How to Use if name == "main":

Understanding how your Python code executes is fundamental to writing maintainable, reusable, and professional scripts. Whether you're building a simple automation tool or developing complex applications, knowing when and how your code runs can make the difference between a script that works only in isolation and one that integrates seamlessly into larger projects. The if __name__ == "__main__": construct is one of Python's most recognizable patterns, yet it remains misunderstood by many developers who encounter it in tutorials and production code alike.

At its core, this pattern is a conditional statement that checks whether a Python file is being run directly or being imported as a module into another script. It's a gatekeeper that determines which parts of your code should execute in different contexts. This simple mechanism provides elegant control over your script's behavior, allowing the same file to serve dual purposes: as a standalone program and as a reusable library of functions and classes.

In this comprehensive guide, you'll discover exactly how this pattern works under the hood, when to use it in your projects, and how it fits into professional Python development practices. We'll explore practical examples, common pitfalls, debugging techniques, and advanced use cases that will transform how you structure your Python code. Whether you're a beginner trying to understand code you've encountered or an experienced developer looking to refine your practices, you'll gain actionable insights that apply to real-world programming scenarios.

Understanding the Fundamentals of __name__ in Python

Every Python module has built-in attributes that provide metadata about the module itself. The __name__ attribute is one of these special variables, and Python automatically sets its value depending on how the module is being used. When you run a Python file directly using the command line, Python sets __name__ to the string "__main__". However, when that same file is imported into another script, __name__ is set to the module's filename without the .py extension.

This behavior creates a powerful distinction between execution contexts. Consider a file named calculator.py that contains functions for mathematical operations. When you run python calculator.py from your terminal, Python executes the file as the main program and sets __name__ to "__main__". But if another file imports calculator with import calculator, then within calculator.py, the __name__ variable will contain the string "calculator" instead.

"The __name__ variable is Python's way of telling your code about its execution context, enabling the same file to behave differently depending on whether it's the star of the show or a supporting actor."

This distinction matters because you often want different behavior in these two scenarios. When running a file directly, you might want to execute test code, run demonstrations, or start a user interface. When importing the same file, you typically only want to load its functions and classes without triggering any automatic execution. The if __name__ == "__main__": pattern provides exactly this control.

How Python Determines the __name__ Value

When Python starts executing a file, it performs several initialization steps before running your actual code. One of these steps involves setting up the module's namespace, which includes assigning a value to __name__. The Python interpreter looks at how the file was invoked and makes a simple determination: if this file is the entry point of the program—the file you explicitly told Python to run—then it's the "main" module and receives the special __name__ value of "__main__".

For imported modules, Python uses the import path to determine the name. If you have a file at myproject/utils/helpers.py and you import it with from myproject.utils import helpers, the __name__ within helpers.py will be "myproject.utils.helpers". This fully qualified name helps Python keep track of different modules and prevents naming conflicts.

The Syntax and Structure

The canonical form of this pattern uses a straightforward conditional statement. The double underscores before and after "name" and "main" indicate these are special Python identifiers, sometimes called "dunder" (double underscore) variables. The comparison uses the equality operator == to check if the string values match exactly. The colon at the end introduces the code block that should execute only when the condition is true.

if __name__ == "__main__":
    # Code here only runs when file is executed directly
    main()

The indented code beneath this conditional statement forms a block that Python will skip entirely when the module is imported. This creates a clean separation between reusable code (functions and classes defined at the module level) and execution code (the specific actions you want to perform when running the script directly).

Practical Implementation Patterns

Understanding the theory is valuable, but seeing how this pattern applies to real code makes the concept concrete. Let's explore various implementation approaches that demonstrate both basic usage and more sophisticated patterns you'll encounter in professional codebases.

