diff --git a/README.md b/README.md index ea0ad533..88dbfc7d 100644 --- a/README.md +++ b/README.md @@ -68,10 +68,12 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php). - [Testing](#testing) - [Code Analysis and Linter](#code-analysis-and-linter) - [Debugging Tools](#debugging-tools) - - [Science and Data Analysis](#science-and-data-analysis) + - [Science](#science) + - [Data Analysis](#data-analysis) - [Data Visualization](#data-visualization) - - [Computer Vision](#computer-vision) - [Machine Learning](#machine-learning) + - [Deep Learning](#deep-learning) + - [Computer Vision](#computer-vision) - [Functional Programming](#functional-programming) - [MapReduce](#mapreduce) - [Third-party APIs](#third-party-apis) @@ -946,48 +948,71 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php). * [pyelftools](https://github.com/eliben/pyelftools) - Parsing and analyzing ELF files and DWARF debugging information. * [python-statsd](https://github.com/WoLpH/python-statsd) - Python Client for the [statsd](https://github.com/etsy/statsd/) server. -## Science and Data Analysis +## Science -*Libraries for scientific computing and data analyzing.* +*Libraries for scientific computing.* * [astropy](http://www.astropy.org/) - A community Python library for Astronomy. * [bcbio-nextgen](https://github.com/chapmanb/bcbio-nextgen) - A toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis. * [bccb](https://github.com/chapmanb/bcbb) - Collection of useful code related to biological analysis. * [Biopython](http://biopython.org/wiki/Main_Page) - Biopython is a set of freely available tools for biological computation. -* [blaze](https://github.com/blaze/blaze) - NumPy and Pandas interface to Big Data. * [cclib](http://cclib.github.io/) - A library for parsing and interpreting the results of computational chemistry packages. * [NetworkX](https://networkx.github.io/) - A high-productivity software for complex networks. -* [Neupy](http://neupy.com/pages/home.html) - Running and testing different Artificial Neural Networks algorithms. * [NIPY](http://nipy.org) - A collection of neuroimaging toolkits. -* [Numba](http://numba.pydata.org/) - Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy. * [NumPy](http://www.numpy.org/) - A fundamental package for scientific computing with Python. * [Open Babel](http://openbabel.org/wiki/Main_Page) - A chemical toolbox designed to speak the many languages of chemical data. -* [Open Mining](https://github.com/mining/mining) - Business Intelligence (BI) in Python (Pandas web interface) -* [orange](http://orange.biolab.si/) - Data mining, data visualization, analysis and machine learning through visual programming or Python scripting. -* [Pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools. * [PyDy](http://www.pydy.org/) - Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib. * [PyMC](https://github.com/pymc-devs/pymc3) - Markov Chain Monte Carlo sampling toolkit. * [RDKit](http://www.rdkit.org/) - Cheminformatics and Machine Learning Software. * [SciPy](http://www.scipy.org/) - A Python-based ecosystem of open-source software for mathematics, science, and engineering. * [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python. * [SymPy](https://github.com/sympy/sympy) - A Python library for symbolic mathematics. -* [zipline](https://github.com/quantopian/zipline) - A Pythonic algorithmic trading library. +* [Zipline](https://github.com/quantopian/zipline) - A Pythonic algorithmic trading library. + +## Data Analysis + +*Libraries for data analyzing.* + +* [blaze](https://github.com/blaze/blaze) - NumPy and Pandas interface to Big Data. +* [Open Mining](https://github.com/mining/mining) - Business Intelligence (BI) in Pandas interface. +* [Orange](http://orange.biolab.si/) - Data mining, data visualization, analysis and machine learning through visual programming or scripts. +* [Pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools. ## Data Visualization *Libraries for visualizing data. See: [awesome-javascript](https://github.com/sorrycc/awesome-javascript#data-visualization).* -* [matplotlib](http://matplotlib.org/) - A Python 2D plotting library. -* [bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python. +* [Altair](https://github.com/altair-viz/altair) - Declarative statistical visualization library for Python. +* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python. * [ggplot](https://github.com/yhat/ggplot) - Same API as ggplot2 for R. -* [plotly](https://plot.ly/python/) - Collaborative web plotting for Python and matplotlib. -* [pygal](http://www.pygal.org/en/latest/) - A Python SVG Charts Creator. -* [pygraphviz](https://pypi.python.org/pypi/pygraphviz) - Python interface to [Graphviz](http://www.graphviz.org/). +* [Matplotlib](http://matplotlib.org/) - A Python 2D plotting library. +* [Pygal](http://www.pygal.org/en/latest/) - A Python SVG Charts Creator. +* [PyGraphviz](https://pypi.python.org/pypi/pygraphviz) - Python interface to [Graphviz](http://www.graphviz.org/). * [PyQtGraph](http://www.pyqtgraph.org/) - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets. -* [SnakeViz](http://jiffyclub.github.io/snakeviz/) - A browser based graphical viewer for the output of Python's cProfile module. -* [seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using matplotlib. -* [vincent](https://github.com/wrobstory/vincent) - A Python to Vega translator. -* [VisPy](http://vispy.org/) - High-performance scientific visualization based on OpenGL. +* [Seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using Matplotlib. +* [VisPy](https://github.com/vispy/vispy) - High-performance scientific visualization based on OpenGL. + +## Machine Learning + +*Libraries for Machine Learning. See: [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python).