np.expand_dims() and np.squeeze() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.expand_dims() on a 1D array

What is the output shape of the array after applying np.expand_dims() on a 1D array along axis 0?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
expanded = np.expand_dims(arr, axis=0)
print(expanded.shape)

A (3,)

B (3, 1)

C (1, 3)

D (1,)

💡 Hint

Think about adding a new dimension at the start of the shape.

The original array has shape (3,). Using np.expand_dims with axis=0 adds a new dimension at the front, resulting in shape (1, 3).

❓ Predict Output

intermediate

Effect of np.squeeze() on a 3D array

Given a 3D array with shape (1, 4, 1), what is the shape after applying np.squeeze()?

NumPy

import numpy as np
arr = np.zeros((1, 4, 1))
squeezed = np.squeeze(arr)
print(squeezed.shape)

A (1, 4, 1)

B (4,)

C (4, 1)

D (1, 4)

💡 Hint

np.squeeze removes all dimensions of size 1.

The original shape is (1, 4, 1). np.squeeze removes all dimensions with size 1, leaving only the dimension with size 4, so the shape becomes (4,).

🔧 Debug

advanced

Identify the error in np.expand_dims usage

Which option will cause an error when trying to expand dimensions of a 2D array with shape (3, 4) using np.expand_dims()?

NumPy

import numpy as np
arr = np.ones((3, 4))
expanded = np.expand_dims(arr, axis=3)
print(expanded.shape)

A axis=4

B axis=3

C axis=-1

D axis=2

💡 Hint

Check if the axis value is valid for the array's dimensions.

The array has 2 dimensions. Valid axis values for np.expand_dims are from -3 to 2 (inclusive). axis=4 is out of range and causes an IndexError.

❓ data_output

advanced

Result of squeezing with specific axis

What is the shape of the array after applying np.squeeze() with axis=0 on an array of shape (1, 5, 1)?

NumPy

import numpy as np
arr = np.zeros((1, 5, 1))
squeezed = np.squeeze(arr, axis=0)
print(squeezed.shape)

A (5, 1)

B (1, 5)

C (5,)

D (1, 5, 1)

💡 Hint

Only the specified axis with size 1 is removed.

Using axis=0 removes the first dimension because it has size 1, resulting in shape (5, 1). The last dimension remains because axis=0 only removes that dimension.

🚀 Application

expert

Using np.expand_dims and np.squeeze to reshape data

You have a 1D array of shape (10,). You want to reshape it to (10, 1) for a machine learning model input, then later revert it back to (10,) after prediction. Which sequence of operations correctly achieves this?

NumPy

import numpy as np
arr = np.arange(10)
# reshape for model input
arr_expanded = np.expand_dims(arr, axis=1)
# ... model prediction happens ...
# revert to original shape
arr_reverted = np.squeeze(arr_expanded, axis=1)
print(arr_reverted.shape)

A Use np.expand_dims(arr, axis=0) then np.squeeze(arr_expanded)

B Use np.expand_dims(arr, axis=0) then np.squeeze(arr_expanded, axis=0)

C Use np.expand_dims(arr, axis=1) then np.squeeze(arr_expanded)

D Use np.expand_dims(arr, axis=1) then np.squeeze(arr_expanded, axis=1)

💡 Hint

Match the axis used in expand_dims and squeeze to correctly revert shape.

Expanding at axis=1 changes shape from (10,) to (10, 1). Squeezing at axis=1 removes that added dimension, reverting to (10,). Using axis=0 in either step changes the shape incorrectly.

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