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Loguru is a library which aims to bring enjoyable logging in Python.

Did you ever feel lazy about configuring a logger and used print() instead?… I did, yet logging is fundamental to every application and eases the process of debugging. Using Loguru you have no excuse not to use logging from the start, this is as simple as from loguru import logger.

Also, this library is intended to make Python logging less painful by adding a bunch of useful functionalities that solve caveats of the standard loggers. Using logs in your application should be an automatism, Loguru tries to make it both pleasant and powerful.

Table of contents

Overview

Installation

pip install loguru

Take the tour

Ready to use out of the box without boilerplate

The main concept of Loguru is that there is one and only one logger.

For convenience, it is pre-configured and outputs to stderr to begin with (but that’s entirely configurable).

from loguru import logger

logger.debug("That's it, beautiful and simple logging!")

The logger is just an interface which dispatches log messages to configured handlers. Simple, right?

No Handler, no Formatter, no Filter: one function to rule them all

How to add an handler? How to setup logs formatting? How to filter messages? How to set level?

One answer: the add() function.

logger.add(sys.stderr, format="{time} {level} {message}", filter="my_module", level="INFO")

This function should be used to register sinks which are responsible of managing log messages contextualized with a record dict. A sink can take many forms: a simple function, a string path, a file-like object, a built-in Handler or a custom class.

Easier file logging with rotation / retention / compression

If you want to send logged messages to a file, you just have to use a string path as the sink. It can be automatically timed too for convenience:

logger.add("file_{time}.log")

It is also easily configurable if you need rotating logger, if you want to remove older logs, or if you wish to compress your files at closure.

logger.add("file_1.log", rotation="500 MB")    # Automatically rotate too big file
logger.add("file_2.log", rotation="12:00")     # New file is created each day at noon
logger.add("file_3.log", rotation="1 week")    # Once the file is too old, it's rotated

logger.add("file_X.log", retention="10 days")  # Cleanup after some time

logger.add("file_Y.log", compression="zip")    # Save some loved space

Modern string formatting using braces style

Loguru favors the much more elegant and powerful {} formatting over %, logging functions are actually equivalent to str.format().

logger.info("If you're using Python {}, prefer {feature} of course!", 3.6, feature="f-strings")

Exceptions catching within threads or main

Have you ever seen your program crashing unexpectedly without seeing anything in the logfile? Did you ever noticed that exceptions occurring in threads were not logged? This can be solved using the catch() decorator / context manager which ensures that any error is correctly propagated to the logger.

@logger.catch
def my_function(x, y, z):
    # An error? It's caught anyway!
    return 1 / (x + y + z)

Pretty logging with colors

Loguru automatically adds colors to your logs if your terminal is compatible. You can define your favorite style by using markup tags in the sink format.

logger.add(sys.stdout, colorize=True, format="<green>{time}</green> <level>{message}</level>")

Asynchronous, Thread-safe, Multiprocess-safe

All sinks added to the logger are thread-safe by default. They are not multiprocess-safe, but you can enqueue the messages to ensure logs integrity. This same argument can also be used if you want async logging.

logger.add("somefile.log", enqueue=True)

Fully descriptive exceptions

Logging exceptions that occur in your code is important to track bugs, but it’s quite useless if you don’t know why it failed. Loguru help you identify problems by allowing the entire stack trace to be displayed, including variables values.

The code:

logger.add("output.log", backtrace=True)  # Set 'False' to avoid leaking sensitive data in prod

def func(a, b):
    return a / b

def nested(c):
    try:
        func(5, c)
    except ZeroDivisionError:
        logger.exception("What?!")

nested(0)

Would result in:

2018-07-17 01:38:43.975 | ERROR    | __main__:nested:10 - What?!
Traceback (most recent call last, catch point marked):

  File "test.py", line 12, in <module>
    nested(0)
    └ <function nested at 0x7f5c755322f0>

> File "test.py", line 8, in nested
    func(5, c)
    │       └ 0
    └ <function func at 0x7f5c79fc2e18>

  File "test.py", line 4, in func
    return a / b
           │   └ 0
           └ 5

ZeroDivisionError: division by zero

Structured logging as needed

Want your logs to be serialized for easier parsing or to pass them around? Using the serialize argument, each log message will be converted to a JSON string before being sent to the configured sink.

logger.add(custom_sink_function, serialize=True)

Using bind() you can contextualize your logger messages by modifying the extra record attribute.

logger.add("file.log", format="{extra[ip]} {extra[user]} {message}")
logger_ctx = logger.bind(ip="192.168.0.1", user="someone")
logger_ctx.info("Contextualize your logger easily")
logger_ctx.bind(user="someoneelse").info("Inline binding of extra attribute")

