How to check if a Pandas DataFrame row or cell contains a string?

In this tutorial we’ll learn how to validate whether a specific pandas DataFrame row or specific one or multiple column cells contain a specific text pattern.

Example DataFrame

We will get started with importing the pandas library and defining some simple data that you are welcome to use in order to follow along this example.

import pandas as pd

month = ['April', 'March', 'January', 'March', 'May']
language = ['Python', 'R', 'Java', 'Javascript', 'Python']
office = ['LA', 'Rome', 'Madrid', 'New York', 'Toronto']
salary = [177.0, 177.0, 155.0, 139.0, 155.0]
salaries = dict(month=month, language=language, salary = salary)
salary_df = pd.DataFrame(data=salaries)
salary_df.head()

Let’s take a quick look at our data:

	month	language	salary
0	April	Python	177.0
1	March	R	177.0
2	January	Java	155.0
3	March	Javascript	139.0
4	May	Python	155.0

Check if a DataFrame row contains specific text

We’ll start by defining a string pattern and then search our DataFrame rows:

# define value to search for
str_pattern = 'Python'

# row by row check for the string pattern in the row values
filt = (salary_df
.apply(lambda r: r.astype('string').str.contains(str_pattern)
.any(), axis=1))

print(filt)

Note tat in order to search row by row, we first need to convert each DataFrame row to the string type. Also note that we have used axis=1 to search across the columns of each row. We’ll get a Series of boolean values highlighting the DataFrame rows contain the string pattern:

0     True
1    False
2    False
3    False
4     True
dtype: bool

We can now filter our DataFrame to highlight the relevant rows:

salary_df[filt]

The result will be:

	month	language	salary
0	April	Python	177.0
4	May	Python	155.0

Verify if a column cell contains a value in list

Let’s start by defining a list of strings we will be searching for:

lst_pattern = ['Python', 'R']

Now filter the our column (Series) accordingly:

filt = salary_df['language'].isin(lst_pattern)
salary_df[filt]

This will be out output:

	month	language	salary
0	April	Python	177.0
1	March	R	177.0
4	May	Python	155.0

Search for text string in multiple columns

In a similar fashion we can search for sub strings in a subset of our DataFrame.

# define list and DataFrame subset
lst_pattern = ['Python', 'R']
subset = salary_df[['month','language']]

# Define a mask to search for a list value in the DataFrame
filt = subset.apply (lambda r: r.isin(lst_pattern).any(), axis=1)

Now we can subset the DataFrame and show only the relevant rows:

subset[filt]

And the output will be:

	month	language
0	April	Python
1	March	R
4	May	Python