Tutorials:
- docs.python.org/py3k/tutorial
- diveintopython.org
- python.org
- code.activestate.com/recipes
- http://openbookproject.net/py4fun
http://webnewage.org/2009/06/23/what-is-pip
http://pypi.python.org/pypi/distribute#distribute-setup-py
http://pypi.python.org/pypi/pip
http://pypi.python.org/pypi/setuptools#files
http://www.komtet.ru/lib/plangs/python/easy-install-zagruzka-i-ustanovka-paketa
http://www.crummy.com/software/BeautifulSoup/#Download
http://peak.telecommunity.com/DevCenter/EasyInstall#installing-easy-install
http://stackoverflow.com/questions/1213690/what-is-the-most-compatible-way-to-install-python-modules-on-a-mac
http://stackoverflow.com/questions/452283/how-can-i-install-the-beautiful-soup-module-on-the-mac
Sources:
http://developer.berlios.de/softwaremap/trove_list.php?form_cat=76
Extensions:
http://pypi.python.org
1. C-Extension for Python: http://cython.org
Cython is a language that makes writing C extensions for the Python language as easy as Python itself. Cython is based on the well-known Pyrex, but supports more cutting edge functionality and optimizations.
The Cython language is very close to the Python language, but Cython additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code.
This makes Cython the ideal language for wrapping external C libraries, and for fast C modules that speed up the execution of Python code.
- Cython Users Guide
- The Cython Wiki.
- Pyrex FAQ and Cython FAQ
- SciPy 2009 proceedings (pdf): Cython Tutorial; Fast Numerical Computations with Cython
2. Mathematics open source library: http://sagemath.org
3. http://www.pythonxy.com
4. http://www.enthought.com/products/epd.php
5. SciPy
6. PyCuda: http://mathema.tician.de/software/pycuda
7. Mrjob: https://github.com/Yelp/mrjob
Run Hadoop Streaming jobs on Amazon Elastic MapReduce or your own Hadoop cluster
8. CRC: http://crcmod.sourceforge.net
9.Twisted: http://twistedmatrix.com/trac An event-driven networking engine written in Python
10. Requests: http://pypi.python.org/pypi/requests
11. MatplotLib: matplotlib.sourceforge.net, http://www.matplotlib.com
12. Py-LLVM: http://www.mdevan.org/llvm-py/index.html
13. Kalman filtering: http://www.scipy.org/Cookbook/KalmanFiltering
14. OpenGL: http://pyopengl.sourceforge.net
15. MapReduce on Python: http://me.lt/4xP7k
16. Natural language toolkit http://www.nltk.org/download
17. Astronomy http://www.astropy.org
Open Source Python Tools for Classification (http://amundblog.blogspot.com)
- Orange - machine learning tool which supports classification (including combining classifiers in ensembles), feature extraction, basic statistical analysis, regression and association rules. It also has an extension modulewhich supports clustering and additional classifier algorithms. Note: Orange is probably the Python-based machine learning tool that is most similar to the more famous tool Weka (which is for Java, or Jython for that matter).
- Monte - less comprehensive than Orange, written purely in Python (i.e. noSWIGed C++). Looks interesting (has several classifiers algorithms), but the APIs seems to be in an early phase (relatively new tool in version 0.1.0)
- libsvm - Python API for most popular open source implementation of SVM.Note: libsvm is also included with Orange and PyML. (I used this tools during my PhD a few years ago)
- RPy - not exactly a classification tool, but it is quite useful with a statistics tool when you are doing classification (it has a nice plotting capability, not unlike matlabs), check out the demo.
- PyML - also less comprehensive than Orange (specialized towards classification and regression, it supports SVM/SMO, ANN and Ridge Regression), but it has a nice API. Example of use:
from PyML import multi, svm, datafunc # read training data, last column has the class mydataset = datafunc.SparseDataSet('iris.data', labelsColumn = -1) myclassifier = multi.OneAgainstRest(svm.SVM()) print "cross-validation results", myclassifier.cv(mydataset)
Catalogue:
http://www.java2s.com/Open-Source/Python/CatalogPython.htm
http://blogs.msdn.com/b/somasegar/archive/2011/08/29/python-tools-for-visual-studio.aspx
P.S. Scientific computing podcas: http://inscight.org
Machine learning:
Tutorials (ru) : http://proft.me/2010/12/8/python-i-nejronnye-seti
http://ffnet.sourceforge.net
http://code.google.com/p/neurolab
http://neuralensemble.org/trac/PyNN
http://sourceforge.net/projects/pylibneural
http://seat.massey.ac.nz/personal/s.r.marsland/Code/9/kmeansnet.py
http://habrahabr.ru/blogs/artificial_intelligence/67194
http://scikit-learn.sourceforge.net/stable
