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.

You can install SciPy using pip:

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pip install scipy

Once installed, import both NumPy and SciPy:

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import numpy as np
from scipy import stats

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

First, use NumPy to calculate the mean:

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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:

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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: 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.