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Python (programming language)

About: Python (programming language) is a(n) research topic. Over the lifetime, 10314 publication(s) have been published within this topic receiving 376099 citation(s). The topic is also known as: py & python 2.

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Papers
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Open accessJournal Article
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

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33,540 Citations


Open accessPosted Content
02 Jan 2012-arXiv: Learning
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

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28,898 Citations


Journal ArticleDOI: 10.1109/MCSE.2007.55
Abstract: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting,and publication-quality image generation across user interfaces and operating systems

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Topics: 2D computer graphics (56%), Computer graphics (55%), Python (programming language) (54%) ...read more

16,056 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTU638
15 Jan 2015-Bioinformatics
Abstract: Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an opensource software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

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11,833 Citations


Open accessProceedings Article
Adam Paszke1, Sam Gross2, Francisco Massa2, Adam Lerer2  +17 moreInstitutions (11)
01 Jan 2019-
Abstract: Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.

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Topics: Programming style (56%), Python (programming language) (53%), Usability (52%) ...read more

9,926 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202222
20211,379
20201,505
20191,327
20181,092
2017759

Top Attributes

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Topic's top 5 most impactful authors

Hans Petter Langtangen

13 papers, 374 citations

Baowen Xu

11 papers, 114 citations

Brian Vinter

9 papers, 58 citations

Hans Fangohr

9 papers, 68 citations

Andrew P. Davison

8 papers, 1.2K citations

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