Basic Script Structure

The simplest and most common pattern involves defining functions at the module level, then calling those functions within the main block. This structure keeps your logic organized and testable while still allowing the file to run as a standalone script.

def calculate_sum(numbers):
    """Calculate the sum of a list of numbers."""
    return sum(numbers)

def calculate_average(numbers):
    """Calculate the average of a list of numbers."""
    if not numbers:
        return 0
    return sum(numbers) / len(numbers)

def display_statistics(numbers):
    """Display various statistics about a list of numbers."""
    print(f"Numbers: {numbers}")
    print(f"Sum: {calculate_sum(numbers)}")
    print(f"Average: {calculate_average(numbers)}")
    print(f"Count: {len(numbers)}")

if __name__ == "__main__":
    data = [10, 20, 30, 40, 50]
    display_statistics(data)

In this example, the three functions can be imported and used by other modules without any side effects. When you run this file directly, however, the code in the main block executes, creating sample data and displaying statistics. This dual-purpose design is remarkably useful for creating utility libraries that also serve as demonstration tools.

Using a Main Function

A more structured approach involves defining a main() function that contains your program's entry point logic. This pattern is especially common in larger applications and follows conventions familiar to developers from other programming languages.

import sys

def process_data(input_file):
    """Process data from the input file."""
    with open(input_file, 'r') as f:
        data = f.read()
    # Processing logic here
    return data.upper()

def save_results(output_file, results):
    """Save results to the output file."""
    with open(output_file, 'w') as f:
        f.write(results)

def main():
    """Main entry point of the program."""
    if len(sys.argv) != 3:
        print("Usage: python script.py  ")
        sys.exit(1)
    
    input_file = sys.argv[1]
    output_file = sys.argv[2]
    
    results = process_data(input_file)
    save_results(output_file, results)
    print(f"Successfully processed {input_file} -> {output_file}")

if __name__ == "__main__":
    main()

"Wrapping your entry point logic in a main function isn't just about organization—it creates a clear contract for what happens when your script runs and makes testing significantly easier."

This pattern provides several advantages. The main() function can accept arguments, making it easier to test different scenarios. It creates a clear entry point that's easy to locate when reading code. And it allows for cleaner error handling and resource management within a well-defined scope.

Command-Line Argument Processing

Many scripts need to accept configuration options or input parameters from the command line. The main block is the ideal place to handle argument parsing, keeping this interface logic separate from your core functionality.

import argparse

def greet(name, enthusiastic=False):
    """Generate a greeting message."""
    greeting = f"Hello, {name}!"
    if enthusiastic:
        greeting = greeting.upper() + "!!"
    return greeting

def main():
    """Parse arguments and execute greeting."""
    parser = argparse.ArgumentParser(description='Greet someone')
    parser.add_argument('name', help='Name of person to greet')
    parser.add_argument('--enthusiastic', '-e', action='store_true',
                       help='Make the greeting enthusiastic')
    
    args = parser.parse_args()
    message = greet(args.name, args.enthusiastic)
    print(message)

if __name__ == "__main__":
    main()

This structure allows the greet() function to remain pure and testable while the argument parsing logic stays confined to the main execution path. Other modules can import and use greet() without worrying about command-line arguments.

Common Use Cases and When to Apply This Pattern

Knowing when to use the main guard is as important as knowing how. While it's tempting to add it to every Python file, there are specific scenarios where it provides clear benefits and others where it might be unnecessary overhead.

✨ Creating Reusable Modules with Test Code

One of the most valuable applications is in utility modules that you want to both import and test independently. You can include test cases or example usage in the main block without affecting other code that imports your functions.

def validate_email(email):
    """Validate email address format."""
    import re
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    return re.match(pattern, email) is not None

def validate_phone(phone):
    """Validate phone number format."""
    import re
    pattern = r'^\+?1?\d{9,15}$'
    return re.match(pattern, phone) is not None

if __name__ == "__main__":
    # Test cases
    test_emails = [
        "user@example.com",
        "invalid.email",
        "another@domain.co.uk"
    ]
    
    print("Email Validation Tests:")
    for email in test_emails:
        result = validate_email(email)
        print(f"  {email}: {'✓' if result else '✗'}")
    
    test_phones = [
        "+1234567890",
        "1234567890",
        "invalid"
    ]
    
    print("\nPhone Validation Tests:")
    for phone in test_phones:
        result = validate_phone(phone)
        print(f"  {phone}: {'✓' if result else '✗'}")

This approach lets you verify your functions work correctly by simply running the file, while other modules can import these validation functions without executing the test code.

