How to create a sequence of rows of Data Frame based on starting and ending value defined by columns

I've got a following Data Frame:

example_df = pd.DataFrame({'id': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},
 'seq_start': {0: 0.0, 1: 2800.0, 2: 6400.0, 3: 8400.0, 4: 9800.0},
 'seq_end': {0: 1400.0, 1: 4700.0, 2: 8400.0, 3: 9800.0, 4: 11400.0}})

I'd like to obtain a Data Frame that has a sequences of values from example_df['seq_start'] to example_df['seq_end'] so that I could later use newly created column in a join.

So the expected output would look like below:

out_df = pd.DataFrame({'id': np.concatenate([[0] * 15, [1] * 20, [2] * 21]),
                       'expected_output': np.concatenate([np.arange(0, 1500, 100), 
                                                          np.arange(2800, 4800, 100),
                                                          np.arange(6400, 8500, 100)])})

    id  expected_output
0    0                0
1    0              100
2    0              200
3    0              300
4    0              400
5    0              500
          ...
12   0             1200
13   0             1300
14   0             1400
15   1             2800
16   1             2900
17   1             3000
          ...

31   1             4400
32   1             4500
33   1             4600
34   1             4700
35   2             6400
36   2             6500
37   2             6600
          ...

54   2             8300
55   2             8400

How can I approach this?

Using pandas.DataFrame.explode:

def listify(x, step=100, right_closed=True):
    lower, upper = sorted(x)
    return range(lower, upper+step*right_closed, step)

example_df['expected'] = example_df[['seq_end', 'seq_start']].astype(int).apply(listify, 1)
new_df = example_df[['id','expected']].explode('expected')
print(new_df)

Output:

    id expected
0    0        0
0    0      100
0    0      200
0    0      300
0    0      400
..  ..      ...
4    4    11000
4    4    11100
4    4    11200
4    4    11300
4    4    11400