Learn scikit and never use R again

Hallelujah, my brothers and sisters. Free yourselves from the <- and the $, the "how do I select a row again? I only did this last week!", and the arcane differences between a table and a data.frame. Join with me in using scikit-learn, and never use R again.

Could it be true? No need to ever use R again? Well, that's how it looks to me. Scikit-learn is a Python module for machine learning which seems to replicate almost all of the multivariate analysis modules I used to use in R. Thanks to Nikolas Fechner at the RDKit UGM for tuning me into this.

Let's see it in action for a simple example that uses SVM to classify irises (not the eyeball type). First, the R:

library(e1071)
library(MASS)
data(iris)

mysvm <- svm(Species ~ ., iris)
mysvm.pred <- predict(mysvm, iris)
table(mysvm.pred,iris$Species)
# mysvm.pred   setosa versicolor virginica
#   setosa     50      0          0
#   versicolor  0     48          2
#   virginica   0      2         48

And now the Python:

from sklearn import svm, datasets
from sklearn.metrics import confusion_matrix
iris = datasets.load_iris()

mysvm = svm.SVC().fit(iris.data, iris.target)
mysvm_pred = mysvm.predict(iris.data)
print confusion_matrix(mysvm_pred, iris.target)
# [[50  0  0]
#  [ 0 48  2]
#  [ 0  0 50]]

This library is quite new, but there seems to be quite a bit of momentum in the data processing space right now in Python. See also Statsmodels and Pandas. These videos from PyData 2012 give an overview of some of these projects.