Python – Numpy Basics

NumPy’s main object is a multidimensional array of variables that are all the same type and is a table of elements which are typically numbers that are indexed by a tuple of positive integers. Dimensions are called axes and the number of axes is a rank. 

For instance if there is a one dimensional array of three elements that array has a rank of one and length three because there is one axis and three elements. If there is a two dimensional array that array has rank two.

NumPy’s class is called ndarray and is also known by the alias array. 

Important attributes of ndarray object:

ndarray.ndim – number of axes (dimensions) of the array (dimensions are known as rank)

ndarray.shape – dimensions of the array; a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the rank or number of dimensions, ndim.

ndarray.size – total number of elements in the array and is equal to the product of the elements of the shape.

ndarray.dtype – describes the type of elements (all homogeneous) in the array.

ndarray.itemsize – the size of bytes of each element of the array.

**This article is written for Python 3.6

Numpy Arrays >>

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