Lesson Plan for Week 8

Objectives

Note: Last week, we did a practice session on working with python. This means, we did not start with the environments. This means we will update the plan as follows.

Monday:

Working with Environments (see week 7)

Wednesday:

• We will check in about the semester project. I will ask each group to share one figure from a dataset they found (see below)
• We will expand our toolkit to read more complex forms of data including big data datasets of satellite and climate model output using xarray

Specific learning goals

• Be able to define big data and what makes working with big data difficult
• Understand many environmental datasets as big data
• Expand our understanding of environmental datasets from tabular data to other data formats including self-desribing datasets such as netcdf
• Use the xarray python package to open, process, and plot geospatial/ multidimensional data.

Continued or postponed:

• Be able to define what an API is
• Understand how APIs can be used to access environmental datasets through computer code
• Describe how APIs contribute to FAIR data and reproducible data analysis pipelines

Class Preparation

Before coming to class, please explore the materials that were posted in the Data Analysis Pipelines section of the lecture notes.

Readings and Materials

APIs (From Last Week)

• Geeks for Geeks: What is an API?
  • This reading provides a basic
• RealPython: Python & APIs: A Winning Combo for Reading Public Data
  • This is a useful resource to understand how API requests can work in Python.

Big Data

• Google: What is big data?

Xarray

• Abernathy: Xarray for multidimensional gridded data
• Xarray Tutorial: Xarray in 45 minutes

Planned Agenda

• Monday:
  • We created python environments to work with Xarray data
• Wednesday:
  • 2-minute stand-up
    • One Figure from your dataset
    • Demonstration and Practice: Working with python environments
  • Housekeeping
  • Xarray:
    • Mini-lecture: Big-Data and Xarray
    • Practice: Working with Xarray

ImportantNavigating directories and files

• When working with files, we need to know the relative location of files on the hard drive.

• This is even more important when using the Terminal.

  • We don’t have to use the Terminal, but it makes many things much quicker.

• How to navigate directories within the Terminal: Linux Command - Shell Tutorial:

  $ pwd # print working directory - will display the path to the current directory
  $ ls  # list - will show content of current directory  - use this frequently
  $ cd  # change directory - allows you to move to a different folder 
  $ cd .. # move one directory up 
  $ cd ./<name of directory> # moves down into that directory
  $ cp # copy <current path to file> <new path to file> - will move the file from the current location to the specified path
  $ mv # move <current path to file> <new path to file> - will move the file from the current location to the specified path

• Practice:

  • Use the Terminal or Anaconda Powershell to cd into your ISAT_420_S26 directory.
    • Hint start with pwd and ls

TipBasic Git Workflow

• See here: Standard workflow for making changes to a repository
• Before leaving class: commit your changes using GitHub-Desktop

Assignments

• Semester project: Exploratory Data Analysis
  • Now that you have at least one dataset, I want you to practice what we did before.
  • By Monday:
    • Use Pandas to open the dataset and explore what it does contain.
    • If you cannot open the dataset, check in with me.
  • By Wednesday:
    • Create at least 1 figure (even better one figure per team member)
    • Present your figure in the standup at the beginning of class.
    • You should present the following information
      • What does the figure show?
      • What does it tell about the dataset
      • What issues did you encounter when accessing the dataset/ making the figure?
      • What’s next?