Build the Story in Plotly - EDA with Python, Pandas and Jupyter

Description

Turn the storyboard into a real, shareable Jupyter notebook: implement every planned chart in Plotly with the exact scale/reference-line/layout decisions from Phase 3, interleave narrative markdown, and export a final notebook/report for the data sponsor.

Activities in this Phase

Order	Name	Actions
Implement Plotly Visuals
#1	Set up the Plotly/Jupyter environment	Read the 'IVS-1 Set up the Plotly/Jupyter environment' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Implement Plotly Visuals'). Follow its guidance when assisting me. Import `plotly.express as px` and `plotly.graph_objects as go`, set the notebook renderer (`pio.renderers.default = "notebook"` or "notebook_connected"), and decide once whether figures need to be interactive (keep Plotly) or also exported statically (`kaleido` for PNG/PDF export).
#2	Implement each storyboard entry as a Plotly figure	Read the 'IVS-2 Implement each storyboard entry as a Plotly figure' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Implement Plotly Visuals'). Follow its guidance when assisting me. Map each storyboard chart type to Plotly: time-series → `px.line`/`go.Scatter(mode="lines")`; part-to-whole/ranking → `px.bar` (sorted, never `px.pie`) or `px.scatter` for a dot plot; deviation → `px.bar`/`px.line` plus a reference shape; distribution → `px.histogram`, `px.box`, or `px.strip`; correlation → `px.scatter` with `trendline="ols"/"lowess"`; multivariate → `px.imshow` (diverging colorscale) or `px.parallel_coordinates`. Use the exact chart type the storyboard specifies — do not substitute a "nicer-looking" one.
#3	Apply scale/reference-line/annotation rules via layout methods	Read the 'IVS-3 Apply scale/reference-line/annotation rules via layout methods' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Implement Plotly Visuals'). Follow its guidance when assisting me. Enforce ECR's decisions in code: `fig.update_yaxes(rangemode="tozero")` for bar charts, explicit `range=[min,max]` with round numbers for line/scatter/box; `fig.add_hline()`/`add_vline()`/`add_shape()` for reference lines/regions and target bands; `fig.update_traces(marker=dict(opacity=...))` or `size=` for over-plotting fixes (jitter via manual x/y offset, density via `px.density_heatmap`).
#4	Build trellis/small-multiples and multi-view linking	Read the 'IVS-4 Build trellis/small-multiples and multi-view linking' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Implement Plotly Visuals'). Follow its guidance when assisting me. Use `facet_col=`/`facet_row=` (with `facet_col_wrap=`) in plotly.express for trellis/visual-crosstab layouts, keeping `category_orders=` set to sort panels by magnitude rather than alphabetically; use `plotly.subplots.make_subplots` for hand-built multi-panel layouts; for linked brushing/filtering across views, use `go.FigureWidget` in the notebook or export to Dash if true cross-filtering is required.
#5	Assemble the Jupyter storybook notebook	Read the 'IVS-5 Assemble the Jupyter storybook notebook' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Implement Plotly Visuals'). Follow its guidance when assisting me. Interleave a markdown cell (the question/insight from the storyboard) before each code cell that renders its Plotly figure, in the sequence fixed by SNK. Keep one figure's setup self-contained per cell group so the notebook can be read top-to-bottom as the story, not just executed.
#6	Review and export the final notebook/report	Read the 'IVS-6 Review and export the final notebook/report' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Implement Plotly Visuals'). Follow its guidance when assisting me. Re-run the notebook top-to-bottom on a clean kernel to confirm every cell executes and every chart still matches its storyboard entry and conclusion. Export to HTML (`jupyter nbconvert --to html`) or PDF for the data sponsor, alongside the notebook itself, per the Framing Brief's audience.