Скачать или смотреть How to Fix an Empty Pandas DataFrame When Appending Rows

Learn how to effectively append data to a `Pandas DataFrame` to resolve issues with an empty DataFrame in Python.
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Resolving the Issue of an Empty Pandas DataFrame

When working with data manipulation in Python, particularly using the Pandas library, encountering an empty DataFrame after attempting to append new rows can be a common problem. In this guide, we will explore a scenario where a developer struggles with appending data to a DataFrame and ultimately find the solution to this issue.

The Problem: Empty DataFrame

Imagine you have a situation where you're parsing an XML file to extract information into a Pandas DataFrame. You might write code similar to the following:

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

When you run this code, even though you can see your temporary data being populated correctly inside the loop, your final DataFrame (df) ends up being empty. The reason for this is found in the line where you attempt to append new_row to df.

The Solution: Correcting the Append Method

To resolve the issue of the empty DataFrame, you must actually assign the result of the append method back to your DataFrame variable. The line in question should be modified as follows:

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

Why the Change Matters

Understanding append(): The append() method in Pandas creates a new DataFrame that contains the data of the original DataFrame plus the new rows you want to add. However, it does not modify the original DataFrame in place.

Reassignment: By reassigning df to the result of df.append(new_row), you ensure that your DataFrame now contains the newly added rows. Without this reassignment, the original df remains unchanged and empty.

Final Code Example

Here's how the corrected part of your code should look:

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

Conclusion

By implementing this simple yet crucial change to your DataFrame manipulation, you can resolve the issue of the empty DataFrame and successfully append new rows from your XML data. This solution is a great reminder of the importance of understanding how DataFrame methods work in Pandas!

Now you can continue your data processing without the frustration of dealing with an empty DataFrame. Happy coding!