Python Numpy Array Shape

In this post, you will learn how to get the shape of an array using Python Numpy.

NumPy is the fundamental package for scientific computing with Python. It is a highly optimised library for numerical operations. The support of NumPy makes the task easier.

The Python numpy module provides a shape function to determine the shape and size of an array or matrix. The shape of an array is defined by the number of elements in each dimension.

Python get shape of an array

In NumPy we will use an attribute called shape which returns a tuple. The components of the tuple give the lengths of the corresponding array dimensions. The one-dimensional array is a row vector and its shape is a single value sequence followed by a comma. One-dimensional arrays don't have rows and columns, so the shape function returns a single value tuple.

Syntax of numpy array

numpy.shape(array)

Here, an array is passed as a parameter. It returns a tuple whose elements give the lengths of the corresponding array dimensions.

Examples of numpy shape

In a one-dimensional array, it is a tuple with one element instead of an integer value. A tuple with one element has a trailing comma.

import numpy as np
 
arr = np.array([5,22,13,64,23,93,10])
print(arr)
 
print('Array Shape = ', np.shape(arr))

Output of the above code:

[ 5 22 13 64 23 93 10]
Array Shape =  (7,)

The example above returns (7,) which means that the array is one dimension and contains 7 elements.

In the case of a two-dimensional array, it will be a number of rows, number of columns.

import numpy as np
 
x = np.array([[10, 20, 30], [40, 50, 60]])
print(x)
print('Array Shape = ', np.shape(x))
print()

Output of the above code:

[[10 20 30]
 [40 50 60]]
Array Shape =  (2, 3)

The example above returns (2, 3), which means that the array has 2 dimensions and each dimension has 3 elements.

Python numpy shape dimensions

The dimension of a matrix is the number of rows and columns in a matrix. We can get the number of dimensions of the NumPy array as an integer value int with the ndim attribute of the numpy.ndarray.

import numpy as np

x = np.array([[6,3],[3,5]])
y = x.ndim
print('Shape dimension: ',y)
print('Shape dimension type: ',type(x.ndim))

Output of the above code:

Shape dimension:  2
Shape dimension type:  <class 'int'>

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