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

Introduction

We use np.expand_dims() to add a new dimension to data, and np.squeeze() to remove dimensions of size one. This helps shape data correctly for analysis or machine learning.

When you have a 1D array but need a 2D array for a function that expects rows and columns.

When you want to remove extra single dimensions from data after processing.

When preparing image data that requires a specific number of dimensions.

When reshaping data to match expected input shapes in machine learning models.

When cleaning up data shapes after slicing or selecting specific elements.

Syntax

NumPy

np.expand_dims(array, axis)
np.squeeze(array, axis=None)

axis in expand_dims is where the new dimension is added.

axis in squeeze specifies which single-sized dimension to remove; if None, all single-sized dimensions are removed.

Examples

Adds a new dimension at the front, changing shape from (3,) to (1, 3).

NumPy

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

Removes all single-sized dimensions, changing shape from (1, 3, 1) to (3,).

NumPy

arr = np.array([[[1], [2], [3]]])
squeezed = np.squeeze(arr)
print(squeezed.shape)

Adds a new dimension as a column, changing shape from (3,) to (3, 1).

NumPy

arr = np.array([1, 2, 3])
new_arr = np.expand_dims(arr, axis=1)
print(new_arr.shape)

Removes the first dimension only, changing shape from (1, 3, 1) to (3, 1).

NumPy

arr = np.array([[[1], [2], [3]]])
squeezed = np.squeeze(arr, axis=0)
print(squeezed.shape)

Sample Program

This program shows how to add new dimensions to a 1D array and how to remove single-sized dimensions from a 3D array using np.expand_dims() and np.squeeze().

NumPy

import numpy as np

# Original 1D array
arr = np.array([10, 20, 30])
print(f"Original shape: {arr.shape}")

# Add new dimension at axis 0
expanded = np.expand_dims(arr, axis=0)
print(f"Shape after expand_dims at axis 0: {expanded.shape}")

# Add new dimension at axis 1
expanded2 = np.expand_dims(arr, axis=1)
print(f"Shape after expand_dims at axis 1: {expanded2.shape}")

# Create array with extra single dimensions
arr2 = np.array([[[10], [20], [30]]])
print(f"Shape before squeeze: {arr2.shape}")

# Remove all single dimensions
squeezed = np.squeeze(arr2)
print(f"Shape after squeeze (all single dims removed): {squeezed.shape}")

# Remove single dimension only at axis 0
squeezed_axis0 = np.squeeze(arr2, axis=0)
print(f"Shape after squeeze at axis 0: {squeezed_axis0.shape}")

Important Notes

Adding or removing dimensions does not change the data, only its shape.

Be careful with axis in squeeze: if the dimension is not size one, it will raise an error.

These functions are useful when working with libraries that expect specific input shapes.

Summary

np.expand_dims() adds a new dimension to an array at the specified axis.

np.squeeze() removes dimensions of size one from an array.

Use these to reshape data for analysis or machine learning without changing the data itself.

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