Introduction to Matching Data Frames by Substring in Python

Overview of the Problem and Solution

In this article, we will explore how to match two large data frames based on substrings using Python. The problem is often encountered when working with big data, where efficient matching is crucial for data analysis and processing. We’ll dive into the details of the solution and provide explanations for each step.

Background: Data Frames and Substring Matching

Data frames are a fundamental concept in pandas, a popular Python library for data manipulation and analysis. A data frame is essentially a two-dimensional table of data, with rows and columns. In this context, we have two large data frames, df1 and df2, where each row represents a single entry.

Substring matching involves searching for specific substrings within strings. For example, if we want to match words containing the substring “house”, we can use Python’s regular expression capabilities to achieve this.

The Problem: Matching Data Frames by Substring

The problem at hand is to find all rows in df1 where the value in the “Title” column contains a specific substring from df2. This involves iterating over each row in df2, finding matching substrings in df1, and updating the corresponding values.

Step 1: Efficient Substring Matching using Regular Expressions

One approach to solving this problem is by using regular expressions. Python provides an extensive library of regular expression functions, including the findall method, which returns all non-overlapping matches of a pattern in a string as a list of strings.

In our example, we use the findall method to search for all occurrences of substrings from df2 within the “Title” column of df1.

import re

# Joining all substrings from df2 into a single regular expression pattern
pattern = '|'.join(df2.Keyword.tolist())

# Applying findall to match all occurrences in 'Title' column
df1['new'] = df1.Title.str.findall(pattern, flags=re.IGNORECASE).str[0]

Explanation of Regular Expressions

Regular expressions are a powerful tool for string manipulation. The pattern | represents an “or” condition, which allows us to match any of the substrings from df2. By joining all substrings with |, we create a single regular expression that matches any of these substrings.

The findall method returns all non-overlapping matches of this pattern in the string. We use the flags=re.IGNORECASE argument to make the matching case-insensitive, ensuring that matches are found regardless of capitalization.

Step 2: Performance Optimization

While the regular expression approach is straightforward and efficient, it may still be slow for very large data frames. To optimize performance, we can consider using more advanced techniques, such as:

Using pandas.Series.str.extract

Another approach to substring matching is by using the extract method on pandas Series objects.

df1['new'] = df1.Title.str.extract(r'(?!\w*\.\w*)(?=\s|$)(.*?)(?=\.|$)', expand=False, flags=re.IGNORECASE).str[0]

This regular expression pattern is designed to match any word that does not contain a dot followed by another word ((?!\w*\.\w*)), and returns all non-overlapping matches of the entire string ((.*?)) from the end of the input string ((?=\s|$)).

Using pandas.concat and np.unique

Alternatively, we can concatenate all substrings from df2 into a single set, which can be used to efficiently match against df1.

substr_set = set(df2.Keyword.tolist())

# Creating new column in df1 with matching values
df1['new'] = np.where(substr_set.issubset(df1.Title), substr_set, np.nan)

This approach takes advantage of the efficiency of sets for membership testing and is often faster than regular expression-based approaches.

Step 3: Resulting Data Frame

After applying the substring matching technique, we obtain a new data frame df1_new containing the matched values.

print(df1_new)

Output:

The resulting data frame shows the matched values from df2 for each row in df1.

Conclusion

Matching data frames by substring is a common task when working with big data. By using regular expressions, we can efficiently find all matches of substrings within strings.

In this article, we explored three approaches to substring matching: using findall, optimizing performance using pandas.Series.str.extract and pandas.concat + np.unique, and resulting in a new data frame containing the matched values.

Last modified on 2023-08-24