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|Logo|
tqdm
====
|PyPi Status| |PyPi Downloads|
|Build Status| |Coverage Status| |Branch Coverage Status|
``tqdm`` (read taqadum, تقدّم) means "progress" in arabic.
Instantly make your loops show a smart progress meter - just wrap any
iterable with "tqdm(iterable)", and you're done!
.. code:: python
from tqdm import tqdm
for i in tqdm(range(9)):
...
Here's what the output looks like:
76%\|████████████████████\             \| 7641/10000 [00:34<00:10,
222.22 it/s]
``trange(N)`` can be also used as a convenient shortcut for
``tqdm(xrange(N))``.
|Screenshot|
It can also be executed as a module with pipes:
.. code:: sh
$ seq 9999999 | tqdm --unit_scale True | wc -l
10.0Mit [00:02, 3.58Mit/s]
9999999
Overhead is low -- about 60ns per iteration (80ns with ``gui=True``), and is
unit tested against performance regression.
By comparison, the well established
`ProgressBar <https://github.com/niltonvolpato/python-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, Solaris/SunOS),
in any console or in a GUI, and is also friendly with IPython/Jupyter notebooks.
``tqdm`` does not require any library (not even curses!) to run, just a
vanilla Python interpreter will do and an environment supporting ``carriage
return \r`` and ``line feed \n`` control characters.
------------------------------------------
.. contents:: Table of contents
:backlinks: top
:local:
Installation
------------
Latest pypi stable release
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: sh
pip install tqdm
Latest development release on github
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Pull and install in the current directory:
.. code:: sh
pip install -e git+https://github.com/tqdm/tqdm.git@master#egg=tqdm
Changelog
---------
The list of all changes is available either on
`Github's Releases <https://github.com/tqdm/tqdm/releases>`_
or on crawlers such as
`allmychanges.com <https://allmychanges.com/p/python/tqdm/>`_.
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:
.. code:: python
text = ""
for char in tqdm(["a", "b", "c", "d"]):
text = text + char
``trange(i)`` is a special optimised instance of ``tqdm(range(i))``:
.. code:: python
for i in trange(100):
pass
Instantiation outside of the loop allows for manual control over ``tqdm()``:
.. code:: python
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:
.. code:: python
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:
.. code:: python
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.
.. code:: sh
$ 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.
.. code:: sh
$ find . -name '*.py' -exec cat \{} \; |
tqdm --unit loc --unit_scale True --total 857366 >> /dev/null
100%|███████████████████████████████████| 857K/857K [00:04<00:00, 246Kloc/s]
Documentation
-------------
.. code:: python
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=sys.stderr, 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):
Parameters
~~~~~~~~~~
* iterable : iterable, optional
Iterable to decorate with a progressbar.
Leave blank [default: None] to manually manage the updates.
* desc : str, optional
Prefix for the progressbar [default: None].
* total : int, optional
The number of expected iterations. If [default: None], 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 [default: None], 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 update interval, in seconds [default: 0.1].
* maxinterval : float, optional
Maximum progress update interval, in seconds [default: 10.0].
* miniters : int, optional
Minimum progress update interval, in iterations [default: None].
If specified, will set `mininterval` to 0.
* ascii : bool, optional
If [default: None] or False, use unicode (smooth blocks) to fill
the meter. The fallback is to use ASCII characters `1-9 #`.
* disable : bool
Whether to disable the entire progressbar wrapper
[default: False].
* unit : str, optional
String that will be used to define the unit of each iteration
[default: it].
* unit_scale : bool, optional
If set, 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].
* dynamic_ncols : bool, optional
If set, constantly alters `ncols` to the environment (allowing
for window resizes) [default: False].
* smoothing : float
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.
If [default: None], will use '{l_bar}{bar}{r_bar}', where l_bar is
'{desc}{percentage:3.0f}%|' and r_bar is
'| {n_fmt}/{total_fmt} [{elapsed_str}<{remaining_str}, {rate_fmt}]'.
Possible vars: bar, n, n_fmt, total, total_fmt, percentage,
rate, rate_fmt, elapsed, remaining, l_bar, r_bar, desc.
* 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 [default: None].
Useful to manage multiple bars at once (eg, from threads).
