Processing CSV (Comma-Separated Values) files in Python is a core skill that every developer will encounter at some point, whether you’re importing data from spreadsheets, exporting database records, or handling data feeds from external APIs. With Python’s built-in csv module and powerful libraries like pandas, you can efficiently parse, manipulate, and analyze CSV data without getting bogged down in the tedious details of format handling. This guide walks you through everything from basic CSV reading to advanced data transformation techniques, plus troubleshooting the weird edge cases that always seem to pop up in production.

How CSV Parsing Works in Python

Python offers two main approaches for handling CSV files: the built-in csv module and the pandas library. The csv module provides low-level control and memory efficiency, making it perfect for processing large files or when you need precise control over parsing behavior. Meanwhile, pandas offers a high-level interface with powerful data manipulation capabilities, ideal for data analysis workflows.

When Python reads a CSV file, it processes each line sequentially, splitting values based on delimiters (usually commas) while respecting quoted strings and escape characters. The csv module handles RFC 4180 compliance automatically, dealing with edge cases like embedded commas, newlines within fields, and various quoting styles.

Step-by-Step Implementation Guide

Basic CSV Reading with csv Module

Let’s start with the fundamentals. Here’s how to read a simple CSV file using Python’s built-in csv module:

import csv

# Basic CSV reading
with open('data.csv', 'r', newline='', encoding='utf-8') as file:
    csv_reader = csv.reader(file)
    
    # Read header row
    headers = next(csv_reader)
    print(f"Headers: {headers}")
    
    # Process each row
    for row_num, row in enumerate(csv_reader, start=1):
        print(f"Row {row_num}: {row}")
        
        # Access specific columns by index
        if len(row) > 2:
            print(f"First column: {row[0]}, Third column: {row[2]}")

Using DictReader for Named Access

The DictReader class makes working with CSV headers much more intuitive:

import csv

with open('employees.csv', 'r', newline='', encoding='utf-8') as file:
    csv_reader = csv.DictReader(file)
    
    for row in csv_reader:
        # Access columns by header name
        print(f"Employee: {row['name']}, Department: {row['department']}")
        print(f"Salary: ${row['salary']}")
        
        # Handle missing or optional fields
        phone = row.get('phone', 'Not provided')
        print(f"Phone: {phone}\n")

Advanced CSV Reading with Custom Dialects

When dealing with non-standard CSV formats, you can define custom dialects:

import csv

# Define custom dialect for pipe-separated values
csv.register_dialect('pipes', delimiter='|', quotechar='"', 
                    doublequote=True, skipinitialspace=True,
                    lineterminator='\r\n', quoting=csv.QUOTE_MINIMAL)

with open('pipe_data.csv', 'r', newline='') as file:
    csv_reader = csv.reader(file, dialect='pipes')
    
    for row in csv_reader:
        print(row)

# Or specify parameters directly
with open('custom_data.csv', 'r', newline='') as file:
    csv_reader = csv.reader(file, delimiter=';', quotechar="'")
    
    for row in csv_reader:
        print(row)

Using Pandas for Powerful CSV Processing

For data analysis and manipulation, pandas provides a more feature-rich approach:

import pandas as pd

# Basic pandas CSV reading
df = pd.read_csv('sales_data.csv')

# Display basic information
print(f"Shape: {df.shape}")
print(f"Columns: {df.columns.tolist()}")
print(df.head())

# Advanced reading with options
df = pd.read_csv('complex_data.csv',
                sep=',',
                header=0,
                index_col=0,
                parse_dates=['date_column'],
                dtype={'id': 'int64', 'amount': 'float64'},
                na_values=['NULL', 'N/A', ''],
                skiprows=1,
                nrows=1000,
                encoding='utf-8')

# Data manipulation examples
filtered_df = df[df['amount'] > 100]
grouped_data = df.groupby('category').sum()
print(grouped_data)

Real-World Examples and Use Cases

Processing Large CSV Files Efficiently

When dealing with large CSV files, memory management becomes crucial. Here’s how to process files that don’t fit in memory:

import csv
from collections import defaultdict

def process_large_csv(filename, chunk_size=1000):
    """Process large CSV files in chunks to manage memory usage"""
    
    stats = defaultdict(int)
    current_chunk = []
    
    with open(filename, 'r', newline='', encoding='utf-8') as file:
        csv_reader = csv.DictReader(file)
        
        for row_num, row in enumerate(csv_reader):
            current_chunk.append(row)
            
            # Process chunk when it reaches the specified size
            if len(current_chunk) >= chunk_size:
                process_chunk(current_chunk, stats)
                current_chunk = []
                
                if row_num % 10000 == 0:
                    print(f"Processed {row_num} rows...")
        
