DataFrames are a powerful tool for working with data in Python, and Pandas provides a number of ways to count duplicate rows in a DataFrame. In this article, we’ll explore a few of the most common methods and highlight their advantages and disadvantages. We will be using the following functions pivot_table() groupby() value_counts() lambda duplicated() len() with drop_duplicates() We’ll also include some code examples to help you get started with counting duplicate rows in your own Pandas DataFrame. In this blog post, we will discuss different ways to count duplicate rows in a pandas dataframe with a sample dataframe with 5 columns and 15 rows. Create Dataframe Let’s start by creating the test dataframe with duplicate rows # importing required libraries import pandas as pd # creating dataframe df = pd.DataFrame( { 'Name': ['John', 'Mary', 'John', 'John', 'John', 'Mary', 'Mary', 'John', 'John', 'John', 'Mary', 'John', 'John', 'Mary', 'John'], 'Age': [30, 23, 30, 30, 30, 23, 23, 30, 30, 30, 23, 30, 30, 23, 30], 'Gender': ['M', 'F', 'M', 'M', 'M', 'F', 'F', 'M', 'M', 'M', 'F', 'M', 'M', 'F', 'M'], 'Job': ['Doctor', 'Teacher', 'Student', 'Doctor', 'Student', 'Teacher', 'Teacher', 'Doctor', 'Student', 'Doctor', 'Teacher', 'Student', 'Doctor', 'Teacher', 'Student'], 'Salary': [100000, 50000, 30000, 100000, 30000, 50000, 50000, 100000, 30000, 100000, 50000, 30000, 100000, 50000, 30000] }) # displaying the dataframe df Out:   Name Age Gender Job Salary 0 John 30 M Doctor 100000 1 Mary 23 F Tea