How to Resample Daily Time Series Data with a Custom Half-Hour Start Time in Python

Описание: Discover how to efficiently resample daily time series data with a half-hour offset using Python and Pandas. Learn step-by-step instructions to get your desired results quickly.
---
This video is based on the question https://stackoverflow.com/q/62882688/ asked by the user 'Zephaniah Irvine' ( https://stackoverflow.com/u/3905233/ ) and on the answer https://stackoverflow.com/a/62882897/ provided by the user 'David Erickson' ( https://stackoverflow.com/u/6366770/ ) 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: Resample daily time series data with half hour start time

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.
---
Resampling Daily Time Series Data with a Custom Half-Hour Start Time in Python

When working with time series data, you may occasionally find yourself needing to resample data to different time intervals. This can become particularly tricky when the starting time of your day does not align with the conventional clock hours, for example, a day beginning at 16:30 instead of the more common 00:00. In this post, we will explore how to accomplish this using Python’s Pandas library.

Understanding the Problem

Consider the following raw data representing transactions, where each entry is timestamped with the time of creation:

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

Our goal is to calculate the sum of transactions over a 24-hour period, starting from 16:30 every day. The desired output should look like this:

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

However, using the built-in resampling function df.resample('24H', base=16).sum() will not yield the correct starting time as it begins at 16:00 instead of 16:30.

The Solution: Using pd.Grouper

To solve this problem, we will use pd.Grouper, which is better suited for summarizing data into different date/time intervals. Below are the steps you need to follow:

Step 1: Prepare Your Data

First, we need to set up our DataFrame with the raw data.

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

Step 2: Convert the Timestamp to DateTime

Next, we need to convert the createdAt column into a DateTime format that Pandas can manipulate.

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

Step 3: Group the Data Accordingly

Now we will utilize the pd.Grouper function to group the data based on our 24-hour interval that starts at 16:30.

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

Step 4: Clean Up the Data

After grouping, we will filter out any rows where the volume is zero:

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

Final Output

Finally, the resulting grouped DataFrame, df1, will hold the calculated sums for each 24-hour interval starting at 16:30. The output should look like:

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

Conclusion

Resampling time series data can pose unique challenges, especially when your starting time doesn't conform to standard practices. By utilizing pd.Grouper, you can effectively manage and analyze your data to match the desired time intervals. With these steps, you can confidently perform similar operations on your datasets with non-standard start times. Happy coding!