Python Email Verification Guide
Learn how to verify your emails
Email is vital for business. You use it for mass communications, promotions, and sharing updates with thousands of customers. Use our guide to learn how you can perform basic Python regex checks.
There’s one major hurdle to improving your email campaign performance - email validity. If the person you’re trying to contact has an invalid email address, your email will bounce.
This guide will help you understand
- Why email verification is necessary for businesses
- How to do Python email verification
- Advanced email validation techniques
- Using the ZeroBounce Python integration
- Limitations of Python email verification
- Real-world examples of email validation using Python
Why email verification is necessary for businesses
Email is a free and accessible resource that virtually anyone can use. Because of that, problems like spam, phishing, and identity theft are abundant.
Why does this matter to you? If you’re bouncing too many emails to invalid addresses, you’ll get grouped in with these spammers and hackers.
Internet service providers (ISPs) are constantly on the lookout for suspicious, abusive email-sending behaviors, and a bounce rate that’s higher than 2% is considered unacceptable.
If your email bounce rate consistently exceeds this, ISPs will be less trusting of your email content and be more likely to relegate it to the spam folder (or block your messages entirely).
But because you likely found this page by searching for Python email verification, you’re already on the right track.
Next, we’ll walk you through the basics of creating a Python script for email checking, as well as the challenges you’re likely to encounter with single or bulk email verification using Python.
How to do Python email verification
At the most basic level, you can implement a simple regular expression (regex). A Python regex for email validation effectively validates the syntax of the email address in question.
Below is an example of a Python script for email checking you can use for regex and string checks:
INPUT (includes the necessary regex and string check)
OUTPUT (provides an email validation result based on regex or string matches)
A regex or string check for single or bulk email verification in Python is the bare minimum that you need to implement.
All services, whether a bulk email checker or real-time email verification, begin their email address checks by confirming the email structure. All emails must be comprised of the following components:
username
@
domain/company name
.com, .net, .edu, etc.
the local part
separator
the email domain
top-level domain
If the email address you’re analyzing using Python email verification doesn’t consist of these parts, the email checker will return an ‘invalid’ result.
In addition to these parts, email addresses may only consist of certain characters, numbers, or symbols to be considered valid. The characters, or the string, that make up the email address are compared with the Python regex. If any characters don’t match the regex, it’s deemed ‘invalid.’
Using this Python script for email checking is an effective baseline for weeding out junk or even risky data that might find its way into your database. By adding Python email verification to your registration forms, checkout screens, or newsletter signups, you can automatically detect and block invalid emails.
It can also stop attackers from inputting something like an SQL injection into your forms, which could grant them backdoor access to your website and database. You can add the above Python script to your site as a baseline protector.
However - it’s important to note that using this Python email verification script will not actually determine if an email is valid and in use. While you can confirm that it uses valid syntax, you need more advanced Python email validation techniques to ensure your contacts are real.
Advanced email validation techniques
While a regex can confirm valid characters in an email address, you need to implement additional tactics to ensure that the mailbox is real.
Let’s start with determining if the address’s described email domain actually exists.
SMTP verification in Python
In place of the example Python code above, you can turn to Python libraries for additional features.
One of these is smtplib - a module that allows you to perform a Simple Mail Transfer Protocol (SMTP) verification check.
SMTP is the protocol we use to send email messages to an email domain. If the domain exists, the receiving server will use Internet Message Access Protocol (IMAP) to download and deliver the message to the user’s mailbox.
Here’s an example of how to use smtplib for SMTP checks:
Therefore, an SMTP verification check in Python allows you to confirm a mailbox’s existence without actually sending an email message. This module lets you confirm an email’s validity without the risk of a bounce.
However - the drawback of a module like smtplib is that it’s not suited for bulk email verification. Not only will the process run slowly or time out, but it’s also easy to get your IP address blacklisted by spam prevention services like Spamhaus.
DNS lookups for domain validity
Next, you’ll want to determine if the email address is configured to receive mail. There are many types of email addresses, and some are configured to send mail only.
To receive mail, the domain needs to have the appropriate Mail Exchange (MX) record present.
You can use another toolkit like dnspython to perform basic queries and testing. Here’s an example they provide to get you started with finding an MX target:
AskPython also provides an excellent breakdown of DNS lookups that you can use for client-side and server-side interactions.
