Quick Intro to Python for Econometrics

3   Reading

  1. Sheppard Chapter 1: Set up Anaconda (Python 3.6). Don’t worry about Jupyter notebooks or Spyder. Our workflow will be to put everything in a script to be run from the command line or in a IPython terminal.
  2. Learn Python3 the Hard Way through Exercise 8.
  3. Learn Python the Hard Way video for Exercise 21 on Functions. Note: This was made for Python 2.7, which didn’t require print to be used as a function.
  4. Sheppard Chapters 3–4, 18, 5, 7, 9 (except 9.2 & 9.4), 10–13, 16. Chapters 6, 8, 14, 15, and 17 can be skimmed quickly or skipped altogether. These chapters just list various useful functions.

4   Exercises

The exercises in Sheppard are okay, but they’re set up more like trivia than actual programming problems. Instead, I would recommend practicing on the following problems found on Project Euler. I’ve added notes for several of the problems. Problem 42 is especially relevant because it requires reading data from a file and processing that data.

  • 1 & 2.
  • 3: The solution should have three functions:
    • is_prime that takes an integer n and returns True if n is prime and False otherwise;
    • find_factor that takes an integer n and finds some factor of that integer
    • factorize that takes an integer n and, using the functions is_prime and find_factor, returns a list of the prime factors of n (that is, a list of prime numbers that, when multiplied together, equal n).
  • 4: The solution should use a function is_palindrome that takes a number n and returns True if the number is a palindrome and False otherwise.
  • 6: Use a Numpy array (e.g., np.arange) and vector dot product.
  • 7: Use your is_prime function from Problem 3.
  • 8: Copy the 1000-digit number directly into your Python script.
  • 9 & 10.
  • 18 & 67*: Use backward induction. 67 might be harder for a beginner because you have to read a file from disk. If you find a general solution to 18 that’s not brute force, you can skip 67 for now.
  • 42:
    • Create a dictionary letter_score whose keys are all the letters of the alphabet and whose values are the letters' letter score, e.g. {'A': 1, 'B': 2, ... }. Hint: you can (and should) do this programmatically using from string import ascii_uppercase and Python's range builtin function.
    • Create a function word_score that takes a word string as an argument and returns the word score. It should use the letter_score: dictionary.
    • Come up with a way to check if a word score is a triangle number.
    • Use the pandas library (import pandas as pd) to read in the file and create a DataFrame with three columns: “word”, “word_score”, “is_triangle”.