Section 1.1: Email Automation

Managing emails can be overwhelming, particularly when faced with a flood of daily messages. Utilizing Python's smtplib and imaplib libraries, I have developed a script that automates both sending and receiving emails. This script helps filter important messages, send routine replies, and categorize my inbox into designated folders.

import smtplib

import imaplib

import email

def send_email(subject, body, to):

smtp_server = 'smtp.gmail.com'

smtp_port = 587

sender_email = '[email protected]'

sender_password = 'yourpassword'

message = f'Subject: {subject}nn{body}'

with smtplib.SMTP(smtp_server, smtp_port) as server:

server.starttls()

server.login(sender_email, sender_password)

server.sendmail(sender_email, to, message)

def check_emails():

imap_server = 'imap.gmail.com'

email_user = '[email protected]'

email_pass = 'yourpassword'

with imaplib.IMAP4_SSL(imap_server) as mail:

mail.login(email_user, email_pass)

mail.select('inbox')

status, messages = mail.search(None, 'UNSEEN')

for num in messages[0].split():

typ, data = mail.fetch(num, '(RFC822)')

msg = email.message_from_bytes(data[0][1])

print(msg['subject'])

send_email('Test Subject', 'This is a test body', '[email protected]')

check_emails()

Section 1.2: Web Scraping

Data collection from the internet is a frequent requirement. By leveraging libraries like BeautifulSoup and requests, I have automated the process of extracting data from various websites, which I then use for analysis and reporting.

import requests

from bs4 import BeautifulSoup

def scrape_website(url):

response = requests.get(url)

soup = BeautifulSoup(response.text, 'html.parser')

titles = soup.find_all('h1')

for title in titles:

print(title.get_text())

Section 1.3: Social Media Automation

Handling social media can take a considerable amount of time. With Python, I automate posting updates and tracking engagement on platforms such as Twitter and LinkedIn using their respective APIs.

import tweepy

def post_tweet(message):

api_key = 'your_api_key'

api_secret_key = 'your_api_secret_key'

access_token = 'your_access_token'

access_token_secret = 'your_access_token_secret'

auth = tweepy.OAuthHandler(api_key, api_secret_key)

auth.set_access_token(access_token, access_token_secret)

api = tweepy.API(auth)

api.update_status(status=message)

post_tweet('Hello, world! This is an automated tweet.')

Section 1.4: File Management

Automating file organization is crucial for maintaining a clutter-free workspace. I have written scripts that categorize files into appropriate folders based on their file types and creation dates.

import os

import shutil

def organize_files(directory):

for filename in os.listdir(directory):

if filename.endswith('.txt'):

shutil.move(os.path.join(directory, filename), os.path.join(directory, 'TextFiles', filename))

elif filename.endswith('.jpg'):

shutil.move(os.path.join(directory, filename), os.path.join(directory, 'Images', filename))

organize_files('/path/to/your/directory')

Section 2.1: Data Backup

Regular backups are critical for data preservation. I utilize Python scripts to back up essential files to cloud storage solutions like Dropbox and Google Drive.

import dropbox

def backup_to_dropbox(file_path):

dbx = dropbox.Dropbox('your_access_token')

with open(file_path, 'rb') as f:

dbx.files_upload(f.read(), '/' + os.path.basename(file_path))

backup_to_dropbox('/path/to/your/file.txt')

Section 2.2: Report Generation

Creating reports can be automated using Python. I employ pandas and matplotlib to extract data from databases, analyze it, and produce visual reports.

import pandas as pd

import matplotlib.pyplot as plt

def generate_report(data):

df = pd.DataFrame(data)

df.plot(kind='bar')

plt.savefig('report.png')

data = {'Category': ['A', 'B', 'C'], 'Values': [10, 20, 30]}

generate_report(data)

Section 2.3: Database Management

Python's sqlite3 library simplifies database management. I automate tasks like inserting, updating, and querying data.

import sqlite3

def manage_database():

conn = sqlite3.connect('example.db')

c = conn.cursor()

c.execute('''CREATE TABLE IF NOT EXISTS users (id INT, name TEXT)''')

c.execute("INSERT INTO users (id, name) VALUES (1, 'John Doe')")

conn.commit()

for row in c.execute('SELECT * FROM users'):

print(row)

conn.close()

manage_database()

These 17 Python automation scripts have revolutionized my daily routine, saving me countless hours and allowing me to focus on more critical tasks. From managing emails and scraping data to generating reports, Python automation truly enhances productivity and efficiency.