Скачать или смотреть How to Properly Convert Float Values to Integers in Pandas DataFrames without Decimal Points

Learn how to successfully convert float values to integers in your Pandas DataFrames and remove unwanted decimal points with step-by-step instructions.
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This video is based on the question https://stackoverflow.com/q/64048662/ asked by the user 'Daniel Poh' ( https://stackoverflow.com/u/9457116/ ) and on the answer https://stackoverflow.com/a/64048759/ provided by the user 'BENY' ( https://stackoverflow.com/u/7964527/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Properly Convert Float Values to Integers in Pandas DataFrames without Decimal Points

When working with data in Pandas, you may encounter scenarios where you need to convert float values within a DataFrame to integers. A common issue arises when, despite using methods like rounding and astype(int), the decimal points do not disappear as expected. In this guide, we will explore the problem and provide a clear solution to achieve the desired output.

Understanding the Problem

Imagine you have a DataFrame filled with float numbers, some of which need to be converted to integers. After applying the .round() method followed by astype(int), you expect to see whole numbers without any decimal points. However, some values still display decimal points, as shown below:

Original DataFrame:

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

Rounding and Conversion Code:

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

Output After Conversion:

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

Expected Output Should Be:

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

What Went Wrong?

The issue here is that when using .iloc to assign the rounded values back to the DataFrame, it does not change the data types of the columns. Thus, while the content appears rounded, it remains a floating-point representation due to the original data type of the columns.

The Solution

To properly convert float values to integers and ensure no decimal points remain, follow these simple steps:

Identify Columns to Convert:
Use df.columns[] to target the specific columns you wish to convert, rather than using .iloc.

Apply Conversion:
Use astype(int) directly on the selected columns after performing the rounding. This will ensure the type change is correctly applied.

Code Implementation

Here is how to implement the solution in code:

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

Expected Output

After executing the above code, your DataFrame will be correctly modified to remove decimal points:

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

Conclusion

By following the proper steps for converting float values to integers within Pandas DataFrames, you can avoid common pitfalls related to datatype retention. This approach ensures that you achieve clean, expected outputs without lingering decimal points. So the next time you encounter uneven decimal points in your DataFrame after a conversion, remember to target your columns correctly!

Feel free to share your thoughts or questions in the comments below, and happy coding!