How to add multiple calculated columns to dataframe at once-CodePudding

I have dataframe as below

Slno Name_x  Age_x   Sex_x Name_y  Age_y   Sex_y
0    1      A     27    Male      A     32    Male
1    2      B     28  Female      B     28  Female
2    3      C      8  Female      C      1  Female
3    4      D     28    Male      D     72    Male
4    5      E     25  Female      E     64  Female

I need to create calculated column , difference between age, check gender match and to achieve this in one go I am using

DF3.loc[:,["Gendermatch","Agematch"]]=  pd.DataFrame([np.where(DF3["Name_x"]==DF3["Name_y"],True,False),np.where(DF3["Age_x"]-DF3["Age_y"]==0,True,False)])

and the resultant dataframe looks like as below

Slno Name_x  Age_x   Sex_x Name_y  Age_y   Sex_y  Gendermatch  Agematch
0    1      A     27    Male      A     32    Male          NaN       NaN
1    2      B     28  Female      B     28  Female          NaN       NaN
2    3      C      8  Female      C      1  Female          NaN       NaN
3    4      D     28    Male      D     72    Male          NaN       NaN
4    5      E     25  Female      E     64  Female          NaN       NaN

Resultant columns shows not a number , what wrong am I doing here?

CodePudding user response：

DF3[["Gendermatch","Agematch"]]=  np.where(DF3["Name_x"]==DF3["Name_y"],True,False),np.where(DF3["Age_x"]-DF3["Age_y"]==0,True,False)

CodePudding user response：

DF3[["Gendermatch","Agematch"]] = pd.DataFrame([np.where(DF3["Name_x"]==DF3["Name_y"],True,False),np.where(DF3["Age_x"]-DF3["Age_y"]==0,True,False)]).T

CodePudding user response：

np.where is useless, Series comparison already returns boolean Series

DF3["Gendermatch"] = DF3["Name_x"]==DF3["Name_y"]
DF3["Agematch"] = DF3["Age_x"]==DF3["Age_y"]
# or in one line
DF3["Gendermatch"], DF3["Agematch"] = (DF3["Name_x"]==DF3["Name_y"]), (DF3["Age_x"]==DF3["Age_y"])

print(DF3)

   Slno Name_x  Age_x   Sex_x Name_y  Age_y   Sex_y  Gendermatch  Agematch
0     1      A     27    Male      A     32    Male         True     False
1     2      B     28  Female      B     28  Female         True      True
2     3      C      8  Female      C      1  Female         True     False
3     4      D     28    Male      D     72    Male         True     False
4     5      E     25  Female      E     64  Female         True     False