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Tqdm

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Tqdm
Tqdm gif logo.gif
Developer(s)Casper da Costa-Luis.[1]
Stable release
4.x.x / February 4, 2016; 3 years ago (2016-02-04)
Repository
Written inPython[2]
Engine
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Operating systemMS Windows, UNIX, MacOS
TypeSoftware profiling tools
LicenseMPLv2.0, MIT
Websitetqdm.github.io

tqdm is a progress bar library designed to be fast and extensible. It is written in Python, though ports in other languages are available. tqdm means progress in Arabic (taqadum, تقدّم)[3] and is an abbreviation for I love you so much in Spanish (te quiero demasiado)[4].

Features[edit | edit source]

Exhaustive documentation may be found on the project's home page.

The package supports both Python versions 2 and 3, and is available for download via conda, pip, and snap.

The two basic use cases are within Python code and within a Command-line interface:

Python Iterable Wrapper[edit | edit source]

tqdm's primary (and original) use is as a wrapper around Python iterables. A simple case would be:

from tqdm import tqdm
from time import sleep
for i in tqdm(range(100)):
    sleep(0.1)
100%|█████████████████████████████████████████| 100/100 [00:10<00:00,  9.95it/s]

Supported features include:

Command-line Interface[edit | edit source]

A Command-line interface is also provided, where tqdm may be used a pipe:

$ cat *.txt | wc -l                                            # count lines of text in all *.txt files
1075075
$ cat *.txt | python3 -m tqdm --unit loc --unit_scale | wc -l  # same but with continuously updating progress information
1.08Mloc [00:07, 142kloc/s] 
1075075

Popularity[edit | edit source]

Last updated: May 2019

Code Metrics[edit | edit source]

The library uses:

Licence[edit | edit source]

tqdm's source code is OSS, and may be cited using the DOI 10.5281/zenodo.595120. The primary maintainer Casper da Costa-Luis[1] releases contributions under the terms of the MPLv2.0, while all other contributions are released under the terms of the MIT licence.

See also[edit | edit source]


Others articles of the Topic Free and open-source software : Aigaion, Ring (programming language), AdoptOpenJDK, Wikimedia Foundation, Koseven (framework), Enguage, VPN.ht
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References in Blogs and Public Media[edit | edit source]

References[edit | edit source]

  1. 1.0 1.1 "@casperdcl". GitHub. Retrieved 22 January 2019.
  2. "tqdm". Python Package Index (PyPI). Python Software Foundation. Retrieved 2019-01-22.
  3. Alī Ġūl, Maḥmūd (1993). Early Southern Arabian Languages and Classical Arabic Sources: A Critical Examination of Literary and Lexicographical Sources by Comparison with the Inscriptions. Irbid: Yarmouk University.
  4. "¿Lenguaje sms que significa esto?". es.answers.yahoo.com (in español).
  5. "Analyzing PyPI package downloads — Python Packaging User Guide". Python Packaging Authority (PyPA). Python Software Foundation. Retrieved 22 January 2019.
  6. "tqdm Code Results". GitHub. Retrieved 22 January 2019.
  7. "tqdm dependents". GitHub. 22 January 2019. Retrieved 22 January 2019.
  8. "tqdm on PyPI". Libraries.io.
  9. "SourceRank Breakdown for tqdm". Libraries.io.
  10. "Libraries - The Open Source Discovery Service". Libraries.io. Retrieved 22 January 2019.
  11. "tqdm Stargazers". GitHub. 22 January 2019. Retrieved 22 January 2019.
  12. Takizawa, Nihey (19 July 2018). "GitHub Trending History". GitHub. Retrieved 25 January 2019.
  13. "tqdm hits". CasperSci. Retrieved 22 January 2019.
  14. Miller, Preston; Bryce, Chapin (2017). Python Digital Forensics Cookbook: Effective Python recipes for digital investigations. Packt Publishing Ltd. ISBN 9781783987474.
  15. Van Boxel, Dan (2017). Hands-On Deep Learning with TensorFlow. Packt Publishing. ISBN 9781787125827.
  16. Nandy, Abhishek; Biswas, Manisha (2018). "Reinforcement Learning with Keras, TensorFlow, and ChainerRL". Reinforcement Learning : With Open AI, TensorFlow and Keras Using Python. Apress. pp. 129–153. ISBN 9781484232859.
  17. Stein, Helge S.; Guevarra, Dan; Newhouse, Paul F.; Soedarmadji, Edwin; Gregoire, John M. (2019). "Machine learning of optical properties of materials – predicting spectra from images and images from spectra". Chemical Science. 10 (1): 47–55. doi:10.1039/C8SC03077D.
  18. Cook, Neil J.; Scholz, Aleks; Jayawardhana, Ray (28 November 2017). "Very Low-mass Stars and Brown Dwarfs in Upper Scorpius Using Gaia DR1: Mass Function, Disks, and Kinematics". The Astronomical Journal. 154 (6): 256. arXiv:1710.11625. Bibcode:2017AJ....154..256C. doi:10.3847/1538-3881/aa9751.
  19. Madhikar, Pranav; Åström, Jan; Westerholm, Jan; Karttunen, Mikko (November 2018). "CellSim3D: GPU accelerated software for simulations of cellular growth and division in three dimensions". Computer Physics Communications. 232: 206–213. Bibcode:2018CoPhC.232..206M. doi:10.1016/j.cpc.2018.05.024.
  20. Palmer, Geraint I.; Knight, Vincent A.; Harper, Paul R.; Hawa, Asyl L. (20 May 2018). "Ciw: An open-source discrete event simulation library". Journal of Simulation: 1–15. doi:10.1080/17477778.2018.1473909.
  21. Knight, Vincent; Campbell, Owen; Harper, Marc; Langner, Karol; Campbell, James; Campbell, Thomas; Carney, Alex; Chorley, Martin; Davidson-Pilon, Cameron; Glass, Kristian; Glynatsi, Nikoleta; Ehrlich, Tomáš; Jones, Martin; Koutsovoulos, Georgios; Tibble, Holly; Jochen, Müller; Palmer, Geraint; Petunov, Piotr; Slavin, Paul; Standen, Timothy; Visintini, Luis; Molden, Karl (31 August 2016). "An open reproducible framework for the study of the iterated prisoner's dilemma". Journal of Open Research Software. 4. doi:10.5334/jors.125. ISSN 2049-9647.


This article "Tqdm" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Tqdm. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.



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