Скачать или смотреть How to Add a New Date Column in a Pandas DataFrame: A Simple Guide to Date Parsing

Learn how to efficiently add a new date column to your Pandas DataFrame by parsing an existing date string. It's easier than you think!
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This video is based on the question https://stackoverflow.com/q/62998563/ asked by the user 'shuntera' ( https://stackoverflow.com/u/11081386/ ) and on the answer https://stackoverflow.com/a/62998650/ provided by the user 'ScootCork' ( https://stackoverflow.com/u/4700327/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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The Problem: Parsing Dates in Pandas

Working with date columns in Pandas can sometimes be a bit confusing, especially when you're trying to convert date formats. Suppose you have a Pandas DataFrame that includes a column with date strings, and you want to create a new column with these dates formatted in a simple way, such as mm/dd/yyyy.

Here’s an example of the data you might be dealing with:

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

Imagine you try to accomplish this task using the apply() function with a lambda expression. Yet, you encounter an error message indicating a TypeError regarding the parser.

The Solution: A More Efficient Approach

Instead of using the apply() method, you can achieve the same result more efficiently by using the pd.to_datetime() function directly on the DataFrame column. Here’s how you can do it:

Steps to Add a New Formatted Date Column

Convert the existing date strings to datetime format: Use pd.to_datetime() to convert the 'checkin' column which contains your date strings.

Format the new date column: After conversion, use the .dt.strftime() method to format the dates to your desired format.

Sample Code

Here’s how the implementation looks:

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

Expected Output

This will give you a new column in the DataFrame:

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

Conclusion

In summary, you can easily add a new date column to your Pandas DataFrame by parsing an existing date string without the need for applying a function row-by-row. By leveraging the pd.to_datetime() function along with dt.strftime(), you can streamline your data manipulation tasks in a more Pythonic way.

Feel free to adapt this method to your projects, and watch your data handling become simpler and more efficient!