must use the csv module for these steps. The file must contain only failed banks in California. It should have the same header row and columns as the source data. And you must convert the Closing Date values to a date formatted as YYYY-MM-DD (e.g. 2020-01-29). You'll need to use strptime and strftime from the datetime module to perform the conversion.
  • Print a count of failed banks in California to the shell. You must print the following text: There are X failed banks in CA, replacing the X with the correct count. For example: There are 41 failed banks in CA. The count (X) should be generated dynamically. In other words, the value of X should be replaced using code; do not hard-code the count directly in the text. For this step, you'll need the print function, of course. You might also find it helpful to learn about string formatting.

Important notes

  • You must write this code exclusively in Python. You should not create a Bash script containing Unix commands, nor should you "shell out" from Python to Unix commands.
  • You must use standard file writing techniques and the CSV module. You should not use pandas or other third-party libraries.
  • Please do NOT use file paths in your code that are specific to your machine. For example, do not use ~/Desktop/code/fdic/scripts/banklist.csv in your Python script. Instead, simply use the base name of the file (banklist.csv). This will make your code more portable so that instructors can download your code and run it. Points will be deducted if user-specific paths are included.

Advice

  • We've provided you with a starter script with guidance on specific techniques. But we're throwing a lot of new skills at you with this assignment. Make sure you take the time to practice each of these skills in the interactive Python interpreter before trying to apply them in the final script. You can always copy/paste the code from the interpreter to your script as you figure out solutions. So be sure to fire up ipython as you work through the assignment!
  • Use print statements in your script to get visibility into the code.

Submitting

To submit the assignment, you must:

  • Use DataKit to create a new code project called FDIC python
  • Save your completed version of failed_banks_ca.py to the project's scripts/ directory
  • Push your code to GitHub
  • Submit the URL to your GitHub repo via Canvas

As a reminder, here's how to create the new project folder:

# Navigate to wherever you store your code
# For example:
cd ~/Desktop/code

# Crate the project
datakit project create

Follow the DataKit prompts to create a project named "FDIC Python", which should generate a new project folder on your machine called fdic-python. This folder will be connected automatically to an identically named GitHub project.

Below is the standard workflow to save and push code to GitHub.

cd ~/Desktop/code/fdic-python

# Install the requirements
pipenv install

# Activate the shell
pipenv shell

# Save and push your code
invoke code.push

For reminders on how to work with DataKit, see here.

NOTE: Grading for this assignment will be based both on the script itself as well as your attention to detail. For example, points will be deducted if the script is not named correctly and placed in the proper folder of the project.