In Pandas, What's the Difference Between `inplace=True` and `inplace=False`?

Exploring inplace=True in pandas

The pandas library frequently provides the option to make modifications to an object in place, as demonstrated by the following statement:

df.dropna(axis='index', how='all', inplace=True)

Understanding how inplace=True operates and what it returns is essential.

Operations with inplace=True

When inplace=True is specified, the original data frame (df) is modified in place. This implies that the operation does not create a new object; instead, it directly changes the existing data frame. The operation does not return any value.

Compared to inplace=False

When inplace=False is passed (or left as the default), a copy of the data frame is created, and the operation is performed on the copy. The modified copy is returned as the result of the operation. Hence, the original data frame (df) remains unaltered.

Return Values

• inplace=True: No return value; the modification is applied in place.
• inplace=False: A new object containing the modified data is returned.

Impact on Subsequent Operations

If you plan to perform subsequent operations on the data frame, consider using inplace=True to avoid creating unnecessary copies. However, if you need to preserve the original data frame or access its original values, use inplace=False to create a separate copy for modification.

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