5 Python Scripts Every Digital Marketer Should Know in 2024

from googleapiclient.discovery import build
from oauth2client.service_account import ServiceAccountCredentials

# Set up authentication
SCOPES = ['https://www.googleapis.com/auth/analytics.readonly']
KEY_FILE_LOCATION = 'path/to/your-service-account-key.json'
VIEW_ID = 'ga:YOUR_VIEW_ID'

# Authenticate and construct the service
credentials = ServiceAccountCredentials.from_json_keyfile_name(KEY_FILE_LOCATION, SCOPES)
analytics = build('analytics', 'v3', credentials=credentials)

# Query the API
response = analytics.data().ga().get(
    ids=VIEW_ID,
    start_date='7daysAgo',
    end_date='today',
    metrics='ga:sessions,ga:pageviews,ga:goalCompletionsAll'
).execute()

# Print the response
print(response)

import tweepy
from textblob import TextBlob
from datetime import datetime, timedelta
import pandas as pd

# Twitter API credentials
API_KEY = 'your_api_key'
API_SECRET_KEY = 'your_api_secret_key'
ACCESS_TOKEN = 'your_access_token'
ACCESS_TOKEN_SECRET = 'your_access_token_secret'

# Authenticate with Twitter
auth = tweepy.OAuth1UserHandler(API_KEY, API_SECRET_KEY, ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
api = tweepy.API(auth)

def analyze_hashtag_engagement(hashtag, days=30):
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days)

    tweets_data = []

    # Fetch tweets
    for tweet in tweepy.Cursor(api.search_tweets, q=f"#{hashtag}", lang="en", since=start_date.strftime('%Y-%m-%d')).items(200):
        tweet_data = {
            "text": tweet.text,
            "likes": tweet.favorite_count,
            "retweets": tweet.retweet_count,
            "date": tweet.created_at,
            "sentiment": TextBlob(tweet.text).sentiment.polarity
        }
        tweets_data.append(tweet_data)

    # Convert to DataFrame
    df = pd.DataFrame(tweets_data)

    # Engagement analysis
    avg_likes = df['likes'].mean()
    avg_retweets = df['retweets'].mean()
    sentiment_avg = df['sentiment'].mean()

    print(f"Hashtag: #{hashtag}")
    print(f"Average Likes: {avg_likes:.2f}")
    print(f"Average Retweets: {avg_retweets:.2f}")
    print(f"Average Sentiment: {sentiment_avg:.2f}")
    return df

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

def send_email(report, sender_email, recipient_emails, smtp_server, smtp_port):
    try:
        # Create the email message
        msg = MIMEMultipart()
        msg['From'] = sender_email
        msg['To'] = ', '.join(recipient_emails)
        msg['Subject'] = 'Weekly Performance Report'
        msg.attach(MIMEText(report, 'plain'))
        
        # Prompt for the sender's email password securely
        password = getpass(f"Enter the password for {sender_email}: ")

        # Connect to the SMTP server
        server = smtplib.SMTP(smtp_server, smtp_port)
        server.starttls()  # Upgrade the connection to secure
        server.login(sender_email, password)
        
        # Send the email
        server.sendmail(sender_email, recipient_emails, msg.as_string())
        print("Email sent successfully!")
    except Exception as e:
        print(f"An error occurred: {e}")
    finally:
        server.quit()

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.keys import Keys
from bs4 import BeautifulSoup
import time

def scrape_product_prices(url):
    # Set up the Selenium WebDriver (ensure you have the correct driver installed)
    service = Service("path/to/chromedriver")  # Update this path with your chromedriver path
    options = webdriver.ChromeOptions()
    options.add_argument('--headless')  # Run in headless mode for scraping without GUI
    driver = webdriver.Chrome(service=service, options=options)

    try:
        # Open the target URL
        driver.get(url)
        time.sleep(3)  # Allow time for page to load

        # Extract page source
        page_source = driver.page_source
        soup = BeautifulSoup(page_source, 'html.parser')

        # Example scraping logic: Update selectors for your target website
        products = []
        for product in soup.find_all('div', class_='product-class'):  # Replace with the actual class
            name = product.find('h2', class_='product-name-class').text.strip()  # Replace class
            price = product.find('span', class_='price-class').text.strip()  # Replace class
            products.append({'name': name, 'price': price})

        return products

    except Exception as e:
        print(f"An error occurred: {e}")
        return []

    finally:
        driver.quit()

import matplotlib.pyplot as plt
import pandas as pd

# Example data
data = {
    "Month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"],
    "Traffic": [1200, 1500, 1700, 1800, 2400, 2500, 2200, 2100, 2600, 2800, 3000, 3500]
}
df = pd.DataFrame(data)

def plot_monthly_traffic(dataframe):
    plt.figure(figsize=(10, 6))
    plt.plot(dataframe['Month'], dataframe['Traffic'], marker='o')
    plt.title('Monthly Traffic Growth Over the Past Year', fontsize=14)
    plt.xlabel('Month', fontsize=12)
    plt.ylabel('Traffic', fontsize=12)
    plt.grid(True)
    plt.tight_layout()
    plt.show()