🚀 Building Command-Line Tools

Scripts designed to be run from the command line benefit significantly from this pattern. It clearly separates the command-line interface from the underlying functionality, making the code more maintainable and testable.

📚 Library Files with Demonstrations

When creating libraries for others to use, including example usage in the main block serves as executable documentation. Users can run your module directly to see demonstrations of its capabilities.

🧪 Development and Debugging

During development, the main block provides a convenient sandbox for testing new functions without creating separate test files. You can quickly iterate on code by running the file directly and observing output.

⚙️ Configuration and Setup Scripts

Installation scripts, configuration utilities, and system setup tools typically need to perform actions when run directly but shouldn't execute those actions when their functions are imported elsewhere.

"The main guard is your insurance policy against unexpected side effects. Even if you think a file will never be imported, adding it costs nothing and prevents future headaches."

Advanced Patterns and Professional Practices

As your Python projects grow in complexity, you'll encounter situations that require more sophisticated approaches to the main guard pattern. Professional codebases often employ variations and extensions that provide additional functionality while maintaining clean separation between importable code and execution logic.

Handling Return Values and Exit Codes

Well-behaved command-line programs should return appropriate exit codes to indicate success or failure. This becomes especially important when your scripts are called by other programs or used in automated workflows.

import sys

def process_file(filename):
    """Process a file and return success status."""
    try:
        with open(filename, 'r') as f:
            content = f.read()
        # Processing logic
        return True, f"Successfully processed {filename}"
    except FileNotFoundError:
        return False, f"Error: File {filename} not found"
    except Exception as e:
        return False, f"Error processing file: {str(e)}"

def main():
    """Main entry point with proper exit code handling."""
    if len(sys.argv) != 2:
        print("Usage: python script.py ")
        return 1
    
    filename = sys.argv[1]
    success, message = process_file(filename)
    print(message)
    
    return 0 if success else 1

if __name__ == "__main__":
    sys.exit(main())

This pattern has the main function return an integer exit code, which is then passed to sys.exit(). By convention, 0 indicates success and non-zero values indicate various error conditions. This makes your script integrate smoothly with shell scripts and automation tools.

Supporting Both Import and Execution

Some modules benefit from providing different levels of functionality depending on how they're used. You might want to expose certain functions for import while providing a command-line interface when run directly.

class DataProcessor:
    """Process and analyze data."""
    
    def __init__(self, data):
        self.data = data
    
    def calculate_statistics(self):
        """Calculate basic statistics."""
        return {
            'mean': sum(self.data) / len(self.data),
            'min': min(self.data),
            'max': max(self.data),
            'count': len(self.data)
        }
    
    def generate_report(self):
        """Generate a formatted report."""
        stats = self.calculate_statistics()
        report = "Data Analysis Report\n"
        report += "=" * 20 + "\n"
        for key, value in stats.items():
            report += f"{key.capitalize()}: {value}\n"
        return report

def run_interactive_mode():
    """Run in interactive mode with user input."""
    print("Enter numbers separated by spaces:")
    user_input = input("> ")
    numbers = [float(x) for x in user_input.split()]
    
    processor = DataProcessor(numbers)
    print("\n" + processor.generate_report())

def main():
    """Main entry point supporting different modes."""
    import sys
    
    if len(sys.argv) > 1:
        # Command-line mode with arguments
        numbers = [float(x) for x in sys.argv[1:]]
        processor = DataProcessor(numbers)
        print(processor.generate_report())
    else:
        # Interactive mode
        run_interactive_mode()

if __name__ == "__main__":
    main()

This design allows other modules to import and use the DataProcessor class while still providing a useful command-line interface when the file is run directly. The main function intelligently chooses between different modes based on the presence of command-line arguments.

