Plain text files with mixed content make finding valid email addresses difficult and time-consuming. Whether it's log files with thousands of lines, database dumps with unstructured data, or legacy system exports with inconsistent formatting, manual copy-paste creates formatting errors and misses hidden addresses.

Our Email Extractor for Text Files solves this problem by scanning text files line by line using powerful regular expressions, automatically detecting character encodings (UTF-8, Latin-1, Windows-1252), validating each email against RFC 5322 standards, removing duplicates, and exporting a clean, ready-to-use email list.

Whether you're a system administrator analyzing server logs, a data analyst processing database exports, or a developer migrating legacy systems, this tool automates email extraction from any text-based file format without external dependencies.

What is the Text File Email Extractor?

The Text File Email Extractor is a lightweight Python script that uses only the standard library to process plain text files. It reads files line by line for memory efficiency, applies RFC 5322-compliant regex patterns to find email addresses, and validates each match before adding it to the output.

How it works: The script opens your text file with automatic encoding detection, reads each line sequentially to minimize memory usage, applies regex patterns to identify email addresses, validates the syntax of each match, removes duplicates using a set-based approach, and writes the results to a clean CSV file with one email per line.

What makes this tool unique is its simplicity and reliability. With zero external dependencies (uses only Python's standard library), it's the fastest way to extract emails from text files without installing packages. It handles massive log files (100MB+) efficiently through line-by-line streaming and works with any text encoding your system throws at it.

50K Emails from Server Logs
100MB+ Large File Support
0 External Dependencies

Key Features

Multi-Format Support

Works with TXT, LOG, DAT, and any plain text file format. No special configuration needed regardless of file extension.

Regex Pattern Matching

Uses RFC 5322 compliant regex patterns to accurately identify email addresses while filtering out false positives and malformed strings.

Encoding Detection

Automatically handles UTF-8, Latin-1, Windows-1252, and other common encodings. No manual configuration for international characters.

Line-by-Line Parsing

Memory-efficient streaming reads files line by line instead of loading entire file into RAM. Handle 100MB+ files with ease.

Context Preservation

Optional feature to export the line number or surrounding text context where each email was found for traceability.

Batch Processing

Process multiple text files at once. Combine email extraction from dozens of log files into a single deduplicated output.

How to Use - Step by Step Guide

Prerequisites

  • Python 3.6 or higher installed on your system
  • No external dependencies required - uses only Python standard library (re, pathlib, csv)
  • Your text file(s) containing email addresses

Step 1: Download the Script

Enter your name and email in the download form on the right sidebar. You'll receive an instant download link to your inbox. The script comes as a ready-to-use .py file inside a ZIP archive.

Step 2: Prepare Your Text File

Place your text file in the same folder as the Python script or note its full path. The script works with any text file format:

  • Server log files (.log, .txt)
  • Database exports (.txt, .dat, .sql)
  • Legacy system dumps
  • Any plain text file with email addresses

Step 3: Run the Script

Open your terminal or command prompt and run:

python email_extractor_text.py your_file.txt

Or for batch processing multiple files:

python email_extractor_text.py file1.log file2.log file3.txt

The script will automatically:

  1. Detect file encoding
  2. Scan each line for email patterns
  3. Validate email syntax
  4. Remove duplicates
  5. Display real-time progress

Step 4: Review the Results

The script creates extracted_emails_YYYY-MM-DD.csv with clean, deduplicated email addresses. Each email is on a separate line, ready to import into your email platform.

Step 5: Import to Your Platform

Use the cleaned CSV file to import contacts into:

  • Postigo - for automated cold email campaigns
  • Mailchimp, ConvertKit, or other email marketing platforms
  • CRM systems like Salesforce or HubSpot
Pro Tip: For large log files (100MB+), the script uses streaming to keep memory usage low. You can process gigabyte-sized files without running out of RAM.

Code Preview

Here's a preview of how the script works:

#!/usr/bin/env python3 """ Email Extractor for Text Files Extracts and validates email addresses from plain text files """ import re import csv from pathlib import Path def extract_emails_from_text(file_path): """Extract emails from text file line by line""" emails = set() email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b' # Try common encodings encodings = ['utf-8', 'latin-1', 'windows-1252', 'utf-8-sig'] for encoding in encodings: try: with open(file_path, 'r', encoding=encoding) as f: for line in f: found = re.findall(email_pattern, line) emails.update(e.lower() for e in found) break except UnicodeDecodeError: continue return sorted(emails) # Full implementation with batch processing in downloaded script...

