Скачать или смотреть Counting String Matches in DataFrames with pandas

Learn how to count occurrences of strings from one column in another using Python's `pandas` library. This guide provides a step-by-step approach to handling data in DataFrames.
---
This video is based on the question https://stackoverflow.com/q/68390475/ asked by the user 'Lee Roy' ( https://stackoverflow.com/u/4250010/ ) and on the answer https://stackoverflow.com/a/68390665/ provided by the user 'mozway' ( https://stackoverflow.com/u/16343464/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Get the count of strings found in column b from column a in any order and return counts in a new column

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Counting String Matches in DataFrames with pandas: A Simple Guide

When working with data in Python, particularly when using the pandas library, you may find the need to analyze relationships between columns in a DataFrame. A common challenge is counting how many times strings from one column (Column A) can be matched within another column (Column B), regardless of their order. In this guide, we'll tackle this problem step by step and provide a solid solution.

The Problem

Let's consider a scenario where we have a DataFrame with two columns: Column A contains phrases, and Column B contains larger sentences. Our goal is to count how many unique phrases from Column A can be found in the text of Column B, while ensuring that the count reflects each unique phrase only once, regardless of its occurrences or order in the sentence. To clarify, here's an example of the data:

Example Data

col Acol Bfrequencybig red carelon musk drives a big red car1elon musk carelon musk drives a big red car1red big carelon musk drives a big red car1red big cardelon musk drives a big red car1Note: We need to ensure that variations of words (like "card" instead of "car") do not result in a match.

The Proposed Solution

How can we implement this functionality using a pandas DataFrame? We'll create a simple function that will help us determine if all words from Column A are present in Column B. Below, we break down the method:

Step 1: Define the Function

To check whether all words in Column A are present in Column B, we will create a function called ismatch. This function will use Python's set data structure to handle word comparisons effectively.

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Apply the Function

Now, we can utilize the apply method from the pandas library to process our DataFrame and apply the ismatch function across it.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Understand the Functionality

Input: The ismatch function takes a series (each row in the DataFrame).

Output: It returns True if all words from Column A are found in Column B and False otherwise.

Example Execution

Using the initial example data, after applying the function, we can see the following outputs:

col Acol BfrequencyOutputbig red carelon musk drives a big red car1Trueelon musk carelon musk drives a big red car1Truered big carelon musk drives a big red car1Truered big cardelon musk drives a big red car1FalseConclusion

In this guide, we explored a straightforward way to count unique matches of strings from one column in another within a pandas DataFrame. By leveraging Python sets and the apply function, we created an efficient solution that addresses both order and exact match criteria.

With this knowledge, you are now equipped to tackle similar string matching challenges in your data analysis projects. Happy coding!