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A Python utility belt containing simple tools, a stdlib like feel, and extra batteries. Hashing, Caching, Timing, Progress, and more made easy!

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Ubelt is a small library of robust, tested, documented, and simple functions that extend the Python standard library. It has a flat API that all behaves similarly on Windows, Mac, and Linux (up to some small unavoidable differences). Almost every function in

was written with a doctest. This provides helpful documentation and example usage as well as helping achieve 100% test coverage (with minor exceptions for Python2, Windows, etc...).
  • Goal: provide simple functions that accomplish common tasks not yet addressed by the python standard library.

  • Constraints: Must be low-impact pure python; it should be easy to install and use.

  • Method: All functions are written with docstrings and doctests to ensure that a baseline level of documentation and testing always exists (even if functions are copy/pasted into other libraries)

  • Motto: Good utilities lift all codes.

Read the docs here:

These are some of the tasks that ubelt's API enables:

  • hash common data structures like list, dict, int, str, etc. (hash_data)

  • hash files (hash_file)

  • cache a block of code (Cacher, CacheStamp)

  • time a block of code (Timerit, Timer)

  • show loop progress (ProgIter)

  • download a file with optional caching and hash verification (download, grabdata)

  • run shell commands (cmd)

  • find a file or directory in candidate locations (findpath, findexe)

  • string-format nested data structures (repr2)

  • color text with ANSI tags (color_text)

  • horizontally concatenate multiline strings (hzcat)

  • make a directory if it doesn't exist (ensuredir)

  • delete a file, link, or entire directory (delete)

  • create cross platform symlinks (symlink)

  • expand environment variables and tildes in path strings (expandpath)

  • import a module using the path to that module (importmodulefrom_path)

  • check if a particular flag or value is on the command line (argflag, argval)

  • get paths to cross platform data/cache/config directories (ensureappcache_dir, ...)

  • memoize functions (memoize, memoizemethod, memoizeproperty)

  • build ordered sets (oset)

  • short defaultdict and OrderedDict aliases (ddict and odict)

  • map a function over the keys or values of a dictionary (mapkeys, mapvals)

  • perform set operations on dictionaries (dictunion, dictisect, dictdiff, dictsubset, ...)

  • perform dictionary operations like histogram, inversion, and sorting (dicthist, invertdict, sortedkeys, sortedvals)

  • argmax/min/sort on lists and dictionaries (argmin, argsort,)

  • find duplicates in a list (find_duplicates)

  • group a sequence of items by some criterion (group_items)

Ubelt is small. Its top-level API is defined using roughly 40 lines:

.. code:: python

from ubelt.util_arg import (argflag, argval,)
from ubelt.util_cache import (CacheStamp, Cacher,)
from ubelt.util_colors import (color_text, highlight_code,)
from ubelt.util_const import (NoParam,)
from ubelt.util_cmd import (cmd,)
from ubelt.util_dict import (AutoDict, AutoOrderedDict, ddict, dict_diff,
                             dict_hist, dict_isect, dict_subset, dict_union,
                             dzip, find_duplicates, group_items, invert_dict,
                             map_keys, map_vals, odict, sorted_keys,
from ubelt.util_download import (download, grabdata,)
from ubelt.util_func import (identity, inject_method,)
from ubelt.util_format import (FormatterExtensions, repr2,)
from ubelt.util_io import (delete, readfrom, touch, writeto,)
from ubelt.util_links import (symlink,)
from ubelt.util_list import (allsame, argmax, argmin, argsort, argunique,
                             boolmask, chunks, compress, flatten, iter_window,
                             iterable, peek, take, unique, unique_flags,)
from ubelt.util_hash import (hash_data, hash_file,)
from ubelt.util_import import (import_module_from_name,
                               import_module_from_path, modname_to_modpath,
                               modpath_to_modname, split_modpath,)
from ubelt.util_memoize import (memoize, memoize_method, memoize_property,)
from ubelt.util_mixins import (NiceRepr,)
from ubelt.util_path import (TempDir, augpath, ensuredir, expandpath,
                             shrinkuser, userhome,)
from ubelt.util_platform import (DARWIN, LINUX, POSIX, WIN32,
                                 ensure_app_cache_dir, ensure_app_config_dir,
                                 ensure_app_data_dir, find_exe, find_path,
                                 get_app_cache_dir, get_app_config_dir,
                                 get_app_data_dir, platform_cache_dir,
                                 platform_config_dir, platform_data_dir,)
from ubelt.util_str import (codeblock, ensure_unicode, hzcat, indent,
from ubelt.util_stream import (CaptureStdout, CaptureStream, TeeStringIO,)
from ubelt.util_time import (timestamp,)
from ubelt.orderedset import (OrderedSet, oset,)
from ubelt.progiter import (ProgIter,)
from ubelt.timerit import (Timer, Timerit,)