Lazy evaluation of expensive functions

Sometime you would like to log verbose information without performance penalty in production, you can use the opt() method to achieve this.

logger.opt(lazy=True).debug("If sink level <= DEBUG: {x}", x=lambda: expensive_function(2**64))

# By the way, "opt()" serves many usages
logger.opt(exception=True).info("Error stacktrace added to the log message")
logger.opt(ansi=True).info("Per message <blue>colors</blue>")
logger.opt(record=True).info("Display values from the record (eg. {record[thread]})")
logger.opt(raw=True).info("Bypass sink formatting\n")
logger.opt(depth=1).info("Use parent stack context (useful within wrapped functions)")

Customizable levels

Loguru comes with all standard logging levels to which trace() and success() are added. Do you need more? Then, just create it by using the level() function.

new_level = logger.level("SNAKY", no=38, color="<yellow>", icon="🐍")

logger.log("SNAKY", "Here we go!")

Better datetime handling

The standard logging is bloated with arguments like datefmt or msecs, %(asctime)s and %(created)s, naive datetimes without timezone information, not intuitive formatting, etc. Loguru fixes it:

logger.add("file.log", format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}")

Suitable for scripts and libraries

Using the logger in your scripts is easy, and you can configure() it at start. To use Loguru from inside a library, remember to never call add() but use disable() instead so logging functions become no-op. If a developer wishes to see your library’s logs, he can enable() it again.

# For scripts
config = {
    "handlers": [
        {"sink": sys.stdout, format="{time} - {message}"},
        {"sink": "file.log", "serialize": True},
    ],
    "extra": {"user": "someone"}
}
logger.configure(**config)

# For libraries
logger.disable("my_library")
logger.info("No matter added sinks, this message is not displayed")
logger.enable("my_library")
logger.info("This message however is propagated to the sinks")

Entirely compatible with standard logging

Wish to use built-in logging Handler as a Loguru sink?

handler = logging.handlers.SysLogHandler(address=('localhost', 514))
logger.add(handler)

Need to propagate Loguru messages to standard logging?

class PropagateHandler(logging.Handler):
    def emit(self, record):
        logging.getLogger(record.name).handle(record)

logger.add(PropagateHandler(), format="{message}")

Want to intercept standard logging messages toward your Loguru sinks?

class InterceptHandler(logging.Handler):
    def emit(self, record):
        logger_opt = logger.opt(depth=6, exception=record.exc_info)
        logger_opt.log(record.levelno, record.getMessage())

logging.getLogger(None).addHandler(InterceptHandler())

Personalizable defaults through environment variables

Don’t like the default logger formatting? Would prefer another DEBUG color? No problem:

# Linux / OSX
export LOGURU_FORMAT="{time} | <lvl>{message}</lvl>"

# Windows
setx LOGURU_DEBUG_COLOR "<green>"

Convenient parser

It is often useful to extract specific information from generated logs, this is why Loguru provides a parse() method which helps dealing with logs and regexes.

pattern = r"(?P<time>.*) - (?P<level>[0-9]+) - (?P<message>.*)"  # Regex with named groups
caster_dict = dict(time=dateutil.parser.parse, level=int)        # Transform matching groups

for groups in logger.parse("file.log", pattern, cast=caster_dict):
    print("Parsed:", groups)
    # {"level": 30, "message": "Log example", "time": datetime(2018, 12, 09, 11, 23, 55)}

Exhaustive notifier

Loguru can easily be combined with the great notifiers library (must be installed separately) to receive an e-mail when your program fail unexpectedly or to send many other kind of notifications.

import notifiers

params = {
    "username": "you@gmail.com",
    "password": "abc123",
    "to": "dest@gmail.com"
}

# Send a single notification
notifier = notifiers.get_notifier("gmail")
notifier.notify(message="The application is running!", **params)

# Be alerted on each error message
from notifiers.logging import NotificationHandler

handler = NotificationHandler("gmail", defaults=params)
logger.add(handler, level="ERROR")

10x faster than built-in logging

Although logging impact on performances is in most cases negligible, a zero-cost logger would allow to use it anywhere without much concern. In an upcoming release, Loguru’s critical functions will be implemented in C for maximum speed.

API Reference

The Loguru library provides a pre-instanced logger to facilitate dealing with logging in Python.

Just from loguru import logger.

loguru.logger

class Logger[source]

An object to dispatch logging messages to configured handlers.

The Logger is the core object of loguru, every logging configuration and usage pass through a call to one of its methods. There is only one logger, so there is no need to retrieve one before usage.

Handlers to which send log messages are added using the add() method. Note that you can use the Logger right after import as it comes pre-configured. Messages can be logged with different severity levels and using braces attributes like the str.format() method do.

