Python is one of the most popular programming language for enterprise applications and Big Data solutions. Big companies like Amazon, Netflix, Google etc use Python in their Data architecture. This course is designed to help you achieve your goals in Python field. Software Engineers with Python knowledge may get more salary than others with similar qualifications without Python knowledge.
In this course, you will learn how to handle interview questions on Python in Software Design and Development. I will explain you the important concepts of Python language.
You will also learn the benefits and use cases of Python in this course.
What is the biggest benefit of this course to me?
Finally, the biggest benefit of this course is that you will be able to demand higher salary in your next job interview.
It is good to learn Python for theoretical benefits. But if you do not know how to handle interview questions on Python, you can not convert your Python knowledge into higher salary.
What are the topics covered in this course?
We cover a wide range of topics in this course. We have questions on Python best practices, Security. tricky questions etc.
How will this course help me?
By attending this course, you do not have to spend time searching the Internet for Python interview questions. We have already compiled the list of the most popular and the latest Python Interview questions.
Are there answers in this course?
Yes, in this course each question is followed by an answer. So you can save time in interview preparation.
What is the best way of viewing this course?
You have to just watch the course from beginning to end. Once you go through all the videos, try to answer the questions in your own words. Also mark the questions that you could not answer by yourself. Then, in second pass go through only the difficult questions. After going through this course 2-3 times, you will be well prepared to face a technical interview in Python language.
What is the level of questions in this course?
This course contains questions that are good for a Fresher to an Architect level. The difficulty level of question varies in the course from a Fresher to an Experienced professional.
What happens if Python technology changes in future?
From time to time, we keep adding more questions to this course. Our aim is to keep you always updated with the latest interview questions on Python.
What are the sample questions covered in this course?
Sample questions covered in this course are as follows:
There are many ways to improve the performance of a Python program. Some of these are as follows:
Python is so much powerful that even Google uses it. Some of the benefits of using Python are as follows:
By default, every source code file in Python is in UTF-8 encoding. But we can also specify our own encoding for source files. This can be done by adding following line after #! line in the source file.
# -*- coding: encoding -*-
In the above line we can replace encoding with the encoding that we want to use.
PEP 8 is a style guide for Python code. This document provides the coding conventions for writing code in Python. Coding conventions are about indentation, formatting, tabs, maximum line length, imports organization, line spacing etc. We use PEP 8 to bring consistency in our code. With consistency it is easier for other developers to read the code.
Pickling is a process by which a Python object hierarchy can be converted into a byte stream. The reverse operation of Pickling is Unpickling.
Python has a module named pickle. This module has the implementation of a powerful algorithm for serialization and de-serialization of Python object structure.
Some people also call Pickling as Serialization or Marshalling. With Serialization we can transfer Python objects over the network. It is also used in persisting the state of a Python object. We can write it to a file or a database.
A Python Decorator is a mechanism to wrap a Python function and modify its behavior by adding more functionality to it. We can use @ symbol to call a Python Decorator function.
Every argument in a Python method is an Object. All the variables in Python have reference to an Object. Therefore arguments in Python method are passed by Reference.
Since some of the objects passed as reference are mutable, we can change those objects in a method. But for an Immutable object like String, any change done within a method is not reflected outside.
Main differences between List and Dictionary data types in Python are as follows:
The use of Pass statement is to do nothing. It is just a placeholder for a statement that is required for syntax purpose. It does not execute any code or command.
Some of the use cases for pass statement are as follows:
>>> while True:
... pass # Wait till user input is received
>>> class MyMinimalClass:
We can also use it as a placeholder for TODO work on a function or code that needs to be implemented at a later point of time.
>>> def initialization(): ... pass # TODO
We can use the unit testing modules unittest or unittest2 to create and run unit tests for Python code.
We can even do automation of tests with these modules. Some of the main components of unittest are as follows:
Some of the main benefits of using Python are as follows:
A metaclass in Python is also known as class of a class. A class defines the behavior of an instance. A metaclass defines the behavior of a class.
