Awesome Scientific Python

A curated list of awesome scientific Python resources.

Contents

• Contents
• Libraries
  • Core libraries
    • IPython/Jupyter
    • NumPy
    • SciPy
    • pandas
    • scikit-learn
    • matplotlib
    • SymPy
  • Other scientific libraries
    • Data visualization
    • 3D visualization
    • Image processing
    • Graphs
    • Neural networks
    • Statistics
    • Compilation
    • Parallel computing
    • GPU computing
  • Domain-specific libraries
    • Geospatial data
    • Astrophysics
    • Molecular simulations
    • Bioinformatics
    • Neuroimaging
    • Neuroscience
    • Mathematics
  • Lists of libraries
• Books
• Courses
• Tutorials
• Videos
• License

Libraries

Core libraries

IPython/Jupyter

Effective interactive computing, data analysis, and visualization.

• IPython - Interactive Python computing in the terminal.
• Jupyter - Open interactive computing in many programming languages.
• Jupyter Notebook - Web-based environment for interactive computing.
• JupyterLab - Next-generation web-based interactive programming and computing environment.

NumPy

Multidimensional array computing.

• NumPy
• NumPy documentation
• NumPy tutorial
• NumPy for MATLAB users

SciPy

Numerical computing library.

• SciPy
• Getting started with SciPy
• SciPy documentation

pandas

Data analysis library.

• pandas
• pandas documentation

scikit-learn

Machine learning library.

• scikit-learn
• scikit-learn tutorial
• scikit-learn user guide

matplotlib

Data visualization and graphics library.

• matplotlib
• matplotlib gallery
• matplotlib tutorials
• matplotlib documentation

SymPy

Symbolic computing library.

• SymPy
• SymPy tutorial
• SymPy documentation

Other scientific libraries

Data visualization

• Bokeh - Interactive visualization for the web.
• Altair - Declarative visualization in Python.
• seaborn - Statistical data visualization.
• bqplot - 2D interactive visualization in Jupyter.
• plotnine - Grammar of Graphics implementation in Python based on ggplot2.
• plotly - Interactive data visualization on the web.
• HoloViews - Data visualization library.

3D visualization

• ipyvolume - 3D visualization with Jupyter.
• VisPy - Interactive GPU-accelerated visualization.
• Glumpy - Scientific visualization in modern OpenGL.
• vedo - Scientific analysis and visualization based on VTK.

Image processing

• scikit-image - Image processing in Python.
• Pillow - Python Imaging Library (PIL) fork in Python.
• OpenCV - Computer vision library.

Graphs

• NetworkX - Graph and network structures and algorithms.
• Graph-tool - Manipulation and statistical analysis of graphs.

Neural networks

• PyTorch - Neural networks and deep learning in Python.
• Keras - Python deep learning library.
• TensorFlow - Machine learning framework.
• Caffe - Deep learning framework.

Statistics

• PyMC3 - Bayesian statistical modeling.
• statsmodels - Statistical models.
• emcee - ensemble sampler for markov chain monte carlo.

Compilation

• Numba - JIT compilation of Python code
• Cython - Combine C and Python

Parallel computing

• ipyparallel - Parallel computing with IPython
• Dask - Parallel computing with task scheduling.

GPU computing

• CuPy - NumPy-like library with CUDA.
• PyCUDA - Use CUDA with Python.

Domain-specific libraries

Geospatial data

• GeoPandas - pandas for geospatial data.
• Shapely - Manipulation and analysis of geometric objects.
• Folium - Interactive maps in Python with leaflet.js.

Astrophysics

• Astropy - Core package for astronomy.
• AstroML - Machine learning for astronomy

Molecular simulations

• MGLTools - Visualization and analysis of molecular structures.
• MDAnalysis - Molecular dynamics simulations
• pysimm - Molecular simulations
• PyMOL - Molecular visualization
• Molecular Modeling Toolkit

Bioinformatics

• Biopython - Biological computations.
• PyBioMed - Descriptors of biological molecules.
• khmer - k-mer counting, filtering, and graph traversal.

