tqdm

tqdm means "progress" in Arabic (taqadum, تقدّم) and is an abbreviation for "I love you so much" in Spanish (te quiero demasiado).

Instantly make your loops show a smart progress meter - just wrap any iterable with tqdm(iterable), and you're done!

from tqdm import tqdm
for i in tqdm(range(10000)):
    ...

76%|████████████████████████████         | 7568/10000 [00:33<00:10, 229.00it/s]

trange(N) can be also used as a convenient shortcut for tqdm(xrange(N)).

REPL: ptpython

It can also be executed as a module with pipes:

$ seq 9999999 | tqdm --unit_scale | wc -l
10.0Mit [00:02, 3.58Mit/s]
9999999
$ 7z a -bd -r backup.7z docs/ | grep Compressing | \
    tqdm --total $(find docs/ -type f | wc -l) --unit files >> backup.log
100%|███████████████████████████████▉| 8014/8014 [01:37<00:00, 82.29files/s]

Overhead is low -- about 60ns per iteration (80ns with tqdm_gui), and is unit tested against performance regression. By comparison, the well-established ProgressBar has an 800ns/iter overhead.

In addition to its low overhead, tqdm uses smart algorithms to predict the remaining time and to skip unnecessary iteration displays, which allows for a negligible overhead in most cases.

tqdm works on any platform (Linux, Windows, Mac, FreeBSD, NetBSD, Solaris/SunOS), in any console or in a GUI, and is also friendly with IPython/Jupyter notebooks.

tqdm does not require any dependencies (not even curses!), just Python and an environment supporting carriage return \r and line feed \n control characters.

---

Installation

Latest PyPI stable release

pip install tqdm

Latest development release on GitHub

Pull and install in the current directory:

pip install -e git+https://github.com/tqdm/tqdm.git@master#egg=tqdm

Latest Conda release

conda install -c conda-forge tqdm

Changelog

The list of all changes is available either on GitHub's Releases: , on the wiki or on crawlers such as allmychanges.com.

Usage

tqdm is very versatile and can be used in a number of ways. The three main ones are given below.

Iterable-based

Wrap tqdm() around any iterable:

text = ""
for char in tqdm(["a", "b", "c", "d"]):
    text = text + char

trange(i) is a special optimised instance of tqdm(range(i)):

for i in trange(100):
    pass

Instantiation outside of the loop allows for manual control over tqdm():

pbar = tqdm(["a", "b", "c", "d"])
for char in pbar:
    pbar.set_description("Processing %s" % char)

Manual

Manual control on tqdm() updates by using a with statement:

with tqdm(total=100) as pbar:
    for i in range(10):
        pbar.update(10)

If the optional variable total (or an iterable with len()) is provided, predictive stats are displayed.

with is also optional (you can just assign tqdm() to a variable, but in this case don't forget to del or close() at the end:

pbar = tqdm(total=100)
for i in range(10):
    pbar.update(10)
pbar.close()

Module

Perhaps the most wonderful use of tqdm is in a script or on the command line. Simply inserting tqdm (or python -m tqdm) between pipes will pass through all stdin to stdout while printing progress to stderr.

The example below demonstrated counting the number of lines in all Python files in the current directory, with timing information included.

$ time find . -name '*.py' -exec cat \{} \; | wc -l
857365

real    0m3.458s
user    0m0.274s
sys     0m3.325s

$ time find . -name '*.py' -exec cat \{} \; | tqdm | wc -l
857366it [00:03, 246471.31it/s]
857365

real    0m3.585s
user    0m0.862s
sys     0m3.358s

Note that the usual arguments for tqdm can also be specified.

$ find . -name '*.py' -exec cat \{} \; |
    tqdm --unit loc --unit_scale --total 857366 >> /dev/null
100%|███████████████████████████████████| 857K/857K [00:04<00:00, 246Kloc/s]

Backing up a large directory?

$ 7z a -bd -r backup.7z docs/ | grep Compressing |
    tqdm --total $(find docs/ -type f | wc -l) --unit files >> backup.log
100%|███████████████████████████████▉| 8014/8014 [01:37<00:00, 82.29files/s]

FAQ and Known Issues

The most common issues relate to excessive output on multiple lines, instead of a neat one-line progress bar.

• Consoles in general: require support for carriage return (CR, \r).

