Calendar management
Embedded contextual email
Scheduling automation
Automated outreach
Telehealth
Marketplaces
Recruiting
Document management
Real estate
Communications software
Docs
SDKs
Tutorials
API status
Changelog
Support
Blog
Events
Guides
Newsroom
Partners
Build vs. buy
Customer stories
Trust & security
Why NylasEmail parsing with Python: A comprehensive guide
Learn email parsing with Python to extract useful information from an inbox.
Learn email parsing with Python to extract useful information from an inbox.
The code in the blog post has been updated to work with Nylas API V3.
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:
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!
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.
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.
To start with Nylas, consider using the Quickstart Guides to grab Python code for reading emails, with environment setup details included. We recently covered using the Nylas API v2 Quickstart Guides on Coding with Nylas:
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:
@flask_app.route('/nylas/read-emails', methods=['GET'])
@is_authenticated
def read_emails():
res = nylas.messages.list(grant_id, query_params={ "limit": 5 } )
# 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]
return res_jsonAlternatively using the Quickstart Guides walkthrough, you can use the Nylas Python SDK directly in your application.
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.list(
grant_id,
query_params={
"from": "devrel@nylas.com"
}
)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:
messages = nylas.messages.list(
grant_id,
query_params={
"search_query_native": 'apidays'
}
)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:
messages = nylas.messages.list(
grant_id,
query_params={
"search_query_native": 'nylas'
}
)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:
messages = nylas.messages.list(
grant_id,
query_params={
"starrred": True
}
)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.
Continue building using the Nylas Email API. You can sign up for Nylas for free and start building! Continue building with Nylas by exploring our quick start guides or visiting our developer documentation.
Please enter your email address and receive the latest updates.
Ram loves teaching, building and exploring technologies. He is passionate about empowering developers to ship amazing products to market as fast as possible ????. Ram is excited to share knowledge and help others. He’s a Relaxed Tomato ????.
