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adrianliechti
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create-viz

Generate publication-quality charts from DataFrames using matplotlib, seaborn, or plotly.

4|4|Updated Dec 15, 2024
Search Tags:#python#data#plotly#matplotlib#charting#seaborn#visualize
One-click install
npx skills add https://github.com/adrianliechti/wingman-chat --skill create-viz-adrianliechti
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: create-viz
Source: https://github.com/adrianliechti/wingman-chat/tree/main/skills/data/create-viz
Command: npx skills add https://github.com/adrianliechti/wingman-chat --skill create-viz-adrianliechti

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create publication-quality data visualizations from raw data for reports, dashboards, and presentations.

Core Features & Use Cases

  • Generate charts from query results or DataFrames following best-practice design
  • Recommend chart types for trends, comparisons, distributions, and relationships
  • Provide production-ready Python code using matplotlib, seaborn, or plotly

Quick Start

Tell me your data source and the chart goal, and I will produce a ready-to-run Python visualization.

Frequently Asked Questions about create-viz

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate publication-quality charts from a DataFrame using Python?▼

You can generate publication-quality charts from a DataFrame by providing your data source and chart goal to get ready-to-run Python code using matplotlib, seaborn, or plotly with best-practice design applied automatically.

What is the best way to visualize query results for a report or dashboard?▼

Visualizing query results is best handled by converting them into charts using Python libraries like seaborn or plotly, which this approach uses to produce production-ready visualizations tailored for reports and dashboards.

Can I get chart type recommendations for comparing distributions and relationships in my data?▼

Yes, you can get chart type recommendations for distributions and relationships by stating your visualization goal, and the system will suggest appropriate chart designs and generate the corresponding Python code.

Does this approach work with pasted datasets to create presentation-ready plots?▼

Yes, it works with pasted datasets to create presentation-ready plots by processing the raw data and generating Python visualization code that follows best-practice design guidance for your presentations.

How do I turn raw data into plotly or matplotlib visualizations without writing code manually?▼

You turn raw data into plotly or matplotlib visualizations by specifying your data source and desired chart, which triggers automatic generation of production-ready Python code that you can run directly.

Why should I use Python for data visualization instead of other charting methods?▼

Using Python for data visualization provides production-ready code with libraries like matplotlib, seaborn, and plotly, ensuring publication-quality outputs with best-practice design that is reproducible for reports and dashboards.

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