Ubelt is distributed on pypi as a universal wheel and can be pip installed on Python 2.7, Python 3.4+. Installations are tested on CPython and PyPy implementations.


pip install ubelt

Note that our distributions on pypi are signed with GPG. The signing public key is

; this should agree with the value in

It is also possible to simply install it from source.


pip install git+


Ubelt is a migration of the most useful parts of

\ ( into a standalone module with minimal dependencies.


library contains a number of useful utility functions, but it also contained non-useful functions, as well as the kitchen sink. A number of the functions were too specific or not well documented. The
is a port of the simplest and most useful parts of

Note that there are other cool things in

that are not in
. Notably, the doctest harness ultimately became
. Code introspection and dynamic analysis tools were ported to
. The more IPython-y tools were ported to
. Parts of it made their way into
. The init-file generation was moved to
. Some vim and system-y things can be found in

Function Usefulness

When I had to hand pick a set of functions that I thought were the most useful I chose these and provided some comment on why:

.. code:: python

import ubelt as ub

ub.ensuredir # os.makedirs(exist_ok=True) is 3 only and too verbose ub.Timerit # powerful multiline alternative to timeit ub.Cacher # configuration based on-disk cachine ub.cmd # combines the best of subprocess.Popen and os.system ub.hash_data # extremely useful with Cacher to config strings ub.repr2 # readable representations of nested data structures # why is this not a one liner --- also see grabdata for the same thing, but builtin caching. ub.AutoDict # one of the most useful tools in Perl, ub.modname_to_modpath # (works via static analysis) ub.modpath_to_modname # (works via static analysis) ub.import_module_from_path # (Unlike importlib, this does not break pytest) ub.import_module_from_name # (Unlike importlib, this does not break pytest)

But a better way might to objectively measure the frequency of usage and built a histogram of usefulness. I generated this histogram using

python dev/

.. code:: python

'repr2': 1598,
'ProgIter': 610,
'expandpath': 610,
'ensuredir': 482,
'take': 337,
'odict': 311,
'map_vals': 272,
'dzip': 246,
'augpath': 209,
'NiceRepr': 197,
'ddict': 191,
'argval': 184,
'cmd': 176,
'argflag': 171,
'flatten': 168,
'codeblock': 159,
'Timerit': 158,
'NoParam': 149,
'dict_hist': 146,
'group_items': 138,
'peek': 134,
'iterable': 124,
'hash_data': 116,
'grabdata': 93,
'delete': 82,
'compress': 76,
'color_text': 76,
'dict_subset': 72,
'Cacher': 68,
'allsame': 66,
'Timer': 57,
'argsort': 53,
'oset': 51,
'invert_dict': 50,
'indent': 47,
'chunks': 45,
'memoize': 44,
'dict_isect': 42,
'timestamp': 40,
'import_module_from_path': 39,
'unique': 36,
'map_keys': 35,
'hzcat': 35,
'find_duplicates': 35,
'writeto': 35,
'dict_union': 34,
'ensure_unicode': 30,
'readfrom': 30,
'iter_window': 29,
'sorted_vals': 29,
'argmax': 26,
'memoize_property': 26,
'modname_to_modpath': 25,
'symlink': 25,
'memoize_method': 23,
'dict_diff': 23,
'identity': 22,
'hash_file': 21,
'touch': 19,
'import_module_from_name': 17,
'highlight_code': 16,
'find_exe': 15,
'CacheStamp': 13,
'find_path': 9,
'AutoDict': 8,
'split_modpath': 7,
'shrinkuser': 7,
'argmin': 6,
'inject_method': 6,
'download': 5,
'modpath_to_modname': 5,
'paragraph': 5,
'CaptureStdout': 4,
'sorted_keys': 3,
'userhome': 2,
'AutoOrderedDict': 2,
'argunique': 2,
'unique_flags': 2,


Be sure to checkout the new Jupyter notebook:

Here are some examples of some features inside



Quickly time a single line.

