Create spectrum of rows using columns as boundary pandas data-frame

Question

I have the following pandas DF:

import pandas as pd

mission_df = pd.DataFrame(
    {'mission': [1, 2, 3],
     'type': ['lift', 'talk', 'run'],
     'boundary_low': [2, 3, 3],
     'boundary_high': [3, 8, 12]})

I would like to add rows to each field (example mission) such that each row will be filled according to the boundaries with discrete jumps, for example mission 1 has bounderies between 2 and 3, so i need for that mission to add 2 rows with values 2 & 3, as following:

desired_df = pd.DataFrame(
    {'mission': [1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3],
     'amount': [2, 3, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6],
     'type': ['lift', 'lift', 'talk', 'talk', 'talk', 'talk', 'talk', 'talk', 'run', 'run', 'run', 'run'],
     'boundary_low': [2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
     'boundary_high': [3, 3, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6]})

Thanks in advance!

Answer 1

Try:

mission_df = mission_df.loc[mission_df.index.repeat(mission_df["boundary_high"]-mission_df["boundary_low"] + 1)]

mission_df['amount'] = mission_df.assign(amount=1).groupby(['mission', 'type'])['amount'].cumsum() + mission_df['boundary_low'].sub(1)

# not sure, if relevant for you:
mission_df.reset_index(drop=True, inplace=True)

The key function in here (to make it simple) is:

pd.Index.repeat(n) , src: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.repeat.html

Outputs:

    mission  type  boundary_low  boundary_high  amount
0         1  lift             2              3       2
1         1  lift             2              3       3
2         2  talk             3              8       3
3         2  talk             3              8       4
4         2  talk             3              8       5
5         2  talk             3              8       6
6         2  talk             3              8       7
7         2  talk             3              8       8
8         3   run             3             12       3
9         3   run             3             12       4
10        3   run             3             12       5
11        3   run             3             12       6
12        3   run             3             12       7
13        3   run             3             12       8
14        3   run             3             12       9
15        3   run             3             12      10
16        3   run             3             12      11
17        3   run             3             12      12