Скачать или смотреть Solving the dtypes as object Problem in Pandas: Converting Dates Smoothly

Struggling with getting dtypes as object in Pandas for date columns? Discover how to easily convert your datetime columns to just the date format without time.
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This video is based on the question https://stackoverflow.com/q/69635983/ asked by the user 'Deke Marquardt' ( https://stackoverflow.com/u/15416969/ ) and on the answer https://stackoverflow.com/a/69636846/ provided by the user 'Jonathan Leon' ( https://stackoverflow.com/u/12133434/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding and Fixing the dtypes as object Problem in Pandas

Working with dates in Python's Pandas library can sometimes lead to frustrating predicaments, particularly when the data types do not match expectations. A common issue many developers face is when trying to convert datetime columns, they find that the dtype is showing up as an object instead of the desired date format. In this guide, we'll explore this issue and learn how to convert datetime columns effectively to remove unwanted time data.

The Problem

One common situation arises when you have a column with datetime values, and you want to extract only the date from those values. For instance, you might have a column like this:

CLOSE_DATE = 2018-07-13 01:04:16

Despite using functions like pd.to_datetime(), the column returns a data type of object, rather than the normalized date format. This can significantly affect how you handle date comparisons and operations.

Sample Code that Failed

Here’s a snippet of code that exemplifies the problem:

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

In this example, the goal is to convert CLOSE_DATE to a date format without the time component. However, the outcome still leads to an object type instead of the expected date format.

The Solution

To resolve this issue, we need to take a couple of steps. Below, we outline a proper method to ensure your datetime data is correctly formatted as desired.

Step 1: Create a Sample DataFrame

First, we can create a sample DataFrame similar to the one mentioned in your question to test our solution:

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

Step 2: Normalize the Dates

Next, we want to create a new column that contains only the date without the time. This can be achieved using the dt.normalize() method, which normalizes the time part of the datetime object to 00:00:00 but still keeps it as a datetime dtype:

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

Step 3: Verify the Result

After normalizing, let’s check the DataFrame and its dtypes:

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

The resulting DataFrame looks like this:

closevalnew_date2021-01-01 01:04:1612021-01-01.........2021-01-19 01:04:16192021-01-19And the dtypes will show:

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

With this method, you have effectively extracted the date component while maintaining the advantages of datetime operations in your DataFrame.

Conclusion

To summarize, dealing with datetime conversions in Pandas does not have to be a daunting task. By using the dt.normalize() approach, you seamlessly transition from datetime with time to a cleaner date format while retaining its dtype as datetime64[ns].

Next time you encounter a situation where Pandas keeps returning an object type for your datetime, remember this solution and implement it to keep your data organized and easy to manage. Happy coding!