I'm trying to do some aggregations on a pandas data frame. Here is a sample code:

import pandas as pd



df = pd.DataFrame({"User": ["user1", "user2", "user2", "user3", "user2", "user1"],

                  "Amount": [10.0, 5.0, 8.0, 10.5, 7.5, 8.0]})



df.groupby(["User"]).agg({"Amount": {"Sum": "sum", "Count": "count"}})



Out[1]: 

      Amount      

         Sum Count

User              

user1   18.0     2

user2   20.5     3

user3   10.5     1

Which generates the following warning:

FutureWarning: using a dict with renaming is deprecated and will be
removed in a future version return super(DataFrameGroupBy,
self).aggregate(arg, *args, **kwargs)

How can I avoid this?

Use groupby apply and return a Series to rename columns

Use the groupby apply method to perform an aggregation that

• Renames the columns


• Allows for spaces in the names


• Allows you to order the returned columns in any way you choose


• Allows for interactions between columns


• Returns a single level index and NOT a MultiIndex

To do this:

• create a custom function that you pass to apply
  


• This custom function is passed each group as a DataFrame


• Return a Series


• The index of the Series will be the new columns

Create fake data

df = pd.DataFrame({"User": ["user1", "user2", "user2", "user3", "user2", "user1", "user3"],

                  "Amount": [10.0, 5.0, 8.0, 10.5, 7.5, 8.0, 9],

                  'Score': [9, 1, 8, 7, 7, 6, 9]})

create custom function that returns a Series

The variable x inside of my_agg is a DataFrame

def my_agg(x):

    names = {

        'Amount mean': x['Amount'].mean(),

        'Amount std':  x['Amount'].std(),

        'Amount range': x['Amount'].max() - x['Amount'].min(),

        'Score Max':  x['Score'].max(),

        'Score Sum': x['Score'].sum(),

        'Amount Score Sum': (x['Amount'] * x['Score']).sum()}



    return pd.Series(names, index=['Amount range', 'Amount std', 'Amount mean',

                                   'Score Sum', 'Score Max', 'Amount Score Sum'])

Pass this custom function to the groupby apply method

df.groupby('User').apply(my_agg)

The big downside is that this function will be much slower than agg for the cythonized aggregations

Using a dictionary with groupby agg method

Using a dictionary of dictionaries was removed because of its complexity and somewhat ambiguous nature. There is an ongoing discussion on how to improve this functionality in the future on github Here, you can directly access the aggregating column after the groupby call. Simply pass a list of all the aggregating functions you wish to apply.

df.groupby('User')['Amount'].agg(['sum', 'count'])

Output

       sum  count

User              

user1  18.0      2

user2  20.5      3

user3  10.5      1

It is still possible to use a dictionary to explicitly denote different aggregations for different columns, like here if there was another numeric column named Other.

df = pd.DataFrame({"User": ["user1", "user2", "user2", "user3", "user2", "user1"],

              "Amount": [10.0, 5.0, 8.0, 10.5, 7.5, 8.0],

              'Other': [1,2,3,4,5,6]})



df.groupby('User').agg({'Amount' : ['sum', 'count'], 'Other':['max', 'std']})

Output

      Amount       Other          

         sum count   max       std

User                              

user1   18.0     2     6  3.535534

user2   20.5     3     5  1.527525

user3   10.5     1     4       NaN

If you replace the internal dictionary with a list of tuples it gets rid of the warning message

import pandas as pd



df = pd.DataFrame({"User": ["user1", "user2", "user2", "user3", "user2", "user1"],

                  "Amount": [10.0, 5.0, 8.0, 10.5, 7.5, 8.0]})



df.groupby(["User"]).agg({"Amount": [("Sum", "sum"), ("Count", "count")]})

This is what I did:

Create a fake dataset:

import pandas as pd

df = pd.DataFrame({"User": ["user1", "user2", "user2", "user3", "user2", "user1", "user3"],

                  "Amount": [10.0, 5.0, 8.0, 10.5, 7.5, 8.0, 9],

                  'Score': [9, 1, 8, 7, 7, 6, 9]})

df

O/P:

    Amount  Score   User

0   10.0    9   user1

1   5.0 1   user2

2   8.0 8   user2

3   10.5    7   user3

4   7.5 7   user2

5   8.0 6   user1

6   9.0 9   user3

I first made the User the index, and then a groupby:

ans = df.set_index('User').groupby(level=0)['Amount'].agg([('Sum','sum'),('Count','count')])

ans

Solution:

    Sum Count

User        

user1   18.0    2

user2   20.5    3

user3   19.5    2