        # Process remaining rows
        if current_chunk:
            process_chunk(current_chunk, stats)
    
    return stats

def process_chunk(chunk, stats):
    """Process a chunk of CSV data"""
    for row in chunk:
        stats['total_rows'] += 1
        stats['total_amount'] += float(row.get('amount', 0))
        
        category = row.get('category', 'unknown')
        stats[f'category_{category}'] += 1

# Usage
results = process_large_csv('massive_sales_data.csv', chunk_size=5000)
print(f"Processed {results['total_rows']} rows")
print(f"Total amount: ${results['total_amount']:,.2f}")

CSV Data Validation and Cleaning

Real-world CSV files often contain inconsistent or invalid data. Here’s a robust validation approach:

import csv
import re
from datetime import datetime
from typing import List, Dict, Any

class CSVValidator:
    def __init__(self):
        self.errors = []
        self.warnings = []
    
    def validate_email(self, email: str) -> bool:
        pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
        return bool(re.match(pattern, email))
    
    def validate_phone(self, phone: str) -> bool:
        # Remove common formatting characters
        cleaned = re.sub(r'[^\d]', '', phone)
        return len(cleaned) >= 10
    
    def validate_date(self, date_str: str, format_str: str = '%Y-%m-%d') -> bool:
        try:
            datetime.strptime(date_str, format_str)
            return True
        except ValueError:
            return False
    
    def validate_row(self, row: Dict[str, Any], row_num: int) -> Dict[str, Any]:
        """Validate and clean a single CSV row"""
        cleaned_row = {}
        
        # Required fields check
        required_fields = ['name', 'email', 'phone']
        for field in required_fields:
            if not row.get(field, '').strip():
                self.errors.append(f"Row {row_num}: Missing required field '{field}'")
                return None
        
        # Clean and validate name
        name = row['name'].strip().title()
        if len(name) < 2:
            self.errors.append(f"Row {row_num}: Invalid name '{name}'")
            return None
        cleaned_row['name'] = name
        
        # Validate email
        email = row['email'].strip().lower()
        if not self.validate_email(email):
            self.errors.append(f"Row {row_num}: Invalid email '{email}'")
            return None
        cleaned_row['email'] = email
        
        # Validate and clean phone
        phone = row['phone'].strip()
        if not self.validate_phone(phone):
            self.warnings.append(f"Row {row_num}: Questionable phone format '{phone}'")
        cleaned_row['phone'] = phone
        
        # Handle optional date field
        if row.get('join_date'):
            if self.validate_date(row['join_date']):
                cleaned_row['join_date'] = row['join_date']
            else:
                self.warnings.append(f"Row {row_num}: Invalid date format '{row['join_date']}'")
        
        return cleaned_row

def process_and_validate_csv(input_file: str, output_file: str):
    validator = CSVValidator()
    valid_rows = []
    
    with open(input_file, 'r', newline='', encoding='utf-8') as infile:
        csv_reader = csv.DictReader(infile)
        
        for row_num, row in enumerate(csv_reader, start=1):
            cleaned_row = validator.validate_row(row, row_num)
            if cleaned_row:
                valid_rows.append(cleaned_row)
    
    # Write cleaned data to output file
    if valid_rows:
        with open(output_file, 'w', newline='', encoding='utf-8') as outfile:
            fieldnames = valid_rows[0].keys()
            csv_writer = csv.DictWriter(outfile, fieldnames=fieldnames)
            csv_writer.writeheader()
            csv_writer.writerows(valid_rows)
    
    # Report results
    print(f"Processed {row_num} rows")
    print(f"Valid rows: {len(valid_rows)}")
    print(f"Errors: {len(validator.errors)}")
    print(f"Warnings: {len(validator.warnings)}")
    
    if validator.errors:
        print("\nErrors:")
        for error in validator.errors[:10]:  # Show first 10 errors
            print(f"  {error}")
    
    return valid_rows, validator.errors, validator.warnings

# Usage
valid_data, errors, warnings = process_and_validate_csv('raw_data.csv', 'cleaned_data.csv')

Performance Comparison: csv vs pandas

Understanding when to use each approach can significantly impact your application's performance:

