np.expand_dims() and np.squeeze() in NumPy - Mini Project: Build & Apply

Using np.expand_dims() and np.squeeze() in NumPy

📖 Scenario: Imagine you are working with image data in a simple computer vision project. Images are stored as arrays, but sometimes you need to add or remove extra dimensions to prepare the data for a model.

🎯 Goal: You will learn how to add a new dimension to a NumPy array using np.expand_dims() and how to remove single-dimensional entries using np.squeeze(). This helps in reshaping data correctly for processing.

📋 What You'll Learn

Create a 2D NumPy array called image with exact values

Create a variable called axis_to_expand to specify where to add a new dimension

Use np.expand_dims() to add a new dimension to image and save it as expanded_image

Use np.squeeze() to remove single-dimensional entries from expanded_image and save it as squeezed_image

Print the shapes of image, expanded_image, and squeezed_image to see the changes

💡 Why This Matters

🌍 Real World

In real-world data science, reshaping arrays is common when preparing data for machine learning models, especially in image processing and deep learning.

💼 Career

Understanding how to manipulate array dimensions is essential for data scientists and machine learning engineers to correctly format data inputs and outputs.

1

Create the initial 2D image array

Create a 2D NumPy array called image with these exact values: [[10, 20, 30], [40, 50, 60]]

NumPy

import numpy as np
# Create the 2D array called image with the exact values
# Your code here

Hint

Use np.array() to create the array with the given nested list.

2

Set the axis to expand

Create a variable called axis_to_expand and set it to 0 to add a new dimension as the first axis

NumPy

import numpy as np
image = np.array([[10, 20, 30], [40, 50, 60]])
# Create axis_to_expand and set it to 0
# Your code here

Hint

Just assign the number 0 to the variable axis_to_expand.

3

Add a new dimension using np.expand_dims()

Use np.expand_dims() with image and axis_to_expand to create expanded_image that has a new dimension added

NumPy

import numpy as np
image = np.array([[10, 20, 30], [40, 50, 60]])
axis_to_expand = 0
# Use np.expand_dims to add a new dimension and save as expanded_image
# Your code here

Hint

Call np.expand_dims() with image and axis=axis_to_expand.

4

Remove single-dimensional entries using np.squeeze() and print shapes

Use np.squeeze() on expanded_image to create squeezed_image. Then print the shapes of image, expanded_image, and squeezed_image each on a new line.

NumPy

import numpy as np
image = np.array([[10, 20, 30], [40, 50, 60]])
axis_to_expand = 0
expanded_image = np.expand_dims(image, axis=axis_to_expand)
# Use np.squeeze on expanded_image to create squeezed_image
# Print the shapes of image, expanded_image, and squeezed_image
# Your code here

Hint

Use np.squeeze() on expanded_image. Then print the .shape attribute of each array.

Practice

1. What does np.expand_dims() do to a NumPy array?

easy

A. Adds a new dimension at the specified axis

B. Removes all dimensions of size one

C. Flattens the array into one dimension

D. Changes the data type of the array

Solution

1. Step 1: Understand the function purpose

   np.expand_dims() is used to add a new axis to an array, increasing its dimensions.

2. Step 2: Compare with other options

   Removing dimensions or flattening is done by other functions like np.squeeze() or reshape(). Changing data type is unrelated.

3. Final Answer:

   Adds a new dimension at the specified axis -> Option A

4. Quick Check:

   np.expand_dims() = Adds dimension [OK]

Hint: Expand dims adds axis, squeeze removes axis [OK]

Common Mistakes:

• Confusing expand_dims with squeeze
• Thinking it changes data values
• Assuming it flattens the array

2. Which of the following is the correct syntax to add a new axis at position 1 using np.expand_dims() on array a?

easy

A. np.expand_dims(a, axis=0)

B. np.expand_dims(a, 0)

C. np.expand_dims(a, axis=1)

D. np.expand_dims(a, axis=2)

Solution

1. Step 1: Check the function signature

   np.expand_dims(array, axis) requires the axis parameter to specify where to add the new dimension.

2. Step 2: Identify correct axis value

   Axis=1 adds the new dimension at position 1. np.expand_dims(a, 0) (positional) and np.expand_dims(a, axis=0) add the dimension at position 0. np.expand_dims(a, axis=2) causes an AxisError since valid axes are -2 to 1 for a 1D array.

3. Final Answer:

   np.expand_dims(a, axis=1) -> Option C

4. Quick Check:

   Use axis=1 to add dimension at position 1 [OK]

Hint: Use axis= to specify position clearly [OK]

Common Mistakes:

• Omitting axis keyword and confusing position
• Using wrong axis number
• Mixing axis=0 and axis=1

3. What is the shape of the array after applying np.squeeze() to arr = np.array([[[1], [2], [3]]])?

medium

A. (3,)

B. (1, 3, 1)

C. (3, 1)

D. (3, 1, 1)

Solution

1. Step 1: Check original shape

   arr has shape (1, 3, 1) because it is a 3D array with one element in first and last dimension.

2. Step 2: Apply np.squeeze()

   np.squeeze() removes all dimensions of size 1, so the first and last dimensions are removed, leaving shape (3,).

3. Final Answer:

   (3,) -> Option A

4. Quick Check:

   squeeze removes size 1 dims = (3,) [OK]

Hint: Squeeze removes all size 1 dims from shape [OK]

Common Mistakes:

• Assuming squeeze removes only one dimension
• Confusing original shape with squeezed shape
• Thinking squeeze flattens the array

4. Given arr = np.array([1, 2, 3]), which code will cause an error?

medium

A. np.expand_dims(arr, axis=-1)

B. np.expand_dims(arr, axis=0)

C. np.squeeze(arr)

D. np.expand_dims(arr, axis=3)

Solution

1. Step 1: Check original array shape

   arr has shape (3,), so it has 1 dimension.

2. Step 2: Validate axis values for expand_dims

   Valid axis values for adding a new dimension are between -2 and 1 (inclusive) because adding a dimension increases rank by 1. Axis=3 is out of bounds and causes an error.

3. Final Answer:

   np.expand_dims(arr, axis=3) causes error -> Option D

4. Quick Check:

   Axis out of range causes error [OK]

Hint: Axis must be in valid range for expand_dims [OK]

Common Mistakes:

• Using axis larger than array ndim + 1
• Confusing axis indexing with array shape
• Assuming squeeze can cause error on 1D arrays

5. You have a 2D array data with shape (5, 1). You want to prepare it for a machine learning model that expects input shape (5,). Which code correctly reshapes data using np.squeeze() and np.expand_dims()?

hard

A. np.expand_dims(np.squeeze(data), axis=1)

B. np.squeeze(data)

C. np.squeeze(np.expand_dims(data, axis=-1))

D. np.expand_dims(data, axis=-1)

Solution

1. Step 1: Understand the original shape and target shape

   data shape is (5, 1). The model expects (5,), so we need to remove the extra dimension.

2. Step 2: Use np.squeeze() to remove size 1 dimension

   np.squeeze(data) removes the dimension of size 1, resulting in shape (5,). Adding dimensions again is unnecessary and would change shape incorrectly.

3. Final Answer:

   np.squeeze(data) -> Option B

4. Quick Check:

   Squeeze removes extra dims to match model input [OK]

Hint: Squeeze to remove size 1 dims for model input [OK]

Common Mistakes:

• Adding dimensions instead of removing
• Using expand_dims unnecessarily
• Not matching model expected input shape