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How to count unique values per groups with Pandas?

Given a DataFrame, we have to count unique values per groups. By Pranit Sharma Last updated : September 20, 2023

Columns are the different fields that contain their particular values when we create a DataFrame. We can perform certain operations on both rows & column values. In this article, we are going to learn how to count unique values in a column.

Unique values are those values that only occur once.

Problem statement

Given a DataFrame, we have to count unique values per groups.

Counting unique values per groups with Pandas

For this purpose, we are going to use a pandas.Series.unique() method inside pandas. This method will remove all the occurrences except the first occurrence. This method selects the specified column passed as a parameter inside it and returns the unique values inside it.

Syntax:

Series.unique()

# or
DataFrame['col_name'].unique()

import pandas as pd

Let us understand with the help of an example.

Python program to count unique values per groups with Pandas

# Importing pandas package
import pandas as pd

# Create a dictionary for the dataframe
dict = {
    'Name': ['Sudhir', 'Pranit', 'Ritesh','Sanskriti', 'Rani','Megha','Suman','Ranveer'],
    'Age': [16, 27, 27, 29, 29,22,19,20],
    'Marks': [40, 24, 50, 48, 33,40,39,48]
}

# Converting Dictionary to Pandas Dataframe
df = pd.DataFrame(dict)

# Display original DataFrame
print("Original DataFrame:\n",df,"\n")

# Selecting column Age and finding unique values
result = df['Age'].unique()

# Display result
print("Unique values in Age are:\n",result)

Output

The output of the above program is:

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