Circumventing greylisting or email spam prevention
The next email validation hurdle you’re likely to encounter is email spam prevention, like email greylisting.
The term ‘email greylisting’ is adapted from the terms whitelist (a list of allowed email addresses) and black/blocklist (automatically rejected emails). Greylisting is the middle ground, as it temporarily rejects any messages from an unknown sender. This temporary rejection period typically lasts around 15 minutes before allowing messages to be received.
To navigate greylisting, senders need to configure their mail servers to reattempt delivery after the greylisting period to reach the inbox.
To get accurate results, your email validation tool must do the same. Similar to SMTP checks, you can use smtplib for better email validation accuracy with Python. You can send a check to the mail server and check for responses.
But be aware that smtplib is not foolproof, as your ability to handle the different responses largely depends on the configuration of the recipient mail server.
How to detect disposable emails with Python email verification
Disposable emails, or temporary emails, make up another category of email addresses that can impact your email validation results.
A disposable email is a valid email address and will successfully pass Python regex or SMTP checks. However, after a period, a disposable email self-destructs, making the mailbox no longer available. It’s easy to acquire disposable emails in your database unless you have a method of identifying disposable email domains.
Unfortunately, Python email verification doesn’t have an effective way of catching these types of domains. You’ll need to leverage a Python API for email validation that has a sophisticated database of disposable domains to reference during the verification process.
Custom email validation rules and error handling
When performing bulk email validation, you’ll inevitably run into various error codes. The recipient email server might experience unexpected issues. Specific complex configurations can also hinder your ability to verify.
For this reason, you’d need an email validator with custom email validation rules to handle specific scenarios. The above issue with greylisting is one of them, as you’re going to encounter a 450 error when it’s present. Instead of continuously retrying attempts and risking a block, you need to configure your Python email verification to wait a period before making another attempt.
Likewise, you need to consider rate limits in general when pinging a server. Without setting rate limits, your email verification tool can be stuck in an indefinite loop if the recipient server is unresponsive.
You’ll want to avoid this as it quickly becomes a resource drain, particularly when performing bulk email verification. You need to know that the server is unresponsive so that you can retry again later and validate the next address on your list. Consider setting up a timeout using the socket.setdefaulttimeout() function.
Using the ZeroBounce Python integration
All of the issues you’ll inevitably face with a basic Python email checker script can be overcome by using a dedicated email validation platform like ZeroBounce.
Instead of performing bulk email verification in Python using your resources, ZeroBounce provides you with a dedicated platform that can perform everything from a regex check to a disposable domain lookup. There’s a 9-step validation process in total that verifies your email address with 99% accuracy.
There are two methods of validating emails using ZeroBounce:
- Upload a file or a list of emails to the platform - This is self-explanatory. All you have to do is create a free account, enter your emails, and get a result.
- Use our Python API for email validation - This uses a script closer to what you’re accustomed to, and you can add it to any platform or website for real-time email verification.
You can make a call to the API from your browser now using the following API endpoint:
https://api.zerobounce.net/v2/validate?api_key=replacewithyours&email=valid@example.com&ip_address=156.124.12.145 - For queries in the EU
https://api-us.zerobounce.net/v2/validate?api_key=replacewithyours&email=valid@example.com&ip_address=156.124.12.145 - For queries in or near the USA
To verify an email address using the ZeroBounce Python script for email checking, start with this:
You’ll need to create a free ZeroBounce to obtain your API key. Then, replace “<YOUR_API_KEY>” with your ZeroBounce API key.
A successful response:
As you can see, ZeroBounce not only guarantees email validation accuracy using Python but also enhances your result with additional data: sub-status information, domain age, SMTP provider, MX record info, name, and location. It’s the definitive way to use an API for email verification.
You can review the complete list of JSON properties for ZeroBounce’s Python email verification API by visiting the complete documentation. There are also Python SDK wrappers available for use.
Limitations of Python email verification
As seen earlier on this page, you’ll inevitably encounter a series of issues when using a traditional Python library or built-in methods when validating an email address.
Be sure to keep these scenarios in mind before deploying a Python email verifier to your website or platform.
A Python email verification tool is limited in what it identifies
Though Python offers more functions than some other languages, such as SMTP pinging and exception handling, it doesn’t offer enough data to determine the actual status of an email address.
