Скачать или смотреть Converting Integer to Datetime in Python

Learn how to easily convert integer date representations into datetime objects using Pandas in Python. Ideal for data manipulation!
---
This video is based on the question https://stackoverflow.com/q/73361400/ asked by the user 'Carlos Juvenal' ( https://stackoverflow.com/u/19109820/ ) and on the answer https://stackoverflow.com/a/73361473/ provided by the user 'azro' ( https://stackoverflow.com/u/7212686/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Convert Integer into Datetime in Python

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Converting Integer into Datetime in Python: A Comprehensive Guide

If you're working with data in Python, you might come across scenarios where dates are represented as integers. This can become a hurdle, especially if you need to perform date manipulations or analyses. In this guide, we'll explore a common problem: converting integer date representations in a Pandas DataFrame into a proper datetime format.

The Problem

Imagine you have a DataFrame with a column labeled DATE, where dates are represented as integers, such as:

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

The format of these integers is as follows:

Year: The first two digits

Month: The next two digits

Day: The following two digits

Hour: The last two digits

While attempting to manipulate these integers into a more traditional datetime format, you may find that your initial attempts might not yield the correct year, as they could convert incorrectly into floats.

The Solution

To successfully convert these integer date representations into a more manageable datetime format, you can use the capabilities of the Pandas library along with regular expressions (regex). Here's how to do it step by step.

Step 1: Import Pandas

First, ensure you import the Pandas library:

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

Step 2: Create Your DataFrame

Next, set up an example DataFrame to work with. Here's what the DataFrame looks like:

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

Step 3: Extract Date Components

Using the str.extract method along with a regex pattern, you can break down the integer into its date components (year, month, day, and hour):

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

Here's how the regex works:

(\d{2}): Matches any two digits and captures them.

The pattern is repeated four times to capture the year, month, day, and hour.

Outcome

After performing the above steps, your DataFrame will now look like this:

DATEyearmonthdayhour18010101180101012012312420123124Additional Remarks

The years captured may need to be converted to a four-digit format if your analysis requires it. You could do this with additional logic, such as adding 2000 for years greater than or equal to 20.

Always ensure your data types are correct before performing any operations to prevent errors and ensure accuracy.

Conclusion

Converting integer representations of dates into usable datetime formats in Python is straightforward with the Pandas library. By using string manipulation and regex, you can efficiently extract and organize your data for further analysis. This method will not only save you time but also enhance your data manipulation capabilities in Python.

Happy coding!