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DAY-8 | Automation With Python Scripting

Sending an Email with smtplib and MIMEText

import smtplib
from email.mime.text import MIMEText

smtp_server = 'smtp.gmail.com'
port = 587  # For TLS
sender_email = 'thenoobgunmaster@gmail.com'
receiver_email = 'thenoobgunmaster@gmail.com'
message = 'Hello, this is an automated message.'

# Create a MIMEText object for the email content
msg = MIMEText(message)
msg['Subject'] = 'Automated Email'
msg['From'] = sender_email
msg['To'] = receiver_email

# Set up the connection to the SMTP server
with smtplib.SMTP(smtp_server, port) as server:
    # Start TLS encryption
    server.starttls()

    # Login to your Gmail account
    password = input("Enter your Gmail password and press enter: ")
    server.login(sender_email, password)

    # Send the email
    server.sendmail(sender_email, [receiver_email], msg.as_string())

Explanation:

  • This script sends an email using the Gmail SMTP server.
  • It uses the smtplib library for sending emails and MIMEText for formatting the email content.
  • You need to replace the sender_email and receiver_email with your own email addresses.
  • The email's subject, sender, and receiver are set using the msg object.
  • It prompts the user for their Gmail password (input is hidden) and uses it to log in to the Gmail account.
  • Finally, it sends the email.

Extracting Links from a Web Page

import requests
from bs4 import BeautifulSoup

url = 'https://example.com'
response = requests.get(url)

soup = BeautifulSoup(response.text, 'html.parser')
links = [link['href'] for link in soup.find_all('a')]

print(links)

Explanation:

  • This script extracts all the links from a webpage.
  • It uses the requests library to fetch the content of the webpage.
  • BeautifulSoup is used to parse the HTML content.
  • It finds all anchor (<a>) tags and extracts the href attribute, which contains the link.
  • The links are stored in the links list and then printed.

Renaming Files in a Directory

import os

directory = 'C:/Users/jaisw/Videos/Python/'

for filename in os.listdir(directory):
    if filename.endswith('.txt'):
        new_name = filename.replace('old_', 'new_')
        os.rename(os.path.join(directory, filename), os.path.join(directory, new_name))

Explanation:

  • This script renames files in a specified directory.
  • It iterates through all files in the directory and checks if they have a .txt extension.
  • If so, it constructs a new name by replacing 'old_' with 'new_' in the filename.
  • Then, it uses os.rename to rename the file.

Removing Old Files from a Directory

import os
import datetime

directory = 'C:/Users/jaisw/Videos/Python/'
threshold_date = datetime.datetime(2023, 10, 1)

for filename in os.listdir(directory):
    filepath = os.path.join(directory, filename)
    if os.path.isfile(filepath) and os.path.getmtime(filepath) < threshold_date.timestamp():
        os.remove(filepath)

Explanation:

  • This script removes files in a directory that are older than a specified date.
  • It uses os.listdir to iterate through all files in the directory.
  • For each file, it checks if it's a file and if its modification time (os.path.getmtime) is earlier than the specified threshold date.
  • If so, it uses os.remove to delete the file.

Scheduled Task using schedule

import schedule
import time

def job():
    print("This is a scheduled task.")

# Schedule a task to run every day at 2 PM
schedule.every().day.at("14:00").do(job)

while True:
    schedule.run_pending()
    time.sleep(1)

Explanation:

  • This script schedules a task to run at a specified time.
  • It defines a function job that prints a message.
  • Using schedule, it sets up the task to run every day at 2:00 PM.
  • The script enters an infinite loop where it checks if there are any pending scheduled tasks to run.

Shutting Down the System

import os

os.system('shutdown /s /t 0')  # Shutdown Windows

# For Linux/macOS
# os.system('shutdown -h now')

Explanation:

  • This script shuts down the system.
  • It uses os.system to execute a command in the system's shell.
  • On Windows, it uses shutdown /s /t 0 to initiate a shutdown.
  • For Linux/macOS, the command would be shutdown -h now to initiate a shutdown.