Once a message is logged, a “record” is associated with it. This record is a dict which contains several information about the logging context: time, function, file, line, thread, level… It also contains the __name__ of the module, this is why you don’t need named loggers.

You should not instantiate a Logger by yourself, use from loguru import logger instead.

add(sink, *, level='DEBUG', format='<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>', filter=None, colorize=None, serialize=False, backtrace=True, enqueue=False, catch=True, **kwargs)[source]

Add an handler sending log messages to a sink adequately configured.

Parameters:
  • sink (file-like object, str, pathlib.Path, function, logging.Handler or class) – An object in charge of receiving formatted logging messages and propagating them to an appropriate endpoint.
  • level (int or str, optional) – The minimum severity level from which logged messages should be send to the sink.
  • format (str or function, optional) – The template used to format logged messages before being sent to the sink.
  • filter (function or str, optional) – A directive used to optionally filter out logged messages before they are send to the sink.
  • colorize (bool, optional) – Whether or not the color markups contained in the formatted message should be converted to ansi codes for terminal coloration, ore stripped otherwise. If None, the choice is automatically made based on the sink being a tty or not.
  • serialize (bool, optional) – Whether or not the logged message and its records should be first converted to a JSON string before being sent to the sink.
  • backtrace (bool, optional) – Whether or not the formatted exception should use stack trace to display local variables values. This probably should be set to False in production to avoid leaking sensitive data.
  • enqueue (bool, optional) – Whether or not the messages to be logged should first pass through a multiprocess-safe queue before reaching the sink. This is useful while logging to a file through multiple processes.
  • catch (bool, optional) – Whether or not errors occurring while sink handles logs messages should be caught or not. If True, an exception message is displayed on sys.stderr but the exception is not propagated to the caller, preventing your app to crash.
  • **kwargs – Additional parameters that will be passed to the sink while creating it or while logging messages (the exact behavior depends on the sink type).

If and only if the sink is a file, the following parameters apply:

Parameters:
  • rotation (str, int, datetime.time, datetime.timedelta or function, optional) – A condition indicating whenever the current logged file should be closed and a new one started.
  • retention (str, int, datetime.timedelta or function, optional) – A directive filtering old files that should be removed during rotation or end of program.
  • compression (str or function, optional) – A compression or archive format to which log files should be converted at closure.
  • delay (bool, optional) – Whether or not the file should be created as soon as the sink is configured, or delayed until first logged message. It defaults to False.
  • mode (str, optional) – The opening mode as for built-in open() function. It defaults to "a" (open the file in appending mode).
  • buffering (int, optional) – The buffering policy as for built-in open() function. It defaults to 1 (line buffered file).
  • encoding (str, optional) – The file encoding as for built-in open() function. If None, it defaults to locale.getpreferredencoding().
  • **kwargs – Others parameters are passed to the built-in open() function.
Returns:

int – An identifier associated with the added sink and which should be used to remove() it.

Notes

Extended summary follows.

The sink parameter

The sink handles incoming log messages and proceed to their writing somewhere and somehow. A sink can take many forms:

  • A file-like object like sys.stderr or open("somefile.log", "w"). Anything with a .write() method is considered as a file-like object. If it has a .flush() method, it will be automatically called after each logged message. If it has a .stop() method, it will be automatically called at sink termination.
  • A file path as str or pathlib.Path. It can be parametrized with some additional parameters, see below.
  • A simple function like lambda msg: print(msg). This allows for logging procedure entirely defined by user preferences and needs.
  • A built-in logging.Handler like logging.StreamHandler. In such a case, the Loguru records are automatically converted to the structure expected by the logging module.
  • A class object that will be used to instantiate the sink using **kwargs attributes passed. Hence the class should instantiate objects which are therefore valid sinks.

Note that you should avoid using the logger inside any of your sinks as this would result in infinite recursion or dead lock if the module’s sink was not explicitly disabled.

The logged message

The logged message passed to all added sinks is nothing more than a string of the formatted log, to which a special attribute is associated: the .record which is a dict containing all contextual information possibly needed (see below).

Logged messages are formatted according to the format of the added sink. This format is usually a string containing braces fields to display attributes from the record dict.

If fine-grained control is needed, the format can also be a function which takes the record as parameter and return the format template string. However, note that in such a case, you should take care of appending the line ending and exception field to the returned format, while "\n{exception}" is automatically appended for convenience if format is a string.

The filter attribute can be used to control which messages are effectively passed to the sink and which one are ignored. A function can be used, accepting the record as an argument, and returning True if the message should be logged, False otherwise. If a string is used, only the records with the same name and its children will be allowed.

