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text_fragment_remover

Remove specified text fragments from CSV, TSV, JSON, and JSONL fields using pandas.

541|171|Updated May 3, 2018
Search Tags:#structured data#data integrity#pandas#text cleaning#fragment removal
One-click install
npx skills add https://github.com/cas-bigdatalab/piflow --skill text-fragment-remover
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: text_fragment_remover
Source: https://github.com/cas-bigdatalab/piflow/tree/main/workspace/skills/text_fragment_remover
Command: npx skills add https://github.com/cas-bigdatalab/piflow --skill text-fragment-remover

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the process of removing specific text fragments from structured data, ensuring that only the desired information remains.

Core Features & Use Cases

  • Selective Text Removal: Eliminate user-defined text fragments while preserving the rest of the data.
  • Data Integrity: Maintains the integrity of the original data structure, leaving other fields unchanged.
  • Use Case: When you need to remove sensitive information like watermarks, placeholders, or specific keywords from your data without altering the rest of the text.

Quick Start

Remove the text "Confidential" and "Draft" from the "Comments" column in the "data.csv" file and save the result as "cleaned.csv".

Frequently Asked Questions about text_fragment_remover

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

FAQPage Schema
How do I remove specific text fragments from a CSV file without altering other fields?▼

To remove text fragments from a CSV file without altering other fields, use this Skill to selectively target and eliminate user-defined strings while preserving the original data structure. It relies on pandas for data manipulation.

Can I clean text fields in JSON and JSONL formats using pandas?▼

Yes, you can clean text fields in JSON and JSONL formats using pandas. This Skill processes structured data files to remove specified text fragments, maintaining data integrity across all supported formats.

What is the best way to remove sensitive keywords or watermarks from structured data?▼

The best way to remove sensitive keywords or watermarks from structured data is through selective text removal. This Skill identifies and eliminates user-defined text fragments while leaving the rest of your dataset unchanged.

Does this text fragment removal tool work with TSV files?▼

Yes, this text fragment removal tool works with TSV files. It supports cleaning text fields in CSV, TSV, JSON, and JSONL formats, ensuring specified text fragments are removed without disrupting other columns.

What do I need to remove placeholders from structured data files?▼

To remove placeholders from structured data files, you need the pandas library installed as a dependency. This Skill uses pandas to manipulate the data and eliminate specified text fragments efficiently.

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