Overview

Overview#

This section introduces the scientific Python ecosystem that powers most data analysis in earth and environmental science: NumPy for numerical arrays, and Matplotlib for visualization. Both libraries underpin everything that comes after (pandas, xarray, scikit-learn, …), so spending time on the fundamentals here pays off across the rest of the course.

Learning objectives#

By the end of this section, you should be able to:

  1. Create and inspect NumPy arrays — using np.array, np.arange, np.linspace, np.zeros, np.ones, and check shape and dtype.

  2. Index and slice arrays — basic indexing, slicing, fancy indexing, and boolean masks; on both 1D and 2D arrays.

  3. Vectorize operations — apply element-wise math and reductions (mean, sum, std) along specific axes.

  4. Reshape and combine arrays — using reshape, transpose, tile, and adding new axes via [None, :].

  5. Understand broadcasting — the rules and common gotchas.

  6. Build a Matplotlib figure — using the explicit Figure / Axes mental model (fig, ax = plt.subplots()).

  7. Make and customize common plots — line, scatter, bar, pcolormesh, contour, quiver — with labels, legends, colors, and annotations.

Pages in this section#

  • NumPy and Matplotlib — array creation and operations, paired with light Matplotlib usage for visualization.

  • More Matplotlib — the Figure/Axes mental model, customization, and 2D plot types.

  • Assignment 4a — numpy reductions, broadcasting, and matplotlib on real ARGO float data.

  • Assignment 4b — replicate three target figures using only numpy and matplotlib.

In-class assignment — 10 points

Your in-class assignment for this section is to complete the “Try it” exercises in the lecture notebooks above. Work through them in your own copy of each notebook. To submit, push your completed notebook(s) to your week folder and post a link to each on the matching Courseworks assignment. (The two at-home assignments — 4a and 4b — are graded separately, 10 points each.)

Working through the lectures#

Both lecture pages are Jupyter notebooks. Use the download button in the top-right (⬇ icon) to grab the .ipynb file, open it in your environment (JupyterLab on LEAP or Colab), and step through the cells. For the shorter notebooks you can also just copy-paste the cells into a fresh notebook. The Try it admonitions invite you to experiment in your own cells before moving on.