Скачать или смотреть How to Create a Date Column with Conditional Format in Pandas

Learn how to handle different date formats in a Pandas DataFrame based on country conditions. This guide helps you create a date column that adheres to specific formats for the USA and the rest of the world.
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This video is based on the question https://stackoverflow.com/q/64599343/ asked by the user 'Gustavo Zárate' ( https://stackoverflow.com/u/14172184/ ) and on the answer https://stackoverflow.com/a/64607232/ provided by the user 'FObersteiner' ( https://stackoverflow.com/u/10197418/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

Are you facing a challenge while trying to create a date column in your Pandas DataFrame, especially when dealing with different date formats based on countries? If your DataFrame has a datetime string column with values formatted differently for the USA and other countries, you might find it tricky to convert them into a unified date format.

In this guide, we will guide you through the necessary steps and solutions to create a new date column that formats date strings conditionally, based on the country specified in your DataFrame.

Problem Breakdown

Imagine you have a DataFrame that looks something like this:

datetimecountry2020-02-10 8:02:55 PM PSTUSA2020-02-10 8:02:55 PM PSTUSA2020-10-02 8:02:55 PM PSTFRANCEIn this DataFrame:

The date format for the USA is %y-%d-%m.

The date format for other countries, such as France, is %y-%m-%d.

The goal is to create a new date column that correctly interprets these formats based on the provided country.

Solution Steps

Step 1: Import Required Libraries

First, you'll need to import the necessary libraries:

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Step 2: Create Example DataFrame

Next, you can create your initial DataFrame using sample data:

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Step 3: Specify Day First

You can use a new column to determine if the day should come first based on the country:

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Step 4: Map Time Zones

To handle time zones, create a mapping dictionary that will map the time zone string to the dateutil timezone:

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Step 5: Custom Date Parsing Function

For clarity and maintainability, you can define a function that will parse the datetime strings based on the dayfirst condition.

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Step 6: Apply the Function to the DataFrame

Finally, use the apply() function to create the new parsed date column:

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Resulting DataFrame

Your DataFrame will now include a new column parsed_dt with uniformly formatted datetime objects:

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Conclusion

Handling multiple date formats based on country conditions in a Pandas DataFrame can initially seem daunting. However, by following the steps outlined above and creating a structured conditional logic, you can successfully resolve this challenge.

If you encounter any issues implementing these solutions or have further questions, feel free to ask for help!