Certainly! I'll provide you with Python code snippets for each of the mentioned automation tasks along with explanations.

Automating Data Entry

Filling out forms or submitting data on websites:

from selenium import webdriver

# Initialize the webdriver (make sure you have chromedriver installed)
driver = webdriver.Chrome()

# Open the website
driver.get('http://example.com')

# Find the form element and fill it out
input_element = driver.find_element_by_name('username')
input_element.send_keys('your_username')

# Submit the form
input_element.submit()

# Close the browser
driver.quit()

Explanation:

  1. We're using the Selenium library to automate web interactions.
  2. Initialize a web driver (in this case, Chrome).
  3. Open a website.
  4. Find the input field and fill it with your desired data.
  5. Submit the form.
  6. Close the browser.

Automating Social Media

Posting updates on platforms like Twitter or Instagram:

from selenium import webdriver

driver = webdriver.Chrome()

# Open Twitter and log in
driver.get('https://twitter.com/login')
username = driver.find_element_by_name('session[username_or_email]')
password = driver.find_element_by_name('session[password]')
username.send_keys('your_username')
password.send_keys('your_password')
password.submit()

# Compose a tweet
tweet_box = driver.find_element_by_xpath('//div[@role="textbox"]')
tweet_box.send_keys('Automating my tweets with Python! #Automation #Python')

# Click the tweet button
tweet_button = driver.find_element_by_xpath('//div[@data-testid="tweetButtonInline"]')
tweet_button.click()

# Close the browser
driver.quit()

Explanation:

  1. We're again using Selenium to automate interactions with the web.
  2. Open Twitter, log in, and compose a tweet.

Automatically responding to messages or comments:

# Note: This example is a simplified demonstration and may not work for all platforms.

# Assuming you're using a hypothetical social media API called 'mysocialapi'
import mysocialapi

def respond_to_messages():
    messages = mysocialapi.get_new_messages()
    for message in messages:
        sender = message.sender
        content = message.content

        response = f"Hi {sender}! Thanks for your message: {content}"
        mysocialapi.send_message(sender, response)

respond_to_messages()

Explanation:

  1. In this example, we're assuming the existence of a hypothetical social media API.
  2. We define a function respond_to_messages which fetches new messages and responds to them.

Automating PDF Operations

Merging, splitting, or extracting pages from PDF files:

from PyPDF2 import PdfFileReader, PdfFileWriter

# Merging PDFs
pdf1 = PdfFileReader(open('file1.pdf', 'rb'))
pdf2 = PdfFileReader(open('file2.pdf', 'rb'))

pdf_writer = PdfFileWriter()
pdf_writer.addPage(pdf1.getPage(0))
pdf_writer.addPage(pdf2.getPage(0))

with open('merged.pdf', 'wb') as out_pdf:
    pdf_writer.write(out_pdf)

# Extracting pages
pdf = PdfFileReader(open('source.pdf', 'rb'))
pdf_writer = PdfFileWriter()

for page_num in range(2, 6):  # Extract pages 2 to 5
    pdf_writer.addPage(pdf.getPage(page_num))

with open('extracted.pdf', 'wb') as out_pdf:
    pdf_writer.write(out_pdf)

Explanation:

  1. We're using the PyPDF2 library to handle PDF operations.
  2. Merging: Open two PDF files, add pages to a new PDF writer, and save the merged file.
  3. Extracting: Open a PDF file, add specific pages to a new PDF writer, and save the extracted file.

Converting PDFs to other formats (e.g., Word, Excel):

from pdf2docx import Converter

pdf_file = "sample.pdf"
docx_file = "output.docx"

cv = Converter(pdf_file)
cv.convert(docx_file, start=0, end=None)
cv.close()

Explanation:

  1. Here, we're using the pdf2docx library to convert PDF to Word.

Automating System Tasks

Scheduling tasks like backups or system maintenance:

You can use system tools like cron (on Linux) or Task Scheduler (on Windows) to schedule the execution of your Python scripts.