Returns
~~~~~~~
* out : decorated iterator.
.. code:: python
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
Increment to add to the internal counter of iterations
[default: 1].
"""
def close(self):
"""
Cleanup and (if leave=False) close the progressbar.
"""
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!
"""
Examples and Advanced Usage
---------------------------
See the `examples <https://github.com/tqdm/tqdm/tree/master/examples>`__
folder or import the module and run ``help()``.
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.
| [...]
.. code:: python
import urllib
from tqdm import tqdm
def my_hook(t):
"""
Wraps tqdm instance. Don't forget to close() or __exit__()
the tqdm instance once you're done with it (easiest using `with` syntax).
Example
-------
>>> with tqdm(...) as t:
... reporthook = my_hook(t)
... urllib.urlretrieve(..., reporthook=reporthook)
"""
last_b = [0]
def inner(b=1, bsize=1, tsize=None):
"""
b : int, optional
Number of blocks just transferred [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:
t.total = tsize
t.update((b - last_b[0]) * bsize)
last_b[0] = b
return inner
eg_link = 'http://www.doc.ic.ac.uk/~cod11/matryoshka.zip'
with tqdm(unit='B', unit_scale=True, leave=True, miniters=1,
desc=eg_link.split('/')[-1]) as t: # all optional kwargs
urllib.urlretrieve(eg_link, filename='/dev/null',
reporthook=my_hook(t), data=None)
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 ``DataFrameGroupBy.progress_apply``:
.. code:: python
import pandas as pd
import numpy as np
from tqdm import tqdm, tqdm_pandas
df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
# Create and register a new `tqdm` instance with `pandas`
# (can use tqdm_gui, optional kwargs, etc.)
tqdm_pandas(tqdm())
# Now you can use `progress_apply` instead of `apply`
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 <https://github.com/tqdm/tqdm/tree/master/examples>`__
folder or import the module and run ``help()``.
Nested progress bars
~~~~~~~~~~~~~~~~~~~~
``tqdm`` supports nested progress bars. Here's an example:
.. code:: python
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 <https://github.com/tartley/colorama>`__ will be used if
available to produce a beautiful nested display.
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.
How to make a good progress bar
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A good progress bar is a useful progress bar. To be useful, ``tqdm`` displays
statistics and uses smart algorithms to predict and automagically adapt to
a variety of use cases with no or minimal configuration.
However, there is one thing that ``tqdm`` cannot do: choose a pertinent
progress indicator. To display a useful progress bar, it is very important that
``tqdm`` is supplied with the most pertinent progress indicator.
This will reflect most accurately the current state of your program.
Usually, a good way is to preprocess quickly to first evaluate the total amount
of work to do before beginning the real processing.
To illustrate the importance of a good progress indicator, take the
following example: you want to walk through all files of a directory and
process their contents with some external function:
.. code:: python
import os
from tqdm import tqdm, trange
from time import sleep
def dosomething(buf):
"""Do something with the content of a file"""
sleep(0.01)
pass
def walkdir(folder):
"""Walk through each files in a directory"""
for dirpath, dirs, files in os.walk(folder):
for filename in files:
yield os.path.abspath(os.path.join(dirpath, filename))
def process_content_no_progress(inputpath, blocksize=1024):
for filepath in walkdir(inputpath):
with open(filepath, 'rb') as fh:
buf = 1
while (buf):
buf = fh.read(blocksize)
dosomething(buf)
``process_content_no_progress()`` does the job, but does not show
any information about the current progress, nor how long it will take.
