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tqdm

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tqdm derives from the Arabic word taqaddum (تقدّم) which can mean "progress," 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(range(N)).

Screenshot

Video Slides Merch

It can also be executed as a module with pipes:

$ seq 9999999 | tqdm --bytes | wc -l
75.2MB [00:00, 217MB/s]
9999999

$ tar -zcf - docs/ | tqdm --bytes --total `du -sb docs/ | cut -f1` \
    > backup.tgz
 32%|██████████▍                      | 8.89G/27.9G [00:42<01:31, 223MB/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.


Table of contents

Installation

Latest PyPI stable release

Versions PyPI-Downloads Libraries-Dependents

pip install tqdm

Latest development release on GitHub

GitHub-Status GitHub-Stars GitHub-Commits GitHub-Forks GitHub-Updated

Pull and install pre-release devel branch:

pip install "git+https://github.com/tqdm/tqdm.git@devel#egg=tqdm"

Latest Conda release

Conda-Forge-Status

conda install -c conda-forge tqdm

Latest Snapcraft release

Snapcraft

There are 3 channels to choose from:

snap install tqdm  # implies --stable, i.e. latest tagged release
snap install tqdm  --candidate  # master branch
snap install tqdm  --edge  # devel branch

Note that snap binaries are purely for CLI use (not import-able), and automatically set up bash tab-completion.

Latest Docker release

Docker

docker pull tqdm/tqdm
docker run -i --rm tqdm/tqdm --help

Other

There are other (unofficial) places where tqdm may be downloaded, particularly for CLI use:

Repology

Changelog

The list of all changes is available either on GitHub's Releases: GitHub-Status, on the wiki, or on the website.

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:

from tqdm import tqdm
from time import sleep

text = ""
for char in tqdm(["a", "b", "c", "d"]):
    sleep(0.25)
    text = text + char

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

from tqdm import trange

for i in trange(100):
    sleep(0.01)

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

pbar = tqdm(["a", "b", "c", "d"])
for char in pbar:
    sleep(0.25)
    pbar.set_description("Processing %s" % char)

Manual

Manual control of tqdm() updates using a with statement:

with tqdm(total=100) as pbar:
    for i in range(10):
        sleep(0.1)
        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):
    sleep(0.1)
    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 demonstrate counting the number of lines in all Python files in the current directory, with timing information included.

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

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

$ time find . -name '*.py' -type f -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' -type f -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?

$ tar -zcf - docs/ | tqdm --bytes --total `du -sb docs/ | cut -f1` \
  > backup.tgz
 44%|██████████████▊                   | 153M/352M [00:14<00:18, 11.0MB/s]

This can be beautified further:

$ BYTES=$(du -sb docs/ | cut -f1)
$ tar -cf - docs/ \
  | tqdm --bytes --total "$BYTES" --desc Processing | gzip \
  | tqdm --bytes --total "$BYTES" --desc Compressed --position 1 \
  > ~/backup.tgz
Processing: 100%|██████████████████████| 352M/352M [00:14<00:00, 30.2MB/s]
Compressed:  42%|█████████▎            | 148M/352M [00:14<00:19, 10.9MB/s]

Or done on a file level using 7-zip:

$ 7z a -bd -r backup.7z docs/ | grep Compressing \
  | tqdm --total $(find docs/ -type f | wc -l) --unit files \
  | grep -v Compressing
100%|██████████████████████████▉| 15327/15327 [01:00<00:00, 712.96files/s]

Pre-existing CLI programs already outputting basic progress information will benefit from tqdm's --update and --update_to flags:

$ seq 3 0.1 5 | tqdm --total 5 --update_to --null
100%|████████████████████████████████████| 5.0/5 [00:00<00:00, 9673.21it/s]
$ seq 10 | tqdm --update --null  # 1 + 2 + ... + 10 = 55 iterations
55it [00:00, 90006.52it/s]

FAQ and Known Issues

GitHub-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).
    • Some cloud logging consoles which don't support \r properly (cloudwatch, K8s) may benefit from export TQDM_POSITION=-1.
  • 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 generators:
    • Generator wrapper functions tend to hide the length of iterables. tqdm does not.
    • Replace tqdm(enumerate(...)) with enumerate(tqdm(...)) or tqdm(enumerate(x), total=len(x), ...). The same applies to numpy.ndenumerate.
    • Replace tqdm(zip(a, b)) with zip(tqdm(a), b) or even zip(tqdm(a), tqdm(b)).
    • The same applies to itertools.
    • Some useful convenience functions can be found under tqdm.contrib.
  • No intermediate output in docker-compose: use docker-compose run instead of docker-compose up and tty: true.
  • Overriding defaults via environment variables: e.g. in CI/cloud jobs, export TQDM_MININTERVAL=5 to avoid log spam. This override logic is handled by the tqdm.utils.envwrap decorator (useful independent of tqdm).

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

Documentation

Py-Versions (Since 19 May 2016)

class tqdm():
  """
  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.
  """

  @envwrap("TQDM_")  # override defaults via env vars
  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, write_bytes=False,
               lock_args=None, nrows=None, colour=None, delay=0):

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 or float, optional

    The number of expected iterations. If unspecified, len(iterable) is used if possible. If float("inf") or 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 number, e.g. 9e9.

  • leave : bool, optional

    If [default: True], keeps all traces of the progressbar upon termination of iteration. If None, will leave only if position is 0.

  • 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. For encoding, see write_bytes.

  • 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 or float, 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 or str, optional

    If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters " 123456789#".

  • 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 and nrows 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, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta. Note that a trailing ": " is automatically removed after {desc} if the latter is empty.

  • initial : int or float, optional

    The initial counter value. Useful when restarting a progress bar [default: 0]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.

  • 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.

  • write_bytes : bool, optional

    Whether to write bytes. If (default: False) will write unicode.

  • lock_args : tuple, optional

    Passed to refresh for intermediate output (initialisation, iterating, and updating).

  • nrows : int, optional

    The screen height. If specified, hides nested bars outside this bound. If unspecified, attempts to use environment height. The fallback is 20.

