Multi-dimensional fancy indexing in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select elements from a 2D array using fancy indexing.

NumPy

import numpy as np
arr = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
result = arr[[1], [0, 2]]

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

A[1, 2]

B1

C2

D[0, 2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single integer instead of a list for row indices.

Confusing row and column indices.

The code uses fancy indexing to select rows 1 and 2 and columns 0 and 2, resulting in elements 40, 60, 70, and 90.

2fill in blank

medium

Complete the code to select elements from a 3x3 array using fancy indexing with two arrays.

NumPy

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

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

A[1, 2, 0]

B[0, 1, 2]

C[2, 1, 0]

D[0, 0, 0]

Attempts:

3 left

💡 Hint

Common Mistakes

Using row indices of different length than column indices.

Using a single integer instead of a list for row indices.

Using row indices [0, 1, 2] and column indices [0, 2, 1] selects elements (0,0), (1,2), and (2,1) from the array.

3fill in blank

hard

Fix the error in the code to correctly select elements using multi-dimensional fancy indexing.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
result = arr[[0, 2], [1]]

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

A[0, 1]

B[1, 0]

C[1, 1]

D[2, 1]

Attempts:

3 left

💡 Hint

Common Mistakes

Using column indices that do not match the length of row indices.

Using invalid column indices.

The code selects elements at positions (0,1) and (2,1), which are 2 and 6 respectively.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = { [1] : len([2]) for word in words if len(word) > 3 }

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

Aword

Bwords

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using the list 'words' instead of the variable 'word'.

Using a string literal instead of a variable.

The dictionary comprehension uses each word as the key and its length as the value, filtering words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths if length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = { [1] : [2] for word in words if len(word) [3] 3 }

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

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word as key without converting to uppercase.

Using incorrect comparison operators.

The comprehension maps each word in uppercase to its length, filtering words longer than 3 characters.

Practice

1. What does multi-dimensional fancy indexing in NumPy allow you to do?

easy

A. Change the shape of the array without copying data.

B. Sort the array elements in ascending order.

C. Calculate the sum of all elements in the array.

D. Select specific elements from an array using lists of row and column indices.

Solution

1. Step 1: Understand the concept of fancy indexing

   Fancy indexing uses arrays or lists of indices to select elements from an array.

2. Step 2: Apply to multi-dimensional arrays

   In multi-dimensional fancy indexing, you provide lists of row and column indices to pick specific elements scattered in the array.

3. Final Answer:

   Select specific elements from an array using lists of row and column indices. -> Option D

4. Quick Check:

   Fancy indexing = element selection by index lists [OK]

Hint: Fancy indexing picks elements by matching row and column lists [OK]

Common Mistakes:

• Confusing fancy indexing with sorting
• Thinking it reshapes arrays
• Assuming it sums elements

2. Which of the following is the correct syntax for multi-dimensional fancy indexing in NumPy to select elements at rows [0, 2] and columns [1, 3] from array arr?

easy

A. arr[[0, 2], [1, 3]]

B. arr[0, 2, 1, 3]

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

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

Solution

1. Step 1: Recall fancy indexing syntax

   Multi-dimensional fancy indexing uses two lists inside square brackets: one for rows, one for columns.

2. Step 2: Match given indices

   To select elements at rows 0 and 2, columns 1 and 3, syntax is arr[[0, 2], [1, 3]].

3. Final Answer:

   arr[[0, 2], [1, 3]] -> Option A

4. Quick Check:

   Two index lists inside brackets = correct fancy indexing [OK]

Hint: Use two lists inside brackets for rows and columns [OK]

Common Mistakes:

• Using slicing instead of lists
• Mixing row and column indices in one list
• Using incorrect bracket placement

3. Given the array arr = np.array([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]), what is the output of arr[[0, 2], [1, 3]]?

medium

A. [20 120]

B. [20 40]

C. [10 120]

D. [50 110]

Solution

1. Step 1: Identify indices from fancy indexing

   Rows are [0, 2], columns are [1, 3]. So elements are arr[0,1] and arr[2,3].

2. Step 2: Find elements at these positions

   arr[0,1] = 20, arr[2,3] = 120, so output is [20 120].

3. Final Answer:

   [20 120] -> Option A

4. Quick Check:

   Elements at (0,1) and (2,3) = [20 120] [OK]

Hint: Match row and column indices element-wise [OK]

Common Mistakes:

• Selecting entire rows or columns instead of elements
• Mixing up row and column indices
• Expecting a 2D array output

4. What error will occur when running the code below?

import numpy as np
arr = np.array([[1,2,3],[4,5,6]])
rows = [0,1]
cols = [1]
print(arr[rows, cols])

medium

A. ValueError because arr is 2D

B. SyntaxError because of wrong brackets

C. IndexError due to mismatched index array shapes

D. No error, prints [2 5]

Solution

1. Step 1: Check shapes of index arrays

   rows has length 2, cols has length 1; shapes differ.

2. Step 2: Understand fancy indexing requirements

   Fancy indexing requires index arrays to have the same shape for element-wise selection.

3. Final Answer:

   IndexError due to mismatched index array shapes -> Option C

4. Quick Check:

   Index arrays must match shape [OK]

Hint: Index arrays must be same shape for fancy indexing [OK]

Common Mistakes:

• Assuming different length index lists work
• Confusing syntax error with runtime error
• Expecting partial output without error

5. You have a 3x4 array data representing sales for 3 stores (rows) over 4 months (columns). You want to select sales for store 0 in month 1, store 2 in month 3, and store 1 in month 0 using multi-dimensional fancy indexing. Which code correctly extracts these values?

hard

A. data[[0, 1, 2], [1, 3, 0]]

B. data[[0, 2, 1], [1, 3, 0]]

C. data[[1, 3, 0], [0, 2, 1]]

D. data[[0, 2, 1]][[1, 3, 0]]

Solution

1. Step 1: Match stores and months as row and column indices

   Stores: 0, 2, 1 correspond to rows; months: 1, 3, 0 correspond to columns.

2. Step 2: Use multi-dimensional fancy indexing syntax

   Use data[[0, 2, 1], [1, 3, 0]] to select elements at these positions element-wise.

3. Final Answer:

   data[[0, 2, 1], [1, 3, 0]] -> Option B

4. Quick Check:

   Row and column lists match stores and months [OK]

Hint: Pair store and month indices in two lists for fancy indexing [OK]

Common Mistakes:

• Swapping row and column lists
• Using chained indexing instead of fancy indexing
• Mismatching lengths of index lists