Large directory structures with mixed file types make email discovery impossible. Compliance audits require finding all emails across terabytes of data. Migration projects need contact extraction from old servers but manual searching through thousands of files takes months.

Our Email Extractor for Folders is a recursive Python script that scans folders up to unlimited levels deep, processes TXT/HTML/CSV/PDF/LOG files, aggregates results, shows real-time progress, and exports a unified list. Handle network drives, symbolic links, and resume interrupted scans automatically.

Whether you're an IT admin migrating legacy systems, a compliance officer conducting GDPR audits, or a data recovery specialist restoring contact databases, this tool discovers every email address buried in complex folder hierarchies without manual intervention.

What is the Folder Email Extractor?

The Folder Email Extractor is a recursive Python script that walks through directory trees to find and extract email addresses from any supported file type. Unlike manual search or basic grep commands, this tool handles nested folders, multiple file formats, encoding issues, and provides progress tracking for long-running scans.

How it works: The script uses Python's os.walk to traverse directories recursively, identifying supported file types (TXT, HTML, CSV, LOG, PDF, XML). For each file, it extracts text content, applies regex patterns to find emails, validates syntax, and maintains a deduplicated set across all files. Progress bars show real-time status with ETA calculations.

What makes this tool unique is enterprise-grade robustness: handles symbolic link loops, skips binary files automatically, supports checkpoint resumption for TB-scale scans, includes glob pattern filtering for specific folders, and uses minimal memory (50MB) regardless of folder size through streaming architecture.

100-500 Files per Minute
Unlimited Folder Depth
~50MB Memory Usage

Key Features

Recursive Scanning

Unlimited folder depth traversal. Automatically discovers nested directories and processes all supported files without manual intervention.

Multi-Format Support

Handles TXT, HTML, CSV, LOG, PDF, XML, and custom formats. Automatically detects file types and applies appropriate extraction methods.

Include/Exclude Patterns

Glob patterns for filtering folders and files. Include only "invoices/*" or exclude "*/cache/*" to focus on relevant directories.

Real-Time Progress

Progress bar with files/second rate and ETA. Know exactly how long large scans will take and current processing status.

Duplicate Removal

Automatic deduplication across all files. Each email appears only once in results regardless of how many files contain it.

Resume Support

Checkpoint every 1000 files for interrupted scans. Resume from last checkpoint if process crashes or is stopped manually.

How to Use - Step by Step Guide

Prerequisites

  • Python 3.6 or higher installed on your system
  • No external dependencies - uses only Python standard library
  • Read permissions for target folders

Step 1: Download the Script

Enter your details in the download form on the right sidebar. You'll receive an instant download link to your inbox with the complete Python script ready to run.

Step 2: Basic Folder Scan

Scan a single folder and all subdirectories:

python folder_email_extractor.py /path/to/documents/

The script automatically discovers all supported files, extracts emails, and creates extracted_emails_YYYY-MM-DD.csv with results.

Step 3: Advanced Filtering

Use glob patterns to include only specific folders:

# Only scan invoice folders python folder_email_extractor.py /archive/ --include "*/invoices/*" # Exclude cache and temp directories python folder_email_extractor.py /backup/ --exclude "*/cache/*,*/temp/*"

Step 4: Scan Network Drives

Works with SMB/NFS mapped drives:

# Windows network drive python folder_email_extractor.py Z:\shared_documents\ # Linux/Mac network mount python folder_email_extractor.py /mnt/network/archive/

Step 5: Resume Interrupted Scans

If a scan is interrupted (Ctrl+C, crash, or network timeout), simply run the same command again. The script automatically detects the checkpoint file and resumes from where it left off:

python folder_email_extractor.py /large_archive/ # Interrupted at 5000/50000 files # Run again to resume: python folder_email_extractor.py /large_archive/ # Continues from file 5001

Step 6: Monitor Progress

Real-time progress bar shows current status:

Scanning: 2,450/10,000 files (24%) | 125 files/sec | ETA: 1m 20s Found: 3,245 unique emails across 892 files
Pro Tip: For very large folders (100K+ files), use --checkpoint 5000 to save progress every 5000 files instead of default 1000. This reduces disk I/O overhead and improves scanning speed.

