Sometimes, you may wish to get a count or tally of the occurrences of particular values in a column. For example, you might have a healthcare dataset and you want to know how many times particular payment methods were used during patient visits. In SQL, you could write a query that uses a GROUP BY clause in conjunction with an aggregate function (in this case, COUNT(*)) to get a tally of the payment methods:

SELECT payment, COUNT(*)
FROM data
GROUP BY payment;

In pandas, the same result is accomplished by chaining together the groupby() and size() functions:

tallies = df3.groupby('new_col4').size()
print(tallies)

The output is as follows:

new_col4
1     3
5     1
7     1
21    1
23    1
dtype: int64