What are the best Python mathematics libraries ?

1. NumPy:

NumPy is an extension to the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode compiler/interpreter.

2. Pandas:

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

3. Scipy :

SciPy (pronounced “Sigh Pie”) is open-source software for mathematics, science, and engineering. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.

4. matplotlib:

matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like wxPython, Qt, orGTK+.

5. Patsy:

Patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. Patsy brings the convenience of R “formulas” to Python.

6. Sympy:

SymPy is a Python library for symbolic mathematics. It aims become a full featured computer algebra system that can compete directly with commercial alternatives (Mathematica, Maple) while keeping the code as simple as possible in order to be comprehensible and easily extensible.

7. Plotly:

Plotly provides online graphing, analytics, and stats tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, and REST.

8. statsmodels:

Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

9. ADiPy:

ADiPy is a fast, pure-python automatic differentiation (AD) library. This package provides Arbitrary order univariate differentiation, First-order multivariate differentiation, Univariate Taylor polynomial function generator, Jacobian matrix generator, Compatible linear algebra routines.

10. matalg27 :

The objective of this matrix algebra module is to provide elementary matrix operations of linear algebra, including the solution of linear equations and matrix inversion. This package is particularly useful for people planning to upgrade to Python 3.x.

11. mplstyler :

An API for ensuring consistent line and marker styles across your plots. Assign colours, markers, line-styles to labels and re-use on subsequent plots. Styles can be matched to specific labels using exact or fuzzy matching to consecutively build up styles. Export styles to XML and re-import in subsequent sessions.

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