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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) and is an abbreviation for I love you so much in Spanish (te quiero demasiado).
Features[edit | edit source]
Python Iterable Wrapper[edit | edit source]
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:
- Display customisation via arguments such as
- Automatic limiting of display updates to avoid slowing down due to excessive iteration rates
- Automatic detection of console width to fill the display
- Automatic use of Unicode to render smooth-filling progress bars on supported terminals
- Support for custom rendering frontends, including:
- Support for custom hooks/callbacks, including:
Command-line Interface[edit | edit source]
$ 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
- Over 39 million downloads
- Over 410 thousand code inclusions
- SourceRank 22, in the world's top 20 Python packages as of early 2019
- 10 thousand stars on GitHub, and the top trending repository during a period in December 2015
- 500 thousand documentation hits
- Used in several textbooks and peer-reviewed scientific publications
Code Metrics[edit | edit source]
The library uses:
- Travis CI for unit testing;
- Codacy for style and security checks;
- Coveralls and Codecov for coverage reporting;
- OpenHub for COCOMO valuation, and
- airspeed velocity for performance testing
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 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 : Shampoo (software), Apache Commons DbUtils, Phorum, Xaraya, AdoptOpenJDK, Australia Cash, Grav (CMS)
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- Ports of tqdm in other languages on GitHub
- Interactive demonstration of tqdm in a Jupyter Notebook
References in Blogs and Public Media[edit | edit source]
- A tqdm release becomes Zenodo's 1 millionth record
- A Hymn to Progress, a poem or song with suggested tune of For those in Peril on the C, where C is a pun on Sea and the C programming language
- My top 5 'new' Python modules of 2015
Seaborn: Three essential python modules (Nov 2018)
References[edit | edit source]
- "@casperdcl". GitHub. Retrieved 22 January 2019.
- "tqdm". Python Package Index (PyPI). Python Software Foundation. Retrieved 2019-01-22.
- 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.
- "¿Lenguaje sms que significa esto?". es.answers.yahoo.com (in español).
- "Analyzing PyPI package downloads — Python Packaging User Guide". Python Packaging Authority (PyPA). Python Software Foundation. Retrieved 22 January 2019.
- "tqdm Code Results". GitHub. Retrieved 22 January 2019.
- "tqdm dependents". GitHub. 22 January 2019. Retrieved 22 January 2019.
- "tqdm on PyPI". Libraries.io.
- "SourceRank Breakdown for tqdm". Libraries.io.
- "Libraries - The Open Source Discovery Service". Libraries.io. Retrieved 22 January 2019.
- "tqdm Stargazers". GitHub. 22 January 2019. Retrieved 22 January 2019.
- Takizawa, Nihey (19 July 2018). "GitHub Trending History". GitHub. Retrieved 25 January 2019.
- "tqdm hits". CasperSci. Retrieved 22 January 2019.
- Miller, Preston; Bryce, Chapin (2017). Python Digital Forensics Cookbook: Effective Python recipes for digital investigations. Packt Publishing Ltd. ISBN 9781783987474.
- Van Boxel, Dan (2017). Hands-On Deep Learning with TensorFlow. Packt Publishing. ISBN 9781787125827.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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