How to calculate the average of a column where the row meets a certain condition in Pandas

Basically I have this Dataframe:

import pandas as pd
dict = {'number': [1,1,1,1,1,2,2,2,4,4,4,4,6,6], 'time':[34,33,41,36,43,22,24,32,29,28,33,32,55,51]}
df = pd.DataFrame(dict)
print(df)

Output:

And I want to transform the df or create another one where instead of being several rows with the same 'number', there is a unique 'number' per row; and in the 'time' column, its average (of the records that had the same 'number'). Also, there should be a 3rd column called 'count' that shows the amount of records each 'number' had.

The output expected is:

Thanks.

>Solution :

Simply use groupby + agg:

agg = df.groupby('number')['time'].agg(['count', 'mean']).reset_index()

Output:

>>> agg
   number  count  mean
0       1      5  37.4
1       2      3  26.0
2       4      4  30.5
3       6      2  53.0