合并熊猫数据透视表

Question

I have a dataframe like this:

Application|Category|Feature|Scenario|Result|Exec_Time  
A1|C1|F1|scenario1|PASS|2.3 
A1|C1|F1|scenario2|FAIL|20.3
A2|C1|F3|scenario3|PASS|12.3 
......

The outcome i am looking for will be a pivot with count of results by Feature and also the sum of exec times. Like this

Application|Category|Feature|Count of PASS|Count of FAIL|SumExec_Time     
A1|C1|F1|200|12|45.62
A1|C1|F2|90|0|15.11
A1|C2|F3|97|2|33.11*

I got individual dataframes to get the pivots of result counts and the sum of execution time by feature but I am not able to merge those dataframes to get my final expected outcome.

dfr = pd.pivot_table(df,index=["Application","Category","Feature"],
values=["Final_Result"],aggfunc=[len])

dft = pd.pivot_table(df,index=["Application","Category","Feature"],
values=["Exec_time_mins"],aggfunc=[np.sum])

Answer 1

You don't need to merge results here, you can create this with a single pivot_table or groupby/apply. I don't have your data but does this get you what you want?

pivot = pd.pivot_table(df, index=["Application","Category","Feature"], 
                           values = ["Final_Result", "Exec_time_mins"], 
                           aggfunc = [len, np.sum])

Answer 2

#Count total records, number of FAILs and total time.
df2 = df.groupby(by=['Application','Category','Feature']).agg({'Result':[len,lambda x: len(x[x=='FAIL'])],'Exec_Time':sum})

#rename columns
df2.columns=['Count of PASS','Count of FAIL','SumExec_Time']

#calculate number of pass
df2['Count of PASS']-=df2['Count of FAIL']

#reset index
df2.reset_index(inplace=True)

df2
Out[1197]: 
  Application Category Feature  Count of PASS  Count of FAIL  SumExec_Time
0          A1       C1      F1              1              1          22.6
1          A2       C1      F3              1              0          12.3