  • colour : str, optional

    Bar colour (e.g. 'green', '#00ff00').

  • delay : float, optional

    Don't display until [default: 0] seconds have elapsed.

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, ignore delim, and default unit_scale to True, unit_divisor to 1024, and unit to 'B'.

  • tee : bool, optional

    If true, passes stdin to both stderr and stdout.

  • update : bool, optional

    If true, will treat input as newly elapsed iterations, i.e. numbers to pass to update(). Note that this is slow (~2e5 it/s) since every input must be decoded as a number.

  • update_to : bool, optional

    If true, will treat input as total elapsed iterations, i.e. numbers to assign to self.n. Note that this is slow (~2e5 it/s) since every input must be decoded as a number.

  • null : bool, optional

    If true, will discard input (no stdout).

  • manpath : str, optional

    Directory in which to install tqdm man pages.

  • comppath : str, optional

    Directory in which to place tqdm completion.

  • log : str, optional

    CRITICALERROR[default: 'INFO']NOTSET.

Returns

  • out : decorated iterator.
class tqdm():
  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 or float, optional
          Increment to add to the internal counter of iterations
          [default: 1]. If using float, consider specifying ``{n:.3f}``
          or similar in ``bar_format``, or specifying ``unit_scale``.

      Returns
      -------
      out  : bool or None
          True if a ``display()`` was triggered.
      """

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

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

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

      Parameters
      ----------
      nolock  : bool, optional
          If ``True``, does not lock.
          If [default: ``False``]: calls ``acquire()`` on internal lock.
      lock_args  : tuple, optional
          Passed to internal lock's ``acquire()``.
          If specified, will only ``display()`` if ``acquire()`` returns ``True``.
      """

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

  def reset(self, total=None):
      """
      Resets to 0 iterations for repeated use.

      Consider combining with ``leave=True``.

      Parameters
      ----------
      total  : int or float, optional. Total to use for the new bar.
      """

  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, **tqdm_kwargs):
      """
      Set/modify postfix (additional stats)
      with automatic formatting based on datatype.

      Parameters
      ----------
      ordered_dict  : dict or OrderedDict, optional
      refresh  : bool, optional
          Forces refresh [default: True].
      kwargs  : dict, optional
      """

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

  @property
  def format_dict(self):
      """Public API for read-only member access."""

  def display(self, msg=None, pos=None):
      """
      Use ``self.sp`` to display ``msg`` in the specified ``pos``.

      Consider overloading this function when inheriting to use e.g.:
      ``self.some_frontend(**self.format_dict)`` instead of ``self.sp``.

      Parameters
      ----------
      msg  : str, optional. What to display (default: ``repr(self)``).
      pos  : int, optional. Position to ``moveto``
        (default: ``abs(self.pos)``).
      """

  @classmethod
  @contextmanager
  def wrapattr(cls, stream, method, total=None, bytes=True, **tqdm_kwargs):
      """
      stream  : file-like object.
      method  : str, "read" or "write". The result of ``read()`` and
          the first argument of ``write()`` should have a ``len()``.

      >>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj:
      ...     while True:
      ...         chunk = fobj.read(chunk_size)
      ...         if not chunk:
      ...             break
      """

  @classmethod
  def pandas(cls, *targs, **tqdm_kwargs):
      """Registers the current `tqdm` class with `pandas`."""

def trange(*args, **tqdm_kwargs):
    """Shortcut for `tqdm(range(*args), **tqdm_kwargs)`."""

Convenience Functions

def tqdm.contrib.tenumerate(iterable, start=0, total=None,
                            tqdm_class=tqdm.auto.tqdm, **tqdm_kwargs):
    """Equivalent of `numpy.ndenumerate` or builtin `enumerate`."""

def tqdm.contrib.tzip(iter1, *iter2plus, **tqdm_kwargs):
    """Equivalent of builtin `zip`."""

def tqdm.contrib.tmap(function, *sequences, **tqdm_kwargs):
    """Equivalent of builtin `map`."""

Submodules

class tqdm.notebook.tqdm(tqdm.tqdm):
    """IPython/Jupyter Notebook widget."""

class tqdm.auto.tqdm(tqdm.tqdm):
    """Automatically chooses beween `tqdm.notebook` and `tqdm.tqdm`."""

class tqdm.asyncio.tqdm(tqdm.tqdm):
  """Asynchronous version."""
  @classmethod
  def as_completed(cls, fs, *, loop=None, timeout=None, total=None,
                   **tqdm_kwargs):
      """Wrapper for `asyncio.as_completed`."""

class tqdm.gui.tqdm(tqdm.tqdm):
    """Matplotlib GUI version."""

class tqdm.tk.tqdm(tqdm.tqdm):
    """Tkinter GUI version."""

class tqdm.rich.tqdm(tqdm.tqdm):
    """`rich.progress` version."""

class tqdm.keras.TqdmCallback(keras.callbacks.Callback):
    """Keras callback for epoch and batch progress."""

class tqdm.dask.TqdmCallback(dask.callbacks.Callback):
    """Dask callback for task progress."""

contrib

The tqdm.contrib package also contains experimental modules:

  • tqdm.contrib.itertools: Thin wrappers around itertools
  • tqdm.contrib.concurrent: Thin wrappers around concurrent.futures
  • tqdm.contrib.slack: Posts to Slack bots
  • tqdm.contrib.discord: Posts to Discord bots
  • tqdm.contrib.telegram: Posts to Telegram bots
  • tqdm.contrib.bells: Automagically enables all optional features
    • auto, pandas, slack, discord, telegram

Examples and Advanced Usage

Description and additional stats

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

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

with trange(10) 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", {"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.

