Menu

Individual skill

python-dataviz-guide

Publication-quality data visualization with matplotlib, seaborn, and plotly

skills/analysis/dataviz/python-dataviz-guide/SKILL.md

Read pinned source

Source commit bf44b3cd617fa94c8a1b254c5d1987142ca3d631

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Instructions only
Last checked
Nov 5, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
310
Repository last updated
Jun 19, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

  • data Derived category

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Python Data Visualization Guide

## Overview

Data visualization is how researchers communicate quantitative findings. A well-designed figure can convey complex relationships instantly, while a poor one buries the signal in clutter. Python's visualization ecosystem -- anchored by matplotlib, seaborn, and plotly -- provides everything needed to produce publication-quality figures for journals, conferences, and presentations.

This guide covers the three major Python visualization libraries, their strengths and trade-offs, and concrete recipes for the chart types researchers use most frequently. Each example is designed to be copy-paste ready and customizable for your specific dataset and venue requirements.

The emphasis is on producing figures that meet journal standards: correct DPI, appropriate font sizes, accessible color palettes, and vector-format exports. We also cover interactive visualization with plotly for exploratory analysis and supplementary materials.

## Matplotlib: The Foundation

Matplotlib is the most flexible Python plotting library. Nearly every other visualization tool in the Python ecosystem builds on it.