I load some machine learning data from a CSV file. The first 2 columns are observations and the remaining columns are features.
Currently, I do the following:
data = pandas.read_csv('mydata.csv')
which gives something like:
data = pandas.DataFrame(np.random.rand(10,5), columns = list('abcde'))
I'd like to slice this dataframe in two data frames: one containing the columns a and b and one containing the columns c, d and e.
It is not possible to write something like
observations = data[:'c']
features = data['c':]
I'm not sure what the best method is. Do I need a pd.Panel?
By the way, I find dataframe indexing pretty inconsistent: data['a'] is permitted, but data is not. On the other side, data['a':] is not permitted but data[0:] is. Is there a practical reason for this? This is really confusing if columns are indexed by Int, given that data != data[0:1]