* + +* [gensim](https://github.com/piskvorky/gensim) - Topic Modelling for Humans. +* [MLlib](http://spark.apache.org/mllib/) - [Apache Spark](http://spark.apache.org/)'s scalable Machine Learning library. +* [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing. +* [pattern](https://github.com/clips/pattern) - Web mining module for Python. +* [Pylearn2](https://github.com/lisa-lab/pylearn2) - A Machine Learning library based on [Theano](https://github.com/Theano/Theano). +* [scikit-learn](http://scikit-learn.org/) - The most popular Python library for Machine Learning. +* [vowpal_porpoise](https://github.com/josephreisinger/vowpal_porpoise) - A lightweight Python wrapper for [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/). + +## Deep Learning + +*Frameworks for Neural Networks and Deep Learning. See: [awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning).* + +* [Caffe](https://github.com/BVLC/caffe) - A fast open framework for deep learning.. +* [Keras](https://github.com/fchollet/keras) - A high-level neural networks library and capable of running on top of either TensorFlow or Theano. +* [Neupy](http://neupy.com/pages/home.html) - Running and testing different Artificial Neural Networks algorithms. +* [TensorFlow](https://github.com/tensorflow/tensorflow) - The most popular Deep Learning framework created by Google. +* [Theano](https://github.com/Theano/Theano) - A library for fast numerical computation. ## Computer Vision @@ -996,24 +1021,6 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php). * [OpenCV](http://opencv.org/) - Open Source Computer Vision Library. * [SimpleCV](http://simplecv.org/) - An open source framework for building computer vision applications. -## Machine Learning - -*Libraries for Machine Learning. See: [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python).* - -* [Crab](https://github.com/muricoca/crab) - A flexible, fast recommender engine. -* [gensim](https://github.com/piskvorky/gensim) - Topic Modelling for Humans. -* [hebel](https://github.com/hannes-brt/hebel) - GPU-Accelerated Deep Learning Library in Python. -* [Keras](https://keras.io/) - a minimalist, highly modular neural networks library, capable of running on top of either [TensorFlow](https://github.com/tensorflow/tensorflow) or [Theano](http://deeplearning.net/software/theano/). -* [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing. -* [pattern](https://github.com/clips/pattern) - Web mining module for Python. -* [PyBrain](https://github.com/pybrain/pybrain) - Another Python Machine Learning Library. -* [Pylearn2](https://github.com/lisa-lab/pylearn2) - A Machine Learning library based on [Theano](https://github.com/Theano/Theano). -* [python-recsys](https://github.com/ocelma/python-recsys) - A Python library for implementing a Recommender System. -* [scikit-learn](http://scikit-learn.org/) - A Python module for machine learning built on top of SciPy. -* [pydeep](https://github.com/andersbll/deeppy) - Deep learning in python -* [vowpal_porpoise](https://github.com/josephreisinger/vowpal_porpoise) - A lightweight Python wrapper for [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit/). -* [skflow](https://github.com/tensorflow/skflow) - A simplified interface for [TensorFlow](https://github.com/tensorflow/tensorflow) (mimicking scikit-learn). - ## MapReduce *Frameworks and libraries for MapReduce.* @@ -1099,6 +1106,7 @@ Inspired by [awesome-php](https://github.com/ziadoz/awesome-php). *Libraries for making Python faster.* * [Cython](http://cython.org/) - Optimizing Static Compiler for Python. Uses type mixins to compile Python into C or C++ modules resulting in large performance gains. +* [Numba](http://numba.pydata.org/) - Python JIT complier to LLVM aimed at scientific Python. * [PeachPy](https://github.com/Maratyszcza/PeachPy) - x86-64 assembler embedded in Python. Can be used as inline assembler for Python or as a stand-alone assembler for Windows, Linux, OS X, Native Client and Go. * [PyPy](http://pypy.org/) - An implementation of Python in Python. The interpreter uses black magic to make Python very fast without having to add in additional type information. * [Pyston](https://github.com/dropbox/pyston) - A Python implementation built using LLVM and modern JIT techniques with the goal of achieving good performance.