Combining fancy and slice indexing in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select rows 1 and 3, and columns 2 to 4 from the array.

NumPy

import numpy as np
arr = np.arange(20).reshape(5,4)
result = arr[[1], 2:4]
print(result)

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

A[0, 2]

B[2, 4]

C[1, 2]

D[1, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a slice instead of a list for row indices.

Selecting columns outside the array shape.

Using fancy indexing with [1, 3] selects rows 1 and 3. The slice 2:4 selects columns 2 to 3.

2fill in blank

medium

Complete the code to select rows 0, 2, 4 and columns 1 and 3 from the array.

NumPy

import numpy as np
arr = np.arange(20).reshape(5,4)
result = arr[[1], [1, 3]]
print(result)

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

A[0, 1, 2]

B[0, 2, 4]

C[1, 3, 5]

D[1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a range instead of a list for rows.

Selecting columns with a slice instead of a list.

Fancy indexing with [0, 2, 4] selects rows 0, 2, and 4. Columns 1 and 3 are selected by the list [1, 3].

3fill in blank

hard

Fix the error in the code to select rows 1 and 2, and columns 0 to 2.

NumPy

import numpy as np
arr = np.arange(15).reshape(3,5)
result = arr[[1], 0:3]
print(result)

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

A[1, 2]

B1, 2

Cslice(1,3)

Dnp.array([1, 2])

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma-separated indices instead of a list.

Using slice object where a list is expected.

Fancy indexing requires a list or array of indices. Using [1, 2] correctly selects rows 1 and 2.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths if length > 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using less than (<) instead of greater than (>) in the condition.

The dictionary comprehension uses len(word) as the value and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

NumPy

data = {'a': 1, 'b': -2, 'c': 3}
result = [1]: [2] for k, v in data.items() if v [3] 0}
print(result)

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys instead of uppercase keys.

Using less than (<) instead of greater than (>) in the condition.

Using keys as values instead of values.

The dictionary comprehension uses uppercase keys with k.upper(), values v, and filters values greater than 0 using >.

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