Aggregation Functions in NumPy (sum, min, max Explained)

📌 Introduction

When working with large datasets, you often need to summarize data quickly.

That’s where aggregation functions in NumPy become essential.

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🔍 What are Aggregation Functions?

Aggregation functions perform operations on a dataset and return a single value.

👉 Example:

• Total sum
• Minimum value
• Maximum value

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🧪 Example Dataset

import numpy as npdata = np.array([10, 20, 30, 40])

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➕ 1. Sum

print(np.sum(data))

👉 Output:

100

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🔽 2. Minimum Value

print(np.min(data))

👉 Output:

10

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🔼 3. Maximum Value

print(np.max(data))

👉 Output:

40

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🔢 4. Product of Elements

print(np.prod(data))

👉 Output:

240000

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📊 5. Mean (Average)

print(np.mean(data))

👉 Output:

25.0

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📐 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

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⚡ Why Use Aggregation?

Using NumPy aggregation:

• Saves time
• Avoids loops
• Handles large datasets efficiently

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📦 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

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🔗 Works with Other Libraries

Aggregation functions are widely used in:

• Pandas
• Scikit-learn
• TensorFlow

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📊 Summary Table

Function	Description
sum()	Total
min()	Smallest value
max()	Largest value
prod()	Product
mean()	Average

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🧠 Pro Tips

• Use axis for multi-dimensional arrays
• Combine aggregation functions for better insights
• Use aggregation in data preprocessing

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🔚 Conclusion

Aggregation functions in NumPy are essential for quickly summarizing and analyzing data.