Flag similar cell values in a pandas dataframe with ease!

Learn how to identify and flag `similar` values in a pandas dataframe using Python efficiently.
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This video is based on the question https://stackoverflow.com/q/72613761/ asked by the user 'Giampaolo Levorato' ( https://stackoverflow.com/u/8964393/ ) and on the answer https://stackoverflow.com/a/72670890/ provided by the user 'Laurent' ( https://stackoverflow.com/u/11246056/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

In the world of data analysis, it is common to encounter similar or duplicated values within a dataset. For instance, you may have a column full of feature names that refer to the same concept but are phrased slightly differently. This can lead to challenges in data cleaning and analysis. In this guide, we will tackle the problem of flagging similar cell values in a pandas DataFrame.

Imagine you are working with a DataFrame that lists various job titles, and you want to recognize which titles are essentially the same but are worded differently. Let's dive into how we can accomplish this in a clean and efficient way using Python.

Setting Up the DataFrame

First, let's set up a pandas DataFrame to work with. We’ll create a DataFrame called df containing different variations of job titles. Here's how to define it:

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

After executing the above code, the DataFrame will look like this:

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

The Similarity Function

To identify similar values, we can utilize Python's difflib.SequenceMatcher, which calculates the similarity ratio between two strings. Here's a simple function that does just that:

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

For instance, by calling similar("Sales Acct.", "Sales Acc."), we get a ratio of approximately 0.95, indicating high similarity between these two strings.

Implementing the Similarity Check

Now that we can calculate similarity, the next step is to flag similar cells in the DataFrame. We will add a new variable called Category based on the similarity ratio.

Step-by-Step Breakdown

Calculate Similarity Ratios:
We need to find the maximum similarity ratio for each job title in the DataFrame. We will create a new column called Match to store these ratios.

Categorize Similar Titles:

If the similarity ratio is >= 0.8, we’ll assign them the same category number (starting from 1).

For titles with a ratio of < 0.8, we’ll assign them a unique category number starting from the highest current category.

Here’s how we can implement this in code:

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

Final Output

After executing the above code, our DataFrame will be updated to include the Category column, which flags similar job titles:

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

Conclusion

Flagging similar cell values in a pandas DataFrame can significantly enhance your data cleaning efforts, making your analysis more precise and streamlined. By utilizing Python's difflib and pandas capabilities, we were able to create a method that not only identifies but also categorizes similar titles expertly. Implementing such techniques can save you a great deal of time and effort in your data processing workflow.

Try it out on your own datasets and see how it transforms your data management strategies!

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