There are numerous types of email addresses, including disposable emails, spam traps, abuse emails, catch-alls, role-based catch-alls, etc., that a basic Python email verification API script won’t be able to identify.
On the other hand, ZeroBounce accurately identifies every type of email address and consistently yields fewer than 1% unknowns. The platform is continuously improved to handle all types of server configurations used by popular as well as lesser-known email domains and can identify the following email types:
Types of Emails ZeroBounce Identifies
Valid
emails
Invalid
emails
Alternate
emails
Disposable
emails
Catch-all
emails
Spam
traps
Abuse
emails
Do not mail
At different steps of the validation process, each email address on your list will undergo rigorous checks to ensure that you’re emailing a safe and valid inbox.
Limited ways to work around antispam technologies
Spam is a significant problem in the email world, which is a big reason why email validation is needed in the first place.
In some cases, a basic Python email validator can handle basic anti-spam practices like greylisting by using a combination of domain pinging and exception-handling rules. However, it doesn’t take much to get your IP blacklisted by sending too many requests. Plus, repeatedly attempting to verify a single email without a dedicated platform is a significant resource drain.
ZeroBounce’s dedicated email validation platform meticulously handles encounters with anti-spam tools and security measures like Spamhaus or Fortiguard. These are something even other email validation platforms struggle with, often returning “unknown” results.
Slower results and unresponsive loops
Another concern you’re likely to have is the speed of email validation. If you’re thinking about using a script like the one above for bulk email verification using Python, think again.
Not only are you unable to obtain truly accurate results, but between getting caught in unresponsive loops and resource drains, it’s nigh impossible to verify an extensive email list effectively in a reasonable time.
Comparatively, ZeroBounce’s Python email verification API can quickly validate roughly 100,000 email addresses in approximately 45 minutes. The email verification process can be even faster or a bit slower, depending on the complexity of the email domains found in your list.
However, that additional processing time is never wasted, as you’re unlikely to find more accurate results with any other tool.
Insufficient resources for bulk email verification
Relating to slower processing speeds, your local system will not be able to verify a significant customer or email database readily.
Not all email domains are created equal, and many can be fairly complex for even a competent email validation service to validate. If you’re attempting to validate complicated domain configurations, temporary domains, or other problematic email types, it’s going to place a significant strain on your platform resources.
Not only is this inefficient when attempting to verify and validate thousands of email addresses, but the resource impact is likely to slow your entire network.
Instead of this fruitless struggle, it’s recommended to validate your database with an email verification service that has the dedicated resources in place to process a massive list of data. Those product engineers will have put in place sophisticated proprietary technologies that can assist with the speedy and accurate validation of even the most complex email domains.
No dedicated support
So, you implement a Python script for email checking on your website. Everything is going well until it isn’t.
Your only option is to stop, diagnose the problem, and attempt trial and error until you find a fix.
Using a dedicated automated email verification tool like ZeroBounce gives the necessary benefit of being backed by a dedicated team of experts. The team of programmers and developers is always on-duty. Any unexpected issues are identified and resolved swiftly, and preventive measures are consistently being implemented to ensure minimal to zero downtime.
And, if you’re ever unsure about anything related to your Python API for email validation, all it takes is a quick message at any hour of the day to get a hand.
Real-world examples of advanced email validation using Python
Email validation isn’t just some niche service for the tech-savvy. It’s critical for keeping your database clean and up-to-date.
Maintaining a list of valid emails gives your business a litany of benefits, including
- The ability to reach a subscriber’s inbox
- Better email deliverability (reach the inbox - not the spam folder)
- Superior email engagement
- More subscribers converting into customers
- Higher email ROI
Let’s take a look at some examples of how you can implement ZeroBounce’s automated email verification using Python.
Manage eCommerce customers and transactions
Protecting your customer data isn’t just good for business; it’s your legal responsibility.
Implementing real-time email verification into your eCommerce store’s account signup and checkout forms will stop visitors and bots from supplying you with invalid or risky data.
All you need to do is add the Python API for email validation to any pages that require email submission, and the tool does the rest. If anything other than valid emails are detected, the validator blocks them and requests a valid entry.
Find real customers for your SaaS platform
Anyone (or anything) can use a submission form. In particular, if you’re giving away freebies or bonuses for creating an account on your platform, you can expect junk data to come your way.
