Problem 😵‍💫

I had a few large collection of emails in the MBOX format, and I needed to filter out emails that contained a particular keyword in either the subject or the body of the email. The goal was to extract these emails and store them in a structured format, such as CSV, for further analysis and reference.

Solution

I used Python to create a script that processes the MBOX file, that i downloaded from my apple mail and extracts the relevant emails. Key steps:

1. Extracting Emails from MBOX File: Using the mailbox module, the script reads the MBOX file and iterates through each email message.

2. Filtering Emails by Keyword: The script checks if the keyword ("bewerbung") is present in either the subject or the body of the email. This way, only relevant emails are selected.

3. Extracting Important Information: The script extracts essential details from each email, such as the date, sender, recipient, subject, and message body.

4. Saving Data to CSV: After filtering and extracting the necessary data, the script writes the relevant information into a CSV file for easy access and analysis.

Outcome 🤓

The filtering process was successfully completed, and a CSV file containing only the relevant emails was generated. This allowed me to isolate and review the specific emails that matched the criteria, enabling a more efficient way to manage and process my inbox.

Processing and Extracting Emails -> Learnings

Extracting Email Content

• processed multipart emails by iterating through different content types.
• used BeautifulSoup to extract text from HTML content, removing unnecessary formatting.
• handled "quoted-printable" encoding with the quopri module to correctly decode email bodies.

Handling Encoding Issues

• a preprocessing step was added to ensure extracted text contained only printable ASCII characters.
• error handling mechanisms were implemented to skip problematic emails instead of terminating the script.

Filtering Emails by Keyword

• converted both the subject and body to lowercase for case-insensitive matching.
• checked if the keyword appeared in either field before extracting the email.

Storing Filtered Results

• extracted key fields: date, sender, recipient, subject, and body.
• saved the filtered data into a CSV file for easy access and further analysis.

Troubleshooting and Optimization

• encoding errors were resolved by ensuring proper decoding of email content.
• virtual environment issues and incorrect Python installations were addressed to enable smooth execution.
• error handling was improved to ensure robustness, allowing the script to continue running even when encountering problematic emails.