I am reading a file in python using pandas and then saving it in a numpy array. The file has the dimension of 11303402 rows x 10 columns. I need to split the data for cross validation and for that I sliced the data into 11303402 rows x 9 columns of examples and 1 array of 11303402 rows x 1 col of labels. The following is the code:

tdata=pd.read_csv('train.csv')
tdata.columns='Arrival_Time','Creation_Time','x','y','z','User','Model','Device','sensor','gt']User_Data = np.array(tdata)
features = User_Data[:,0:9]
labels = User_Data[:,9:10]

The error comes in the following code:

classes=np.unique(labels)
idx=labels==classes[0]
Yt=labels[idx]
Xt=features[idx,:]

On the line:

Xt=features[idx,:]

it says 'too many indices for array'

The shapes of all 3 data sets are:

print np.shape(tdata) = (11303402, 10)
print np.shape(features) = (11303402, 9)
print np.shape(labels) = (11303402, 1)

If anyone knows the problem, please help.

The problem is idx has shape (11303402,1) because the logical comparison returns an array of the same shape as labels. These two dimensions use all of the indexes in features. The quick work around is

Xt=features[idx[:,0],:]