How to automate stuff in Python

1 min readFundamentalsAutomationAdvanced
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There are several ways to automate tasks in Python, depending on the specific task you're trying to accomplish. Here are a few examples:

  • Scheduling tasks: You can use the schedule library to schedule tasks to run at specific intervals. For example, you can schedule a script to run every day at a certain time.
import schedule
import time

def my_task():
    print("Running my task...")

schedule.every().day.at("10:30").do(my_task)

while True:
    schedule.run_pending()
    time.sleep(1)
  • Web scraping: You can use the beautifulsoup4 and requests libraries to scrape data from websites. For example, you can extract information from a webpage and save it to a file or a database.
import requests
from bs4 import BeautifulSoup

url = "https://www.example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
title = soup.find('title').get_text()
print(title)
  • Automating GUI interactions: You can use the pyautogui library to automate GUI interactions such as clicking on buttons, typing text, and moving the mouse. For example, you can automate filling out a form on a website.
import pyautogui

pyautogui.click(x=100, y=200)
pyautogui.typewrite("Hello World!")
  • Automating file operations: You can use the os and shutil libraries to automate file operations such as creating, renaming, moving, and deleting files and directories. For example, you can automate the process of copying files from one location to another.
import os
import shutil

src = '/path/to/src/'
dst = '/path/to/dst/'

for file in os.listdir(src):
    s = os.path.join(src, file)
    d = os.path.join(dst, file)
    shutil.copy2(s, d)

It's worth noting that these are just a few examples and the possibilities of automating tasks in Python are endless. It's also important to consider the security and ethical implications of automating certain tasks, particularly when working with sensitive data or interacting with external systems.

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A proven roadmap to prepare for $75K+ entry-level data roles. Perfect for Data Scientist ready to level up their career.

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