| import pandas as pd | |
| # Input and output file paths | |
| input_csv = 'telegram_links.csv' # Replace with your input CSV file | |
| output_csv = 'cleaned_telegram_links.csv' # Output file after cleaning | |
| # Read the CSV file into a pandas DataFrame | |
| df = pd.read_csv(input_csv) | |
| # Remove duplicate links | |
| df.drop_duplicates(subset=['Telegram Links'], inplace=True) | |
| # Function to clean and filter URLs | |
| def clean_url(url): | |
| # Fix malformed URLs (e.g., 'tps://' -> 'https://') | |
| if url.startswith('tps://'): | |
| url = url.replace('tps://', 'https://') | |
| # Remove everything after '"' if present | |
| if '"' in url: | |
| url = url.split('"')[0] | |
| # Remove trailing symbols like ')๐ธ', ')๐น', etc. | |
| if ')' in url: | |
| url = url.split(')')[0] | |
| # Remove any non-URL text (e.g., "๐ค", "ุงููู ุฃูุจุฑ") | |
| if ' ' in url or '\n' in url: | |
| url = url.split()[0] # Keep only the first part (assumed to be the URL) | |
| # Ensure the URL starts with 'https://' and has a valid format | |
| if not url.startswith('https://t.me/'): | |
| return None | |
| # Count the number of '/' characters after 'https://' | |
| if url.count('/') > 3: # Allow up to 2 '/' after 'https://' | |
| return None | |
| return url | |
| # Apply the cleaning function to the DataFrame | |
| df['Telegram Links'] = df['Telegram Links'].apply(clean_url) | |
| # Drop rows with None values (filtered out URLs) | |
| df.dropna(subset=['Telegram Links'], inplace=True) | |
| # Save the cleaned DataFrame to a new CSV file | |
| df.to_csv(output_csv, index=False) | |
| print(f"Cleaned links saved to {output_csv}") |