How to Replace Dataframe Column Values Based on Condition of Second Dataframe Values скачать в хорошем качестве

How to Replace Dataframe Column Values Based on Condition of Second Dataframe Values

Learn how to efficiently replace specific values in a Pandas DataFrame based on conditions from another DataFrame, particularly for CSV files involving fruit links. --- This video is based on the question https://stackoverflow.com/q/73318287/ asked by the user 'MarkWP' ( https://stackoverflow.com/u/16440431/ ) and on the answer https://stackoverflow.com/a/73322048/ provided by the user 'Pierre D' ( https://stackoverflow.com/u/758174/ ) 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 Replace Dataframe Column Values Based on Condition of Second Dataframe Values 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 Replace Dataframe Column Values Based on Condition of Second Dataframe Values In data analysis and manipulation using Python's Pandas library, one common scenario is needing to replace values in one DataFrame based on conditions from another DataFrame. This can be particularly useful in cases where you need to update links or references in your dataset. In this post, we'll walk through a practical example of how to replace the values in a column of a main.csv DataFrame based on conditions from a replacements.csv DataFrame that contains links for specific fruits. Let's dive into the details. The Problem Imagine you have two CSV files: replacements.csv - Contains fruit names along with their corresponding links, and an indicator to show whether the link should be changed. main.csv - Contains titles and a list of fruit names in the External Links column. Example Files replacements.csv: [[See Video to Reveal this Text or Code Snippet]] main.csv: [[See Video to Reveal this Text or Code Snippet]] Required Output Our goal is to update the External Links in main.csv wherever a fruit appears in replacements.csv with Link Changed marked as Yes. The expected output looks like this: [[See Video to Reveal this Text or Code Snippet]] The Solution Here’s how we can achieve this using Pandas: Step 1: Read the CSV Files First, we load both CSV files into Pandas DataFrames. [[See Video to Reveal this Text or Code Snippet]] Step 2: Convert the Strings in External Links to Lists The External Links column in main.csv contains strings that need to be converted into proper Python lists. [[See Video to Reveal this Text or Code Snippet]] Step 3: Create a Mapping Dictionary We will create a dictionary from replacements.csv to easily map fruits to their respective links. [[See Video to Reveal this Text or Code Snippet]] Step 4: Update the External Links Using the dictionary we created, we will map the fruits in the External Links column of main.csv with their corresponding links. [[See Video to Reveal this Text or Code Snippet]] Step 5: Save the Results Finally, we can save the modified DataFrame back to CSV format. [[See Video to Reveal this Text or Code Snippet]] Conclusion By following these steps, you efficiently replaced values based on conditions from another DataFrame. Using tools like Pandas, you can perform transformations on your datasets without losing time on manual data entry or inaccurate replacements. This method not only simplifies your data management but also helps improve the accuracy of your information, ensuring that fruits in main.csv point to the correct URLs from replacements.csv. Feel free to adapt this code to suit your specific dataset needs. Happy coding with Pandas!