Drop Columns in a Pandas DataFrame

June 01, 2019

This post shows how to drop one or more columns from a Pandas DataFrame using df.drop.

Imports

import pandas as pd

Create Example Data

The following DataFrame has three columns: Name, Age, and Sex.

data = {"Name": ["James", "Alice", "Phil", "James"],
		"Age": [24, 28, 40, 24],
		"Sex": ["Male", "Female", "Male", "Male"]}
df = pd.DataFrame(data)
print(df)
Name Age Sex
James 24 Male
Alice 28 Female
Phil 40 Male
James 24 Male

Drop One Column

df.drop accepts two arguments: the name of the column you’re dropping, and the axis. The axis argument is always equal to 1 when dropping columns. Dropping the Age column would look like this:

df_without_age = df.drop('Age', 1)
print(df_without_age)
Name Sex
James Male
Alice Female
Phil Male

Drop Multiple Columns

Dropping multiple columns has similar syntax to dropping a single column. The only difference is that you pass a list of column names rather than a single string value. Here’s how you would drop both the Age and Sex columns in a single command:

df_without_age_or_sex = df.drop(['Age', 'Sex'], 1)
print(df_without_age_or_sex)
Name
James
Alice
Phil

One additional trick is that you can do this in place without assigning the DataFrame you just dropped a column from to a new or existing variable. To do this, specify inplace=True inside the df.drop command.