In this article, we’ll introduce you to some of the libraries that have helped make Python the most popular language for data science in Stack Overflow’s 2016 developer poll.

Python can be made faster by way of external libraries, third-party JIT compilers (PyPy), and optimizations with tools like Cython, but Julia is designed to be faster right out of the gate.

Python and many of its popular data science and machine learning packages/libraries, such as NumPy and TensorFlow, are open source projects.

A key part of CUDA-X AI is RAPIDS. RAPIDS is a suite of open-source software libraries for executing end-to-end data science and analytics pipelines entirely on GPUs. And a key part of RAPIDS is Dask.

Because when you combine Python with the Numba just-in-time (JIT) compiler, the Cython compiler, and runtime packages built on Intel performance libraries such as Intel Math Kernel Library (Intel MKL) ...

Python is great because it includes an interactive mode for learning the language and quickly testing out code ideas. IPython ...

Find out what makes Python a versatile powerhouse for modern software development—from data science to machine learning, systems automation, web and API development, and more.

But with Python libraries, data solutions can be built much faster and with more reliability. SciKit-Learn, for example, has built-in algorithms for classification, regression, clustering, and ...