data_block = np.append(training_values, target_value)  # merge
print('data_block: ', data_block)
data_block = tuple(data_block)
print('data_block tuple: ', data_block)

data_block:  [ 0.03478261  0.00869565  0.03478261  0.07826087  0.05217391  0.07826087 0.14782609]
data_block tuple:  (0.034782608695652174, 0.0086956521739130436, 0.034782608695652174, 0.078260869565217397, 0.052173913043478258, 0.078260869565217397, 0.14782608695652172)

def series_to_supervised(data_list, look_back=1, look_forward=0):
print(look_back)
data, labels = [], []
for i in range(len(data_list) - look_back):
    training_values = data_list[i:(i + look_back)]
    target_value = data_list[i + look_back + look_forward]
    print('target_value: ', target_value)

    data_block = np.append(training_values, target_value)  # merge
    data_block = tuple(data_block)
    data = np.append(data, data_block)  # add to data as tuple

for i in range(look_back):
    labels.append("lb_" + str(i))
labels.append("target_value")
print(labels)

df = pandas.DataFrame(data=data)
return df