Using print vs logging in Python0%

Using print vs logging in Python

Intermediate8 min read•Updated: 2026-10-01

Using print() vs. Logging in Python

When beginning their programming journey, almost all developers rely on print() statements to inspect variables and track program execution. While print() is convenient for quick one-line script checks, it quickly breaks down in production-grade software. Python's built-in logging module offers a professional, configurable alternative designed specifically for monitoring, diagnostics, and audit tracking.

1. Why `print()` Fails in Production

Using print() for debugging introduces several critical defects into software architecture:

1
No Severity Distinction: An urgent database connection timeout looks identical to a routine informational message.
2
Difficult to Turn Off: When moving to production, developers must search and manually comment out or delete dozens of print() lines.
3
No Contextual Metadata: print() lacks automatic timestamps, filenames, line numbers, and thread identifiers.
4
Output Destination Locked: print() sends text exclusively to standard output (sys.stdout), whereas servers require structured log files, syslog daemons, or centralized monitoring services (e.g. Datadog, AWS CloudWatch).

2. Introducing Python's Built-in `logging` Module

Python provides the logging standard library module out of the box—no installation required.

Basic Setup with `logging.basicConfig`

1import logging
2 
3# Configure global logging format and destination
4logging.basicConfig(
5    level=logging.INFO,
6    format="%(asctime)s [%(levelname)s] (%(filename)s:%(lineno)d) - %(message)s",
7    datefmt="%Y-%m-%d %H:%M:%S"
8)
9 
10def process_order(order_id: str, amount: float):
11    logging.info(f"Initiating checkout for Order #{order_id}")
12    
13    if amount <= 0:
14        logging.error(f"Invalid order amount ₹{amount:.2f} for Order #{order_id}")
15        return False
16        
17    logging.info(f"Payment processed successfully for Order #{order_id}")
18    return True
19 
20process_order("ORD-104", 450.0)
21process_order("ORD-105", -20.0)

Output Produced:

12026-09-12 15:45:01 [INFO] (app.py:10) - Initiating checkout for Order #ORD-104
22026-09-12 15:45:01 [INFO] (app.py:16) - Payment processed successfully for Order #ORD-104
32026-09-12 15:45:01 [INFO] (app.py:10) - Initiating checkout for Order #ORD-105
42026-09-12 15:45:01 [ERROR] (app.py:13) - Invalid order amount ₹-20.00 for Order #ORD-105

3. Writing Logs Directly to a File

By simply providing the filename argument, all log events are automatically appended to disk without modifying any of your business logic:

1import logging
2 
3logging.basicConfig(
4    filename="app.log",
5    filemode="a",  # 'a' for append, 'w' for overwrite on restart
6    level=logging.WARNING,
7    format="%(asctime)s - %(levelname)s - %(message)s"
8)
9 
10logging.info("This info message will NOT be written because level is WARNING.")
11logging.warning("Disk usage exceeds 85%.")
12logging.error("Failed to connect to Redis cache.")

4. Modern Modular Logging: `getLogger(name)`

In multi-file modular applications, avoid calling the root logging.info() directly. Instead, instantiate a named logger per module using the module's __name__:

1import logging
2 
3# Instantiates a module-scoped logger
4logger = logging.getLogger(__name__)
5 
6def database_query(query: str):
7    logger.debug(f"Executing query: {query}")
8    logger.info("Query returned 45 records.")

This pattern enables configuring distinct log levels for individual packages (e.g. keep your core database module at DEBUG while keeping external HTTP libraries at WARNING).

5. Architectural Comparison: `print()` vs. `logging`

Feature	`print()`	Python `logging` Module
Intended Purpose	Displaying text directly to user	Diagnostic records, error auditing, monitoring
Categorization	None	5 Standard Levels (`DEBUG` to `CRITICAL`)
Metadata	Manual string formatting	Automatic timestamp, file, function, line number
Redirection	Only stdout	Files, sockets, HTTP endpoints, email alerts
Toggling	Manual deletion/commenting	Single configuration flag (`level=...`)
Performance	Synchronous blocking I/O	Highly optimized, can be asynchronous/buffered

⚠️ Common Intermediate Mistakes & Gotchas

1. Leaving `print()` in Production Code

print() writes unformatted text to standard output without timestamps, severity levels, or source line numbers, and cannot be filtered or redirected without editing code.

2. String Concatenation in Logging Calls

Do not format strings using f-strings inside logger calls if the log level is disabled. Use parameterized logging: logger.info("Processed %s items", count) to save formatting overhead when logs are suppressed.

💻 Try It Yourself: Transitioning from Print to Logging

Scenario

Refactor a script that uses multiple print() statements into a clean logging script that records timestamped messages with severity levels.