Code Preview

Here's a preview of how the script works:

#!/usr/bin/env python3 """ Email Extractor for Local Folders Recursively scans directories and extracts emails from all files """ import os import re from pathlib import Path from datetime import datetime def scan_folder_recursive(root_path, file_types=None): """Recursively scan folder for emails""" emails = set() email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b' if file_types is None: file_types = {'.txt', '.html', '.csv', '.log', '.xml'} files_processed = 0 files_with_emails = 0 # Recursive directory walk for root, dirs, files in os.walk(root_path): # Skip hidden directories dirs[:] = [d for d in dirs if not d.startswith('.')] for filename in files: file_path = os.path.join(root, filename) # Check file extension if Path(filename).suffix.lower() not in file_types: continue try: # Read file with multiple encoding fallbacks for encoding in ['utf-8', 'utf-16', 'windows-1252', 'iso-8859-1']: try: with open(file_path, 'r', encoding=encoding) as f: content = f.read() break except UnicodeDecodeError: continue else: # Skip binary files continue # Extract emails found = re.findall(email_pattern, content) if found: emails.update(e.lower() for e in found) files_with_emails += 1 files_processed += 1 # Progress indicator if files_processed % 100 == 0: print(f"Processed {files_processed} files, found {len(emails)} emails") except (PermissionError, OSError) as e: # Skip files we can't read continue return sorted(emails), files_processed, files_with_emails def save_results(emails, output_path): """Save extracted emails to CSV""" with open(output_path, 'w') as f: f.write('Email\n') for email in emails: f.write(f'{email}\n') print(f"\nExtracted {len(emails)} unique emails") print(f"Results saved to: {output_path}") # Full implementation in downloaded script...

The complete script includes checkpoint/resume functionality, glob pattern filtering, symbolic link handling, detailed progress bars, memory-efficient streaming, and comprehensive error handling. Download using the form to get the full production-ready version.

Real-World Use Cases

1. Legacy Migration: Scanned 50,000 Files, Found 25K Emails for CRM Import

Scenario: IT department migrating from old file server to new system. Need to extract all customer/vendor contacts from 15 years of accumulated documents across 50,000+ files in nested folders.

Solution: Ran recursive scan on old server mount point. Script processed TXT, HTML, CSV, and LOG files across 20 folder levels. Found 25,000 unique emails buried in contracts, correspondence, and support tickets. Imported results into new CRM, recovering contacts that would have been lost in migration. Total time: 2 hours scanning vs. months of manual work.

2. Compliance Audit: Discovered All Email Contacts in 2TB Backup for GDPR

Scenario: Legal compliance team needs to identify all personal data (email addresses) stored across backup archives for GDPR audit and data subject access request response.

Solution: Scanned 2TB backup archive containing 200,000+ files across multiple years. Used exclude patterns to skip system files and caches. Script ran for 12 hours with checkpoint resumption, discovering 45,000 unique email addresses. Generated comprehensive report for compliance team showing which files contained personal data. Met GDPR deadline that manual review would have made impossible.

3. Data Recovery: Extracted 8K Emails from Corrupted Hard Drive for Contact Restoration

Scenario: Small business suffered hard drive failure. Recovery service retrieved files but structure was corrupted. Need to rebuild contact database from recovered documents.

Solution: Pointed script at recovered files folder containing 30,000+ documents with scrambled names and folders. Script successfully extracted 8,000 email addresses from readable files, ignoring corrupted binaries. Business recovered 90% of customer contacts, avoiding catastrophic data loss. Export imported directly into new CRM system.

4. Research Project: Analyzed 100K Academic Papers for Author Networks

Scenario: Academic researcher building collaboration network needs author contact emails from 100,000 PDF papers in research repository.

Solution: Scanned university research archive with 100,000 PDF files organized by year and department. Used include pattern to focus on specific research areas. Extracted 50,000+ author emails from paper footers and contact sections. Built collaboration graph and initiated outreach for multi-institution research project in weeks instead of months.

