How to search DataFrames using pandas isin()

The Python pandas function DataFrame.isin() is designed to quickly and ef­fi­cient­ly check whether certain values exist in a DataFrame. This function is par­tic­u­lar­ly useful for checking for multiple values at once.

What is the syntax for pandas isin()?

Pandas isin() takes one parameter and looks like this:

DataFrame.isin(values)

The values parameter can be a Python list, a Python dic­tio­nary or another DataFrame. It contains the values you want to search for in the DataFrame.

How to use isin() with DataFrames in pandas

You can use isin() for different purposes. In addition to checking for values, it can also be used to filter DataFrames.

Checking for values in a column

First, let’s take a look at a DataFrame that contains in­for­ma­tion about different people and where they live.

import pandas as pd
# Creating a DataFrame
data = {
    'Name': ['Amir', 'Bella', 'Charlize', 'David'],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']
}
df = pd.DataFrame(data)
print(df)

The DataFrame looks like this:

Name      City
0    Amir    New York
1    Bella  	Los Angeles
2  Charlize     Chicago
3    David    Houston

Now, we want to use pandas isin() to check whether the cities in the City column appear in a separate list of cities we’ve created. Once we’ve created the list with the reference cities, we’ll run the function on the DataFrame column “City”:

# Cities for the list to be compared to
cities_to_check_against = ['Chicago', 'Houston', 'Miami']
# Using the isin() method
result = df['City'].isin(cities_to_check_against)
print(result)

The result is a series of Boolean values in­di­cat­ing whether each city in the City column is present in the cities_to_check_against list:

0    False
1    False
2     True
3     True
Name: City, dtype: bool

Filtering a DataFrame using isin()

You can also use pandas isin() to filter a DataFrame, keeping only the rows with cities that appear in the cities_to_check_against list.

# Filtering a DataFrame using isin()
filtered_df = df[df['City'].isin(cities_to_check_against)]
print(filtered_df)

The result is a DataFrame that contains only the rows with cities that are also in the cities_to_check_against list:

Name    City
2  Charlize  Chicago
3    David  Houston

Checking multiple columns in a DataFrame

For more complex filtering op­er­a­tions, you can also use pandas isin() with dic­tio­nar­ies. In the following example, you’ll see how you can use a dic­tio­nary to si­mul­ta­ne­ous­ly check multiple columns of a DataFrame. First, we’ll add a column to the original DataFrame and then use isin():

# Creating a DataFrame
data = {
    'Name': ['Amir', 'Bella', 'Charlize', 'David'],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston'],
    'Age': [25, 30, 35, 40]
}
df = pd.DataFrame(data)
# Dictionary with values that the DataFrame should be checked against
values_to_check_against = {
    'City': ['Chicago', 'Houston'],
    'Age': [30, 40]
}
# Using isin() with a dictionary
result = df.isin(values_to_check_against)
print(result)

In this case, calling isin() returns a DataFrame with Boolean values, which indicate whether the con­di­tions have been met in each column:

Name  City  Age
0  False  False  False
1  False  False   True
2  False   True  False
3  False   True   True