How to Filter DataFrame Columns in Python Based on Substrings?

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In this article, we'll explore how to filter DataFrame columns in Python using a specified list of substrings. Specifically, we want to select all columns from a DataFrame that contain any elements from a given list named 'animals'. This can be particularly useful in scenarios where column names might include multiple similar identifiers, such as variations of 'cat' and 'rabbit'. Let's dive into the solutions!

Understanding the Issue


Before we delve into the solution, let's understand why it is important to filter DataFrame columns based on a list of substrings. Often in data manipulation, especially in pandas, you might have a situation where your DataFrame consists of multiple columns, and you wish to filter these based on keywords. For example, if you have a DataFrame tracking animal adoptions and fosterings, the columns might include details about various animals.

When we attempt to filter using just exact matches, we run into limitations. If our list of animals contains elements like 'cat' and 'rabbit', columns titled 'cats_fostered', 'cats_adopted', 'rabbits_fostered', and 'rabbits_adopted' won't be matched correctly since they do not exactly match the items in the list. So, let's explore how to achieve the desired filtering.

Solution: Using Regular Expressions


To filter the DataFrame based on substrings effectively, we can utilize regular expressions (regex) in pandas. However, you need to make sure that you construct a regex pattern that works with the list elements.

Step 1: Setup the DataFrame


First, let's create the sample DataFrame using the provided animal_data dictionary:

import pandas as pd

data = {
"date": ["2023-01-22", "2023-11-16", "2024-06-30", "2024-08-16", "2025-01-22"],
"cats_fostered": [1, 2, 3, 4, 5],
"cats_adopted": [1, 2, 3, 4, 5],
"dogs_fostered": [1, 2, 3, 4, 5],
"dogs_adopted": [1, 2, 3, 4, 5],
"rabbits_fostered": [1, 2, 3, 4, 5],
"rabbits_adopted": [1, 2, 3, 4, 5]
}

animal_data = pd.DataFrame(data)

Step 2: Create a Regex Pattern


Next, we will create a regex pattern that matches any of the substrings present in the animals list. Here's how you can construct that:

animals = ["cat", "rabbit"]
regex_pattern = '|'.join(animals)


This regex pattern will match any column that contains either 'cat' or 'rabbit'.

Step 3: Filter the DataFrame


Now we can filter the DataFrame using the filter method with the regex pattern we created:

filtered_data = animal_data.filter(regex=regex_pattern)
print(filtered_data)

Complete Code Example


Here’s the complete code for convenience:

import pandas as pd

data = {
"date": ["2023-01-22", "2023-11-16", "2024-06-30", "2024-08-16", "2025-01-22"],
"cats_fostered": [1, 2, 3, 4, 5],
"cats_adopted": [1, 2, 3, 4, 5],
"dogs_fostered": [1, 2, 3, 4, 5],
"dogs_adopted": [1, 2, 3, 4, 5],
"rabbits_fostered": [1, 2, 3, 4, 5],
"rabbits_adopted": [1, 2, 3, 4, 5]
}

animal_data = pd.DataFrame(data)

animals = ["cat", "rabbit"]
regex_pattern = '|'.join(animals)
filtered_data = animal_data.filter(regex=regex_pattern)

print(filtered_data)

Output


When you run the above code, you will get a DataFrame that includes only the columns containing the keywords defined in the animals list:

cats_fostered cats_adopted rabbits_fostered rabbits_adopted
0 1 1 1 1
1 2 2 2 2
2 3 3 3 3
3 4 4 4 4
4 5 5 5 5

Frequently Asked Questions


Q: Can I filter DataFrame columns based on more complex patterns?
A: , you can extend the regex pattern to include more complex expressions based on your filtering requirements.

Q: What if I want to filter based on exact matches sometimes?
A: You can use the isin() method for exact matches while using the filter method for substring-based filtering.

Q: How can I ensure that my filtering does not miss any data?
A: Always check the column names and ensure they're formatted correctly before filtering to avoid missing any data.

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