Скачать или смотреть How to Use pandas.cut to Set a Bin from

Learn how to effectively use `pandas.cut` to define a custom time bin from 22:00 to 6:00, alongside payload strategies for accurate time handling in your data analysis.
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This video is based on the question https://stackoverflow.com/q/68262127/ asked by the user 'polaris' ( https://stackoverflow.com/u/12335080/ ) and on the answer https://stackoverflow.com/a/68262723/ provided by the user 'Henry Ecker' ( https://stackoverflow.com/u/15497888/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

When working with time-based data in Python, especially with the powerful library Pandas, you might run into the need to categorize or bin your data into specific time intervals. This is particularly useful for analysis in various domains like business, healthcare, and more.

A common question arises when dealing with time segments that span across midnight, such as setting a bin from 22:00 (10 PM) to 06:00 (6 AM). This can present challenges because the standard time representation resets after midnight. In this guide, we'll explore how to effectively achieve this using the pd.cut function in Pandas.

The Problem

Suppose you have a dataset represented by datetime objects that span a complete day (24 hours) and you wish to divide them into several intervals. In our case, we want to categorize these into intervals including the one from 22:00 to 06:00.

The required intervals are:

[6,11)

[11,14)

[14,17)

[17,22)

[22,6)

To address this, let's delve into a structured solution.

The Solution

To successfully cut this data into the desired intervals, we can use the pd.cut function with an adjustment to handle the 24-hour format. Specifically, we can shift our datetime values so that the lower bound becomes midnight for easier classification. Here’s how to do it step-by-step:

Step 1: Initialize Your Data

First, create a sample DataFrame containing datetime objects for each hour of a day.

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

Step 2: Adjust the Datetime

Next, we will offset the datetime by 6 hours. This offsetting moves our time frame, allowing us to work comfortably within the bounds of the 24-hour clock while ensuring that our earlier hours (like 1 AM) can now be accurately classified:

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

Step 3: Review the Results

After running the above code, you can check the new classifications in the DataFrame:

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

The output will show that the datetime values are now categorized as follows:

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

Explanation of the Code

Offsetting: By subtracting pd.Timedelta(hours=6) from the datetime, we shift our entire timeline 6 hours back. Therefore, midnight becomes our new starting point at 0.

Binning: We define bins that categorize the hours into our required ranges, ensuring that any hour from 22:00 (10 PM) to 06:00 (6 AM) is captured correctly in the [22,6) category.

Conclusion

Setting bins for time intervals using pandas.cut can initially seem daunting, especially when dealing with ranges that cross midnight. However, by utilizing datetime offsetting, you can simplify this process dramatically.

Now, you can apply this technique to your datasets whenever the need arises to categorize time-based data effectively, ensuring that your analysis is precise and insightful.

If you have any more questions about handling datetime objects in Pandas, feel free to leave a comment below!