awesome-scientific-python

A curated list of awesome scientific Python resources
https://github.com/rossant/awesome-scientific-python

Last synced: 3 days ago
JSON representation

Core libraries
- SciPy
- IPython/Jupyter
  - Jupyter Notebook - Web-based environment for interactive computing.
  - JupyterLab - Next-generation web-based interactive programming and computing environment.
- NumPy
- pandas
- matplotlib
- SymPy
- scikit-learn
  - scikit-learn
  - scikit-learn user guide
Other scientific libraries
- 3D visualization
  - Glumpy - Scientific visualization in modern OpenGL.
  - Glumpy - Scientific visualization in modern OpenGL.
  - PyVista - 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK).
  - Datoviz - 2D/3D high-performance GPU visualization with Vulkan.
  - ipyvolume - 3D visualization with Jupyter.
  - vedo - Scientific analysis and visualization based on VTK.
- Statistics
  - emcee - ensemble sampler for markov chain monte carlo.
  - statsmodels - Statistical models.
  - emcee - ensemble sampler for markov chain monte carlo.
- GPU computing
  - PyCUDA - Use CUDA with Python.
  - CuPy - NumPy-like library with CUDA.
  - PyCUDA - Use CUDA with Python.
- Data visualization
  - bqplot - 2D interactive visualization in Jupyter.
  - plotnine - Grammar of Graphics implementation in Python based on ggplot2.
  - Napari - Multi-dimensional image viewer for python.
  - Altair - Declarative visualization in Python.
  - seaborn - Statistical data visualization.
- Neural networks
  - PyTorch - Neural networks and deep learning in Python.
  - Keras - Python deep learning library.
  - TensorFlow - Machine learning framework.
  - Caffe - Deep learning framework.
- Compilation
  - Numba - JIT compilation of Python code
- Image processing
  - scikit-image - Image processing in Python.
  - Pillow - Python Imaging Library (PIL) fork in Python.
  - OpenCV - Computer vision library.
- Graphs
  - Graph-tool - Manipulation and statistical analysis of graphs.
- Parallel computing
  - ipyparallel - Parallel computing with IPython
  - Dask - Parallel computing with task scheduling.
Domain-specific libraries
- Geospatial data
  - GeoPandas - pandas for geospatial data.
  - GeoPandas - pandas for geospatial data.
  - Folium - Interactive maps in Python with leaflet.js.
  - Shapely - Manipulation and analysis of geometric objects.
- Molecular simulations
  - MGLTools - Visualization and analysis of molecular structures.
  - MGLTools - Visualization and analysis of molecular structures.
  - MDAnalysis - Molecular dynamics simulations
  - pysimm - Molecular simulations
  - Molecular Modeling Toolkit
- Neuroimaging
  - NiTime - Time series.
  - NiBabel - Neuro-imaging file formats.
  - Nilearn - Machine learning for neuro-imaging.
  - NiTime - Time series.
  - MNE - MEG and EEG.
  - Expyriment - Behavioral and neuroimaging experiments.
- Neuroscience
  - Neo - File formats for neuroscience.
  - PsychoPy - Psychology and neuroscience experiments.
  - PyGaze - Eye tracking.
  - Neo - File formats for neuroscience.
  - PsychoPy - Psychology and neuroscience experiments.
  - Nengo - Simulation of large-scale brain models
  - Klusta - Spike detection and clustering-based spike sorting.
  - phy - Manual spike sorting for high-density multielectrode arrays.
  - NeuroTools - Tools for neural simulations.
  - PyGaze - Eye tracking.
  - Brian2 - Simulations of spiking neural networks.
  - Spyking Circus - Spike sorting on large extracellular recordings.
- Mathematics
  - mpmath - arithmetic with arbitrary precision.
  - Sage - Mathematics software system.
  - mpmath - arithmetic with arbitrary precision.
- Bioinformatics
  - Biopython - Biological computations.
  - PyBioMed - Descriptors of biological molecules.
  - khmer - k-mer counting, filtering, and graph traversal.
- Astrophysics
  - AstroML - Machine learning for astronomy
Lists of libraries
- Mathematics
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - Scientific Computing Tools for Python - on scipy.org.
  - Python for Data Analysis - William McKinney, O'Reilly, 2017, 544 pages (second edition).
  - Learning IPython for Interactive Computing and Data Analysis
  - IPython Interactive Computing and Visualization Cookbook
  - Python Machine Learning - Sebastian Raschka & Vahid Mirjalili, Packt Publishing, 2017, 622 pages (second edition).
