Design the Visual Story - EDA with Python, Pandas and Jupyter

Description

A correct conclusion still fails if it's shown badly. Apply a systematic analytical-pattern method to pick, per finding, the right chart type, quantitative scale, reference lines and layout, then lay those choices out as a Jupyter storyboard before writing any plotting code.

Activities in this Phase

Order	Name	Actions
Encode and Reduce Clutter
#1	Identify the analytical pattern type per conclusion	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	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	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	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	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	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	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.
Choose Chart per Analysis Type
#1	Design a time-series display	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	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	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	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	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	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).
Storyboard the Notebook
#1	Sequence the narrative
#2	Draft one storyboard entry per chart	Read the 'SNK-2 Draft one storyboard entry per chart' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Storyboard the Notebook'). Follow its guidance when assisting me. For every chart in the sequence, write down: chart type (from CTE), axis/scale choice and reference lines (from ECR), any trellis/layout decision, and the one-sentence insight/conclusion it must support. This entry-per-chart table is the literal blueprint Phase 4 codes against.
#3	Review the storyboard against conclusions	Read the 'SNK-3 Review the storyboard against conclusions' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Storyboard the Notebook'). Follow its guidance when assisting me. Check the storyboard against Phase 2's Conclusions Summary: does every conclusion that matters have a designated chart, and does every planned chart trace back to a real conclusion (not just an interesting-looking variable)? Fix gaps before writing any Plotly code.