Additional bar_format parameters may also be defined by overriding format_dict, and the bar itself may be modified using ascii:

from tqdm import tqdm
class TqdmExtraFormat(tqdm):
    """Provides a `total_time` format parameter"""
    @property
    def format_dict(self):
        d = super(TqdmExtraFormat, self).format_dict
        total_time = d["elapsed"] * (d["total"] or 0) / max(d["n"], 1)
        d.update(total_time=self.format_interval(total_time) + " in total")
        return d

for i in TqdmExtraFormat(
      range(9), ascii=" .oO0",
      bar_format="{total_time}: {percentage:.0f}%|{bar}{r_bar}"):
    if i == 4:
        break
00:00 in total: 44%|0000.     | 4/9 [00:00<00:00, 962.93it/s]

Note that {bar} also supports a format specifier [width][type].

  • width
    • unspecified (default): automatic to fill ncols
    • int >= 0: fixed width overriding ncols logic
    • int < 0: subtract from the automatic default
  • type
    • a: ascii (ascii=True override)
    • u: unicode (ascii=False override)
    • b: blank (ascii=" " override)

This means a fixed bar with right-justified text may be created by using: bar_format="{l_bar}{bar:10}|{bar:-10b}right-justified"

Nested progress bars

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

from tqdm.auto import trange
from time import sleep

for i in trange(4, desc='1st loop'):
    for j in trange(5, desc='2nd loop'):
        for k in trange(50, desc='3rd loop', leave=False):
            sleep(0.01)

For manual control over positioning (e.g. for multi-processing use), you may specify position=n where n=0 for the outermost bar, n=1 for the next, and so on. However, it's best to check if tqdm can work without manual position first.

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

L = list(range(9))

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

if __name__ == '__main__':
    freeze_support()  # for Windows support
    tqdm.set_lock(RLock())  # for managing output contention
    p = Pool(initializer=tqdm.set_lock, initargs=(tqdm.get_lock(),))
    p.map(progresser, L)

Note that in Python 3, tqdm.write is thread-safe:

from time import sleep
from tqdm import tqdm, trange
from concurrent.futures import ThreadPoolExecutor

L = list(range(9))

def progresser(n):
    interval = 0.001 / (n + 2)
    total = 5000
    text = f"#{n}, est. {interval * total:<04.2}s"
    for _ in trange(total, desc=text):
        sleep(interval)
    if n == 6:
        tqdm.write("n == 6 completed.")
        tqdm.write("`tqdm.write()` is thread-safe in py3!")

if __name__ == '__main__':
    with ThreadPoolExecutor() as p:
        p.map(progresser, L)

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
urllib = getattr(urllib, 'request', urllib)

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
        return self.update(b * bsize - self.n)  # also sets self.n = b * bsize

eg_link = "https://caspersci.uk.to/matryoshka.zip"
with TqdmUpTo(unit='B', unit_scale=True, unit_divisor=1024, 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)
    t.total = t.n

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).

Wrapping read/write methods

To measure throughput through a file-like object's read or write methods, use CallbackIOWrapper:

from tqdm.auto import tqdm
from tqdm.utils import CallbackIOWrapper

with tqdm(total=file_obj.size,
          unit='B', unit_scale=True, unit_divisor=1024) as t:
    fobj = CallbackIOWrapper(t.update, file_obj, "read")
    while True:
        chunk = fobj.read(chunk_size)
        if not chunk:
            break
    t.reset()
    # ... continue to use `t` for something else

Alternatively, use the even simpler wrapattr convenience function, which would condense both the urllib and CallbackIOWrapper examples down to:

import urllib, os
from tqdm import tqdm

eg_link = "https://caspersci.uk.to/matryoshka.zip"
response = getattr(urllib, 'request', urllib).urlopen(eg_link)
with tqdm.wrapattr(open(os.devnull, "wb"), "write",
                   miniters=1, desc=eg_link.split('/')[-1],
                   total=getattr(response, 'length', None)) as fout:
    for chunk in response:
        fout.write(chunk)

The requests equivalent is nearly identical:

import requests, os
from tqdm import tqdm

eg_link = "https://caspersci.uk.to/matryoshka.zip"
response = requests.get(eg_link, stream=True)
with tqdm.wrapattr(open(os.devnull, "wb"), "write",
                   miniters=1, desc=eg_link.split('/')[-1],
                   total=int(response.headers.get('content-length', 0))) as fout:
    for chunk in response.iter_content(chunk_size=4096):
        fout.write(chunk)

Custom callback

tqdm is known for intelligently skipping unnecessary displays. To make a custom callback take advantage of this, simply use the return value of update(). This is set to True if a display() was triggered.

from tqdm.auto import tqdm as std_tqdm

def external_callback(*args, **kwargs):
    ...

class TqdmExt(std_tqdm):
    def update(self, n=1):
        displayed = super(TqdmExt, self).update(n)
        if displayed:
            external_callback(**self.format_dict)
        return displayed

asyncio

Note that break isn't currently caught by asynchronous iterators. This means that tqdm cannot clean up after itself in this case:

from tqdm.asyncio import tqdm

async for i in tqdm(range(9)):
    if i == 2:
        break

Instead, either call pbar.close() manually or use the context manager syntax:

from tqdm.asyncio import tqdm

with tqdm(range(9)) as pbar:
    async for i in pbar:
        if i == 2:
            break

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`, `tqdm.notebook.tqdm`, 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().

Keras Integration

A keras callback is also available:

from tqdm.keras import TqdmCallback

...

model.fit(..., verbose=0, callbacks=[TqdmCallback()])

Dask Integration

A dask callback is also available:

from tqdm.dask import TqdmCallback

with TqdmCallback(desc="compute"):
    ...
    arr.compute()

# or use callback globally
cb = TqdmCallback(desc="global")
cb.register()
arr.compute()

IPython/Jupyter Integration

IPython/Jupyter is supported via the tqdm.notebook submodule:

from tqdm.notebook import trange, tqdm
from time import sleep

for i in trange(3, desc='1st loop'):
    for j in tqdm(range(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 colour hints (blue: normal, green: completed, red: error/interrupt, light blue: no ETA); as demonstrated below.

Screenshot-Jupyter1 Screenshot-Jupyter2 Screenshot-Jupyter3

The notebook version supports percentage or pixels for overall width (e.g.: ncols='100%' or ncols='480px').

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.