The record dict

The record is just a Python dict, accessible from sinks by message.record. It contains all contextual information of the logging call (time, function, file, line, level, etc.). Each of its key can be used in the handler’s format so the corresponding value is properly displayed in the logged message (eg. "{level}" -> "INFO"). Some record’s values are objects with two or more attributes, those can be formatted with "{key.attr}" ("{key}" would display one by default). Formatting directives like "{key: >3}" also works and is particularly useful for time (see below).

Key Description Attributes
elapsed The time elapsed since the start of the program See datetime.timedelta
exception The formatted exception if any, None otherwise type, value, traceback
extra The dict of attributes bound by the user (see bind()) None
file The file where the logging call was made name (default), path
function The function from which the logging call was made None
level The severity used to log the the message name (default), no, icon
line The line number in the source code None
message The logged message (not yet formatted) None
module The module where the logging call was made None
name The __name__ where the logging call was made None
process The process in which the logging call was made name, id (default)
thread The thread in which the logging call was made name, id (default)
time The aware local time when the logging call was made See datetime.datetime

The time formatting

To use your favorite time representation, you can precise it directly in the time formatter specifier of you handler format, like for example format="{time:HH:mm:ss} {message}". Note that this datetime represents your local time, and it is also made timezone-aware, so you can display the UTC offset to avoid ambiguities.

The time field can be formatted using more human-friendly tokens. Those constitute a subset of the one used by the Pendulum library of @sdispater. To escape a token, just add square brackets around it, for example "[YY]" would display literally "YY".

If no time formatter specifier is used, like for example if format="{time} {message}", the default one will use ISO 8601.

  Token Output
Year YYYY 2000, 2001, 2002 … 2012, 2013
YY 00, 01, 02 … 12, 13
Quarter Q 1 2 3 4
Month MMMM January, February, March …
MMM Jan, Feb, Mar …
MM 01, 02, 03 … 11, 12
M 1, 2, 3 … 11, 12
Day of Year DDDD 001, 002, 003 … 364, 365
DDD 1, 2, 3 … 364, 365
Day of Month DD 01, 02, 03 … 30, 31
D 1, 2, 3 … 30, 31
Day of Week dddd Monday, Tuesday, Wednesday …
ddd Mon, Tue, Wed …
d 0, 1, 2 … 6
Days of ISO Week E 1, 2, 3 … 7
Hour HH 00, 01, 02 … 23, 24
H 0, 1, 2 … 23, 24
hh 01, 02, 03 … 11, 12
h 1, 2, 3 … 11, 12
Minute mm 00, 01, 02 … 58, 59
m 0, 1, 2 … 58, 59
Second ss 00, 01, 02 … 58, 59
s 0, 1, 2 … 58, 59
Fractional Second S 0 1 … 8 9
SS 00, 01, 02 … 98, 99
SSS 000 001 … 998 999
SSSS… 000[0..] 001[0..] … 998[0..] 999[0..]
SSSSSS 000000 000001 … 999998 999999
AM / PM A AM, PM
Timezone Z -07:00, -06:00 … +06:00, +07:00
ZZ -0700, -0600 … +0600, +0700
zz EST CST … MST PST
Seconds timestamp X 1381685817, 1234567890.123
Microseconds timestamp x 1234567890123

The file sinks

If the sink is a str or a pathlib.Path, the corresponding file will be opened for writing logs. The path can also contains a special "{time}" field that will be formatted with the current date at file creation.

The rotation check is made before logging each messages. If there is already an existing file with the same name that the file to be created, then the existing file is renamed by appending the date to its basename to prevent file overwriting. This parameter accepts:

  • an int which corresponds to the maximum file size in bytes before that the current logged file is closed and a new one started over.
  • a datetime.timedelta which indicates the frequency of each new rotation.
  • a datetime.time which specifies the hour when the daily rotation should occur.
  • a str for human-friendly parametrization of one of the previously enumerated types. Examples: "100 MB", "0.5 GB", "1 month 2 weeks", "4 days", "10h", "monthly", "18:00", "sunday", "w0", "monday at 12:00", …
  • a function which will be called before logging. It should accept two arguments: the logged message and the file object, and it should return True if the rotation should happen now, False otherwise.

The retention occurs at rotation or at sink stop if rotation is None. Files are selected according to their basename, if it is the same that the sink file, with possible time field being replaced with .*. This parameter accepts:

  • an int which indicates the number of log files to keep, while older files are removed.
  • a datetime.timedelta which specifies the maximum age of files to keep.
  • a str for human-friendly parametrization of the maximum age of files to keep. Examples: "1 week, 3 days", "2 months", …
  • a function which will be called before the retention process. It should accept the list of log files as argument and process to whatever it wants (moving files, removing them, etc.).

