Myujth

I am using the below code to do groupby calaculation.

**Input**
ORG DSTN FLAG
LON SIN Y
ADL SIN N
SIN LON N
LON SIN Y
LON SIN N
ADL SIN Y
ADL SIN N
SIN LON Y
SIN LON Y
SIN LON Y
SIN LON N
LON SIN N

My Code

data.assign(Lane_Score=data.groupby(['ORIGIN_CITY','DEST_CITY']).Delay_Flag.apply(lambda x:x.replace('YES|NO',(x=='YES').mean(),regex=True)))

I am getting the output by its not getting attached to dataframe.When I try to extract that column alone its not working.

data['Lane_Score']

How to fix this.

According to pandas documentation DataFrame.assign

keywords are the column names. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

So you either need to assign it to original df or use explicit general assignment, Also you don't need replace use transform:

df['Lane_Score'] = df.groupby(['ORG','DSTN']).FLAG.transform(lambda x: (x=='Y').mean())

Or Faster approach would be:

df['Lane_Score'] = df['FLAG']=='Y'
df['Lane_Score'] = df.groupby(['ORG','DSTN']).Lane_Score.transform('mean')

Or:

df = df.assign(Lane_Score=df.groupby(['ORG','DSTN']).FLAG.apply(lambda x: x.replace('Y|N',(x=='Y').mean(),regex=True)))

print(df)
 ORG DSTN FLAG Lane_Score
0 LON SIN Y 0.500000
1 ADL SIN N 0.333333
2 SIN LON N 0.600000
3 LON SIN Y 0.500000
4 LON SIN N 0.500000
5 ADL SIN Y 0.333333
6 ADL SIN N 0.333333
7 SIN LON Y 0.600000
8 SIN LON Y 0.600000
9 SIN LON Y 0.600000
10 SIN LON N 0.600000
11 LON SIN N 0.500000

Try this:

data['Lane_Score'] = data.groupby(['ORIGIN_CITY','DEST_CITY']).Delay_Flag.apply(lambda x:x.replace('YES|NO',(x=='YES').mean(),regex=True)))