Does `inplace=True` Modify the Original Pandas DataFrame or Return a New One?

Inplace Editing in Pandas: Understanding inplace=True

In the realm of Pandas data manipulation, one often encounters the option of performing operations inplace, denoted by the inplace=True parameter. This parameter has a profound impact on how operations are applied and data is handled.

Impact of inplace=True on Return Value

When inplace=True is specified, the operation is directly applied to the original DataFrame object and returns None. This effectively modifies the object in place without creating a new one. In contrast, when inplace=False (which is the default), a new DataFrame object is created with the modified data and returned.

Object Handling with inplace=True and inplace=False

With inplace=True, the original DataFrame is directly modified and updated. However, when inplace=False, a new DataFrame is created using the original object. This new DataFrame reflects the applied operation and becomes the result.

Modifying Self with inplace=True

When inplace=True is employed, it's important to understand that all operations are modifying the original object itself. This means any subsequent operations on that object will be based on the updated data.

Example Usage

To illustrate the difference, consider the following operations:

# Inplace Drop (returns None)
df.dropna(axis='index', how='all', inplace=True)

# Non-inplace Drop (returns a new DataFrame)
new_df = df.dropna(axis='index', how='all', inplace=False)

In the first case, the original DataFrame df is modified in place by removing all rows with all NaN values. In the second case, a new DataFrame new_df is created with the modifications, while the original df remains unchanged.

Understanding the behavior of inplace=True when working with Pandas ensures efficient data handling and avoids unintended modifications to objects.

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