Скачать или смотреть How to Concatenate Rows Based on a Condition in Python with Pandas

A comprehensive guide on how to concatenate rows with conditions in Python using Pandas, perfect for managing datasets that include 'nan' string values.
---
This video is based on the question https://stackoverflow.com/q/74354757/ asked by the user 'Mando' ( https://stackoverflow.com/u/18614437/ ) and on the answer https://stackoverflow.com/a/74357074/ provided by the user 'Bushmaster' ( https://stackoverflow.com/u/15415267/ ) 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: Concatenate rows based on a condition in Python

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.
---
How to Concatenate Rows Based on a Condition in Python with Pandas

Handling datasets can be tricky, especially when dealing with missing values or placeholders such as 'nan'. In this guide, we will explore how you can effectively concatenate rows in a pandas DataFrame based on a specific condition: when a column contains the string 'nan'. We’ll walk you through an example to clarify the process and help you implement it in your own projects.

The Problem

Let’s say you have a DataFrame that contains several rows of data, some of which have the title 'nan'. You want to consolidate these rows by combining their contents with the last valid title that isn’t 'nan'. Here's a quick look at our starting table:

TitleContentMeasureBackgroundtext1Measure 1MethodabcMeasure 1nandfgMeasure 1Backgroundtext2Measure 2MethodabcdfgMeasure 2Backgroundtext3Measure 3MethodabMeasure 3nancdMeasure 3nanfgMeasure 3The desired table should look like this:

TitleContentMeasureBackgroundtext1Measure 1MethodabcdfgMeasure 1Backgroundtext2Measure 2MethodabcdfgMeasure 2Backgroundtext3Measure 3MethodabcdfgMeasure 3The Solution

To achieve the above transformation, we can follow a systematic approach involving a few steps:

Step 1: Setup Your DataFrame

First, let's create our initial DataFrame. You can do this using the following code:

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

Step 2: Create a New Column to Track Last Valid Title

We need to replace 'nan' strings with the last valid title. For this, we can utilize the fillna method combined with forward fill:

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

Step 3: Assign IDs to Each Group

Next, we will assign an ID to each group of rows based on the last valid title generated in the previous step. This helps us segregate the rows by title changes:

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

Step 4: Aggregate Rows Based on IDs

Now that we have groups identified by unique IDs, we can aggregate the rows. Here’s how:

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

Step 5: Review the Final DataFrame

Running the above code will give you a new DataFrame that combines the rows correctly, ready for use:

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

Conclusion

With this step-by-step guide, you can easily concatenate rows based on conditions in a dataset using Python’s Pandas library. By carefully filling and aggregating your DataFrame, you transform messy data into cleaner, more usable structures. Remember, handling 'nan' appropriately is crucial in data processing, ensuring that your analyses yield accurate insights.

Feel free to adapt this example to your datasets, and happy coding!