Create a dummy column and input value based on condition

I have data containing a list of topics (topics 1-6; and 0 meaning no topic is assigned) and their value. I want to create a new column for each topic and each label, and fill the column with the value. Here's what the table looks like...

reviewId   topic   value     label
      01       2      -4  negative
      02       2       9  positive
      03       0      -7  negative
      04       5      -1  negative
      05       1      38  positive

What should I do to create a table looking like this?

reviewId	topic	value	label	t1p	t2p	t2n	t5n
01	2	-4	negative	0	0	-4	0
02	2	9	positive	0	9	0	0
03	0	-7	negative	0	0	0	0
04	5	-1	negative	0	0	0	-1
05	1	38	positive	38	0	0	0

Here's reproducible data to work on.

raw_df = pd.DataFrame({
    'reviewId': ['01', '02', '03', '04', '05'],
    'topic': [2, 2, 0, 5, 1],
    'value': ['-4', '9', '-7', '-1', '38']})

Here's a link similar topic on this problem..

Problem about inputting value: link
Problem about creating columns based on label: link

Solution

from itertools import product

(df.join(pd.get_dummies(zip(df["topic"], df["label"]))
           .reindex(columns=product(range(1, 5+1), ["positive", "negative"]),
                    fill_value=0)
           .mul(df["value"], axis="rows")
           .rename(columns=lambda c: f"t{c[0]}{c[1][0]}")))

dummify the "topic"-"label" pairs
- reindex with the entire (1...5) X (p, n) possible values to ensure all are here
- and initiate the missings, e.g., "4"-"negative" pair in the sample data with 0
given the 1/0 frame at this point, multiply it with "values" row-wise
- 1/0s will act as a selector of values
column names are like "(2, positive)" etc.; rename them to "t2p" etc.

and join it with the original frame to get

   reviewId  topic  value     label  t1p  t1n  t2p  t2n  t3p  t3n  t4p  t4n  t5p  t5n
0         1      2     -4  negative    0    0    0   -4    0    0    0    0    0    0
1         2      2      9  positive    0    0    9    0    0    0    0    0    0    0
2         3      0     -7  negative    0    0    0    0    0    0    0    0    0    0
3         4      5     -1  negative    0    0    0    0    0    0    0    0    0   -1
4         5      1     38  positive   38    0    0    0    0    0    0    0    0    0