1. Your first matplotlib figure
    Plot a function in the Code editor's Python mode — data, labels, styling — and export it as a publication figure.
  2. The notebook workflow
    Split an analysis into cells — load, inspect, plot — run them in order, and keep the printed output alongside the figure.
  3. Wrangling data with pandas
    Load a CSV into a DataFrame, filter and aggregate it, and plot straight from the frame — the shortest path from raw data to figure.
  4. Fitting a model with scipy
    Fit a function to noisy data with curve_fit, plot the fit over the points, and quote the parameters with real uncertainties.
  5. Statistical plots with seaborn
    Distributions, group comparisons, and regressions in a few lines — seaborn's statistical shorthand on top of matplotlib.
  6. Multi-panel and print-ready figures
    Subplots with shared axes, panel labels, error bars, and the sizing that survives a journal's column width.