Python Logging: An In-Depth Tutorial

Python Logging: An In-Depth Tutorial - The Python logging module comes with the standard library and provides basic logging features. By setting it up correctly, a log message can

As applications become more complex, having good logs can be very useful, not only when debugging but also to provide insight for application issues/performance.

The Python standard library comes with a logging module that provides most of the basic logging features. By setting it up correctly, a log message can bring a lot of useful information about when and where the log is fired as well as the log context, such as the running process/thread.

Despite the advantages, the logging module is often overlooked as it takes some time to set up properly and, although complete, in my opinion, the official logging doc at does not really give logging best practices or highlight some logging surprises.

This Python logging tutorial is not meant to be a complete document on the logging module but rather a “getting started” guide that introduces some logging concepts as well as some “gotchas” to watch out for. The post will end with best practices and contain some pointers to more advanced logging topics.

Please note that all code snippets in the post suppose that you have already imported the logging module:

import logging

Concepts for Python Logging

This section gives an overview on some concepts that are often encountered in the logging module.

Python Logging Levels

The log level corresponds to the “importance” a log is given: an “error” log should be more urgent then than the “warn” log, whereas a “debug” log should be useful only when debugging the application.

There are six log levels in Python; each level is associated with an integer that indicates the log severity: NOTSET=0, DEBUG=10, INFO=20, WARN=30, ERROR=40, and CRITICAL=50.

All the levels are rather straightforward (DEBUG < INFO < WARN ) except NOTSET, whose particularity will be addressed next.

Python Logging Formatting

The log formatter basically enriches a log message by adding context information to it. It can be useful to know when the log is sent, where (Python file, line number, method, etc.), and additional context such as the thread and process (can be extremely useful when debugging a multithreaded application).

For example, when a log “hello world” is sent through a log formatter:

"%(asctime)s — %(name)s — %(levelname)s — %(funcName)s:%(lineno)d — %(message)s"

it will become

2018-02-07 19:47:41,864 - a.b.c - WARNING - <module>:1 - hello world

Python Logging Handler

The log handler is the component that effectively writes/displays a log: Display it in the console (via StreamHandler), in a file (via FileHandler), or even by sending you an email via SMTPHandler, etc.

Each log handler has 2 important fields:

  • A formatter which adds context information to a log.
  • A log level that filters out logs whose levels are inferior. So a log handler with the INFO level will not handle DEBUG logs.

The standard library provides a handful of handlers that should be enough for common use cases: The most common ones are StreamHandler and FileHandler:

console_handler = logging.StreamHandler()
file_handler = logging.FileHandler("filename")

Python Logger

Logger is probably the one that will be used directly the most often in the code and which is also the most complicated. A new logger can be obtained by:

toto_logger = logging.getLogger("toto")

A logger has three main fields:

  • Propagate: Decides whether a log should be propagated to the logger’s parent. By default, its value is True.
  • A level: Like the log handler level, the logger level is used to filter out “less important” logs. Except, unlike the log handler, the level is only checked at the “child” logger; once the log is propagated to its parents, the level will not be checked. This is rather an un-intuitive behavior.
  • Handlers: The list of handlers that a log will be sent to when it arrives to a logger. This allows a flexible log handling—for example, you can have a file log handler that logs all DEBUG logs and an email log handler that will only be used for CRITICAL logs. In this regard, the logger-handler relationship is similar to a publisher-consumer one: A log will be broadcast to all handlers once it passes the logger level check.

A logger is unique by name, meaning that if a logger with the name “toto” has been created, the consequent calls of logging.getLogger("toto") will return the same object:

assert id(logging.getLogger("toto")) == id(logging.getLogger("toto"))

As you might have guessed, loggers have a hierarchy. On top of the hierarchy is the root logger, which can be accessed via logging.root. This logger is called when methods like logging.debug() is used. By default, the root log level is WARN, so every log with lower level (for example via"info")) will be ignored. Another particularity of the root logger is that its default handler will be created the first time a log with a level greater than WARN is logged. Using the root logger directly or indirectly via methods like logging.debug() is generally not recommended.

