As with built-in Python sequences, NumPy arrays are “0-indexed”: the first element of the array is accessed using index 0, not 1. Like the original list, the array is mutable. >>> a [ 0 ] = 10 >>> a …

numpy.array# numpy. array (object, dtype = None, *, copy = True, order = 'K', subok = False, ndmin = 0, like = None) # Create an array. Parameters: object array_like. An array, any object …

There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the …

There are 6 general mechanisms for creating arrays: Conversion from other Python structures (i.e. lists and tuples) Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.) …

Arrays can be indexed using an extended Python slicing syntax, array[selection]. Similar syntax is also used for accessing fields in a structured data type.

>>> import numpy as np >>> a = np. arange (10) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> np. where (a < 5, a, 10 * a) array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90]) This can be used on …

ndarrays can be indexed using the standard Python x[obj] syntax, where x is the array and obj the selection. There are different kinds of indexing available depending on obj: basic indexing, …

numpy.zeros# numpy. zeros (shape, dtype = float, order = 'C', *, like = None) # Return a new array of given shape and type, filled with zeros. Parameters: shape int or tuple of ints. Shape …

First array elements raised to powers from second array, element-wise. fmod (x1, x2, /[, out, where, casting, ...]) Returns the element-wise remainder of division.

numpy.dot# numpy. dot (a, b, out = None) # Dot product of two arrays. Specifically, If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation). If both a and b are …