Create new column for each different value including sum of appearances of corresponding value in Python/Pandas

Question

I've got a dataframe like this:

import pandas as pd
data = {
    'POS': ['1','2','1','3','4'],
    'TYPE': ['A','A','A','B','C'],
    'VOLUME': [34,2,12,200,1],

}
df = pd.DataFrame(data)
df

Table:

    POS TYPE VOLUME
0   1   A    34
1   2   A    2
2   1   A    12
3   3   B    200
4   4   C    1

Task:

I want to automatically create new columns for each different value in column TYPE and get the number of appearances of each value grouped by POS (assume that there are a lot of different values, not only A, B and C). Additionally I simply want to sum VOLUME .

The result should look like this:

|--------------|--------------|--------------|--------------|--------------|
|      POS     |   Amount_A   |   Amount_B   |   Amount_C   |  Sum_Volume  |
|--------------|--------------|--------------|--------------|--------------|
|       1      |      2       |      0       |       0      |     46       |
|       2      |      1       |      0       |       0      |     2        |
|       3      |      0       |      1       |       0      |     200      |
|       4      |      0       |      0       |       1      |     1        |
|--------------|--------------|--------------|--------------|--------------|

Attempt:

I know how to do it for VOLUME : df.groupby(['POS'])['VOLUME'].sum() . But I dont't konw how to manage getting new columns without something like "If TYPE == 'A' then ...".

Answer 1

Try this:

import pandas as pd
data = {
'POS': ['1','2','1','3','4'],
'TYPE': ['A','A','A','B','C'],
'VOLUME': [34,2,12,200,1],

}
df = pd.DataFrame(data)
df = pd.concat([df,pd.get_dummies(df["TYPE"])],axis=1)
print(df.groupby("POS").sum())

OUTPUT:

  VOLUME ABC POS 1 46 2 0 0 2 2 1 0 0 3 200 0 1 0 4 1 0 0 1