The compression happens at rotation or at sink stop if rotation is None. This parameter accepts:

  • a str which corresponds to the compressed or archived file extension. This can be one of: "gz", "bz2", "xz", "lzma", "tar", "tar.gz", "tar.bz2", "tar.xz", "zip".
  • a function which will be called before file termination. It should accept the path of the log file as argument and process to whatever it wants (custom compression, network sending, removing it, etc.).

The color markups

To add colors to your logs, you just have to enclose your format string with the appropriate tags. This is based on the great ansimarkup library from @gvalkov. Those tags are removed if the sink don’t support ansi codes.

The special tag <level> (abbreviated with <lvl>) is transformed according to the configured color of the logged message level.

Here are the available tags (note that compatibility may vary depending on terminal):

Color (abbr) Styles (abbr)
Black (k) Bold (b)
Blue (e) Dim (d)
Cyan (c) Normal (n)
Green (g) Italic (i)
Magenta (m) Underline (u)
Red (r) Strike (s)
White (w) Reverse (r)
Yellow (y) Blink (l)
  Hide (h)

Usage:

Description Examples
Foreground Background
Basic colors <red>, <r> <GREEN>, <G>
Light colors <light-blue>, <le> <LIGHT-CYAN>, <LC>
Xterm colors <fg 86>, <fg 255> <bg 42>, <bg 9>
Hex colors <fg #00005f>, <fg #EE1> <bg #AF5FD7>, <bg #fff>
RGB colors <fg 0,95,0> <bg 72,119,65>
Stylizing <bold>, <b> , <underline>, <u>
Shorthand (FG, BG) <red, yellow>, <r, y>
Shorthand (Style, FG, BG) <bold, cyan, white>, <b,,w>, <b,c,>

The environment variables

The default values of sink parameters can be entirely customized. This is particularly useful if you don’t like the log format of the pre-configured sink.

Each of the add() default parameter can be modified by setting the LOGURU_[PARAM] environment variable. For example on Linux: export LOGURU_FORMAT="{time} - {message}" or export LOGURU_BACKTRACE=NO.

The default levels attributes can also be modified by setting the LOGURU_[LEVEL]_[ATTR] environment variable. For example, on Windows: setx LOGURU_DEBUG_COLOR "<blue>" or setx LOGURU_TRACE_ICON "🚀".

If you want to disable the pre-configured sink, you can set the LOGURU_AUTOINIT variable to False.

On Linux, you will probably need to edit the ~/.profile file to make this persistent. On Windows, don’t forget to restart your terminal for the change to be taken into account.

Examples

>>> logger.add(sys.stdout, format="{time} - {level} - {message}", filter="sub.module")

>>> logger.add("file_{time}.log", level="TRACE", rotation="100 MB")

>>> def my_sink(message):
...     record = message.record
...     update_db(message, time=record.time, level=record.level)
...
>>> logger.add(my_sink)

>>> from logging import StreamHandler
>>> logger.add(StreamHandler(sys.stderr), format="{message}")

>>> class RandomStream:
...     def __init__(self, seed, threshold):
...         self.threshold = threshold
...         random.seed(seed)
...     def write(self, message):
...         if random.random() > self.threshold:
...             print(message)
...
>>> stream_object = RandomStream(seed=12345, threhold=0.25)
>>> logger.add(stream_object, level="INFO")
>>> logger.add(RandomStream, level="DEBUG", seed=34567, threshold=0.5)
remove(handler_id=None)[source]

Remove a previously added handler and stop sending logs to its sink.

Parameters:handler_id (int or None) – The id of the sink to remove, as it was returned by the add() method. If None, all handlers are removed. The pre-configured handler is guaranteed to have the index 0.
Raises:ValueError – If handler_id is not None but there is no active handler with such id.

Examples

>>> i = logger.add(sys.stderr, format="{message}")
>>> logger.info("Logging")
Logging
>>> logger.remove(i)
>>> logger.info("No longer logging")
catch(exception=<class 'Exception'>, *, level='ERROR', reraise=False, message="An error has been caught in function '{record[function]}', process '{record[process].name}' ({record[process].id}), thread '{record[thread].name}' ({record[thread].id}):")[source]

Return a decorator to automatically log possibly caught error in wrapped function.

This is useful to ensure unexpected exceptions are logged, the entire program can be wrapped by this method. This is also very useful to decorate threading.Thread.run() methods while using threads to propagate errors to the main logger thread.

Note that the visibility of variables values (which uses the cool better_exceptions library from @Qix-) depends on the backtrace option of each configured sinks.

The returned object can also be used as a context manager.

