Efficiently Group Your Pandas Dataframe into 10-Minute Intervals with Customized Aggregations

Learn how to effectively group your Pandas dataframe into `10-minute intervals`, applying different aggregation methods for each column like `last`, `max`, and `mean`. --- This video is based on the question https://stackoverflow.com/q/71024507/ asked by the user 'Stanisla Vavrika' ( https://stackoverflow.com/u/11517700/ ) and on the answer https://stackoverflow.com/a/71025164/ provided by the user 'S.Fadaei' ( https://stackoverflow.com/u/8895679/ ) 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: Pandas dataframe group by 10 min intervals with different actions on other columns 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. --- Efficiently Group Your Pandas Dataframe into 10-Minute Intervals with Customized Aggregations When working with time series data in Pandas, it’s often necessary to analyze data over specific intervals. For instance, you may want to aggregate your data into 10-minute intervals, applying different operations to different columns. This can help you summarize and analyze your data more effectively. Let's take a closer look at how you can achieve this using Pandas. The Problem Imagine you have a Pandas dataframe with a timestamp and several columns, which might look something like this: [[See Video to Reveal this Text or Code Snippet]] Your goal is to create 10-minute intervals where: The close_price column displays the last value of that interval. The highest_price column shows the maximum value of the interval. The volume column represents the mean value of the interval. Initially, you might attempt to use the following code: [[See Video to Reveal this Text or Code Snippet]] However, you noticed that the output seemed incorrect or included unexpected results, such as null rows for intervals lacking data. The Solution Your initial approach using dataframe.resample() combined with agg() is fundamentally correct. However, the issue arises due to several factors. Let’s break down the solution: 1. Understanding Resampling The resample() method aggregates data by continuously adding 10 minutes to the timestamp and calculating the requested aggregations. If there are periods (intervals) where there is no data, Pandas will create a NULL row for that interval. 2. Handling NULL Rows To address the come across NULL values in your resultant dataframe, use the dropna() method to remove these rows. This will ensure that your aggregated results are clean and easy to interpret. Here’s how you can include it in your code: [[See Video to Reveal this Text or Code Snippet]] 3. Customizing Your Aggregation Function In some cases, you may want to add other custom aggregation functions for additional columns. You can simply extend the agg dictionary as needed: [[See Video to Reveal this Text or Code Snippet]] Conclusion By utilizing the resample() method along with appropriate aggregation functions and cleaning the resultant dataframe of NULL rows, you can efficiently group and summarize your time series data into specified intervals. Keep in mind that a well-structured approach will enhance your analysis and recovery of insights from your data. Transform your dataframe today and enjoy the clarity that 10-minute intervals can bring to your data analysis!