Скачать или смотреть How to Replace NaN in DataFrames with Previous Values Using pandas

Learn how to efficiently fill `NaN` values in your pandas DataFrame by referencing previous records. Streamline your data with these easy-to-follow steps!
---
This video is based on the question https://stackoverflow.com/q/67697412/ asked by the user 'MTALY' ( https://stackoverflow.com/u/3135025/ ) and on the answer https://stackoverflow.com/a/67697428/ provided by the user 'BENY' ( https://stackoverflow.com/u/7964527/ ) 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: Fill NAN if previous record found in a combined dataframe

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.
---
Filling NaN Values in Pandas DataFrames: A Step-by-Step Guide

When working with data in Python, especially with the pandas library, it is common to encounter NaN (Not a Number) values in a DataFrame. These can arise from missing or incomplete record information. In this post, we will tackle a specific problem: how to fill these NaN values based on existing data in the same DataFrame. Let’s explore this challenge with a practical example.

The Problem

Imagine that you have a DataFrame containing information about clients, orders, and their corresponding countries, as shown below:

CountryClient_idOrder_dateUSAaaa11/1/2020CAbbb22/2/2020JPccc32/2/2020USAaaa13/10/2020NaNaaa11/9/2020NaNbbb220/5/2021NaNccc320/5/2021NaNccc320/5/2021In this DataFrame, you'll notice that while some rows contain valid country information, other rows have NaN in the Country column. The goal is to find the first non-NaN country for each Client_id and fill in the NaN entries.

The Solution

To achieve this, we can leverage the pandas library’s powerful data manipulation capabilities. Here’s how you can fill in the NaN values based on the previous records of each Client_id using a two-step approach:

Step 1: Grouping and Transforming the Data

We will group the DataFrame by Client_id, and for each group, we will extract the first available non-null value of Country. This way, we can fill the NaN entries with the appropriate country.

Step 2: Filling the NaN Values

With the transformation ready, we can replace the NaN values directly in the Country column. Here’s the code you would implement:

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

Expected Output

After running the above code, the DataFrame will reflect the filled NaN values as follows:

CountryClient_idOrder_dateUSAaaa11/1/2020CAbbb22/2/2020JPccc32/2/2020USAaaa13/10/2020USAaaa11/9/2020CAbbb220/5/2021JPccc320/5/2021JPccc320/5/2021Conclusion

Filling in NaN values can significantly enhance the quality of your DataFrames, allowing for better data analysis and visualization. By using pandas to group by unique identifiers and then transforming the data accordingly, you can efficiently handle missing values. This method not only saves time but also keeps your data accurate and reliable for future analysis!

With this approach, you'll find that managing NaN values in your datasets becomes significantly simpler.