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programmatic-eda

Analyze datasets with pandas, seaborn, and matplotlib to profile quality and generate visual summaries.

351|70|Updated Jan 11, 2026
Search Tags:#visualization#eda#duplicates#data-quality#outliers#data-profiling#exploratory-analysis
One-click install
npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill programmatic-eda
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: programmatic-eda
Source: https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/programmatic-eda
Command: npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill programmatic-eda

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Systematic exploratory data analysis following best practices helps analysts understand data structure, identify data quality issues (duplicates, missing values, inconsistencies, outliers), examine distributions, detect correlations, and generate visualizations.

Core Features & Use Cases

  • Automated data profiling and sanity checks before analysis.
  • Identification of data quality issues such as duplicates, missing values, and outliers.
  • Generation of distributions, correlations, and visual summaries for quick decision support.
  • Use Case: Apply to a new dataset to surface quality issues and initial insights, enabling rapid storytelling.

Quick Start

Load a dataset and generate an initial profiling report that includes distributions, relationships, and quality checks.

Frequently Asked Questions about programmatic-eda

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

FAQPage Schema
How do I perform exploratory data analysis to uncover data quality issues in my dataset?▼

Exploratory data analysis involves analyzing a dataset to uncover structure and quality issues like duplicates and missing values. This Skill uses pandas, seaborn, and matplotlib to compute profiling metrics and generate visual summaries for actionable insights.

What's the best way to automate data profiling and sanity checks before analysis?▼

Automated data profiling applies systematic checks to surface inconsistencies and structure in your data. It handles distributions, correlations, and outliers, outputting a structured report that enables rapid storytelling and quick decision support.

Do I need Python and pandas to generate visual summaries for exploratory analysis?▼

Yes, you need Python-based tooling including pandas, seaborn, and matplotlib to compute profiling metrics and generate visual summaries. These dependencies handle the heavy lifting for data manipulation and visualization.

Can I use this approach to detect outliers and correlations across diverse data sizes?▼

Yes, the approach is applicable to diverse domains and data sizes for detecting outliers and correlations. It handles distributions and relationships systematically, producing actionable insights regardless of the dataset scale.

How does systematic EDA handle missing values and duplicates during data profiling?▼

Systematic EDA handles missing values and duplicates by identifying them as data quality issues during profiling. It analyzes the dataset comprehensively to uncover these inconsistencies, ensuring the final structured report accurately reflects data health.

What limitations should I expect when generating distributions and correlations for large datasets?▼

While applicable to diverse data sizes, limitations depend on the Python-based tooling capacity of pandas, seaborn, and matplotlib. Processing very large datasets may require managing memory constraints when computing profiling metrics and rendering visual summaries.

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