Conditional operation based on column label in pandas dataframe

Question

I have a dataframe below

df=pd.DataFrame(np.random.randn(6,3),index=list("ABCDEF"),columns=list("XYZ"))
df.reset_index(inplace=True)
df

I want to have a new column named "Q". The values under column "Q" shall be calculated based on the labels under index column with the following three conditions:

conditions=[(df["index"]== "A"|"B"|"C"|"D"),(df["index"]== "E"),(df["index"]== "F")]
returned_value=[df["X"]+df["Y"],df["Y"]*2,df["Z"]]

So I was thinking using

df["Q"]=np.select(conditions, returned_value)

I however got the error after defining the conditions. I first used or, and got another error, and then changed to |, but got the following. Any hints on how can I achieve what I want?

TypeError: unsupported operand type(s) for |: 'str' and 'str'

Answer 1

Use isin for check membership of multiple values:

np.random.seed(1213)
df=pd.DataFrame(np.random.randn(6,3),index=list("ABCDEF"),columns=list("XYZ"))
df.reset_index(inplace=True)

conditions=[df["index"].isin(["A","B","C","D"]),(df["index"]== "E"),(df["index"]== "F")]
returned_value=[df["X"]+df["Y"],df["Y"]*2,df["Z"]]
df["Q"]=np.select(conditions, returned_value)
print (df)
  index         X         Y         Z         Q
0     A  0.511604 -0.217660 -0.521060  0.293943
1     B  1.253270  1.104554 -0.770309  2.357825
2     C  0.632975 -1.322322 -0.936332 -0.689347
3     D  0.436361  1.233744  0.527565  1.670105
4     E -0.369576  1.820059 -1.373630  3.640118
5     F -0.414554 -0.098443  0.904791  0.904791

But reset index is not necessary, then check df.index:

np.random.seed(1213)
df=pd.DataFrame(np.random.randn(6,3),index=list("ABCDEF"),columns=list("XYZ"))

conditions=[df.index.isin(["A","B","C","D"]),(df.index == "E"),(df.index== "F")]
returned_value=[df["X"]+df["Y"],df["Y"]*2,df["Z"]]
df["Q"]=np.select(conditions, returned_value)
print (df)
          X         Y         Z         Q
A  0.511604 -0.217660 -0.521060  0.293943
B  1.253270  1.104554 -0.770309  2.357825
C  0.632975 -1.322322 -0.936332 -0.689347
D  0.436361  1.233744  0.527565  1.670105
E -0.369576  1.820059 -1.373630  3.640118
F -0.414554 -0.098443  0.904791  0.904791