Note that notebooks will display the bar in the cell where it was created. This may be a different cell from the one where it is used. If this is not desired, either

  • delay the creation of the bar to the cell where it must be displayed, or
  • create the bar with display=False, and in a later cell call display(bar.container):
from tqdm.notebook import tqdm
pbar = tqdm(..., display=False)
# different cell
display(pbar.container)

The keras callback has a display() method which can be used likewise:

from tqdm.keras import TqdmCallback
cbk = TqdmCallback(display=False)
# different cell
cbk.display()
model.fit(..., verbose=0, callbacks=[cbk])

Another possibility is to have a single bar (near the top of the notebook) which is constantly re-used (using reset() rather than close()). For this reason, the notebook version (unlike the CLI version) does not automatically call close() upon Exception.

from tqdm.notebook import tqdm
pbar = tqdm()
# different cell
iterable = range(100)
pbar.reset(total=len(iterable))  # initialise with new `total`
for i in iterable:
    pbar.update()
pbar.refresh()  # force print final status but don't `close()`

Custom Integration

To change the default arguments (such as making dynamic_ncols=True), simply use built-in Python magic:

from functools import partial
from tqdm import tqdm as std_tqdm
tqdm = partial(std_tqdm, dynamic_ncols=True)

For further customisation, tqdm may be inherited from to create custom callbacks (as with the TqdmUpTo example above) or for custom frontends (e.g. GUIs such as notebook or plotting packages). In the latter case:

  1. def __init__() to call super().__init__(..., gui=True) to disable terminal status_printer creation.
  2. Redefine: close(), clear(), display().

Consider overloading display() to use e.g. self.frontend(**self.format_dict) instead of self.sp(repr(self)).

Some submodule examples of inheritance:

Dynamic Monitor/Meter

You can use a tqdm as a meter which is not monotonically increasing. This could be because n decreases (e.g. a CPU usage monitor) or total changes.

One example would be recursively searching for files. The total is the number of objects found so far, while n is the number of those objects which are files (rather than folders):

from tqdm import tqdm
import os.path

def find_files_recursively(path, show_progress=True):
    files = []
    # total=1 assumes `path` is a file
    t = tqdm(total=1, unit="file", disable=not show_progress)
    if not os.path.exists(path):
        raise IOError("Cannot find:" + path)

    def append_found_file(f):
        files.append(f)
        t.update()

    def list_found_dir(path):
        """returns os.listdir(path) assuming os.path.isdir(path)"""
        listing = os.listdir(path)
        # subtract 1 since a "file" we found was actually this directory
        t.total += len(listing) - 1
        # fancy way to give info without forcing a refresh
        t.set_postfix(dir=path[-10:], refresh=False)
        t.update(0)  # may trigger a refresh
        return listing

    def recursively_search(path):
        if os.path.isdir(path):
            for f in list_found_dir(path):
                recursively_search(os.path.join(path, f))
        else:
            append_found_file(path)

    recursively_search(path)
    t.set_postfix(dir=path)
    t.close()
    return files

Using update(0) is a handy way to let tqdm decide when to trigger a display refresh to avoid console spamming.

Writing messages

This is a work in progress (see #737).

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.auto 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
from tqdm.contrib import DummyTqdmFile


@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!")

Redirecting logging

Similar to sys.stdout/sys.stderr as detailed above, console logging may also be redirected to tqdm.write().

Warning: if also redirecting sys.stdout/sys.stderr, make sure to redirect logging first if needed.

Helper methods are available in tqdm.contrib.logging. For example:

import logging
from tqdm import trange
from tqdm.contrib.logging import logging_redirect_tqdm

LOG = logging.getLogger(__name__)

if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)
    with logging_redirect_tqdm():
        for i in trange(9):
            if i == 4:
                LOG.info("console logging redirected to `tqdm.write()`")
    # logging restored

Monitoring thread, intervals and miniters

tqdm implements a few tricks 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 display 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 instantiation of any tqdm bar.

Merch

You can buy tqdm branded merch now!

Contributions

GitHub-Commits GitHub-Issues GitHub-PRs OpenHub-Status GitHub-Contributions CII Best Practices

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

See the CONTRIBUTING file for more information.

Developers who have made significant contributions, ranked by SLoC (surviving lines of code, git fame -wMC --excl '\.(png|gif|jpg)$'), are:

Name ID SLoC Notes
Casper da Costa-Luis casperdcl ~80% primary maintainer Gift-Casper

Stephen Larroque Martin Zugnoni Daniel Ecer Richard Sheridan Guangshuo Chen Helio Machado Kyle Altendorf

lrq3000 martinzugnoni de-code richardsheridan chengs 0x2b3bfa0 altendky

~9% ~3% ~2% ~1% ~1% ~1% <1%

team member

Noam Yorav-Raphael Matthew Stevens

noamraph mjstevens777

<1% <1%

original author

Hadrien Mary hadim <1% team member
Mikhail Korobov kmike <1% team member

Ports to Other Languages

A list is available on this wiki page.

LICENCE

Open Source (OSI approved): LICENCE

Citation information: DOI

(Since 19 May 2016)

tqdm's People

Contributors

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tqdm's Issues

add codecov.io support

What about adding codecov.io support?

Advantages of codecov:

  1. it can mark build as failed if a patch coverage is less than X% (a good value is 100, this is configurable);
  2. it uses branch coverage by default;
  3. it can show coverage report for a patch (see example at scrapy/scrapy#1528).

They can run with coveralls in parallel for some time, then we can disable one of them. In Scrapy (1) works wonderful: contributors are writing more and better tests, and merging an undercovered PR is now a deliberate decision.

jitter in IPython notebook

Hey,

This is how tqdm 2.2.4 looks in IPython notebook: http://www.youtube.com/watch?v=GjRHAmj_xfc

This is how it used to look in 1.0: http://www.youtube.com/watch?v=t7e6IEdEaTc

Red color is fine (we're using stderr now, and it makes sense), but I think text shouldn't jump by default, and progress bar looks untidy (it is not image compression artifact - there are vertical lines, and the last symbol doesn't have the same height as other symbols).

