Example analysis notebook and (*very*) rudimentary notes for a customized python-novice-gapminder. http://blog.byronjsmith.com/python-lesson-balance.html

gapminder-analysis.ipynb

lesson-notes.md

Introduction

• Demonstrate "complex" analysis notebook.

Pandas/Matplotlib

• Load data
  • Talk about the optional parameter index_col=
• Walk through the gapminder_all data.
• Plot 1957 life-expectancy against GDP/pop
  • Note the column-as-attribute syntax
  • Note outlier
• data.gdpPercap_1957.argmax()
• data.drop(...)
• Plot Africa data alone
  • Use data[data.continent == 'Africa'] syntax
  • Plot with a color
• CHALLENGE: Select the subset of the data from the continent which contains the country with the maximum life-expectancy in 1967

For loops

• Talk about wanting to repeat something for each continent.

• For loop syntax

• CHECK YOUR UNDERSTANDING:

  Fill in the blanks in the program below so that it prints “nit” (the reverse of the original character string “tin”).

  original = "tin"
  result = ____
  for char in original:
      result = ____
  print(result)

• Plot only a subset of the continents (africa, asia, europe)

If statements

• Talk about wanting to do something different depending on which continent we're considering.
• If statement syntax (notice the 'block' syntax)
  • if
  • if, else
  • if, elif, else
• Plot a different color for several of the continents.

Plot Aesthetics

• Add x/y axis labels
• Add a title
• Size the points by np.sqrt(population) (so that the area is proportional)
• Add a legend (dummy plotting)
• Note the uglyness of the numbers

Defining Functions

• Celsius to fahrenheit conversion
• Kelvin to C conversion
• Talk about function composition
• What are the benefits of using functions?
• Let's write a function to convert large numbers into scientific notation
• Add an optional argument to set the precision
• Add some documentation and show how help(scientific_notation) works

Defensive programming

• How do we know if it's working? (unit testing, assert)

Statsmodels

• Line of best fit
• Statsmodels
• formula
• fitting the model and viewing the summary
• Getting the coefficients out and using them
• Plotting the line
• CHALLENGE: extend this analysis and share with your neighbors