Fix set_index() Returning None in Pandas 🐼

Discover the correct way to use pandas `set_index()` for your DataFrame and understand why it might return None.
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This video is based on the question https://stackoverflow.com/q/74105646/ asked by the user 'Callum' ( https://stackoverflow.com/u/19142056/ ) and on the answer https://stackoverflow.com/a/74105690/ provided by the user 'Aldiandya Irsyad Nur Farizi' ( https://stackoverflow.com/u/7338064/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding Why set_index() Returns None in Pandas

If you are new to Python and Pandas, you may encounter a situation where you try to change the index of a DataFrame using the set_index() function, but the function seems to return None. This issue can be confusing, especially if you expect your DataFrame to be modified with the new index. Let's delve into the problem and clarify how to properly use this function.

The Problem: Attempting to Change the Index

Let's say you have the following DataFrame:

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

You tried to change its index to the 'Close Time' column using the following code:

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

However, you found that the DataFrame did not change, and when you checked the return value using:

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

You saw None. This behavior might leave you perplexed as a beginner.

The Solution: Proper Use of set_index()

The confusion arises from the use of the inplace=True parameter. Here's what you need to understand about set_index():

Inplace Modification: When you use inplace=True, set_index() modifies the original DataFrame directly and does not return a new DataFrame. Therefore, if you try to assign the result to a new variable, it will receive None.

To properly set the index, you can follow either of the two approaches below:

Option 1: Use Inplace and Print the Original DataFrame

If you simply want to modify your existing DataFrame and print it, you can do:

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

Option 2: Assign to a New Variable Without Inplace

If you prefer to create a new DataFrame while keeping the original one unchanged, you can omit the inplace argument and do:

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

Final Thoughts

Using the set_index() method correctly will adapt your DataFrame structure as needed. Always remember that inplace=True changes the object directly without returning it, while omitting it allows you to create a new DataFrame. Understanding these distinctions will pave the way for a smoother experience as you dive deeper into Pandas.

If you try the suggested fixes, you should see your DataFrame updated as expected. Happy coding!