I also find old elapsed: ... left: ... to be easier to understand than new ...<..., but that's a personal preference :)

Code:

import time
import random
from tqdm import tqdm

x = range(343)

for el in tqdm(x, "Loading", mininterval=0, leave=True):
    time.sleep(random.random() * 0.01)

Progress bar stays on 0%

Using Python 3.5 on Ubuntu 14.04 64b with the native OS terminal and vanilla Python shell:

>>> for i in tqdm(range(9)):
...     print('test')
... 
  0%|                                                                                                                   | 0/9 [00:00<?, ?it/s]test
test
test
test
test
test
test
test
test
>>> tqdm.__version__
'3.7.1'

The bar stays in that state:

0%|                                                                                                                   | 0/9 [00:00<?, ?it/s]

Sub stats showed as an unique progress bar

I have thinking about this for a couple of days.

Lets say I have two threads working in parallel in a single problem. The counter update would be something similar to this: long pause, an update (thread 1), short pause, another update (thread 2), long pause... Those two close updates break the speed and ETA estimation.

Thinking of more sophisticated algorithms, I realized that the problem is actually pretty simple if we keep average speed for both threads separated and just accumulate it when printed.

I realize that this code is a bit specialized but I think it would be a nice feature. If you are not interested or you are worried about this change impacting performance, would be nice to provide some hooks to specialize via the constructor of thru subclassing.

What do you think?

Thanks.

when I run make, give this error

rm -f .coverage # coverage erase
nosetests tqdm --with-coverage --cover-package=tqdm --cover-erase --cover-min-percentage=80 --ignore-files="tests_perf.py" -d -v
nose.plugins.cover: ERROR: Coverage not available: unable to import coverage module
tests_pandas.test_pandas ... ok
tests_pandas.test_pandas_leave ... ok
Test time interval format ... ok
Test statistics and progress bar formatting ... ok
Test SI unit prefixes ... ok
tests_tqdm.test_all_defaults ... ok
Test csv iterator ... ok
Test output to arbitrary file-like objects ... ok
Test leave=True always prints info about the last iteration ... ok
Test trange ... ok
Test mininterval ... ok
Test maxinterval ... ok
Test miniters ... ok
Test purely dynamic miniters ... ok
Test large mininterval ... ok
Test smoothed dynamic miniters ... ok
Test smoothed dynamic miniters with mininterval ... ok
Test disable ... ok
Test SI unit prefix ... ok
Test ascii/unicode bar ... ok
Test manual creation and updates ... ok
Test manual creation and closure ... ok
Test exponential weighted average smoothing ... ok
Test nested progress bars ... ok
0it [00:00, ?it/s]okatting ...
Test unpause ... FAIL
Hello: 0it [00:00, ?it/s]ok
Test internal GUI properties ... ok
Test version string ... ok

FAIL: Test unpause

Traceback (most recent call last):
File "/Users/HWJ/anaconda2/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/Users/HWJ/pkgs/tqdm/tqdm/tests/tests_tqdm.py", line 734, in test_unpause
assert abs(r_before - r_after) < 1 # TODO: replace equal when DiscreteTimer
AssertionError:

assert abs(91.41 - 87.61) < 1 # TODO: replace equal when DiscreteTimer


Ran 29 tests in 2.318s

FAILED (failures=1)
make[1]: *** [testcoverage] Error 1
make: *** [alltests] Error 2

curious about the gif

Could you tell me what kind of plug-in you are using? that looks fantastic.

Thanks
oliver

Resume progress, add start offset

When resuming a job, it would be nice if we could display that to the user, without simply quickly skipping a bunch of iterations. Which would throw off the ETA, as well as wouldn't show visual feedback to the user that their job has been resumed.

I propose adding a start offset, which will just offset the existing total & starting position of the iterator passed in.

So if you pass in an total of 10, it would look like this.

0% [    ] 0/10 [....]

Then adding start=10 will offset the starting index, and total, like this:

50% [----    ] 10/20 [....]

The resume part of the bar should look different, not sure how exactly, maybe just a different pattern or darker color. Here's wget for inspiration.

image

Support for nested progress bars + readme updates

tqdm should support displaying nested progress bars, in particular when using nested loops:

from tqdm import trange
from time import sleep
for i in trange(10, desc='outer loop', leave=True):
    for j in trange(100, desc='inner loop'):
        sleep(0.01)

This currently does not work because the outer loop is always deleted and replaced by the inner loop.

A simple fix would be to allow StatusPrinter to avoid using the \r replacing character when displaying the inner loop to not replace the outer loop. This is the approach taken by ipy-progressbar.

Another way would be to update both bars independently, which is currently done for tqdm_notebook. But I'm not sure this is possible inside a terminal (is there a way to replace not only the current line but also the x previous lines? ie, the opposite of a line return).

We should then update the README to add this use case.

Also, we should add in the README that tqdm does not require any dependency, in particular curses (which is commonly required for various console utilities), this would fix noamraph/tqdm#16.

GUI?

Ok. This may be a really bad idea. But it must be said... GUI?

  • add a default kwarg gui=False
  • if gui=True attempt to use matplotlib animations
  • can display rate graph for last 60 secs

Pros

  • More end-user app friendly
  • Windows users no longer get terminated ( 😉 ) by \r and \n
  • Would have numpy so can try fitting different polynomials/regressions etc on rate to get better ETA
  • GUIs are expected to be a bit slow anyway so enabling more features as above should be fine

Cons

  • Will be slower even after optimisations. Probably will require mininterval > 0.5
  • Could mess with other code that lazily relies on gcf()
  • May not be a feature people are interested in?

low efficiency

When I run the example,
for i in trange(1000):
sleep(0.01)

it seems only 88 iters per second, a decreasing of 12% performance is noticed

Add an option to disable progress bar

Sometime it's convenient to not have progress bar. So it would be really nice to add an option to disable tqdm so the code does not need to be modified, only an option to turn off.

for i in tqdm.tqdm(range(10), total=10, disable=True):
    print(i)

Whith disable being False by default of course.