Integration with Testing Frameworks

Professional projects use dedicated testing frameworks like pytest or unittest, but the main guard can still play a role in making individual modules testable during development.

def add(a, b):
    """Add two numbers."""
    return a + b

def multiply(a, b):
    """Multiply two numbers."""
    return a * b

def divide(a, b):
    """Divide two numbers."""
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b

if __name__ == "__main__":
    # Quick manual tests during development
    assert add(2, 3) == 5, "Addition test failed"
    assert multiply(4, 5) == 20, "Multiplication test failed"
    assert divide(10, 2) == 5, "Division test failed"
    
    try:
        divide(10, 0)
        assert False, "Should have raised ValueError"
    except ValueError:
        pass  # Expected behavior
    
    print("All manual tests passed!")

"While manual tests in the main block shouldn't replace proper unit tests, they provide a quick feedback loop during development that can catch obvious errors before committing code."

Configuring Logging and Debug Output

The main block is an appropriate place to configure logging behavior, especially when you want different logging levels for direct execution versus imported usage.

import logging

def setup_logging(level=logging.INFO):
    """Configure logging for the application."""
    logging.basicConfig(
        level=level,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
    )

def complex_operation(data):
    """Perform a complex operation with logging."""
    logger = logging.getLogger(__name__)
    logger.info(f"Starting operation with {len(data)} items")
    
    result = []
    for item in data:
        logger.debug(f"Processing item: {item}")
        # Processing logic
        result.append(item * 2)
    
    logger.info("Operation completed successfully")
    return result

def main():
    """Main entry point with debug logging enabled."""
    setup_logging(level=logging.DEBUG)
    
    test_data = [1, 2, 3, 4, 5]
    result = complex_operation(test_data)
    print(f"Results: {result}")

if __name__ == "__main__":
    main()

When this module is imported, the logging configuration isn't applied automatically, allowing the importing code to control logging behavior. When run directly, debug logging is enabled to help with development and troubleshooting.

Common Mistakes and How to Avoid Them

Even experienced developers occasionally make mistakes when implementing the main guard pattern. Understanding these common pitfalls helps you write more robust code and debug issues more quickly when they arise.

Incorrect String Comparison

One frequent error is using single equals instead of double equals, or misquoting the string values. Python's syntax checking will catch some of these, but others might lead to subtle bugs.

# Wrong - assignment instead of comparison
if __name__ = "__main__":  # SyntaxError
    main()

# Wrong - incorrect string
if __name__ == "main":  # Will never be true
    main()

# Wrong - missing quotes
if __name__ == __main__:  # NameError
    main()

# Correct
if __name__ == "__main__":
    main()

Placing Imports Inside the Main Block

While sometimes intentional for optional dependencies, placing standard imports inside the main block is generally considered poor practice. It can hide import errors and makes the module's dependencies unclear.

# Problematic pattern
if __name__ == "__main__":
    import sys
    import os
    main()

# Better approach
import sys
import os

if __name__ == "__main__":
    main()

"Module-level imports should remain at the top of your file where they're immediately visible. The main block is for execution logic, not dependency management."

Forgetting to Use the Pattern When Needed

Scripts that perform actions at the module level without a main guard can cause problems when imported. This is especially troublesome with code that has side effects like printing output, modifying files, or making network requests.

# Problematic - runs on import
print("Starting application...")
data = load_data_from_api()  # Makes API call on import!
process_data(data)

# Better approach
def run_application():
    print("Starting application...")
    data = load_data_from_api()
    process_data(data)

if __name__ == "__main__":
    run_application()

Overcomplicating the Main Block

The main block should primarily serve as an entry point, not contain extensive business logic. Complex code in the main block becomes difficult to test and maintain.

