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time_currency_normalizer

Convert date and currency formats in structured data files to standardized formats.

541|171|Updated May 3, 2018
Search Tags:#structured data#pandas#data cleaning#currency conversion#date normalization
One-click install
npx skills add https://github.com/cas-bigdatalab/piflow --skill time-currency-normalizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: time_currency_normalizer
Source: https://github.com/cas-bigdatalab/piflow/tree/main/workspace/skills/time_currency_normalizer
Command: npx skills add https://github.com/cas-bigdatalab/piflow --skill time-currency-normalizer

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill normalizes date and currency formats in structured data, saving time and ensuring consistency in data processing.

Core Features & Use Cases

  • Date Normalization: Converts various date formats to a standardized YYYY-MM-DD format.
  • Currency Normalization: Converts currency symbols to their respective ISO codes (USD, EUR, CNY).
  • Use Case: Ideal for data scientists and analysts who need to clean and standardize financial or temporal data across different sources.

Quick Start

Normalize the date and currency in the input file 'data.csv' and save the output to 'normalized_data.csv'.

Frequently Asked Questions about time_currency_normalizer

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

FAQPage Schema
How do I normalize various date formats to YYYY-MM-DD in structured data?▼

To normalize date formats to YYYY-MM-DD in structured data, you can use this Skill to convert inconsistent date entries in your files. It leverages pandas for data manipulation and regex for pattern matching to ensure consistent date formatting.

Can I convert currency symbols to ISO codes like USD and EUR using pandas?▼

Yes, you can convert currency symbols to ISO codes like USD and EUR using this Skill. It processes structured data files to standardize currency formats, replacing symbols with their respective ISO codes for consistent financial data processing.

What is the best way to clean financial data with mixed date and currency formats?▼

The best way to clean financial data with mixed formats is to standardize both fields simultaneously. This Skill normalizes dates to YYYY-MM-DD and currencies to ISO codes, ensuring consistency across different data sources for time series analysis.

Do I need pandas to standardize date and currency formats in my CSV files?▼

Yes, you need pandas to standardize date and currency formats using this Skill. Pandas is required for data manipulation during the normalization process, while regex handles the pattern matching for various date and currency formats.

Why does date normalization fail when processing structured data from multiple sources?▼

Date normalization may fail when structured data contains unrecognized date patterns. This Skill uses regex for pattern matching to handle various date formats, but extremely irregular or ambiguous formats might require pre-processing to ensure accurate conversion to YYYY-MM-DD.

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