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SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.
scikit-learn is a Python module for machine learning built on top of SciPy. It is simple and efficient tools for data mining and data analysis. It supports automatic classification, clustering, model selection, pre processing and lot more.
web2py is a full-stack framework for rapid development of fast, scalable, secure and portable database-driven web-based applications. This framework is based on Model-View-Controller design and it has support to Server-side form validation and postbacks. It has the ability to generate forms from database tables and it includes an extensive set of validators.
PyPy is a fast, compliant alternative implementation of the Python language. It supports most of the commonly used Python standard library modules. Its feature include Just-in-Time compiler, Less memory usage, support Stackless and lot more. PyPy focuses on speed, efficiency and 100% compatibility with the original CPython interpreter. It was conceived as an implementation of Python written in Python, which enables Python developers to hack the implementation.
PyBossa is an open source platform for crowd-sourcing online (volunteer) assistance to perform tasks that require human cognition, knowledge or intelligence (e.g. image classification, transcription, information location etc). PyBossa is not designed to handle payment or money — it is designed to support volunteer-driven projects.
PyML is an interactive object oriented framework for machine learning written in python. PyML focuses on kernel classifiers, providing tools for feature selection, model selection, and methods for assessing classifier performance.
NumPy is the fundamental package needed for scientific computing with Python. Numerical Python adds a fast and sophisticated N-dimensional array facility to the Python language. NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.