What are the differences between NumPy and SciPy with examples?

What are the differences between NumPy and SciPy with examples?

NumPy is primarily for numerical operations, while SciPy builds on NumPy for scientific computing. For example, NumPy can create arrays and perform basic operations, while SciPy can solve differential equations or perform optimization.

Can you give me a specific example of using NumPy?

Sure! Here's a simple example: you can create a NumPy array and calculate the mean.

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
mean = np.mean(arr)
print(mean)  # Output: 3.0

And what about an example using SciPy?

Absolutely! Here's an example of using SciPy to optimize a function:

from scipy.optimize import minimize

# Define a simple quadratic function
def func(x):
    return x**2 + 5*x + 10

result = minimize(func, 0)
print(result.x)  # Output: the x value that minimizes the function

How do I decide when to use NumPy vs SciPy?

Use NumPy for basic array operations and mathematical functions. Opt for SciPy when you need advanced scientific computations like optimization, integration, or statistics.