k-fold crossvalidation with sklearn

from sklearn.model_selection import KFold

kf = KFold(n_splits=2)

kf.split(df_train)

step = 0 # set counter to 0
for train_index, val_index in kf.split(df_train): # for each fold
    step = step + 1 # update counter

    print('Step ', step)

    features_fold_train = df_train.iloc[train_index, [4, 5]] # features matrix of training data (of this step)
    features_fold_val = df_train.iloc[val_index, [4, 5]] # features matrix of validation data (of this step) 

    target_fold_train = df_train.iloc[train_index, 6] # target vector of training data (of this step)
    target_fold_val = df_train.iloc[val_index, 6] # target vector of validation data (of this step) 

    print("VALIDATE:", val_index)
    print('Dimensions features matrix for validation: ', features_fold_val.shape)
    print("TRAIN:", train_index)
    print('Dimensions features matrix for training: ',features_fold_train.shape, '\n')

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