Neuroimaging

• NiBabel - Neuro-imaging file formats.
• Nilearn - Machine learning for neuro-imaging.
• NiTime - Time series.
• MNE - MEG and EEG.
• DIPY - Diffusion MR imaging.
• Expyriment - Behavioral and neuroimaging experiments.

Neuroscience

• Brian2 - Simulations of spiking neural networks.
• Spyking Circus - Spike sorting on large extracellular recordings.
• Klusta - Spike detection and clustering-based spike sorting.
• phy - Manual spike sorting for high-density multielectrode arrays.
• NeuroTools - Tools for neural simulations.
• Neo - File formats for neuroscience.
• PsychoPy - Psychology and neuroscience experiments.
• Nengo - Simulation of large-scale brain models
• PyGaze - Eye tracking.

Mathematics

• Sage - Mathematics software system.
• mpmath - arithmetic with arbitrary precision.

Lists of libraries

• Python Numeric and Scientific - on python.org.
• Scientific Computing Tools for Python - on scipy.org.
• Useful libraries for data science in Python - by Sebastian Raschka.
• Python for Scientific Audio - by Fabian-Robert Stöter.

Books

• Python Data Science Handbook - Jake VanderPlas, O'Reilly, 2016, 541 pages.
• Python for Data Analysis - William McKinney, O'Reilly, 2017, 544 pages (second edition).
• Learning IPython for Interactive Computing and Data Analysis, Cyrille Rossant, Packt Publishing, 2015, 200 pages (second edition).
• IPython Interactive Computing and Visualization Cookbook, Cyrille Rossant, Packt Publishing, 2018, 548 pages (second edition).
• A Primer on Scientific Programming with Python - Hans Petter Langtangen, Springer, 2014, 872 pages.
• Exploring Data with Python - Naomi Ceder, Manning 2018, 110 pages.
• Deep Learning with Python - François Chollet, Manning, 2017, 384 pages.
• Python Machine Learning - Sebastian Raschka & Vahid Mirjalili, Packt Publishing, 2017, 622 pages (second edition).

Courses

• Stat 159/259, Reproducible and Collaborative Data Science - Fernando Perez, Berkeley University, 2017.
• CME 193, Introduction to Scientific Python - Stanford University, Sven Schmit, 2015.
• Using Python for Research - Jukka-Pekka Onnela, Harvard University Online Learning.
• Introduction to Data Analytics and Machine Learning with Python - University of London.
• PHY 546: Python for Scientific Computing - Stony Brook University, Michael Zingale, 2018.
• Python for Data Analysis - Luke Thompson, NOAA.
• Coursera Data Science with Python - University of Michigan.
• edX Python for Data Science - UC San Diego, Ilkay Altintas, Leo Porter.
• edX Foundations of Data Science: Computational Thinking with Python - UC Berkeley, Ani Adhikari, John DeNero, David Wagner.
• Python Course - Bernd Klein.
• Intro to Python for Data Science - DataCamp, Filip Schouwenaars.
• Schools using Python - on python.org.

Tutorials

• SciPy Lecture Notes
• Lectures on scientific computing with Python - Robert Johansson.
• Python NumPy tutorial - Justin Johnson, Stanford University.
• Real Python Python Data Science Tutorials
• A gallery of interesting Jupyter Notebooks
• List of Python Data Science Tutorials - Ujjwal Karn.

Videos

• SciPy 2018: Scientific Computing with Python Conference - 97 YouTube videos.
• SciPy 2017: Scientific Computing with Python Conference - 91 YouTube videos.
• SciPy 2016: Scientific Computing with Python Conference - 92 YouTube videos.
• SciPy 2015: Scientific Computing with Python Conference - 116 YouTube videos.
• SciPy 2014: Scientific Computing with Python Conference - 121 YouTube videos.
• SciPy 2013: Scientific Computing with Python Conference - 33 YouTube videos.

License

To the extent possible under law, Cyrille Rossant has waived all copyright and related or neighboring rights to this work.