.. code:: python

>>> import math
>>> import ubelt as ub
>>> timer = ub.Timer('Timer demo!', verbose=1)
>>> with timer:
>>>     math.factorial(100000)
tic('Timer demo!')
...toc('Timer demo!')=0.1453s

Robust Timing and Benchmarking

Easily do robust timings on existing blocks of code by simply indenting them. There is no need to refactor into a string representation or convert to a single line. With

there is no need to resort to the

The quick and dirty way just requires one indent.

Note: Timerit is also defined in a standalone module:

pip install timerit

.. code:: python

>>> import math
>>> import ubelt as ub
>>> for _ in ub.Timerit(num=200, verbose=3):
>>>     math.factorial(10000)
Timing for 200 loops
Timed for: 200 loops, best of 3
    time per loop: best=2.055 ms, mean=2.145 ± 0.083 ms

Use the loop variable as a context manager for more accurate timings or to incorporate an setup phase that is not timed. You can also access properties of the

class to programmatically use results.

.. code:: python

>>> import math
>>> import ubelt as ub
>>> t1 = ub.Timerit(num=200, verbose=2)
>>> for timer in t1:
>>>     setup_vars = 10000
>>>     with timer:
>>>         math.factorial(setup_vars)
>>> print('t1.total_time = %r' % (t1.total_time,))
Timing for 200 loops
Timed for: 200 loops, best of 3
    time per loop: best=2.064 ms, mean=2.115 ± 0.05 ms
t1.total_time = 0.4427177629695507

Loop Progress

is a no-threads attached Progress meter that writes to stdout. It is a mostly drop-in alternative to
__. The advantage of
is that it does not use any python threading
, and therefore can be safer with code that makes heavy use of multiprocessing.


is also defined in a standalone module:
pip install progiter

.. code:: python

>>> import ubelt as ub
>>> def is_prime(n):
...     return n >= 2 and not any(n % i == 0 for i in range(2, n))
>>> for n in ub.ProgIter(range(1000), verbose=2):
>>>     # do some work
>>>     is_prime(n)
    0/1000... rate=0.00 Hz, eta=?, total=0:00:00, wall=14:05 EST 
    1/1000... rate=82241.25 Hz, eta=0:00:00, total=0:00:00, wall=14:05 EST 
  257/1000... rate=177204.69 Hz, eta=0:00:00, total=0:00:00, wall=14:05 EST 
  642/1000... rate=94099.22 Hz, eta=0:00:00, total=0:00:00, wall=14:05 EST 
 1000/1000... rate=71886.74 Hz, eta=0:00:00, total=0:00:00, wall=14:05 EST 


Cache intermediate results in a script with minimal boilerplate. It looks like 4 lines of boilerplate is the best you can do with Python 3.8 syntax. See`__ for details.

.. code:: python

>>> import ubelt as ub
>>> cfgstr = 'repr-of-params-that-uniquely-determine-the-process'
>>> cacher = ub.Cacher('test_process', cfgstr)
>>> data = cacher.tryload()
>>> if data is None:
>>>     myvar1 = 'result of expensive process'
>>>     myvar2 = 'another result'
>>>     data = myvar1, myvar2
>>> myvar1, myvar2 = data



constructs a hash corresponding to a (mostly) arbitrary ordered python object. A common use case for this function is to construct the
mentioned in the example for
. Instead of returning a hex, string,
encodes the hash digest using the 26 lowercase letters in the roman alphabet. This makes the result easy to use as a filename suffix.

.. code:: python

>>> import ubelt as ub
>>> data = [('arg1', 5), ('lr', .01), ('augmenters', ['flip', 'translate'])]
>>> ub.hash_data(data)[0:8]

There exists an undocumented plugin architecture to extend this function to arbitrary types. See

for details.

Command Line Interaction

The builtin Python

module is great, but it can be a bit clunky at times. The
command is easy to use, but it doesn't have much flexibility. The
function aims to fix this. It is as simple to run as
, but it returns a dictionary containing the return code, standard out, standard error, and the
object used under the hood.

.. code:: python

>>> import ubelt as ub
>>> info = ub.cmd('gcc --version')
>>> print(ub.repr2(info))
    'command': 'gcc --version',
    'err': '',
    'out': 'gcc (Ubuntu 5.4.0-6ubuntu1~16.04.9) 5.4.0 20160609\nCopyright (C) 2015 Free Software Foundation, Inc.\nThis is free software; see the source for copying conditions.  There is NO\nwarranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n\n',
    'proc': ,
    'ret': 0,

Also note the use of

to nicely format the output dictionary.

Additionally, if you specify

will simultaneously capture the standard output and display it in real time.