Parameters:
  • exception (Exception, optional) – The type of exception to intercept. If several types should be caught, a tuple of exceptions can be used too.
  • level (str or int, optional) – The level name or severity with which the message should be logged.
  • reraise (bool, optional) – Whether or not the exception should be raised again and hence propagated to the caller.
  • message (str, optional) – The message that will be automatically logged if an exception occurs. Note that it will be formatted with the record attribute.
Returns:

decorator / context manager – An object that can be used to decorate a function or as a context manager to log exceptions possibly caught.

Examples

>>> @logger.catch
... def f(x):
...     100 / x
...
>>> def g():
...     f(10)
...     f(0)
...
>>> g()
ERROR - An error has been caught in function 'g', process 'Main' (367), thread 'ch1' (1398):
Traceback (most recent call last, catch point marked):
  File "program.py", line 12, in <module>
    g()
    └ <function g at 0x7f225fe2bc80>
> File "program.py", line 10, in g
    f(0)
    └ <function f at 0x7f225fe2b9d8>
  File "program.py", line 6, in f
    100 / x
          └ 0
ZeroDivisionError: division by zero

>>> with logger.catch(message="Because we never know..."):
...    main()  # No exception, no logs
...
opt(*, exception=None, record=False, lazy=False, ansi=False, raw=False, depth=0)[source]

Parametrize a logging call to slightly change generated log message.

Parameters:
  • exception (bool, tuple or Exception, optional) – If it does not evaluate as False, the passed exception is formatted and added to the log message. It could be an Exception object or a (type, value, traceback) tuple, otherwise the exception information is retrieved from sys.exc_info().
  • record (bool, optional) – If True, the record dict contextualizing the logging call can be used to format the message by using {record[key]} in the log message.
  • lazy (bool, optional) – If True, the logging call attribute to format the message should be functions which will be called only if the level is high enough. This can be used to avoid expensive functions if not necessary.
  • ansi (bool, optional) – If True, logged message will be colorized according to the markups it possibly contains.
  • raw (bool, optional) – If True, the formatting of each sink will be bypassed and the message will be send as is.
  • depth (int, optional) – Specify which stacktrace should be used to contextualize the logged message. This is useful while using the logger from inside a wrapped function to retrieve worthwhile information.
Returns:

Logger – A logger wrapping the core logger, but transforming logged message adequately before sending.

Examples

>>> try:
...     1 / 0
... except ZeroDivisionError:
...    logger.opt(exception=True).debug("Exception logged with debug level:")
...
[18:10:02] DEBUG in '<module>' - Exception logged with debug level:
Traceback (most recent call last, catch point marked):
> File "<stdin>", line 2, in <module>
ZeroDivisionError: division by zero

>>> logger.opt(record=True).info("Current line is: {record[line]}")
[18:10:33] INFO in '<module>' - Current line is: 1

>>> logger.opt(lazy=True).debug("If sink <= DEBUG: {x}", x=lambda: math.factorial(2**5))
[18:11:19] DEBUG in '<module>' - If sink <= DEBUG: 263130836933693530167218012160000000

>>> logger.opt(ansi=True).warning("We got a <red>BIG</red> problem")
[18:11:30] WARNING in '<module>' - We got a BIG problem

>>> logger.opt(raw=True).debug("No formatting\n")
No formatting

>>> def wrapped():
...     logger.opt(depth=1).info("Get parent context")
...
>>> def func():
...     wrapped()
...
>>> func()
[18:11:54] DEBUG in 'func' - Get parent context
bind(**kwargs)[source]

Bind attributes to the extra dict of each logged message record.

This is used to add custom context to each logging call.

Parameters:**kwargs – Mapping between keys and values that will be added to the extra dict.
Returns:Logger – A logger wrapping the core logger, but which sends record with the customized extra dict.

Examples

>>> logger.add(sys.stderr, format="{extra[ip]} - {message}")
1
>>> class Server:
...     def __init__(self, ip):
...         self.ip = ip
...         self.logger = logger.bind(ip=ip)
...     def call(self, message):
...         self.logger.info(message)
...
>>> instance_1 = Server("192.168.0.200")
>>> instance_2 = Server("127.0.0.1")
>>> instance_1.call("First instance")
192.168.0.200 - First instance
>>> instance_2.call("Second instance")
127.0.0.1 - Second instance
level(name, no=None, color=None, icon=None)[source]

Add, update or retrieve a logging level.

Logging levels are defined by their name to which a severity no, an ansi color and an icon are associated and possibly modified at run-time. To log() to a custom level, you should necessarily use its name, the severity number is not linked back to levels name (this implies that several levels can share the same severity).

To add a new level, all parameters should be passed so it can be properly configured.

