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The NumPy module of Python provides a function called NumPy.histogram(). This function represents the frequency of the number of values that are compared with a set of values ranges.

NumPy ndarray objects can be indexed using Python x[selection_obj] syntax, where x is the array. There are three kinds of indexing available in numpy: field access, basic slicing, advanced indexing.

Ndarray is the n-dimensional array object defined in the numpy. It stores the collection of elements of the same type. Elements in the collection can be accessed using a zero-based index. Each element in an ndarray takes the same size in memory.

The NumPy sum() function is used to compute sum of array elements over a given axis. The function calculates sum of all elements present in the array by default, otherwise over the specified axis.

The NumPy reshape() function is used to give a new shape to an array without changing its data.

The NumPy package contains matlib module. This module contains all the functions in the numpy namespace that return matrices instead of ndarray objects.

The NumPy package contains a number of functions which provides all the functionality required for linear algebra. Few of these functions are - dot(), vdot(), inner(), matmul(), det(), solve() and inv().

Array attributes reflect information that is intrinsic to the array. Accessing an array through its attributes allows to get and sometimes set intrinsic properties of the array without creating a new array.

The NumPy package contains a number of functions which can be used to create an array from an existing data. asarray(), frombuffer() and fromiter() fuctions.