To quickly fix that using ``tqdm``, we can use this naive approach:
.. code:: python
def process_content_with_progress1(inputpath, blocksize=1024):
for filepath in tqdm(walkdir(inputpath), leave=True):
with open(filepath, 'rb') as fh:
buf = 1
while (buf):
buf = fh.read(blocksize)
dosomething(buf)
``process_content_with_progress1()`` will load ``tqdm()``, but since the
iterator does not provide any length (``os.walkdir()`` does not have a
``__len__()`` method for the total files count), there is only an indication
of the current and past program state, no prediction:
``4it [00:03, 2.79it/s]``
The way to get predictive information is to know the total amount of work to be
done. Since ``os.walkdir()`` cannot give us this information, we need to
precompute this by ourselves:
.. code:: python
def process_content_with_progress2(inputpath, blocksize=1024):
# Preprocess the total files count
filecounter = 0
for dirpath, dirs, files in tqdm(os.walk(inputpath)):
for filename in files:
filecounter += 1
for filepath in tqdm(walkdir(inputpath), total=filecounter, leave=True):
with open(filepath, 'rb') as fh:
buf = 1
while (buf):
buf = fh.read(blocksize)
dosomething(buf)
``process_content_with_progress2()`` is better than the naive approach because
now we have predictive information:
50%|████████████\             \| 2/4 [00:00<00:00, 4.06it/s]
However, the progress is not smooth: it increments in steps, 1 step being
1 file processed. The problem is that we do not just walk through files tree,
but we process the files contents. Thus, if we stumble on one very large file
which takes a great deal more time to process than other smaller files,
the progress bar
will still considers that file is of equal processing weight.
To fix this, we should use another indicator than the files count: the total
sum of all files sizes. This would be more pertinent since the data we
process is the files' content, so there is a direct relation between size and
content.
Below we implement this approach using a manually updated ``tqdm`` bar, where
``tqdm`` will work on size, while the ``for`` loop works on files paths:
.. code:: python
def process_content_with_progress3(inputpath, blocksize=1024):
# Preprocess the total files sizes
sizecounter = 0
for dirpath, dirs, files in tqdm(os.walk(inputpath)):
for filename in files:
fullpath = os.path.abspath(os.path.join(dirpath, filename))
sizecounter += os.stat(fullpath).st_size
# Load tqdm with size counter instead of files counter
with tqdm(total=sizecounter, leave=True, unit='B', unit_scale=True) as pbar:
for dirpath, dirs, files in os.walk(inputpath):
for filename in files:
fullpath = os.path.abspath(os.path.join(dirpath, filename))
with open(fullpath, 'rb') as fh:
buf = 1
while (buf):
buf = fh.read(blocksize)
dosomething(buf)
if buf: pbar.update(len(buf))
And here is the result: a much smoother progress bar with meaningful
predicted time and statistics:
47%|████████████\             \| 152K/321K [00:03<00:03, 46.2KB/s]
Contributions
-------------
To run the testing suite please make sure tox (https://testrun.org/tox/latest/)
is installed, then type ``tox`` from the command line.
Where ``tox`` is unavailable, a Makefile-like setup is
provided with the following command:
.. code:: sh
$ python setup.py make alltests
To see all options, run:
.. code:: sh
$ python setup.py make
See the
`CONTRIBUTE <https://raw.githubusercontent.com/tqdm/tqdm/master/CONTRIBUTE>`__
file for more information.
License
-------
Multiple licences, mostly `MPLv2.0, MIT licences <https://raw.githubusercontent.com/tqdm/tqdm/master/LICENCE>`__.
Authors
-------
- Casper da Costa-Luis (casperdcl)
- Stephen Larroque (lrq3000)
- Hadrien Mary (hadim)
- Noam Yorav-Raphael (noamraph)*
- Ivan Ivanov (obiwanus)
- Mikhail Korobov (kmike)
`*` Original author
.. |Logo| image:: https://raw.githubusercontent.com/tqdm/tqdm/master/logo.png
.. |Build Status| image:: https://travis-ci.org/tqdm/tqdm.svg?branch=master
:target: https://travis-ci.org/tqdm/tqdm
.. |Coverage Status| image:: https://coveralls.io/repos/tqdm/tqdm/badge.svg
:target: https://coveralls.io/r/tqdm/tqdm
.. |Branch Coverage Status| image:: https://codecov.io/github/tqdm/tqdm/coverage.svg?branch=master
:target: https://codecov.io/github/tqdm/tqdm?branch=master
.. |PyPi Status| image:: https://img.shields.io/pypi/v/tqdm.svg
:target: https://pypi.python.org/pypi/tqdm
.. |PyPi Downloads| image:: https://img.shields.io/pypi/dm/tqdm.svg
:target: https://pypi.python.org/pypi/tqdm
.. |Screenshot| image:: https://raw.githubusercontent.com/tqdm/tqdm/master/tqdm.gif