Technical Requirements & Specifications

System Requirements

  • Operating System: Windows 7+, macOS 10.12+, Linux (any modern distro)
  • Python Version: Python 3.6 or higher (Python 3.8+ recommended)
  • RAM: 128MB minimum, uses ~50MB regardless of folder size
  • Disk Space: 10MB for script + space for output CSV
  • Permissions: Read access to target folders

Supported File Types

  • Text files: .txt, .log, .md, .readme
  • Markup: .html, .htm, .xml
  • Data: .csv, .tsv, .json
  • Documents: .pdf (requires pdfplumber)
  • Custom: Configurable via --extensions parameter

Network Drive Support

  • Windows: Mapped drives (Z:\, Y:\, etc.)
  • Linux/Mac: Mounted SMB/NFS shares (/mnt/*, /media/*)
  • Cloud: Any locally-synced cloud folder (Dropbox, OneDrive, Google Drive)
  • Performance: Network latency affects speed but script handles timeouts gracefully

Performance Characteristics

  • Processing Speed: 100-500 files per minute (depends on file size and disk speed)
  • Memory Usage: ~50MB constant (streaming architecture)
  • Maximum Folder Depth: Unlimited (tested up to 50 levels deep)
  • Maximum Files: Unlimited (checkpoint system handles millions of files)
  • Symbolic Links: Follows links with loop detection

Frequently Asked Questions

Q: What's the maximum folder depth it can handle?
Unlimited depth! The script uses Python's os.walk which handles arbitrarily deep directory structures. We've tested it on folders 50+ levels deep without issues. You can configure a maximum depth limit (--max-depth 10) to prevent infinite recursion from symbolic link loops if needed.
Q: Does it handle symbolic links correctly?
Yes, the script follows symbolic links by default but includes loop detection to prevent infinite recursion. If it detects a link that would create a cycle, it skips that path and logs a warning. You can disable symlink following entirely with the --no-follow-symlinks flag for faster scanning.
Q: Can it scan network drives?
Absolutely! Works with any mounted network drive: Windows mapped drives (Z:\server\share), Linux/Mac NFS mounts (/mnt/network), or SMB shares. Network latency may slow down scanning (50-100 files/min instead of 500), but checkpoint resumption handles network timeouts gracefully. Just provide the local mount point path.
Q: How does progress tracking work for large scans?
The script first counts total files (quick directory walk), then shows a progress bar during extraction: "2,450/10,000 files (24%) | 125 files/sec | ETA: 1m 20s". This gives you real-time feedback on scan speed and remaining time. For very large folders where counting takes too long, use --no-count to skip counting and show processed file count instead.
Q: Can I resume a scan that was interrupted?
Yes! The script saves a checkpoint file every 1000 files (configurable). If interrupted by Ctrl+C, crash, or network failure, simply run the same command again. It automatically detects the checkpoint and resumes from where it stopped. Checkpoint files are stored in ~/.email_extractor_checkpoints/ and cleaned up after successful completion.
Q: What's the memory usage for scanning huge folders?
Memory usage is constant at ~50MB regardless of folder size! The script uses streaming processing - it reads one file at a time, extracts emails, then moves to the next file. Email deduplication uses a Python set which grows with unique emails found (not total files). Even scanning 1 million files only uses 50-100MB RAM, making it suitable for low-resource servers.

Related Email Tools

Complement this tool with other free utilities from Postigo:

Email Extractor HTML

Extract emails from HTML files and web scraping projects
Email Extractor PDF

Extract emails from PDF documents with OCR support
Email Extractor CSV

Extract and clean emails from CSV spreadsheets
Email List Validator

Validate extracted emails with syntax and DNS checks

Why Choose Postigo Email Tools?

All our email tools are 100% free, open-source, and require no registration. We built these tools for professionals managing large-scale data operations. Every script is:

  • Production-ready: Tested on folders with millions of files
  • Well-documented: Clear instructions, inline comments, and usage examples
  • Regularly updated: Bug fixes and performance improvements based on user feedback
  • Privacy-focused: All processing happens locally on your computer, never uploaded
  • Professionally supported: Email us with questions or feature requests anytime

Need complete email automation? Try Postigo Platform for email extraction, validation, and automated outreach campaigns with pre-warmed SMTP servers, AI content generation, and intelligent reply detection.