Python

In addition to Python’s built-in data structures (such as tuples, dicts, and lists), a library module called collections provides data structures with additional features, some of which are specializations of the built-in ones. Import the module: import collections Specialized container datatypes are usually dict subclasses or wrappers around other classes like lists, tuples, etc. Notable implementations are : the Counter class used for counting hashable objects. defaultdict class used as a faster implementation of a specialised dictionary....

An assignment only creates a “binding” (an association) between a name and a “target” (object of some type). A copy is sometimes necessary so you can change the value of one object without changing the other (when two names are pointing to the same object). # Assignment: bind the name y to # the list [1, 2]. y = [1, 2 ] # Create another binding - # bind the name x to the same # object that y is currently bound to....

Normal Python references to objects increment the object’s reference count thus preventing it from being garbage collected. If a user desires creating weak references, the weakref module can be used: import weakref class Rezha(object): pass To create a weak reference, the ref class is used: # object instance rezha = Rezha() # weak reference to our object r = weakref.ref(rezha) Then, you can call the reference object: print(r) # <weakref at 0x01414E40; to 'Rezha'....

While in Python 2 it was possible to use both the function next() and the .next() method to iterate over the resulting values of a generator, the later has been removed with the introduction of Python 3. Consider the sample generator: def sample_generator(): yield "a" yield "b" yield "c" In Python 2: a = sample_generator() print(next(a)) # prints 'a' print(a.next()) # prints 'b' But in Python 3: print(next(a)) # prints 'a' print(a....

Generator expressions are a high performance and memory efficient generalization of list comprehensions and generators. Imagine we want to sum up all even number ranging from 1 to 100. Using list comprehension: even_sum = sum([x for x in range(1, 100) if x % 2 == 0]) print(even_sum) #2450 This will prove inefficient in the case of a large range because it first creates a list, it iterates over it and then returns the sum....