Combining fancy and slice indexing in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of combining fancy and slice indexing on a 2D array

What is the output of this code snippet?

import numpy as np
arr = np.arange(16).reshape(4,4)
result = arr[[1,3], 1:3]

NumPy

import numpy as np
arr = np.arange(16).reshape(4,4)
result = arr[[1,3], 1:3]
print(result)

A

[[1 2]
 [9 10]]

B

[[1 2 3]
 [9 10 11]]

C

[[5 6 7]
 [13 14 15]]

D

[[5 6]
 [13 14]]

💡 Hint

Remember that fancy indexing selects specific rows, and slice indexing selects columns.

The fancy index [1,3] selects rows 1 and 3. The slice 1:3 selects columns 1 and 2. So the output is a 2x2 array with elements from rows 1 and 3 and columns 1 and 2.

❓ data_output

intermediate

Shape of array after combining fancy and slice indexing

Given the code below, what is the shape of the resulting array?

import numpy as np
arr = np.arange(27).reshape(3,3,3)
result = arr[[0,2], :, 1:3]

NumPy

import numpy as np
arr = np.arange(27).reshape(3,3,3)
result = arr[[0,2], :, 1:3]
print(result.shape)

A (2, 3, 2)

B (3, 2, 2)

C (2, 2, 3)

D (3, 3, 2)

💡 Hint

Fancy indexing on the first axis selects 2 slices, then full slice on second axis, then slice on third axis.

Fancy indexing selects 2 elements along the first axis, full slice selects all 3 elements along the second axis, and slice 1:3 selects 2 elements along the third axis, so shape is (2,3,2).

🔧 Debug

advanced

Identify the error in combining fancy and slice indexing

What error does this code raise?

import numpy as np
arr = np.arange(9).reshape(3,3)
result = arr[1:3, [0, 2], 1]

NumPy

import numpy as np
arr = np.arange(9).reshape(3,3)
result = arr[1:3, [0, 2], 1]

A TypeError: unhashable type: 'slice'

B IndexError: too many indices for array

C ValueError: shape mismatch in fancy indexing

D No error, outputs a 2D array

💡 Hint

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

The array is 2D but the code uses 3 indices, causing IndexError: too many indices for array.

🚀 Application

advanced

Extract specific rows and columns using combined indexing

You have a 5x5 numpy array. You want to extract rows 0, 2, and 4, but only columns 1 to 3 (inclusive start, exclusive end). Which code snippet correctly does this?

A arr[1:4, 0:3]

B arr[1:4, [0, 2, 4]]

C arr[[0, 2, 4], 1:4]

D arr[[1, 2, 3], 0:3]

💡 Hint

Remember fancy indexing selects rows, slice selects columns.

Option C selects rows 0, 2, 4 and columns 1, 2, 3 correctly. Others select wrong axes or ranges.

🧠 Conceptual

expert

Understanding the output shape when combining fancy and slice indexing

Consider a 3D numpy array with shape (4, 5, 6). If you use fancy indexing on the first axis with 2 indices and slice indexing on the second axis with 3:5, and slice indexing on the third axis with 1:4, what will be the shape of the resulting array?

A (2, 2, 3)

B (4, 2, 3)

C (2, 3, 3)

D (2, 5, 3)

💡 Hint

Fancy indexing changes the size of the first axis to the number of indices selected.

Fancy indexing selects 2 elements on axis 0, slice 3:5 selects 2 elements on axis 1, slice 1:4 selects 3 elements on axis 2, so shape is (2, 2, 3).

Practice

1. What does combining fancy indexing and slice indexing in NumPy allow you to do?
arr[[0, 2], 1:4] selects:

easy

A. Rows 0 to 2, columns 1 and 4

B. Rows 1 to 4, columns 0 and 2

C. Rows 0 and 2, columns 1 to 3

D. Rows 1 and 4, columns 0 to 2

Solution

1. Step 1: Understand fancy indexing for rows

   The fancy index [0, 2] selects rows 0 and 2 specifically.

2. Step 2: Understand slice indexing for columns

   The slice 1:4 selects columns starting at 1 up to but not including 4, so columns 1, 2, and 3.