To update an existing level, pass its name with the parameters to be changed.

To retrieve level information, the name solely suffices.

Parameters:
  • name (str) – The name of the logging level.
  • no (int) – The severity of the level to be added or updated.
  • color (str) – The color markup of the level to be added or updated.
  • icon (str) – The icon of the level to be added or updated.
Returns:

Level – A namedtuple containing information about the level.

Examples

>>> level = logger.level("ERROR")
Level(no=40, color='<red><bold>', icon='❌')
>>> logger.add(sys.stderr, format="{level.no} {icon} {message}")
>>> logger.level("CUSTOM", no=15, color="<blue>", icon="@")
>>> logger.log("CUSTOM", "Logging...")
15 @ Logging...
>>> logger.level("WARNING", icon=r"/!\")
>>> logger.warning("Updated!")
30 /!\ Updated!
disable(name)[source]

Disable logging of messages coming from name module and its children.

Developers of library using Loguru should absolutely disable it to avoid disrupting users with unrelated logs messages.

Parameters:name (str) – The name of the parent module to disable.

Examples

>>> logger.info("Allowed message by default")
[22:21:55] Allowed message by default
>>> logger.disable("my_library")
>>> logger.info("While publishing a library, don't forget to disable logging")
enable(name)[source]

Enable logging of messages coming from name module and its children.

Logging is generally disabled by imported library using Loguru, hence this function allows users to receive these messages anyway.

Parameters:name (str) – The name of the parent module to re-allow.

Examples

>>> logger.disable("__main__")
>>> logger.info("Disabled, so nothing is logged.")
>>> logger.enable("__main__")
>>> logger.info("Re-enabled, messages are logged.")
[22:46:12] Re-enabled, messages are logged.
configure(*, handlers=None, levels=None, extra=None, activation=None)[source]

Configure the core logger.

It should be noted that extra values set using this function are available across all modules, so this is the best way to set overall default values.

Parameters:
  • handlers (list of dict, optional) – A list of each handler to be added. The list should contains dicts of params passed to the add() function as keyword arguments. If not None, all previously added handlers are first removed.
  • levels (list of dict, optional) – A list of each level to be added or updated. The list should contains dicts of params passed to the level() function as keyword arguments. This will never remove previously created levels.
  • extra (dict, optional) – A dict containing additional parameters bound to the core logger, useful to share common properties if you call bind() in several of your files modules. If not None, this will remove previously configured extra dict.
  • activation (list of tuple, optional) – A list of (name, state) tuples which denotes which loggers should be enabled (if state is True) or disabled (if state is False). The calls to enable() and disable() are made accordingly to the list order. This will not modify previously activated loggers, so if you need a fresh start preprend your list with ("", False) or ("", True).
Returns:

list of int – A list containing the identifiers of added sinks (if any).

Examples

>>> logger.configure(
...     handlers=[dict(sink=sys.stderr, format="[{time}] {message}"),
...            dict(sink="file.log", enqueue=True, serialize=True)],
...     levels=[dict(name="NEW", no=13, icon="¤", color="")],
...     extra={"common_to_all": "default"},
...     activation=[("my_module.secret": False, "another_library.module": True)]
... )
[1, 2]
static parse(file, pattern, *, cast={}, chunk=65536)[source]

Parse raw logs and extract each entry as a dict.

The logging format has to be specified as the regex pattern, it will then be used to parse the file and retrieve each entries based on the named groups present in the regex.

Parameters:
  • file (str, pathlib.Path or file-like object) – The path of the log file to be parsed, or alternatively an already opened file object.
  • pattern (str or re.Pattern) – The regex to use for logs parsing, it should contain named groups which will be included in the returned dict.
  • cast (function or dict, optional) – A function that should convert in-place the regex groups parsed (a dict of string values) to more appropriate types. If a dict is passed, its should be a mapping between keys of parsed log dict and the function that should be used to convert the associated value.
  • chunk (int, optional) – The number of bytes read while iterating through the logs, this avoid having to load the whole file in memory.
Yields:

dict – The dict mapping regex named groups to matched values, as returned by re.Match.groupdict() and optionally converted according to cast argument.

Examples

>>> reg = r"(?P<lvl>[0-9]+): (?P<msg>.*)"    # If log format is "{level.no} - {message}"
>>> for e in logger.parse("file.log", reg):  # A file line could be "10 - A debug message"
...     print(e)                             # => {'lvl': '10', 'msg': 'A debug message'}
...

