Скачать с ютуб How to Generate Random Duration in Minutes and Add to Pandas DataFrame Dates в хорошем качестве

How to Generate Random Duration in Minutes and Add to Pandas DataFrame Dates

Learn how to create random durations between `45` and `60` minutes and apply them to a pandas DataFrame to compute checkout times. --- This video is based on the question https://stackoverflow.com/q/77065389/ asked by the user 'Splendor Ijeh' ( https://stackoverflow.com/u/22521541/ ) and on the answer https://stackoverflow.com/a/77065418/ provided by the user 'nicholaspooran' ( https://stackoverflow.com/u/22434294/ ) 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: Generate a list of random duration in minutes between 45 and 60, and add each random minute to a datetime column in a pandas 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. --- How to Generate Random Duration in Minutes and Add to Pandas DataFrame Dates In data processing and analysis, it's common to work with time-based data, especially when handling booking systems, appointments, or time tracking. A common task is to calculate a checkout time based on a random duration added to a check-in time. In this guide, we will explore how to generate a list of random durations between 45 and 60 minutes and use these durations to populate a checkout column in a Pandas DataFrame. The Problem You may find yourself in need of adding random durations to a datetime column in a Pandas DataFrame. For example, you might have a list of check-in times and want to create corresponding checkout times that vary randomly within a specified duration (in this case, between 45 and 60 minutes). Many may run into issues when trying to implement this, as shown in the code you've attempted: [[See Video to Reveal this Text or Code Snippet]] The problem here is that every iteration of the loop assigns a single duration value to the whole checkout column, rather than applying a unique random duration for each check-in. The Solution To solve this problem, we need to create a random duration for each row in the DataFrame and add that duration to its corresponding check-in time. Here’s how you can do that step-by-step. Step 1: Import Necessary Libraries You will need to import the random, datetime, and pandas libraries. [[See Video to Reveal this Text or Code Snippet]] Step 2: Create a Sample DataFrame Start by creating a sample DataFrame that includes a column with check-in times. [[See Video to Reveal this Text or Code Snippet]] Step 3: Generate Random Durations Next, create a random list of duration values between 45 and 60 minutes, ensuring the list matches the number of rows in your DataFrame. [[See Video to Reveal this Text or Code Snippet]] Step 4: Add Durations to Check-in Times Now, use the apply function to add the random durations to their corresponding check-in times. [[See Video to Reveal this Text or Code Snippet]] Step 5: Preview the DataFrame Finally, display the DataFrame to see the newly generated checkout times. [[See Video to Reveal this Text or Code Snippet]] Expected Output This code results in a DataFrame similar to the following, where each checkout time reflects the specified random duration added to the corresponding check-in time: [[See Video to Reveal this Text or Code Snippet]] Conclusion By following these steps, you can easily generate random durations in minutes and apply them to datetime columns in a pandas DataFrame. This approach can greatly enhance your data processing capabilities, especially for applications requiring random intervals or variations. Feel free to tweak the random duration range or extend the functionality as per your needs!