.. code:: python

>>> import ubelt as ub
>>> info = ub.cmd('gcc --version', verbose=True)
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.9) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO

A common use case for

is parsing version numbers of programs

.. code:: python

>>> import ubelt as ub
>>> cmake_version = ub.cmd('cmake --version')['out'].splitlines()[0].split()[-1]
>>> print('cmake_version = {!r}'.format(cmake_version))
cmake_version = 3.11.0-rc2

This allows you to easily run a command line executable as part of a python process, see what it is doing, and then do something based on its output, just as you would if you were interacting with the command line itself.


removes the need to think about if you need to pass a list of args, or a string. Both will work. This utility has been tested on both Windows and Linux.

Cross-Platform Resource and Cache Directories

If you have an application which writes configuration or cache files, the standard place to dump those files differs depending if you are on Windows, Linux, or Mac. Ubelt offers a unified functions for determining what these paths are.


functions find the correct platform-specific location for these files and ensures that the directories exist. (Note: replacing "ensure" with "get" will simply return the path, but not ensure that it exists)

The resource root directory is

on Windows,
on Linux and
~/Library/Application Support
on Mac. The cache root directory is
on Windows,
on Linux and
on Mac.

Example usage on Linux might look like this:

.. code:: python

>>> import ubelt as ub
>>> print(ub.compressuser(ub.ensure_app_cache_dir('my_app')))
>>> print(ub.compressuser(ub.ensure_app_resource_dir('my_app')))



function will create a symlink similar to
. The main differences are that 1) it will not error if the symlink exists and already points to the correct location. 2) it works* on Windows (*hard links and junctions are used if real symlinks are not available)

.. code:: python

>>> import ubelt as ub
>>> dpath = ub.ensure_app_cache_dir('ubelt', 'demo_symlink')
>>> real_path = join(dpath, 'real_file.txt')
>>> link_path = join(dpath, 'link_file.txt')
>>> ub.writeto(real_path, 'foo')
>>> ub.symlink(real_path, link_path)

Downloading Files

The function
provides a simple interface to download a URL and save its data to a file.

.. code:: python

>>> import ubelt as ub
>>> url = ''
>>> fpath =, verbose=0)
>>> print(ub.compressuser(fpath))

The function

works similarly to
, but whereas
will always re-download the file,
will check if the file exists and only re-download it if it needs to.

.. code:: python

>>> import ubelt as ub
>>> url = ''
>>> fpath = ub.grabdata(url, verbose=0, hash_prefix='944389a39')
>>> print(ub.compressuser(fpath))

New in version 0.4.0: both functions now accepts the

keyword argument, which if specified will check that the hash of the file matches the provided value. The
keyword argument can be used to change which hashing algorithm is used (it defaults to


Group items in a sequence into a dictionary by a second id list

.. code:: python

>>> import ubelt as ub
>>> item_list    = ['ham',     'jam',   'spam',     'eggs',    'cheese', 'bannana']
>>> groupid_list = ['protein', 'fruit', 'protein',  'protein', 'dairy',  'fruit']
>>> ub.group_items(item_list, groupid_list)
{'dairy': ['cheese'], 'fruit': ['jam', 'bannana'], 'protein': ['ham', 'spam', 'eggs']}

Dictionary Histogram

Find the frequency of items in a sequence

.. code:: python

>>> import ubelt as ub
>>> item_list = [1, 2, 39, 900, 1232, 900, 1232, 2, 2, 2, 900]
>>> ub.dict_hist(item_list)
{1232: 2, 1: 1, 2: 4, 900: 3, 39: 1}

Find Duplicates

Find all duplicate items in a list. More specifically,

searches for items that appear more than
times, and returns a mapping from each duplicate item to the positions it appeared in.

.. code:: python

>>> import ubelt as ub
>>> items = [0, 0, 1, 2, 3, 3, 0, 12, 2, 9]
>>> ub.find_duplicates(items, k=2)
{0: [0, 1, 6], 2: [3, 8], 3: [4, 5]}

Dictionary Manipulation

Take a subset of a dictionary.

.. code:: python

>>> import ubelt as ub
>>> dict_ = {'K': 3, 'dcvs_clip_max': 0.2, 'p': 0.1}
>>> subdict_ = ub.dict_subset(dict_, ['K', 'dcvs_clip_max'])
>>> print(subdict_)
{'K': 3, 'dcvs_clip_max': 0.2}

Take only the values, optionally specify a default value.