Complete Solution

1import logging
2 
3# Configure basic logger
4logging.basicConfig(
5    level=logging.INFO,
6    format="%(asctime)s [%(levelname)s] %(message)s",
7    datefmt="%Y-%m-%d %H:%M:%S"
8)
9 
10def process_payment(account_id, amount):
11    logging.info("Initiating payment of ₹%s for account %s", amount, account_id)
12    if amount <= 0:
13        logging.error("Invalid transaction amount: ₹%s", amount)
14        return False
15    logging.info("Payment of ₹%s processed successfully.", amount)
16    return True
17 
18process_payment("ACC-101", 500)
19process_payment("ACC-102", -50)

Expected Output

12026-10-01 14:00:00 [INFO] Initiating payment of ₹500 for account ACC-101
22026-10-01 14:00:00 [INFO] Payment of ₹500 processed successfully.
32026-10-01 14:00:00 [INFO] Initiating payment of ₹-50 for account ACC-102
42026-10-01 14:00:00 [ERROR] Invalid transaction amount: ₹-50

Interactive Quiz• Knowledge Check

Practice Quiz

Question 1

What is the primary disadvantage of using print() statements for debugging in production applications?

Question 2

Which function in the logging module is used to establish baseline formatting and logging levels?

Question 3

What does %(asctime)s represent inside a logging format string?

Question 4

What is the recommended way to create a module-level logger in modular Python applications?

Question 5

If logging.basicConfig(level=logging.WARNING) is set, which of the following log calls will NOT produce any output?

🚀 What's Next?

In the next lesson, we will continue your intermediate Python journey with Logging Levels (9: Debugging and Logging).

👉 Continue to Next Lesson: Logging Levels →

Next Lesson

Logging Levels

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

Related Lessons

Previous Lesson	Next Lesson
Project: Weather Data Fetcher	Logging Levels

Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

Previous Next

PreviousPrevNext

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Logging Levels

Using print vs logging in Python

Intermediate8 min read•Updated: 2026-10-01

Study Materials Cheatsheet

Using print() vs. Logging in Python

1. Why `print()` Fails in Production

Using print() for debugging introduces several critical defects into software architecture:

1
No Severity Distinction: An urgent database connection timeout looks identical to a routine informational message.
2
Difficult to Turn Off: When moving to production, developers must search and manually comment out or delete dozens of print() lines.
3
No Contextual Metadata: print() lacks automatic timestamps, filenames, line numbers, and thread identifiers.
4
Output Destination Locked: print() sends text exclusively to standard output (sys.stdout), whereas servers require structured log files, syslog daemons, or centralized monitoring services (e.g. Datadog, AWS CloudWatch).

2. Introducing Python's Built-in `logging` Module

Python provides the logging standard library module out of the box—no installation required.

Basic Setup with `logging.basicConfig`

1import logging
2 
3# Configure global logging format and destination
4logging.basicConfig(
5    level=logging.INFO,
6    format="%(asctime)s [%(levelname)s] (%(filename)s:%(lineno)d) - %(message)s",
7    datefmt="%Y-%m-%d %H:%M:%S"
8)
9 
10def process_order(order_id: str, amount: float):
11    logging.info(f"Initiating checkout for Order #{order_id}")
12    
13    if amount <= 0:
14        logging.error(f"Invalid order amount ₹{amount:.2f} for Order #{order_id}")
15        return False
16        
17    logging.info(f"Payment processed successfully for Order #{order_id}")
18    return True
19 
20process_order("ORD-104", 450.0)
21process_order("ORD-105", -20.0)

Output Produced:

12026-09-12 15:45:01 [INFO] (app.py:10) - Initiating checkout for Order #ORD-104
22026-09-12 15:45:01 [INFO] (app.py:16) - Payment processed successfully for Order #ORD-104
32026-09-12 15:45:01 [INFO] (app.py:10) - Initiating checkout for Order #ORD-105
42026-09-12 15:45:01 [ERROR] (app.py:13) - Invalid order amount ₹-20.00 for Order #ORD-105

3. Writing Logs Directly to a File

By simply providing the filename argument, all log events are automatically appended to disk without modifying any of your business logic:

1import logging
2 
3logging.basicConfig(
4    filename="app.log",
5    filemode="a",  # 'a' for append, 'w' for overwrite on restart
6    level=logging.WARNING,
7    format="%(asctime)s - %(levelname)s - %(message)s"
8)
9 
10logging.info("This info message will NOT be written because level is WARNING.")
11logging.warning("Disk usage exceeds 85%.")
12logging.error("Failed to connect to Redis cache.")

4. Modern Modular Logging: `getLogger(name)`

In multi-file modular applications, avoid calling the root logging.info() directly. Instead, instantiate a named logger per module using the module's __name__:

1import logging
2 
3# Instantiates a module-scoped logger
4logger = logging.getLogger(__name__)
5 
6def database_query(query: str):
7    logger.debug(f"Executing query: {query}")
8    logger.info("Query returned 45 records.")

