Скачать или смотреть Understanding Why timestamp() Returns the Same Time in Python

Discover why the Python `timestamp()` method may return the same time across multiple function calls and learn how to achieve second-level precision in your timestamps.
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Understanding Why timestamp() Returns the Same Time in Python

If you've ever worked with time in Python, you might have encountered an issue where the timestamp() function returns the same value for consecutive calls, despite waiting a few seconds in between. This can be puzzling, especially when you're trying to set up time-based callbacks or log events accurately. In this post, we'll explore why this happens and how you can resolve it effectively.

The Problem

Let's look at an example to illustrate this problem. You might have implemented a callback function that prints the current time in UNIX epoch format as follows:

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

When calling this function multiple times, you observe the following output:

First call:

1614270080.0

2021-02-25 19:22:14.304776

Second call after a second:

1614270080.0

2021-02-25 19:22:15.498516

Last call after 2 seconds:

1614270080.0

2021-02-25 19:22:17.701154

Notice how the timestamp remains the same across these calls, while the human-readable time changes. This leads to a critical question: Why does datetime.now().timestamp() return the same time?

Understanding the Root Cause

The underlying issue stems from how Python represents floating-point numbers, particularly with regard to the system you are using. In Python, the timestamp() method returns a floating-point number (float) representing the current time in seconds since the epoch. The type of that float depends on the architecture of your Python build - 32-bit or 64-bit.

Here’s why this matters:

32-bit Floats: When using a 32-bit float, the precision it can express while representing a timestamp is insufficient, leading to approximations. In our given example, all timestamps that fall within the same second will be rounded down to the same representation, causing them to appear identical when printed.

64-bit Floats: On the other hand, a 64-bit float provides much higher precision, allowing for more distinct timestamps even when the function is called in quick succession.

Example of Precision Issues

To illustrate the difference in precision between 32-bit and 64-bit floats, let's run the following code:

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

This illustrates that with a 32-bit float, you cannot express different timestamps within a second, whereas 64-bit floats allow you to represent each one accurately.

Solution: Opt for a 64-bit Build

If you require second-level precision in your timestamps, the solution is straightforward: Use a 64-bit Python build. This will ensure that your timestamp() calls accurately reflect the time down to the sub-second level. Switching to a 64-bit version will unlock the full potential of the floating-point representation, thereby solving the issue you're facing.

Conclusion

Understanding why Python's timestamp() method can give the same result for multiple calls requires a deep dive into how floats are represented in memory. By ensuring that you are using a 64-bit build, you can achieve the precision you need for time-sensitive applications. Next time you face this issue, remember that it's not just about the code but also about the underlying architecture that impacts how time is reported.

With this guide, you're now equipped to handle timing in your Python applications with greater confidence. Don't hesitate to reach out if you have further questions or need additional guidance!