Inserting new rows in pandas data frame at specific indices

Question

I have a following data frame df with two columns \"identifier\", \"values\" and \"subid\":

     identifier   values    subid
0      1              


        

Answer 1

Preserving the index order is the tricky part. I'm not sure this is the most efficient way to do this, but it should work.

x = [2,8,12]
rows = []
cur = {}

for i in df.index:
    if i in x:
        cur['index'] = i
        cur['identifier'] = df.iloc[i].identifier
        cur['values'] = df.iloc[i]['values']
        cur['subid'] = df.iloc[i].subid - 1
        rows.append(cur)
        cur = {}

Then, iterate through the new rows list, and perform an incremental concat, inserting each new row into the correct spot.

offset = 0; #tracks the number of rows already inserted to ensure rows are inserted in the correct position

for d in rows:
    df = pd.concat([df.head(d['index'] + offset), pd.DataFrame([d]), df.tail(len(df) - (d['index']+offset))])
    offset+=1


df.reset_index(inplace=True)
df.drop('index', axis=1, inplace=True)
df

    level_0 identifier  subid   values
0         0          1      1      101
1         1          1      1      102
2         0          1      1      103
3         2          1      2      103
4         3          1      2      104
5         4          1      2      105
6         5          2      3      106
7         6          2      3      107
8         7          2      3      108
9         0          2      3      109
10        8          2      4      109
11        9          2      4      110
12       10          3      5      111
13       11          3      5      112
14        0          3      5      113
15       12          3      6      113

Answer 2

subtract where the prior row is different than the current row

# edit in place
df['values'] -= df.identifier.ne(df.identifier.shift().bfill())
df

    identifier  values
0            1     101
1            1     102
2            1     103
3            1     104
4            1     105
5            2     105
6            2     107
7            2     108
8            2     109
9            2     110
10           3     110
11           3     112
12           3     113

Or

# new dataframe
df.assign(values=df['values'] - df.identifier.ne(df.identifier.shift().bfill()))

    identifier  values
0            1     101
1            1     102
2            1     103
3            1     104
4            1     105
5            2     105
6            2     107
7            2     108
8            2     109
9            2     110
10           3     110
11           3     112
12           3     113