Pandas groupby mean - into a dataframe?

Question

Say my data looks like this:

date,name,id,dept,sale1,sale2,sale3,total_sale
1/1/17,John,50,Sales,50.0,60.0,70.0,180.0
1/1/17,Mike,21,Engg,43.0,55.0,2.0,100.0
1/1/17,Jane,99,Tech,90.0,80.0,70.0,240.0
1/2/17,John,50,Sales,60.0,70.0,80.0,210.0
1/2/17,Mike,21,Engg,53.0,65.0,12.0,130.0
1/2/17,Jane,99,Tech,100.0,90.0,80.0,270.0
1/3/17,John,50,Sales,40.0,50.0,60.0,150.0
1/3/17,Mike,21,Engg,53.0,55.0,12.0,120.0
1/3/17,Jane,99,Tech,80.0,70.0,60.0,210.0

I want a new column average, which is the average of total_sale for each name,id,dept tuple

I tried

df.groupby(['name', 'id', 'dept'])['total_sale'].mean()

And this does return a series with the mean:

name  id  dept 
Jane  99  Tech     240.000000
John  50  Sales    180.000000
Mike  21  Engg     116.666667
Name: total_sale, dtype: float64

but how would I reference the data? The series is a one dimensional one of shape (3,). Ideally I would like this put back into a dataframe with proper columns so I can reference properly by name/id/dept.

Answer 1

If you call .reset_index() on the series that you have, it will get you a dataframe like you want (each level of the index will be converted into a column):

df.groupby(['name', 'id', 'dept'])['total_sale'].mean().reset_index()

EDIT: to respond to the OP's comment, adding this column back to your original dataframe is a little trickier. You don't have the same number of rows as in the original dataframe, so you can't assign it as a new column yet. However, if you set the index the same, pandas is smart and will fill in the values properly for you. Try this:

cols = ['date','name','id','dept','sale1','sale2','sale3','total_sale']
data = [
['1/1/17', 'John', 50, 'Sales', 50.0, 60.0, 70.0, 180.0],
['1/1/17', 'Mike', 21, 'Engg', 43.0, 55.0, 2.0, 100.0],
['1/1/17', 'Jane', 99, 'Tech', 90.0, 80.0, 70.0, 240.0],
['1/2/17', 'John', 50, 'Sales', 60.0, 70.0, 80.0, 210.0],
['1/2/17', 'Mike', 21, 'Engg', 53.0, 65.0, 12.0, 130.0],
['1/2/17', 'Jane', 99, 'Tech', 100.0, 90.0, 80.0, 270.0],
['1/3/17', 'John', 50, 'Sales', 40.0, 50.0, 60.0, 150.0],
['1/3/17', 'Mike', 21, 'Engg', 53.0, 55.0, 12.0, 120.0],
['1/3/17', 'Jane', 99, 'Tech', 80.0, 70.0, 60.0, 210.0]
]
df = pd.DataFrame(data, columns=cols)

mean_col = df.groupby(['name', 'id', 'dept'])['total_sale'].mean() # don't reset the index!
df = df.set_index(['name', 'id', 'dept']) # make the same index here
df['mean_col'] = mean_col
df = df.reset_index() # to take the hierarchical index off again

Answer 2

Adding to_frame

df.groupby(['name', 'id', 'dept'])['total_sale'].mean().to_frame()