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NumPy Array Shape

Learn all about NumPy Array Shape in this comprehensive tutorial.

5 min read intermediate

•The shape of an array is the number of elements in each dimension.
•NumPy arrays have an attribute called shape that returns a tuple with each index having the number of corresponding elements.
•Integers at every index tells about the number of elements the corresponding dimension has.

Shape of an Array

The shape of an array is the number of elements in each dimension.

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

python

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

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.

python

import numpy as nparr = np.array([1, 2, 3, 4], ndmin=5)print(arr)
  print('shape of array :', arr.shape)

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.

2 questions

Which of the following is true about NumPy Array Shape?

It is strictly typedIt is evaluated at runtimeIt requires explicit memory managementIt is deprecated

What is the most common pitfall when working with NumPy Array Shape?

SyntaxErrorIndentationErrorNameErrorTypeError

Answer all questions to submit.