np.newaxis for adding dimensions in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a new axis to the 1D array to make it 2D.

NumPy

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

Drag options to blanks, or click blank then click option'

A[np.newaxis, np.newaxis]

B[np.newaxis, :]

C[np.newaxis]

D[:, np.newaxis]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [np.newaxis] alone does not add a new axis correctly.

Adding np.newaxis in the wrong position changes the shape differently.

Using [:, np.newaxis] adds a new axis as a column, changing shape from (3,) to (3, 1).

2fill in blank

medium

Complete the code to add a new axis at the start of the array shape.

NumPy

import numpy as np
arr = np.array([4, 5, 6])
new_arr = arr[1]
print(new_arr.shape)

Drag options to blanks, or click blank then click option'

A[np.newaxis, :]

B[:, np.newaxis]

C[np.newaxis]

D[np.newaxis, np.newaxis]

Attempts:

3 left

💡 Hint

Common Mistakes

Placing np.newaxis after the colon adds the axis at the end, not the start.

Using just [np.newaxis] does not specify the position clearly.

Using [np.newaxis, :] adds a new axis at the start, changing shape from (3,) to (1, 3).

3fill in blank

hard

Fix the error in the code to correctly add a new axis to the array.

NumPy

import numpy as np
arr = np.array([7, 8, 9])
new_arr = arr[1]
print(new_arr.shape)

Drag options to blanks, or click blank then click option'

A[np.newaxis]

B[np.newaxis, :]

C[:, np.newaxis]

Dnp.newaxis

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.newaxis without slicing causes errors.

Placing np.newaxis inside brackets incorrectly changes the shape.

The correct way to add a new axis as a column is [:, np.newaxis]. Using np.newaxis alone or in wrong positions causes errors or wrong shapes.

4fill in blank

hard

Fill both blanks to create a 3D array by adding two new axes to the 1D array.

NumPy

import numpy as np
arr = np.array([10, 20, 30])
new_arr = arr[1][2]
print(new_arr.shape)

Drag options to blanks, or click blank then click option'

A[np.newaxis]

B[:, np.newaxis]

C[np.newaxis, np.newaxis]

D[np.newaxis, :]

Attempts:

3 left

💡 Hint

Common Mistakes

Using combined axes like [np.newaxis, np.newaxis] once instead of chaining twice.

Mixing slicing and brackets incorrectly.

Adding [np.newaxis] twice adds two new axes, changing shape from (3,) to (1, 1, 3).

5fill in blank

hard

Fill all three blanks to create a 4D array with shape (1, 1, 1, 3) from a 1D array.

NumPy

import numpy as np
arr = np.array([100, 200, 300])
new_arr = arr[1][2][3]
print(new_arr.shape)

Drag options to blanks, or click blank then click option'

A[np.newaxis]

B[:, np.newaxis]

C[np.newaxis, :]

D[np.newaxis, np.newaxis]

Attempts:

3 left

💡 Hint

Common Mistakes

Adding axes in wrong order changes the shape.

Using combined axes incorrectly.

Using [np.newaxis, np.newaxis] adds two new axes at the start, then [np.newaxis] adds another, and [:, np.newaxis] adds a new axis as column, resulting in shape (1, 1, 1, 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