loo encoding

def leave_one_out(df_train, df_test, var, noise=.01, drop=True):

    new_var = 'mean_{}'.format(var)
    df_train[new_var] = np.nan
    df_test[new_var] = np.nan

    # training set
    loo = LeaveOneOut()
    for train_index, test_index in tqdm_notebook(loo.split(df_train)):
        loo_train = df_train.iloc[train_index]
        loo_test = df_train.iloc[test_index]
        calc_mean_var = loo_test[var].iloc[0]
        df_train.iloc[test_index][new_var] = loo_train[loo_train[var] == calc_mean_var]['y'].mean()

    # test set
    grouped = df_train.groupby(var)[['y']].mean().reset_index()
    grouped.columns = [var, new_var]
    df_test = df_test.merge(grouped, on=var)

    # drop column (optional)
    if drop:
        df_train.drop(var, axis=1, inplace=True)
        df_test.drop(var, axis=1, inplace=True)

    return df_train, df_test

train, test = leave_one_out(train, test, 'X0')