Metadata-Version: 1.1
Name: tqdm
Version: 4.7.4
Summary: A Fast, Extensible Progress Meter
Home-page: https://github.com/tqdm/tqdm
Author: tqdm developers
Author-email: python.tqdm@gmail.com
License: MPLv2.0, MIT Licenses
Description: |Logo|
        
        tqdm
        ====
        
        |PyPi-Status| |PyPi-Versions|
        
        |Build-Status| |Coverage-Status| |Branch-Coverage-Status|
        
        |DOI-URI| |Licence|
        
        
        ``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 | wc -l
            10.0Mit [00:02, 3.58Mit/s]
            9999999
        
        Overhead is low -- about 60ns per iteration (80ns with ``tqdm_gui``), 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
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        |PyPi-Status|
        
        .. code:: sh
        
            pip install tqdm
        
        Latest development release on github
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        |Github-Status| |Github-Stars| |Github-Forks|
        
        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:
        |Github-Status| 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 --total 857366 >> /dev/null
            100%|███████████████████████████████████| 857K/857K [00:04<00:00, 246Kloc/s]
        
        Backing up a large directory?
        
        .. code:: sh
        
            $ 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]
        
        
        Documentation
        -------------
        
        |PyPi-Versions| |Readme-Hits| (Since 19 May 2016)
        
        .. 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 to manually manage the updates.
        * desc  : str, optional  
            Prefix for the progressbar.
        * 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 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 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.
            If specified, will set `mininterval` to 0.
        * 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].
        * 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, 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.
            If unspecified, 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 unspecified.
            Useful to manage multiple bars at once (eg, from threads).
        
        Extra CLI Options
        ~~~~~~~~~~~~~~~~~
        
        * delim  : chr, optional  
            Delimiting character [default: '\n']. Use '\0' for null.
            N.B.: on Windows systems, Python converts '\n' to '\r\n'.
        * buf_size  : int, optional  
            String buffer size in bytes [default: 256]
            used when `delim` is specified.
        
        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 clear(self):
                  """
                  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 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 <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.
        
        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, 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 ``DataFrame.progress_apply`` and ``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.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 <https://github.com/tqdm/tqdm/tree/master/examples>`__
        folder or import the module and run ``help()``.
        
        IPython/Jupyter Integration
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        IPython/Jupyter is supported via the `tqdm_notebook` submodule:
        
        .. code:: python
        
            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.
        
        |Screenshot-Jupyter1|
        |Screenshot-Jupyter2|
        |Screenshot-Jupyter3|
        
        Writing messages
        ~~~~~~~~~~~~~~~~
        
        Since ``tqdm`` uses a simple printing mechanism to display progress bars,
        you should not write any message in the terminal using ``print()``.
        
        To write messages in the terminal without any collision with ``tqdm`` bar
        display, a ``.write()`` method is provided:
        
        .. code:: python
        
            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:
        
        .. code:: python
        
            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)
        
            @contextlib.contextmanager
            def stdout_redirect_to_tqdm():
                save_stdout = sys.stdout
                try:
                    sys.stdout = DummyTqdmFile(sys.stdout)
                    yield save_stdout
                # Relay exceptions
                except Exception as exc:
                    raise exc
                # Always restore sys.stdout if necessary
                finally:
                    sys.stdout = save_stdout
        
            def blabla():
                print("Foo blabla")
        
            # Redirect stdout to tqdm.write() (don't forget the `as save_stdout`)
            with stdout_redirect_to_tqdm() as save_stdout:
                # tqdm call need to specify sys.stdout, not sys.stderr (default)
                # and dynamic_ncols=True to autodetect console width
                for _ in tqdm(range(3), file=save_stdout, dynamic_ncols=True):
                    blabla()
                    sleep(.5)
        
            # After the `with`, printing is restored
            print('Done!')
        
        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)):
                    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):
                    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, 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.
        
        
        Licence
        -------
        
        Open Source (OSI approved): |Licence|
        
        Citation information: |DOI-URI|
        
        
        Authors
        -------
        
        Ranked by contributions.
        
        -  Casper da Costa-Luis (casperdcl)
        -  Stephen Larroque (lrq3000)
        -  Hadrien Mary (hadim)
        -  Noam Yorav-Raphael (noamraph)*
        -  Ivan Ivanov (obiwanus)
        -  Mikhail Korobov (kmike)
        
        `*` Original author
        
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Keywords: progressbar progressmeter progress bar meter rate eta console terminal time
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Classifier: License :: OSI Approved :: MIT License
Classifier: Environment :: Console
Classifier: Framework :: IPython
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: POSIX :: BSD
Classifier: Operating System :: POSIX :: BSD :: FreeBSD
Classifier: Operating System :: POSIX :: SunOS/Solaris
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Programming Language :: Python :: Implementation :: IronPython
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: User Interfaces
Classifier: Topic :: System :: Monitoring
Classifier: Topic :: Terminals
Classifier: Topic :: Utilities
Classifier: Intended Audience :: Developers
