Скачать или смотреть How to Calculate Date + 1 in Pandas Based on Time Conditions

Learn how to adjust dates in your Pandas DataFrame based on time conditions, excluding weekends and holidays.
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Adjusting Dates in Pandas with Time Conditions

Working with dates and times in Pandas can be tricky, especially when certain conditions apply that require adjusting the date. In this guide, we'll address a common scenario: how to calculate the date plus one day if the time is greater than 4 PM (16:00 in 24-hour format), and minus one day if the time is less than 9:30 AM. We will also handle weekends and holidays, ensuring that we only operate on business days.

The Problem

Let's say you have a DataFrame that looks like this:

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Here, the PDate column holds timestamps in the format of 'YYYYMMDDTHHMMSS.sss'. Your goal is to adjust the date based on the specified conditions:

If time is greater than 4 PM: Add one business day to the date.

If time is less than 9:30 AM: Subtract one business day from the date.

Business days are from Monday to Friday; if the current day is Friday and you add a day, it should skip to Monday. Conversely, if the current day is Monday and you subtract a day, it should go back to Friday.

Steps to Solve the Problem

Let's break down the solution step-by-step:

Step 1: Convert to datetime

The first thing we need to do is convert the PDate column from its string format into a Pandas datetime format. This can be achieved using the pd.to_datetime() function.

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Step 2: Define Conditions for Time

Next, we need to define our conditions based on the time extracted from the PDate column.

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Step 3: Apply Business Day Adjustments

Now, we can utilize np.select() to create new dates based on these conditions. We will use BDay from pandas.tseries.offsets to manage business days.

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Step 4: Viewing the Results

Finally, let’s take a look at how our new_date column looks in the DataFrame.

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The complete DataFrame will show you the original and adjusted dates:

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Conclusion

By following these steps, you can efficiently calculate the date adjustments needed based on specific time conditions using Pandas. Remember, managing business days is crucial in such applications. This approach is useful not only in financial analysis but in any situation where date accuracy is essential.

Now you have the tools to handle dates and times effectively in your data analysis tasks. Happy coding!