Aspect	csv Module	pandas	Best Use Case
Memory Usage	Low (row-by-row)	High (loads entire dataset)	csv for large files, pandas for analysis
Reading Speed	Fast for simple parsing	Faster for complex operations	csv for ETL, pandas for data science
Data Types	Strings only	Automatic type inference	pandas for numerical analysis
Data Manipulation	Manual implementation	Built-in methods	pandas for transformations
Dependencies	Standard library	Requires installation	csv for minimal environments

Here's a performance benchmark comparing both approaches:

import csv
import pandas as pd
import time
import psutil
import os

def benchmark_csv_methods(filename, num_rows=100000):
    """Compare performance between csv module and pandas"""
    
    # Create test data
    create_test_csv(filename, num_rows)
    
    results = {}
    
    # Benchmark csv module
    start_time = time.time()
    process = psutil.Process(os.getpid())
    start_memory = process.memory_info().rss / 1024 / 1024  # MB
    
    row_count = 0
    with open(filename, 'r', newline='') as file:
        csv_reader = csv.DictReader(file)
        for row in csv_reader:
            row_count += 1
            # Simulate some processing
            _ = float(row['amount']) * 1.1
    
    csv_time = time.time() - start_time
    csv_memory = process.memory_info().rss / 1024 / 1024 - start_memory
    
    results['csv'] = {
        'time': csv_time,
        'memory': csv_memory,
        'rows_processed': row_count
    }
    
    # Benchmark pandas
    start_time = time.time()
    start_memory = process.memory_info().rss / 1024 / 1024
    
    df = pd.read_csv(filename)
    df['amount'] = df['amount'] * 1.1
    row_count = len(df)
    
    pandas_time = time.time() - start_time
    pandas_memory = process.memory_info().rss / 1024 / 1024 - start_memory
    
    results['pandas'] = {
        'time': pandas_time,
        'memory': pandas_memory,
        'rows_processed': row_count
    }
    
    return results

def create_test_csv(filename, num_rows):
    """Create test CSV file for benchmarking"""
    import random
    
    with open(filename, 'w', newline='') as file:
        writer = csv.writer(file)
        writer.writerow(['id', 'name', 'amount', 'category'])
        
        for i in range(num_rows):
            writer.writerow([
                i,
                f'User_{i}',
                round(random.uniform(10.0, 1000.0), 2),
                random.choice(['A', 'B', 'C', 'D'])
            ])

# Run benchmark
results = benchmark_csv_methods('test_data.csv', 50000)

print("Performance Comparison:")
print(f"CSV Module: {results['csv']['time']:.2f}s, {results['csv']['memory']:.1f}MB")
print(f"Pandas: {results['pandas']['time']:.2f}s, {results['pandas']['memory']:.1f}MB")

Common Pitfalls and Troubleshooting

Encoding Issues

One of the most frequent problems when parsing CSV files is character encoding. Here's how to handle various encoding scenarios:

import csv
import chardet

def detect_encoding(filename):
    """Detect file encoding automatically"""
    with open(filename, 'rb') as file:
        raw_data = file.read(10000)  # Read first 10KB
        result = chardet.detect(raw_data)
        return result['encoding']

def safe_csv_read(filename):
    """Safely read CSV with encoding detection"""
    
    # Try common encodings first
    encodings = ['utf-8', 'utf-8-sig', 'iso-8859-1', 'cp1252']
    
    for encoding in encodings:
        try:
            with open(filename, 'r', newline='', encoding=encoding) as file:
                csv_reader = csv.reader(file)
                # Try to read first few rows to validate
                for i, row in enumerate(csv_reader):
                    if i >= 5:  # Check first 5 rows
                        break
                print(f"Successfully opened with encoding: {encoding}")
                return encoding
        except UnicodeDecodeError:
            continue
        except Exception as e:
            print(f"Error with {encoding}: {e}")
            continue
    
    # Fall back to automatic detection
    detected_encoding = detect_encoding(filename)
    print(f"Using detected encoding: {detected_encoding}")
    return detected_encoding

# Usage
encoding = safe_csv_read('problematic_file.csv')

# Now read the file with correct encoding
with open('problematic_file.csv', 'r', newline='', encoding=encoding) as file:
    csv_reader = csv.DictReader(file)
    for row in csv_reader:
        print(row)

