17 Practical Python Scripts for Automating Common Tasks

The article introduces a series of Python automation scripts aimed at reducing repetitive work for developers, data analysts, and anyone looking to streamline workflows. Each script is accompanied by a brief description and the full source code.

1. File Management

<code># Python script to sort files in a directory by their extension
import os
from shutil import move

def sort_files(directory_path):
    for filename in os.listdir(directory_path):
        if os.path.isfile(os.path.join(directory_path, filename)):
            file_extension = filename.split('.')[-1]
            destination_directory = os.path.join(directory_path, file_extension)
            if not os.path.exists(destination_directory):
                os.makedirs(destination_directory)
            move(os.path.join(directory_path, filename), os.path.join(destination_directory, filename))
</code>

2. Web Scraping

<code># Python script for web scraping to extract data from a website
import requests
from bs4 import BeautifulSoup

def scrape_data(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    # Your code here to extract relevant data from the website
</code>

3. Email Automation

<code># Python script to send personalized emails to a list of recipients
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

def send_personalized_email(sender_email, sender_password, recipients, subject, body):
    server = smtplib.SMTP('smtp.gmail.com', 587)
    server.starttls()
    server.login(sender_email, sender_password)
    for recipient_email in recipients:
        message = MIMEMultipart()
        message['From'] = sender_email
        message['To'] = recipient_email
        message['Subject'] = subject
        message.attach(MIMEText(body, 'plain'))
        server.sendmail(sender_email, recipient_email, message.as_string())
    server.quit()
</code>

4. Excel Automation

<code># Python script to read and write data to an Excel spreadsheet
import pandas as pd

def read_excel(file_path):
    df = pd.read_excel(file_path)
    return df

def write_to_excel(data, file_path):
    df = pd.DataFrame(data)
    df.to_excel(file_path, index=False)
</code>

5. Database Interaction

<code># Python script to connect to a database and execute queries
import sqlite3

def connect_to_database(database_path):
    connection = sqlite3.connect(database_path)
    return connection

def execute_query(connection, query):
    cursor = connection.cursor()
    cursor.execute(query)
    result = cursor.fetchall()
    return result
</code>

6. System Task Automation

<code># Python script to manage system processes
import psutil

def get_running_processes():
    return [p.info for p in psutil.process_iter(['pid', 'name', 'username'])]

def kill_process_by_name(process_name):
    for p in psutil.process_iter(['pid', 'name', 'username']):
        if p.info['name'] == process_name:
            p.kill()
</code>

7. Image Processing

<code># Python script to resize and crop images
from PIL import Image

def resize_image(input_path, output_path, width, height):
    image = Image.open(input_path)
    resized_image = image.resize((width, height), Image.ANTIALIAS)
    resized_image.save(output_path)

def crop_image(input_path, output_path, left, top, right, bottom):
    image = Image.open(input_path)
    cropped_image = image.crop((left, top, right, bottom))
    cropped_image.save(output_path)
</code>

8. Network Automation

<code># Python script to check the status of a website
import requests

def check_website_status(url):
    response = requests.get(url)
    if response.status_code == 200:
        # Handle successful response
        pass
    else:
        # Handle unsuccessful response
        pass
</code>

9. Data Cleaning & Transformation

<code># Python script to remove duplicates from data
import pandas as pd

def remove_duplicates(data_frame):
    cleaned_data = data_frame.drop_duplicates()
    return cleaned_data
</code>

10. PDF Automation

<code># Python script to extract text from PDFs
import PyPDF2

def extract_text_from_pdf(file_path):
    with open(file_path, 'rb') as f:
        pdf_reader = PyPDF2.PdfFileReader(f)
        text = ''
        for page_num in range(pdf_reader.numPages):
            page = pdf_reader.getPage(page_num)
            text += page.extractText()
    return text
</code>

11. GUI Automation

<code># Python script for GUI automation using pyautogui
import pyautogui

def automate_gui():
    # Your code here for GUI automation using pyautogui
    pass
</code>

12. Testing Automation

<code># Python script for unit testing with the unittest module
import unittest

def add(a, b):
    return a + b

class TestAddFunction(unittest.TestCase):
    def test_add_positive_numbers(self):
        self.assertEqual(add(2, 3), 5)
    def test_add_negative_numbers(self):
        self.assertEqual(add(-2, -3), -5)
    def test_add_zero(self):
        self.assertEqual(add(5, 0), 5)

if __name__ == '__main__':
    unittest.main()
</code>

13. Cloud Service Automation

<code># Python script to manage AWS resources using Boto3
import boto3

def create_ec2_instance(instance_type, image_id, key_name, security_group_ids):
    ec2 = boto3.resource('ec2')
    instance = ec2.create_instances(
        ImageId=image_id,
        InstanceType=instance_type,
        KeyName=key_name,
        SecurityGroupIds=security_group_ids,
        MinCount=1,
        MaxCount=1
    )
    return instance[0].id
</code>

14. Financial Automation

<code># Python script for stock price analysis (placeholder for actual implementation)
# Your code here to fetch stock data using a financial API (e.g., Yahoo Finance)
# Your code here to analyze the data and derive insights
</code>

15. Natural Language Processing

<code># Python script for sentiment analysis using NLTK
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

def analyze_sentiment(text):
    nltk.download('vader_lexicon')
    sia = SentimentIntensityAnalyzer()
    sentiment_score = sia.polarity_scores(text)
    return sentiment_score
</code>

The article concludes by encouraging readers to start using Python for automation to save time, reduce errors, and boost overall productivity across a wide range of domains.