Python programming might seem simple and you might find it really easy to code. However, sometimes python interview questions might be tricky and you mind may run opposite to the right answer. You always need to take your programming skills seriously and go through conceptual learning of codes.
Besides simple syntax and semantics, most of the interview questions are to test programming aptitude of candidate. You might not be lucky to face the most common python interview questions while looking for a job.
In this python Interview questions article, I am listing down a couple of questions related to the latest language Python, which is high in demand among several top IT companies. So, if you are preparing for an interview, just go through it. All the Best!
Python memory manager allocates heap space to Python objects. The core API of Python gives a few tools for the programmer to code unfailing and highly robust program.
Additionally, Python contains a built-in garbage collector that recycles the memory which is unused. Once an object is no more referenced by the program, it frees up the heap space it occupied. The garbage collector determines objects that are no more referenced by the program liberated the in used memory as well as make it available to the heap space.
Python also supports Inheritance as well as Multiple Inheritance. Any class can inherit behaviour as well as attributes methods from different class, known as superclass. A class that inherits from a superclass is known as subclass. Another name for superclasses is ancestors too. There presents a hierarchy association between classes.
Syntax of Inheritance in Python:
class BaseClass: Body of base class class DerivedClass(BaseClass): Body of derived class
Various types of Inheritance
Class variables are stated with keyword static as well as Instance variables are stated with static keyword.
Class variables can be obtained anywhere in the class while an instance variable can be obtained in a specific object of the class.
Class variables can be obtained by making use of class name of object reference. Instance variables can be obtained only with the help of object reference.
A construct called the Global Interpreter Lock (GIL) is available in Python. It is the work of the GIL to make sure that only one of the ‘threads’ can execute at any one point of time. Normally a thread accepts the GIL, does a little work, then passes it on the GIL to the next thread. All this happens very quickly that will seem so to the human eye and it may seem like your threads are executing in parallel, but they are really just taking turns using the same CPU core. All this GIL passing adds overhead to execution. This means that if you want to make your code run faster then using the threading package often isn’t a good idea.
Whereas, **kwargs is used when you are unsure about the number of keyword arguments that will be passed to a function, or it can be also used to pass the values of a dictionary as keyword arguments. The identifier args and kwargs are a convention, you could also use *bob and **billy but that would not be wise.
There are basically two frameworks available in the Python programming that are mainly
Firstly, Flask is a “microframework” that is built with simpler applications that are on primary basis. Flask uses external libraries and is always ready to use. Secondly, Pyramid is generally built for larger applications. As it provides a lot of flexibility and allows the developer to use the right kind of tools for the project, which makes it developer friendly.
The developer can easily choose the database, template style, URL structure, and much more. Pyramid is heavily configurable. Lastly, Django can be also used for larger applications just like Pyramid. It basically includes an ORM.
Java and Python are way different from each other, but both of them can be useful tools for high-tech developers. Also, Python is quite easies to master than Java if you are new to learning how to write programs. Below mentioned are the few points which clearly shows that python is different from Java. Here they are-
On the other side, Packages are namespaces which has various packages and modules themselves. They are merely directories.
Every package in Python programming language is a directory which should have a special file known as _init_.py. This file can be void and it shows that the directory it consists is a Python package. This can be imported similar to the module which can be imported.
To install python on different machine follow the steps below:
Python is the most flexible language due to its dynamic nature.
Functions in Python are first-class objects. Python follows OOPS paradigm that makes it more real-time coding experience.
Python code is very clean by using indentation syntax. So its code is more clear than any other language.
More natural language: you can express your ideas in more natural language compared to other languages.
It has a huge community and growing faster so that it can be best promising language in the world.
It has large number of packages based on almost every area of programming like machine learning, AI,
Python has lots of interesting features you can check out on the official website.
Generally, we use copy function of the copy module to make a duplicate data. But this copy function only makes the copy of reference that means if we change the new object then this will change the original as well.
So, we use deep copy of the copy module so that we can create a new object with original one and can modify the newly created object separately.
Monkey patching is a concept by which we can change the function behavior in runtime. Due to the dynamic nature of the python, we can replace the body of a class function to new one. It is very useful in testing the python application. For example, if we have any class method which returns some api data and during testing, we don’t want the api data and use some local data then we can change the function and assign another function to it.
These are just arguments variable passed to function in the definition part. When we don’t know the number of arguments passed to a function then we use *args. Same is applied to *kwargs but it accepts keyword arguments(Python dictionary).
The name can be anything like a variable name. We just use the above name (args, kwargs) because it is very understandable to programmers.
If you had to open large files, you could operate on chunks, and not one byte at a time. To be precocious, make sure RAM of the target machine is enough How are you operating on the file? What are you returning from the file? In what pattern are you accessing the date on the file (like maybe in random sampling or using some serialized mechanism)? There could be many factors that could affect your response to this question.
Docstring refers to documentation string for a function. It must be defined at first, within a function that defines it. Though there’s not much difference between the two, one could put it this way– Docstrings are for documentation, however, comments are for code readers/reviewers.
Few methods like str and gt are examples of the special method. They override the behavior of other global functions/operators and will be used with the with keyword. Overriding those functions might lead to unintended behaviors in a dynamic language like Python, thus, they are meant to be used very carefully.
In Python, variables that are only called and declared inside a function become inherently global. But, If a variable is assigned a new value within the same function, it will then be a local variable.
But, you also have the flexibility of explicitly declaring a variable as "global" within the same function.
Range returns a list. Xrange object which takes the same memory independent of the range size.
While using range, one can have all items already generated which can consume a lot of memory. Using xrange, one can get the elements one by one i.e. only one element is generated per loop.
Decorators are not exclusively made for python and are functions that have a capability of accepting a function as an argument and could return functions. A simple example might be a decorator that takes a function, then outputs(or prints it argument) with stdout, prints the return value using stdout, then returns that returns that value. The syntax for decorators in python uses the @decorator_name above any function definition.
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