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Astropy, a library of Python tools for astronomy and astrophysics. Biopython, a Python molecular biology suite; Gensim, a library for natural language processing, including unsupervised topic modeling and information retrieval; graph-tool, a Python module for manipulation and statistical analysis of graphs.
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Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [32] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. [4]
Pages in category "Python (programming language) software" The following 56 pages are in this category, out of 56 total. This list may not reflect recent changes .
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Python Tools for Visual Studio (PTVS) is a free and open-source plug-in for versions of Visual Studio up to VS 2015 providing support for programming in Python. Since VS 2017, it is integrated in VS and called Python Support in Visual Studio. It supports IntelliSense, debugging, profiling, MPI cluster debugging, mixed C++/Python debugging, and ...
Python is a high-level, general-purpose programming language that is popular in artificial intelligence. [1] It has a simple, flexible and easily readable syntax. [2] Its popularity results in a vast ecosystem of libraries, including for deep learning, such as PyTorch, TensorFlow, Keras, Google JAX.