This pattern enables configuring distinct log levels for individual packages (e.g. keep your core database module at DEBUG while keeping external HTTP libraries at WARNING).

5. Architectural Comparison: `print()` vs. `logging`

Feature	`print()`	Python `logging` Module
Intended Purpose	Displaying text directly to user	Diagnostic records, error auditing, monitoring
Categorization	None	5 Standard Levels (`DEBUG` to `CRITICAL`)
Metadata	Manual string formatting	Automatic timestamp, file, function, line number
Redirection	Only stdout	Files, sockets, HTTP endpoints, email alerts
Toggling	Manual deletion/commenting	Single configuration flag (`level=...`)
Performance	Synchronous blocking I/O	Highly optimized, can be asynchronous/buffered

⚠️ Common Intermediate Mistakes & Gotchas

1. Leaving `print()` in Production Code

print() writes unformatted text to standard output without timestamps, severity levels, or source line numbers, and cannot be filtered or redirected without editing code.

2. String Concatenation in Logging Calls

💻 Try It Yourself: Transitioning from Print to Logging

Scenario

Refactor a script that uses multiple print() statements into a clean logging script that records timestamped messages with severity levels.

Complete Solution

1import logging
2 
3# Configure basic logger
4logging.basicConfig(
5    level=logging.INFO,
6    format="%(asctime)s [%(levelname)s] %(message)s",
7    datefmt="%Y-%m-%d %H:%M:%S"
8)
9 
10def process_payment(account_id, amount):
11    logging.info("Initiating payment of ₹%s for account %s", amount, account_id)
12    if amount <= 0:
13        logging.error("Invalid transaction amount: ₹%s", amount)
14        return False
15    logging.info("Payment of ₹%s processed successfully.", amount)
16    return True
17 
18process_payment("ACC-101", 500)
19process_payment("ACC-102", -50)

Expected Output

12026-10-01 14:00:00 [INFO] Initiating payment of ₹500 for account ACC-101
22026-10-01 14:00:00 [INFO] Payment of ₹500 processed successfully.
32026-10-01 14:00:00 [INFO] Initiating payment of ₹-50 for account ACC-102
42026-10-01 14:00:00 [ERROR] Invalid transaction amount: ₹-50

Interactive Quiz• Knowledge Check

Practice Quiz

Question 1

What is the primary disadvantage of using print() statements for debugging in production applications?

Question 2

Which function in the logging module is used to establish baseline formatting and logging levels?

Question 3

What does %(asctime)s represent inside a logging format string?

Question 4

What is the recommended way to create a module-level logger in modular Python applications?

Question 5

If logging.basicConfig(level=logging.WARNING) is set, which of the following log calls will NOT produce any output?

🚀 What's Next?

In the next lesson, we will continue your intermediate Python journey with Logging Levels (9: Debugging and Logging).

👉 Continue to Next Lesson: Logging Levels →

Next Lesson

Logging Levels

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

Related Lessons

Previous Lesson	Next Lesson
Project: Weather Data Fetcher	Logging Levels

Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

Previous Next

Using print() vs. Logging in Python

1. Why print() Fails in Production

2. Introducing Python's Built-in logging Module

Basic Setup with logging.basicConfig

Output Produced:

3. Writing Logs Directly to a File

4. Modern Modular Logging: getLogger(__name__)

5. Architectural Comparison: print() vs. logging

⚠️ Common Intermediate Mistakes & Gotchas

1. Leaving print() in Production Code

2. String Concatenation in Logging Calls

💻 Try It Yourself: Transitioning from Print to Logging

Scenario

Complete Solution

Expected Output

Practice Quiz

🚀 What's Next?

Logging Levels

Related Lessons

Practice Quiz

Loading Page Content...

Using print() vs. Logging in Python

1. Why print() Fails in Production

2. Introducing Python's Built-in logging Module

Basic Setup with logging.basicConfig

Output Produced:

3. Writing Logs Directly to a File

4. Modern Modular Logging: getLogger(__name__)

5. Architectural Comparison: print() vs. logging

⚠️ Common Intermediate Mistakes & Gotchas

1. Leaving print() in Production Code

2. String Concatenation in Logging Calls

💻 Try It Yourself: Transitioning from Print to Logging

Scenario

Complete Solution

Expected Output

Practice Quiz

🚀 What's Next?

Logging Levels

Related Lessons

Practice Quiz

1. Why `print()` Fails in Production

2. Introducing Python's Built-in `logging` Module

Basic Setup with `logging.basicConfig`

4. Modern Modular Logging: `getLogger(name)`

5. Architectural Comparison: `print()` vs. `logging`

1. Leaving `print()` in Production Code

1. Why `print()` Fails in Production

2. Introducing Python's Built-in `logging` Module

Basic Setup with `logging.basicConfig`

4. Modern Modular Logging: `getLogger(name)`

5. Architectural Comparison: `print()` vs. `logging`

1. Leaving `print()` in Production Code