np.newaxis for adding dimensions in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of adding a new axis to a 1D array

What is the shape of the array b after adding a new axis using np.newaxis?

NumPy

import numpy as np

a = np.array([1, 2, 3])
b = a[:, np.newaxis]
print(b.shape)

A (3,)

B (3, 1)

C (1, 3)

D (1,)

💡 Hint

Think about how adding a new axis in the second position changes the shape.

Using np.newaxis in the second position converts the 1D array of shape (3,) into a 2D array with shape (3, 1).

❓ data_output

intermediate

Resulting array after adding new axis

What is the output of the array b after adding a new axis?

NumPy

import numpy as np

a = np.array([4, 5, 6])
b = a[np.newaxis, :]
print(b)

A [[4 5 6]]

B

[4
 5
 6]

C

[[4]
 [5]
 [6]]

D [4 5 6]

💡 Hint

Adding np.newaxis at the start adds a new row dimension.

Using np.newaxis at the start changes the shape from (3,) to (1, 3), making it a 2D array with one row.

❓ visualization

advanced

Visualizing shape changes with np.newaxis

Which option correctly describes the shape change when adding np.newaxis to a 2D array arr of shape (2, 3) as arr[:, :, np.newaxis]?

NumPy

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
new_arr = arr[:, :, np.newaxis]
print(new_arr.shape)

A (2, 3) - shape unchanged

B (2, 1, 3) - adds a new second dimension

C (1, 2, 3) - adds a new first dimension

D (2, 3, 1) - adds a new third dimension

💡 Hint

Adding np.newaxis at the end adds a new dimension at the last position.

The original array has shape (2, 3). Adding np.newaxis as the third index adds a new dimension at the end, resulting in shape (2, 3, 1).

🔧 Debug

advanced

Identify the error when using np.newaxis incorrectly

What error will this code raise?

NumPy

import numpy as np

a = np.array([1, 2, 3])
b = a[np.newaxis, np.newaxis]
print(b.shape)

A SyntaxError: invalid syntax

B TypeError: unhashable type: 'slice'

C (1, 1, 3) - no error, shape is correct

D IndexError: too many indices for array

💡 Hint

Check how many dimensions the original array has and how many indices are used.

The original array is 1D with shape (3,). Using two np.newaxis adds two new dimensions, resulting in shape (1, 1, 3). This is valid and does not raise an error.

🚀 Application

expert

Using np.newaxis to prepare data for broadcasting

Given two arrays a with shape (5,) and b with shape (3,), which option correctly uses np.newaxis to add dimensions so that a + b broadcasts to shape (5, 3)?

NumPy

import numpy as np

a = np.arange(5)
b = np.arange(3)

# Which option below correctly reshapes a and b for broadcasting?

A a[:, np.newaxis] + b[np.newaxis, :]

B a[np.newaxis, :] + b[:, np.newaxis]

C a[:, np.newaxis] + b[:, np.newaxis]

D a[np.newaxis, :] + b[np.newaxis, :]

💡 Hint

Broadcasting works when dimensions are compatible or equal to 1.

Adding np.newaxis to a as a[:, np.newaxis] changes shape from (5,) to (5, 1). Adding np.newaxis to b as b[np.newaxis, :] changes shape from (3,) to (1, 3). These shapes broadcast to (5, 3).

Practice

1. What does np.newaxis do when used inside array indexing?

easy

A. Changes the data type of the array elements

B. Deletes a dimension from the array

C. Flattens the array into one dimension

D. Adds a new dimension to the array without copying data

Solution

1. Step 1: Understand the role of np.newaxis

   np.newaxis is used to add a new axis (dimension) to an existing array, increasing its dimensionality by one.

2. Step 2: Confirm what np.newaxis does not do

   It does not remove dimensions, change data types, or flatten arrays.

3. Final Answer:

   Adds a new dimension to the array without copying data -> Option D

4. Quick Check:

   np.newaxis adds dimension = C [OK]

Hint: np.newaxis adds a dimension, never removes or flattens [OK]

Common Mistakes:

• Thinking np.newaxis removes dimensions
• Confusing np.newaxis with flattening
• Assuming it changes data types

2. Which of the following is the correct syntax to add a new axis to a 1D numpy array a to make it a column vector?

easy

A. a[:, np.newaxis]

B. a[np.newaxis, :]

C. a[np.newaxis]

D. a[:, :np.newaxis]

Solution

1. Step 1: Understand array shape and axis addition

   To convert a 1D array to a column vector, we add a new axis as the second dimension using a[:, np.newaxis].

