How to Get NumPy Array Shape?

To get the shape of a Python NumPy array use numpy.ndarray.shape property. The array shape can be defined as the number of elements in each dimension and dimension is defined as a number of indices or subscripts, that can specify an individual element of an array.

1. Quick Examples of Array Shape

If you are in a hurry, below are some quick examples of how to get the shape of an array. Use the size property to get the length of the NumPy array.


# Quick examples of numpy array shape

# Example 1: Get the shape of the array
arr = np.array([[3, 6, 7, 9], [2, 4, 6, 8]])
array_shape = arr.shape

# Example 2: Creating 3 dimension array
arr = np.array([[[2, 3],[3, 4]],[[5, 6], [8, 9]]])
array_shape = arr.shape

# Example 3: Using Multi dimension
arr = np.array([1, 3, 5, 7, 9], ndmin=6)
array_shape = arr.shape

2. Get NumPy Array shape

To get the shape of a NumPy array, you can use the shape attribute of the NumPy array object. For example, the shape attribute is used to obtain the shape of the NumPy array arr.

Use ndarray.shape to get the shape of the NumPy array. This returns a tuple with each index having the number of corresponding elements. The below examples return (2,4) which means that the arr has 2 dimensions and each dimension has 4 elements (2 rows & 4 columns).


# Import numpy
import numpy as np

# Create a NumPy array 
arr = np.array([[3, 6, 7, 9], [2, 4, 6, 8]])
print("Original array:\n", arr)

# Get the shape of the array
array_shape = arr.shape
print("Array shape:\n", array_shape)

Yields below output.

Similarly, it creates a 3-dimensional NumPy array and then prints its shape. This means the array arr has 2 dimensions along the first axis, 2 dimensions along the second axis, and 2 dimensions along the third axis.


# Creating a 3 dimensions array
arr = np.array([[[2, 3],[3, 4]],[[5, 6], [8, 9]]])

# Get the shape of the array
array_shape = arr.shape
print("Array shape:\n", array_shape)

# Output:
# Array shape:
#  (2, 2, 2)

3. Get the Shape of a Multi-Dimensional Array

To create a multi-dimensional NumPy array with ndmin=6, which means you’re specifying that the array should have a minimum of 6 dimensions. However, the array you’ve provided contains only 1 element. When you specify ndmin=6 for such a small array, NumPy will add additional singleton dimensions to meet the specified minimum dimensions.

In this case, the array has been expanded to have 6 dimensions with the shape of (1, 1, 1, 1, 1, 5) because of the ndmin=6 argument.


# Create multi dimensional array
arr = np.array([1, 3, 5, 7, 9], ndmin=6)
print("Original array:", arr)

# Use shape of Multi-Dimensional Array
array_shape = arr.shape
print("Shape of multi dimensional array:", array_shape)

Yields below output.


# Output:
# Original array: [[[[[[1 3 5 7 9]]]]]]
# Shape of multi dimensional array: (1, 1, 1, 1, 1, 5)

Frequently Asked Questions

How do I get the shape of a NumPy array?

To get the shape of a NumPy array, you can use the shape attribute of the NumPy array object. For example, the shape attribute is used to obtain the shape of the NumPy array array.

What does the shape of a NumPy array represent?

The shape of a NumPy array is a tuple representing its dimensions. For a 2D array, the shape is (rows, columns). For a 3D array, the shape is (depth, rows, columns) , and so on for higher dimensions.

Can the shape of a NumPy array be modified?

The shape of a NumPy array is immutable. You cannot change the shape of an existing array. However, you can create a new array with a different shape based on the original array’s data.

What does the shape of (n,) mean in NumPy?

A shape of (n,) indicates a 1-dimensional array with n elements. It’s a special case where the array has a single dimension with size n.

How do I get the number of dimensions of a NumPy array?

To get the number of dimensions of a NumPy array, you can use the ndim attribute. For instance, the ndim attribute is used to obtain the number of dimensions of the NumPy array array.

4. Conclusion

In this article, I have explained how to get the shape of a Python NumPy array by using numpy.ndarray.shape properties with examples. This returns a tuple with the number of rows and columns in an array. The array shape can be defined as the number of elements in each dimension and dimension is defined as a number of indices or subscripts, that can specify an individual element of an array.

Happy Learning!!

References

https://numpy.org/doc/stable/reference/generated/numpy.ndarray.shape.html