• Nested progress bars:

  • Consoles in general: require support for moving cursors up to the previous line. For example, IDLE, ConEmu and PyCharm (also here, here, and here) lack full support.
  • Windows: additionally may require the Python module colorama to ensure nested bars stay within their respective lines.

• Unicode:

  • Environments which report that they support unicode will have solid smooth progressbars. The fallback is an ascii-only bar.
  • Windows consoles often only partially support unicode and thus often require explicit ascii=True (also here). This is due to either normal-width unicode characters being incorrectly displayed as "wide", or some unicode characters not rendering.

• Wrapping enumerated iterables: use enumerate(tqdm(...)) instead of tqdm(enumerate(...)). The same applies to numpy.ndenumerate. This is because enumerate functions tend to hide the length of iterables. tqdm does not.

• Wrapping zipped iterables has similar issues due to internal optimisations. tqdm(zip(a, b)) should be replaced with zip(tqdm(a), b) or even zip(tqdm(a), tqdm(b)).

If you come across any other difficulties, browse and file .

Documentation

(Since 19 May 2016)

class tqdm(object):
  """
  Decorate an iterable object, returning an iterator which acts exactly
  like the original iterable, but prints a dynamically updating
  progressbar every time a value is requested.
  """

  def __init__(self, iterable=None, desc=None, total=None, leave=True,
               file=None, ncols=None, mininterval=0.1,
               maxinterval=10.0, miniters=None, ascii=None, disable=False,
               unit='it', unit_scale=False, dynamic_ncols=False,
               smoothing=0.3, bar_format=None, initial=0, position=None,
               postfix=None, unit_divisor=1000):

Parameters

• iterable : iterable, optional
  Iterable to decorate with a progressbar. Leave blank to manually manage the updates.

• desc : str, optional
  Prefix for the progressbar.

• total : int, optional
  The number of expected iterations. If unspecified, len(iterable) is used if possible. As a last resort, only basic progress statistics are displayed (no ETA, no progressbar). If gui is True and this parameter needs subsequent updating, specify an initial arbitrary large positive integer, e.g. int(9e9).

• leave : bool, optional
  If [default: True], keeps all traces of the progressbar upon termination of iteration.

• file : io.TextIOWrapper or io.StringIO, optional
  Specifies where to output the progress messages (default: sys.stderr). Uses file.write(str) and file.flush() methods.

• ncols : int, optional
  The width of the entire output message. If specified, dynamically resizes the progressbar to stay within this bound. If unspecified, attempts to use environment width. The fallback is a meter width of 10 and no limit for the counter and statistics. If 0, will not print any meter (only stats).

• mininterval : float, optional
  Minimum progress display update interval [default: 0.1] seconds.

• maxinterval : float, optional
  Maximum progress display update interval [default: 10] seconds. Automatically adjusts miniters to correspond to mininterval after long display update lag. Only works if dynamic_miniters or monitor thread is enabled.

• miniters : int, optional
  Minimum progress display update interval, in iterations. If 0 and dynamic_miniters, will automatically adjust to equal mininterval (more CPU efficient, good for tight loops). If > 0, will skip display of specified number of iterations. Tweak this and mininterval to get very efficient loops. If your progress is erratic with both fast and slow iterations (network, skipping items, etc) you should set miniters=1.

• ascii : bool, optional
  If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters 1-9 #.

• disable : bool, optional
  Whether to disable the entire progressbar wrapper [default: False]. If set to None, disable on non-TTY.

• unit : str, optional
  String that will be used to define the unit of each iteration [default: it].

• unit_scale : bool or int or float, optional
  If 1 or True, the number of iterations will be reduced/scaled automatically and a metric prefix following the International System of Units standard will be added (kilo, mega, etc.) [default: False]. If any other non-zero number, will scale total and n.

• dynamic_ncols : bool, optional
  If set, constantly alters ncols to the environment (allowing for window resizes) [default: False].

• smoothing : float, optional
  Exponential moving average smoothing factor for speed estimates (ignored in GUI mode). Ranges from 0 (average speed) to 1 (current/instantaneous speed) [default: 0.3].

