How To Use A List Of Values To Select Rows from Pandas Dataframe – Definitive Guide

Pandas allow you to select a subset of rows based on column or row values.

You can use a list of values to select rows from the pandas dataframe using the df[df[‘column_name’].isin([list_of_values])] statement.

Basic Example

import pandas as pd 

# List of Tuples
users = [ ('Shivam', 'Pandey', 'India', 1),
             ('Kumar', 'Ram' , 'US', 2 ),
         ('Felix','John' , 'Germany', 3 ),
         ('Michael','John' , 'India', 1 ),
              ]

#Create a DataFrame object
df = pd.DataFrame(  users, 
                    columns = ['First Name' , 'Last Name', 'Country', 'Country_Code']
                    ) 

df[df['Country_Code'].isin([1, 2])]

The rows with country_code 1 and 2 are selected and displayed.

Dataframe Will Look Like

	First Name	Last Name	Country	Country_Code
0	Shivam	Pandey	India	1
1	Kumar	Ram	US	2
3	Michael	John	India	1

This tutorial teaches you how to use a list of values to select rows from the Pandas dataframe.

Sample Dataframe

First, let us create a sample dataframe used for demonstration purposes.

import pandas as pd 

# List of Tuples
users = [ ('Shivam', 'Pandey', 'India', 1),
             ('Kumar', 'Ram' , 'US', 2 ),
         ('Felix','John' , 'Germany', 3 ),
         ('Michael','John' , 'India', 1 ),
              ]

#Create a DataFrame object
df = pd.DataFrame(  users, 
                    columns = ['First Name' , 'Last Name', 'Country', 'Country_Code']
                    ) 

df

Dataframe Will Look Like

	First Name	Last Name	Country	Country_Code
0	Shivam	Pandey	India	1
1	Kumar	Ram	US	2
2	Felix	John	Germany	3
3	Michael	John	India	1

Using isin

You can use the isin() method to use a list of values to select rows from the dataframe.

The condition df['Country_Code'].isin([1, 2])] creates a Mask for each row with

• True where the Country_Code is 1 or 2
• False for other country_codes.

Based on these masks, the df[ ] will return the rows where the mask is True.

Code

The following code will return the rows from the dataframe where the country_code is in 1 or 2.

df[df['Country_Code'].isin([1, 2])]

Dataframe Will Look Like

	First Name	Last Name	Country	Country_Code
0	Shivam	Pandey	India	1
1	Kumar	Ram	US	2
3	Michael	John	India	1

Using Query

The query() method queries the coloums of the dataframe with a boolean expression.

• It returns the rows where the expression evaluates to True.
• Similar to using SQL query statements.

This method will throw an error when the column name contains space.

Code

df.query('Country_Code in [1, 2]')

Dataframe Will Look Like

The rows with country codes 1 and 2 are selected and displayed.

	First Name	Last Name	Country	Country_Code
0	Shivam	Pandey	India	1
1	Kumar	Ram	US	2
3	Michael	John	India	1

Select Rows With Values Not In the list

To select rows with values that are not present in the list,

• use the isin() method and the ~ operator to specify NOT IN.

It’ll return the rows that don’t match the values specified in the list.

In other words, it is also known as excluding a list of rows.

Code

The following code selects rows with country codes, not in 1 and 2.

df[~df['Country_Code'].isin([1, 2])]

Dataframe Will look Like

	First Name	Last Name	Country	Country_Code
2	Felix	John	Germany	3

Select Rows By Index In List Of Values

To select rows using a list of its indexes,

• Use the iloc statement.
• Pass the list of indexes to the iloc, and it’ll return those rows.

An error will be raised if the specified indexes are out of range.

Code
The following code demonstrates how to select rows by the index in the values list.

df.iloc[[1,3]]

Dataframe Will Look Like

The rows with indexes 1 and 3 are selected and displayed.

	First Name	Last Name	Country	Country_Code
1	Kumar	Ram	US	2
3	Michael	John	India	1

Additional Resources

• How To Select Rows From Pandas Dataframe
• How To Select Rows From Pandas Dataframe Based On Column Values
• How To Filter Pandas Dataframe Using In And NOT IN like SQL