How to Pass datetime64[ns] from Pandas DataFrame to Function for Calculating Working Days

A detailed guide on how to calculate working days in a pandas DataFrame by correctly passing datetime objects to a function using apply.
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How to Pass datetime64[ns] from Pandas DataFrame to Function for Calculating Working Days

If you're working with Python's Pandas library and need to calculate the number of working days between two dates while accounting for public holidays, you may run into some frustration when trying to pass datetime64[ns] objects directly to your function. This guide will not only address this common issue but also guide you towards a clear solution.

The Challenge

Imagine you have a DataFrame that consists of start and end dates, and you want to add a new column that indicates the number of working days, excluding weekends and specified public holidays. Here’s a simplified view of your approach:

You created a function working_days(start, end, holidays) that calculates the number of network days between two dates. When you tried to apply this function directly to your DataFrame, you ended up with errors like:

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

or

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

These errors indicate that you're trying to pass entire series (or numpy arrays), which your function is not prepared to handle.

The Solution

To resolve this issue, you can leverage the apply() method that Pandas provides. This method is perfect for applying a function along the axis (rows) of a DataFrame. Here’s how you can implement this:

Step 1: Define Your Function

You already have your function working_days(start, end, holidays) defined. For reference, here’s the complete function:

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

Step 2: Create a DataFrame

You have your DataFrame structured as follows:

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

Step 3: Apply the Function

Here is the crucial part: using the .apply() method to iterate over each row of your DataFrame and apply the working_days function to the Start_Date and End_Date columns.

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

Explanation of the Code

Lambda Function: The lambda x: allows us to refer to each row in the DataFrame one by one as x.

Axis Parameter: We set axis=1 to ensure we are applying the function across rows.

Arguments Passed: Each row's Start_Date, End_Date, and public_holidays are passed to our working_days function.

Conclusion

Using .apply() is a powerful way to efficiently work with DataFrames in Pandas, especially when custom functions are needed for row-wise operations. By implementing this solution, you can now accurately compute the number of working days within your DataFrame without running into the common pitfalls related to datatype errors.

If you have more questions or run into issues, feel free to ask! Happy coding!