awesome-python

🥈Silver

Hand-picked Python libraries and frameworks for automation. Organized by category, this skill helps operations teams streamline workflows. Connects to Jupyter, pandas, and other data tools.

446370Updated Today

Intermediate30min to implementautomation

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/dylanhogg/awesome-python.git

Works with:

Claude

Overview

About This Skill

https://www.awesomepython.org/

Use Cases

Quickly find and integrate libraries for data manipulation and analysis in your Python projects.

Discover the latest tools for machine learning and deep learning to enhance your AI applications.

Utilize libraries for web scraping and data collection to automate data gathering tasks.

Implement testing and debugging tools to improve code quality and reliability in your software.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/dylanhogg/awesome-python

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Can you recommend some awesome Python libraries for [TASK] in the [INDUSTRY]? I'm particularly interested in [SPECIFIC NEED].

Example Output

### Recommended Python Libraries for Data Analysis

1. **Pandas**: A powerful data manipulation and analysis library that provides data structures like DataFrames to work with structured data.
2. **NumPy**: Fundamental package for scientific computing with Python, offering support for large multidimensional arrays and matrices.
3. **Matplotlib**: A plotting library for creating static, animated, and interactive visualizations in Python.
4. **SciPy**: A library used for scientific and technical computing that builds on NumPy, providing additional functionality for optimization, integration, and statistics.

These libraries will help you efficiently handle data analysis tasks in your projects.