Activities - Encode and Reduce Clutter

#	Name	Abbrev.	Guidance	Actions
Design the Visual Story 7
1	Identify the analytical pattern type per conclusion	ECR-1	For each conclusion from Phase 2, classify it as time-series, part-to-whole/ranking, deviation, distribution, correlation, or …	Read the 'ECR-1 Identify the analytical pattern type per conclusion' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Encode and Reduce Clutter'). Follow its guidance when assisting me. For each conclusion from Phase 2, classify it as time-series, part-to-whole/ranking, deviation, distribution, correlation, or multivariate before choosing anything about how to draw it — this single classification drives every remaining choice in this workflow and the next.
2	Select the optimal quantitative scale	ECR-2	Bar graphs must start their scale at zero (never narrow it — that's a visual …	Read the 'ECR-2 Select the optimal quantitative scale' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Encode and Reduce Clutter'). Follow its guidance when assisting me. Bar graphs must start their scale at zero (never narrow it — that's a visual lie); every other chart type (line, scatter, dot plot, box plot) should start a little below the lowest value and end a little above the highest. Begin/end/interval values should all be round numbers.
3	Add reference lines and regions	ECR-3	Whenever a value must be judged against a threshold or norm (budget, target, mean±SD, percentile …	Read the 'ECR-3 Add reference lines and regions' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Encode and Reduce Clutter'). Follow its guidance when assisting me. Whenever a value must be judged against a threshold or norm (budget, target, mean±SD, percentile band), add a labeled reference line or shaded region instead of leaving the comparison to be computed mentally. Set the reference to 0 / 0% / 100% to match whether the deviation is absolute, a percentage difference, or a percentage of the reference.
4	Choose pre-attentive encodings by precision hierarchy	ECR-4	Reserve length and 2-D position — the only two pre-attentive attributes perceived with real quantitative …	Read the 'ECR-4 Choose pre-attentive encodings by precision hierarchy' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Encode and Reduce Clutter'). Follow its guidance when assisting me. Reserve length and 2-D position — the only two pre-attentive attributes perceived with real quantitative precision — for the values that must be compared precisely. Use hue or shape (the best categorical discriminators) to separate groups, and treat size/color-intensity/blur as secondary/tertiary encodings only.
5	Plan trellis or multi-view layout	ECR-5	If one chart per conclusion would be cluttered, or you need to compare shapes across …	Read the 'ECR-5 Plan trellis or multi-view layout' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Encode and Reduce Clutter'). Follow its guidance when assisting me. If one chart per conclusion would be cluttered, or you need to compare shapes across many groups, plan a trellis/small-multiples grid (one categorical variable) or a visual crosstab (two categorical variables): every panel must share the same chart type, size and scale, sequenced by magnitude rather than alphabetically. For open-ended exploration, plan tightly-coupled multiple views with shared filtering/brushing instead of one fixed chart.
6	Reduce over-plotting	ECR-6	If points visibly collide in a scatter or dense line chart, apply remedies in order …	Read the 'ECR-6 Reduce over-plotting' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Encode and Reduce Clutter'). Follow its guidance when assisting me. If points visibly collide in a scatter or dense line chart, apply remedies in order of increasing intervention: shrink point size → remove fill (outline only) → change shape → jitter → add transparency → encode density (contours/heatmap cells) → reduce the data itself (aggregate, filter, trellis, or sample). Stop at the mildest remedy that solves the problem.
7	Check for spurious or missed patterns	ECR-7	Before locking in the encoding plan, verify every apparent pattern against the underlying detail data, …	Read the 'ECR-7 Check for spurious or missed patterns' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Encode and Reduce Clutter'). Follow its guidance when assisting me. Before locking in the encoding plan, verify every apparent pattern against the underlying detail data, and deliberately look again with fresh eyes for a real pattern you may have missed. This is the QA gate before any chart gets built.

Activities in Encode and Reduce Clutter