How to split data into 3 sets (train, validation and test)?

Question

I wish to divide pandas dataframe to 3 separate sets. I know by using train_test_split from sklearn.cross_validation, one can divide the data in two sets (train and test). but, to perform these I couldn't find any solution about splitting the data into three sets. most preferably, I would like to have the indices of the original data. I know here we would be using train_test_split two times and somehow we can adjust the indices. But is these a standard or built-in way to split the data into 3 sets instead of 2? kindly help Select the correct answer from above options

Answer 1

You can split your dataset into train,validation and test using the numpy.split() method: Syntax- numpy.split(array,indices_or_sections,axis=0) Example- >>> a= np.arange(9.0) >>> np.split(a, 3) [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7., 8.])] To Learn what is python and python applications then visit this python for data science course.