Nearly every scientist working in Python draws on the power of NumPy. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to …

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 …

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

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 …

NumPy reference Routines and objects by topic Array manipulation routines numpy.reshape

The reference guide contains a detailed description of the functions, modules, and objects included in NumPy. The reference describes how the methods work and which parameters …

numpy.random.normal # random.normal(loc=0.0, scale=1.0, size=None) # Draw random samples from a normal (Gaussian) distribution.

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.

numpy.ndarray # class numpy.ndarray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None) [source] # An array object represents a multidimensional, homogeneous array of …

For values exactly halfway between rounded decimal values, NumPy rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0, -0.5 and 0.5 round to 0.0, etc.