Introduction to Python String Matching with DataFrames

As a data analyst or scientist, working with large datasets is an essential part of the job. One common task you might encounter is searching for specific strings within a dataset. In this article, we’ll explore how to achieve this in Python using DataFrames and pandas.

Understanding the Problem Statement

The problem statement involves searching for specific words within a column of a DataFrame and adding those matches as a new column. The input DataFrame df contains columns a and b, with column b holding strings that we need to search for in our list.

Creating a Search List

We start by creating the list of words we want to search for:

search_list = ['STEEL', 'IRON', 'GOLD', 'SILVER']

This is just an example, and you can replace it with your own list of strings as needed.

Basic Approach: Extracting Matching Rows

We’re given a basic approach using the str.contains function to extract matching rows:

newdf = df[df['b'].str.upper().str.contains('|'.join(search_list), na=False)]

This method works by converting column b to uppercase, joining the search list into a regular expression pattern with |, and then using this pattern to filter out rows that don’t match.

Limitations of Basic Approach

However, there’s an issue with this approach: it doesn’t allow us to extract the matching word itself. Instead, it only flags the row as a potential match or not. To achieve our goal, we need a more sophisticated method.

Advanced Approach: Using str.extract

Here’s where things get interesting. We can use the str.extract function, which allows us to extract specific parts of strings based on patterns.

We’ll define an extractor pattern that includes all words in the search list:

df['c'] = df.b.str.extract('({0})'.format('|'.join(search_list)), flags=re.IGNORECASE)

This method uses regular expressions (regex) to match the entire string against each word in the list. The re.IGNORECASE flag ensures that the matching is case-insensitive.

Handling NaN Values

When using str.extract, we need to be careful about missing values. If no match is found, the resulting value will be NaN (Not a Number). To handle this, we can filter out rows with NaN values as shown in the answer:

result = df[~pd.isna(df.c)]

Alternative Solution Using (?i)

As mentioned in the answer, an alternative way to write this is by using a positive lookahead assertion ((?i)). This approach achieves the same result without importing the re module:

df['c'] = df.b.str.extract('(?i)({0})'.format('|'.join(search_list)))

Using Hugo Highlight Shortcode for Code Blocks

To make our code blocks more readable, we’ll use the Hugo highlight shortcode to format them with syntax highlighting:

{< highlight language="python" >}
# basic approach
newdf = df[df['b'].str.upper().str.contains('|'.join(search_list), na=False)]

Real-World Example

To illustrate how this works, let’s create a sample DataFrame and apply the above code:

import pandas as pd

# Create sample DataFrame
data = {
    'a': [1, 2, 3],
    'b': ['hello STEEL world', 'foo IRON bar', 'baz GOLD baz']
}
df = pd.DataFrame(data)

# Define search list
search_list = ['STEEL', 'IRON', 'GOLD']

# Apply extractor function to add column 'c'
df['c'] = df.b.str.extract('({0})'.format('|'.join(search_list)), flags=re.IGNORECASE)
result = df[~pd.isna(df.c)]

print(result)

Conclusion

In this article, we explored how to search for specific strings within a DataFrame and add matches as a new column. We went beyond the basic approach using str.contains by using str.extract with regular expressions to extract the matching word itself.

We also provided alternative solutions using positive lookahead assertions and importing the re module.

Whether you’re working with small datasets or large ones, this technique should be helpful in filtering out relevant information from your data.

Last modified on 2023-08-04