Fancy indexing with integer arrays in NumPy - Mini Project: Build & Apply

Fancy indexing with integer arrays

📖 Scenario: Imagine you have a list of daily temperatures for a week. You want to pick specific days to check the temperatures, but not in order. This is like picking favorite days to remember.

🎯 Goal: You will create a NumPy array of temperatures, then use fancy indexing with an integer array to select temperatures from specific days.

📋 What You'll Learn

Create a NumPy array called temps with these exact values: [22, 19, 24, 21, 20, 23, 18]

Create a NumPy array called days with these exact indices: [0, 3, 5]

Use fancy indexing with days to select temperatures from temps and store in selected_temps

Print the selected_temps array

💡 Why This Matters

🌍 Real World

Fancy indexing helps quickly pick specific data points from large datasets, like selecting certain days' temperatures from a weather dataset.

💼 Career

Data scientists often use fancy indexing to filter and analyze subsets of data efficiently without loops.

1

Create the temperature array

Import NumPy as np and create a NumPy array called temps with these exact values: [22, 19, 24, 21, 20, 23, 18]

NumPy

# Import numpy and create the temps array
# Your code here

Hint

Use import numpy as np first. Then create temps using np.array() with the exact list of numbers.

2

Create the days index array

Create a NumPy array called days with these exact indices: [0, 3, 5]

NumPy

import numpy as np

temps = np.array([22, 19, 24, 21, 20, 23, 18])

# Create the days array
# Your code here

Hint

Use np.array() to create days with the exact indices.

3

Select temperatures using fancy indexing

Use fancy indexing with the days array to select temperatures from temps and store the result in a variable called selected_temps

NumPy

import numpy as np

temps = np.array([22, 19, 24, 21, 20, 23, 18])
days = np.array([0, 3, 5])

# Use fancy indexing to select temperatures
# Your code here

Hint

Use temps[days] to pick the temperatures at the indices in days.

4

Print the selected temperatures

Print the selected_temps array to see the temperatures from the chosen days

NumPy

import numpy as np

temps = np.array([22, 19, 24, 21, 20, 23, 18])
days = np.array([0, 3, 5])
selected_temps = temps[days]

# Print the selected_temps array
# Your code here

Hint

Use print(selected_temps) to display the selected temperatures.

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