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Learn seaborn

How this course works, what the workbench can do, and the chapter list.

This is a full seaborn course for the Swiftener Seaborn editor. Pandas is already imported as pd. NumPy is np. Seaborn is sns. Matplotlib is plt. You type in that tab, press Ctrl+Enter (or Cmd+Enter), and call plt.show() so the figure appears in the Plot panel.

You do not install Python, pip, or Jupyter. Code and uploaded files stay in your browser.

Tables without plots belong in Learn pandas. Raw matplotlib belongs in Learn matplotlib. Models belong in Learn scikit-learn. This course stays on statistical plots of DataFrames.

Goal

Finish every chapter by running the copy-paste blocks in the workbench. The last chapter is a small sales dashboard you can reuse.

How to take the course

  1. Keep this tutorial tab and the Seaborn workbench side by side.
  2. Wait until the workbench status says seaborn is ready. The first load installs seaborn in the tab and can take a minute.
  3. Copy an exercise, paste it as the whole editor, run it, then look at the Plot panel. Each shaded block is a complete script — it does not rely on leftover variables.
  4. Every block ends with plt.show(). Without it, the figure may not appear. Figure-level calls such as relplot and catplot still need plt.show().
  5. When a chapter asks for a file, download it from the buttons above, then click Add files in the workbench.

The workbench Snippets menu is a cheat sheet. Prefer the exercises here — they are written for this editor.

What this editor can and cannot do

  • Can: pd, np, sns, plt, print, CSV uploads, plt.show(), plt.savefig to /uploads, downloads.
  • Cannot: Jupyter magics (%matplotlib inline), sns.load_dataset() (it fetches GitHub and needs extra HTTP), 3-D / animation, installing extra PyPI packages beyond what the app already loads (pandas, numpy, matplotlib, seaborn).
  • Always print(...) for console text. Always plt.show() for figures.

City names in the examples are Nairobi, Mombasa, Kisumu (then Nakuru and Eldoret on the sample files).

Sample files

Download any of these from the banner, then attach them with Add files:

  • sales.csv — tiny city × product snapshot (already has revenue)
  • sales_log.csv — dated sales rows, including product C
  • products.csv — product category and cost
  • regions.csv — city to region lookup

Chapter list

Work in order the first time. After that, jump around from the sidebar.