  - Using Python for Research - Jukka-Pekka Onnela, Harvard University Online Learning.
  - Introduction to Data Analytics and Machine Learning with Python - University of London.
  - edX Python for Data Science - UC San Diego, Ilkay Altintas, Leo Porter.
  - Python Course - Bernd Klein.
  - SciPy 2021: Scientific Computing with Python Conference - 61 YouTube videos.
  - SciPy 2019: Scientific Computing with Python Conference - 102 YouTube 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.
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - Scientific Computing Tools for Python - on scipy.org.
  - Python for Data Analysis - William McKinney, O'Reilly, 2017, 544 pages (second edition).
  - Learning IPython for Interactive Computing and Data Analysis
  - IPython Interactive Computing and Visualization Cookbook
  - Python Machine Learning - Sebastian Raschka & Vahid Mirjalili, Packt Publishing, 2017, 622 pages (second edition).
  - Using Python for Research - Jukka-Pekka Onnela, Harvard University Online Learning.
  - edX Python for Data Science - UC San Diego, Ilkay Altintas, Leo Porter.
  - Python Course - Bernd Klein.
  - SciPy 2024: Scientific Computing with Python Conference - 60 YouTube videos.
  - SciPy 2023: Scientific Computing with Python Conference - 59 YouTube videos.
  - SciPy 2022: Scientific Computing with Python Conference - 10 YouTube videos.
  - SciPy 2021: Scientific Computing with Python Conference - 61 YouTube videos.
  - SciPy 2019: Scientific Computing with Python Conference - 102 YouTube 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.
  - 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.
  - edX Foundations of Data Science: Computational Thinking with Python - UC Berkeley, Ani Adhikari, John DeNero, David Wagner.
  - Schools using Python - on python.org.
  - Python NumPy for Data Science - by Programiz PRO.
  - SciPy Lecture Notes
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - Real Python Python Data Science Tutorials
  - A gallery of interesting Jupyter Notebooks
  - List of Python Data Science Tutorials - Ujjwal Karn.
  - pyOpenSci Python Package Guide
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - NumPy Illustrated - The Visual Guide to NumPy - Lev Maximov.
  - Python Machine Learning - Sebastian Raschka & Vahid Mirjalili, Packt Publishing, 2017, 622 pages (second edition).
  - Python for Scientific Audio - by Fabian-Robert Stöter.
  - A Primer on Scientific Programming with Python - Hans Petter Langtangen, Springer, 2014, 872 pages.
  - Deep Learning with Python - François Chollet, Manning, 2017, 384 pages.
  - CME 193, Introduction to Scientific Python - Stanford University, Sven Schmit, 2015.
  - Coursera Data Science with Python - University of Michigan.
  - Intro to Python for Data Science - DataCamp, Filip Schouwenaars.
  - Python Numeric and Scientific - on python.org.
  - Useful libraries for data science in Python - by Sebastian Raschka.
  - Python Data Science Handbook - Jake VanderPlas, O'Reilly, 2016, 541 pages.
  - Exploring Data with Python - Naomi Ceder, Manning 2018, 110 pages.
  - Stat 159/259, Reproducible and Collaborative Data Science - Fernando Perez, Berkeley University, 2017.
  - Lectures on scientific computing with Python - Robert Johansson.
  - SciPy 2024: Scientific Computing with Python Conference - 60 YouTube videos.
  - SciPy 2023: Scientific Computing with Python Conference - 59 YouTube videos.
  - SciPy 2022: Scientific Computing with Python Conference - 10 YouTube videos.
  - SciPy 2021: Scientific Computing with Python Conference - 61 YouTube videos.
  - SciPy 2019: Scientific Computing with Python Conference - 102 YouTube 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.
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Programming Languages

Python 9 Jupyter Notebook 3

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

awesome-scientific-python

Core libraries

SciPy

IPython/Jupyter

NumPy

pandas

matplotlib

SymPy

scikit-learn

Other scientific libraries

3D visualization

Statistics

GPU computing

Data visualization

Neural networks

Compilation

Image processing

Graphs

Parallel computing

Domain-specific libraries

Geospatial data

Molecular simulations

Neuroimaging

Neuroscience

Mathematics

Bioinformatics

Astrophysics

Lists of libraries

Mathematics