Python Advanced - Data Manipulation with NumPy

Performing array manipulation and numerical operations using NumPy

NumPy is a powerful library for numerical computing in Python. It provides support for arrays, matrices, and a wide range of mathematical functions to perform operations on these data structures. This tutorial explores how to use NumPy for array manipulation and numerical operations.

Installing NumPy

To use NumPy, you need to install it using pip:

pip install numpy
            

Creating Arrays

NumPy provides various ways to create arrays. Here are some examples:

import numpy as np

# Creating an array from a list
arr = np.array([1, 2, 3, 4, 5])
print(arr)

# Creating an array of zeros
zeros = np.zeros((3, 3))
print(zeros)

# Creating an array of ones
ones = np.ones((2, 2))
print(ones)

# Creating an array with a range of values
range_arr = np.arange(0, 10, 2)
print(range_arr)

# Creating an array with random values
random_arr = np.random.rand(3, 3)
print(random_arr)
            

Array Indexing and Slicing

NumPy allows you to index and slice arrays efficiently. Here are some examples:

# Indexing an array
print(arr[0])  # Output: 1

# Slicing an array
print(arr[1:4])  # Output: [2 3 4]

# Indexing a 2D array
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix[0, 0])  # Output: 1
print(matrix[:, 1])  # Output: [2 5 8]
print(matrix[1, :])  # Output: [4 5 6]
            

Array Operations

NumPy provides a wide range of functions for performing operations on arrays. Here are some examples:

# Element-wise addition
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(arr1 + arr2)  # Output: [5 7 9]

# Element-wise multiplication
print(arr1 * arr2)  # Output: [ 4 10 18]

# Dot product
print(np.dot(arr1, arr2))  # Output: 32

# Transpose of a matrix
print(matrix.T)

# Sum of elements
print(np.sum(arr1))  # Output: 6

# Mean of elements
print(np.mean(arr1))  # Output: 2.0

# Standard deviation
print(np.std(arr1))  # Output: 0.816496580927726
            

Broadcasting

Broadcasting allows NumPy to perform operations on arrays of different shapes. Here is an example:

# Broadcasting example
arr = np.array([1, 2, 3])
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix + arr)
# Output:
# [[ 2  4  6]
#  [ 5  7  9]
#  [ 8 10 12]]
            

Linear Algebra

NumPy provides functions for performing linear algebra operations. Here are some examples:

# Matrix multiplication
matrix1 = np.array([[1, 2], [3, 4]])
matrix2 = np.array([[5, 6], [7, 8]])
print(np.matmul(matrix1, matrix2))

# Determinant of a matrix
print(np.linalg.det(matrix1))

# Inverse of a matrix
print(np.linalg.inv(matrix1))

# Eigenvalues and eigenvectors
eigenvalues, eigenvectors = np.linalg.eig(matrix1)
print(eigenvalues)
print(eigenvectors)
            

Advanced Indexing

NumPy supports advanced indexing techniques for selecting and modifying elements in arrays. Here are some examples:

# Boolean indexing
arr = np.array([1, 2, 3, 4, 5])
print(arr[arr > 2])  # Output: [3 4 5]

# Fancy indexing
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix[[0, 1], [1, 2]])  # Output: [2 6]
            

Reshaping and Resizing

NumPy allows you to reshape and resize arrays. Here are some examples:

# Reshaping an array
arr = np.arange(1, 10)
reshaped_arr = arr.reshape((3, 3))
print(reshaped_arr)

# Flattening an array
flattened_arr = reshaped_arr.flatten()
print(flattened_arr)

# Resizing an array
resized_arr = np.resize(arr, (2, 5))
print(resized_arr)
            

Saving and Loading Arrays

NumPy provides functions for saving and loading arrays to and from files. Here are some examples:

# Saving an array to a file
np.save('array.npy', arr)

# Loading an array from a file
loaded_arr = np.load('array.npy')
print(loaded_arr)

# Saving an array to a text file
np.savetxt('array.txt', arr)

# Loading an array from a text file
loaded_txt_arr = np.loadtxt('array.txt')
print(loaded_txt_arr)
            

Summary

In this tutorial, you learned about performing array manipulation and numerical operations using NumPy in Python. NumPy provides support for efficient array and matrix operations, a wide range of mathematical functions, and various tools for manipulating and processing numerical data. Understanding NumPy is essential for numerical computing and data analysis in Python.