📌 Introduction
When working with large datasets, you often need to summarize data quickly.
That’s where aggregation functions in NumPy become essential.
🔍 What are Aggregation Functions?
Aggregation functions perform operations on a dataset and return a single value.
👉 Example:
- Total sum
- Minimum value
- Maximum value
🧪 Example Dataset
import numpy as npdata = np.array([10, 20, 30, 40])
➕ 1. Sum
print(np.sum(data))
👉 Output:
100
🔽 2. Minimum Value
print(np.min(data))
👉 Output:
10
🔼 3. Maximum Value
print(np.max(data))
👉 Output:
40
🔢 4. Product of Elements
print(np.prod(data))
👉 Output:
240000
📊 5. Mean (Average)
print(np.mean(data))
👉 Output:
25.0
📐 6. Aggregation in 2D Arrays
data = np.array([[1,2,3],[4,5,6]])print(np.sum(data, axis=0)) # column-wise
print(np.sum(data, axis=1)) # row-wise
⚡ Why Use Aggregation?
Using NumPy aggregation:
- Saves time
- Avoids loops
- Handles large datasets efficiently
📦 Real-World Example
sales = np.array([1000, 2000, 3000])total_sales = np.sum(sales)
print("Total:", total_sales)
✔ Used in:
- Business analytics
- Finance reports
- Data science
🔗 Works with Other Libraries
Aggregation functions are widely used in:
- Pandas
- Scikit-learn
- TensorFlow
📊 Summary Table
| Function | Description |
|---|---|
| sum() | Total |
| min() | Smallest value |
| max() | Largest value |
| prod() | Product |
| mean() | Average |
🧠 Pro Tips
- Use
axisfor multi-dimensional arrays - Combine aggregation functions for better insights
- Use aggregation in data preprocessing
🔚 Conclusion
Aggregation functions in NumPy are essential for quickly summarizing and analyzing data.