This blog will cover the Q&As of Python for Data Science (AI/ML) and Data Engineer Training covering Python Decorators and Generators Q & A: Day 6 Live Session Review. This blog will help you to clear your concepts in Python
We also covered hands-on Lab 19 and Lab 20 out of our 25+ extensive labs.
The previous week, In Day 5 Live Session, we have covered the basic concepts of Python OOPs Concepts, Error And Exception Handling.
So, here are some of the Q & As asked during the Live session from Module 7: Python Decorators and Generators.
Generators in Python
Generators are just like functions that give us a sequence of values one as an iterable (which can be iterated upon using loops). Generators contain yield statements just as functions contain return statements.
Q1: Why python uses generators?
Ans: Python uses the conception of generators because of many reasons. allow us to discuss a number of them.
1. Easy to implement: We will implement the generators simply in a very straightforward and clear manner. It significantly reduces the code compared to iterators. this can be as a result of in contrast to iterators, generators will decision the functions like iter() and next() mechanically.
2. Memory efficiency: before the returning of the result, a standard operation creates a complete sequence of memory. however, if the item range is massive memory becomes overkill. however, with generators, the sequence is memory-friendly. as a result of it produces one item at a time.
3. Infinite stream representation: Generators are a superb medium to represent infinite steam knowledge. Since the generators turn out just one at a time, infinite streams are often kept within the memory.
def gen(): a = 10 print('This is printed first') yield a a += 1 print('This is printed second') yield a a += 1 print('This is printed at last') yield a #Using loop for i in gen(): print(i)
Output:
Also Read: About Python Data Types.
Q2: What is the Application of Generators?
Ans: Suppose we need to create a stream of Fibonacci numbers, adopting the generator approach makes it trivial; we just have to call next(x) to get the next Fibonacci number without bothering about where or when the stream of numbers ends
Q3: How To Create Generators in Python?
Ans: It is as easy as defining a normal function, but with a yield statement instead of a return statement.
If a function contains at least one yield statement (it may contain other yield or return statements), it becomes a generator function. Both yield and return will return some value from a function.
The difference is that while a return statement terminates a function entirely, the yield statement pauses the function saving all its states and later continues from there on successive calls.
Q4: What are the generator function and generator object?
Ans: Generator Function: A generator function is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return. If the body of a def contains yield, the function automatically becomes a generator function.
def simpleGeneratorFun(): yield 1 yield 2 yield 3 # Driver code to check above generator function for value in simpleGeneratorFun(): print(value)
Read More: About Python Function.
Generator-Object :
Generator functions return a generator object. Generator objects are used either by calling the next method on the generator object or using the generator object in a “for in” loop (as shown in the above program).
Q5: What is the difference between Yield and return?
Ans: The return statement terminates the function entirely. And the yield function passes the function. This can be done by saving all the states and later continues from there on the successive calls.
Q6: What is the difference between a generator and a normal function?
Ans: There are a few main differences between generators and normal functions:
- A generator can contain more than one yield statement.
- When the iteration object calls the generator, execution does not start immediately.
- There is no necessity to call the functions. Additionally, the generator automatically calls methods like iter() and next. Usually next() will iterate these functions
- When the function gets yielded, the function is paused and the control is transferred to the caller.
- Between the sequential calls, local variables and their states were remembered.
- Finally, if the function terminates, the generator mechanically calls the stop iteration()
Also Read: Our blog post on Data Science Career.
Decorators in Python
Decorators are a very powerful and useful tool in Python since it allows programmers to modify the behavior of function or class. Decorators allow us to wrap another function in order to extend the behavior of the wrapped function, without permanently modifying it. But before diving deep into decorators let us understand some concepts that will come in handy in learning the decorators.
Q7: What is Python Decorator?
Ans: A decorator is a python with interesting features that add functionality to the existing code. This is also called metaprogramming. Decorators are also known as metaprogramming. It was said because it was trying to modify another programming part at compile time. Decorators allow us to wrap up another function in order to extend the behavior of another function.
#python program to illustrate functions # can be treated as objects def shout(text): return text.upper() print(shout('Hello')) yell = shout print(yell('Hello')) Output= HELLO HELLO
Read More: About Python IDE.
Q8: What is the function of Decorators?
Ans: Usually, a decorator is any callable object that is used to modify the function. A reference to the function is passed to the decorator and the decorator returns the modified function. Usually, the modified functions contain the function call.
Q9: What is @ Symbol in Python?
Ans: In short, it is used in decorator syntax
In the context of decorators, this syntax:
@decorator def decorated_function(): """this function is decorated"""
is equivalent to this:
def decorated_function(): """this function is decorated""" decorated_function = decorator(decorated_function)
Q10: What are chaining Decorators in Python?
Ans: Chaining decorators means applying more than one decorator inside a function. Python allows us to implement more than one decorator to a function. It makes decorators useful for reusable building blocks as it accumulates several effects together. It is also known as nested decorators in Python.
Syntax:
@decor1 @decor def num(): statement(s)
Related References
- Python Loops and Control Statements
- Python Methods and Functions
- Python OOPs Concepts, Error And Exception Handling
- Python For Data Science: Why, How & Libraries Used
- Natural Language Processing with Python
- Python For Beginners: Overview, Features & Career Opportunities
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