📘 Chapter 8: Boolean Indexing and Masking in NumPy — Smart Filtering Made Simple

🧠 “Boolean indexing in NumPy is like having a magical magnifying glass — you only see the data you care about.”

Welcome to Chapter 8 of your NumPy learning series!

You’ve already mastered creating, reshaping, and manipulating arrays. Now, let’s move into one of the most powerful features of NumPy — Boolean indexing and masking.

This concept is a game-changer for:

• Filtering data

• Making condition-based selections

• Applying vectorized logic without loops

Whether you’re cleaning data, extracting rows that meet a condition, or highlighting specific values — Boolean indexing helps you do it quickly, efficiently, and cleanly.

🔍 What You’ll Learn

• How to create Boolean masks using conditions

• Select elements that meet one or more criteria

• Combine multiple conditions using logical operators

• Real-life examples: filtering, cleaning, and subsetting data

Let’s begin!

✅ 1. What is Boolean Indexing?

Boolean indexing means selecting data based on whether a condition is True or False.

Let’s start simple:

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
mask = arr > 30
print(mask)

Output:

[False False False  True  True]

Now apply the mask:

filtered = arr[mask]
print(filtered)  # Output: [40 50]

🎯 This is Boolean indexing in action: filtering without loops.

🧠 2. Creating Boolean Masks

You can use any comparison operator:

📌 Examples

arr = np.array([5, 10, 15, 20, 25])

print(arr < 20)   # [ True  True  True False False]
print(arr == 10)  # [False  True False False False]
print(arr != 15)  # [ True  True False  True  True]

🧊 3. Selecting Elements That Meet Criteria

Use a Boolean mask directly inside the array’s brackets:

arr = np.array([12, 23, 34, 45, 56])

# Select values > 30
print(arr[arr > 30])  # [34 45 56]

# Select even numbers
print(arr[arr % 2 == 0])  # [12 34 56]

This can be used in real-life for:

• Selecting rows with high sales

• Removing missing data

• Filtering scores above average

🔗 4. Combining Multiple Conditions

Just like in logic, you can combine multiple conditions using:

Wrap conditions in parentheses to avoid precedence errors!

🔸 Example: AND

arr = np.array([10, 15, 20, 25, 30])

# Select values between 15 and 30
filtered = arr[(arr >= 15) & (arr <= 30)]
print(filtered)  # [15 20 25 30]

🔸 Example: OR

print(arr[(arr < 15) | (arr > 25)])  # [10 30]

🔸 Example: NOT

🧾 5. Boolean Indexing with 2D Arrays

Let’s take a matrix:

matrix = np.array([
    [10, 20, 30],
    [40, 50, 60],
    [70, 80, 90]
])

# Mask values > 50
print(matrix[matrix > 50])  # [60 70 80 90]

🔁 6. Updating Elements Based on Conditions

You can also modify values using Boolean masks:

arr = np.array([1, 2, 3, 4, 5])
arr[arr > 3] = 99
print(arr)  # [1 2 3 99 99]

Useful for:

• Replacing outliers

• Censoring negative numbers

• Standardizing data ranges

💡 7. Where to Use Boolean Indexing in Real Life?

🔍 8. Bonus: Using np.where()

Sometimes you don’t just want to filter — you want to choose between two values based on a condition.

Syntax:

np.where(condition, value_if_true, value_if_false)

Example:

arr = np.array([10, 20, 30, 40])
new_arr = np.where(arr > 25, 1, 0)
print(new_arr)  # [0 0 1 1]

This is especially useful in label encoding, binarizing data, or applying simple transformations.

🧪 Real-World Use Case: Filter Students Above Average

scores = np.array([55, 70, 90, 60, 85])
avg = np.mean(scores)

above_avg = scores[scores > avg]
print("Above Average Scores:", above_avg)

You can also extract their indices:

indices = np.where(scores > avg)
print("Indices:", indices)  # (array([1, 2, 4]),)

⚠️ Common Mistakes to Avoid

📌 Summary Table: Boolean Indexing Essentials

🔚 Wrapping Up Chapter 8

And that’s a wrap! 🎉
You’ve just unlocked one of the most powerful tools in NumPy: Boolean indexing and masking.

This feature allows you to:

• Filter smartly

• Replace conditionally

• Combine logic in a clean, vectorized way

You’ll use Boolean indexing in every serious NumPy project, whether you’re analyzing data, building AI, or visualizing stats.

🔜 Coming Next in Chapter 9: Sorting and Searching in NumPy

We’ll explore:

• Sorting arrays with np.sort(), argsort()

• Finding values with searchsorted(), nonzero()

• Real-world examples with data processing