NumPy is a powerful library for numerical computing in Python. It provides a wide range of mathematical functions and tools for working with arrays and matrices. In this tutorial, we will learn how to use NumPy for numerical computing.
The first step is to install NumPy. You can do this using the pip command:
pip install numpyOnce NumPy is installed, you can import it into your Python program:
import numpy as npOnce NumPy is imported, you can create a NumPy array. A NumPy array is a multi-dimensional array of numbers. You can create a NumPy array from a list of numbers:
my_array = np.array([1, 2, 3, 4, 5])You can also create a NumPy array from a range of numbers:
my_array = np.arange(1, 10)NumPy provides a wide range of mathematical functions for working with arrays. You can use these functions to perform mathematical operations on NumPy arrays. For example, you can add two NumPy arrays together:
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
c = a + b
print(c) # [5, 7, 9]NumPy also provides a wide range of functions for working with arrays. For example, you can use the np.mean() function to calculate the mean of an array:
a = np.array([1, 2, 3])
mean = np.mean(a)
print(mean) # 2.0You can save and load NumPy arrays using the np.save() and np.load() functions. For example, you can save an array to a file:
a = np.array([1, 2, 3])
np.save('my_array', a) # saves the array to a file called 'my_array.npy'You can then load the array from the file:
b = np.load('my_array.npy')
print(b) # [1, 2, 3]You can visualize NumPy arrays using the Matplotlib library. For example, you can plot a NumPy array as a line graph:
import matplotlib.pyplot as plt
a = np.array([1, 2, 3])
plt.plot(a)
plt.show()NumPy can also be used for linear algebra. You can use the np.linalg module to perform linear algebra operations. For example, you can calculate the inverse of a matrix:
A = np.array([[1, 2], [3, 4]])
A_inv = np.linalg.inv(A)
print(A_inv) # [[-2. 1. ]
# [ 1.5 -0.5]]