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Life is short, use PythonWant to share your content on R-bloggers? click here if you have a blog, or here if you don't.
I started to play with Python two weeks ago due to the limitation of R in terms of handling large data, then a friend of mine suggested me to try Python since I had to do data massage frequently, “Python is the best choice, trust me”, he said. Although I was unwilling to learn another new software, I couldn’t bear with the low efficiency of R (or of my work) for large data. You may realize my learning curve as: Excellent free CSV splitter –> MySQL+RMySQL package –> Several R packages including bigmemory and ff. But to be honest, none of them satisfies me either because of the limitation of the method (slow + malfunction) or of my own computer (short of memory).
I am shocked by python’s extreme power and easy-to-use design after nearly two weeks, dealing with a 10GB CSV had never become so easy. More importantly, you can access R from Python almost seamlessly with the package RPY. To get started, I would like to recommend the following readings to all Python newbies like me:
1, commands dictionary Matlab vs R vs Python;
2, free ebook Dive Into Python;
3, a text book Machine Learning: An Algorithmic Perspective
The third book is especially useful for data analysis, as there are lots of Python code examples in the book, the code and dataset are available to download @ the author’s website http://www-ist.massey.ac.nz/smarsland/MLBook.html, take a look before deciding to add it to your shelf.
Tags – python , r
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