The full script includes batch processing, progress indicators, context preservation, and advanced regex options. Download it using the form to get the complete version.

Real-World Use Cases

1. Log File Analysis - 50K Emails from Server Logs

Scenario: Your web server logs contain thousands of user registration attempts, contact form submissions, and API requests. You need to extract all unique email addresses for analysis or compliance reporting.

Solution: Run this script on your server log files (access.log, error.log) to extract all emails. One system administrator extracted 50,000 unique emails from 2GB of log files in under 5 minutes.

2. Database Dump Parsing

Scenario: You've exported a database table as a text dump (SQL INSERT statements, CSV without proper formatting). The data is messy with mixed content but contains valuable email addresses.

Solution: Process the dump file to extract all emails regardless of formatting. The script ignores SQL syntax, commas, quotes, and other noise to find only valid email addresses.

3. Document Exports from Legacy Systems

Scenario: Migrating from a legacy CRM or accounting system that only exports to TXT files with custom formatting. You need to extract client emails for import into your new system.

Solution: The script works on any text format without needing to understand the file structure. Simply point it at the export files and get a clean email list in seconds.

Technical Requirements & Specifications

System Requirements

  • Operating System: Windows 7+, macOS 10.12+, Linux (any modern distro)
  • Python Version: Python 3.6 or higher (Python 3.9+ recommended)
  • RAM: 128MB minimum (handles files up to 1GB)
  • Disk Space: 1MB for script + space for output files

Supported File Formats

  • Plain text files (.txt)
  • Log files (.log)
  • Data files (.dat)
  • SQL dumps (.sql)
  • Any file readable as text

Supported Encodings

  • UTF-8 (default)
  • UTF-8 with BOM (utf-8-sig)
  • Latin-1 (ISO-8859-1)
  • Windows-1252 (CP1252)
  • Auto-detection with fallback

Performance

  • Process 10,000 lines per second on average hardware
  • Memory-efficient streaming for unlimited file size
  • Batch processing supports unlimited file count

Frequently Asked Questions

Q: Can it handle very large files (100MB+ or 1GB+)?
Absolutely! The script uses line-by-line streaming, meaning it reads one line at a time instead of loading the entire file into memory. We've successfully tested it with multi-gigabyte log files without any memory issues.
Q: What happens if my file has encoding errors or special characters?
The script tries multiple common encodings (UTF-8, Latin-1, Windows-1252) automatically. If one fails, it tries the next until it finds one that works. This handles 99% of real-world text files without manual intervention.
Q: Can it preserve the line number or context where each email was found?
Yes! The script includes an optional --context flag that exports the line number and surrounding text for each email. This is useful for auditing or understanding the source of each email address.
Q: Can I process an entire folder of text files at once?
Yes! Run the script with a wildcard pattern (e.g., python email_extractor_text.py *.log) or use the --folder flag to process all text files in a directory recursively. All emails are combined and deduplicated in a single output file.
Q: What export formats are supported besides CSV?
The default is CSV (one email per line), but the script also supports TXT (plain list), JSON (array format), and Excel XLSX. Use the --format flag to specify your preferred output format.

Related Email Tools

Complement this tool with other free utilities from Postigo:

Email Extractor CSV

Extract emails from CSV files and spreadsheets
Email Extractor HTML

Extract emails from HTML files and web pages
Email Deduplicator

Remove duplicate emails from your lists across multiple sources
Email Normalizer

Standardize email formats and remove dots/plus signs

Why Choose Postigo Email Tools?

All our email tools are 100% free, open-source, and require no registration. We built these tools for data analysts and system administrators, by professionals. Every script is:

  • Production-ready: Tested with millions of lines of logs and data
  • Well-documented: Clear instructions and code comments
  • Regularly updated: Bug fixes and improvements based on user feedback
  • Privacy-focused: All processing happens locally on your computer
  • Professionally supported: Email us with questions anytime

Need more automation? Try Postigo Platform for complete email outreach with pre-warmed SMTP, AI content generation, and smart reply filtering.