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SciPy Cookbook¶ This is the “SciPy Cookbook” — a collection of various user-contributed recipes, which once lived under wiki.scipy.org. If you have a nice notebook you’d like to add here, or you’d like to make some other edits, please see the SciPy-CookBook repository.
A beginners guide to using Python for performance computing A comparison of weave with NumPy, Pyrex, Psyco, Fortran (77 and 90) and C++ for solving Laplace's equation. This article was originally written by Prabhu Ramachandran. laplace.py is the complete Python code discussed below. The source tarball ( perfpy_2.tgz ) contains in addition the Fortran code, the pure C++ code, the Pyrex sources and
For newer versions see https://www.numpy.org/doc Numpy (development version) Reference Guide Numpy (development version) User Guide Numpy 1.17.0 Reference Guide, [HTML+zip], [PDF] Numpy 1.17.0 User Guide, [PDF] Numpy 1.16.1 Reference Guide, [HTML+zip], [PDF] Numpy 1.16.1 User Guide, [PDF] Numpy 1.16.0 Reference Guide, [HTML+zip], [PDF] Numpy 1.16.0 User Guide, [PDF] Numpy 1.15.4 Reference Guide, [
This tutorial is unfinished. The original authors were not NumPy experts nor native English speakers so it needs reviewing. Please do not hesitate to click the edit button. You will need to create a User Account first. Quick Tour NumPy is a Python library for working with multidimensional arrays. The main data type is an array. An array is a set of elements, all of the same type, indexed by a vec
Introduction MATLAB® and NumPy/SciPy have a lot in common. But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB® clones. This page is intended to be a place to collect wisdom about the differences, mostly for the purpose of helping proficient MATLAB® users become proficient NumPy and SciPy users.
Scientific Tools for Python SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-frien
Topical software¶ This page indexes add-on software and other resources relevant to SciPy, categorized by scientific discipline or computational topic. It is intended to be exhaustive. If you know of an unlisted resource, see About this page, below. Additional useful software packages can be found on the Python Package Index, especially in its Science/Research category. You may also want to take a
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