Python Pandas - 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', how='all', inplace=True)

I am curious what is being returned as well as how the object is handled when inplace=True is passed vs. when inplace=False.

Are all operations modifying self when inplace=True? And when inplace=False is a new object created immediately such as new_df = self and then new_df is returned?

Answer 1

When inplace=True is passed, the data is renamed in place (it returns nothing), so you'd use:

df.an_operation(inplace=True)

When inplace=False is passed (this is the default value, so isn't necessary), performs the operation and returns a copy of the object, so you'd use:

df = df.an_operation(inplace=False) 

So:

if inplace == False:
    Assign your result to a new variable
else
    No need to assign

Answer 2

The way I use it is

# Have to assign back to dataframe (because it is a new copy)
df = df.some_operation(inplace=False) 

Or

# No need to assign back to dataframe (because it is on the same copy)
df.some_operation(inplace=True)

CONCLUSION:

 if inplace is False
      Assign to a new variable;
 else
      No need to assign

Answer 3

I usually use with numpy.

you use inplace=True, if you don't want to save the updated data to the same variable

data["column1"].where(data["column1"]< 5, inplace=True)

this is same as...

data["column1"] = data["column1"].where(data["column1"]< 5)