12.2.1 Automating repetitive tasks with Python scripts

One of the most powerful uses of Python is its ability to automate repetitive tasks. By writing Python scripts, you can save a significant amount of time and effort by handling tasks that would otherwise need to be done manually. This could include tasks like file management, web scraping, sending automated emails, data processing, and more.

Here’s a breakdown of how to automate common repetitive tasks with Python:

1. Automating File Management Tasks

One of the most common automation tasks is managing files—whether it’s renaming files, organizing files into directories, or moving files based on certain conditions.

Example 1: Renaming Files in a Directory

You can automate the process of renaming files in a directory. Let’s say you have a folder with images, and you want to add a prefix or suffix to all file names.

import os

# Directory containing the files
directory = '/path/to/your/folder/'

# Loop through all files in the directory
for filename in os.listdir(directory):
    if filename.endswith(".jpg"):
        new_name = "prefix_" + filename
        os.rename(os.path.join(directory, filename), os.path.join(directory, new_name))
        print(f"Renamed: {filename} -> {new_name}")

This script renames all .jpg files by adding a "prefix_" to their original names.

Example 2: Moving Files Based on File Extension

Another automation task could be moving files of certain types to specific folders.

import shutil
import os

source_directory = '/path/to/source/folder'
destination_directory = '/path/to/destination/folder'

# Move all text files to another folder
for filename in os.listdir(source_directory):
    if filename.endswith(".txt"):
        shutil.move(os.path.join(source_directory, filename), os.path.join(destination_directory, filename))
        print(f"Moved: {filename}")

This script moves all .txt files from one directory to another.

2. Automating Web Scraping

Web scraping allows you to automate the process of extracting data from websites, which can be useful for gathering product prices, news, stock data, etc.

Example: Scraping Data from a Website

Using libraries like requests and BeautifulSoup, you can automate the extraction of data from HTML pages.

import requests
from bs4 import BeautifulSoup

# URL of the website
url = 'https://example.com'

# Get the page content
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

# Extracting all headlines (assuming headlines are in <h2> tags)
headlines = soup.find_all('h2')
for headline in headlines:
    print(headline.text)

This script automates the task of extracting headlines from a webpage.

3. Automating Email Notifications

Automating the sending of email notifications can be helpful for alerting users about specific events, such as when a report is generated or when an error occurs in a task.

Example: Sending Automated Emails

Here’s how to send an email notification automatically using Python’s smtplib.

import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

# Email credentials and message details
sender_email = 'your-email@example.com'
receiver_email = 'receiver@example.com'
password = 'your-email-password'

# Setting up the MIME message
message = MIMEMultipart()
message['From'] = sender_email
message['To'] = receiver_email
message['Subject'] = 'Automated Email Notification'

# Body of the email
body = 'This is an automated email sent by Python script.'
message.attach(MIMEText(body, 'plain'))

# Sending the email
try:
    server = smtplib.SMTP('smtp.example.com', 587)
    server.starttls()
    server.login(sender_email, password)
    server.sendmail(sender_email, receiver_email, message.as_string())
    print('Email sent successfully.')
except Exception as e:
    print(f"Error: {e}")
finally:
    server.quit()

This script sends an automated email with a specified message.

4. Automating Data Processing

You can automate the process of processing data, such as reading data from CSV files, performing calculations, and saving the results.

Example: Processing Data from a CSV File

Let’s say you have a CSV file with sales data, and you want to automate the process of calculating the total sales.

import pandas as pd

# Read the CSV file
data = pd.read_csv('sales_data.csv')

# Calculate total sales
total_sales = data['Sales'].sum()

# Print the result
print(f'Total Sales: {total_sales}')

This script automates the process of reading the data from a CSV file and calculating the total sales.

5. Automating System Administration Tasks

System administration tasks like checking disk usage, monitoring system performance, and cleaning up old log files can also be automated.

Example: Deleting Old Log Files

If you need to delete log files that are older than 30 days, you can use Python to automate this task.

import os
import time

log_directory = '/path/to/logs/'
current_time = time.time()

# Loop through the files in the directory
for filename in os.listdir(log_directory):
    file_path = os.path.join(log_directory, filename)
    # Check if file is older than 30 days
    if os.path.getmtime(file_path) < current_time - 30 * 86400:
        os.remove(file_path)
        print(f'Deleted: {filename}')

This script deletes log files that have not been modified in the last 30 days.

6. Scheduling Automation Tasks

Once you’ve written your Python scripts to automate tasks, you might want them to run at regular intervals. You can schedule Python scripts to run automatically using:

• Windows Task Scheduler: To run a Python script at a specific time.
• Cron Jobs: For Linux/macOS to schedule tasks.

For example, on Linux, use crontab to schedule a script to run every day at 8 AM:

0 8 * * * /usr/bin/python3 /path/to/script.py

7. Conclusion

Automating repetitive tasks with Python scripts allows you to save time, reduce human error, and increase productivity. By automating tasks like file management, data processing, web scraping, and email notifications, you can focus on more strategic activities. Python's rich ecosystem of libraries and frameworks makes it an excellent choice for automating tasks across different domains.

Next Steps:

• Explore more Python libraries for automation, like selenium for browser automation or schedule for task scheduling.
• Build more complex workflows by combining multiple automated tasks.