• bar_format : str, optional
  Specify a custom bar string formatting. May impact performance. [default: '{l_bar}{bar}{r_bar}'], where l_bar='{desc}: {percentage:3.0f}% {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ' '{rate_fmt}{postfix}]' Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt, percentage, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, elapsed, remaining, desc, postfix. Note that a trailing ": " is automatically removed after {desc} if the latter is empty.

• initial : int, optional
  The initial counter value. Useful when restarting a progress bar [default: 0].

• position : int, optional
  Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).

• postfix : dict or *, optional
  Specify additional stats to display at the end of the bar. Calls set_postfix(**postfix) if possible (dict).

• unit_divisor : float, optional
  [default: 1000], ignored unless unit_scale is True.

Extra CLI Options

• delim : chr, optional
  Delimiting character [default: 'n']. Use '0' for null. N.B.: on Windows systems, Python converts 'n' to 'rn'.

• buf_size : int, optional
  String buffer size in bytes [default: 256] used when delim is specified.

• bytes : bool, optional
  If true, will count bytes and ignore delim.

Returns

• out : decorated iterator.

def update(self, n=1):
    """
    Manually update the progress bar, useful for streams
    such as reading files.
    E.g.:
    >>> t = tqdm(total=filesize) # Initialise
    >>> for current_buffer in stream:
    ...    ...
    ...    t.update(len(current_buffer))
    >>> t.close()
    The last line is highly recommended, but possibly not necessary if
    ``t.update()`` will be called in such a way that ``filesize`` will be
    exactly reached and printed.

    Parameters
    ----------
    n  : int, optional
        Increment to add to the internal counter of iterations
        [default: 1].
    """

def close(self):
    """
    Cleanup and (if leave=False) close the progressbar.
    """

def unpause(self):
    """
    Restart tqdm timer from last print time.
    """

def clear(self, nomove=False):
    """
    Clear current bar display
    """

def refresh(self):
    """
    Force refresh the display of this bar
    """

def write(cls, s, file=sys.stdout, end="\n"):
    """
    Print a message via tqdm (without overlap with bars)
    """

def set_description(self, desc=None, refresh=True):
    """
    Set/modify description of the progress bar.

    Parameters
    ----------
    desc  : str, optional
    refresh  : bool, optional
        Forces refresh [default: True].
    """

def set_postfix(self, ordered_dict=None, refresh=True, **kwargs):
    """
    Set/modify postfix (additional stats)
    with automatic formatting based on datatype.

    Parameters
    ----------
    refresh  : bool, optional
        Forces refresh [default: True].
    """

def trange(*args, **kwargs):
  """
  A shortcut for tqdm(xrange(*args), **kwargs).
  On Python3+ range is used instead of xrange.
  """

class tqdm_gui(tqdm):
  """
  Experimental GUI version of tqdm!
  """

def tgrange(*args, **kwargs):
  """
  Experimental GUI version of trange!
  """

class tqdm_notebook(tqdm):
  """
  Experimental IPython/Jupyter Notebook widget using tqdm!
  """

def tnrange(*args, **kwargs):
  """
  Experimental IPython/Jupyter Notebook widget using tqdm!
  """

Examples and Advanced Usage

• See the examples folder;

• import the module and run help(), or

• consult the wiki.

  • this has an excellent article on how to make a great progressbar.

Description and additional stats

Custom information can be displayed and updated dynamically on tqdm bars with the desc and postfix arguments:

from tqdm import trange
from random import random, randint
from time import sleep

with trange(100) as t:
    for i in t:
        # Description will be displayed on the left
        t.set_description('GEN %i' % i)
        # Postfix will be displayed on the right,
        # formatted automatically based on argument's datatype
        t.set_postfix(loss=random(), gen=randint(1,999), str='h',
                      lst=[1, 2])
        sleep(0.1)

with tqdm(total=10, bar_format="{postfix[0]} {postfix[1][value]:>8.2g}",
          postfix=["Batch", dict(value=0)]) as t:
    for i in range(10):
        sleep(0.1)
        t.postfix[1]["value"] = i / 2
        t.update()

Points to remember when using {postfix[...]} in the bar_format string:

• postfix also needs to be passed as an initial argument in a compatible format, and
• postfix will be auto-converted to a string if it is a dict-like object. To prevent this behaviour, insert an extra item into the dictionary where the key is not a string.

