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Lessons

  • Lesson 0: Configuring your computer
  • Lesson 1: Welcome and Introduction to JupyterLab
  • Lesson 2: Basic command line skills
  • Lesson 3: Variables, operators, and types
  • Lesson 4: More operators and conditionals
  • Lesson 5: Lists and tuples
  • Lesson 6: Iteration
  • Lesson 7: Introduction to functions
  • Lesson 8: String methods
  • Lesson 9: Dictionaries
  • Lesson 10: Packages and modules
  • Lesson 11: File I/O
  • Lesson 12: Version control with Git
  • Lesson 13: Errors and exception handling
  • Lesson 14: Style
  • Lesson 15: Comprehensions
  • Lesson 16: Introduction to Pandas
  • Lesson 17: Tidy data and split-apply-combine
  • Lesson 18: Making plots
  • Lesson 19: High level plotting with iqplot
  • Lesson 20: Styling Bokeh plots
  • Lesson 21: Introduction to Numpy and Scipy
  • Lesson 22: Plotting time series and generated data
  • Lesson 23: Random number generation
  • Lesson 24: Hacker stats I
  • Lesson 25: Hacker stats II
  • Lesson 26: Dashboards
  • Lesson 27: JavaScript for stand-alone Bokeh apps
  • Lesson 28: Survey of other packages and languages
  • Lesson 29: Bootcamp recap

Auxiliary lessons

  • Lesson 30: Control of external devices
  • Lesson 31. Apps for controlling external devices
  • Lesson 32: Control panels
  • Lesson 33: More about the command line
  • Lesson 34: Regular expressions
  • Lesson 35: Introduction to scripting
  • Lesson 36: Introduction to object-oriented programming
  • Lesson 37: Algorithmic complexity
  • Lesson 38: Testing and test-driven development
  • Lesson 39: Examples of TDD
  • Lesson 40: High level plotting with HoloViews
  • Lesson 41: High level plotting with Vega-Altair
  • Lesson 42: More plotting with Vega-Altair
  • Lesson 43: Dealing with overplotting
  • Lesson 44: Introduction to image processing with scikit-image
  • Lesson 45: Basic image quantification
  • Lesson 46: Plotting with Matplotlib and Seaborn

Exercises

  • Exercise 1
  • Exercise 2
  • Exercise 3
    • Exercise 3.1: Mastering .loc for Pandas data frames
    • Exercise 3.2: Split-Apply-Combine of the frog data set
    • Exercise 3.3: Adding data to a data frame
    • Exercise 3.4: Axes with logarithmic scale and error bars
    • Exercise 3.5: Automating scatter plots
  • Exercise 4
  • Exercise 5
  • Exercise 6

Exercise solutions

  • Exercise 1 solutions
  • Exercise 2 solutions
  • Exercise 3 solutions
  • Exercise 4 solutions
  • Exercise 5 solutions
  • Exercise 6 solutions

Schedule

  • Schedule overview
  • Daily schedule

Resources

  • Scientific Python distribution
  • Online instruction
  • Books
  • Griffin Chure’s templates for reproducible publishing
Programming Bootcamp
  • View page source

Exercise 3

  • Exercise 3.1: Mastering .loc for Pandas data frames
  • Exercise 3.2: Split-Apply-Combine of the frog data set
  • Exercise 3.3: Adding data to a data frame
  • Exercise 3.4: Axes with logarithmic scale and error bars
  • Exercise 3.5: Automating scatter plots
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Last updated on Sep 08, 2023.

© 2023 Justin Bois. With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.

This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center.



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