Handling Malformed CSV Data

Real-world CSV files often contain inconsistencies. Here's how to handle them gracefully:

import csv
import logging

# Set up logging for error tracking
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def robust_csv_parser(filename):
    """Parse CSV with error handling for malformed data"""
    
    successful_rows = []
    error_rows = []
    
    with open(filename, 'r', newline='', encoding='utf-8') as file:
        # Use csv.Sniffer to detect dialect
        sample = file.read(1024)
        file.seek(0)
        
        try:
            dialect = csv.Sniffer().sniff(sample)
            has_header = csv.Sniffer().has_header(sample)
        except csv.Error:
            # Fall back to default dialect
            dialect = csv.excel
            has_header = True
        
        csv_reader = csv.reader(file, dialect=dialect)
        
        if has_header:
            headers = next(csv_reader)
            logger.info(f"Detected headers: {headers}")
        
        for row_num, row in enumerate(csv_reader, start=1):
            try:
                # Validate row has expected number of columns
                if has_header and len(row) != len(headers):
                    logger.warning(f"Row {row_num}: Expected {len(headers)} columns, got {len(row)}")
                    
                    # Pad short rows or truncate long rows
                    if len(row) < len(headers):
                        row.extend([''] * (len(headers) - len(row)))
                    else:
                        row = row[:len(headers)]
                
                # Additional validation can go here
                if any(len(cell) > 1000 for cell in row):  # Check for unusually long fields
                    logger.warning(f"Row {row_num}: Contains unusually long field")
                
                successful_rows.append(row)
                
            except Exception as e:
                logger.error(f"Row {row_num}: {e}")
                error_rows.append((row_num, row, str(e)))
    
    logger.info(f"Successfully parsed {len(successful_rows)} rows")
    logger.info(f"Errors in {len(error_rows)} rows")
    
    return successful_rows, error_rows, headers if has_header else None

# Usage with error handling
try:
    good_rows, bad_rows, headers = robust_csv_parser('messy_data.csv')
    
    # Write clean data to new file
    with open('cleaned_output.csv', 'w', newline='', encoding='utf-8') as outfile:
        writer = csv.writer(outfile)
        if headers:
            writer.writerow(headers)
        writer.writerows(good_rows)
    
    # Log problematic rows for manual review
    if bad_rows:
        with open('error_log.txt', 'w') as error_file:
            for row_num, row, error in bad_rows:
                error_file.write(f"Row {row_num}: {error}\nData: {row}\n\n")
                
except Exception as e:
    logger.error(f"Critical error: {e}")

Best Practices and Advanced Techniques

Memory-Efficient CSV Processing

For applications running on resource-constrained servers, efficient memory usage is crucial:

import csv
from itertools import islice
import gc

class MemoryEfficientCSVProcessor:
    def __init__(self, chunk_size=1000):
        self.chunk_size = chunk_size
        
    def process_in_chunks(self, filename, processor_func):
        """Process CSV file in memory-efficient chunks"""
        
        with open(filename, 'r', newline='', encoding='utf-8') as file:
            csv_reader = csv.DictReader(file)
            
            while True:
                # Read chunk using islice
                chunk = list(islice(csv_reader, self.chunk_size))
                
                if not chunk:
                    break
                
                # Process chunk
                result = processor_func(chunk)
                
                # Yield results instead of storing everything
                yield result
                
                # Force garbage collection for memory management
                del chunk
                gc.collect()

def aggregate_sales_data(chunk):
    """Example processor function for sales data"""
    totals = {}
    
    for row in chunk:
        category = row.get('category', 'unknown')
        amount = float(row.get('amount', 0))
        
        if category not in totals:
            totals[category] = {'count': 0, 'total': 0}
        
        totals[category]['count'] += 1
        totals[category]['total'] += amount
    
    return totals

# Usage example
processor = MemoryEfficientCSVProcessor(chunk_size=5000)
overall_totals = {}

for chunk_result in processor.process_in_chunks('large_sales.csv', aggregate_sales_data):
    # Merge chunk results
    for category, data in chunk_result.items():
        if category not in overall_totals:
            overall_totals[category] = {'count': 0, 'total': 0}
        
        overall_totals[category]['count'] += data['count']
        overall_totals[category]['total'] += data['total']

