NumPy 1.20 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.19 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.18 Manual [HTML+zip] [Reference …

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

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

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.

The matmul function implements the semantics of the @ operator defined in PEP 465. It uses an optimized BLAS library when possible (see numpy.linalg). Examples Try it in your browser! For 2-D …

The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so …

The NumPy library contains multidimensional array data structures, such as the homogeneous, N-dimensional ndarray, and a large library of functions that operate efficiently on these data structures.

numpy.logical_and # numpy.logical_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature]) = <ufunc 'logical_and'> # Compute the truth value …

NumPy numerical types are instances of numpy.dtype (data-type) objects, each having unique characteristics. Once you have imported NumPy using import numpy as np you can create arrays …

Handling complex numbers # ... Extrema finding # ... Miscellaneous # ... numpy.not_equal