Statistical Functions in NumPy (mean, median, std Explained)

📌 Introduction

When working with data, understanding statistics is essential.

With NumPy, you can easily perform statistical analysis using built-in functions.

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🔍 Why Use Statistical Functions?

Statistical functions help you:

• Analyze data trends
• Summarize datasets
• Prepare data for machine learning

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🧪 Example Dataset

import numpy as npdata = np.array([10, 20, 30, 40, 50])

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📊 1. Mean (Average)

The mean is the average of all values.

print(np.mean(data))

👉 Output:

30.0

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📈 2. Median

The median is the middle value.

print(np.median(data))

👉 Output:

30.0

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📉 3. Standard Deviation (std)

Shows how spread out values are.

print(np.std(data))

👉 Output:

14.1421356237

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📦 4. Variance

Variance is the square of standard deviation.

print(np.var(data))

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📊 5. Min & Max

print(np.min(data))
print(np.max(data))

👉 Output:

10
50

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📐 6. Axis-Based Operations (2D Arrays)

data = np.array([[1,2,3],[4,5,6]])print(np.mean(data, axis=0))  # column-wise
print(np.mean(data, axis=1))  # row-wise

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⚡ Why NumPy is Efficient?

NumPy performs:

• Fast calculations
• Vectorized operations
• Optimized performance

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📦 Real-World Use Case

marks = np.array([70, 80, 90, 60])average = np.mean(marks)
print("Average Marks:", average)

✔ Useful in:

• Student analysis
• Sales reports
• Data science

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🔗 Used with Other Libraries

Statistical functions are widely used in:

• Pandas
• Scikit-learn
• TensorFlow

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📊 Summary Table

Function	Description
mean()	Average
median()	Middle value
std()	Standard deviation
var()	Variance
min()	Minimum
max()	Maximum

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🧠 Pro Tips

• Use axis for multi-dimensional data
• Combine multiple functions for insights
• Always clean data before analysis

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🔚 Conclusion

Statistical functions in NumPy make data analysis simple, fast, and powerful.