# Overcomplicated main block
if __name__ == "__main__":
    # Dozens of lines of logic here
    for item in items:
        if condition1:
            # More nested logic
            if condition2:
                # Even more logic
                result = complex_calculation()
        else:
            # Alternative logic
            result = other_calculation()
    # Continues for many more lines...

# Better approach
def process_items(items):
    """Process items with complex logic."""
    results = []
    for item in items:
        if condition1:
            if condition2:
                result = complex_calculation()
        else:
            result = other_calculation()
        results.append(result)
    return results

if __name__ == "__main__":
    items = get_items()
    results = process_items(items)
    display_results(results)

Debugging and Troubleshooting

When working with the main guard pattern, you might encounter situations where code doesn't behave as expected. Understanding how to diagnose and fix these issues is essential for efficient development.

Verifying __name__ Values

Sometimes you need to see exactly what value __name__ contains in different contexts. Adding diagnostic print statements helps understand execution flow.

print(f"Module name: {__name__}")

def my_function():
    print(f"Function called from module: {__name__}")

if __name__ == "__main__":
    print("Running as main program")
    my_function()
else:
    print("Module was imported")

Running this file directly shows "__main__" as the module name, while importing it from another file shows the actual module name. This technique quickly reveals whether your code is executing in the expected context.

Debugging Import Issues

When modules aren't behaving correctly after import, the issue often relates to how the main guard is structured or what code exists outside it.

# debug_module.py
print("This always prints on import or execution")

def utility_function():
    return "Utility result"

print("This also always prints")

if __name__ == "__main__":
    print("This only prints when run directly")
    result = utility_function()
    print(f"Result: {result}")

Understanding which print statements execute during import versus direct execution helps identify where code should be moved into or out of the main guard.

Testing Module Behavior

Creating a simple test file that imports your module helps verify it behaves correctly in both contexts.

# test_import.py
print("About to import module...")
import my_module
print("Import completed")
print(f"Calling function: {my_module.utility_function()}")

Running this test file shows exactly what happens during import, making it clear whether unwanted code is executing.

Real-World Examples from Popular Python Projects

Examining how established Python projects use the main guard pattern provides insight into professional practices and design decisions. These examples demonstrate patterns you can adapt for your own projects.

Web Framework Entry Points

Web frameworks like Flask commonly use the main guard to start development servers without affecting production deployments where the application is imported by WSGI servers.

from flask import Flask

app = Flask(__name__)

@app.route('/')
def home():
    return "Hello, World!"

@app.route('/api/data')
def get_data():
    return {"status": "success", "data": [1, 2, 3]}

if __name__ == "__main__":
    # Development server
    app.run(debug=True, host='0.0.0.0', port=5000)

This pattern allows the application to be imported by production servers like Gunicorn without starting the development server, while still providing an easy way to test locally.

Data Science Scripts

Data analysis scripts benefit from separating analysis functions from execution, making notebooks and other scripts able to reuse the analysis code.

import pandas as pd
import matplotlib.pyplot as plt

def load_and_clean_data(filepath):
    """Load and clean dataset."""
    df = pd.read_csv(filepath)
    df = df.dropna()
    return df

def analyze_trends(df):
    """Analyze trends in the dataset."""
    summary = df.describe()
    correlations = df.corr()
    return summary, correlations

def visualize_data(df):
    """Create visualizations."""
    df.plot(kind='scatter', x='feature1', y='feature2')
    plt.savefig('analysis.png')

def main():
    """Run complete analysis pipeline."""
    df = load_and_clean_data('data.csv')
    summary, correlations = analyze_trends(df)
    print(summary)
    print(correlations)
    visualize_data(df)

if __name__ == "__main__":
    main()

"Structuring data science code with proper separation between functions and execution makes it infinitely more reusable. Your analysis becomes a library, not just a script."

