Activities - Choose Chart per Analysis Type

#	Name	Abbrev.	Guidance	Actions
Design the Visual Story 6
1	Design a time-series display	CTE-1	Default to a line graph for continuous change through time; use bars for period-to-period comparison, …	Read the 'CTE-1 Design a time-series display' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Choose Chart per Analysis Type'). Follow its guidance when assisting me. Default to a line graph for continuous change through time; use bars for period-to-period comparison, a dot plot (never connected by a line) for irregularly-spaced samples, box plots to show a distribution shifting over time, and radar/cycle plots or heatmaps for cyclical/high-volume data. Smooth volatility with a running average rather than a single trend line, use a log scale or % change when comparing growth rates of very different magnitude, and never render a missing value as zero — break the line instead.
2	Design a part-to-whole/ranking display	CTE-2	Sort items by value and use a bar graph (never a pie chart) for ranking/part-to-whole …	Read the 'CTE-2 Design a part-to-whole/ranking display' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Choose Chart per Analysis Type'). Follow its guidance when assisting me. Sort items by value and use a bar graph (never a pie chart) for ranking/part-to-whole comparison; switch to a dot plot when the scale must be narrowed off zero; use a Pareto chart (bars + cumulative-% line) for 80/20 analysis; re-express with √ (mildest), then log, then inverse when values span a huge range; use a line graph only to show rank changes over time (a bumps chart), never a single ranking snapshot.
3	Design a deviation display	CTE-3	Pair a bar or line graph with a 0 / 0% / 100% reference line …	Read the 'CTE-3 Design a deviation display' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Choose Chart per Analysis Type'). Follow its guidance when assisting me. Pair a bar or line graph with a 0 / 0% / 100% reference line depending on whether the deviation (actual vs. budget/target/forecast/prior period/peer) is shown as an absolute difference, a percentage difference, or a percentage of the reference value. Use bars to emphasize individual-period deviations and lines when the overall shape of deviation through time matters more.
4	Design a distribution display	CTE-4	For a single distribution use a histogram (shape+magnitude, consistent bin widths, bin count tuned by …	Read the 'CTE-4 Design a distribution display' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Choose Chart per Analysis Type'). Follow its guidance when assisting me. For a single distribution use a histogram (shape+magnitude, consistent bin widths, bin count tuned by trial and error), a frequency polygon (shape only), a strip plot (every raw value, small N), or a stem-and-leaf plot (≤100 values, quick by hand). For comparing several distributions, box plots beat everything else — add outlier dots beyond the whiskers rather than stretching them. Prefer median/percentiles over mean/SD when outliers are present.
5	Design a correlation display	CTE-5	Use a scatterplot with a square aspect ratio, both axes trimmed near min/max, for two …	Read the 'CTE-5 Design a correlation display' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Choose Chart per Analysis Type'). Follow its guidance when assisting me. Use a scatterplot with a square aspect ratio, both axes trimmed near min/max, for two variables; add a trend line matching the actual shape (linear/logarithmic/exponential/low-order polynomial — never an overfit high-order polynomial), with a separate trend line per subgroup if slopes visibly differ. For 3+ variables use a scatterplot matrix, table lens, or parallel-coordinates plot; de-clutter dense scatterplots (transparency/jitter) and add grid lines when comparing several side by side.
6	Design a multivariate display	CTE-6	Avoid glyph-based displays (Chernoff faces, stars, whiskers) — no demonstrated real-world effectiveness. Prefer a parallel-coordinates …	Read the 'CTE-6 Design a multivariate display' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Choose Chart per Analysis Type'). Follow its guidance when assisting me. Avoid glyph-based displays (Chernoff faces, stars, whiskers) — no demonstrated real-world effectiveness. Prefer a parallel-coordinates plot with brushing/filtering, or a colorblind-safe diverging-color heatmap (never red/green, never black as the "near zero" color), to compare multivariate profiles. Use a clustering/similarity-ranking algorithm, not eyes alone, to group items, then visualize the groups (e.g. in a trellis).

Activities in Choose Chart per Analysis Type