I can make a PR but I don't want to bother if the project is not maintained anymore... (see #18)

Doesn't work in Sublime + SublimeREPL?

I'm using Sublime + SublimeREPL to run some Python scripts.

This package does not seem to work, the progress is not updated "in place" but repeated over and over on the same line.

This is what I see:

CR  0%|          | 0/25000 [00:00<?, ?it/s]CR  0%|          | 2/25000 [00:00<22:34, 8.46it/s]CR  0%|          | 4/25000 [00:00<22:36, 18.42it/s]CR  0%|          | 6/25000 [00:00<22:37, 18.41it/s]CR  0%|          | 8/25000 [00:00<22:38, 18.39it/s]

Update examples

Update the examples in the README so that it looks like tqdm is doing something. Maybe even have leave=True by default.

Memory optimization

We can optimize the memory usage of tqdm by defining a __slots__ property in tqdm class (see this link for an impressive account! -- of course, the gain will be a lot less for us, and even maybe negligible, but I think it can also be a good practice to list all the object properties).

However, we need to test if this won't break the inheriting classes.

On the matter, there's an interesting progressbar library called python-progressbar, it claims to be very flexible and customizable.

Change tqdm project name

I think the best way to do it is keep this repo, empty it and put a message in the README saying that the project has been moved to another repo.

If others agree, this is the way I think we should do it :

  • find a name
  • rename the organization
  • rename the repo
  • tag a version on git (v1.0 ???)
  • create a new account on pypi
  • share password with @lrq3000, @casperdcl, @kmike and @hadim
  • and finally we can release it on pypi !!!

PS: for the pypi release I would like us to register a new account on pypi with project name as login and a password (shared by mail) to avoid any issue if someone disapear.

New string formatting speed issues

Hi there,

I noted that the previous string formatting, like:

'%5.2f' % (n / elapsed)

Was systematically changed to:

'{0:5.2f}'.format(float(n) / elapsed)

However, this is about 2x slower than the old string formatting. Is there any reason to keep using the new formatting style?

make tqdm a class

And all parameters fields of that class.

This way, we can do

t = tqdm(foo)
for elem in t:
    do_stuff(elem)
    if condition:
        t.desc = 'floob'

use 80 columns limit again

Orginal tqdm followed 80 columns limit in its source code; I think it is easy to support (just need to wrap a couple of new docstrings). What about making the limit 80 again?

Being able to show s/it instead of "it/s"

I have operations that takes several seconds. Lets say, process datablocks. Lets say that each datablock takes 5 seconds.

I rather prefer to see "5s/block" than "0.2block/s".

I would like to have a flag to reverse the units displayed. Even something automatic like a flag to request "if it/s<1 -> show s/it".

Add support for data streams (with known total size)

Hi,

It is easy to integrate tqdm with urllib when downloading a file, but when something is being uploaded, it is not so easy. One needs to intercept .read() or .write() calls and update tqdm manually.

I needed it in b2 command line tool, so I have implemented it there, but maybe a stream wrapper could be useful for other tqdm users? This code is more specific to tqdm than to b2 command line tool.

Do you want to support progress tracking of data streams?

Be able to display the instantaneous speed of the loop

Currently, tqdm shows the average speed of the loop, given the time it took since it started and the amount of iterations done. In some common cases this is not helpful, and the instantaneous speed would be better (the instantaneous speed being the inverse of the amount of time since the last iteration).

The classic case where this would be useful is for stoppable scripts that skip what was already done when you run them a second time after stopping them. For example, a script that converts a million images and checks before that the converted image already exists. If you already converted 20% of the images, the second run of the script will execute 20% of the iterations at 300 its/s, while the standard speed should be 1 it/s. When the script reaches the first unconverted image, it still says that the speed is around 300 its/s, and you’ll have to wait several thousands of iterations (so several hours) before the speed comes back to something more reasonable (but the estimated remaining time will still not be correct).

For such scripts, it could therefore be useful to display the instantaneous speed, or we could even create an algorithm that uses the minimum, maximum, and median speed per iteration to give a realistic estimation of speed and remaining time that would work for any use case, instead of using only the average or the instantaneous speed depending on a boolean setting. The new estimations will be much more dynamic, but not ridiculously changing like Windows progress bars that only use the instantaneous speed and can switch from a few seconds to several hours in a heartbeat.

More statistics than minimum, maximum, and median speed (like quartiles) would increase the precision of the estimation, but these three values are enough for now in my opinion.

A few suggestions

Hello, I am Miss Abbie, a representative from http://socialjusticecode.org, this is a message on behalf of the organization about this repo. We've noticed that there are a few triggering things included in here that could scare some members of the gay/trans/gender-fluid community away. In your readme you have what the name of this repo means in Arabic, and it is fantastic that you are including other languages, however this is cultural appropriation. Please give credit to the arabic community if you insist on using their language in the context of a project, or arabic members of the code community may be too disgusted to use the code. It would be better not to include this at all, as you are basically stealing the language from a culture and using it for your own purposes. Also there is a lot of capital letters in your file names, including "license" (all in capital letters), "contribute", "manifest", "readme", and "release." While it is fine to have one or two of these, this many may scare off some who find this triggering.

It would be fantastic if you could clear all of these up and become SJC approved, making GitHub and the coding community a better place. (✿ -‿-)

[Regression] Tqdm freezes when iteration speed changes drastically

That’s a regression that happened between version 1.0 and 2.0.
Try this minimal example with several tqdm versions:

from tqdm import tqdm
from time import sleep

for i in tqdm(range(10000)):
    if i > 3000:
        sleep(1)

As you’ll see, it works great with tqdm 1.0, but it takes almost forever to update with version 2.0 and 3.0. In fact, it takes about 3000 seconds to update, because the first update did 3000 updates in one second, so tqdm assumes it’s ok to wait for 3000 more updates before updating. But that’s only an acceptable behaviour for loops with a rather constant iteration speed. The smoothing argument was developed for such cases (see #48, great work on this by the way!), but the current miniters/mininterval behaviour contradicts it.