.. code:: python

>>> import ubelt as ub
>>> dict_ = {1: 'a', 2: 'b', 3: 'c'}
>>> print(list(ub.take(dict_, [1, 2, 3, 4, 5], default=None)))
['a', 'b', 'c', None, None]

Apply a function to each value in the dictionary (see also


.. code:: python

>>> import ubelt as ub
>>> dict_ = {'a': [1, 2, 3], 'b': []}
>>> newdict = ub.map_vals(len, dict_)
>>> print(newdict)
{'a': 3, 'b': 0}

Invert the mapping defined by a dictionary. By default

assumes that all dictionary values are distinct (i.e. the mapping is one-to-one / injective).

.. code:: python

>>> import ubelt as ub
>>> mapping = {0: 'a', 1: 'b', 2: 'c', 3: 'd'}
>>> ub.invert_dict(mapping)
{'a': 0, 'b': 1, 'c': 2, 'd': 3}

However, by specifying

the inverted dictionary builds a set of keys that were associated with each value.

.. code:: python

>>> import ubelt as ub
>>> mapping = {'a': 0, 'A': 0, 'b': 1, 'c': 2, 'C': 2, 'd': 3}
>>> ub.invert_dict(mapping, unique_vals=False)
{0: {'A', 'a'}, 1: {'b'}, 2: {'C', 'c'}, 3: {'d'}}

AutoDict - Autovivification

While the

is nice, it is sometimes more convenient to have an infinitely nested dictionary of dictionaries.

.. code:: python

>>> import ubelt as ub
>>> auto = ub.AutoDict()
>>> print('auto = {!r}'.format(auto))
auto = {}
>>> auto[0][10][100] = None
>>> print('auto = {!r}'.format(auto))
auto = {0: {10: {100: None}}}
>>> auto[0][1] = 'hello'
>>> print('auto = {!r}'.format(auto))
auto = {0: {1: 'hello', 10: {100: None}}}

String-based imports

Ubelt contains functions to import modules dynamically without using the python

statement. While
exists, the
implementation is simpler to user and does not have the disadvantage of breaking


simply provides an interface to this functionality, the core implementation is in
(over as of version
, the code is statically copied into an autogenerated file such that
does not actually depend on
during runtime).

.. code:: python

>>> import ubelt as ub
>>> module = ub.import_module_from_path(ub.expandpath('~/code/ubelt/ubelt'))
>>> print('module = {!r}'.format(module))
module = 
>>> module = ub.import_module_from_name('ubelt')
>>> print('module = {!r}'.format(module))
module = 

Related to this functionality are the functions

, which statically transform (i.e. no code in the target modules is imported or executed) between module names (e.g.
) and module paths (e.g.

.. code:: python

>>> import ubelt as ub
>>> modpath = ub.util_import.__file__
>>> print(ub.modpath_to_modname(modpath))
>>> modname = ub.util_import.__name__
>>> assert ub.modname_to_modpath(modname) == modpath

Horizontal String Concatenation

Sometimes its just prettier to horizontally concatenate two blocks of text.

.. code:: python

>>> import ubelt as ub
>>> B = ub.repr2([[1, 2], [3, 4]], nl=1, cbr=True, trailsep=False)
>>> C = ub.repr2([[5, 6], [7, 8]], nl=1, cbr=True, trailsep=False)
>>> print(ub.hzcat(['A = ', B, ' * ', C]))
A = [[1, 2], * [[5, 6],
     [3, 4]]    [7, 8]]

External tools.

Some of the tools in

also exist as standalone modules. I haven't decided if its best to statically copy them into ubelt or require on pypi to satisfy the dependency. There are some tools that are not used by default unless you explicitly allow for them.

Code that is currently statically included:

  • ProgIter -
  • Timerit -

Code that is currently linked via pypi:

  • OrderedSet -

Code that is completely optional, and only used in specific cases:

  • Numpy -
    will format a numpy array nicely by default
  • xxhash - this can be specified as a hasher to
  • Pygments - used by the

Also, in the future some of the functionality in ubelt may be ported and integrated into the



Ubelt will support Python2 for the foreseeable future (at least until the projects I work on are off it followed by a probation period).

PRs are welcome. If you have a utility function that you think is useful then write a PR. I'm likely to respond promptly.

Also check out my other projects (many of which are powered by ubelt):

  • ProgIter
  • Timerit
  • mkinit
  • xdoctest
  • xinspect
  • xdev
  • vimtk
  • graphid
  • ibeis

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