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update(bump-basic): altair — comprehensive quality review
Restructured data and color encoding for cleaner Altair idiom.
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‎plots/bump-basic/implementations/altair.py‎

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""" pyplots.ai
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"""pyplots.ai
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bump-basic: Basic Bump Chart
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Library: altair 6.0.0 | Python 3.13.11
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Quality: 92/100 | Created: 2025-12-23
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Library: altair 6.0.0 | Python 3.14.3
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Quality: /100 | Updated: 2026-02-22
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"""
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import altair as alt
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import pandas as pd
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# Data - Sports league standings over 6 matchweeks
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data = {
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"Team": (
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["Arsenal"] * 6
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+ ["Chelsea"] * 6
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+ ["Liverpool"] * 6
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+ ["Man City"] * 6
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+ ["Man United"] * 6
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+ ["Tottenham"] * 6
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),
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"Week": ["Week 1", "Week 2", "Week 3", "Week 4", "Week 5", "Week 6"] * 6,
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"Rank": [
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# Arsenal: Starts 3rd, rises to 1st, stays competitive
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3,
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2,
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1,
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1,
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2,
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1,
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# Chelsea: Mid-table consistency
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4,
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4,
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3,
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4,
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3,
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3,
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# Liverpool: Starts 1st, drops mid-season, recovers
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1,
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1,
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2,
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3,
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1,
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2,
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# Man City: Slow start, climbs steadily
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5,
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5,
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4,
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2,
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4,
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4,
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# Man United: Volatile rankings
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2,
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3,
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5,
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5,
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5,
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5,
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# Tottenham: Bottom position throughout
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6,
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6,
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6,
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6,
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6,
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6,
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],
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}
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# Data - Sports league standings over 6 matchweeks (no random data)
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teams = ["Arsenal", "Chelsea", "Liverpool", "Man City", "Man United", "Tottenham"]
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weeks = ["Week 1", "Week 2", "Week 3", "Week 4", "Week 5", "Week 6"]
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ranks = [
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# Arsenal: Starts 3rd, rises to 1st, stays competitive
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3,
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2,
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1,
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1,
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2,
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1,
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# Chelsea: Mid-table consistency
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4,
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4,
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3,
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4,
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3,
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3,
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# Liverpool: Starts 1st, drops mid-season, recovers
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1,
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1,
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2,
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3,
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1,
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2,
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# Man City: Slow start, climbs steadily
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5,
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5,
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4,
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2,
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4,
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4,
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# Man United: Volatile rankings
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2,
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3,
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5,
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5,
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5,
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5,
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# Tottenham: Bottom position throughout
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6,
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6,
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6,
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6,
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6,
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6,
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]
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df = pd.DataFrame(data)
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df = pd.DataFrame({"Team": [t for t in teams for _ in weeks], "Week": weeks * len(teams), "Rank": ranks})
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# Define color palette (Python Blue first, then colorblind-safe colors)
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colors = ["#306998", "#FFD43B", "#E15759", "#59A14F", "#9C755F", "#BAB0AC"]
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# Colorblind-safe palette (Python Blue first)
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colors = ["#306998", "#FFD43B", "#E15759", "#59A14F", "#B07AA1", "#76B7B2"]
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# Create bump chart - lines connecting rankings
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# Interactive highlight — distinctive Altair feature
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highlight = alt.selection_point(fields=["Team"], on="pointerover")
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# Shared encodings
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x = alt.X("Week:O", title="Match Week", axis=alt.Axis(labelFontSize=18, titleFontSize=22))
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y = alt.Y(
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"Rank:Q",
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title="League Position",
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scale=alt.Scale(domain=[1, 6], reverse=True),
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axis=alt.Axis(labelFontSize=18, titleFontSize=22, tickMinStep=1, values=[1, 2, 3, 4, 5, 6]),
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)
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color = alt.Color(
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"Team:N",
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title="Team",
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scale=alt.Scale(domain=teams, range=colors),
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legend=alt.Legend(labelFontSize=16, titleFontSize=18, symbolSize=200),
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)
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# Lines — dim non-highlighted teams on hover
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lines = (
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alt.Chart(df)
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.mark_line(strokeWidth=4, opacity=0.8)
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.encode(
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x=alt.X("Week:O", title="Match Week", axis=alt.Axis(labelFontSize=18, titleFontSize=22)),
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y=alt.Y(
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"Rank:Q",
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title="League Position",
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scale=alt.Scale(domain=[1, 6], reverse=True),
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axis=alt.Axis(labelFontSize=18, titleFontSize=22, tickMinStep=1, values=[1, 2, 3, 4, 5, 6]),
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),
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color=alt.Color(
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"Team:N",
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title="Team",
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scale=alt.Scale(domain=df["Team"].unique().tolist(), range=colors),
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legend=alt.Legend(labelFontSize=16, titleFontSize=18),
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),
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)
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.mark_line(strokeWidth=4)
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.encode(x=x, y=y, color=color, opacity=alt.condition(highlight, alt.value(1), alt.value(0.3)))
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.add_params(highlight)
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)
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# Add points at each period for clarity
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# Points at each period for clarity
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points = (
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alt.Chart(df)
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.mark_point(size=250, filled=True, opacity=1)
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.mark_point(size=250, filled=True)
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.encode(
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x=alt.X("Week:O"),
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y=alt.Y("Rank:Q", scale=alt.Scale(domain=[1, 6], reverse=True)),
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color=alt.Color("Team:N", scale=alt.Scale(domain=df["Team"].unique().tolist(), range=colors)),
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color=alt.Color("Team:N", scale=alt.Scale(domain=teams, range=colors)),
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opacity=alt.condition(highlight, alt.value(1), alt.value(0.3)),
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tooltip=["Team:N", "Week:O", "Rank:Q"],
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)
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)
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chart = (
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(lines + points)
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.properties(width=1600, height=900, title=alt.Title("bump-basic · altair · pyplots.ai", fontSize=28))
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.configure_axis(grid=True, gridOpacity=0.3, gridDash=[4, 4])
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.configure_axis(grid=True, gridOpacity=0.2, gridDash=[4, 4])
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.configure_view(strokeWidth=0)
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)
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# Save as PNG (1600 × 900 × 3 = 4800 × 2700)
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chart.save("plot.png", scale_factor=3.0)
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# Save interactive HTML version
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chart.interactive().save("plot.html")
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# Save interactive HTML version (hover highlights team)
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chart.save("plot.html")

