Python Programming for Earth Science Researchers

This website contains content for learning Python as an Earth Science researcher. It focuses on building core programming and computational skills that are applicable to researchers.

The materials were developed at the NASA Student Airborne Research Program (SARP), and continues to be used by instructors. SARP is an earth science reasearch internship for undergraduates held every summer. The program is based out of California and Virginia. At SARP, students experience 8 weeks of hands on NASA research and data training [SBB+22].

This year, SARP is fortunate to have access to the Cryocloud platform for student research projects. The cloud is an exciting new science tool that allows researchers to more easily collaborate, spend less time setting up software environments, and scale their data analysis. Tutorials relating to doing science on the cloud are also hosted on this website.

Organization

The contents of this website is split into three sections:

• Core Python Progression - Instructional material for learning Python. Many lessons include practice problems and answers in the Practice Sheets section.

• Computational Skills - Notebooks focusing on more general computational skills to complement and round out the Python lessons.

• Open Science - Notebooks focusing on additional technical competencies relating to open science.

• Practice Problems & Answers - Practice problems to accompany selected lessons.

• Example Gallery - Example code for common analyses or visualizations.

• Contributor’s Guide - Want to help make this website better? Find out more about how you can do that in the Contributor Guide.

Where to start

If you’re a student, check out the Core Python Progression. This section can be followed as a step by step flow to build Python skills. Once you’ve gotten through the first two lessons, intermittently explore some of the lessons in the Computational Skills section. These are invaluable for developing a holistic understanding of programming.

If your an educator, check out the Instructor’s Guide. This section gives an introduction to the core instructional concepts and provides suggestions for how this material might be used in your classroom.

Table of Contents

Core Python Progression

• 1. Python Building Blocks
  • 1.1 Variables, Data Types, and if Statements
  • 1.2 Lists and Dictionaries
• 2. Tabular Data
  • 2.1 The pandas Library
  • 2.2 Filtering and groupby
• 3. Gridded Data
• 4. Spatio-temporal Python
• 5. Visualization

Computational Skills

• Organizing your Files
• Communicating when something isn’t working
• Debugging
• Hierarchical Data Structure

Open Science

• Science on the Cloud

Practice Problems & Answers

• Python Practice
  • 1.1 Problems
  • 1.1 Answers
  • 1.2 Problems
  • 1.2 Answers
  • 2.1 Problems
  • 2.1 Answers
  • 2.2 Problems
  • 2.2 Answers
  • 3.1 Problems
  • 3.1 Answers
  • 3.2 Problems
  • 4.1 Problems
  • 4.1 Answers
  • 4.2 Problems
  • 4.2 Answers
  • 4.3 Problems

Example Gallery

• Visualization Examples

Contributor Guide

• Instructor’s Guide
• Contributing to SARP Lessons

Jupyter Book Citations

[SBB+22]

Emily L. Schaller, J. Ryan Bennett, Donald R. Blake, Raphael M. Kudela, Barry L. Lefer, Melissa Yang Martin, Dar A. Roberts, Richard E. Shetter, Bruce A. Tagg, and Jack A. Kaye. High-flying interns: nasa's student airborne research program (sarp). Bulletin of the American Meteorological Society, 103(4):E1061 – E1077, 2022. URL: https://journals.ametsoc.org/view/journals/bams/103/4/BAMS-D-19-0269.1.xml, doi:10.1175/BAMS-D-19-0269.1.