2. Step 2: Check each option

   a[np.newaxis, :] adds a new axis at the front, making a row vector (1, 3). a[np.newaxis] does the same, making a row vector (1, 3). a[:, :np.newaxis] runs but does not add a dimension since :np.newaxis is equivalent to :, keeping shape (3,).

3. Final Answer:

   a[:, np.newaxis] -> Option A

4. Quick Check:

   Column vector = a[:, np.newaxis] [OK]

Hint: Use a[:, np.newaxis] to add a column dimension [OK]

Common Mistakes:

• Using np.newaxis in wrong position
• Confusing row and column vector shapes
• Syntax errors like a[:, :np.newaxis]

3. Given the code:

import numpy as np
arr = np.array([1, 2, 3])
new_arr = arr[:, np.newaxis]
print(new_arr.shape)

What is the output?

medium

A. (3, 1)

B. (1, 3)

C. (3,)

D. (1,)

Solution

1. Step 1: Check original array shape

   The original array arr is 1D with shape (3,).

2. Step 2: Adding new axis with arr[:, np.newaxis]

   This adds a new axis as the second dimension, changing shape to (3, 1).

3. Final Answer:

   (3, 1) -> Option A

4. Quick Check:

   Shape after newaxis = (3, 1) [OK]

Hint: Adding np.newaxis in second position adds a new column dimension [OK]

Common Mistakes:

• Confusing (1, 3) and (3, 1) shapes
• Forgetting newaxis adds dimension, not removes
• Assuming shape stays (3,)

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([1, 2, 3])
new_arr = arr[np.newaxis:]
print(new_arr.shape)

medium

A. new_arr will have shape (3, 1)

B. new_arr will be a flattened array

C. Fails to add a new dimension due to incorrect slicing with np.newaxis

D. Code runs fine and adds a new axis

Solution

1. Step 1: Analyze the slicing syntax

   The code uses arr[np.newaxis:], which does not cause SyntaxError but treats np.newaxis (alias for None) as slice start, equivalent to arr[:], so shape remains (3,).

2. Step 2: Understand correct usage of np.newaxis

   np.newaxis must be used inside index brackets as a separate index, not as part of a slice.

3. Final Answer:

   Fails to add a new dimension due to incorrect slicing with np.newaxis -> Option C

4. Quick Check:

   np.newaxis must be separate index, not slice [OK]

Hint: np.newaxis is an index, not a slice; avoid np.newaxis: [OK]

Common Mistakes:

• Using np.newaxis as part of slice syntax
• Expecting code to run without error
• Confusing np.newaxis with slice objects

5. You have a 2D numpy array data with shape (100, 50). You want to add a new dimension at the front to represent batch size 1, making the shape (1, 100, 50). Which code correctly does this using np.newaxis?

hard

A. data[np.newaxis:]

B. data[np.newaxis, :, :]

C. data[:, :, np.newaxis]

D. data[:, np.newaxis, :]

Solution

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

   Original shape is (100, 50). We want to add a new axis at the front to get (1, 100, 50).

2. Step 2: Use np.newaxis at the front index

   Using data[np.newaxis, :, :] adds a new axis at position 0, resulting in shape (1, 100, 50).

3. Step 3: Check other options

   data[np.newaxis:] does not add a new dimension (acts as slice data[:], shape (100,50)). data[:, np.newaxis, :] adds axis in middle (100, 1, 50), data[:, :, np.newaxis] adds axis at end (100, 50, 1).

4. Final Answer:

   data[np.newaxis, :, :] -> Option B

5. Quick Check:

   New axis front = data[np.newaxis, :, :] [OK]

Hint: Add np.newaxis at front index for batch dimension [OK]

Common Mistakes:

• Adding newaxis in wrong position
• Using fewer indices than array dimensions
• Confusing axis order for shape