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csv-analyzer

Analyze, clean, transform, and summarize CSV files with pandas.

22|8|Updated Mar 14, 2026
Search Tags:#data analysis#data transformation#pandas#statistics#csv#data cleaning
One-click install
npx skills add https://github.com/inbharatai/claude-skills --skill csv-analyzer-inbharatai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csv-analyzer
Source: https://github.com/inbharatai/claude-skills/tree/main/skills/csv-analyzer
Command: npx skills add https://github.com/inbharatai/claude-skills --skill csv-analyzer-inbharatai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of analyzing, cleaning, and transforming CSV files, enabling users to quickly derive meaningful insights and detect anomalies.

Core Features & Use Cases

  • Data Cleaning: Handle missing values, correct data types, and remove duplicates.
  • Statistical Analysis: Compute descriptive statistics (mean, median, standard deviation) and identify outliers.
  • Data Transformation: Reshape data, create new features, and aggregate information.
  • Use Case: Analyze a customer transaction CSV to identify the top 10% of customers by spending and detect any unusual transaction patterns.

Quick Start

Use the csv-analyzer skill to calculate the average of the 'price' column in the attached 'sales_data.csv' file.

Frequently Asked Questions about csv-analyzer

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

FAQPage Schema
How do I analyze a CSV file to compute descriptive statistics and detect outliers?▼

You can analyze a CSV file by loading it into pandas to compute descriptive statistics like mean and standard deviation, identify outliers, and detect anomalies to produce actionable insights from your dataset.

What is the best way to clean and transform CSV data for analytical workflows?▼

Cleaning and transforming CSV data involves handling missing values, correcting data types, and removing duplicates using pandas. You can then reshape data, create new features, and aggregate information for various analytical workflows.

Does the csv-analyzer skill require pandas to perform data wrangling tasks?▼

Yes, the csv-analyzer requires pandas as a dependency for efficient data manipulation, transformation, and analysis when wrangling CSV files for various analytical workflows.

How do I identify the top percentage of customers by spending from a transaction CSV?▼

To identify top customers by spending from a transaction CSV, you can use pandas to aggregate transaction data, compute spending totals per customer, and detect unusual transaction patterns or anomalies.

Can I use this approach to detect anomalies and handle missing values in large CSV files?▼

Yes, you can use pandas to handle missing values, correct data types, and detect anomalies in CSV files. It streamlines data wrangling to compute statistics and derive meaningful insights efficiently.

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