The Python is more powerful language which offer great tools for data crunching and preparation, as well as for complex scientific data analysis and modelling. The Python frameworks are allows you to carry out complex mathematical computations and create sophisticated models that make sense of your data. Based on that you need to prepare yourself accordingly if you need to be a good Python Developer.

Why we should go for Python:

1. As the Python is already a proven language in the data science industry and it is widely accepted by most of the industry, so it is now taken the lead as the toolkit for scientific data analysis and modelling. 

2. These are open-sourced libraries, offering alternate ways of deriving the same output. 

3. As the technology now a days gets more and more competitive, data scientists and engineers are continually striving for ways to process information, extract insights and model, by processing massive datasets. 

4. Python is the only platform where we can be able to explore the various, so you need to be well versed in the various Python libraries that support your data science tasks and the benefits they offer to make your outputs more robust and speedier.

Here I would like to discuss the some important interview Questions on python and its related areas which is mostly required by the candidates to prepare who are willing to appear for Python developer.

Basic Interview Questions:

1. In python environment what is the difference between list and tuples in Python?
2. What are the key features of Python?
3. What type of language is python does belong?
4. How the Python is considered as an interpreted language?
5. What is pep 8 concepts in python? How will you relate it?
6. How the memory management is done in Python?
7. What is name space in Python?
8. What is PYTHON PATH?
9. What are python modules?
10. What are local variables and global variables in Python?

11. What are negative indexes and why are they used?

12. What are Python packages?

13.How can files be deleted in Python?

14. What are the built-in types of python?

15. What advantages do NumPy arrays offer over (nested) Python lists?

16. How to add values to a python array?

17. How to remove values to a python array?

18. Does Python have OOps concepts fully supported? If yes then how?

19. What is the difference between deep and shallow copy in python? Explain with suitable syntax?

20. Is python case sensitive language like other languages?

21.What is type conversion in Python? Explain with suitable approach?

22. How to install Python on Windows and set path variable?

23. What do you mean by the term “indentation”? Is it required in python?

24. What is the difference between Arrays, Tuple and lists? Discuss?

25. What are functions in Python? Explain the importance of function in python?

26.What is “init” in python? Explain with suitable syntax?

27.What is a lambda function? What is the main utility of Lambda in python?

28. What do you mean by the term “self-Python”? Explain?

Basic Python Programs based questions:

1. Write a program in Python to execute the Bubble sort, selection sort algorithm.

2. Write a program in Python to produce pascal triangle.

3. Write a program to produce Fibonacci series in Python.

4. Write a program to perform the factorial of number?

5. Write a program to compute the number of factorial number between a given range of values?

6. Write a program in Python to check if a number is prime.

7. Write a program in Python to check if a sequence is a Palindrome.

8. Write a one-liner that will count the number of capital letters in a file. Your code should work

even if the file is too big to fit in memory.

9. With suitable approach, Write a sorting algorithm for a numerical dataset in Python.

10. Explain how the python support the dual indexing approach?

11. How does break, continue and pass work in the segment of the Python code?

12. What is the meaning of the segment mentioned as [::-1} do?

13. How can you randomize the items of a list in place in Python? Is it possible in Python? 

14. What are python iterators? Discuss with suitable code?

15. How can you generate random numbers in Python?

16. What is the difference between range & xrange in python?

17. How do you write comments lines in python while writing the program?

18. What is the usage of help() and dir() function in Python?

19. Whenever Python exits, why isn’t all the memory de-allocated?

20. What is a dictionary in Python?

21. How can the ternary operators be used in python?

22. What does this mean: *args, **kwargs? And why would we use it?

23. What is the purpose of the function len()? Explain with example?

24. Explain split(), sub(), subn() methods of “re” module in Python.

Python Libraries Interview Questions:

1. What is pickling and unpickling process in Python? Which library is being used for this process?

2. What are the generators in python? Discuss with suitable example?

3. For a given code in python, How will you capitalize the first letter of string?

4. How will you convert a string to all lowercase using the inbuilt code in python?

5. How to comment multiple lines in python?

6.What are docstrings in Python? Discuss with suitable example?

