NumPy Array Filtering
In NumPy, filtering allows you to extract specific elements from an array based on conditions.
Instead of manually looping through data, NumPy lets you filter arrays using simple expressions.
This is called Array Filtering.
What is Array Filtering in NumPy?
Array filtering means:
Selecting elements from an array that satisfy a condition.
It is done using Boolean indexing.
Why Use Array Filtering?
- Fast data selection
- No loops required
- Clean and readable code
- Essential for data science
- Used in machine learning preprocessing
1. Basic Filtering Example
import numpy as np
arr = np.array([10, 20, 30, 40, 50])
filtered = arr[arr > 25]
print(filtered)
Output:
[30 40 50]
2. How Filtering Works
arr = [10, 20, 30, 40, 50]
condition: > 25
Result:
[False False True True True]
Filtered values:
[30 40 50]
3. Filtering Even Numbers
arr = np.array([1, 2, 3, 4, 5, 6])
even = arr[arr % 2 == 0]
print(even)
Output:
[2 4 6]
4. Filtering Odd Numbers
arr = np.array([1, 2, 3, 4, 5, 6])
odd = arr[arr % 2 != 0]
print(odd)
Output:
[1 3 5]
5. Filtering with Multiple Conditions
arr = np.array([10, 15, 20, 25, 30, 35])
result = arr[(arr > 15) & (arr < 35)]
print(result)
Output:
[20 25 30]
Logical Operators in Filtering
| Operator | Meaning |
|---|---|
| & | AND |
| | | OR |
| ~ | NOT |
6. Filtering Using Comparison Arrays
arr = np.array([5, 10, 15, 20])
mask = arr > 10
print(arr[mask])
Output:
[15 20]
7. Filtering 2D Arrays
arr = np.array([
[10, 20, 30],
[40, 50, 60]
])
filtered = arr[arr > 30]
print(filtered)
Output:
[40 50 60]
8. Real-World Example: Student Scores
scores = np.array([45, 60, 75, 30, 90])
passed = scores[scores >= 50]
print(passed)
Output:
[60 75 90]
9. Real-World Example: Data Cleaning
data = np.array([100, -5, 50, -10, 200])
cleaned = data[data > 0]
print(cleaned)
Output:
[100 50 200]
10. Filtering vs Loop Method
❌ Using Loop:
result = []
for x in arr:
if x > 20:
result.append(x)
✅ Using NumPy Filtering:
result = arr[arr > 20]
✔ Faster
✔ Cleaner
✔ More efficient
Advantages of NumPy Filtering
- High performance
- Simple syntax
- No loops needed
- Works on large datasets
- Essential for AI/ML workflows
Summary
NumPy filtering allows you to extract data based on conditions using boolean indexing.
It is a powerful feature in NumPy and widely used in data analysis and machine learning with Python.
Conclusion
Mastering array filtering helps you efficiently process, clean, and analyze data in Python, making your code faster and more powerful.
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