Learn email parsing with Python to extract useful information from an inbox.
In this blog post, we explore email parsing with Python. We’ll look at how to start with Nylas. Check out how to parse emails with Python on Coding with Nylas:
What is email parsing?
Let’s first understand email parsing before we jump into specific code examples. Email parsing is transforming data from emails into a structured format helpful in completing different tasks.
The benefit of email parsing includes retrieving specific information for use in web applications and automating specific tasks, such as extracting meaningful contacts for future communication. Access to first-party data via email communication is crucial for building functionality that provides value for your users. To learn more, check out our deeper dive into email parsing!
Data available using an email parser
Let’s look at the different types of information we can collect from an email inbox. We’ll dive into examples of email parsing with Python right after.
The email header consists of the sender, receiver, subject, and timestamp. Headers are part of every email and is has many usages. One example includes cross-checking the sender with the user’s contact lists to determine if the email is relevant to the user.
The message or body of the email is where a lot of the information about the email exists. Using this information, we can extract key details about the purpose of the message. For example, knowing the email is a transaction or receipt, we can extract payment information for certain expenses like business travel.
The email attachments are files that are part of the email body. Working with the attachment’s contents can be a feature on its own. Since data is sent in various forms, parsing attachments sent through email is important to create a powerful user experience.
Emails generally come in plain text and HTML. Parsing HTML emails is a powerful way to get extra information to display in a different format and from various sources such as newsletters and transactional emails.
Email specific data fields
We can parse emails using different fields available to use. For example, if you want to find more important emails, we can consider using the starred field with communication providers like Gmail. This is another way we can explore and extract important details from emails.
This section briefly explored the different types of data we can extract by email parsing with Python.
In this blog, we will provide code snippets for updating the backend route /nylas/read-emails for email parsing with Python, which uses the Nylas SDK:
Retrieve the first 20 threads of the authenticated account from the Nylas API.
This endpoint is a GET request and accepts no parameters.
The threads are retrieved using the Nylas API client, with the view set to "expanded".
The threads are then returned as a JSON object.
See our docs for more information about the thread object.
# where() sets the query parameters for the request
# all() executes the request and return the results
# TODO: update res to parse emails
res = nylas.threads.where(limit=5, view="expanded").all()
# enforce_read_only=False is used to return the full thread objects
res_json = [item.as_json(enforce_read_only=False) for item in res]
Integrating Nylas with Python applications
Alternatively using the Quickstart Guides walkthrough, you can use the Nylas Python SDK directly in your application. We provide a step-by-step guide to adding the Nylas SDK to your backend code to get started:
Parse email headers
Let’s look at how to parse email headers. We can use the Nylas Email API to parse email headers. An example of parsing email headers is selecting emails that are from a specific sender:
res = nylas.messages.where(from_="firstname.lastname@example.org").all()
Parse email message
Let’s take a look at how to parse an email message. Using the Nylas Email API, we can search the contents of an email to find specific information. Searching an entire email is not as efficient as other fields since it searches the entire email contents. However, it is very powerful when you are first parsing emails and want to figure out what information is available at first glance. Let’s take a look at a code sample searching for emails referencing the API Days conference:
res = nylas.messages.search("apidays")
Parse email attachment
When building applications that work with communication data, understanding when you have attachments is useful if you want to work with the attachments and provide additional functionality to your users. The Nylas Email API lets you check which emails contain attachments:
res = nylas.messages.where(has_attachment='true').all()
Parse specific email data fields
Now that we’ve explored a few ways to parse emails, let’s explore specific data fields that can be used to parse emails. Using the Nylas Email API, we have access to many different types of fields that allow for a more granular approach to parsing emails. Check out the documentation to learn more about different wants to parse emails by data fields. Here is an example of parsing for all emails the user has starred:
res = nylas.messages.where(starred='true').all()
Best practices for email parsing
Let’s wrap up by discussing best practices for email parsing with Python.
Ensuring the data is useful for building functionality requires verifying the accuracy of the data parsed from the email. An example of ways to validate the data could be checking the format of the data using regular expressions.
When parsing emails, it’s important to normalize the data being parsed to ensure consistency for further analysis. An example would be removing certain characters or keeping consistent character casing (uppercase vs. lowercase) so that the data will be useful for building features in your application.
Handling edge cases and exceptions is important when parsing emails to ensure process or application does not fail and that the parsed data can be stored correctly. With error handling, it’s important to keep track of errors by logging in to troubleshoot and modify the parsers to be more robust easily.
Be sure to protect information that is considered sensitive or personally identifiable. This may require updates to user terms and conditions or compliance with various privacy regulators. Considerations are required when it comes to the storage of sensitive information.
When parsing emails, performance will be important. Given the vast amount of communication available from just one user, implementing efficient and memory-optimized parsing will be important for consuming large amounts of emails. Another consideration is handling the parsing asynchronously to avoid impacting the user experience.
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