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exploratory-data-analysis

Automate exploratory data analysis on CSV and Excel datasets with Python.

3|Updated Dec 3, 2025
Search Tags:#data quality#visualization#pandas#statistics#EDA#outliers
One-click install
npx skills add https://github.com/braselog/researchAssistant --skill exploratory-data-analysis-braselog
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/braselog/researchAssistant/tree/main/.ra/skills/exploratory-data-analysis
Command: npx skills add https://github.com/braselog/researchAssistant --skill exploratory-data-analysis-braselog

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps researchers automatically explore datasets to reveal data structure, data quality, distributions, and relationships, enabling informed decisions before formal analysis.

Core Features & Use Cases

  • Load and inspect datasets (CSV, Excel, etc.) to summarize structure and data types.
  • Assess data quality by detecting missing values, duplicates, and outliers.
  • Visualize distributions and relationships to generate an initial, reproducible EDA report.
  • Use Case: before modeling, quickly understand data health and key drivers to guide analysis plans.

Quick Start

Place your dataset in the working directory and run the EDA workflow to generate an initial report and visualizations.

Frequently Asked Questions about exploratory-data-analysis

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

FAQPage Schema
How do I automate exploratory data analysis on a CSV file?▼

Automated exploratory data analysis loads your CSV file to compute statistical summaries, detect missing values, identify outliers, and generate a reproducible report with visualizations. This reveals data structure and quality before formal analysis.

What is the best way to check data quality and find outliers in a pandas dataset?▼

The best way to check data quality is to run an automated EDA workflow that assesses missing values, duplicates, and outliers. It leverages pandas and numpy to inspect data types and compute summaries, producing a structured report on data health.

Does this EDA workflow support Excel files along with CSV?▼

Yes, the EDA workflow supports loading and inspecting both CSV and Excel files. It automatically summarizes the structure and data types of your dataset to help you understand initial data health before modeling.

When do I need to run an exploratory data analysis report?▼

You need to run an exploratory data analysis report when you receive a new dataset and must understand its distributions and relationships before formal analysis. It is essential for assessing data quality and identifying key drivers to guide your analysis plans.

How do I visualize distributions and relationships in my dataset before modeling?▼

You can visualize distributions and relationships by running the EDA workflow, which uses Python-based visualization libraries to generate plots from your loaded data. This produces an initial reproducible report to guide your modeling decisions.

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