Is `inplace=True` in Pandas Considered Harmful?

In pandas, is inplace = True considered harmful, or not?

In spite of previous discussions with conflicting conclusions, inplace = True is considered harmful in pandas. Plans are underway for its future deprecation.

Why is inplace = False the default behavior?

It offers several advantages:

• Chained operations: It enables method chaining, such as df.dropna().rename().sum(), providing clarity and potentially optimizing execution order.
• Safety and clarity: Inplace modifications can raise SettingWithCopyWarning and exhibit unpredictable behavior when called on DataFrame columns. inplace = False avoids these issues.
• Predictable results: Out-of-place operations always return a new DataFrame, ensuring consistency.

When should inplace = True be used?

Despite its general drawbacks, inplace = True might be useful in specific circumstances:

• Improved memory usage: In some cases, it can reduce memory consumption compared to chained operations.

Can you anticipate whether inplace = True operations will truly be carried out in-place?

No, you cannot guarantee it. pandas may still create a copy behind the scenes and reassign the reference later.

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