Fancy indexing with integer arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is fancy indexing in NumPy?

Fancy indexing is a way to select elements from a NumPy array using integer arrays or lists as indices, allowing you to pick multiple elements at once in any order.

beginner

How does fancy indexing differ from slicing in NumPy?

Slicing selects a continuous range of elements, while fancy indexing lets you select arbitrary elements using arrays of indices, which can be non-continuous and repeated.

beginner

Given array arr = np.array([10, 20, 30, 40, 50]), what does arr[[1, 3, 4]] return?

It returns a new array with elements at positions 1, 3, and 4: [20, 40, 50].

intermediate

Can fancy indexing be used to modify elements in a NumPy array? How?

Yes. You can assign new values to elements selected by fancy indexing. For example, arr[[1, 3]] = [100, 200] changes elements at positions 1 and 3.

intermediate

What happens if you use repeated indices in fancy indexing, like arr[[2, 2, 0]]?

The output array will include repeated elements corresponding to the repeated indices. For example, it will return elements at positions 2, 2, and 0, repeating values accordingly.

What type of object can be used for fancy indexing in NumPy?

A Only slices

B Strings

C Integer arrays or lists

D Boolean values

Fancy indexing uses integer arrays or lists to select elements by their positions.

What does arr[[0, 2, 4]] do if arr = np.array([5, 10, 15, 20, 25])?

A Raises an error

B Returns <code>[10, 20]</code>

C Returns <code>[5, 10, 15, 20, 25]</code>

D Returns <code>[5, 15, 25]</code>

It selects elements at indices 0, 2, and 4.

Can fancy indexing select elements in any order?

A Yes, order can be arbitrary

B No, only ascending order

C No, only descending order

D Only continuous ranges

Fancy indexing allows arbitrary order and repetition of indices.

What happens if you assign values using fancy indexing with repeated indices?

A All assignments are summed

B The last assignment to a repeated index is kept

C An error occurs

D Values are averaged

When assigning, repeated indices get overwritten in order, so the last value sticks.

Which of these is NOT a use case for fancy indexing?

A Selecting a continuous slice of elements

B Duplicating elements in a new array

C Reordering elements arbitrarily

D Selecting specific rows from a 2D array

Continuous slices are done with slicing, not fancy indexing.

Explain fancy indexing with integer arrays in NumPy and give a simple example.

Think about how you pick specific items from a list using their positions.

Describe how you can modify elements in a NumPy array using fancy indexing.

Consider how you change values at certain positions in a list.

Practice

1. What does fancy indexing with integer arrays in NumPy allow you to do?

easy

A. Create a new array filled with zeros

B. Sort an array in ascending order

C. Select multiple elements from an array using a list of positions

D. Change the data type of an array

Solution

1. Step 1: Understand fancy indexing

   Fancy indexing uses a list or array of integers to pick elements by their positions.

2. Step 2: Identify the correct operation

   It selects multiple elements from the original array based on given indices.

3. Final Answer:

   Select multiple elements from an array using a list of positions -> Option C

4. Quick Check:

   Fancy indexing = select elements by positions [OK]

Hint: Fancy indexing picks elements by position list [OK]

Common Mistakes:

• Confusing fancy indexing with sorting
• Thinking it changes data type
• Assuming it creates arrays of zeros

2. Which of the following is the correct syntax to select elements at positions 0, 2, and 4 from a NumPy array arr using fancy indexing?

easy

A. arr{0, 2, 4}

B. arr(0, 2, 4)

C. arr[0:2:4]

D. arr[[0, 2, 4]]

Solution

1. Step 1: Recall fancy indexing syntax

   Fancy indexing uses double square brackets with a list or array of indices inside.

2. Step 2: Match syntax to options

   Only arr[[0, 2, 4]] uses correct double brackets and list format.

3. Final Answer:

   arr[[0, 2, 4]] -> Option D

4. Quick Check:

   Double brackets with list = correct fancy indexing [OK]

Hint: Use double brackets with list for fancy indexing [OK]

Common Mistakes:

• Using parentheses instead of brackets
• Using curly braces which are invalid
• Using slicing syntax incorrectly

3. What is the output of the following code?

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
indices = np.array([1, 3, 4])
result = arr[indices]
print(result)

medium

A. [20 40 50]

B. [10 30 40]

C. [20 30 40]

D. [50 40 20]

Solution

1. Step 1: Understand the array and indices

   Original array is [10, 20, 30, 40, 50]. Indices are [1, 3, 4].

2. Step 2: Select elements at given indices

   Elements at positions 1, 3, 4 are 20, 40, 50 respectively.

3. Final Answer:

   [20 40 50] -> Option A

4. Quick Check:

   arr[1,3,4] = [20 40 50] [OK]

Hint: Match indices to array positions to get output [OK]

Common Mistakes:

• Mixing up indices and values
• Using wrong positions from array
• Confusing slicing with fancy indexing

4. Identify the error in the following code snippet:

import numpy as np
arr = np.array([5, 10, 15, 20])
indices = [1, 4]
print(arr[indices])

medium

A. IndexError because index 4 is out of bounds

B. SyntaxError due to wrong brackets

C. TypeError because indices is a list

D. No error, prints [10 20]

Solution

1. Step 1: Check array size and indices

   Array length is 4, valid indices are 0 to 3. Index 4 is invalid.

2. Step 2: Understand error type

   Accessing index 4 causes IndexError: index out of bounds.

3. Final Answer:

   IndexError because index 4 is out of bounds -> Option A

4. Quick Check:

   Index 4 invalid for length 4 array [OK]

Hint: Check if all indices are within array length [OK]

Common Mistakes:

• Assuming list indices are always valid
• Confusing syntax errors with index errors
• Thinking list indices cause type errors

5. Given a 2D NumPy array arr = np.array([[1,2,3],[4,5,6],[7,8,9]]), how can you use fancy indexing to select elements at positions (0,2), (1,0), and (2,1) and create a new 1D array with these values?

hard

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

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

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

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

Solution

1. Step 1: Understand 2D fancy indexing

   To select elements at (row, col) positions, use two index arrays: one for rows, one for columns.

2. Step 2: Match given positions to index arrays

   Positions are (0,2), (1,0), (2,1), so rows = [0,1,2], cols = [2,0,1].

3. Step 3: Use fancy indexing syntax

   Use arr[[0,1,2], [2,0,1]] to get elements [3,4,8].

4. Final Answer:

   arr[[0,1,2], [2,0,1]] -> Option B

5. Quick Check:

   Rows and cols arrays match positions [OK]

Hint: Use two index arrays: rows and columns [OK]

Common Mistakes:

• Mixing row and column indices order
• Using chained indexing instead of fancy indexing
• Using wrong index arrays for positions