How do I check a CSV of emails for typos and bad domains?

A practical way is to validate in three layers:

1. Basic email format check
   • Looks for things like missing @, spaces, or no domain.
2. Domain check
   • Confirms the domain part exists and looks valid.
3. Typos / bad-domain check
   • Catches common mistakes like gamil.com instead of gmail.com.

Option 1: Use Python with a CSV

If you have a CSV with a column like email, you can use pandas.

Install

pip install pandas email-validator

Script

import pandas as pd
from email_validator import validate_email, EmailNotValidError

# Common domain typos / bad domains
COMMON_DOMAIN_FIXES = {
    "gamil.com": "gmail.com",
    "gnail.com": "gmail.com",
    "hotnail.com": "hotmail.com",
    "yahooo.com": "yahoo.com",
    "outlok.com": "outlook.com",
    "icloud.con": "icloud.com",
}

def check_email(email):
    if pd.isna(email) or str(email).strip() == "":
        return "missing"

    email = str(email).strip()

    # Format check
    try:
        v = validate_email(email, check_deliverability=False)
        normalized = v.email
    except EmailNotValidError:
        return "bad_format"

    # Domain typo check
    domain = normalized.split("@")[-1].lower()
    if domain in COMMON_DOMAIN_FIXES:
        return f"typo_domain -> {COMMON_DOMAIN_FIXES[domain]}"

    return "ok"

df = pd.read_csv("emails.csv")
df["email_check"] = df["email"].apply(check_email)

# Save results
df.to_csv("emails_checked.csv", index=False)

# Show suspicious rows
print(df[df["email_check"] != "ok"])

Option 2: Add live domain/deliverability checking

If you want to catch domains that don’t actually exist, set:

validate_email(email, check_deliverability=True)

That checks DNS/MX records, but note:

• it may be slower
• it can be blocked by network/DNS issues
• some valid domains don’t have MX in the usual way

Option 3: Use Excel / Google Sheets

If you just need a quick pass:

• Use formulas to flag malformed emails
• Extract the domain and compare it to a list of known-good domains
• Use conditional formatting to highlight suspicious entries

Example to get the domain from A2:

=RIGHT(A2,LEN(A2)-FIND("@",A2))

Then compare against a reference list of accepted domains.

Good typo checks to include

Common misspellings:

• gamil.com → gmail.com
• hotnail.com → hotmail.com
• outlok.com → outlook.com
• yaho.com → yahoo.com
• icloud.con → icloud.com

You can expand the mapping based on your audience.

Best practice

For a CSV list, I’d recommend:

• syntax validation
• domain typo mapping
• optional DNS/MX verification
• export flagged rows for review

If you want, I can give you:

• a ready-to-run Python script
• an Excel formula solution
• or a version that checks common typos automatically with fuzzy matching