Boolean indexing for filtering in NumPy - Mini Project: Build & Apply

Boolean indexing for filtering

📖 Scenario: Imagine you have a list of daily temperatures recorded in a city. You want to find out which days were warm, meaning the temperature was above 20 degrees Celsius.

🎯 Goal: You will create a NumPy array of temperatures, set a threshold for warm days, use boolean indexing to filter the warm days, and then display those warm temperatures.

📋 What You'll Learn

Create a NumPy array called temperatures with the exact values: 15, 22, 19, 24, 18, 30, 21

Create a variable called warm_threshold and set it to 20

Use boolean indexing with temperatures and warm_threshold to create a new array called warm_days containing only temperatures above the threshold

Print the warm_days array

💡 Why This Matters

🌍 Real World

Filtering data based on conditions is common in weather analysis, finance, and health monitoring to focus on important values.

💼 Career

Data scientists often use boolean indexing to quickly select and analyze subsets of data without loops, making their code faster and cleaner.

1

Create the temperatures array

Import NumPy as np and create a NumPy array called temperatures with these exact values: 15, 22, 19, 24, 18, 30, 21

NumPy

import numpy as np
# Create the temperatures array with the given values
# Your code here

Hint

Use np.array([...]) to create the array with the exact numbers.

2

Set the warm day threshold

Create a variable called warm_threshold and set it to the number 20

NumPy

import numpy as np

temperatures = np.array([15, 22, 19, 24, 18, 30, 21])
# Set warm_threshold to 20
# Your code here

Hint

Just assign the number 20 to the variable warm_threshold.

3

Filter warm days using boolean indexing

Use boolean indexing with temperatures and warm_threshold to create a new NumPy array called warm_days that contains only the temperatures greater than warm_threshold

NumPy

import numpy as np

temperatures = np.array([15, 22, 19, 24, 18, 30, 21])

warm_threshold = 20
# Create warm_days using boolean indexing
# Your code here

Hint

Use temperatures > warm_threshold inside the square brackets to filter.

4

Print the warm days

Print the warm_days array to display the temperatures above the threshold

NumPy

import numpy as np

temperatures = np.array([15, 22, 19, 24, 18, 30, 21])

warm_threshold = 20

warm_days = temperatures[temperatures > warm_threshold]
# Print warm_days
# Your code here

Hint

Use print(warm_days) to show the filtered temperatures.

Practice

1. What does boolean indexing in numpy allow you to do?

easy

A. Select elements from an array based on True/False conditions

B. Sort an array in ascending order

C. Change the data type of an array

D. Calculate the sum of all elements in an array

Solution

1. Step 1: Understand boolean indexing concept

   Boolean indexing uses a True/False array to pick elements from another array.

2. Step 2: Compare with other options

   Sorting, changing data type, and summing are different numpy operations, not boolean indexing.

3. Final Answer:

   Select elements from an array based on True/False conditions -> Option A

4. Quick Check:

   Boolean indexing = filtering by True/False [OK]

Hint: Boolean indexing picks elements where condition is True [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Assuming it calculates sums

2. Which of the following is the correct syntax to filter array arr for values greater than 5 using boolean indexing?

easy

A. arr > 5[arr]

B. arr[arr > 5]

C. arr.filter(arr > 5)

D. arr[arr < 5]

Solution

1. Step 1: Identify correct boolean indexing syntax

   In numpy, filtering uses arr[condition] where condition is a boolean array.

2. Step 2: Check each option

   arr[arr > 5] uses correct syntax. arr > 5[arr] is invalid syntax. arr.filter(arr > 5) is not a numpy method. arr[arr < 5] filters for less than 5, not greater.

3. Final Answer:

   arr[arr > 5] -> Option B

4. Quick Check:

   Correct syntax is arr[condition] [OK]

Hint: Use arr[condition] to filter arrays in numpy [OK]

Common Mistakes:

• Placing condition outside brackets
• Using non-existent filter method
• Mixing up greater than and less than

3. What is the output of the following code?

import numpy as np
arr = np.array([2, 7, 4, 9, 1])
filtered = arr[arr % 2 == 1]

medium

A. [7 4 9]

B. [2 4]

C. [7 9 1]

D. [2 7 4 9 1]

Solution

1. Step 1: Understand the condition arr % 2 == 1

   This condition selects odd numbers because odd numbers have remainder 1 when divided by 2.

2. Step 2: Apply condition to array elements

   Elements 7, 9, and 1 are odd, so they are selected.

3. Final Answer:

   [7 9 1] -> Option C

4. Quick Check:

   Filter odd numbers = [7 9 1] [OK]

Hint: Use modulo (%) to filter odd/even numbers [OK]

Common Mistakes:

• Selecting even numbers instead of odd
• Including all elements without filtering
• Misunderstanding modulo operator

4. The following code throws an error. What is the mistake?

import numpy as np
arr = np.array([10, 15, 20, 25])
filtered = arr[arr > 15 and arr < 25]

medium

A. Using 'and' instead of '&' for element-wise condition

B. Missing parentheses around conditions

C. Using 'or' instead of 'and'

D. Array is not defined properly

Solution

1. Step 1: Identify boolean operator error

   In numpy, element-wise logical operations require '&' instead of Python's 'and'.

2. Step 2: Understand why 'and' causes error

   'and' expects single boolean, but arr > 15 and arr < 25 returns arrays, causing TypeError.

3. Final Answer:

   Using 'and' instead of '&' for element-wise condition -> Option A

4. Quick Check:

   Use '&' for element-wise logical AND [OK]

Hint: Use & with parentheses for multiple conditions [OK]

Common Mistakes:

• Using 'and' instead of '&' in numpy conditions
• Forgetting parentheses around each condition
• Assuming 'or' works like '|'

5. Given a numpy array data = np.array([3, 6, 9, 12, 15, 18]), how would you filter values that are divisible by 3 but not by 6 using boolean indexing?

hard

A. data[(data % 3 == 0) & (data % 6 == 0)]

B. data[(data % 3 == 0) | (data % 6 != 0)]

C. data[(data % 3 != 0) & (data % 6 == 0)]

D. data[(data % 3 == 0) & (data % 6 != 0)]

Solution

1. Step 1: Define conditions for filtering

   We want numbers divisible by 3 (data % 3 == 0) but not divisible by 6 (data % 6 != 0).

2. Step 2: Combine conditions with element-wise AND

   Use '&' to combine both conditions inside parentheses for correct boolean indexing.

3. Step 3: Apply combined condition to data array

   data[(data % 3 == 0) & (data % 6 != 0)] correctly applies both conditions with '&'. Others use wrong operators or conditions.

4. Final Answer:

   data[(data % 3 == 0) & (data % 6 != 0)] -> Option D

5. Quick Check:

   Use & and parentheses for combined conditions [OK]

Hint: Combine conditions with & and parentheses for filtering [OK]

Common Mistakes:

• Using | instead of & for AND condition
• Mixing up divisibility conditions
• Forgetting parentheses around each condition