Converting 360-day timeseries to standard date format in Python with Pandas

Learn how to convert a custom timeseries format into a standard date format using Python and Pandas! This guide will help you analyze your data more effectively.
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Converting a 360-day Timeseries into a Usable Date Format in Python

Dealing with custom timeseries data can be quite tricky, especially when the date format deviates from standard practices. If you're working with a timeseries where each year consists of only 360 days—using 30 days for each month—understanding how to convert that into a more traditional date format becomes crucial for analysis. In this guide, we will guide you through the process of transforming a timeseries column in Python using the Pandas library so you can analyze your data based on years, months, days, and hours.

The Problem

Imagine you have a dataset with a column that counts hours since January 1, 1970, but it follows a unique format where a year is just 360 days long. Therefore, the need arises to convert these hours into a format that can easily be broken down into year, month, day, and hour. For instance, the input of 233280.5 hours should be converted to a human-readable date format like 1997-01-01-01 (year-month-day-hour) for better analysis.

The Solution

In order to convert your timeseries data, we can leverage Python’s Pandas library. We'll take a systematic approach to break down the solution:

Step 1: Import Necessary Libraries

First, you'll need to import the necessary libraries into your Python environment. Add the following code at the top of your script:

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Step 2: Create Your Dataframe

Next, define the initial timeseries data that you need to convert:

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Step 3: Calculate Years and Fractional Years

Using numpy, calculate the number of full years and the remaining fraction of the year from the 360d_year_hours column:

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

Step 4: Create the New Datetime Column

With y being the array representing whole years, add those to your reference date in order to compute the new date:

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

Step 5: Determine Days in the Year

Now you’ll need to figure out how many days are in the new year, considering whether it's a leap year or not:

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Step 6: Update and Round the Datetime

Finally, update the datetime to include fractional days, round it to the nearest hour, and you’re done:

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Step 7: View the Results

After executing the above code, if you check the updated datetime column, it will display:

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

Conclusion

With these steps, you now have a functional method to convert a custom timeseries column based on a 360-day year into a conventional date format. You can apply this approach to your own datasets to facilitate better time-based analysis. Python’s Pandas library offers powerful functionalities that make these transformations straightforward and efficient.

If you have any questions or run into any issues, feel free to leave a comment below. Happy coding!