Category / python

by mlopezm

IPython Notebook + nbviewer

With IPython Notebook + nbviewer  you got a similar functionality to using Rmarkdown + RPubs

You create your markdown documents and then you can access them using nbviewer. Actually in nbviewer you don´t leave any doc, the we justa access your docs which are usually in github

http://nbviewer.ipython.org/

http://ipython.org/notebook.html

by mlopezm

Using Python for data analysis, machine learning

Here is an excellent list of tools from python that you can use for your machine learning projects:

http://stats.stackexchange.com/questions/1595/python-as-a-statistics-workbench

extracted from this reference:

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook’s Distributions in Scipy.
  • pandas This is a really nice library for working with statistical data — tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.
  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.
  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you’re not using NumPy or pandas.
  • statsmodels Statistical modeling: Linear models, GLMs, among others.
  • scikits Statistical and scientific computing packages — notably smoothing, optimization and machine learning.
  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.
  • PyMix Mixture models.

If speed becomes a problem, consider Theano — used with good success by the deep learning people.

Information for the tools:

For Pandas:

http://pandas.pydata.org/pandas-docs/dev/10min.html

for a short summary on pandas:

http://www.bigdataexaminer.com/exploratory-data-analysis-in-python-using-pandas-matplotlib-and-numpy/

For Numpy/Scipy:

http://wiki.scipy.org/Cookbook

For GLM:

http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/glm.html

Monte Carlo

http://pymc-devs.github.io/pymc/tutorial.html#