Impara Automating Email List Cleaning | Automating Growth Tasks

When running email campaigns, having a clean and accurate email list is essential for growth hackers. Clean lists help you avoid sending duplicate messages, reduce bounce rates, and ensure your outreach reaches only valid, engaged users. Manually cleaning large lists can be tedious and error-prone. Python offers a way to automate this process, saving you time and improving the effectiveness of your campaigns.


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# Hardcoded list of emails with duplicates and extra spaces
emails = [
    "alice@example.com ",
    "bob@example.com",
    "alice@example.com",
    " carol@example.com",
    "dave@example.com",
    "bob@example.com ",
    "eve@example.com"
]

# Step 1: Trim whitespace from each email
trimmed_emails = [email.strip() for email in emails]

# Step 2: Remove duplicates by converting to a set, then back to a list
unique_emails = list(set(trimmed_emails))

print("Cleaned email list:")
for email in unique_emails:
    print(email)

In this code, you start with a list of email addresses that may contain duplicates and unwanted spaces. The first step uses a list comprehension: [email.strip() for email in emails]. This goes through each email and applies the strip() method, which removes any leading or trailing whitespace. This is important because even a small space can make two identical emails appear different.

Next, you remove duplicates by converting the list to a set: set(trimmed_emails). Sets in Python automatically discard duplicate values, making them perfect for deduplication. Finally, you convert the set back to a list using list() so you can work with the cleaned emails as a normal list.


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# Filter out invalid emails using basic string checks
emails = [
    "alice@example.com",
    "bobexample.com",
    "carol@example.com",
    "dave@.com",
    "eve@example.com"
]

# Only keep emails that contain "@" and "."
valid_emails = [email for email in emails if "@" in email and "." in email]

print("Valid email addresses:")
for email in valid_emails:
    print(email)

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Sezione 1. Capitolo 1

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Suggested prompts:

Can you explain how to make the email validation more accurate?

What are some common mistakes to avoid when cleaning email lists?

Can you show how to combine deduplication and validation in one script?

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              1234567891011121314151617181920
            
# Hardcoded list of emails with duplicates and extra spaces
emails = [
    "alice@example.com ",
    "bob@example.com",
    "alice@example.com",
    " carol@example.com",
    "dave@example.com",
    "bob@example.com ",
    "eve@example.com"
]

# Step 1: Trim whitespace from each email
trimmed_emails = [email.strip() for email in emails]

# Step 2: Remove duplicates by converting to a set, then back to a list
unique_emails = list(set(trimmed_emails))

print("Cleaned email list:")
for email in unique_emails:
    print(email)


              123456789101112131415
            
# Filter out invalid emails using basic string checks
emails = [
    "alice@example.com",
    "bobexample.com",
    "carol@example.com",
    "dave@.com",
    "eve@example.com"
]

# Only keep emails that contain "@" and "."
valid_emails = [email for email in emails if "@" in email and "." in email]

print("Valid email addresses:")
for email in valid_emails:
    print(email)

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 1. Capitolo 1

Automating Email List Cleaning

1. Why is it important to remove duplicate emails before launching a campaign?

2. Which Python data structure is most efficient for removing duplicates from a list?

3. What is the purpose of using the strip() method on email addresses?

Automating Email List Cleaning

1. Why is it important to remove duplicate emails before launching a campaign?

2. Which Python data structure is most efficient for removing duplicates from a list?

3. What is the purpose of using the strip() method on email addresses?