Here is my dataframe
import pandas as pd
df = pd.DataFrame({'A': ['one', 'one', 'two', 'two', 'one'] ,
'B': ['Ar', 'Br', 'Cr', 'Ar','Ar'] ,
'C': ['12/15/2011', '11/11/2001', '08/30/2015', '07/3/1999','03/03/2000' ],
'D':[1,7,3,4,5]})
My goal is to group by column A
and sort within grouped results by column B
.
Here is what I came up with:
sort_group = df.sort_values('B').groupby('A')
I was hoping that grouping operation would not distort order, but it does not work and also returns not a dataframe, but groupby
object
<pandas.core.groupby.DataFrameGroupBy object at 0x0000000008B190B8>
Any suggestions?
By using the sort_values() method you can sort multiple columns in DataFrame by ascending or descending order.
groupby() and pass the name of the column that you want to group on, which is "state" . Then, you use ["last_name"] to specify the columns on which you want to perform the actual aggregation. You can pass a lot more than just a single column name to . groupby() as the first argument.
You cannot apply sort_values
directly to a groupby
object but you need an apply
:
df.groupby('A').apply(lambda x: x.sort_values('B'))
gives you the desired output:
A B C D
A
one 0 one Ar 12/15/2011 1
4 one Ar 03/03/2000 5
1 one Br 11/11/2001 7
two 3 two Ar 07/3/1999 4
2 two Cr 08/30/2015 3
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