How to check if a value is in a pandas DataFrame?

Today we will learn how to check whether a specific text value exists across a DataFrame columns and rows.

Step # 1: Create Example DataFrame

We will start by creating a simple DataFrame that you can use to follow along with this example.

import pandas as pd
office = ['Toronto', 'Paris', 'Rio de Janeiro', 'Buenos Aires', 'Paris']
salary = [192.0, 217.0, 230.0, 203.0, 117.0]
mydf = pd.DataFrame(dict(office = office, salary = salary))
mydf.head()

Here’s our DataFrame:

officesalary
0Toronto192.0
1Paris217.0
2Rio de Janeiro230.0
3Buenos Aires203.0
4Paris117.0

Step #2: Check if string exists in a DataFrame column

You can use the standard pandas str accessor to check whether a string exists in a column:

search_str = 'Rio'
filt = mydf['office'].str.contains(search_str)
mydf[filt].head()

This returns the following DataFrame subset:

officesalary
2Rio de Janeiro230.0

Step #3: Check if DataFrame row contains value

We now would like to check whether a string exists in any of the DataFrame rows:

search_str = 'Paris'
filt = mydf.apply (lambda row: row.str.contains(search_str).any(), axis = 1)
mydf[filt].head()

This will return the following rows:

officesalary
1Paris217.0
4Paris117.0

Step # 4: Check multiple items are in DataFrame columns

Here we will use the Series isin method, and loop across the columns of our DataFrame (therefore, axis=0), then we’ll filter our DataFrame to show only columns containing values from the list:

search_lst = ['Paris', 'Buenos Aires']
filt = mydf.apply (lambda cell: cell.isin(search_lst), axis = 0)
mydf.loc[:,mydf[filt].any()]
officesalary
1Paris217.0
3Buenos Aires203.0
4Paris117.0

Step # 5: Show cell containing specific value from list

In the next case we would like to find the exact Dataframe cells (row and column intersection) that contain a specific set of values. Here we go:

row = mydf.apply (lambda cell: cell.isin(search_lst).any(), axis = 1)
col = mydf.apply (lambda cell: cell.isin(search_lst).any(), axis = 0)

mydf.loc[row, col]

Here’s the result:

office
1Paris
3Buenos Aires
4Paris

Step #6: Check if a column contains values greater than a constant

For completeness – we can find rows that contains values greater than a specific number . In this example, we will search for all rows with salaries bigger than the mean salary. Here we go:

filt = mydf['salary'] > mydf['salary'].mean()
mydf[filt]

Additional learning

How to assign a Series as a pandas DataFrame column object?