Calculating Horse Ages: Master Age Determination with Python and Pandas

Learn how to accurately calculate horse ages using Python and Pandas, regardless of their birth date. This step-by-step guide simplifies your horse age calculations!
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This video is based on the question https://stackoverflow.com/q/74793515/ asked by the user 'onthepunt' ( https://stackoverflow.com/u/19794510/ ) and on the answer https://stackoverflow.com/a/74793723/ provided by the user 'Panda Kim' ( https://stackoverflow.com/u/20430449/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Calculating Horse Ages: Master Age Determination with Python and Pandas

When it comes to horses, their age classification can be quite fascinating and somewhat unique. All horses share the same birthday of 1st August when it comes to their age classification, regardless of their actual birth dates. This means that a horse born just one day before this date is considered to be 1 year old when that date arrives.

The Challenge

As an example, if a horse was born on 31st July, they would still be classified as 1 year old when 1st August rolls around. This particular rule can lead to some confusion, especially if you're trying to calculate a horse's age on a specific race meeting date.

Consider this scenario:

You have a DataFrame with three columns:

FoalDate: The horse's actual birthday.

MeetingDate: The date of a race meeting.

Age: The age of the horse, which currently is filled with zeros in your DataFrame.

You want to calculate the correct horse age for each meeting date based on their birthday classification. For example, a horse born on 20/11/2014 would be 2 years old on 30/04/2017, as they had celebrated their birthday two times by that meeting date.

Let's Dive into the Solution

Step 1: Set Up Your DataFrame

First, you need to create the DataFrame that contains the foal dates and meeting dates of the horses. Below is a sample code to create your DataFrame:

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

This gives you the following DataFrame:

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

Step 2: Convert Dates to Datetime Format

Next, you need to convert the dates into a datetime format for better manipulation. Use the following code:

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

Step 3: Calculate the Age

With your dates in the correct format, you can now proceed to calculate the horse's age using logical conditions. The logic goes like this:

Check if the month of the foal date is before August (this means the horse has not turned a year older yet).

Check if the meeting date is in August or later (this indicates that the horse can age).

Here’s how it looks in code:

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

Step 4: Assign Age Back to DataFrame

Finally, you will want to assign these calculated ages back to your original DataFrame:

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

Final Output

Your DataFrame now displays the horses' accurate ages:

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

Conclusion

Using this method, you can accurately determine the age of any horse at various meeting dates while respecting their 1st August birthday classification. This process can be applied to similar problems in animal age calculations by simply adjusting the birthday date logic.

Feel free to adapt this code for your own use case and enjoy the ride!

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