What types of file encodings can CSV Wrangler handle?

It uses chardet for initial detection and employs a robust fallback chain including UTF-8, Latin-1, and CP1252 to ensure data is readable even with unknown encodings.

Does this skill support Excel files?

Yes, it includes patterns for 'smart' Excel reading that drops empty rows/columns and identifies sheet names using pandas and openpyxl.

How does it manage rows with an incorrect number of columns?

CSV Wrangler can capture malformed rows in a separate report for inspection or use specialized regex splitting to handle delimiters trapped inside quoted fields.

Can it handle CSV files with headers hidden under metadata rows?

Yes, the skill includes header detection logic that scans the file to find the first row containing valid column-like data, skipping top-level metadata automatically.

CSV Wrangler

Name: CSV Wrangler
Author: majesticlabs-dev

bymajesticlabs-dev

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Data Science & ML

Cleans and processes malformed or messy CSV files using automated detection of encodings, delimiters, and data types.

About

CSV Wrangler provides robust patterns for handling real-world data files that are often improperly formatted or inconsistent. It automates the detection of character encodings and delimiters, recovers data from malformed rows, and handles complex scenarios like embedded delimiters in quoted fields. This skill is essential for data engineers and developers who need to ingest diverse datasets without manual cleanup, ensuring reliable data loading into pandas DataFrames or databases through standardized cleaning pipelines.

Key Features

Malformed row recovery and metadata/header detection
Automatic encoding detection with intelligent fallback chains
Support for quoted fields and escaped character handling
Flexible date parsing and numeric column normalization
Heuristic delimiter inference for varied flat-file formats
18 GitHub stars

Use Cases

Ingesting legacy data exports with inconsistent formatting or encodings
Pre-processing messy datasets for machine learning and analytics pipelines
Standardizing diverse CSV sources into a unified database schema

About

Key Features

Malformed row recovery and metadata/header detection
Automatic encoding detection with intelligent fallback chains
Support for quoted fields and escaped character handling
Flexible date parsing and numeric column normalization
Heuristic delimiter inference for varied flat-file formats
18 GitHub stars

Use Cases

Ingesting legacy data exports with inconsistent formatting or encodings
Pre-processing messy datasets for machine learning and analytics pipelines
Standardizing diverse CSV sources into a unified database schema