SciPy vs NumPy

NumPy and SciPy work hand in hand — but each has its own role.

NumPy provides the foundation for fast array operations and basic math, while SciPy builds on top of it to offer advanced scientific and statistical tools.

The easiest way to see their difference is through a quick example.

To get started, install SciPy with pip:

Copy
pip install scipy

Then, import both NumPy and SciPy into your code:

Copy
import numpy as np
from scipy import stats

Suppose you have two datasets and want to check if their average values differ.

First, use NumPy to calculate each mean:

Copy
data1 = [5.1, 5.5, 5.8, 6.0, 6.2]
data2 = [5.0, 5.1, 5.4, 5.6, 5.9]

mean1 = np.mean(data1)
mean2 = np.mean(data2)

print("Mean of data1:", mean1)
print("Mean of data2:", mean2)

Next, use SciPy to perform an independent t-test, which checks whether the difference between the two means is statistically significant:

Copy
t_stat, p_value = stats.ttest_ind(data1, data2)

print("t-statistic:", t_stat)
print("p-value:", p_value)

NumPy helps you calculate basic statistics like the mean, while SciPy gives you ready-made functions to test hypotheses and perform advanced analysis.

• NumPy – Performs fast numerical operations and array manipulation
• SciPy – Extends NumPy with higher-level functions for statistics, optimization, and scientific computing

Use NumPy for core math and array work, and SciPy when you need ready-made tools for deeper scientific analysis.