# Display results
for category, data in overall_totals.items():
    avg = data['total'] / data['count'] if data['count'] > 0 else 0
    print(f"{category}: {data['count']} items, total: ${data['total']:.2f}, avg: ${avg:.2f}")

CSV Data Transformation Pipeline

Building a reusable pipeline for CSV data transformation:

import csv
from abc import ABC, abstractmethod
from typing import List, Dict, Any, Iterator

class CSVTransformer(ABC):
    """Abstract base class for CSV transformers"""
    
    @abstractmethod
    def transform(self, row: Dict[str, Any]) -> Dict[str, Any]:
        pass

class EmailNormalizer(CSVTransformer):
    def transform(self, row: Dict[str, Any]) -> Dict[str, Any]:
        if 'email' in row:
            row['email'] = row['email'].strip().lower()
        return row

class PhoneFormatter(CSVTransformer):
    def transform(self, row: Dict[str, Any]) -> Dict[str, Any]:
        if 'phone' in row:
            # Remove all non-digits
            digits = ''.join(filter(str.isdigit, row['phone']))
            
            # Format as (XXX) XXX-XXXX for US numbers
            if len(digits) == 10:
                row['phone'] = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
            elif len(digits) == 11 and digits[0] == '1':
                row['phone'] = f"({digits[1:4]}) {digits[4:7]}-{digits[7:]}"
        return row

class DateStandardizer(CSVTransformer):
    def transform(self, row: Dict[str, Any]) -> Dict[str, Any]:
        from datetime import datetime
        
        date_fields = ['created_date', 'updated_date', 'birth_date']
        
        for field in date_fields:
            if field in row and row[field]:
                try:
                    # Try multiple date formats
                    formats = ['%m/%d/%Y', '%Y-%m-%d', '%d-%m-%Y', '%m-%d-%Y']
                    
                    for fmt in formats:
                        try:
                            date_obj = datetime.strptime(row[field], fmt)
                            row[field] = date_obj.strftime('%Y-%m-%d')  # Standardize to ISO format
                            break
                        except ValueError:
                            continue
                except:
                    pass  # Keep original value if conversion fails
        
        return row

class CSVPipeline:
    def __init__(self):
        self.transformers: List[CSVTransformer] = []
        self.filters = []
    
    def add_transformer(self, transformer: CSVTransformer):
        self.transformers.append(transformer)
        return self
    
    def add_filter(self, filter_func):
        self.filters.append(filter_func)
        return self
    
    def process_file(self, input_file: str, output_file: str) -> Dict[str, int]:
        stats = {'processed': 0, 'filtered_out': 0, 'errors': 0}
        
        with open(input_file, 'r', newline='', encoding='utf-8') as infile:
            csv_reader = csv.DictReader(infile)
            
            with open(output_file, 'w', newline='', encoding='utf-8') as outfile:
                # Initialize writer with fieldnames from first row
                fieldnames = csv_reader.fieldnames
                csv_writer = csv.DictWriter(outfile, fieldnames=fieldnames)
                csv_writer.writeheader()
                
                for row in csv_reader:
                    try:
                        # Apply transformers
                        for transformer in self.transformers:
                            row = transformer.transform(row)
                        
                        # Apply filters
                        should_include = True
                        for filter_func in self.filters:
                            if not filter_func(row):
                                should_include = False
                                stats['filtered_out'] += 1
                                break
                        
                        if should_include:
                            csv_writer.writerow(row)
                            stats['processed'] += 1
                            
                    except Exception as e:
                        print(f"Error processing row: {e}")
                        stats['errors'] += 1
        
        return stats

# Usage example
def valid_email_filter(row):
    """Filter to keep only rows with valid email addresses"""
    import re
    email = row.get('email', '')
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    return bool(re.match(pattern, email))

def active_users_filter(row):
    """Filter to keep only active users"""
    return row.get('status', '').lower() == 'active'

# Build and run pipeline
pipeline = CSVPipeline()
pipeline.add_transformer(EmailNormalizer()) \
        .add_transformer(PhoneFormatter()) \
        .add_transformer(DateStandardizer()) \
        .add_filter(valid_email_filter) \
        .add_filter(active_users_filter)

results = pipeline.process_file('raw_users.csv', 'processed_users.csv')
print(f"Pipeline results: {results}")

