How to Resample Daily Data to Annual Frequency Using the Most Recent Day

Learn how to convert a daily time series to an annual frequency using the latest observation date in Python with Pandas. This guide breaks down the steps for clarity and ease of understanding.
---
This video is based on the question https://stackoverflow.com/q/62228721/ asked by the user 'stacksideways' ( https://stackoverflow.com/u/13465113/ ) and on the answer https://stackoverflow.com/a/62228967/ provided by the user 'filippo' ( https://stackoverflow.com/u/5629339/ ) 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 to annual from the most recent day?

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 Resample Daily Data to Annual Frequency Using the Most Recent Day

When working with time series data, especially in financial contexts, you may find yourself needing to convert daily observations to an annual frequency. However, you might want to base this conversion on the last observation rather than the last calendar day of each year. This guide will guide you through the steps to achieve this in Python using the Pandas library.

The Problem: Annual Resampling from Daily Data

In our scenario, we have a daily time series with observations that extend over several years. Let's say the latest observation is from June 6, 2020. We want to transform our daily data into annual data, but instead of using the default end-of-year dates (like December 31), we want to fix our annual timestamps to June 6 for each year.

For example, if your daily data runs from January 1, 2010, to June 6, 2020, the goal is to resample the data so that it reflects:

2010-06-06

2011-06-06

2012-06-06

2013-06-06

2014-06-06

2015-06-06

2016-06-06

2017-06-06

2018-06-06

2019-06-06

2020-06-06

Default Behavior of resample

When using Pandas' resample function, if you do not specify any adjustments, it will assume you want yearly entries to end on December 31 of each year. This is not suitable for our needs, as we require the records to reflect our latest observation date.

The Solution: Leveraging loffset

To achieve our desired resampling, we can use the loffset parameter in the resample() function. This adjustment allows us to manipulate the end-of-year frequency so that we can align with our required dates.

Step-by-Step Implementation

Let’s break down the implementation in Python:

Import Required Libraries:
Begin by importing the necessary libraries – Pandas for data manipulation and NumPy for numerical data generation.

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

Create Daily Time Series:
We will create a daily time series dataset that spans from January 1, 2010, to the current day.

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

Resample with loffset:
Now, we apply the resample() method on the time series. The loffset parameter will adjust the timestamps as needed.

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

Result

After executing the above code segment, you will get a series that looks like this:

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

Important Note

Keep in mind that this method may behave differently in the case of leap years or if your latest observation happens to fall on February 29. If you need to handle these cases, consider implementing additional checks or adjustments to your logic.

Conclusion

Transforming daily time series data to an annual frequency based on the last observation date can be accomplished seamlessly with Pandas. By utilizing the loffset parameter in the resample() method, you can tailor your data to meet specific requirements without altering the original dataset substantially.

Feel free to try out this method with your own datasets and adapt it any way to fit your needs!