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Basic Array Operations in Python Using NumPy

In this post, I demonstrate a simple and reproducible example of numerical array operations using the NumPy library in Python. NumPy is a foundational tool for scientific computing, simulations, and data analysis.

Background

NumPy provides efficient support for multi-dimensional arrays and vectorized mathematical operations. These capabilities are widely used in areas such as signal processing, machine learning, traffic modeling, and V2X latency analysis.

Test Objective

The goal of this test is to:

• Create numerical arrays
• Perform element-wise addition
• Compute a statistical summary (mean)

Test Code

import numpy as np

a = np.array([1, 2, 3, 4])
b = np.array([10, 20, 30, 40])

c = a + b
mean_val = np.mean(c)

print("Array a:", a)
print("Array b:", b)
print("a + b:", c)
print("Mean of (a + b):", mean_val)

Observed Output (Text)

Array a: [1 2 3 4]
Array b: [10 20 30 40]
a + b: [11 22 33 44]
Mean of (a + b): 27.5

Observed Output (Screenshot)

Figure 1: Terminal output showing NumPy array addition and mean computation.

Explanation

NumPy performs element-wise addition between arrays of the same shape. The resulting array c contains the sum of corresponding elements from a and b.

The function np.mean() computes the arithmetic mean of all elements in the resulting array.

Why This Matters

This simple example illustrates core NumPy concepts that are essential for:

• Numerical simulations
• Data preprocessing pipelines
• Statistical analysis
• Scientific and engineering research

Vectorized operations such as these are significantly faster and more readable than equivalent loop-based implementations.

Key Lesson Learned

NumPy enables concise, efficient, and reliable numerical computation through vectorized array operations.

Conclusion

This post demonstrates a basic but fundamental use of NumPy for array arithmetic and statistical analysis. The same principles extend to higher-dimensional arrays and more complex numerical models used in advanced research and simulations.