All of the operators used in this recipe have method equivalents that produce the exact same result. For instance, in step 1, imdb_score + 1 may be reproduced with the add method. Check the following code to see the method version of each step in the recipe:

>>> imdb_score.add(1)              # imdb_score + 1
>>> imdb_score.mul(2.5)            # imdb_score * 2.5
>>> imdb_score.floordiv(7)         # imdb_score // 7
>>> imdb_score.gt(7)               # imdb_score > 7
>>> director.eq('James Cameron')   # director == 'James Cameron'

Why does pandas offer a method equivalent to these operators? By its nature, an operator only operates in exactly one manner. Methods, on the other hand, can have parameters that allow you to alter their default functionality:

You may be curious as to how a Python Series object, or any object for that matter, knows what to do when it encounters an operator. For example, how does the expression imdb_score * 2.5 know to multiply each element in the Series by 2.5? Python has a built-in, standardized way for objects to communicate with operators using special methods. 

Special methods are what objects call internally whenever they encounter an operator. Special methods are defined in the Python data model, a very important part of the official documentation, and are the same for every object throughout the language. Special methods always begin and end with two underscores. For instance, the special method __mul__ is called whenever the multiplication operator is used. Python interprets the imdb_score * 2.5 expression as imdb_score.__mul__(2.5).

There is no difference between using the special method and using an operator as they are doing the exact same thing. The operator is just syntactic sugar for the special method.