What file formats does this EDA skill support?

It supports a wide range of common data formats including CSV, Excel (.xlsx, .xls), JSON, Parquet, TSV, Feather, HDF5, Pickle, and more.

What kind of visualizations are generated?

The toolkit generates high-resolution (300 DPI) plots including distribution histograms with KDE overlays, correlation heatmaps, box/violin plots for outliers, and scatter matrices.

Are the reports generated by this skill shareable?

Yes, it produces professional markdown reports that integrate visualizations and automated insights, making them perfect for project documentation or sharing with team members.

Can it handle large datasets with over a million rows?

For very large datasets, the skill is designed to run analysis on sampled data first to provide quick exploration, while recommending distributed computing for full-scale processing.

Does this skill help with data cleaning?

While its primary focus is exploration, it automatically flags data quality issues like duplicates, missing values, and imbalances, providing specific recommendations for cleaning and feature engineering.

Exploratory Data Analysis Toolkit

Name: Exploratory Data Analysis Toolkit
Author: lifangda

bylifangda

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Ciencia de Datos y ML

Automates comprehensive statistical analysis and visual profiling for diverse datasets to uncover hidden patterns, anomalies, and actionable insights.

Acerca de

This skill equips Claude with a robust suite of data science tools to perform automated Exploratory Data Analysis (EDA) on various file formats including CSV, Excel, Parquet, and JSON. It streamlines the analytical workflow by generating detailed statistical summaries, identifying outliers, detecting correlations, and producing production-ready visualizations. Designed for data scientists and analysts, it transforms raw data files into structured markdown reports and actionable intelligence, facilitating rapid data profiling and quality assessment directly within the Claude environment.

Características Principales

Intelligent outlier detection and comprehensive data quality assessment
16 GitHub stars
Automated markdown report generation using professional insight templates
Multi-format data support including CSV, Excel, JSON, Parquet, and HDF5
High-quality visualization suite featuring heatmaps, scatter matrices, and box plots
Automated statistical summaries, distribution analysis, and normality tests

Casos de Uso

Statistical validation and hypothesis testing during scientific research or business analysis
Initial data profiling for new datasets to identify quality issues and missing values
Generating comprehensive visual data reports for stakeholders and project documentation

Acerca de

Características Principales

Intelligent outlier detection and comprehensive data quality assessment
16 GitHub stars
Automated markdown report generation using professional insight templates
Multi-format data support including CSV, Excel, JSON, Parquet, and HDF5
High-quality visualization suite featuring heatmaps, scatter matrices, and box plots
Automated statistical summaries, distribution analysis, and normality tests

Casos de Uso

Statistical validation and hypothesis testing during scientific research or business analysis
Initial data profiling for new datasets to identify quality issues and missing values
Generating comprehensive visual data reports for stakeholders and project documentation