NumPy is a powerful library for numerical computing in Python. It provides support for arrays, matrices, and many mathematical functions. In this tutorial, we will explore the basics of NumPy arrays, how to create them, and some common operations you can perform on them.
First, we need to import the NumPy library. It is usually imported with the alias np:
import numpy as npYou can create NumPy arrays in several ways. The most common method is to convert a Python list to a NumPy array using the np.array() function:
# Create a 1D array
arr = np.array([1, 2, 3, 4, 5])
print(arr)
# Output: [1 2 3 4 5]
NumPy also provides functions to create arrays of zeros, ones, and random numbers:
# Array of zeros
zeros = np.zeros(5)
print(zeros)
# Output: [0. 0. 0. 0. 0.]
# Array of ones
ones = np.ones(5)
print(ones)
# Output: [1. 1. 1. 1. 1.]
# Array of random numbers
random = np.random.rand(5)
print(random)
# Output: [0.5488135 0.71518937 0.60276338 0.54488318 0.4236548 ] (example output, will vary)
How can you create a 2D array (matrix) with NumPy?
You can create a 2D array by passing a list of lists to the np.array() function. For example:
# Create a 2D array (matrix)
matrix = np.array([[1, 2, 3], [4, 5, 6],
[7, 8, 9]])
print(matrix)
# Output:
# [[1 2 3]
# [4 5 6]
# [7 8 9]]
NumPy arrays have several attributes that are useful for understanding the array's properties:
arr = np.array([[1, 2, 3], [4, 5, 6]])
# Shape of the array
print(arr.shape)
# Output: (2, 3)
# Number of dimensions
print(arr.ndim)
# Output: 2
# Size of the array (total number of elements)
print(arr.size)
# Output: 6
# Data type of the elements
print(arr.dtype)
# Output: int64
What will be the output of the following code?
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)
print(arr.ndim)
print(arr.size)
print(arr.dtype)
The output will be:
(2, 3)
2
6
int64
This indicates that the array has 2 rows and 3 columns, is 2-dimensional, contains 6 elements, and the data type of the elements is int64.
You can access elements of a NumPy array using indexing and slicing:
arr = np.array([1, 2, 3, 4, 5])
# Access the first element
print(arr[0])
# Output: 1
# Access elements from index 1 to 3 (excluding 3)
print(arr[1:3])
# Output: [2 3]
For 2D arrays, you can use two indices to access elements:
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
# Access the element at row 1, column 2
print(matrix[1, 2])
# Output: 6
# Access the first two rows and columns 1 and 2
print(matrix[:2, 1:3])
# Output:
# [[2 3]
# [5 6]]
What will be the output of the following code?
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix[2, 1])
print(matrix[:2, 1:3])
The output will be:
8
[[2 3]
[5 6]]
This shows that the element at row 2, column 1 is 8, and the slice of the first two rows and columns 1 and 2 is:
[[2 3]
[5 6]]
NumPy allows you to perform element-wise operations on arrays:
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
# Element-wise addition
print(arr1 + arr2)
# Output: [5 7 9]
# Element-wise multiplication
print(arr1 * arr2)
# Output: [ 4 10 18]
# Element-wise square root
print(np.sqrt(arr1))
# Output: [1. 1.41421356 1.73205081]
What will be the output of the following code?
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(arr1 - arr2)
print(arr1 / arr2)
The output will be:
[-3 -3 -3]
[0.25 0.4 0.5 ]
This shows the element-wise subtraction and division of the arrays arr1 and arr2.
You can reshape an array using the reshape() method:
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
# Reshape to 3x3 matrix
matrix = arr.reshape((3, 3))
print(matrix)
# Output:
# [[1 2 3]
# [4 5 6]
# [7 8 9]]
What will be the output of the following code?
arr = np.array([1, 2, 3, 4, 5, 6])
# Reshape to 2x3 matrix
matrix = arr.reshape((2, 3))
print(matrix)
The output will be:
[[1 2 3]
[4 5 6]]
This shows that the array arr has been reshaped into a 2x3 matrix.
Broadcasting allows you to perform operations on arrays of different shapes. NumPy automatically handles the element-wise operations:
arr = np.array([1, 2, 3])
scalar = 2
# Multiply array by scalar
print(arr * scalar)
# Output: [2 4 6]
# Add a 1D array to a 2D array
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix + arr)
# Output:
# [[2 4 6]
# [5 7 9]]
What will be the output of the following code?
arr = np.array([1, 2, 3])
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix - arr)
The output will be:
[[0 0 0]
[3 3 3]]
This shows that the 1D array arr is subtracted from each row of the 2D array matrix due to broadcasting.
This tutorial covered the basics of NumPy arrays, including how to create, manipulate, and perform operations on them. NumPy is a versatile library that provides many more functions and capabilities for numerical computing in Python. For more information, refer to the official NumPy documentation.