Numerical computing tools NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

NumPy documentation # Version: 2.3 Download documentation: Historical versions of documentation Useful links: Installation | Source Repository | Issue Tracker | Q&A Support | Mailing List NumPy is …

The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, …

Scientific Python Lectures Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. NumPy: the absolute basics for beginners NumPy tutorial by Nicolas …

The basics # NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy …

NumPy user guide # This guide is an overview and explains the important features; details are found in NumPy reference.

What is NumPy? # NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays …

coefficient matrix rcond – value of rcond. For more details, see numpy.linalg.lstsq. Vndarray, shape (deg + 1, deg + 1) or (deg + 1, deg + 1, K) Present only if full == False and cov == True. The covariance …

numpy.where # numpy.where(condition, [x, y, ]/) # Return elements chosen from x or y depending on condition.

numpy.matmul # numpy.matmul(x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None, subok=True[, signature, axes, axis]) = <ufunc 'matmul'> # Matrix product of two arrays. Parameters: …