* [NLTK](http://www.nltk.org/) - A leading platform for building Python programs to work with human language data.
* [Pattern](http://www.clips.ua.ac.be/pattern) - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
* [TextBlob](http://textblob.readthedocs.org/) - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
* [jieba](https://github.com/fxsjy/jieba#jieba-1) - Chinese Words Segementation Utilities.
* [SnowNLP](https://github.com/isnowfy/snownlp) - A library for processing Chinese text.
* [loso](https://github.com/victorlin/loso) - Another Chinese segmentation library.
* [genius](https://github.com/duanhongyi/genius) - A Chinese segment base on Conditional Random Field.
* [scikit-learn](http://scikit-learn.org/) - A Python module for machine learning built on top of SciPy.
* [pattern](https://github.com/clips/pattern) - Web mining module for Python.
* [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing.
* [Pylearn2](https://github.com/lisa-lab/pylearn2) - A Machine Learning library based on [Theano](https://github.com/Theano/Theano).
* [hebel](https://github.com/hannes-brt/hebel) - GPU-Accelerated Deep Learning Library in Python.
* [gensim](https://github.com/piskvorky/gensim) - Topic Modelling for Humans.
* [PyBrain](https://github.com/pybrain/pybrain) - Another Python Machine Learning Library.
* [Crab](https://github.com/muricoca/crab) - A flexible, fast recommender engine.
* [python-recsys](https://github.com/ocelma/python-recsys) - A Python library for implementing a Recommender System.
#### Data Analysis / Data Visualization
* [SciPy](http://www.scipy.org/) - A Python-based ecosystem of open-source software for mathematics, science, and engineering.
* [NumPy](http://www.numpy.org/) - A fundamental package for scientific computing with Python.
* [Numba](http://numba.pydata.org/) - Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy.
* [NetworkX](https://networkx.github.io/) - A high-productivity software for complex networks.
* [Pandas](http://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.
* [Open Mining](https://github.com/avelino/mining) - Business Intelligence (BI) in Python (Pandas web interface)
* [PyMC](https://github.com/pymc-devs/pymc) - Markov Chain Monte Carlo sampling toolkit.
* [zipline](https://github.com/quantopian/zipline) - A Pythonic algorithmic trading library.
* [PyDy](https://pydy.org/) - Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
* [SymPy](https://github.com/sympy/sympy) - A Python library for symbolic mathematics.
* [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.
* [astropy](http://www.astropy.org/) - A community Python library for Astronomy.
* [matplotlib](http://matplotlib.org/) - A Python 2D plotting library.
* [bokeh](https://github.com/ContinuumIO/bokeh) - Interactive Web Plotting for Python.
* [plotly](https://plot.ly/python) - Collaborative web plotting for Python and matplotlib.
* [vincent](https://github.com/wrobstory/vincent) - A Python to Vega translator.
* [d3py](https://github.com/mikedewar/d3py) - A plottling library for Python, based on [D3.js](http://d3js.org/).
* [ggplot](https://github.com/yhat/ggplot) - Same API as ggplot2 for R.
* [Kartograph.py](https://github.com/kartograph/kartograph.py) - Rendering beautiful SVG maps in Python.
* [pygal](http://pygal.org/) - A Python SVG Charts Creator.
* [Treat](https://github.com/louismullie/treat) - Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby
* [Ruby Linguistics](http://www.deveiate.org/projects/Linguistics/) - NLTK for Ruby
