1. Using the duplicated() function:
Using the duplicated() function, which returns a boolean mask indicating which rows are duplicates.
2. Using the drop_duplicates() function:
Using the drop_duplicates() function, which returns a new DataFrame with duplicate rows removed.
3. Using the groupby() function:
Using the groupby() function, which groups the DataFrame by all columns and returns the count of occurrences for each group.
4. Using apply() function with tuple():
Using Python’s built-in tuple() function and apply() method to convert each row to a tuple, and then checking for duplicates using the duplicated() function.
5.Using the Counter() class:
Using Python’s built-in Counter() function to count the occurrences of each tuple of values, and then checking if any of the counts are greater than 1.