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smart_quote_normalizer

Normalizes non-standard quotes and dashes to ASCII in CSV, TSV, Excel, and SPSS files using Python.

541|171|Updated May 3, 2018
Search Tags:#text processing#data cleaning#text normalization#formatting standardization#character conversion
One-click install
npx skills add https://github.com/cas-bigdatalab/piflow --skill smart-quote-normalizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: smart_quote_normalizer
Source: https://github.com/cas-bigdatalab/piflow/tree/main/workspace/skills/smart_quote_normalizer
Command: npx skills add https://github.com/cas-bigdatalab/piflow --skill smart-quote-normalizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps streamline text processing by normalizing formatting characters like quotes and dashes, making it easier to handle and analyze text.

Core Features & Use Cases

  • Formatting Normalization: Converts characters such as curly quotes and em dashes to their standard ASCII counterparts.
  • File Processing: Works with CSV, TSV, Excel, and SPSS files for flexible data handling.
  • Use Case: If you're working with text that includes non-standard formatting and need to ensure consistency for analysis or further processing, this skill can be very useful.

Quick Start

Run the smart_quote_normalizer skill to clean up the formatting of your text in the 'input.csv' file and output the results to 'output.csv'.

Frequently Asked Questions about smart_quote_normalizer

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

FAQPage Schema
How do I normalize non-standard quote characters in a CSV file for text processing?▼

To normalize non-standard quote characters in a CSV file, you can run this skill to convert curly quotes and em dashes into standard ASCII counterparts, outputting a cleanly formatted file for analysis.

What is the best way to standardize formatting characters across Excel and SPSS files?▼

Standardizing formatting characters across Excel and SPSS files involves converting non-standard quotes and dashes to ASCII standards, which this skill handles efficiently using Python character replacement.

Does text normalization work with TSV file formats or only CSV?▼

Text normalization works with TSV file formats as well as CSV, Excel, and SPSS files, ensuring consistent character conversion across various data handling formats.

Why do I need to convert curly quotes and em dashes before data analysis?▼

You need to convert curly quotes and em dashes before data analysis to ensure formatting consistency, preventing parsing errors and character encoding issues during downstream text processing.

Can I use Python to automate text cleaning for formatting standardization?▼

You can use Python to automate text cleaning for formatting standardization, as this skill utilizes Python scripts for efficient character replacement to prepare text for analysis.

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