Using print vs logging0%
Logging Levels

Using print vs logging

Beginner12 min read•Updated: Jul 11, 2026
Study Materials

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. 1
    No Severity Distinction: An urgent database connection timeout looks identical to a routine informational message.
  2. 2
    Difficult to Turn Off: When moving to production, developers must search and manually comment out or delete dozens of print() lines.
  3. 3
    No Contextual Metadata: print() lacks automatic timestamps, filenames, line numbers, and thread identifiers.
  4. 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

Python
import logging
 
# Configure global logging format and destination
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] (%(filename)s:%(lineno)d) - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S"
)
 
def process_order(order_id: str, amount: float):
logging.info(f"Initiating checkout for Order #{order_id}")
if amount <= 0:
logging.error(f"Invalid order amount ₹{amount:.2f} for Order #{order_id}")
return False
logging.info(f"Payment processed successfully for Order #{order_id}")
return True
 
process_order("ORD-104", 450.0)
process_order("ORD-105", -20.0)

Output Produced:

Output
2026-09-12 15:45:01 [INFO] (app.py:10) - Initiating checkout for Order #ORD-104
2026-09-12 15:45:01 [INFO] (app.py:16) - Payment processed successfully for Order #ORD-104
2026-09-12 15:45:01 [INFO] (app.py:10) - Initiating checkout for Order #ORD-105
2026-09-12 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:

Python
import logging
 
logging.basicConfig(
filename="app.log",
filemode="a", # 'a' for append, 'w' for overwrite on restart
level=logging.WARNING,
format="%(asctime)s - %(levelname)s - %(message)s"
)
 
logging.info("This info message will NOT be written because level is WARNING.")
logging.warning("Disk usage exceeds 85%.")
logging.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__:

Python
import logging
 
# Instantiates a module-scoped logger
logger = logging.getLogger(__name__)
 
def database_query(query: str):
logger.debug(f"Executing query: {query}")
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

Featureprint()Python logging Module
Intended PurposeDisplaying text directly to userDiagnostic records, error auditing, monitoring
CategorizationNone5 Standard Levels (DEBUG to CRITICAL)
MetadataManual string formattingAutomatic timestamp, file, function, line number
RedirectionOnly stdoutFiles, sockets, HTTP endpoints, email alerts
TogglingManual deletion/commentingSingle configuration flag (level=...)
PerformanceSynchronous blocking I/OHighly optimized, can be asynchronous/buffered

Multiple Choice Questions

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

A. print() only supports ASCII characters B. print() cannot be toggled off globally, lacks timestamps/severity, and clutters stdout C. print() is deprecated in Python 3.12 D. print() causes memory leaks Answer: B Explanation: print() lacks severity categorization, automatic timestamps, and file routing, and requires manual code deletion before shipping to production.

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

A. logging.init() B. logging.configure() C. logging.basicConfig() D. logging.setup() Answer: C Explanation: logging.basicConfig(**kwargs) configures the root logger with formatting strings, log levels, and destination filenames.

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

A. The execution duration of the current function B. Human-readable creation time of the log record C. System CPU clock cycles D. The timezone offset Answer: B Explanation: %(asctime)s inserts the timestamp when the LogRecord was created.

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

A. logger = logging.new() B. logger = logging.getLogger(__name__) C. logger = logging.RootLogger() D. logger = logging.create_stream() Answer: B Explanation: logging.getLogger(__name__) creates or retrieves a logger identified by the current module path, facilitating hierarchical configuration.

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

A. logging.warning("Disk full") B. logging.error("Crash detected") C. logging.info("User logged in") D. logging.critical("Power outage") Answer: C Explanation: logging.INFO is of lower severity than WARNING, so all INFO and DEBUG calls are suppressed.

Next Lesson

Logging Levels

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

Related Lessons

Previous LessonNext Lesson
Project: Weather Data FetcherLogging Levels

Practice Quiz

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

PreviousNext
PrevNext