PPOL564 Slides and Activities

Repo for Georgetown McCourt's School of Public Policy's Data Science I (PPOL 564) in Fall of 2022

Slides and Activities (Fall '22)

These are jupyter notebook-based activities to practice Python or other concepts, along with accompanying slides:

Python basics

• 00_introclass.ipynb
  • Concepts covered:
    • Creating variables
    • Checking types
    • Lists
    • Basic list comprehension
    • Basic numpy arrays
  • Slides: 01_ppol564_f22_intro_pythonbasics.pdf

Data wrangling

• 01_pandas_datacleaning_blank.ipynb

  • Data: DC crime reports in 2020
  • Concepts covered:
    • Aggregation using groupby and agg
    • Lambda functions within aggregation
    • Recoding variables using np.where
    • Recoding variables using np.select
    • Recoding variables using map and dictionary
  • Slides: 02_ppol564_f22_datawrangling.pdf
  • Solutions: 01_pandas_datacleaning_solutions.ipynb

• 02_functions_part1_blank.ipynb

  • Data: DC crime reports in 2020
  • Concepts covered:
    • user-defined function to find matches within a broader pool of data
    • using list comprehension to apply a function iteratively over list elements
  • Slides: 03_ppol564_f22_userdefinedfunctions_part1.pdf
  • Solutions: 02_functions_part1_solutions.ipynb

• 03_plottingexamples_plotnine.ipynb

  • Data: DC crime reports in 2020
  • Concepts covered:
    • Bar chart
    • Line graph
    • Coloring and/or faceting by group

• 04_latex_output_examples_blank.ipynb

  • Data: DC crime reports in 2020
  • Concepts covered:
    • Creating tables to export to LaTeX
    • Saving figures to export
    • Using user-defined function to iterate over entities and efficiently save many figures
    • Solutions: 04_latex_output_examples_solutions.ipynb

• 05_reviewsession_blank.ipynb

  • Data: simulated data
  • Concepts covered:
    • Methods versus attributes
    • Reshaping using pivot and pivot_table()
    • User-defined functions and if/elif/else control flow
  • Solutions: 05_reviewsession_solutions.ipynb

• 06_reshaping_merging_blank.ipynb

  • Data: San Diego business tax certificate data; Census NAICS code data
  • Concepts covered:
    • Reshaping between long and wide
    • Data cleaning such as extraneous rows/columns
    • Recasting join cols to allow join (e.g., converting int to character)
    • pd.merge and different types of exact joins using join keys
    • Post-merge diagnostics
  • Slides: 05_ppol564_f22_mergereshape.pdf
  • Solutions: 06_reshaping_merging_solutions.ipynb

• 07_regex_blank.ipynb

  • Data: Food Research Action Center (FRAC) data on district and school's election of community eligibility provision (CEP) for Free or Reduced Price Lunch (FRPL)
  • Concepts covered:
    • Pattern construction using re module
    • re.sub for replacement
    • re.findall
    • re.match and how to work with match objects using .group()
    • Throughout, review of list comprehension
  • Slides: 06_ppol564_f22_regex.pdf
  • Solutions: 07_regex_solutions.ipynb