Numpy Library

Creating Numpy Arrays

Numpy is a powerful Python library for numerical computing. It provides support for efficient array operations and mathematical functions. Numpy is widely used in scientific computing, data analysis, and machine learning. In this section, we will explore some of the key features and functionalities of the Numpy library.

Numpy arrays are used to store and manipulate homogeneous data efficiently. You can create Numpy arrays using various methods, such as converting lists or tuples to arrays, using built-in functions, or reading data from files.

import numpy as np
# Create an array from a list
my_list = [1, 2, 3, 4, 5]
my_array = np.array(my_list)
# Create an array of zeros
zeros_array = np.zeros(5)
# Create an array of ones
ones_array = np.ones(5)
# Create a range of values
range_array = np.arange(1, 10, 2)
# Read data from a file
data_array = np.loadtxt('data.txt')

Array Operations

Numpy provides a wide range of array operations for performing mathematical calculations and manipulations on arrays. You can perform element-wise operations, array broadcasting, array reshaping, and more.import numpy as np# Perform element-wise operationsa = np.array([1, 2, 3])b = np.array([4, 5, 6])c = a + b# Perform array broadcastingd = np.array([[1, 2, 3], [4, 5, 6]])e = np.array([1, 2, 3])f = d + e# Reshape an arrayg = np.array([1, 2, 3, 4, 5, 6])h = g.reshape(2, 3)

Mathematical Functions

Numpy provides a wide range of mathematical functions for performing calculations on arrays. These functions can be applied to individual elements or entire arrays.

import numpy as np
# Apply mathematical functions
a = np.array([1, 2, 3, 4, 5])
mean = np.mean(a)median = np.median(a)
std_dev = np.std(a)
max_value = np.max(a)
min_value = np.min(a)