Of course, specifying miniters=1 does the expected behaviour, so I’m wondering: why not do that by default?

In tqdm 1.0, miniters was set to 1 and mininterval was set to 0.5, which meant: "we update the display at every iteration if the time taken by the iteration is longer than 0.5 seconds, otherwise we wait several iterations, enough to make at least a 0.5 second interval".

Since tqdm 2.0, miniters is set to None and mininterval is set to 0.1. From what I understand, it means "we update the display after waiting for several iterations, enough to make at least a 0.1 second interval".

Unfortunately, from what the example above shows, tqdm doesn’t respect this rule since we don’t have an update every 0.1 second. The behaviour seems more complex now, it tries to assume how much time tqdm should wait instead of basically counting when it should update its display, like it was in tqdm 1.0.

Reduce overhead automatically

The code below shows that for the simplest of generators, there's quite an overhead. This is to be expected as there's not much content in the loop itself.

The 6x increase isn't that bad (especially when compared to the de-facto ProgressBar, hah). However, this is reduced to about 3x if we set miniters to be high enough that it limits update more than the mininterval. This is due to the internal time updating of the function.

I think it's best to attempt to set a higher miniters by default (maybe based on iterator arguments, like max(int(args[0] / 100), 1)) or at least document this effect somewhere...

Still. 100x faster that ProgressBar in some scenarios. And prettier by default. Nice 👍

xrange: 3.02 sec
tqdm.trange: 19.6 sec
tqdm.trange, miniters=niters/100: 9.71 sec
progressbar.ProgressBar()(xrange): 87.8 sec

#!/usr/bin/env python2.7

# Profiling/overhead tests
from timeit import timeit


stmts = ('[i for i in xrange(int(1e8))]',
    'import tqdm; [i for i in tqdm.trange(int(1e8))]',
    'import tqdm; [i for i in tqdm.trange(int(1e8), miniters=int(1e6))]',
    'import progressbar; [i for i in progressbar.ProgressBar()(xrange(int(1e8)))]')
for s in stmts:
    print s
    print timeit(stmt=s, number=1), 'seconds'

Force refresh by user

We already have failsafes to try to prevent tqdm from taking too much time between two updates, but they are triggered only after the first long gap, not before (else, we would need to check time() often, and this would kill the performance).

An alternative would be to allow the user to force the bar to print the current state (whether it has changed or not). I will here focus on a console environment, we can see later for other environments (IPython, GUI, etc. they should be easier).

To do that in a console, we can either activate mouse clicking, or register and catch some kind of interrupt that would be triggered by the user pressing a set of keys.

I think both are portable to any OS, so the only two criteria we should meet are: reliability (triggering the refresh should always work instantly) and no conflict (with the console's predefined shortcuts or the parent application using tqdm).

NB: And if multiple bars are displayed at the same time, we can just force them all to refresh without discrimination, that's not a problem.

Garbled output on ipython console in windows

Hi, i am getting the following output in windows when i import tqdm

Python 2.7.11 |Anaconda 2.0.1 (32-bit)| (default, Dec  7 2015, 14:13:17) [MSC v.
1500 32 bit (Intel)]
Type "copyright", "credits" or "license" for more information.

IPython 2.1.0 -- An enhanced Interactive Python.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://binstar.org
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: from tqdm import tqdm

☺☻In [☺☻2☺☻]: ☺☻

Any idea why this happens?
tqdm version is 3.4.0

Performance test

Since we aim for tqdm to stay fast compared to the other similar projects, we should maybe make an automatic performance test that should be run everytime we make additions.

I've looked if Travis or another free CI provided performance tests, but it's not currently in the plans:
travis-ci/travis-ci#352
travis-ci/travis-ci#3313

Meanwhile, we could at least make a performance test to run locally and manually by us. There are a few tools to do that:

So, what do you think guys? Do you know any other alternative? Or do we settle with vbench or codespeed for now to avoid performance regressions?

Restarting a progress meter without getting crazy

In my program I can restart a calculation. If I restart a calculation, I do something like:

class progress :
    def __init__(self, path, num_fragmentos) :
        self.num_fragmentos = num_fragmentos
        self._first = True
        self._progress = tqdm.tqdm(
                total = num_fragmentos,
                unit = "block",
                miniters = 1)

    def __call__(self, num_fragmento) :
        if self._first :
            self._progress.update(num_fragmento)
            self._first = False
        else :
            self._progress.update()

I create a regular tqdm and in the first call I "jump" to the right position. This shows the progress bar, but "it/s", ETA, etc, are unrealistic for a while.

I would like to add an initial value to tqdm constructor. I should be able to do something like "tqdm(total=100, initial=71)", and not mess with time estimations.

Progress bar prints through eol on fedora/bpython

Following the gif from the readme on my distro, tqdm does not play well with bpython (Fedora 21 Linux 4.1.7, bash on xterm-256color, Python 2.7.8 on bpython 0.14.2 (via yum) running tqdm 3.4.0 (via pip)).

After:

>>> for _ in tqdm.tqdm(xrange(10)):
...     time.sleep(.1)

The output writes blank characters to the end (right) of the terminal (the progress bar does not extend all the way to the end either) until the new line is reached and is then reprinted on the next line, the very last write eliminates the last 100% line, leaving this printed in the terminal:

40%|####      | 4/10 [00:00<00:00,  9.07it/s]
80%|########  | 8/10 [00:00<00:00,  8.37it/s]

On both ipython and regular interactive python sessions the result prints properly:

50%|███████████████████████████████████████████████████████████████████████████████████▌                                                                                   | 5/10 [00:05<00:05,  1.00it/s]

Moving the GUI into an inheriting object

I just gave a thought on how the GUI could be moved to its own module. As I said in another comment, I think it would be better to modularize the GUI logic, keeping it separated from the console logic, as to avoid potential conflicts (ie, if we update the console code, it could break the GUI, or the other way around), for ease of maintenance and more robustness.

