Skip to main content
Practice

SciPy vs NumPy

NumPy and SciPy are closely related, but they serve different purposes.

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

The best way to understand their differences is to see them in action.


Installing and Importing SciPy​

You can install SciPy using pip:

Install SciPy
pip install scipy

Once installed, import both NumPy and SciPy:

Import NumPy and SciPy
import numpy as np
from scipy import stats

Example: Mean and t-Test​

Let's say you have two datasets and want to compare their means.

First, use NumPy to calculate the mean:

Mean with NumPy
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 a t-test and check if the difference between the means is statistically significant:

Independent t-Test with SciPy
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.


Key Takeaway​

  • NumPy: Handles core numerical operations and array manipulation.
  • SciPy: Builds on NumPy to provide advanced scientific and statistical tools.

Use NumPy for calculations and SciPy when you need higher-level functions to solve complex problems.

Want to learn more?

Join CodeFriends Plus membership or enroll in a course to start your journey.