One of the most common metaclass in Python is type. We can subclass type to create our own metaclass.
We can use metaclass as a class-factory to create different types of classes.
A frozenset is a collection of unique values in Python. In addition to all the properties of set, a frozenset is immutable and hashable.
Once we have set the values in a frozenset, we cannot change. So we cannot use and update methods from set on frozenset. Being hashable, we can use the objects in frozenset as keys in a Dictionary.
Python Flask is a micro-framework based on Python to develop a web application.
It is a very simple application framework that has many extensions to build an enterprise level application.
Flask does not provide a data abstraction layer or form validation by default. We can use external libraries on top of Flask to perform such tasks.
None is a reserved keyword used in Python for null objects. It is neither a null value nor a null pointer. It is an actual object in Python. But there is only one instance of None in a Python environment.
We can use None as a default argument in a function. During comparison we have to use “is” operator instead of “==” for None.
A Module is a script written in Python with import statements, classes, functions etc. We can use a module in another Python script by importing it or by giving the complete namespace.
With Modules, we can divide the functionality of our application in smaller chunks that can be easily managed.
In a normal dictionary in Python, there is no order maintained between keys. To solve this problem, we can use OrderDict class in Python. This class is available for use since version 2.7. It is similar to a dictionary in Python, but it maintains the insertion order of keys in the dictionary collection.
Python uses most of the Object Oriented programming concepts. But we can also do functional programming in Python. As per the opinion of experts, Python is a multi-paradigm programming language.
We can do functional, procedural, object-oriented and imperative programming with the help of Python.
We can implement exception handling to handle error conditions in Python code. If we are expecting an error condition that we cannot handle, we can raise an error with appropriate message.
>>> if student_score < 0: raise ValueError(“Score can not be negative”)
If we do not want to stop the program, we can just catch the error condition, print a message and continue with our program.
E.g. In following code snippet we are catching the error and continuing with the default value of age.
print("duration has to be a number")
Python provides a useful method issubclass(a,b) to check whether class a is a subclass of b.
E.g. int is not a subclass of long
bool is a subclass of int
In Python, we can use the debugger pdb for debugging the code. To start debugging we have to enter following lines on the top of a Python script.
After adding these lines, our code runs in debug mode. Now we can use commands like breakpoint, step through, step into etc for debugging.
Python provides a profiler called cProfile that can be used for profiling Python code.
We can call it from our code as well as from the interpreter.
It gives use the number of function calls as well as the total time taken to run the script.
We can even write the profile results to a file instead of standard out.
We use ‘is’ to check an object against its identity.
We use ‘==’ to check equality of two objects.
>>> lst = [10,20, 20]
>>> lst == lst[:]
>>> lst is lst[:]
We can create a common module with variables that we want to share.
This common module can be imported in all the modules in which we want to share the variables.
In this way, all the shared variables will be in one module and available for sharing with any new module as well.
In Functional Programming, we decompose a program into functions. These functions take input and after processing give an output. The function does not maintain any state.
Python provides built-in functions that can be used for Functional programming. Some of these functions are:
Event iterators and generators can be used for Functional programming in Python.
In Python, enumerate() function is an improvement over regular iteration. The enumerate() function returns an iterator that gives (0, item).
>>> for i,j in enumerate(thelist):
... print i,j
To execute a Python script in Unix, we need to have Python executor in Unix environment.
In addition to that we have to add following line as the first line in a Python script file.
This will tell Unix to use Python interpreter to execute the script.
Some of the popular libraries of Python used for Data analysis are:
I am a Software Architect with expertise in Cloud Computing, Amazon Web Services, Microservices, Data Science, Hadoop, Spark, Machine Learning and Java architecture. Learning, using and sharing Technology is my passion.
I have built systems that are running enterprise software of companies across the world. I have gained a lot of knowledge by working hands-on on these large scale software projects.
With these courses I aim to share my knowledge with the future Software Engineers, Developers, Leaders and Architects .
I am sure the knowledge in these courses can give you extra power to win in life.
All the best!!