How To Drop One Or More Columns In Pandas Dataframe

For this exercise, I am using College.csv data.

import pandas as pd

df = pd.read_csv('College.csv')

df.head(1)

Pandas How To Drop One Column By Name

Lets say we want to drop column 'Private' using df.drop()

df.drop('Private',axis=1).head(1)

With above command, we see that 'Private' column has been removed. axis=1 means remove the column. axis=0 means work on row.

With the above command data has not saved. To make the changes, use inplace=True option as shown below.

df.drop('Private',axis=1,inplace=True)

Lets check our dataframe again.

df.head(1)

Pandas How To Drop Multiple Columns By Name

Similarly we can run the same command to drop multiple columns. Lets say we want to drop next two columns 'Apps' and 'Accept'

df.drop(['Apps','Accept'],axis=1).head(1)

To remove multiple columns, we have provided list of columns to df.drop() as shown above. Again for making the change, we need to pass option inplace=True.

df.drop(['Apps','Accept'],axis=1,inplace=True)

Pandas How To Drop One Column By Index Number

We can also remove the column the index number. Let say we want to remove the column 'Enroll' which is index 1. We can get the index using df.columns[index].

df.drop(df.columns[1],axis=1,inplace=True)

df.head(1)

Pandas How To Drop Multiple Columns By Index Number

Lets say we want to drop column Top10perc which is index 1 and F.Undergrad which is index 3. We can do that passing index in a list.

df.drop(df.columns[[1, 3]], axis=1,inplace=True)

df.head(1)

Yes, the columns 'Top10perc' and F.Undergrad have been removed.

Pandas How To Drop Range Of Multiple Columns By Index

Now lets say we want to drop columns 'Top25perc', 'P.Undergrad' and 'Outstate' that are columns from index 1 to 3. We can do that by specifying the index range.

df.drop(df.columns[1:3],axis=1,inplace=True)

df.head(1)

Note, we got only two columns removed, 1:3, 3 is not inclusive. so we have to mention 1:4. Lets do it again. Remove the columns 'Outstate', 'Room.Board' and 'Books' using range 1:4

df.drop(df.columns[1:4],axis=1,inplace=True)

df.head(1)

Wrap Up!

In this post, I have covered the basics of how to drop columns in Pandas dataframe. In the next post, I will cover how to drop rows of Pandas DataFrame.