Integration with Server Environments

When deploying CSV processing applications on VPS or dedicated servers, consider these production-ready patterns:

import csv
import os
import logging
import tempfile
from pathlib import Path
import shutil

class ProductionCSVProcessor:
    def __init__(self, work_dir="/tmp/csv_processing", max_file_size=100*1024*1024):
        self.work_dir = Path(work_dir)
        self.work_dir.mkdir(exist_ok=True)
        self.max_file_size = max_file_size
        
        # Configure logging
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler('csv_processor.log'),
                logging.StreamHandler()
            ]
        )
        self.logger = logging.getLogger(__name__)
    
    def validate_file(self, filepath):
        """Validate file before processing"""
        if not os.path.exists(filepath):
            raise FileNotFoundError(f"File not found: {filepath}")
        
        file_size = os.path.getsize(filepath)
        if file_size > self.max_file_size:
            raise ValueError(f"File too large: {file_size} bytes (max: {self.max_file_size})")
        
        # Check if file is actually a CSV
        with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
            sample = f.read(1024)
            if not any(delimiter in sample for delimiter in [',', ';', '\t', '|']):
                raise ValueError("File doesn't appear to be a valid CSV")
        
        return True
    
    def process_file_safely(self, input_path, output_path, processor_func):
        """Process CSV file with error handling and cleanup"""
        
        temp_output = None
        try:
            # Validate input
            self.validate_file(input_path)
            self.logger.info(f"Starting processing: {input_path}")
            
            # Use temporary file for output
            temp_output = tempfile.NamedTemporaryFile(
                mode='w', 
                delete=False, 
                dir=self.work_dir,
                suffix='.csv'
            )
            
            # Process the file
            stats = processor_func(input_path, temp_output.name)
            temp_output.close()
            
            # Move temporary file to final location atomically
            shutil.move(temp_output.name, output_path)
            
            self.logger.info(f"Successfully processed: {stats}")
            return stats
            
        except Exception as e:
            self.logger.error(f"Processing failed: {e}")
            
            # Cleanup temporary file
            if temp_output and os.path.exists(temp_output.name):
                os.unlink(temp_output.name)
            
            raise
    
    def cleanup_old_files(self, max_age_hours=24):
        """Clean up old temporary files"""
        import time
        
        current_time = time.time()
        cleaned_count = 0
        
        for file_path in self.work_dir.glob("*"):
            if file_path.is_file():
                file_age = current_time - file_path.stat().st_mtime
                if file_age > (max_age_hours * 3600):
                    file_path.unlink()
                    cleaned_count += 1
        
        self.logger.info(f"Cleaned up {cleaned_count} old files")
        return cleaned_count

# Example processor function
def example_processor(input_file, output_file):
    """Example CSV processor with statistics"""
    
    stats = {'rows_processed': 0, 'rows_filtered': 0, 'errors': 0}
    
    with open(input_file, 'r', newline='', encoding='utf-8') as infile:
        reader = csv.DictReader(infile)
        
        with open(output_file, 'w', newline='', encoding='utf-8') as outfile:
            writer = csv.DictWriter(outfile, fieldnames=reader.fieldnames)
            writer.writeheader()
            
            for row in reader:
                try:
                    # Example processing logic
                    if row.get('status') == 'active':
                        writer.writerow(row)
                        stats['rows_processed'] += 1
                    else:
                        stats['rows_filtered'] += 1
                        
                except Exception as e:
                    stats['errors'] += 1
                    logging.error(f"Error processing row: {e}")
    
    return stats

# Usage in production
processor = ProductionCSVProcessor()

# Process file with error handling
try:
    results = processor.process_file_safely(
        '/uploads/data.csv',
        '/processed/clean_data.csv',
        example_processor
    )
    print(f"Processing completed: {results}")
    
except Exception as e:
    print(f"Processing failed: {e}")

# Regular cleanup
processor.cleanup_old_files()

For additional information on CSV file format specifications and advanced parsing techniques, check out the official Python CSV documentation and the pandas read_csv reference.

CSV parsing in Python becomes straightforward once you understand the tools available and common edge cases. Whether you're building ETL pipelines, processing user uploads, or analyzing datasets, the techniques covered here will handle most real-world scenarios you'll encounter. Remember to always validate your data, handle encoding issues gracefully, and choose the right tool for your specific use case based on performance requirements and data complexity.

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