🔥Limited Offer: Get 50% OFFon AI & Full Stack Courses🔥
Back to NumPy Notes
Topic #9

NumPy Array Shape


Shape of an Array

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


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

  import numpy as np

arr = 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.

Example

  import numpy as np

arr = np.array([1, 2, 3, 4], ndmin=5)

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

What does the shape 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.

Want to go beyond the notes?

Join CodingNow 2.0's NumPy course — live mentorship, real projects, and 100% placement support.

Enroll Now — Free Demo Available

NumPy Array Shape – FAQs

Quick answers about learning NumPy Array Shape in NumPy.

This free note from CodingNow 2.0 explains NumPy Array Shape in NumPy — concept, syntax and worked code examples you can copy, run and revise before interviews.
Yes. Every NumPy topic on CodingNow 2.0, including NumPy Array Shape, is 100% free with no signup required.
With focused practice, most students grasp NumPy Array Shape in 1–3 days from these notes; pairing it with CodingNow 2.0's mentor-led course takes you to job-ready depth faster.
Use the code examples in this note, then ask doubts for free on the CodingNow 2.0 Community (/community) — expert instructors answer within 24 hours.
WhatsApp
Call NowEnroll Now