numpy-structured
CommunityHandle C-like structs in NumPy arrays.
Authorcuba6112
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill enables efficient handling of complex, multi-typed data within NumPy arrays, mimicking C-style structs for better interoperability and memory management.
Core Features & Use Cases
- Structured Data: Define arrays where each element has named fields of different data types (integers, floats, strings, etc.).
- Binary Data Interpretation: Directly interpret raw binary data from files or network buffers as structured records.
- Use Case: When working with sensor data that has a fixed binary format defined by a C header file, use this Skill to load and access fields like 'timestamp', 'temperature', and 'status_code' directly from the byte stream.
Quick Start
Use the numpy-structured skill to create an array of 10 points, each with an integer ID and a 3-element float position.
Dependency Matrix
Required Modules
numpy
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: numpy-structured Download link: https://github.com/cuba6112/skillfactory/archive/main.zip#numpy-structured Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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