‎plots/bump-basic/metadata/altair.yaml‎

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library: altair
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specification_id: bump-basic
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created: '2025-12-23T09:18:27Z'
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updated: '2025-12-23T09:23:38Z'
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generated_by: claude-opus-4-5-20251101
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updated: '2026-02-22T20:55:00+00:00'
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generated_by: claude-opus-4-6
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workflow_run: 20456609780
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issue: 0
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python_version: 3.13.11
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library_version: 6.0.0
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python_version: '3.14.3'
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library_version: '6.0.0'
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preview_url: https://storage.googleapis.com/pyplots-images/plots/bump-basic/altair/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bump-basic/altair/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/bump-basic/altair/plot.html
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quality_score: 92
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quality_score: null
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impl_tags:
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dependencies: []
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techniques:

‎plots/bump-basic/specification.md‎

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- `period` (categorical or time) - Time points for ranking snapshots
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- `rank` (integer) - Position at each period (1 = highest rank)
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- Size: 5-10 entities, 4-8 periods typical
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- Example: Formula 1 driver standings over a 10-race season
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## Notes
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‎plots/bump-basic/specification.yaml‎

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# Specification tracking
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created: 2025-12-15T20:42:43Z
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updated: 2025-12-15T20:42:43Z
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updated: 2026-02-22T12:00:00Z
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issue: 982
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suggested: MarkusNeusinger
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data_type:
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- categorical
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- ordinal
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- timeseries
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domain:
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- general
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features:
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- basic
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- ranking
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- temporal
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- comparison

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