dpilger26/numcpp

C++ implementation of the Python Numpy library

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Summary Information

Updated 54 minutes ago
Added to GitGenius on September 17th, 2026
Created on February 7th, 2018
Open Issues & Pull Requests: 7 (+0)
GitHub issues: Enabled
Number of forks: 575
Total Stargazers: 3,964 (+0)
Total Subscribers: 82 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.1 days
Mean response time: 148.5 days
90th percentile: 803.6 days
Tracked items: 28

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Issue Activity (beta)

Open issues: 4
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 1,079 days
Stale 30+ days: 4
Stale 90+ days: 4

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Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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  • enhancement (7)
  • help wanted (3)

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Detailed Description

NumCpp is a header-only C++ library that implements the Python NumPy API for numerical computing in C++.

The library addresses the need to perform NumPy-style array operations and numerical algorithms in C++ without requiring Python or external dependencies beyond Boost. It provides a templatized NdArray class as its core data structure, which represents 2D arrays with 1D arrays implemented as 1xN variants. The approach mirrors NumPy's interface closely, allowing developers familiar with NumPy to write equivalent code in C++ using similar function names and patterns. The library includes initializer functions for common array creation tasks, reshaping operations, type conversion methods, and a broad range of mathematical and algorithmic functions.

Developers should choose this tool when they need NumPy-like functionality in a compiled C++ application where Python integration is impractical or undesirable. It suits scientific computing, numerical analysis, and data processing projects that require performance benefits of native C++ while maintaining a familiar API. The header-only design means no separate compilation step is needed, only inclusion in source files. The library supports modern C++ standards and works across multiple compilers and platforms, making it suitable for cross-platform numerical applications.

The project maintains active testing across multiple C++ standards and compiler versions, with continuous integration workflows validating builds. Development activity shows sustained attention to code quality and compatibility across a range of modern compiler releases and C++ language versions.