skills.rest
PedroHBO
PedroHBOCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

data-processing

Clean, transform, and analyze datasets with Pandas, Polars, and NumPy.

1|1|Updated Mar 26, 2026
Search Tags:#pandas#pipelines#polars#numpy#data-transformation#data-processing#data-cleaning
One-click install
npx skills add https://github.com/PedroHBO/opencode-config-skills --skill data-processing-pedrohbo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-processing
Source: https://github.com/PedroHBO/opencode-config-skills/tree/main/skills/data-processing
Command: npx skills add https://github.com/PedroHBO/opencode-config-skills --skill data-processing-pedrohbo

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you perform data cleaning, transformation, and analysis in Python by leveraging Pandas, Polars, and NumPy, reducing manual scripting and improving performance.

Core Features & Use Cases

  • Data cleaning and wrangling across Pandas, Polars, and NumPy pipelines.
  • Memory optimization and dtype best practices for large datasets.
  • Use Case: Build end-to-end data pipelines that preprocess input data and feed analytics or ML models.

Quick Start

Load a dataset with Pandas, apply standard cleaning and transformation steps, and run a quick analysis.

Frequently Asked Questions about data-processing

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

FAQPage Schema
How do I optimize memory usage when processing large datasets with Pandas?▼

Optimize memory in Pandas by applying dtype best practices and chunking large files. This data-processing approach reduces memory overhead and enables efficient handling of variable-sized datasets during transformation.

What is the best way to build a data pipeline for cleaning and transforming datasets in Python?▼

Build data pipelines using Pandas, Polars, and NumPy for cleaning and transforming datasets. This Skill provides data-processing capabilities to preprocess input data and feed analytics or ML models efficiently.

Can I use Polars and NumPy for feature engineering and data normalization?▼

Yes, you can use Polars and NumPy for feature engineering and data normalization. The Skill supports vectorized operations across these frameworks to apply data-transformation steps within Python pipelines.

Do I need Pandas installed to run data-transformation and data-cleaning scripts?▼

Yes, Pandas is a required dependency to run data-transformation and data-cleaning scripts. The Skill relies on it alongside Polars and NumPy to execute data-processing workflows and memory profiling.

How does vectorized data processing compare to standard row-by-row operations?▼

Vectorized data processing applies operations across entire arrays simultaneously, significantly outperforming standard row-by-row operations. The Skill leverages NumPy and Pandas for performance optimization in data pipelines.

Related Skills

Explore More Data & Analytics Skills→
Thiago-Cruz-engThiago-Cruz-eng

data-analysis

Analyze datasets to extract insights and produce structured reports.

Community
Intermediate
bcastelinobcastelino
2

data-scientist

Perform statistical analysis and build predictive models using Python and R.

Community
Advanced
vuralserhat86vuralserhat86
43

data_visualization

Generate bar, line, and scatter visualizations from data using matplotlib and seaborn.

Community
Intermediate
pluginagentmarketplacepluginagentmarketplace
5

big-data

Process and analyze petabyte-scale data with Spark and Hadoop.

Community
Advanced