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data-cleaning

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Transform messy data into insights.

Data & Analytics#validation#data quality#pandas#data cleaning#data preprocessing#outlier detection
Authorseb1n
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill tackles the pervasive issue of messy, inconsistent, and incomplete data, transforming raw datasets into a reliable format ready for analysis and machine learning.

Core Features & Use Cases

  • Handles Missing Values: Imputes or flags missing data based on column type and missingness patterns.
  • Deduplication: Identifies and removes exact and near-duplicate records.
  • Type Coercion & Standardization: Corrects data types and standardizes formats (dates, strings, numbers).
  • Outlier Detection & Treatment: Identifies and handles outliers using statistical methods.
  • Schema Validation: Enforces data quality rules to ensure consistency.
  • Use Case: Clean a customer database with missing emails, duplicate entries, and inconsistent region names before running a marketing campaign.

Quick Start

Use the data-cleaning skill to clean the attached file 'customer_data.csv' and save the output to 'cleaned_customer_data.csv'.

Dependency Matrix

Required Modules

pandaspyjanitorgreat_expectationsfuzzywuzzynumpy

Components

scriptsreferences

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: data-cleaning
Download link: https://github.com/seb1n/awesome-ai-agent-skills/archive/main.zip#data-cleaning

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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