#Training data is in a CSV file called train.csv df = pd.read_csv(“train.csv”)
#we create a new column called kfold and fill it with -1
df[“kfold”]=-1
#the next step is to randomize the rows of the data
df = df.sample(frac=1).reset_index(drop=True)
#initiate the kfold class from model_selection module
kf = model_selection.KFold(n_splits=5)
#fill the new kfold column
for fold, (trn_, val_) in enumerate(kf.split(X=df)):
df.loc[val_, ‘kfold’] = fold
For the given code, the kf.split(X=df) method takes the 'df' dataframe as the input and splits the indices of the df dataframe into train and test sets. The split() method returns a list of indices, one for train set and another one for test set, in the form of tuple (trn_, val_). In addition, the split() method is wrapped in the enumerate() method, which acts as a counter to the split() iterable and returns enumerate objects. As there will be 5 folds returned from the split() method, the enumerate indices will range from 0-4, indicating the i-th fold. So, the 'enumerate(kf.split(X=df))' statement returns 'fold, (trn_, val_)'.
For every returned enumerate object from the split() method, which contains a counter index (fold) and a tuple of train and test indices (trn_, val_), the index (fold) is assigned as the value of the 'kfold' column, where the rows are in the 'val_' indices list.
That means the value of 'kfold' column is the i-th fold the respective row/sample is assigned as the validation sample. For example, if df.loc[0, 'kfold'] = 2, it means that the row 0 sample of the df dataframe is assigned as part of the validation set when fold=2.