7. What is the purpose of is, not and in operators? Discuss with suitable example?

8. With suitable approach explain how Multithreading can be achieved in Python?

9. What is the process of compilation and linking in python?

10. What are Python libraries? Name a few of them.

11. What is split used for?

12. How to import modules in python while doing the program? Explain?

OOPS Interview Questions

1. Explain Inheritance in Python with an example.

2. How are classes created in Python? 

3. What is monkey patching in Python?

4. Does python support multiple inheritance?

5. What is Polymorphism in Python?

6. Define encapsulation in Python?

7. How do you do data abstraction in Python?

8. Does python make use of access specifiers?

7. How to create an empty class in Python? 

8. What does an object() do?

9. Explain what Flask is and its benefits?

10. Is Django better than Flask?

11. Mention the differences between Django, Pyramid and Flask.

12. Discuss Django architecture with suitable approach?

13. Explain in python environment how can set up the Database in Django?

14. Give an example how to write a VIEW in Django? Discuss?

15. Mention what the Django templates consist of.

16. Explain the use of session in Django framework?

17.  List out the inheritance styles in Django with suitable approach?

Web Scraping Based Interview Questions on Python:

1. How To Save An Image Locally Using Python Whose URL Address I Already Know?

2. How can you Get the Google cache age of any URL or web page?

3. You are required to scrap data from IMDb top 250 movies page. It should only have fields

movie name, year, and rating.

Data Analysis Based Interview Questions on Python:

1. What is map function in Python?

2. Is python numpy better than lists?

3. How to get indices of N maximum values in a NumPy array?

4.  How do you calculate percentiles with Python/ NumPy?

5. What is the difference between NumPy and SciPy?

6. How do you make 3D plots/visualizations using NumPy/SciPy?

Multiple Choice Questions (MCQ)

1. Which of the following statements create a dictionary? (Multiple Correct Answers Possible)

d = {}
b) d = {“john”:40, “peter”:45}
c) d = {40:”john”, 45:”peter”}
d) d = (40:”john”, 45:”50”)

2. Which one of these is floor division?

1. /
   b) //
   c) %
   d) None of the mentioned

3. What is the maximum possible length of an identifier?
a) 31 characters
b) 63 characters
c) 79 characters
d) None of the above

4. Why are local variable names beginning with an underscore discouraged?

1. they are used to indicate a private variables of a class
   b) they confuse the interpreter
   c) they are used to indicate global variables
   d) they slow down execution

5. Which of the following is an invalid statement?
a) abc = 1,000,000
b) a b c = 1000 2000 3000

c) a,b,c = 1000, 2000, 3000
d) a_b_c = 1,000,000

6. Suppose list1 is [2, 33, 222, 14, 25], What is list1[-1] ?
a) Error
b) None
c) 25
d) 2

7. To open a file c:scores.txt for writing, we use
a)outfile = open(“c:scores.txt”, “r”)
b) outfile = open(“c:scores.txt”, “w”)
c) outfile = open(file = “c:scores.txt”, “r”)
d) outfile = open(file = “c:scores.txt”, “o”)

8. When will the else part of try-except-else be executed?

a) always
b) when an exception occurs
c) when no exception occurs
d) when an exception occurs into except block

Scope @ NareshIT:

1. At NareshIT’s Python application Development program you will be able to get the extensive hands-on training in front-end, middleware, and back-end technology.

2. It skilled you along with phase-end and capstone projects based on real business scenarios.

3. Here you learn the concepts from leading industry experts with content structured to ensure industrial relevance.

4. An end-to-end application with exciting features

5. Earn an industry-recognized course completion certificate.

Introduction:

1. Python is a popular programming language created by Guido van Rossum and released in the year 1991. 

2. Python is a more powerful language that offers great tools for data crunching and preparation, as well as for complex scientific data analysis and modeling. 

3. Python today has multiple implementations including Jython, scripted in Java language for Java Virtual Machine.

4. IronPython is the new variety that is written in C# for the Common Language Infrastructure, and the PyPy version is written in RPython and translated into C. 

5. Most of the Python modules work on a community development model and are open-source and free.

Zip Function in Python:

1. The zip() function in Python is basically used to return a zip object.

2. The zip object is basically an iterator of tuples. 

3. Here in the iterator the first item or value in each passed iterator is paired together, and then the second item in each passed iterator is paired together, etc.

4. During the process, if the passed iterators have different lengths, the iterator with the least items decides the length of the new iterator.

Syntax:

zip(iterator1, iterator2, iterator3 ...)

Note:  Here the Iterator objects that will be joined together.

