NumPy Advanced Indexing – Boolean & Fancy Indexing Explained with Examples

🐍 NumPy – Advanced Indexing

NumPy provides powerful advanced indexing techniques that go beyond simple element access and slicing.

Advanced indexing allows you to:

• Select multiple elements at once
• Filter data using conditions
• Extract complex patterns
• Modify specific elements efficiently

It is widely used in:

• Data science
• Machine learning
• Data filtering
• Image processing
• Feature engineering

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What is Advanced Indexing?

Advanced indexing refers to techniques that allow non-continuous or conditional selection of array elements.

It mainly includes:

• Fancy Indexing
• Boolean Indexing
• Conditional Filtering

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🔵 Fancy Indexing

Fancy indexing allows you to select multiple elements using a list of indices.

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Example

import numpy as np

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

result = arr[[0, 2, 4]]

print(result)

Output:

[10 30 50]

✔ You can select elements in any order

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Fancy Indexing in 2D Arrays

arr = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])

result = arr[[0, 2]]
print(result)

Output:

[[1 2 3]
 [7 8 9]]

✔ Selects entire rows

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🟢 Boolean Indexing

Boolean indexing selects elements based on conditions.

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Example

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

result = arr[arr > 20]

print(result)

Output:

[25 30]

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Multiple Conditions

arr = np.array([10, 15, 20, 25, 30, 35])

result = arr[(arr > 15) & (arr < 35)]

print(result)

Output:

[20 25 30]

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Using OR Condition

result = arr[(arr < 15) | (arr > 30)]

print(result)

Output:

[10 35]

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🔴 Modifying Values with Boolean Indexing

You can directly update selected values.

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

arr[arr > 30] = 99

print(arr)

Output:

[10 20 30 99 99]

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🟣 Fancy Indexing with 2D Arrays

arr = np.array([
    [10, 20],
    [30, 40],
    [50, 60]
])

result = arr[[0, 2], [1, 0]]

print(result)

Output:

[20 50]

✔ Picks (0,1) and (2,0)

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🟡 Conditional Filtering

arr = np.array([5, 12, 18, 25, 40])

even = arr[arr % 2 == 0]

print(even)

Output:

[12 18 40]

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🔵 Combining Fancy + Boolean Indexing

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

result = arr[[True, False, True, False, True]]

print(result)

Output:

[10 30 50]

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🧠 Difference Between Basic and Advanced Indexing

Feature	Basic Indexing	Advanced Indexing
Access type	Single element	Multiple elements
Condition support	No	Yes
Flexibility	Low	High
Performance	Fast	Slightly slower
Use case	Simple access	Filtering & selection

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⚡ Real-World Example

Filtering student scores

scores = np.array([45, 67, 89, 30, 90, 55])

passed = scores[scores >= 50]

print(passed)

Output:

[67 89 90 55]

✔ Common in data analytics

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🖼️ Image Processing Example

image = np.array([
    [100, 150, 200],
    [50,  80,  120],
    [90,  60,  30]
])

bright_pixels = image[image > 100]

print(bright_pixels)

Output:

[150 200 120]

✔ Used in computer vision

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🚀 Performance Tips

• Boolean indexing is very efficient for filtering
• Avoid unnecessary fancy indexing on large datasets
• Use vectorized conditions instead of loops
• Combine conditions for better performance

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⚠️ Common Errors

1. Shape mismatch

IndexError: shape mismatch

✔ Fix: Ensure index arrays match dimensions

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2. Using Python lists instead of NumPy arrays

✔ Always convert data to NumPy arrays for advanced indexing

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🧾 Summary

NumPy advanced indexing includes:

• Fancy indexing → select multiple indices
• Boolean indexing → filter using conditions
• Conditional indexing → apply logical operations

These techniques are essential for:

• Data filtering
• Machine learning preprocessing
• Data transformation
• Image analysis

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🏁 Conclusion

Advanced indexing in NumPy provides powerful and flexible ways to access and manipulate data. It allows you to write clean, efficient, and highly expressive code for real-world data science and machine learning applications.