What Python dependencies are required?

The skill requires pandas, plotly, and numpy. For exporting static images (PNG/PDF/SVG), the kaleido package is also required.

How do I create a custom dashboard with specific plots?

You can provide a JSON configuration file to the dashboard script, allowing you to specify the exact chart types, columns, and titles you want to display in a single layout.

Can I export the charts for use in external presentations?

Yes, you can export visualizations as interactive HTML files for web use or as static image formats like PNG, PDF, and SVG for documents and presentations.

Do I need to clean my data before using the visualizer?

While clean data is best, the skill includes a 'Data Profile' feature that automatically analyzes missing values, data types, and quality issues to help you understand what cleaning might be necessary.

What visualization types are supported by this skill?

The skill supports a wide range of plots including histograms, box plots, violin plots, scatter plots with trend lines, correlation heatmaps, line charts for time series, and categorical bar or pie charts.

CSV Data Visualizer

Name: CSV Data Visualizer
Author: nguyendinhquocx

bynguyendinhquocx

0•

数据科学与机器学习

Transforms raw CSV data into interactive Plotly visualizations, comprehensive statistical reports, and professional multi-plot dashboards.

This skill provides a comprehensive toolkit for exploratory data analysis (EDA) directly within the Claude Code environment. It enables users to automatically profile datasets for quality and distribution, generate a wide array of interactive charts—including scatter plots, correlation heatmaps, and time-series trends—and compile multiple visualizations into sophisticated dashboards. Designed to streamline the transition from raw data to actionable insights, it handles the complex scripting required for high-quality data storytelling while offering flexible output formats for both interactive exploration and static reporting.

主要功能

01Support for multiple output formats including interactive HTML, PNG, PDF, and SVG

02Advanced relationship analysis with correlation heatmaps and trend line generation

03Multi-plot dashboard generation using automatic analysis or custom JSON configs

04Automated data profiling with statistical summaries and data quality checks

050 GitHub stars

06Interactive Plotly visualizations including scatter, box, histogram, and violin plots

使用场景

01Generating presentation-ready dashboards and reports for business stakeholders

02Analyzing data quality and missing value distributions before machine learning preprocessing

03Performing rapid exploratory data analysis on unfamiliar datasets to identify patterns and outliers

主要功能

01Support for multiple output formats including interactive HTML, PNG, PDF, and SVG

02Advanced relationship analysis with correlation heatmaps and trend line generation

03Multi-plot dashboard generation using automatic analysis or custom JSON configs

04Automated data profiling with statistical summaries and data quality checks

050 GitHub stars

06Interactive Plotly visualizations including scatter, box, histogram, and violin plots

使用场景

01Generating presentation-ready dashboards and reports for business stakeholders

02Analyzing data quality and missing value distributions before machine learning preprocessing

03Performing rapid exploratory data analysis on unfamiliar datasets to identify patterns and outliers