altair

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Guide and best practices for creating data visualizations using Altair in Python. Always use this over matplotlib or seaborn.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

المصدر

jswortz

jswortz/my-skills

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

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SKILL.md

readonly

name	altair
description	Guide and best practices for creating data visualizations using Altair in Python. Always use this over matplotlib or seaborn.

Altair Data Visualization Skill

Use this skill when tasked with generating charts, graphs, and plots using Python. Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. Our environment relies on Altair instead of Matplotlib and Seaborn for all new plots.

Setup

Ensure altair and vl-convert-python are available locally via uv.

import altair as alt
import pandas as pd

Creating Charts

Altair expects data in a Pandas DataFrame, preferably in long format (melted).

Example: Line Chart with Multiple Series

import pandas as pd
import altair as alt

# 1. Prepare Data
df = pd.DataFrame({
    'Epoch': [1, 2, 3],
    'Metric A': [0.5, 0.6, 0.7],
    'Metric B': [0.4, 0.5, 0.9]
})

# 2. Melt Data
df_melt = df.melt('Epoch', var_name='Metric', value_name='Score')

# 3. Create Base Chart
base = alt.Chart(df_melt).encode(
    x=alt.X('Epoch:Q', title='Generation (Epoch)', axis=alt.Axis(tickMinStep=1))
)

# 4. Create Line Marks
lines = base.mark_line(point=True).encode(
    y=alt.Y('Score:Q', title='Evaluation Score', scale=alt.Scale(domain=[0, 1.0])),
    color=alt.Color('Metric:N', scale=alt.Scale(
        domain=['Metric A', 'Metric B'],
        range=['#4A90E2', '#F5A623']
    )),
    tooltip=['Epoch', 'Metric', 'Score']
)

# 5. Add Properties and Config
chart = lines.properties(
    title="Evolution Performance",
    width=700,
    height=400
).configure_title(
    fontSize=14
)

# 6. Save Chart
chart.save("output.png")

Supported Output Formats

.html (Interactive web page)
.png (Requires vl-convert-python)
.svg (Requires vl-convert-python)
.json (Vega-Lite spec)

Common Scenarios

Scatter Plot: mark_circle(size=60)
Bar Chart: mark_bar()
Area Chart: mark_area()
Dual Axis: alt.layer(chart1, chart2).resolve_scale(y='independent') Note: To resolve dual axis you simply create 2 independent charts and use alt.layer(c1, c2).resolve_scale(y='independent').
Horizontal Rule / Threshold:

threshold = alt.Chart(pd.DataFrame({'y': [0.70]})).mark_rule(color='red', strokeDash=[5,5]).encode(y='y:Q')
chart = alt.layer(lines, threshold)

Best Practices

Avoid matplotlib or seaborn unless specifically requested. Altair handles complex legends and multi-series plots in a cleaner declarative way.
If data is large (>5000 rows), use alt.data_transformers.disable_max_rows() with caution, or aggregate the dataframe using Pandas before passing to Altair.
Keep tooltips explicit for better interactivity if saving as HTML.
Customize colors with scale=alt.Scale(scheme='set2') (or other vega schemes) or precise hex arrays range=['#ff0000', '#00ff00'].

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Altair Data Visualization Skill

Setup

Ensure altair and vl-convert-python are available locally via uv.

import altair as alt import pandas as pd

Creating Charts

Altair expects data in a Pandas DataFrame, preferably in long format (melted).

Example: Line Chart with Multiple Series

import pandas as pd import altair as alt # 1. Prepare Data df = pd.DataFrame({ 'Epoch': [1, 2, 3], 'Metric A': [0.5, 0.6, 0.7], 'Metric B': [0.4, 0.5, 0.9] }) # 2. Melt Data df_melt = df.melt('Epoch', var_name='Metric', value_name='Score') # 3. Create Base Chart base = alt.Chart(df_melt).encode( x=alt.X('Epoch:Q', title='Generation (Epoch)', axis=alt.Axis(tickMinStep=1)) ) # 4. Create Line Marks lines = base.mark_line(point=True).encode( y=alt.Y('Score:Q', title='Evaluation Score', scale=alt.Scale(domain=[0, 1.0])), color=alt.Color('Metric:N', scale=alt.Scale( domain=['Metric A', 'Metric B'], range=['#4A90E2', '#F5A623'] )), tooltip=['Epoch', 'Metric', 'Score'] ) # 5. Add Properties and Config chart = lines.properties( title="Evolution Performance", width=700, height=400 ).configure_title( fontSize=14 ) # 6. Save Chart chart.save("output.png")

Supported Output Formats

.html (Interactive web page)

.png (Requires vl-convert-python)

.svg (Requires vl-convert-python)

.json (Vega-Lite spec)

Common Scenarios

Scatter Plot: mark_circle(size=60)

Bar Chart: mark_bar()

Area Chart: mark_area()

Dual Axis: alt.layer(chart1, chart2).resolve_scale(y='independent') Note: To resolve dual axis you simply create 2 independent charts and use alt.layer(c1, c2).resolve_scale(y='independent').

Horizontal Rule / Threshold:

threshold = alt.Chart(pd.DataFrame({'y': [0.70]})).mark_rule(color='red', strokeDash=[5,5]).encode(y='y:Q') chart = alt.layer(lines, threshold)

Best Practices

Avoid matplotlib or seaborn unless specifically requested. Altair handles complex legends and multi-series plots in a cleaner declarative way.

If data is large (>5000 rows), use alt.data_transformers.disable_max_rows() with caution, or aggregate the dataframe using Pandas before passing to Altair.

Keep tooltips explicit for better interactivity if saving as HTML.

Customize colors with scale=alt.Scale(scheme='set2') (or other vega schemes) or precise hex arrays range=['#ff0000', '#00ff00'].