Nested progress bars

tqdm supports nested progress bars. Here's an example:

from tqdm import trange
from time import sleep

for i in trange(10, desc='1st loop'):
    for j in trange(5, desc='2nd loop', leave=False):
        for k in trange(100, desc='3nd loop'):
            sleep(0.01)

On Windows colorama will be used if available to keep nested bars on their respective lines.

For manual control over positioning (e.g. for multi-threaded use), you may specify position=n where n=0 for the outermost bar, n=1 for the next, and so on:

from time import sleep
from tqdm import trange, tqdm
from multiprocessing import Pool, freeze_support, RLock

L = list(range(9))

def progresser(n):
    interval = 0.001 / (n + 2)
    total = 5000
    text = "#{}, est. {:<04.2}s".format(n, interval * total)
    for i in trange(total, desc=text, position=n):
        sleep(interval)

if __name__ == '__main__':
    freeze_support()  # for Windows support
    p = Pool(len(L),
             # again, for Windows support
             initializer=tqdm.set_lock, initargs=(RLock(),))
    p.map(progresser, L)
    print("\n" * (len(L) - 2))

Hooks and callbacks

tqdm can easily support callbacks/hooks and manual updates. Here's an example with urllib:

urllib.urlretrieve documentation

> [...]
> If present, the hook function will be called once
> on establishment of the network connection and once after each block > read
> thereafter. The hook will be passed three arguments; a count of > blocks
> transferred so far, a block size in bytes, and the total size of the > file.
> [...]

import urllib, os
from tqdm import tqdm

class TqdmUpTo(tqdm):
    """Provides `update_to(n)` which uses `tqdm.update(delta_n)`."""
    def update_to(self, b=1, bsize=1, tsize=None):
        """
        b  : int, optional
            Number of blocks transferred so far [default: 1].
        bsize  : int, optional
            Size of each block (in tqdm units) [default: 1].
        tsize  : int, optional
            Total size (in tqdm units). If [default: None] remains unchanged.
        """
        if tsize is not None:
            self.total = tsize
        self.update(b * bsize - self.n)  # will also set self.n = b * bsize

eg_link = "https://caspersci.uk.to/matryoshka.zip"
with TqdmUpTo(unit='B', unit_scale=True, miniters=1,
              desc=eg_link.split('/')[-1]) as t:  # all optional kwargs
    urllib.urlretrieve(eg_link, filename=os.devnull,
                       reporthook=t.update_to, data=None)

Inspired by twine#242. Functional alternative in examples/tqdm_wget.py.

It is recommend to use miniters=1 whenever there is potentially large differences in iteration speed (e.g. downloading a file over a patchy connection).

Pandas Integration

Due to popular demand we've added support for pandas -- here's an example for DataFrame.progress_apply and DataFrameGroupBy.progress_apply:

import pandas as pd
import numpy as np
from tqdm import tqdm

df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))

# Register `pandas.progress_apply` and `pandas.Series.map_apply` with `tqdm`
# (can use `tqdm_gui`, `tqdm_notebook`, optional kwargs, etc.)
tqdm.pandas(desc="my bar!")

# Now you can use `progress_apply` instead of `apply`
# and `progress_map` instead of `map`
df.progress_apply(lambda x: x**2)
# can also groupby:
# df.groupby(0).progress_apply(lambda x: x**2)

In case you're interested in how this works (and how to modify it for your own callbacks), see the examples folder or import the module and run help().

IPython/Jupyter Integration

IPython/Jupyter is supported via the tqdm_notebook submodule:

from tqdm import tnrange, tqdm_notebook
from time import sleep

for i in tnrange(10, desc='1st loop'):
    for j in tqdm_notebook(xrange(100), desc='2nd loop'):
        sleep(0.01)

In addition to tqdm features, the submodule provides a native Jupyter widget (compatible with IPython v1-v4 and Jupyter), fully working nested bars and color hints (blue: normal, green: completed, red: error/interrupt, light blue: no ETA); as demonstrated below.

It is also possible to let tqdm automatically choose between console or notebook versions by using the autonotebook submodule:

from tqdm.autonotebook import tqdm
tqdm.pandas()

Note that this will issue a TqdmExperimentalWarning if run in a notebook since it is not meant to be possible to distinguish between jupyter notebook and jupyter console. Use auto instead of autonotebook to suppress this warning.

Writing messages

Since tqdm uses a simple printing mechanism to display progress bars, you should not write any message in the terminal using print() while a progressbar is open.

