How to merge rows in dataframe with different columns?

Question

I want to merge rows of dataframe with one common column value and then merge rest of the column values separated by comma for string values and convert to array/list for int values.

A   B     C    D
1  one   100  value
4  four  400  value
5  five  500  value
2  two   200  value

Expecting result like:

   A                B                 C            D
[1,4,5,2]  one,four,five,two  [100,400,500,200]  value

I can use groupby for column D but how can I use apply for columns A,C as apply(np.array) and apply(','.join) for column B in df all at once?

Answer 1

Dynamic solution - strings columns are joined and numeric are converted to lists with GroupBy.agg:

f = lambda x: x.tolist() if np.issubdtype(x.dtype, np.number) else ','.join(x)
#similar for test strings - https://stackoverflow.com/a/37727662
#f = lambda x: ','.join(x) if np.issubdtype(x.dtype, np.flexible) else x.tolist()
df1 = df.groupby('D').agg(f).reset_index().reindex(columns=df.columns)
print (df1)
              A                  B                     C      D
0  [1, 4, 5, 2]  one,four,five,two  [100, 400, 500, 200]  value

Another solution is specify each functions separately for each column:

df2 = (df.groupby('D')
        .agg({'A': lambda x: x.tolist(), 'B': ','.join, 'C':lambda x: x.tolist()})
        .reset_index()
        .reindex(columns=df.columns))

print (df2)

              A                  B                     C      D
0  [1, 4, 5, 2]  one,four,five,two  [100, 400, 500, 200]  value

Answer 2

df = df.groupby('D').apply(lambda x: pd.Series([list(x.A),','.join(x.B),list(x.C)])).reset_index().rename({0:'A',1:'B',2:'C'}, axis=1)

df = df[['A','B','C','D']]

Output

              A                  B                     C      D
0  [1, 4, 5, 2]  one,four,five,two  [100, 400, 500, 200]  value