I thought of two ways to do that:

1- move the GUI into its own module (eg, _tqdmgui.py) and in its own class, that would inherit from the _tqdm.tqdm() class. This would give something like this:

from ._tqdm import tqdm

class tqdmgui(tqdm):

    def __init__(self, **kwargs):

        tqdm.__init__(self, **kwargs)
        self.sp = None
        # Gui logic here in addition to tqdm.__init__()

    def __iter__(self):
        # Gui logic here that overrides __iter__()

    def update(self, n=1):
        # Gui logic that overrides update()

    def close(self):
        # Gui logic that overrides close()

This would totally modularize the code, but there would be a bit of redundancy and it wouldn't be possible to use tqdm(..., gui=True) anymore to use the GUI, you'd have to do tqdmgui(...).

2- if we want to keep the gui argument inside the main tqdm() class, we can move the GUI management to a class or function that would replace StatusPrinter. For example, we could replace in tqdm() class the direct calls to format_meter() or StatusPrinter by a class property that would point to the correct functions/objects to use:

class tqdm(object):
    def __init__(self, iterable=None, desc=None, total=None, leave=False,
                 file=sys.stderr, ncols=None, mininterval=0.1,
                 miniters=None, ascii=None, disable=False,
                 unit='it', unit_scale=False, gui=False):

        # Initialization code here...

        if not gui:
            self.sp = StatusPrinter(self.file)
            self.sp_call = lambda *args, **kwargs: self.sp(format_meter(*args, **kwargs))
            # etc.
        else:
            self.sp = GUIPrinter()
            self.sp_call = self.sp_gui.gui_format_meter

Then in the code, to call, you'd have to replace self.sp(format_meter(...)) by self.sp_call(...).

However, I'm not sure that this latter option can be realized. @casperdcl, could you tell me if the GUI would have all the necessary info to work if it is supplied the arguments of the format_meter() function?

3- I just thought we can also mix both 1 and 2: we can change self.sp in the inherited tqdmgui class to call a specifically designed StatusPrinter-like object.


What do you think guys? Would you like the GUI to be modularized? If yes, what option would you prefer? (you can also propose other options)

run make fail on linux

make
make[1]: Entering directory '/home/wenjian/pkgs/tqdm'
rm -f .coverage # coverage erase
nosetests tqdm --with-coverage --cover-package=tqdm --cover-erase --cover-min-percentage=80 --ignore-files="tests_perf.py" -d -v
nose.plugins.cover: ERROR: Coverage not available: unable to import coverage module
tests_pandas.test_pandas ... ok
tests_pandas.test_pandas_leave ... ok
Test time interval format ... ok
Test statistics and progress bar formatting ... ok
Test SI unit prefixes ... ok
tests_tqdm.test_all_defaults ... ok
Test csv iterator ... ok
Test output to arbitrary file-like objects ... ok
Test leave=True always prints info about the last iteration ... ok
Test trange ... ok
Test mininterval ... ok
Test maxinterval ... ok
Test miniters ... ok
Test purely dynamic miniters ... ok
Test large mininterval ... ok
Test smoothed dynamic miniters ... ok
Test smoothed dynamic miniters with mininterval ... ok
Test disable ... ok
Test SI unit prefix ... ok
Test ascii/unicode bar ... ok
Test manual creation and updates ... ok
Test manual creation and closure ... ok
Test exponential weighted average smoothing ... ok
Test nested progress bars ... ok
0it [00:00, ?it/s]okatting ...
Test unpause ... ok
Hello: 0it [00:00, ?it/s]ok
Test internal GUI properties ... ok
Test version string ... ok


Ran 29 tests in 1.000s

OK
make[1]: Leaving directory '/home/wenjian/pkgs/tqdm'
make[1]: Entering directory '/home/wenjian/pkgs/tqdm'
nosetests tqdm/tests/tests_perf.py -d -v
Test overhead of iteration based tqdm ... FAIL
Test overhead of manual tqdm ... FAIL

FAIL: Test overhead of iteration based tqdm

Traceback (most recent call last):
File "/home/wenjian/anaconda2/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/wenjian/pkgs/tqdm/tqdm/tests/tests_perf.py", line 58, in test_iter_overhead
(total, time_tqdm, total, time_bench))
AssertionError: trange(1e+06): 0.539649, range(1e+06): 0.023868

FAIL: Test overhead of manual tqdm

Traceback (most recent call last):
File "/home/wenjian/anaconda2/lib/python2.7/site-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/home/wenjian/pkgs/tqdm/tqdm/tests/tests_perf.py", line 85, in test_manual_overhead
(total, time_tqdm, total, time_bench))
AssertionError: tqdm(1e+06): 0.670235, range(1e+06): 0.023699


Ran 2 tests in 1.267s

FAILED (failures=2)
Makefile:64: recipe for target 'testperf' failed
make[1]: *** [testperf] Error 1
make[1]: Leaving directory '/home/wenjian/pkgs/tqdm'
Makefile:34: recipe for target 'alltests' failed
make: *** [alltests] Error 2

Asynchronous tqdm

From Python 3.4, the stdlib provides an asynchronous decorator. Maybe this can be used to make tqdm asynchronous, thus lowering even further the overhead?

Eg,

try:
    from asyncio import coroutine
except ImportError:
    def coroutine(func):
        return func

From the excellent ipy_progressbar module.

Also, it's interesting to note that they used a deque(maxlen=100) to memorize all 100 previous delta_t to compute their eta. This is I think quite efficient (but not as efficient as us), and it can be a useful trick for us if we want to implement more complex etas.

Add non-iterator operating mode

Some interfaces don’t work well with iterators, e.g.

urllib.request.urlretrieve(url, filename=None, reporthook=None, data=None)

[…]
The third argument, if present, is a hook function that 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.
[…]

So for this to work we need a class that can be created with all but tqdm’s first argument, and has a value property, which on setting redraws the progress bar.

bar_format option does not exist?

In docs there is option bar_format but module from PyPy has not it:

pbar = tqdm(total=post_count, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt}'
TypeError: __init__() got an unexpected keyword argument 'bar_format'

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