ValueError: operands could not be broadcast together with shapes (7410,) (3,)

I have a df in below format:

Priority Mined_Category server date_reported Count Zscore_Volume
1 - Critical Memory issue xxxxxx111 2018-07-11 1 nan
1 - Critical Memory issue xxxxxx111 2018-08-11 1 nan
1 - Critical Memory issue yyyyyy195 2018-07-06 1 1.71
1 - Critical Memory issue yyyyyy195 2018-07-08 1 1.71
2 - High Memory issue abcabcabcba1410 2018-08-21 1 nan


my aim is to replace nan with 100 whenever Priority Mined_Category and Server groupby count is 1 and replace nan with 1000 whenever Priority Mined_Category and Server groupby count is >1

Priority

Mined_Category

Server

Priority

Mined_Category

Server

I tried below code:

> df_aggegrate_Volume.loc[(df_aggegrate_Volume.groupby(["Priority","Mined_Category","server"]).count()>1)&(df_aggegrate_Volume['Zscore_Volume'].isnull()) ,"Zscore_Volume"]= -100


but I get below error:

ValueError: operands could not be broadcast together with shapes
(7410,) (3,)

1 Answer
1

Need GroupBy.transform for return Series with same size as original df filled by aggregate values:

GroupBy.transform

Series

df

m1 = (df_aggegrate_Volume.groupby(["Priority","Mined_Category","server"])["server"]
.transform('count')>1)

m2 = df_aggegrate_Volume['Zscore_Volume'].isnull()

df_aggegrate_Volume.loc[m1 & m2 ,"Zscore_Volume"]= -100

print (df_aggegrate_Volume)
Priority Mined_Category server date_reported Count
0 1 - Critical Memory issue xxxxxx111 2018-07-11 1
1 1 - Critical Memory issue xxxxxx111 2018-08-11 1
2 1 - Critical Memory issue yyyyyy195 2018-07-06 1
3 1 - Critical Memory issue yyyyyy195 2018-07-08 1
4 2 - High Memory issue abcabcabcba1410 2018-08-21 1

Zscore_Volume
0 -100.00
1 -100.00
2 1.71
3 1.71
4 NaN


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