NumPy

The official version of NumPy is a programming tool developed by Python. The latest version of NumPy has built-in professional and powerful N-dimensional array objects, complex (broadcasting) functions, and tools integrating C/C++ and Fortran codes, which can help users store and process large matrices. NumPy software supports a variety of personalized editing adjustments, allowing users to easily create higher-quality coding services.

Introduction to NumPy software

NumPy(Numeric Python) provides many advanced numerical programming tools, such as matrix data types, vector processing, and sophisticated arithmetic libraries. Built for rigorous number crunching. Mostly used by many large financial companies, as well as core scientific computing organizations such as: Lawrence Livermore, NASA uses it to handle some tasks that would otherwise be done using C++, Fortran or Matlab.

NumPy software features

​ ​ ※Deep learning frameworks accelerate the process from research prototypes to production deployment.

​​​​※An end-to-end platform for machine learning that makes it easy to build and deploy ML-based applications.

​ ​ ※Deep learning framework is suitable for flexible research prototyping and production.

​ ​ ※Cross-language development platform for column-memory data and analysis.

​ ​ ※Multidimensional arrays with broadcast and lazy evaluation for numerical analysis.

​ ​ ※Develop a library for array calculations and recreate the basic concepts of NumPy.

​ ​ ※ Python backend system that decouples API from implementation; unumpy provides a NumPy API.

​ ​ ※Distributed arrays and advanced parallel analysis capabilities enable large-scale performance.

​ ​ ※Array library compatible with NumPy, used for GPU accelerated calculations using Python.

​​​ ※Composable transformation of NumPy programs: diff, vectorization, on-the-fly compilation to GPU/TPU.

​ ​ ※Tagged indexed multidimensional arrays for advanced analysis and visualization

​​​ ※Compatible with NumPy’s sparse array library, which is integrated with Dask and SciPy’s sparse linear algebra.

​​​​※Tensor learning, algebra and backend work seamlessly using NumPy, MXNet, PyTorch, TensorFlow or CuPy.

NumPy software features

​​​Powerful N-dimensional array

                          Fast and versatile NumPy vectorization, indexing and broadcasting concepts are the de facto standard for array computing today.

​Numerical calculation tools

NumPy provides a comprehensive range of mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

​​​interoperable

NumPy supports a wide range of hardware and computing platforms and works well with distributed, GPU and sparse array libraries.

​​​performance

The core of NumPy is well-optimized C code. Enjoy the flexibility of Python at the speed of compiled code.

​​​Easy to use

NumPy’s high-level syntax makes it accessible and efficient for programmers of any background or experience level.

​​​Open source

NumPy is released under the liberal BSD license and is publicly developed and maintained on GitHub by a vibrant, responsive, and diverse community.

NumPy change log

​​ 1.Fix BUG, ​​the new version has a better experience

​ 2. Some pages have been changed