Custom Sorting in Python 3: How to Sort Strings with Pandas

Discover how to sort a list of strings in Python using `Pandas` so that non-numeric values come before numeric ones, all in ascending order.
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Custom Sorting in Python 3: How to Sort Strings with Pandas

Sorting data efficiently is a fundamental requirement in many programming tasks. In this guide, we will tackle a specific problem: sorting a list of strings where non-numeric values should appear before numeric ones, and both categories sorted in ascending order. This guide will help you understand how to implement this in Python using the Pandas library.

The Problem

Imagine you have a list of strings representing both numeric and non-numeric values. For example:

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

We want to sort this list such that:

Non-numeric strings are sorted alphabetically first.

Numeric strings are sorted in ascending order following the non-numeric ones.

The desired output would be:

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

The Solution

To achieve this sorting mechanism, we will implement a two-step sorting strategy using a custom sorting key in Python. Let’s break down the solution into manageable parts.

Step 1: Define a Function to Identify Numeric Values

First, we need a helper function that can distinguish between numeric and non-numeric strings. Here’s how this function works:

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

This function attempts to convert the string to a float. If successful, it returns True; otherwise, it returns False for non-numeric values.

Step 2: Sort the List Using a Custom Key

Next, we can sort the list using the built-in sort() method along with a custom key. The idea is to first use is_numeric to determine if a string is numeric or not, and then sort based on its value or alphabetical order.

Here’s how we can implement this:

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

Explanation of the Key:

The key function uses a tuple:

is_numeric(x): This will place all non-numeric values before numeric ones since False sorts before True.

float(x) if is_numeric(x) else x: This converts numeric strings to floats for proper numeric sorting; for non-numeric strings, it keeps them as they are.

Alternative Method for Elegance

Another elegant approach can integrate the numeric value conversion directly into the is_numeric function return. Here’s how it looks:

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

Using this method, we still achieve the same result but in a more compact manner.

Example Output

By applying either of the sorting strategies mentioned above, the output will be as follows:

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

Conclusion

Custom sorting can be very efficient with the right approach. By defining a clear function to distinguish numeric strings from non-numeric ones, we can easily sort mixed data types in Python. Keep this technique in mind while working on data-related tasks in Python, especially when using the Pandas library! Happy coding!