Here are the best open-source Python scientific computing libraries for your applications. These helps building applications with powerful array manipulation and scientific computing capabilities.
It provides many scientific computing tools, like optimization, integration, interpolation, and signal processing. It helps developers with linear algebra, Fourier transforms, and other mathematical operations. These libraries offer options to create static, interactive plots, histograms, and scatter plots. You get powerful tools for data manipulation, large dataset manipulation. It also helps working with multi-dimensional arrays.
We have handpicked the top Python libraries that make scientific computing more accessible and efficient. Moreover, they are maintained by the global developer community. They are an excellent choice for any scientific computing and application development project.
NumPy:
- Used in Utilities, Data Manipulation, NumPy applications, etc.
- Known for efficient manipulation and computation of arrays and matrices.
- Offers linear algebra, Fourier transform, and random number capabilities.
numpyby numpy
The fundamental package for scientific computing with Python.
numpyby numpy
Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)
SciPy:
- Used for scientific computing, like optimization, signal processing, linear algebra, and more.
- Built to work with NumPy arrays.
- Provides many user-friendly and efficient numerical routines, like numerical integration and optimization.
Matplotlib:
- Used for creating static, animated, and interactive visualizations in Python.
- Offers data visualization and plotting, including 2D and 3D plotting and animation capabilities.
- Works with Python scripts, Python/IPython shells, web application servers.
- Works with several graphical user interface toolkits.
matplotlibby matplotlib
matplotlib: plotting with Python
matplotlibby matplotlib
Python 17559 Version:v3.7.1 License: No License
Pandas:
- Used for data manipulation and analysis.
- Offers powerful data analysis tools.
- Includes data structures for efficient handling of data.
- Supports multiple data formats.
pandasby pandas-dev
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandasby pandas-dev
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)
Seaborn:
- Used in Analytics, Data Visualization, Pandas applications, etc.
- Allows visualizing statistical models and distributions.
- Built on top of Matplotlib.
seabornby mwaskom
Statistical data visualization in Python
seabornby mwaskom
Python 10797 Version:v0.12.2 License: Permissive (BSD-3-Clause)
scikit-learn:
- Used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Pandas applications, etc.
- Offers various algorithms for classification, regression, clustering, and more.
- Built on top of SciPy.
scikit-learnby scikit-learn
scikit-learn: machine learning in Python
scikit-learnby scikit-learn
Python 54584 Version:1.2.2 License: Permissive (BSD-3-Clause)
SymPy:
- Used for symbolic mathematics in Institutions, Learning, Education applications, etc.
- Includes algorithms for algebra, calculus, and equation solving.
- Written entirely in Python and depends on mpmath Python library.
sympyby sympy
A computer algebra system written in pure Python
sympyby sympy
Python 10857 Version:sympy-1.12 License: Others (Non-SPDX)
Statsmodels:
- Used for statistical modeling and hypothesis testing.
- Offers linear and non-linear regression and time series analysis capabilities.
- Provides a complement to SciPy for statistical computations.
statsmodelsby statsmodels
Statsmodels: statistical modeling and econometrics in Python
statsmodelsby statsmodels
Python 8572 Version:v0.14.0 License: Permissive (BSD-3-Clause)