Example:

We can have the following example which shows how the zip function is being used in Python. Here it should be noted that If one tuple contains more items, these items are ignored.

a = ("John", "Charles", "Mike")
b = ("Jenny", "Christy", "Monica", "Vicky")

x = zip(a, b)

Zip in Python 3:

 As you know Python released version 3.0 which is also called Python 3 in December 2008. Technically this version was mainly released in order to fix the major problems that are found in Python 2. As you know Python 3 was incompatible with Python 2 in many aspects. It is backward incompatible and Some features of Python 3 have been backported to Python 2. x versions to make the migration process easy in Python 3.

When we are going to consider the zip function utility Python 3 doesn’t have many differences in terms of syntax but yes in terms of values, it does have. Let us consider the following example which will let you know how the zip function is applied in Python3.

Syntax:
zip(*iterators)
Parameters:
Here the Python iterables or containers that we used to have are ( list, string, etc ).
Return Value :

It is used to return a single iterator object which is used to contains the mapped values from all the containers that are present.

Example:

# Python code to demonstrate the working of zip() 

  

# initializing lists 

name = [ "Manjeet", "Nikhil", "Shambhavi", "Astha" ] 

roll_no = [ 4, 1, 3, 2 ] 

marks = [ 40, 50, 60, 70 ] 

  

# using zip() to map values 

mapped = zip(name, roll_no, marks) 

  

# converting values to print as set 

mapped = set(mapped) 

  

# printing resultant values  

print ("The zipped result is : ",end="") 

print (mapped) 

Note: If we are going to compare with Python2 and Python3 then you will find the similarity in maximum cases.

Unzipping in Python:

Similar to zip() the Unzipping is also get implemented. The Unzipping operation in Python means we are going to convert the zipped values back to the individual self as they were initially before the zip() was applied. This is done with the help of “*” operator.

Let us consider the following example which will let you know how the unzipping will work. Here I am going to consider the same example that I have taken in the zip() implementation so that you can easily understand the zip() and unzip().

Example:

# Python code to demonstrate the working of  unzipping 

  

# First we need to initialize the lists 

  

name = [ "Manjeet", "Nikhil", "Shambhavi", "Astha" ] 

roll_no = [ 4, 1, 3, 2 ] 

marks = [ 40, 50, 60, 70 ] 

  

# using zip() to map values 

mapped = zip(name, roll_no, marks) 

  

# converting values to print as list 

mapped = list(mapped) 

  

# printing resultant values  

print ("The zipped result is : ",end="") 

print (mapped) 

  

print("\n") 

  

# unzipping values 

namz, roll_noz, marksz = zip(*mapped) 

  

print ("The unzipped result: \n",end="") 

  

# printing initial lists 

print ("The name list is : ",end="") 

print (namz) 

  

print ("The roll_no list is : ",end="") 

print (roll_noz) 

  

print ("The marks list is : ",end="") 

print (marksz)

Note:

It should be noted that there are many possible applications present that can be said to be expected using Zip. 

We have many relevant examples where we are going to apply such functionality in a wider range. For example, student databases scorecards, or any other utility that requires mapping of groups. 

Scope @ NareshIT:

1. At Naresh IT you will get a good Experienced faculty who will guide you, mentor you, and nurture you to achieve your dream goal.

2. Here you will get good hands-on practice in terms of the practical industry-oriented environment which will definitely help you a lot to shape your future.

3. During the designing process of the application, we will let you know about the other aspects of the application too. 

4. Our Expert trainer will let you know about every in and out of the problem scenario.

Achieving your dream goal is our motto. Our excellent team is working restlessly for our students to click their target. So, believe in us and our advice, and we assure you about your success.

The Python libraries offer great tools for data crunching and preparation, as well as for complex scientific data analysis and modeling. Here I am going to discuss the list of top Python frameworks that allow you to carry out complex mathematical computations and create sophisticated models that make sense of your data.

Introduction:

1. As Python is already a proven language in the data science industry and is widely accepted by most of the industry, it is now taken the lead as the toolkit for scientific data analysis and modeling. 

2. Here I would like to highlight some of the most popular and go-to Python libraries for data science. 

3. These are open-sourced libraries, offering alternate ways of deriving the same output. 

4. As technology nowadays gets more and more competitive, data scientists and engineers are continually striving for ways to process information, extract insights, and model, by processing massive datasets. 

5. Python is the only platform where we can be able to explore the various, so you need to be well-versed in the various Python libraries that support your data science tasks and the benefits they offer to make your outputs more robust and speedier.

