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KaranKathur06
KaranKathur06COMMUNITY
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data-visualization

Transform datasets into exploratory charts and interactive dashboards with Matplotlib, Seaborn, Plotly, and Dash.

Updated Feb 22, 2026
Search Tags:#data visualization#dashboards#plotly#matplotlib#seaborn#eda#chart styling
One-click install
npx skills add https://github.com/KaranKathur06/Metal-Hub --skill data-visualization-karankathur06
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/KaranKathur06/Metal-Hub/tree/main/.cursor/skills/data-visualization
Command: npx skills add https://github.com/KaranKathur06/Metal-Hub --skill data-visualization-karankathur06

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, pandas, numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

It helps you quickly translate raw datasets into clear visual insights so you can explore patterns, explain results, and communicate findings to others.

Core Features & Use Cases

  • Exploratory Data Analysis (EDA): Use summary stats, missing-value checks, distributions, and correlation heatmaps to understand what your data is telling you.
  • Charting Across Popular Python Stacks: Build plots with Matplotlib and statistical graphics with Seaborn for common analysis tasks.
  • Interactive Dashboards and Visuals: Create interactive Plotly charts and a Plotly Dash dashboard with filters like dropdowns and range sliders.
  • Reusable Guidance: Apply chart-selection and styling references to choose appropriate chart types and keep visuals consistent.

Quick Start

Use the data-visualization skill to generate Matplotlib, Seaborn, and Plotly examples for your dataset, including an EDA view and an interactive dashboard.

Frequently Asked Questions about data-visualization

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

FAQPage Schema
How do I create interactive dashboards with dropdowns and range sliders in Python?▼

Interactive dashboards with dropdowns and range sliders are built using Plotly Dash. This skill generates Dash applications with filter components, applying interactive visualization scenarios to your dataset for analysis and communication.

What's the best way to run exploratory data analysis and visualize correlations?▼

Exploratory data analysis and correlation visualization are handled using summary statistics, missing-value checks, and correlation heatmaps. This EDA workflow leverages Seaborn and Matplotlib to explore distributions and identify patterns in raw datasets.

Can I use Matplotlib and Seaborn to export publication-quality figures?▼

Matplotlib and Seaborn can export publication-quality figures in common formats like PNG, PDF, and SVG. This skill applies chart styling references and best practices to ensure visual consistency for communicating findings.

Do I need pandas and numpy installed to generate statistical visualizations?▼

Pandas and numpy are required dependencies to generate statistical visualizations. This skill requires a Python-based plotting environment to transform datasets into exploratory charts and interactive visuals.

How does chart selection guidance work for choosing the right plot type?▼

Chart selection guidance provides reusable references to choose appropriate chart types based on your data. This skill applies chart best-practice guidance to keep visuals consistent and match chart types to EDA workflows.

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