Create column on two conditions with pandas - python-3.x

Question

I'm utilizing pandas to do some analysis exercise. I want to create a new column that the value is the sum of two rows. The original data set is as follow...
Admit Gender Dept Freq
0 Admitted Male A 512
1 Rejected Male A 313
2 Admitted Female A 89
3 Rejected Female A 19
4 Admitted Male B 353
5 Rejected Male B 207
6 Admitted Female B 17
7 Rejected Female B 8
8 Admitted Male C 120
9 Rejected Male C 205
10 Admitted Female C 202
11 Rejected Female C 391
12 Admitted Male D 138
13 Rejected Male D 279
14 Admitted Female D 131
15 Rejected Female D 244
16 Admitted Male E 53
17 Rejected Male E 138
18 Admitted Female E 94
19 Rejected Female E 299
20 Admitted Male F 22
21 Rejected Male F 351
22 Admitted Female F 24
23 Rejected Female F 317
I want to create a new column utilizing the following data frame...
Dept Gender Freq
0 A Female 108
1 A Male 825
2 B Female 25
3 B Male 560
4 C Female 593
5 C Male 325
6 D Female 375
7 D Male 417
8 E Female 393
9 E Male 191
10 F Female 341
11 F Male 373
I want to create a new column in the first data frame utilizing the Freq column of the second data frame. I need to insert the 108 value if Detp and Gender are the same in both data frames. The new data frame should look like this...
Admit Gender Dept Freq Total
0 Admitted Male A 512 825
1 Rejected Male A 313 825
2 Admitted Female A 89 108
3 Rejected Female A 19 108
4 Admitted Male B 353 560
5 Rejected Male B 207 560
6 Admitted Female B 17 25
7 Rejected Female B 8 25
I have tried the following code...
for i in data.iterrows():
for j in total_freq.iterrows():
if i[1].Gender == total_freq.Gender & i[1].Dept == total_freq.Dept:
data['Total'] = total_freq.Freq
I get the following error... TypeError: cannot compare a dtyped [object] array with a scalar of type [bool]
Any help to create the column with the correct values?

Answer 1

You can use transform
df['Total'] = df.groupby(['Dept', 'Gender']).Freq.transform('sum')
You get
Admit Gender Dept Freq Total
0 Admitted Male A 512 825
1 Rejected Male A 313 825
2 Admitted Female A 89 108
3 Rejected Female A 19 108
4 Admitted Male B 353 560
5 Rejected Male B 207 560
6 Admitted Female B 17 25
7 Rejected Female B 8 25
8 Admitted Male C 120 325
9 Rejected Male C 205 325
10 Admitted Female C 202 593
11 Rejected Female C 391 593
12 Admitted Male D 138 417
13 Rejected Male D 279 417
14 Admitted Female D 131 375
15 Rejected Female D 244 375
16 Admitted Male E 53 191
17 Rejected Male E 138 191
18 Admitted Female E 94 393
19 Rejected Female E 299 393
20 Admitted Male F 22 373
21 Rejected Male F 351 373
22 Admitted Female F 24 341
23 Rejected Female F 317 341

Answer 2

You can use pandas.DataFrame.merge() to left join your totals from the second dataframe to the first. First, rename freq in the totals df.
df1 = df1.rename(columns={'Freq':'Total'})
df_totals = pd.merge(df, df1['Total'], how='left', on=['Gender', 'Dept'])