5 Best Ways to Check if Pandas DataFrame Column Values Contain Specific Text

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💡 Problem Formulation: When working with text data in pandas DataFrames, a common task is to filter rows based on whether a column contains a specific substring. For instance, if we have a DataFrame employees with a column "Name", we might want to find all employees whose name contains “Smith”. The desired output would be a filtered DataFrame where every row has “Smith” within the “Name” column.

Method 1: Using str.contains()

One common way to filter rows based on a text pattern is to use the str.contains() method. This method allows case-sensitive searching within a column for a pattern or regex and returns a boolean Series. It’s versatile and supports regex operations directly.

Here’s an example:

import pandas as pd

# Create a dummy DataFrame
df = pd.DataFrame({'Name': ['Alice Smith', 'Bob Johnson', 'Charlie Smith', 'David Brown']})

# Filter rows where the name contains 'Smith'
contains_smith = df['Name'].str.contains('Smith')
filtered_df = df[contains_smith]
print(filtered_df)

Output:

           Name
0   Alice Smith
2  Charlie Smith

This snippet first creates a DataFrame with a column "Name". Using the .str.contains('Smith'), it creates a boolean Series that is True for rows where “Name” contains “Smith”. The DataFrame is then filtered using this Series to produce a Dataframe filtered_df containing the matching rows.

Method 2: Case-Insensitive Filtering with str.contains()

To perform case-insensitive matching, you can pass an optional argument case=False to the str.contains() function. This method is helpful when the casing of your search term shouldn’t affect the filtering process.

Here’s an example:

contains_smith_case_insensitive = df['Name'].str.contains('smith', case=False)
filtered_df_case_insensitive = df[contains_smith_case_insensitive]
print(filtered_df_case_insensitive)

Output:

           Name
0   Alice Smith
2  Charlie Smith

The code filters the DataFrame by checking for ‘smith’ in a case-insensitive manner. It uses the same str.contains() method but with the additional case=False argument. Thus, it doesn’t matter whether ‘smith’ is capitalized or not in the DataFrame.

Method 3: Using query() Method with String Containing

The query() method allows you to filter rows using a query expression. It’s particularly useful when you want to write concise and readable code. This method can also use the str.contains() function within the query string.

Here’s an example:

filtered_query = df.query('Name.str.contains("Smith")', engine='python')
print(filtered_query)

Output:

           Name
0   Alice Smith
2  Charlie Smith

Here, the query() method is used with an expression that checks for the presence of ‘Smith’ in the “Name” column. Note that specifying engine='python' is essential when using string methods within the query() method.

Method 4: Using Regular Expressions with str.contains()

For more complex pattern matching, you can use regular expressions in conjunction with str.contains(). Regular expressions are powerful for pattern searching in strings and fit naturally with the str.contains() method for advanced text filtering.

Here’s an example:

import re

regex_pattern = r'^[A|C].*Smith$'  # Names starting with A or C followed by Smith
contains_regex = df['Name'].str.contains(regex_pattern, flags=re.I)
filtered_df_regex = df[contains_regex]
print(filtered_df_regex)

Output:

           Name
0   Alice Smith
2  Charlie Smith

This code uses a regular expression that matches names starting with ‘A’ or ‘C’ followed by ‘Smith’, ignoring the case (as indicated by the flags=re.I for case insensitivity). The str.contains() function is employed to create a boolean Series, which is then used to filter the DataFrame.

Bonus One-Liner Method 5: Lambda Function with apply()

If you prefer using lambda functions for inline operations, you can apply a lambda function to each row to check for substrings using the apply() method. It’s a flexible approach, allowing for complex conditions.

Here’s an example:

filtered_lambda = df[df['Name'].apply(lambda name: 'Smith' in name)]
print(filtered_lambda)

Output:

           Name
0   Alice Smith
2  Charlie Smith

In this snippet, a lambda function is applied to each element in the “Name” column. The lambda checks if ‘Smith’ is in the name string. Rows satisfying this condition remain in the resulting filtered_lambda DataFrame.

Summary/Discussion

  • Method 1: str.contains(). Straightforward. Case-sensitive by default. Good for simple substring matching.
  • Method 2: Case-Insensitive str.contains(). Enhances method 1. Useful when case of the substring should be ignored. Adds minor complexity.
  • Method 3: Using query(). Enables more readable code. Suitable for complex DataFrame filtering. Requires knowledge of query syntax.
  • Method 4: Regular Expressions. Most powerful for pattern matching. Potentially overkill for simple tasks. Regex can be complex to write and read.
  • Method 5: Lambda Function with apply(). Highly flexible, inline operations. Can be slower on large DataFrames. Allows complex logic per row.