[FIXED] Best way to count number of values present in a dataframe column

Issue

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' ]
                            })

If I want to find the number of instances of a category is this the best way to do it?

print(dfsupport.groupby(dfsupport['Category'],dropna=True).apply(lambda y: y['Category'].count()))

output:

Category
Chair      2
Cushion    1
Large      2
Mats       2
Table      2
dtype: int64

Solution

You can instead use the value_counts method to get a series with a count of each unique value. In your specific example the code would look like this:

dfsupport["Category"].value_counts()

Answered By - JohnnyPro