Instead of dealing with that hassle, add a Python script for email checking to your account registration webpage or forms. It’ll automatically detect invalid characters and undesirable domains, while helping real customers correct their entries.
You don’t want just any emails. Those aren’t real accounts. You want those valid email addresses so that you can continuously engage your platform users and communicate critical updates.
Verify contacts in your CRM
Everyone has a contact list or address book. But, even if you’re confident that you’re adding valid data now, there’s no guarantee it will remain valid later.
Our Email Decay Report shows that every email list decays by nearly 25% annually. If you’ve got a customer list of 100,000, that’s 25,000 bounced emails.
Stay up to date with a clean database by verifying your contacts at any time. As an example, Salesforce built a native API integration that lets its users validate any email address in their CRM or the entire list with just a click.
Ensuring applicants are legitimate customers
Getting the occasional junk email from a free account signup form isn’t the end of the world. However, other forms like loans or employment applications are more significant.
As another example, Infinity Software, a cloud-based platform designed for money lenders, uses ZeroBounce email validation to check emails on loan applications.
Communication via these transactions is critical, and you need a valid way to contact your applicants. The applicants themselves also want to know if they misentered their email addresses so that they don’t miss out on their results.
Weed out irrelevant, spammy comments on your website posts
Have a popular blog or forum where your visitors can leave comments and links? It’s a fast way to pick up harmful content or toxic links that can drive away visitors and harm your SEO.
Ensure Python email validation accuracy by using the Python API to check any account using your website’s comment sections or threads. This makes it difficult for any unwanted visitors or bots to post submissions without using valid data you can trace back to their real identity.
Preparing an email marketing list for campaigns
Bulk email verification in Python or any other language is an email marketer’s best friend.
Their goal is to send high-quality emails to your subscribers and customers to educate, engage, and convert. But none of that’s possible if they’re working with a junk email list.
What’s more - not using best email marketing practices sabotages overall email deliverability. Not cleaning your list and bouncing too many emails means all of your emails will struggle to reach the inbox.
Be sure to use a bulk email verification tool or a dedicated Python email address validation library to check email addresses as you acquire them.
Use a dedicated Python email verification API
The TL;DR - email verification is a necessity. No matter your industry, if you’re a business sending mass emails, it’s up to you to clean your data for
- Website, platform, and customer security
- Email marketing best practices
- Maximum email deliverability
- Company/domain protection
- More user engagement
- Better email ROI year-round
While a Python regex for email validation is a great place to start, you’ll be quick to encounter problems with accuracy, reliability, and resource management. That’s where the ZeroBounce real-time email verification service can help.
Create your free account today to try! Once you have your API key, you can find Python email verification API code samples by visiting our email validation API page or documentation.
Create your free account!
Get 100 free email validations monthly
FAQs
The quickest way to check an email’s validity is to use a Python regex for email validation like the following:
import re
def is_valid_email(email):
"""Check if the email is a valid format."""
# Regular expression for validating an Email
regex = r'^[a-z0-9]+[._]?[a-z0-9]+[@]w+[.]w+$'
# If the string matches the regex, it is a valid email
if re.match(regex, email):
return True
else:
return False
# Test the function
emails = ["test@example.com", "invalid-email.com", "another.test@example.co.uk"]
for email in emails:
if is_valid_email(email):
print(f"'{email}' is a valid email address.")
else:
print(f"'{email}' is an invalid email address.")
However, a regex check merely validates the email format and does not include vital verification checks like SMTP or domain pinging, DNS record lookups, or disposable domain checks. For those, you need to use a dedicated email validation platform that supports additional verification.
A regular expression pattern you can use for email validation in Python is: regex = r'^[a-z0-9]+[\._]?[a-z0-9]+[@]\w+[.]\w+$'
To verify an email’s existence, you need to perform a variety of checks, including regex checks for syntax errors, SMTP checks for domain and user verification, and DNS lookups to ensure the email is configured correctly. Additional lookups like disposable domain or spam trap domain checks are also helpful in avoiding risky, temporary email addresses.
A Python regex is an excellent way to check an email address’s syntax and structure quickly. This ensures that the email is comprised of the proper components and doesn’t use any unallowed characters. However, be mindful that a Python regex check doesn’t check for SMTP, MX, or DNS records, all of which are necessary for an email address to receive an incoming email message.
