How to check if Pandas column has value from list of string?

In pandas, we often need to check if values in a DataFrame column match any items from a given list of strings. This is useful for filtering data, data validation, or finding specific patterns in your dataset.

Pandas is a powerful Python library for data analysis and manipulation. It provides several methods to check if column values exist in a list of strings, with isin() being the most commonly used approach.

Creating Sample Data

Let's first create a sample DataFrame to demonstrate different methods ?

import pandas as pd

data = {
    "Name": ["Anil", "Srivatsav", "Prasad", "Mitra"],
    "Class": [10, 30, 20, 50],
    "Subject": ["Python", "Java", "C", "Android"]
}

df = pd.DataFrame(data)
print(df)

       Name  Class  Subject
0      Anil     10   Python
1  Srivatsav     30     Java
2     Prasad     20        C
3     Mitra     50  Android

Using pandas isin() Method

The isin() method is the most efficient way to check if column values exist in a list. It returns a Boolean Series indicating matches ?

Syntax

DataFrame.column.isin(values)

Example

import pandas as pd

data = {
    "Name": ["Anil", "Srivatsav", "Prasad", "Mitra"],
    "Class": [10, 30, 20, 50],
    "Subject": ["Python", "Java", "C", "Android"]
}

df = pd.DataFrame(data)
search_list = ["Anil", "Prasad"]

# Check which names are in the search list
result = df["Name"].isin(search_list)
print("Boolean result:")
print(result)
print("\nFiltered rows:")
print(df[result])

Boolean result:
0     True
1    False
2     True
3    False
Name: Name, dtype: bool

Filtered rows:
    Name  Class Subject
0   Anil     10  Python
2  Prasad     20       C

Using NumPy isin() Function

NumPy also provides an isin() function that works similarly to pandas but offers more flexibility for complex operations ?

Example

import pandas as pd
import numpy as np

data = {
    "Name": ["Anil", "Srivatsav", "Prasad", "Mitra"],
    "Class": [10, 30, 20, 50],
    "Subject": ["Python", "Java", "C", "Android"]
}

df = pd.DataFrame(data)
search_names = ["Anil", "Srivatsav"]

# Using numpy isin()
result = np.isin(df["Name"], search_names)
print("NumPy isin() result:")
print(result)

NumPy isin() result:
[ True  True False False]

Checking Multiple Columns

You can check multiple columns simultaneously using various approaches ?

import pandas as pd

data = {
    "Name": ["Anil", "Srivatsav", "Prasad", "Mitra"],
    "Class": [10, 30, 20, 50],
    "Subject": ["Python", "Java", "C", "Android"]
}

df = pd.DataFrame(data)

# Check if any value in Name or Subject columns matches the list
search_values = ["Anil", "Java", "C"]

name_match = df["Name"].isin(search_values)
subject_match = df["Subject"].isin(search_values)

print("Name matches:")
print(name_match)
print("\nSubject matches:")
print(subject_match)
print("\nRows with either match:")
print(df[name_match | subject_match])

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

Subject matches:
0    False
1     True
2     True
3    False
Name: Subject, dtype: bool

Rows with either match:
       Name  Class Subject
0      Anil     10  Python
1  Srivatsav     30    Java
2     Prasad     20       C

Comparison of Methods

Conclusion

Use pandas.isin() for simple column value checking against a list. For more complex operations or better performance with large datasets, consider numpy.isin(). Both methods return Boolean arrays that can be used for filtering DataFrames efficiently.