How to Remove Duplicates from Pandas DataFrame

Need to remove duplicates from Pandas DataFrame?

If so, you can apply the following syntax in Python to remove duplicates from your DataFrame:

pd.DataFrame.drop_duplicates(df)

In the next section, you’ll see the steps to apply this syntax in practice.

Steps to Remove Duplicates from Pandas DataFrame

Step 1: Gather the data that contains duplicates

Firstly, you’ll need to gather the data that contains the duplicates.

For example, let’s say that you have the following data about boxes, where each box may have a different color or shape:

Color	Shape
Green	Rectangle
Green	Rectangle
Green	Square
Blue	Rectangle
Blue	Square
Red	Square
Red	Square
Red	Rectangle

As you can see, there are duplicates under both columns.

Before you remove those duplicates, you’ll need to create Pandas DataFrame to capture that data in Python.

Step 2: Create Pandas DataFrame

Next, create Pandas DataFrame using this code:

import pandas as pd

boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'],
         'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle']
        }
df = pd.DataFrame(boxes, columns = ['Color', 'Shape'])

print(df)

Once you run the code in Python, you’ll get the same values as in step 1:

Step 3: Remove duplicates from Pandas DataFrame

To remove duplicates from the DataFrame, you may use the following syntax that you saw at the beginning of this guide:

pd.DataFrame.drop_duplicates(df)

Let’s say that you want to remove the duplicates across the two columns of Color and Shape.

In that case, apply the code below in order to remove those duplicates:

import pandas as pd

boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'],
         'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle']
        }
df = pd.DataFrame(boxes, columns = ['Color', 'Shape'])

df_duplicates_removed = pd.DataFrame.drop_duplicates(df)
print(df_duplicates_removed)

As you can see, only the distinct values across the two columns remain:

But what if you want to remove the duplicates under a single column?

For example, what if you want to remove the duplicates under the Color column only?

In that case, you should just keep the Color column when assigning the columns to the DataFrame:

df = pd.DataFrame(boxes, columns= [‘Color’])

So the full Python code to remove the duplicates under the Color column would look like this:

import pandas as pd

boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'],
         'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle']
        }
df = pd.DataFrame(boxes, columns = ['Color'])

df_duplicates_removed = pd.DataFrame.drop_duplicates(df)
print(df_duplicates_removed)

As you may observe, only the distinct values under the Color column remain:

That’s it! You may want to check the Pandas Documentation to learn more about removing duplicates from a DataFrame.