How to get frequency counts of a column in Pandas DataFrame

In this post, we will see how to get frequency counts of a column in Pandas DataFrame.

Sometimes, you might have to find counts of each unique value for the categorical column. You can use value_count() to get frequency counts easily. value_count() returns series object with frequency counts data for a column.

Here is sample Employee data that will be used in below example:
[table id=29 /]

import pandas as pd
emp_df = pd.DataFrame({'Name': ['Adam','Reena','Mahesh','Supriya','Aman'], 
                        'Age': [27,22,33,26,34], 
                       'Department':['HR','Sales','Tech','HR','Tech']})
print("-------Original Dataframe-------\n",emp_df)

# Get unique values using unique() method
deptCounts = emp_df.Department.value_counts()
print("-------Frequency counts for Department column-------\n",deptCounts)
print(type(deptCounts))

Output:

-------Original Dataframe-------
       Name  Age Department
0     Adam   27         HR
1    Reena   22      Sales
2   Mahesh   33       Tech
3  Supriya   26         HR
4     Aman   34       Tech
-------Frequency counts for Department column-------
 HR       2
Tech     2
Sales    1
Name: Department, dtype: int64

As you can see, we got Frequency counts for each unique value in column Department.
That’s all about how to get frequency counts of a column in Pandas DataFrame.