Pandas: Insert missing row data and iterate with conditions within groups

We can come up this , Idea here is use shift get the match row and add the not match row to original df

s=df.groupby('name',sort=False).From_num.shift()
addingdata=pd.concat([s,df.drop('From_num',1)],axis=1)[df.To_num.ne(s)&s.notnull()]
addingdata.index-=1 
addingdata.columns=['To_num','name', 'From_num']
df=df.append(addingdata).sort_index()
df
    name From_num To_num
0    Jim       80     99
1    Jim       68     80
2    Jim      751     68
3    Jim  Started    751
4   Mike       32    105
5   Mike       68     32
6   Mike      126     68
7   Mike       49    126
8   Mike  Started     49
9   Polo      105    324
10  Polo       68    105
10  Polo      114     68
11  Polo       76    114
12  Polo  Started     76
13   Tom      251     96
13   Tom      115    251
14   Tom       49    115
15   Tom       23     49
16   Tom  Started     23

We can do the following:

1. Check if next row of To_num is equal to current row From_num
2. Do this check per group of name
3. For these rows, replace To_num by From_num
4. Finally fill in To_num of next row in From_num

This solution should be fast, since it is all vectorized, except that we have to check the booleans for each group with GroupBy.apply, but that is an oké scenario to use apply.

def create_masks(d):
    shift = d['To_num'].shift(-1)
    m1 = d['From_num'].ne(shift)
    m2 = shift.notna()
    
    return m1 & m2


def create_rows(d):
    bools =  d.groupby('name').apply(create_masks).reset_index(drop=True)
    vals = d[bools].copy()
    vals['To_num'] = vals['From_num']
    vals.loc[:, 'From_num'] = d.loc[bools.shift().fillna(False), 'To_num'].to_numpy()
    d = d.append(vals).sort_index().reset_index(drop=True)
    
    return d

df = create_rows(df)

Output

    name From_num To_num
0    Jim       80     99
1    Jim       68     80
2    Jim      751     68
3    Jim  Started    751
4   Mike       32    105
5   Mike       68     32
6   Mike      126     68
7   Mike       49    126
8   Mike  Started     49
9   Polo      105    324
10  Polo       68    105
11  Polo      114     68
12  Polo       76    114
13  Polo  Started     76
14   Tom      251     96
15   Tom      115    251
16   Tom       49    115
17   Tom       23     49
18   Tom  Started     23