Create a new column with unique values from another in python Pandas - without grouping

Question

I already posted a question, but I presented it badly. Here my problem:

I have a dataframe like that:

Col1	Col2	Col3	Col4	DESIRED COLUMN
SF	123	01/02	UP	UP, WA
BF	543	30/12	DO	DO, AF
QW	241	30/12	AF	DO, AF
SF	123	01/02	WA	UP, WA
QW	789	20/11	D	D
SF	678	31/12	OT	OT

I wish to obtain the DESIRED COLUMN. The logic is the following:

• Have a condition that distinguishes the cases when Col1 is equal to SF or not
• In case Col1 is NOT equal to SF then I would groupby for Col3 and I would aggregate items of Col4
• In case Col1 is equal to SF then I would groupby for Col2 and Col3 and I would aggregate items of Col4

Where is my problem? I don't know if I overthought but using groupby pandas reduces the rows of the dataframe. My objective is simply to add a column, like I showed above.

Thank you in advance!!!

Answer 1

Use GroupBy.transform with numpy.where:

m = df.Col1.eq('SF')

s1 = df.groupby(['Col2','Col3'])['Col4'].transform(', '.join)
s2 = df.groupby(['Col3'])['Col4'].transform(', '.join)
df['DESIRED COLUMN'] = np.where(m, s1, s2)
print (df)
  Col1  Col2   Col3 Col4 DESIRED COLUMN
0   SF   123  01/02   UP         UP, WA
1   BF   543  30/12   DO         DO, AF
2   QW   241  30/12   AF         DO, AF
3   SF   123  01/02   WA         UP, WA
4   QW   789  20/11    D              D
5   SF   678  31/12   OT             OT

Possible solution if NaNs in Col2, Col3:

m = df.Col1.eq('SF')

df1 = df.fillna({'Col2':'nan', 'Col3':'nan'})
s1 = df1.groupby(['Col2','Col3'])['Col4'].transform(', '.join)
s2 = df1.groupby(['Col3'])['Col4'].transform(', '.join)
df['DESIRED COLUMN'] = np.where(m, s1, s2)