Automation and DevOps Scripts

System administration and deployment scripts often need to be both executable tools and importable libraries for larger automation frameworks.

import subprocess
import sys

def deploy_application(environment, version):
    """Deploy application to specified environment."""
    commands = [
        f"git checkout {version}",
        "docker build -t myapp .",
        f"docker tag myapp myapp:{version}",
        f"kubectl set image deployment/myapp myapp=myapp:{version} -n {environment}"
    ]
    
    for cmd in commands:
        print(f"Executing: {cmd}")
        result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
        if result.returncode != 0:
            print(f"Error: {result.stderr}")
            return False
    return True

def rollback_deployment(environment, version):
    """Rollback to previous version."""
    cmd = f"kubectl rollout undo deployment/myapp -n {environment}"
    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
    return result.returncode == 0

def main():
    """Main deployment script entry point."""
    if len(sys.argv) < 3:
        print("Usage: python deploy.py  ")
        return 1
    
    environment = sys.argv[1]
    version = sys.argv[2]
    
    success = deploy_application(environment, version)
    if success:
        print(f"Successfully deployed version {version} to {environment}")
        return 0
    else:
        print("Deployment failed")
        return 1

if __name__ == "__main__":
    sys.exit(main())

This structure allows the deployment functions to be imported and used by other automation tools while still providing a command-line interface for manual deployments.

Performance Considerations

While the main guard pattern itself has negligible performance impact, understanding how Python's import system works helps you write more efficient code, especially in large projects with many modules.

Import Time Execution

Every statement at module level executes when the module is imported, even if it's outside the main block. This includes function and class definitions, which must be processed even though they don't execute their contents until called.

import time

# This executes on import
start_time = time.time()

def expensive_function():
    """This definition runs on import but content doesn't execute."""
    result = sum(range(1000000))
    return result

# This also executes on import
print(f"Module loaded in {time.time() - start_time} seconds")

if __name__ == "__main__":
    # This only executes when run directly
    result = expensive_function()
    print(f"Result: {result}")

Minimizing module-level code that performs expensive operations keeps import times fast, which matters when your module is imported frequently or by many other modules.

Lazy Loading Patterns

For expensive operations that aren't always needed, consider lazy loading within functions rather than at module level.

# Inefficient - loads large dataset on every import
import pandas as pd
large_dataset = pd.read_csv('huge_file.csv')

def analyze_data():
    return large_dataset.describe()

# Better - loads only when needed
import pandas as pd

_dataset_cache = None

def get_dataset():
    global _dataset_cache
    if _dataset_cache is None:
        _dataset_cache = pd.read_csv('huge_file.csv')
    return _dataset_cache

def analyze_data():
    dataset = get_dataset()
    return dataset.describe()

Alternative Patterns and When to Use Them

While the standard main guard is the most common pattern, Python offers other approaches for controlling code execution that suit different scenarios.

Using setuptools Entry Points

For distributable packages, setuptools entry points provide a more robust way to create command-line tools without relying on the main guard pattern.

# In your module file
def main():
    """Entry point for command-line tool."""
    print("Running application...")
    # Application logic here

# In setup.py
from setuptools import setup

setup(
    name='myapp',
    entry_points={
        'console_scripts': [
            'myapp=mypackage.module:main',
        ],
    },
)

This approach creates a proper command-line script that users can run without needing to know the internal module structure or use python -m syntax.

Module-Level __main__.py Files

Python packages can include a __main__.py file that executes when the package is run with python -m package_name. This provides an alternative to putting main guard code in regular modules.

# mypackage/__main__.py
from .core import run_application

if __name__ == "__main__":
    run_application()

This pattern cleanly separates the package's importable code from its command-line interface, making the structure more explicit and maintainable.

