How do I remove invalid emails from a CSV list?

To remove invalid emails from a CSV list, you generally:

1. Load the CSV
2. Check each email against a validation rule
3. Keep only the valid ones
4. Save the cleaned CSV

Quick options

1) Using Excel / Google Sheets

If your CSV is small:

• Open it in Excel or Sheets
• Add a helper column with a formula to check email format
• Filter out invalid rows
• Export/save the cleaned file

Example basic check:

• In Excel, you can use formulas or data validation, but email validation is limited and not perfect.

2) Using Python

This is the most reliable for a CSV.

import pandas as pd
import re

# Load CSV
df = pd.read_csv("input.csv")

# Basic email regex
pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'

# Keep rows where email is valid
df_clean = df[df["email"].astype(str).str.match(pattern, na=False)]

# Save cleaned CSV
df_clean.to_csv("cleaned.csv", index=False)

If your email column has a different name, replace "email" with that column name.

3) Using a command-line tool

If you’re comfortable with terminal tools, you can use Python one-liner scripts or CSV filters, but Python is usually easier and safer.

Important note

A regex can catch format errors, but it cannot guarantee an address actually exists or can receive mail. For that, you’d need:

• DNS/MX lookup
• SMTP verification
• An email verification service

Common cleanup steps

You may also want to:

• Trim spaces: " test@example.com " → test@example.com
• Convert to lowercase
• Remove duplicates
• Remove empty cells

Example in Python:

df["email"] = df["email"].astype(str).str.strip().str.lower()

If you want, I can give you:

• a Python script that removes invalid emails from your CSV,
• an Excel formula,
• or a version that also deduplicates and trims spaces.