for...
new_line['x'] = a
new_line['y'] = b
new_line['z'] = a + math.cos(math.pi * b)
rows_list.append(new_line)

dataset = pd.DataFrame(rows_list, columns=['x','y','z'])
...


    model = keras.Sequential([
        layers.Dense(8, activation='relu', input_shape=[len(features.keys())]),
        layers.Dense(8, activation='relu'),
        layers.Dense(1, activation='relu')
    ])

    optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.005)

    model.compile(loss='mse',
                  optimizer=optimizer,
                  metrics=['mae', 'mse'])

    model.fit(features, labels, validation_split=0.2, epochs=500)


...
print("Expected | Predicted")
#  iterating over rows using iterrows() function
for i, j in example_labels.iterrows():
    print(j[0], example_result[i][0])