Context Managers for Resource Management

When your main code needs to manage resources like files or network connections, combining the main guard with context managers ensures proper cleanup.

import contextlib

@contextlib.contextmanager
def setup_environment():
    """Set up and tear down execution environment."""
    print("Setting up environment...")
    # Setup code
    resources = {"database": "connected", "cache": "initialized"}
    
    try:
        yield resources
    finally:
        print("Cleaning up environment...")
        # Cleanup code

def main():
    """Main application logic with resource management."""
    with setup_environment() as resources:
        print(f"Running with resources: {resources}")
        # Application logic here

if __name__ == "__main__":
    main()

"Combining the main guard with context managers creates bulletproof entry points that properly handle resources regardless of how the program exits."

Best Practices Summary

Professional Python development involves following established conventions that make code more maintainable, testable, and collaborative. Here are the key practices to remember when using the main guard pattern.

🎯 Always Use the Standard Form

Stick to the canonical if __name__ == "__main__": syntax. Variations like if __name__ == '__main__': work identically, but consistency across projects helps readability. Most style guides prefer double quotes for this particular idiom.

📦 Structure Your Code Logically

Organize files with imports at the top, followed by constants and configuration, then function and class definitions, and finally the main guard at the bottom. This creates a predictable structure that's easy to navigate.

🔍 Keep the Main Block Simple

The code inside the main guard should primarily call other functions rather than contain complex logic itself. This makes testing easier and keeps your entry point clean and understandable.

📝 Document Your Entry Points

Add docstrings to your main function explaining what the script does when run directly, what arguments it expects, and what it returns. This helps users understand how to use your code without reading the implementation.

🧪 Design for Testability

Structure your code so that functions can be tested independently of the main execution path. Avoid global state and side effects outside the main block that might interfere with testing.

⚠️ Handle Errors Gracefully

Include proper error handling in your main function and return appropriate exit codes. This makes your scripts reliable components in larger systems.

🔄 Consider Reusability from the Start

Even if you're writing a one-off script, structure it as if it might be imported later. This habit costs little extra effort and often pays dividends when requirements change.

What happens if I don't use if __name__ == "__main__": in my script?

Your script will still run correctly when executed directly, but all code at the module level will also execute when the file is imported by another module. This can cause unwanted side effects like printing output, making network requests, or starting services when you only wanted to import functions. For scripts that will never be imported, the main guard is optional but still recommended as good practice.

Can I use if __name__ == "__main__": multiple times in one file?

Technically yes, Python will execute all blocks where the condition is true, but this is considered poor practice and confusing. Instead, use a single main guard at the bottom of your file and call different functions from within it if you need to perform multiple operations. Multiple main guards make code harder to understand and maintain.

Does the main guard affect performance?

The performance impact is negligible—it's a simple string comparison that happens once when the module loads. The real performance consideration is what code exists at module level outside the guard, since that executes on every import. Keep expensive operations inside functions that are only called when needed, whether from the main block or elsewhere.

Should I use this pattern in Jupyter notebooks?

In Jupyter notebooks, the __name__ variable is always set to "__main__" for the notebook itself, so the guard doesn't provide the same import protection. However, if you're developing code in a notebook that you plan to move to a module file later, using the pattern helps with the transition. For pure notebook work, it's generally unnecessary.

How do I test code that's inside the main guard?

The best approach is to move logic from the main guard into separate functions that you can test independently. The main guard itself should primarily call these functions. If you need to test the main function directly, you can import it and call it in your test code—it's just a regular function. Some testing frameworks also provide ways to simulate running modules as main programs.

What's the difference between if __name__ == "__main__": and using a main() function?

The if __name__ == "__main__": guard is a conditional check that determines whether code should run, while main() is a function containing your program's entry point logic. They serve different purposes and are often used together: the guard checks the execution context, and main() contains the actual code to run. You can put code directly in the guard without a main() function, but using a main() function is considered better practice for organization and testability.