Find The Duplicate Rows Of One Column Then Add The Corresponding Rows Of Other Columns

I want to check the duplicate rows of one column and add the corresponding rows of other columns. If the dateframe is as follows: A B C D E F G 13348 xyz

Solution 1:

I think need:

cols = ['D','E','F','G']
#for each group transpose df and check if all duplicates
df1 = df.groupby('A')[cols].apply(lambda x: x.T.duplicated(keep=False))
#for duplicates aggregate sum else 0
arr = np.where(df1.all(axis=1), df.groupby('A')[cols[0]].sum(), 0)
#remove unnecessary columns and add new, get first rows per column A
df = df.drop(cols, axis=1).drop_duplicates('A').assign(D=arr)
print (df)
        A        B        C  D
0   13348    xyzqr   324580  5
2   45832  gberthh   258729  0
4   58712    bgrtw   984562  2
5   76493     hzrt   638495  0
6  643509        .  T648501  2

Alternative solution with check each group if all values are dupes:

cols = ['D','E','F','G']
m = df.groupby('A')[cols].apply(lambda x: x.T.duplicated(keep=False).all())
print (m)
A
13348     True
45832    False
dtype: bool

arr = np.where(m, df.groupby('A')[cols[0]].sum(), 0)
df = df.drop(cols, axis=1).drop_duplicates('A').assign(D=arr)
print (df)
        A        B        C  D
0   13348    xyzqr   324580  5
2   45832  gberthh   258729  0
4   58712    bgrtw   984562  2
5   76493     hzrt   638495  0
6  643509        .  T648501  2