How to Use Regex in Python to Find + - 1 Numeric Matches in Strings

Discover how to efficiently use Python regex to extract rows from a DataFrame that have similar prefixes and differ by one in digits.
---

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: finding + - 1 with regex python

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Finding Rows with Matching Prefixes and Numeric Variance in Python

When working with data, especially strings that follow a specific pattern, you may encounter situations where you need to extract subsets of that data based on certain conditions. One such case involves matching rows with the same prefix and whose numeric suffix differs by one. In this guide, we will go through a practical solution to this problem using Python's regex capabilities along with the powerful Pandas library.

The Problem Abstracted

Imagine you have a dataset that contains strings formatted as follows:

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

You want to identify rows where the first two characters are the same and the numeric portion differs by exactly + 1 or -1. For example:

If you search for AB1, the result should include only AB2.

If you search for AB3, the results should include both AB2 and AB4.

Let's delve into how we can achieve this using regex and Pandas.

Setting Up the Data

First, we need to prepare our DataFrame with the strings that we want to analyze. In this case, we are working with names that follow a specific format of two letters followed by a number. Here's how we could start:

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

Extracting Prefixes and Numbers

To implement the desired solution, we begin by using regex to extract the prefixes and the numeric parts from our DataFrame. The following code snippet demonstrates how to do this:

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

Breakdown:

Regex Pattern: The pattern (?P<name1>\D+ )(?P<Num>\d+ ) is used where:

\D+ matches any non-digit characters (the prefix).

\d+ matches the numeric digits (the suffix).

Conversion: We convert the numeric portion to an integer type for easy comparison.

Sorting: The resulting DataFrame is sorted by the prefix and the number.

The output of this data extraction will look like:

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

Identifying Matches with Shift Function

Next, we need to check for the numeric difference of ±1. To accomplish this, we create a function that identifies and returns the matching rows:

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

Explanation:

Input Row: The function identifies which row corresponds to the input parameter.

Prefix Extraction: It gets the relevant prefix.

Filtering: It then selects all rows that have the same prefix.

Shift Logic: By using the shift() function, we can determine whether the numbers immediately before or after the input row's number match our ±1 criteria.

Testing the Function

We can now test our function to see how it works for different cases:

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

Expected Output:

For fun('AB1', u) the output will be:

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

And for fun('AB3', u) the output will be:

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

Conclusion

By utilizing the power of regex along with Pandas, we can effectively filter through data to find rows that meet our specific criteria. This method ensures that you can manipulate and analyze data robustly and efficiently, combining the best of both worlds. Start implementing this logic in your data processing tasks, and enhance your analytical capabilities with Python!