By default, when a new logger is created, its parent will be set to the root logger:

lab = logging.getLogger("a.b")
assert lab.parent == logging.root # lab's parent is indeed the root logger

However, the logger uses the “dot notation,” meaning that a logger with the name “a.b” will be the child of the logger “a.” However, this is only true if the logger “a” has been created, otherwise “ab” parent is still the root.

la = logging.getLogger("a")
assert lab.parent == la # lab's parent is now la instead of root

When a logger decides whether a log should pass according to the level check (e.g., if the log level is lower than logger level, the log will be ignored), it uses its “effective level” instead of the actual level. The effective level is the same as logger level if the level is not NOTSET, i.e., all the values from DEBUG up to CRITICAL; however, if the logger level is NOTSET, then the effective level will be the first ancestor level that has a non-NOTSET level.

By default, a new logger has the NOTSET level, and as the root logger has a WARN level, the logger’s effective level will be WARN. So even if a new logger has some handlers attached, these handlers will not be called unless the log level exceeds WARN:

toto_logger = logging.getLogger("toto")
assert toto_logger.level == logging.NOTSET # new logger has NOTSET level
assert toto_logger.getEffectiveLevel() == logging.WARN # and its effective level is the root logger level, i.e. WARN

# attach a console handler to toto_logger
console_handler = logging.StreamHandler()
toto_logger.debug("debug") # nothing is displayed as the log level DEBUG is smaller than toto effective level
toto_logger.debug("debug message") # now you should see "debug message" on screen

By default, the logger level will be used to decide of the a log passes: If the log level is lower than logger level, the log will be ignored.

Python Logging Best Practices

The logging module is indeed very handy, but it contains some quirks that can cause long hours of headache for even the best Python developers. Here are the best practices for using this module in my opinion:

  • Configure the root logger but never use it in your code—e.g., never call a function like, which actually calls the root logger behind the scene. If you want to catch error messages from libraries you use, make sure to configure the root logger to write to a file, for example, to make the debugging easier. By default, the root logger only outputs to stderr, so the log can get lost easily.
  • To use the logging, make sure to create a new logger by using logging.getLogger(logger name). I usually use __name__ as the logger name, but anything can be used, as long as it is consistent. To add more handlers, I usually have a method that returns a logger (you can find the gist on
import logging
import sys
from logging.handlers import TimedRotatingFileHandler
FORMATTER = logging.Formatter("%(asctime)s — %(name)s — %(levelname)s — %(message)s")
LOG_FILE = "my_app.log"

def get_console_handler():
   console_handler = logging.StreamHandler(sys.stdout)
   return console_handler
def get_file_handler():
   file_handler = TimedRotatingFileHandler(LOG_FILE, when='midnight')
   return file_handler
def get_logger(logger_name):
   logger = logging.getLogger(logger_name)
   logger.setLevel(logging.DEBUG) # better to have too much log than not enough
   # with this pattern, it's rarely necessary to propagate the error up to parent
   logger.propagate = False
   return logger

After you can create a new logger and use it:

my_logger = get_logger("my module name")
my_logger.debug("a debug message")

  • Use RotatingFileHandler classes, such as the TimedRotatingFileHandler used in the example instead of FileHandler, as it will rotate the file for you automatically when the file reaches a size limit or do it everyday.
  • Use tools like Sentry, Airbrake, Raygun, etc., to catch error logs automatically for you. This is especially useful in the context of a web app, where the log can be very verbose and error logs can get lost easily. Another advantage of using these tools is that you can get details about variable values in the error so you can know what URL triggers the error, which user is concerned, etc.


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Python Logging: An In-Depth Tutorial
Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Art  Lind

Art Lind


How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.


In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

  • md5
  • sha1
  • sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python