To write messages in the terminal without any collision with tqdm bar display, a .write() method is provided:

from tqdm import tqdm, trange
from time import sleep

bar = trange(10)
for i in bar:
    # Print using tqdm class method .write()
    sleep(0.1)
    if not (i % 3):
        tqdm.write("Done task %i" % i)
    # Can also use bar.write()

By default, this will print to standard output sys.stdout. but you can specify any file-like object using the file argument. For example, this can be used to redirect the messages writing to a log file or class.

Redirecting writing

If using a library that can print messages to the console, editing the library by replacing print() with tqdm.write() may not be desirable. In that case, redirecting sys.stdout to tqdm.write() is an option.

To redirect sys.stdout, create a file-like class that will write any input string to tqdm.write(), and supply the arguments file=sys.stdout, dynamic_ncols=True.

A reusable canonical example is given below:

from time import sleep
import contextlib
import sys
from tqdm import tqdm

class DummyTqdmFile(object):
    """Dummy file-like that will write to tqdm"""
    file = None
    def __init__(self, file):
        self.file = file

    def write(self, x):
        # Avoid print() second call (useless \n)
        if len(x.rstrip()) > 0:
            tqdm.write(x, file=self.file)

    def flush(self):
        return getattr(self.file, "flush", lambda: None)()

@contextlib.contextmanager
def std_out_err_redirect_tqdm():
    orig_out_err = sys.stdout, sys.stderr
    try:
        sys.stdout, sys.stderr = map(DummyTqdmFile, orig_out_err)
        yield orig_out_err[0]
    # Relay exceptions
    except Exception as exc:
        raise exc
    # Always restore sys.stdout/err if necessary
    finally:
        sys.stdout, sys.stderr = orig_out_err

def some_fun(i):
    print("Fee, fi, fo,".split()[i])

# Redirect stdout to tqdm.write() (don't forget the `as save_stdout`)
with std_out_err_redirect_tqdm() as orig_stdout:
    # tqdm needs the original stdout
    # and dynamic_ncols=True to autodetect console width
    for i in tqdm(range(3), file=orig_stdout, dynamic_ncols=True):
        sleep(.5)
        some_fun(i)

# After the `with`, printing is restored
print("Done!")

Monitoring thread, intervals and miniters

tqdm implements a few tricks to to increase efficiency and reduce overhead.

• Avoid unnecessary frequent bar refreshing: mininterval defines how long to wait between each refresh. tqdm always gets updated in the background, but it will diplay only every mininterval.
• Reduce number of calls to check system clock/time.
• mininterval is more intuitive to configure than miniters. A clever adjustment system dynamic_miniters will automatically adjust miniters to the amount of iterations that fit into time mininterval. Essentially, tqdm will check if it's time to print without actually checking time. This behaviour can be still be bypassed by manually setting miniters.

However, consider a case with a combination of fast and slow iterations. After a few fast iterations, dynamic_miniters will set miniters to a large number. When iteration rate subsequently slows, miniters will remain large and thus reduce display update frequency. To address this:

• maxinterval defines the maximum time between display refreshes. A concurrent monitoring thread checks for overdue updates and forces one where necessary.

The monitoring thread should not have a noticeable overhead, and guarantees updates at least every 10 seconds by default. This value can be directly changed by setting the monitor_interval of any tqdm instance (i.e. t = tqdm.tqdm(...); t.monitor_interval = 2). The monitor thread may be disabled application-wide by setting tqdm.tqdm.monitor_interval = 0 before instantiatiation of any tqdm bar.

Contributions

All source code is hosted on GitHub. Contributions are welcome.

See the CONTRIBUTING file for more information.

Ports to Other Languages

A list is available on this wiki page.

LICENCE

Open Source (OSI approved):

Citation information:

Authors

The main developers, ranked by surviving lines of code (git fame -wMC), are:

• Casper da Costa-Luis (casperdcl, ~2/3, )
• Stephen Larroque (lrq3000, ~1/5)
• Hadrien Mary (hadim, ~2%)
• Guangshuo Chen (chengs, ~1%)
• Noam Yorav-Raphael (noamraph, ~1%, original author)
• Mikhail Korobov (kmike, ~1%)

There are also many which we are grateful for.

(Since 19 May 2016)