Pandas pivot_table 多个 aggfunc 带边距

Question

I've noticed that I can't set margins=True when having multiple aggfunc such as ("count","mean","sum").

It will vomit KeyError: 'Level None not found'

This is the example code.

df.pivot_table(values=A,index=[B,C,D],columns=E,aggfunc=("count","mean","sum"),
margins=True,margins_name="Total",fill_value=0)

Update:

This is the sample df:

[{'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 1, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 1, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 1, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'}]

And the code throwing errors.

df.pivot_table(values="Results",index="Game_ID",
columns="Team",aggfunc=("count","mean","sum"),margins=True)

Answer 1

I see the error you are talking about. I got around it by using the function calls instead of the string names "count","mean", and "sum."

First, we start with your dataframe:

import pandas as pd

df=pd.DataFrame([{'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 1, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'},
 {'Game_ID': 'no.1', 'Results': 1, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 1, 'Team': 'A'},
 {'Game_ID': 'no.1', 'Results': 0, 'Team': 'B'}])

Then just replace the aggregate functions with standard library call to len and the numpy aggregate functions.

• "count" becomes len
• "mean" becomes np.mean
• "sum" becomes np.sum

The pivot table is made with the following lines:

import numpy as np
df.pivot_table(values="Results",
               index="Game_ID",
               columns="Team",
               aggfunc=[len,np.mean,np.sum],
               margins=True)

Note, len might not be what you want, but in this example it gives the same answer as "count" would on its own. Look at numpy.count_nonzero , for example.