Explode multiple coumns with different size list columns in pandas

Question

I have following situation where i may get 300 columns in csv file and some of them are list parameter (50 columns)and they can uneven size including no values.

Answer 1

First you can create the exploded columns like you want with concat, str.split and stack. Use reset_index and join to be able to get the column 'Time' associated. Then you need to change the values in the column 'Time' to create the interpolation. I'm not sure if one can interpolate directly a datetime column, but you can change the type to int64, mask the values if same than previous row with shift and interpolate. So like this:

l_col = ['COL1', 'COL2']

df_f = pd.concat([df[col].str.split(' ', expand=True) for col in l_col ], 
                 axis=1, keys=l_col)\
         .stack()\
         .reset_index(level=1, drop=True)\
         .join(df[['Time']])\
         .reset_index(drop=True)

df_f['Time'] = pd.to_datetime(df_f['Time'].astype('int64')
                                          .mask(df_f.Time.eq(df_f.Time.shift()))
                                          .interpolate(method='linear'))

print (df_f)
    COL1  COL2                    Time
0    0.0  50.0 2020-03-13 10:43:00.500
1   10.0  60.0 2020-03-13 10:43:00.580
2   20.0  70.0 2020-03-13 10:43:00.660
3   30.0  80.0 2020-03-13 10:43:00.740
4   40.0  90.0 2020-03-13 10:43:00.820
5   10.0  10.0 2020-03-13 10:43:00.900
6   20.0  20.0 2020-03-13 10:43:15.700
7   30.0  None 2020-03-13 10:43:30.500
8   70.0  None 2020-03-13 10:43:45.300
9   10.0       2020-03-13 10:44:00.100
10  20.0  None 2020-03-13 10:44:00.100
11  30.0  None 2020-03-13 10:44:00.100
12  70.0  None 2020-03-13 10:44:00.100

I'm not sure what you want for the missing values in COL2 e.g. so you may need some fillna to work this out.