Monitoring system resources and taking actions based on conditions:

import psutil

# Example: If CPU usage is above 90%, send an alert
if psutil.cpu_percent() > 90:
    send_alert_email("High CPU Usage", "CPU usage is above 90%")

Explanation:

  1. We're using the psutil library to monitor system resources.
  2. If a condition is met (e.g., high CPU usage), you can take appropriate actions.

Automating Data Analysis

Running regular reports and sending them to stakeholders:

import pandas as pd
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication

# Generate report using pandas
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Score': [85, 90, 75]}
df = pd.DataFrame(data)

# Save the report to a file (e.g., CSV)
df.to_csv('report.csv', index=False)

# Send the report via email
msg = MIMEMultipart()
msg['From'] = 'your_email@gmail.com'
msg['To'] = 'recipient@example.com'
msg['Subject'] = 'Monthly Report'

body = "Please find attached the monthly report."
msg.attach(MIMEText(body, 'plain'))

with open('report.csv', 'rb') as attachment:
    part = MIMEApplication(attachment.read(), Name='report.csv')
    part['Content-Disposition'] = f'attachment; filename={"report.csv"}'
    msg.attach(part)

server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login('your_email@gmail.com', 'your_password')
server.sendmail('your_email@gmail.com', 'recipient@example.com', msg.as_string())
server.quit()

Explanation:

  1. We're using pandas for data analysis and smtplib for sending emails.
  2. Generate a report (in this case, a CSV file), attach it to an email, and send it.

Processing data and generating visualizations:

import pandas as pd
import matplotlib.pyplot as plt

# Load data
data = pd.read_csv('data.csv')

# Data processing
# (e.g., calculate summary statistics, filter data)

# Generate visualizations
plt.figure(figsize=(10, 5))
plt.plot(data['Date'], data['Value'])
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Data Visualization')
plt.savefig('visualization.png')

Explanation:

  1. Load data using pandas.
  2. Process the data (e.g., calculate summary statistics, filter).
  3. Generate visualizations using matplotlib.

Automating Tests

Writing and running automated tests for software applications:

Example using unittest:

import unittest

class TestMathFunctions(unittest.TestCase):

    def test_addition(self):
        self.assertEqual(1 + 1, 2)

    def test_subtraction(self):
        self.assertEqual(3 - 1, 2)

    def test_multiplication(self):
        self.assertEqual(2 * 2, 4)

    def test_division(self):
        self.assertEqual(6 / 2, 3)

if __name__ == '__main__':
    unittest.main()

Explanation:

  1. We're using the built-in unittest framework for writing and running tests.
  2. Define test cases as classes inheriting from unittest.TestCase.
  3. Write individual test methods (e.g., test_addition, test_subtraction).
  4. Run the tests using unittest.main().

Automating Social Media Data Analysis

Extracting data from social media APIs for analysis or reporting:

import requests

# Assuming we're using a hypothetical social media API
api_url = 'https://api.example.com/posts'

response = requests.get(api_url)
data = response.json()

# Process and analyze data
# (e.g., calculate engagement metrics, sentiment analysis)

Explanation:

  1. Make a request to a hypothetical social media API to retrieve data.
  2. Process and analyze the data as per your specific requirements (e.g., calculating engagement metrics, sentiment analysis).

Monitoring social media trends or mentions:

import tweepy

# Assuming you have Twitter API credentials
consumer_key = 'your_consumer_key'
consumer_secret = 'your_consumer_secret'
access_token = 'your_access_token'
access_token_secret = 'your_access_token_secret'

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)

# Search for tweets with a specific keyword
tweets = api.search(q='python', count=10)

for tweet in tweets:
    print(tweet.text)

Explanation:

  1. Use the Tweepy library to access the Twitter API.
  2. Search for tweets containing a specific keyword (in this case, 'python').