ARRAY ATTRIBUTES IN NUMPY

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In this tutorial, we are going to learn about different array attributes in Numpy that are essential to know if you want to transform your arrays.



Array Attributes in Numpy

Array attributes are essential to find out the shape, dimension, item size etc. If connected with the ndarray object of numpy then we can find about these in detail. Let us look at some of these attributes with their respective examples as well.

ndarray.shape

By using this method in numpy you can know the array dimensions. It can also be used to resize the array.

import numpy as np
arr = np.array([[1,2,3,4],[5,6,7,8]])
arr

Output:

array([[1, 2, 3, 4],
[5, 6, 7, 8]])


You can change the shape of the array by rearranging the tuple.

arr.shape = (4,2)
arr

Output:

array([[1, 2],
[3, 4],
[5, 6],
[7, 8]])

ndarray.ndim

This method returns the number of dimensions of an array.

arr = n.array([[1,2,3,4],[5,6,7,8]])
arr

Output:

array([[1, 2, 3, 4],
[5, 6, 7, 8]])
arr = np.arange(10).reshape(2,5)
arr

Output:

array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
arr.ndim

Output:

2



ndarray.itemsize

This method returns the length of the array of each component in bytes.

import numpy as np 
arr = np.array([1,2,3,4,5]) 
arr.itemsize

Output:

8

ndarray.T

This method creates the transpose method for the array i.e. converts rows into columns and columns into rows:

arr = np.array([[1,2,3,4],[5,6,7,8]])
arr

Output:

array([[1, 2, 3, 4],[5, 6, 7, 8]])

Applying the transpose method

arr.T

Output:

array([[1, 5],
[2, 6],
[3, 7],
[4, 8]])

References: Scipy Docs

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