Scientific computing tools for Python

SciPy refers to several related but distinct entities:

  • The SciPy ecosystem, a collection of open source software for scientific computing in Python.

  • The community of people who use and develop this stack.

  • Several conferences dedicated to scientific computing in Python - SciPy, EuroSciPy, and

  • The SciPy library, one component of the SciPy stack, providing many numerical routines.

The SciPy ecosystem

Scientific computing in Python builds upon a small core of packages:

  • Python, a general purpose programming language. It is interpreted and dynamically typed and is very well suited for interactive work and quick prototyping, while being powerful enough to write large applications in.

  • NumPy, the fundamental package for numerical computation. It defines the numerical array and matrix types and basic operations on them.

  • The SciPy library, a collection of numerical algorithms and domain-specific toolboxes, including signal processing, optimization, statistics, and much more.

  • Matplotlib, a mature and popular plotting package that provides publication-quality 2-D plotting, as well as rudimentary 3-D plotting.

On this base, the SciPy ecosystem includes general and specialised tools for data management and computation, productive experimentation, and high-performance computing. Below, we overview some key packages, though there are many more relevant packages.

Data and computation:

  • pandas, providing high-performance, easy-to-use data structures.

  • SymPy, for symbolic mathematics and computer algebra.

  • NetworkX, is a collection of tools for analyzing complex networks.

  • scikit-image is a collection of algorithms for image processing.

  • scikit-learn is a collection of algorithms and tools for machine learning.

  • h5py and PyTables can both access data stored in the HDF5 format.

Productivity and high-performance computing:

  • IPython, a rich interactive interface, letting you quickly process data and test ideas.

  • The Jupyter notebook provides IPython functionality and more in your web browser, allowing you to document your computation in an easily reproducible form.

  • Cython extends Python syntax so that you can conveniently build C extensions, either to speed up critical code or to integrate with C/C++ libraries.

  • Dask, Joblib or IPyParallel for distributed processing with a focus on numeric data.

Quality assurance:

  • nose, a framework for testing Python code, being phased out in preference for pytest.

  • numpydoc, a standard and library for documenting Scientific Python libraries.