Module 10 Activity

Scenario

Exploration is slow in raw Matplotlib. This activity uses Seaborn for the statistical work without inheriting its opinions.

What you build

A reshaped dataset, faceted exploration, and a finished figure where the statistical defaults have been examined and stated.

Steps

1. Reshape your data to long form once, near the load. Report the shape before and after.
2. Rebuild one multi-series chart as a single Seaborn call and list what it decided for you.
3. Facet one crowded chart into small panels and confirm the axes are shared.
4. For every statistical chart, state what the bar height and the whisker actually represent, in the axis label.
5. Take one Seaborn figure and finish it with Matplotlib: limits, a finding-carrying title, an annotation, house style.
6. Say which parts of your workflow belong in each library.

Evidence to hand in

• The reshape with both shapes.
• The Seaborn call and what it decided.
• The faceted chart with shared axes confirmed.
• Statistical labels stating the statistic.
• The finished figure.
• Your library split.

Review checklist

• The reshape to long form happens once, near the data load.
• Faceted panels share axes.
• Every statistical chart states its statistic in the axis label.
• At least one Seaborn figure was finished with Matplotlib.
• The library split is stated as a workflow, not a preference.

Mark complete