>>> caster = dict(lvl=int)                   # Parse 'lvl' key as an integer
>>> for e in logger.parse("file.log", reg, cast=caster):
...     print(e)                             # => {'lvl': 10, 'msg': 'A debug message'}

>>> def cast(groups):
...     if "date" in groups:
...         groups["date"] = datetime.strptime(groups["date"], "%Y-%m-%d %H:%M:%S")
...
>>> with open("file.log") as file:
...     for log in logger.parse(file, reg, cast=cast):
...         print(log["date"], log["something_else"])
trace(_message, *args, **kwargs)

Log _message.format(*args, **kwargs) with severity 'TRACE'.

debug(_message, *args, **kwargs)

Log _message.format(*args, **kwargs) with severity 'DEBUG'.

info(_message, *args, **kwargs)

Log _message.format(*args, **kwargs) with severity 'INFO'.

success(_message, *args, **kwargs)

Log _message.format(*args, **kwargs) with severity 'SUCCESS'.

warning(_message, *args, **kwargs)

Log _message.format(*args, **kwargs) with severity 'WARNING'.

error(_message, *args, **kwargs)

Log _message.format(*args, **kwargs) with severity 'ERROR'.

critical(_message, *args, **kwargs)

Log _message.format(*args, **kwargs) with severity 'CRITICAL'.

log(_level, _message, *args, **kwargs)[source]

Log _message.format(*args, **kwargs) with severity _level.

exception(_message, *args, **kwargs)[source]

Convenience method for logging an 'ERROR' with exception information.

start(*args, **kwargs)[source]

Deprecated function to add() a new handler.

Warning

Deprecated since version 0.2.2: start() will be removed in Loguru 1.0.0, it is replaced by add() which is a less confusing name.

stop(*args, **kwargs)[source]

Deprecated function to remove() an existing handler.

Warning

Deprecated since version 0.2.2: stop() will be removed in Loguru 1.0.0, it is replaced by remove() which is a less confusing name.

Project Information

Contributing

Thank you for considering improving Loguru, any contribution is much welcome!

Asking questions

If you have any question about Loguru, if you are seeking for help, or if you would like to suggest a new feature, you are encouraged to open a new issue so we can discuss it. Bringing new ideas and pointing out elements needing clarification allows to make this library always better!

Reporting a bug

If you encountered an unexpected behavior using Loguru, please open a new issue so we can fix it as soon as possible! Be as specific as possible in the description of your problem so we can fix it as quickly as possible.

An ideal bug report includes:

  • The Python version you are using
  • The Loguru version you are using (you can find it with print(loguru.__version__))
  • Your operating system name and version
  • Your development environment and local setup (IDE, Terminal, project context, anything that could be useful)
  • Some minimal reproducible example

Implementing changes

If you are willing to enhance Loguru by implementing non-trivial changes, please open a new issue first to keep a reference about why such modifications are made (and potentially avoid unneeded work). Then, the workflow would look as follow:

  1. Fork the Loguru project from GitHub

  2. Clone the repository locally:

    $ git clone git@github.com:your_name_here/loguru.git
$ cd loguru

  3. Activate your virtual environment:

    $ python -m virtualenv env
$ source env/bin/activate

  4. Create a new branch from master:

    $ git checkout master
$ git branch fix_bug
$ git checkout fix_bug

  5. Install Loguru in development mode:

    $ pip install -e .[dev]

  6. Implement the modifications wished. During the process of development, honor PEP 8 as much as possible.

  7. Add unit tests (don’t hesitate to be exhaustive!) and ensure none are failing using:

    $ pytest tests

  8. Remember to update documentation if required

  9. Update the changelog.rst file with what you improved

  10. add and commit your changes, rebase your branch on master, push your local project:

    $ git add .
$ git commit -m 'Add succinct explanation of what changed'
$ git rebase master
$ git push origin fix_bug

  11. Finally open a pull request before getting it merged!

License

MIT License

Copyright (c) 2017

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Changelog

0.2.4 (2018-12-26)

  • Fix adding handler while logging which was not thread-safe (#22)

0.2.3 (2018-12-16)

  • Add support for PyPy
  • Add support for Python 3.5
  • Fix incompatibility with awscli by downgrading required colorama dependency version (#12)

0.2.2 (2018-12-12)

  • Deprecate logger.start() and logger.stop() methods in favor of logger.add() and logger.remove() (#3)
  • Fix ignored formatting while using logging.Handler sinks (#4)
  • Fix impossibility to set empty environment variable color on Windows (#7)

0.2.1 (2018-12-08)

  • Fix typo preventing README to be correctly displayed on PyPI

0.2.0 (2018-12-08)

  • Remove the parser and refactor it into the logger.parse() method
  • Remove the notifier and its dependencies, just pip install notifiers if user needs it

0.1.0 (2018-12-07)

  • Add logger
  • Add notifier
  • Add parser

0.0.1 (2017-09-04)

Initial release