3. Final Answer:

   Rows 0 and 2, columns 1 to 3 -> Option C

4. Quick Check:

   Fancy rows + slice columns = selected subarray [OK]

Hint: Fancy picks rows, slice picks continuous columns [OK]

Common Mistakes:

• Confusing rows and columns in indexing
• Forgetting slice end is exclusive
• Mixing up fancy and slice roles

2. Which of the following is the correct syntax to select rows 1 and 3 and columns 2 to 4 from a NumPy array arr?

easy

A. arr[[1, 3], 2:5]

B. arr[1:3, 2:5]

C. arr[1, 3, 2:5]

D. arr[[1, 3], [2:5]]

Solution

1. Step 1: Identify fancy indexing for rows

   To select rows 1 and 3, use fancy indexing with a list: [1, 3].

2. Step 2: Use slice for columns

   To select columns 2 to 4, use slice 2:5 (end exclusive).

3. Final Answer:

   arr[[1, 3], 2:5] -> Option A

4. Quick Check:

   Fancy rows + slice columns syntax correct [OK]

Hint: Fancy indexing uses lists, slice uses colon syntax [OK]

Common Mistakes:

• Using commas inside fancy index list
• Using slice inside list brackets
• Incorrect slice end index

3. Given the array:

import numpy as np
arr = np.array([[10, 20, 30, 40],
                [50, 60, 70, 80],
                [90, 100, 110, 120],
                [130, 140, 150, 160]])
print(arr[[1, 3], 1:3])

What is the output?

medium

A. [[ 70 80]
[150 160]]

B. [[ 60 70]
[140 150]]

C. [[ 50 60]
[130 140]]

D. [[ 60 70 80]
[140 150 160]]

Solution

1. Step 1: Select rows using fancy indexing

   Rows 1 and 3 are:
   [50, 60, 70, 80]
   [130, 140, 150, 160]

2. Step 2: Select columns 1 to 2 (slice 1:3)

   Columns 1 and 2 for these rows are:
   Row 1: 60, 70
   Row 3: 140, 150

3. Final Answer:

   [[ 60 70] [140 150]] -> Option B

4. Quick Check:

   Fancy rows + slice columns output matches [OK]

Hint: Pick rows first, then slice columns [OK]

Common Mistakes:

• Selecting wrong rows or columns
• Including column 3 by mistake
• Confusing row and column indices

4. What is wrong with this code snippet?

import numpy as np
arr = np.arange(16).reshape(4,4)
result = arr[1:3, [0, 2]]
print(result)

medium

A. It prints rows 1 and 2 with columns 0 and 2 correctly

B. It raises an error because slice and fancy indexing cannot be combined

C. It prints rows 0 and 2 with columns 1 and 3 incorrectly

D. It raises an error because the fancy index list is invalid

Solution

1. Step 1: Understand slice and fancy indexing combination

   Slice 1:3 selects rows 1 and 2. Fancy index [0, 2] selects columns 0 and 2.

2. Step 2: Check if code runs and output

   This combination is valid in NumPy and prints the selected subarray without error.

3. Final Answer:

   It prints rows 1 and 2 with columns 0 and 2 correctly -> Option A

4. Quick Check:

   Slice rows + fancy columns works fine [OK]

Hint: Slice and fancy indexing can combine on different axes [OK]

Common Mistakes:

• Thinking slice and fancy indexing cannot mix
• Confusing which axis uses slice or fancy
• Expecting an error when code is valid

5. You have a 5x5 NumPy array arr. You want to select rows 0, 2, and 3, but only columns 1 to 3 inclusive. Which code correctly does this?

hard

A. arr[1:4, [0, 2, 4]]

B. arr[0:5:2, 1:4]

C. arr[[0, 2, 4], [1, 2, 3]]

D. arr[[0, 2, 3], 1:4]

Solution

1. Step 1: Identify fancy indexing for rows

   Rows 0, 2, and 3 are selected using fancy indexing: [0, 2, 3].

2. Step 2: Use slice for columns 1 to 3 inclusive

   Slice 1:4 selects columns 1, 2, and 3 (end index exclusive).

3. Final Answer:

   arr[[0, 2, 3], 1:4] -> Option D

4. Quick Check:

   Fancy rows + slice columns selects correct subarray [OK]

Hint: Fancy list for rows, slice for continuous columns [OK]

Common Mistakes:

• Using slice for rows instead of fancy list
• Using fancy list for columns incorrectly
• Off-by-one error in slice end index