Understanding inplace=True

Question

In the pandas library many times there is an option to change the object inplace such as with the following statement...

df.dropna(axis=\'index\         


        

Answer 1

Yes, in Pandas we have many functions has the parameter inplace but by default it is assigned to False.

So, when you do df.dropna(axis='index', how='all', inplace=False) it thinks that you do not want to change the orignial DataFrame, therefore it instead creates a new copy for you with the required changes.

But, when you change the inplace parameter to True

Then it is equivalent to explicitly say that I do not want a new copy of the DataFrame instead do the changes on the given DataFrame

This forces the Python interpreter to not to create a new DataFrame

But you can also avoid using the inplace parameter by reassigning the result to the orignal DataFrame

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