Скачать или смотреть Resolving the cannot do slice indexing Error in Pandas

Learn how to effectively resolve the `cannot do slice indexing` error in Pandas. This comprehensive guide will help you use the correct methods for DataFrame slicing in Python.
---
This video is based on the question https://stackoverflow.com/q/62357554/ asked by the user 'San9096' ( https://stackoverflow.com/u/13393830/ ) and on the answer https://stackoverflow.com/a/62357606/ provided by the user 'John Zwinck' ( https://stackoverflow.com/u/4323/ ) 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: cannot do slice indexing with these indexes error in Pandas

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.
---
Resolving the cannot do slice indexing Error in Pandas: A Step-by-Step Guide

If you're working with Pandas, you might have encountered the frustrating error message: "cannot do slice indexing with these indexes." This often occurs when you're trying to slice a DataFrame using an incorrect indexing method. Let's break down this problem, diagnose its cause, and provide a clear solution.

Understanding the Problem

Imagine you have a DataFrame in Pandas that looks something like this:

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

You might want to extract the last two rows of this DataFrame, which is a common requirement. However, when you try to do:

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

You receive the error:

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

This error arises because you are using .loc[] with incorrect indexers. In this case, loc expects labels from the DataFrame's index (which is of type DatetimeIndex), but you're providing integer values.

Understanding Indexing Methods in Pandas

Before diving into the solution, it's important to understand the difference between the two main indexing methods in Pandas:

.loc[]: This is primarily label-based indexing, which means you use the row labels (in this case, the dates) within the DataFrame to access data. If you try passing integer indices, you'll encounter errors like the one above.

.iloc[]: This is integer-location based indexing. With iloc, you can access data based on its integer position within the DataFrame, making it suitable for situations where you want to select rows based on their order rather than their labels.

Solution: Using iloc for Slicing

To successfully retrieve the last two rows of the DataFrame without causing any indexing errors, you should use the iloc[] method. Here’s how you can do it:

Step 1: Use iloc[]

Instead of using loc, use iloc to slice the DataFrame by integer location:

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

Explanation of the Code

df.iloc[-2:] retrieves the last two rows of the DataFrame.

The -2: means start from the second-to-last row and go to the end of the DataFrame.

Complete Example

Here's the complete code you can run to see it in action:

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

Conclusion

Utilizing the correct indexing methods in Pandas is essential for effective data manipulation and analysis. By understanding and applying .iloc, you can avoid common errors such as the cannot do slice indexing message and retrieve data precisely as you need.

Now you can confidently slice your Pandas DataFrames without running into errors. Happy coding!