Скачать или смотреть How to Fill a New Column in a DataFrame Based on Multiple Conditions in Python

Learn how to create a new column in a pandas DataFrame by filling it with values that meet specific criteria using regex in Python.
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Filling a New Column in a DataFrame Based on Multiple Conditions in Python

When working with data in Python, especially with libraries like pandas, you may come across scenarios where you need to create a new column based on certain conditions from other columns in your DataFrame. In this guide, we'll tackle a specific problem: how to fill a new column in a pandas DataFrame where the values should be 17 characters long and should follow the format XXMPXXXXXXXXXXXX.

The Problem

Imagine you have the following DataFrame that contains some serial numbers, and you want to extract or derive a new serial number that matches specific criteria. Here’s the data you need to work with:

Serial Number NewSerial Number + KeywordSerial Number Old12MP322115673224312MP3221156732243 Restaurant12MP32211567322430Retail 12MP325145373082732514537308270K312MP3251773832657325177383265711MP322115673224311MP3221156732243MP322115673224311MP32511567322670MP3251156732267The goal is to create a new column called "Serial Number Final" that meets the specified conditions.

The Solution

Using Regular Expressions

To achieve this, you can utilize the power of regular expressions (regex) with the str.extract() function. Here are a couple of methods to extract the desired serial number format:

Method 1: General Regex Pattern

Create a regex pattern: The pattern (..MP.{13}) will help us identify values with the required format – two characters followed by “MP” and 13 additional characters.

Implement the extraction: Apply this regex pattern to the 'Serial Number + Keyword' column.

Here's the Python code to do this:

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

This will give you the following output:

Serial Number NewSerial Number + KeywordSerial Number OldSerial Number Final12MP322115673224312MP3221156732243 Restaurant12MP322115673224312MP32211567322430Retail 12MP3251453730827325145373082712MP32514537308270K312MP3251773832657325177383265712MP325177383265711MP322115673224311MP3221156732243MP322115673224311MP322115673224311MP32511567322670MP325115673226711MP3251156732267Method 2: Numeric Regex Pattern

If you know that the serial numbers will consist only of digits followed by "MP", you can refine the pattern. The regex (\d\dMP\d{13}) ensures that the output consists strictly of digits.

Here's how to apply this:

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

Picking the First Match from Multiple Columns

If you want to consider multiple columns for deriving the new serial number, you can use the apply function combined with bfill. Here’s an example of how to do that:

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

This approach ensures that you look through both relevant columns and pick the first match that meets the criteria.

Conclusion

With this, you can successfully create a new column in your pandas DataFrame based on specified conditions from other columns. Regular expressions are a powerful tool for string manipulation and can save you significant time when dealing with data processing tasks in Python.

Feel free to experiment with the code snippets provided and adjust your regex patterns according to your data’s structures!