12 Python Scripting Techniques for Automation

Python is a versatile programming language that can be used to automate various tasks, from simple file management to complex data analysis and manipulation. In this article, we will explore 12 Python scripting techniques for automation that you can use in your daily work.

1. File Management with os Module

The os module provides a way to interact with the operating system, including working with files and directories. You can use it to automate tasks such as:

• Creating new directories
• Copying and moving files
• Listing file contents

Here’s an example script that uses the os module to create a new directory and copy a file into it:
“`python
import os

Create a new directory

os.mkdir(‘new_directory’)

Copy a file into the new directory

os.system(‘cp example.txt new_directory’)
“`

2. String Manipulation with Regular Expressions

Regular expressions are powerful patterns that can be used to match and manipulate strings. Python’s re module provides support for regular expressions, which you can use to automate tasks such as:

• Extracting specific information from text
• Validating user input

Here’s an example script that uses the re module to extract email addresses from a string:
“`python
import re

Define a regular expression pattern for email addresses

pattern = r’\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Z|a-z]{2,}\b’

Apply the pattern to a string and extract matches

matches = re.findall(pattern, ‘Contact me at john.doe@example.com or jane.smith@example.com’)

print(matches) # Output: [‘john.doe@example.com’, ‘jane.smith@example.com’]
“`

3. Data Analysis with Pandas

Pandas is a powerful library for data manipulation and analysis in Python. You can use it to automate tasks such as:

• Importing and merging datasets
• Filtering and sorting data

Here’s an example script that uses the Pandas library to import a CSV file and filter out rows based on a condition:
“`python
import pandas as pd

Load a CSV file into a Pandas DataFrame

df = pd.read_csv(‘data.csv’)

Filter out rows where the ‘age’ column is less than 18

filtered_df = df[df[‘age’] >= 18]

print(filtered_df) # Output: filtered DataFrame
“`

4. Web Scraping with requests and BeautifulSoup

The requests library provides a simple way to make HTTP requests in Python, while BeautifulSoup can be used to parse HTML content. You can use them to automate tasks such as:

• Scrolling through web pages to extract data
• Submitting forms and gathering responses

Here’s an example script that uses the requests and BeautifulSoup libraries to scrape a website:
“`python
import requests
from bs4 import BeautifulSoup

Send a GET request to the website

response = requests.get(‘https://www.example.com’)

Parse the HTML content with BeautifulSoup

soup = BeautifulSoup(response.content, ‘html.parser’)

Extract specific information from the parsed HTML

title = soup.title.text
print(title) # Output: webpage title
“`

5. Automated Testing with Unittest

Unittest is a built-in Python library for unit testing and debugging. You can use it to automate tasks such as:

• Writing test cases for functions and methods
• Running tests and analyzing results

Here’s an example script that uses the Unittest library to write and run test cases:
“`python
import unittest

Define a function to be tested

def add(x, y):
return x + y

class TestAddFunction(unittest.TestCase):
def test_add(self):
self.assertEqual(add(2, 3), 5)
self.assertEqual(add(-1, 1), 0)

if name == ‘main‘:
unittest.main()
“`

6. Database Interaction with SQLite

SQLite is a lightweight database library that can be used to store and manage data in Python. You can use it to automate tasks such as:

• Creating tables and inserting data
• Querying data and retrieving results

Here’s an example script that uses the SQLite library to interact with a database:
“`python
import sqlite3

Connect to the SQLite database

connection = sqlite3.connect(‘example.db’)

Create a table

cursor = connection.cursor()
cursor.execute(‘CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT)’)

Insert data into the table

cursor.execute(“INSERT INTO users VALUES (‘1’, ‘John Doe’)”)

Query data from the table and print results

cursor.execute(‘SELECT * FROM users’)
results = cursor.fetchall()
for row in results:
print(row)

Close the database connection

connection.close()
“`

7. GUI Automation with Tkinter

Tkinter is a built-in Python library for creating graphical user interfaces (GUIs). You can use it to automate tasks such as:

• Creating GUI windows and buttons
• Handling events and input

Here’s an example script that uses the Tkinter library to create a simple GUI:
“`python
import tkinter as tk

Create a Tkinter window

window = tk.Tk()
window.title(‘Example Window’)

Create labels and buttons within the window

label = tk.Label(window, text=’Hello World!’)
button = tk.Button(window, text=’Click Me!’, command=lambda: print(‘Button clicked!’))

Layout the widgets within the window

label.pack()
button.pack()

Start the Tkinter event loop

window.mainloop()
“`

8. Network Automation with Scapy

Scapy is a powerful library for network exploration and automation in Python. You can use it to automate tasks such as:

• Sniffing packets and analyzing protocols
• Sending custom packets and simulating network scenarios

Here’s an example script that uses the Scapy library to send a simple packet:
“`python
from scapy.all import IP, TCP, Raw

Create a new IP packet with a destination IP address of ‘192.168.1.100’

packet = IP(dst=’192.168.1.100′) / TCP(dport=80) / Raw(b’Hello World!’)

Send the packet using Scapy’s sr function

response = scapy.sr(packet)

print(response)
“`

9. Image and Video Processing with OpenCV

OpenCV is a popular library for image and video processing in Python. You can use it to automate tasks such as:

• Capturing and displaying images and videos
• Applying filters and performing object detection

Here’s an example script that uses the OpenCV library to display a captured image:
“`python
import cv2

Capture an image from the default camera device

image = cv2.imread(‘example.jpg’)

Display the image using OpenCV’s imshow function

cv2.imshow(‘Image’, image)

Wait for a key press before closing the window

cv2.waitKey(0)
cv2.destroyAllWindows()
“`

10. Machine Learning with scikit-learn

scikit-learn is a popular library for machine learning in Python. You can use it to automate tasks such as:

• Training and evaluating models on datasets
• Selecting and tuning hyperparameters

Here’s an example script that uses the scikit-learn library to train a simple model:
“`python
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

Load the Iris dataset

iris = load_iris()

Split the data into training and test sets

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

Train a logistic regression model on the training data

model = LogisticRegression()
model.fit(X_train, y_train)

Evaluate the model on the test data and print results

accuracy = model.score(X_test, y_test)
print(accuracy)
“`

11. System Administration with Pygame

Pygame is a cross-platform set of Python modules designed for writing video games. You can use it to automate tasks such as:

• Creating graphical windows and handling events
• Drawing shapes and text within the window

Here’s an example script that uses the Pygame library to create a simple window:
“`python
import pygame

Initialize the Pygame module

pygame.init()

Create a new window with a size of 800×600 pixels

window = pygame.display.set_mode((800, 600))

Set the title of the window to ‘Example Window’

pygame.display.set_caption(‘Example Window’)

Run the Pygame event loop until the user closes the window

running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False

Quit the Pygame module

pygame.quit()
“`

12. Cloud Computing with Boto3

Boto3 is a Python SDK for Amazon Web Services (AWS). You can use it to automate tasks such as:

• Creating and managing AWS resources like EC2 instances, S3 buckets, and Lambda functions
• Interacting with other AWS services like DynamoDB and SQS

Here’s an example script that uses the Boto3 library to create a simple S3 bucket:
“`python
import boto3

Create a new S3 client using the Boto3 library

s3 = boto3.client(‘s3’)

Define the name of the S3 bucket to be created

bucket_name = ‘example-bucket’

Use the Boto3 library to create the S3 bucket

response = s3.create_bucket(Bucket=bucket_name)

print(response)
“`

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