Reading time: 30 minutes | Coding time: 10 minutes

Python is a dynamic language which is strongly typed. Dynamic binding is the capability to use an instance without regard for its type. It is handled entirely through a simple attribute lookup process. Whenever an attribute is accessed as object.attribute:

1. attribute is located by searching within the instance itself. This yields a positive result when the attribute being searched for is an instance variable or method.

2. If the instance does not have the required attribute, the instance's class definition is searched. All the class variables, class methods and static methods fall into this category.

3. If both these lookups do not return any result, the interpreter then proceeds to the base classes of the object. The first match found is returned.

The critical aspect of this binding process is its independence of the type of object. Thus, if you try a lookup such as object.name, it will work on any object that happens to have a name attribute independent of the class of the object. This behavior is colloquially referred to as duck typing about the adage “if it looks like, quacks like, and walks like a duck, then it is a duck.”

The idea is that you don't need a type to invoke an existing method on an object - if a method is defined on it, you can invoke it.

Where should I use it in my code?

More often than not we write programs, either deliberately or unwittingly that rely on duck typing.

While defining classes, the magic methods(or dunder methods) implement some sort of protocol supported by the language. For example,

• __iter__ and __next__ methods are used to implement the Iterator protocol. The instances of the class can now be used in for loops.
• if we declare the __len__ and __getitem__ methods inside a class, it's instances are called sequences. All the objects of this class are now subscribable by index (similar to list) as well as can be iterated over.

The Problem of Tabulation

Suppose we are asked to display a dictionary of country names and it's capital in a tabular format. Let us try to write a generic utility function which displays a given input mapping in a tabular form:

Attempt 01

def tabulate_mapping(mapping, headers):
    '''Tabulates the input mapping as columns with given headers.
    
    >>> mapping = {'India': 'New Delhi',
    ...             'USA': 'Washington',
    ...             'England': 'London'}
    >>> headers = ('Country', 'Captial')
    >>> tabulate_mapping_01(mapping, headers)
            Country |        Capital |
    ----------------------------------
            England |         London |
              India |      New Delhi |
                USA |     Washington |
    '''
    heading = ''
    for header in headers:  ##### NOTE 1
        heading += '{:>15}'.format(header) + ' |'
    print(heading)
    print('-'*len(heading))

    for k, v in mapping.items():  ##### NOTE 2
        print('{:>15} |{:>15} |'.format(k, v))

The code should look pretty straightforward to any programmer who has written even a few lines of Python. However, there are two salient features about the code which do not strike us at first sight. After some experimenting, you will figure out the following:

1. Any iterable can be passed as headers i.e. list, tuple, user-defined sequences. Even unordered collections such as dict, set would also work.
2. Any object which implements an items method can be printed using tabulate_mapping function.

Duck Typing At Work!

Let us create a class which extends the Python built-in list and implements a items method.

class MyList(list):
    ''' A user-defined collection data type which extends the built-in list.
    
    >>> alphabet = MyList(*'ABC')
    >>> print(alphabet)
    ['A', 'B', 'C']
    >>> alphabet[1]
    'B'
    >>> for i, item in alphabet.items():
    ...     print(i, item)
    1 A
    2 B
    3 C
    '''
    def __init__(self, *args):
        super().__init__(args)
        
    def items(self):
        '''Alternate utility to `enumerate` on the instance'''
        for i, item in enumerate(self, start=1):
            yield i, item

Now, by the grace of Duck Typing, we can use the tabulate_mapping function with instances of MyList class. However, what if we do not want malicious objects to use our code this way. We all know about SQL Injection. It's about time we become aware of our privacy.

The Curious Case of Input Validation

In the function tabulate_mapping, observe that we have not written any code for validating the inputs. With Duck Typing in the fray, it is important to understand the need for a guideline on validating inputs.

Programmers coming from a background of a statically typed background would be inclined to perform static type checks on the object. As we have seen, strict type check can be severely restrictive to the point where the developers miss out on the cool features Python has to offer.
i.e Too much of isinstance and hasattr works but it is neither fun nor Pythonic!

We will perform different kinds of input validation for the tabulate_mapping function based on the requirements:

1. If we want the tabulate_mapping to be a generic utility which can work on any mapping object with items and iterable headers.

def tabulate_mapping_v1(mapping, headers):
        
        # Validate headers
        try:
            headers_iter = iter(headers)
        except TypeError as e:
            raise ValueError('headers needs to be an iterable.')

        # Validate mapping
         try:
             mapping_items = mapping.items()
         except AttributeError as e:
             raise ValueError('mapping should have an items method.')

        heading = ''
        
         for header in headers_iter:
             heading += '{:>15}'.format(header) + ' |'

         print(heading)
         print('-'*len(heading))
         
         for k, v in mapping_items:
             print('{:>15} |{:>15} |'.format(k, v))

2. If we want to restrict the mapping object to an instance of a subclass of the built-in dict and the headers are specified to be a list object:

def tabulate_mapping_v2(mapping, headers):
        
        # Validate headers
        if not isinstance(headers, list)
            raise ValueError('headers needs to be a list object.')

        # Validate mapping
        if not isinstance(mapping, dict):
            raise ValueError('mapping needs to be a dict object.')
     
        heading = ''
        
         for header in headers_iter:
             heading += '{:>15}'.format(header) + ' |'

         print(heading)
         print('-'*len(heading))
         
         for k, v in mapping_items:
             print('{:>15} |{:>15} |'.format(k, v))

Conclusion

Whenever writing Python, we need to clearly define the allowed interfaces that can be used in place of an object rather than based on classes the object.

There is no distinction between the right way and the wrong way when it comes to dealing with objects in Python. The most Pythonic code at a given situation depends entirely on the requirements of the user and the practicality of the use cases. The consideration of Python's Data Model and duck typing can help one write clearer, readable and Pythonic code.