📌 Introduction
When working with datasets, you often need to filter specific values.
That’s where Boolean Indexing in NumPy becomes extremely powerful.
🔍 What is Boolean Indexing?
Boolean indexing allows you to:
- Filter data based on conditions
- Extract only required values
- Perform dynamic queries
🧪 Example Array
import numpy as nparr = np.array([10, 20, 30, 40, 50])
✅ 1. Basic Boolean Condition
print(arr > 25)
👉 Output:
[False False True True True]
✔ Returns True/False for each element
🔎 2. Filtering Values
print(arr[arr > 25])
👉 Output:
[30 40 50]
✔ Only values satisfying condition are returned
🔗 3. Multiple Conditions
print(arr[(arr > 20) & (arr < 50)])
👉 Output:
[30 40]
✔ Operators:
&→ AND|→ OR
📊 4. Boolean Indexing in 2D Arrays
arr = np.array([[1,2,3],[4,5,6]])print(arr[arr > 3])
👉 Output:
[4 5 6]
🔄 5. Modify Values Using Condition
arr = np.array([10, 20, 30, 40])arr[arr > 25] = 0
print(arr)
👉 Output:
[10 20 0 0]
✔ Replace values dynamically
⚡ Why Boolean Indexing is Powerful?
Using NumPy, you can:
- Avoid loops
- Filter large datasets instantly
- Write clean & efficient code
📦 Real-World Use Cases
Boolean indexing is widely used in:
- Data cleaning
- Filtering datasets
- Machine learning preprocessing
🔗 Used with Other Libraries
Works seamlessly with:
- Pandas
- Scikit-learn
- TensorFlow
📊 Summary Table
| Operation | Example |
|---|---|
| Condition | arr > 25 |
| Filter | arr[arr > 25] |
| AND | (arr > 20) & (arr < 50) |
| Modify | arr[arr > 25] = 0 |
🧠 Pro Tips
- Always use parentheses in multiple conditions
- Use boolean indexing instead of loops
- Combine with slicing for advanced queries
🔚 Conclusion
Boolean indexing in NumPy is a must-know skill for efficient data filtering and manipulation.