drop_duplicates is a method in Pandas, a Python library used for data manipulation and analysis, that allows you to remove duplicate rows from a dataframe.
When you apply the drop_duplicates() method on a pandas dataframe, it will return a new dataframe with only the unique rows, excluding any duplicates. The method considers all columns by default, but you can specify only certain columns to consider when identifying duplicates using the subset parameter.
For example, if you have a dataframe with multiple rows containing the same values in all columns, you can use the drop_duplicates() method to remove the duplicate rows and return a new dataframe with only the unique values.
Here’s an example code snippet:
import pandas as pd
# create a dataframe with duplicate rows
df = pd.DataFrame({ ‘name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘Alice’, ‘Charlie’],
‘age’: [25, 30, 35, 25, 35],
‘location’: [‘New York’, ‘San Francisco’, ‘Boston’, ‘New York’, ‘Boston’] })
# drop duplicate rows based on all columns
df_unique = df.drop_duplicates()
# drop duplicate rows based on a subset of columns
df_unique_subset = df.drop_duplicates(subset=[‘name’, ‘age’])
In this example, df_unique will contain only the unique rows in the original dataframe df, while df_unique_subset will contain only the unique rows based on the name and age columns, but considering all columns when identifying duplicates.