Скачать или смотреть How to Create New Rows from Column Values in a Pandas DataFrame

Discover how to transform data in a Pandas DataFrame by creating new rows from specific column values. This guide provides practical code snippets and explanations.
---
This video is based on the question https://stackoverflow.com/q/62337224/ asked by the user 'aeapen' ( https://stackoverflow.com/u/8888469/ ) and on the answer https://stackoverflow.com/a/62337423/ provided by the user 'rpanai' ( https://stackoverflow.com/u/4819376/ ) 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: How to create a new rows from column values of pandas data frame

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 Create New Rows from Column Values in a Pandas DataFrame: A Step-by-Step Guide

When working with data in Python using the Pandas library, you often encounter situations where you need to manipulate the data to obtain the desired format. One common challenge is transforming your DataFrame in such a way that new rows are created based on the values present in specific columns.

In this guide, we will explore a practical scenario where we need to generate new rows in a DataFrame based on conditions derived from the column values. Let’s start by looking at our initial DataFrame structure and the desired outcome.

The Problem

Imagine we have the following DataFrame containing information about regions and postal codes:

DateCountryTypeZip_InclZip_Excl10/4/2020FRRegional57_67_682/1/2020GBRegionalAB_DD17/3/2021GBRegionalBT_TYTS_TN18/3/2021GBRegional19/1/2021INRegional68Transformation Rules

We want to transform this DataFrame based on the following rules:

If the Zip_Incl is not empty, that value should populate the Zip_Final.

If both Zip_Incl and Zip_Excl have values, Zip_Incl should take precedence.

If Zip_Incl is empty and Zip_Excl has a value, then Zip_Excl should be used for Zip_Final.

Desired Output

After applying the transformation rules, our resulting DataFrame should look like this:

DateCountryTypeZip_InclZip_ExclZip_Final10/4/2020FRRegional575710/4/2020FRRegional676710/4/2020FRRegional68682/1/2020GBRegionalAB_DDAB2/1/2020GBRegionalAB_DDDD17/3/2021GBRegionalBTTS_TNBT17/3/2021GBRegionalTYTS_TNTY18/3/2021GBRegional19/1/2021INRegional6868The Solution

Let's take a look at how we can achieve this transformation step by step using Python's Pandas library.

Step 1: Setup the DataFrame

First, we need to create our initial DataFrame:

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

Step 2: Identify Values

Next, we identify which entries are not empty in the Zip_Incl and Zip_Excl columns:

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

Step 3: Select the First Non-Empty Value

Using numpy, we can extract the first non-empty value in each row:

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

Step 4: Populate the Zip_Final Column

We then use this selection to populate our new Zip_Final column:

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

Step 5: Split and Explode for Final Output

Finally, we want to split the Zip_Final by underscores and create multiple rows for each value:

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

Final Output

After executing the above code, you will obtain the desired DataFrame with new rows created based on the values from the Zip_Incl and Zip_Excl columns.

Conclusion

Transforming a Pandas DataFrame can be straightforward with the right approach. By applying the outlined method, you can efficiently create new rows based on existing column values. Whether you're using numpy for performance or Pandas methods for simplicity, adjusting your DataFrame requires just a few key steps and careful attention to the rules defining your transformations.

If you have any questions or need further assistance with Pandas DataFrames, feel free to ask!