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# Python Data Science NumPy Array Shape

Shape of an Array: The shape of an array is refered to as the number of elements in each dimension.

How to Get the Shape of an Array?

NumPy arrays have an attribute called shape that returns a tuple with each index having the number of corresponding elements.

Example 1: Print the shape of a 2-D array.

Code

import numpy as np

arr = np.array([[11, 12, 13, 14], [15, 16, 17, 18]])

print(arr.shape)

the output will be

(2, 4)

Note: The example above returns (2, 4), which means that the array has 2 dimensions, where the first dimension has 2 elements and the second has 4.

Example 2: Create an array with 5 dimensions using ndmin using a vector with values 11,12,13,14 and verify that last dimension has value 4.

Code

import numpy as np

arr = np.array([11, 12, 13, 14], ndmin=5)

print(arr)
print('shape of array :', arr.shape)

the output will be

[[[[[11 12 13 14]]]]]
shape of array : (1, 1, 1, 1, 4)

What does the shape of tuple represent?

Integers at every index tells about the number of elements the corresponding dimension has.

In the example above at index-4 we have value 4, so we can say that 5th ( 4 + 1 th) dimension has 4 elements.