[FIXED] How to print out another column after a value_counts in dataframe

Issue

I am learning pandas and python.

I have this dataframe:

dfsupport = pd.DataFrame({'Date': ['8/12/2020','8/12/2020','13/1/2020','24/5/2020','31/10/2020','11/7/2020','11/7/2020','4/4/2020','1/2/2020'],
                            'Category': ['Table','Chair','Cushion','Table','Chair','Mats','Mats','Large','Large'],
                            'Sales': ['1 table','3chairs','8 cushions','3Tables','12 Chairs','12Mats','4Mats','13 Chairs and 2 Tables', '3 mats, 2 cushions 4@chairs'],
                            'Paid': ['Yes','Yes','Yes','Yes','No','Yes','Yes','No','Yes'],
                            'Amount': ['93.78','$51.99','44.99','38.24','£29.99','29 21 only','18','312.8','63.77' ]
                            })

which produces:

         Date Category                        Sales Paid Amount
0   8/12/2020    Table                      1 table  Yes  93.78
1   8/12/2020    Chair                      3chairs  Yes  51.99
2   13/1/2020  Cushion                   8 cushions  Yes  44.99
3   24/5/2020    Table                      3Tables  Yes  38.24
4  31/10/2020    Chair                    12 Chairs   No  29.99
5   11/7/2020     Mats                       12Mats  Yes  29.21
6   11/7/2020     Mats                        4Mats  Yes     18
7    4/4/2020    Large       13 Chairs and 2 Tables   No  312.8
8    1/2/2020    Large  3 mats, 2 cushions 4@chairs  Yes  63.77

I want to find the date with the most sale, so I ran:

print("######\n",dfsupport['Date'].value_counts().max())

which gives:

2

What I would now like to do is to unpack that 2 and find out which dates that was for and also which "Sales" occurred in each of those instances.

I'm stuck and don't know how to print out those columns. Would appreciate some guidance.

Solution

Another possible solution, which uses pandas.DataFrame.groupby, pandas.DataFrame.transform and boolean indexing:

s = dfsupport.groupby('Date')['Date'].transform(len)
dfsupport[s.eq(s.max())]

Output:

        Date Category    Sales Paid      Amount
0  8/12/2020    Table  1 table  Yes       93.78
1  8/12/2020    Chair  3chairs  Yes      $51.99
5  11/7/2020     Mats   12Mats  Yes  29 21 only
6  11/7/2020     Mats    4Mats  Yes          18

Answered By - PaulS