altair

Estrelas2

Forks1

Atualizado9 de abril de 2026 às 01:56

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

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Fonte

jswortz

jswortz/my-skills

Abrir repositório GitHub Ver repositórios do creator

Download

Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

Explorador de arquivos

2 arquivos

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'].

Mais deste repositório

mesmo repositório

dream-analyzer

jswortz/my-skills

Analyze agent session transcripts to find looping behavior, missing capabilities, or token waste, and leverage framework-native concepts and skill-creator patterns to propose optimizations.

2026-04-162

a2a

jswortz/my-skills

Build multi-agent systems using Google ADK with A2A protocol, deployed on Agent Engine. Use when creating agents that communicate via A2A, building multi-tier agent hierarchies, connecting ADK agents with RemoteA2aAgent, exposing agents with to_a2a(), or deploying agent stacks to Vertex AI Agent Engine. Covers leaf agents with tools, functional agents that delegate, orchestrators that route, local testing with uvicorn, and phased cloud deployment.

2026-04-102

adk

jswortz/my-skills

Build AI agents using Google's Agent Development Kit (ADK). Use when creating LLM agents with tools, building workflow agents (Sequential, Parallel, Loop), composing multi-agent systems, or developing custom agents. Covers agent creation patterns, function tools, agent configuration, session management, and running agents locally with CLI or web interface.

2026-04-102

agent-engine

jswortz/my-skills

Deploy and manage AI agents on Vertex AI Agent Engine. Use when deploying ADK agents to production, configuring Agent Engine runtime, managing deployed agents, setting up sessions and memory, or integrating with A2A protocol. Covers deployment from agent objects and source files, environment configuration, scaling, sessions, memory bank, and agent management operations.

2026-04-102

agile

jswortz/my-skills

Sprint-based agile development with parallel agent execution. Use when the user wants to run an agile sprint, plan sprint work, conduct standups, run retrospectives, manage a backlog, estimate story points, track velocity, or coordinate parallel development across multiple agents acting as Scrum team roles (Scrum Master, Product Owner, Tech Lead, Frontend Dev, Backend Dev, QA Engineer). Also use when the user mentions sprints, user stories, acceptance criteria, definition of done, kanban, or SAFe. Triggers on: 'run a sprint', 'sprint planning', 'standup', 'retrospective', 'backlog grooming', 'agile workflow', 'scrum team'.

2026-04-102

ask-questions-if-underspecified

jswortz/my-skills

Clarify requirements before implementing. Use when serious doubts araise.

2026-04-102

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'].