Here I would like to discuss some important libraries which is mostly required by Python developers.

TensorFlow:

1. It is the best and the Ultimate Machine Learning and Deep Learning Framework, which consists of many libraries that use a system of multi-layered nodes to enable the setting up, training, and deployment of artificial neural networks when working with large datasets. 

2. It was set up by Google Brain and is written in C++ but can be called in Python. 

3. The most prolific applications of TensorFlow are object identification, speech recognition, word embedding, and recurrent neural networks.

4. It is also used for sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. 

5. It also supports production prediction at scale, using the same models used for training.

6. It has many features such as a high level of performance, flexible architecture, and the ability to run on any target like a local machine, a cluster in the cloud, iOS and Android devices, CPUs, or GPUs.

Keras:

1. It is the Library for Neural Networks.

2. Keras is a high-performing library for working with neural networks, running on top of TensorFlow, Theano, and CNTK (Microsoft’s Cognitive Toolkit). 

3. Keras is user-friendly, with simple APIs and easy fast experimentation, making it possible to work on more complex models. 

4. Its modular and extendable nature allows you to use a variety of modules from neural layers, to optimizers, and activation functions to develop a new model. 

5. This makes Keras a good option for data scientists when they want to add a new module as classes and functions.

NumPy :

1. It is being considered as the Core Numeric and Scientific Computation Library.

2. The NumPy is also refer as Numerical Python and it is the core library that forms the mainstay of the ecosystem of data science tools in Python. 

3. It supports scientific computing with high-quality mathematical functions and logical operations on built-in multi-dimensional arrays and matrices. 

4. Besides n-dimensional array objects, NumPy provides functionality in basic algebraic functions, random numbers, basic Fourier transforms, sophisticated random number capabilities, and tools for integrating Fortran code and C/C++ code. 

5. The Array interface of NumPy also allows multiple options to reshape large datasets.

6. It is one of the best data science toolkits and is being used by most other data science or machine learning Python packages (SciPy, MatplotLib, ScikitLearn, etc.) are built on it.

SciPy:

1. As we have already discussed above regarding the NumPy, the SciPy is the Numeric and Scientific Computation Library.

2. SciPy is an important Python library for researchers, developers, and data scientists

3. SciPy is also referred to as Scientific Python which is considered as another core library for scientific computing with algorithms and complex mathematical tools for Python.

4. It contains tools for numerical integration, interpolation, optimization, etc., and helps to solve problems in linear algebra, probability theory, integral calculus, fast Fourier transform, signal processing, and other such tasks of data science.

5. The SciPy key data structure is also a multidimensional array, implemented by NumPy.

6. It basically gets set up after the NumPy installation is done on the environment.

7. It offers an edge to NumPy by improving useful functions for regression, minimization, Fourier transformation, and more.

Pandas:

1. It is considered the Data Analysis Library and is a dedicated library for data analysis, data cleaning, data handling, data discovery, and steps executed prior to machine learning projects.

2. It is basically used to provide tools for shaping, merging, reshaping, and slicing datasets.

3. Here we have three types of data structures such as “series” (single-dimensional, homogenous array), “data frames” (two-dimensional, heterogeneous columns), and “panel” (three-dimensional, size mutable array). 

4. These are used to enable merging, grouping, filtering, slicing, and combining data, besides providing a built-in time-series functionality. Data in multiple formats such as CSV, SQL, HDFS, or Excel can also be processed easily.

5. The Panda is the go-to library for data analysis in domains like finance, statistics, social sciences, and engineering. 

6. Its easy adaptability, and ability to work well with incomplete, unstructured, and uncategorized data, make it popular among data scientists.

SciKit-Learn:

1. It is basically used for the Data Analysis and Machine Learning Library to solve complex machine learning problems.

2. It basically used to provide algorithms for common machine learning and data mining tasks such as clustering, regression, classification, dimensionality reduction, feature extraction, image processing, model selection, and pre-processing. 

3. It is built on the top of SciPy, Numpy, and Matplotlib.

4. SciKit-Learn has great supporting documentation that makes it user-friendly.

5. The various functionalities of SciKit-Learn help data scientists in use cases like spam filters, image recognition, drug response, stock pricing, and customer segmentation.

PyTorch:

1. It is the other Largest Machine Learning Framework used to solve the more complex problems.

2. The PyTorch library has several features that make it the ultimate choice for data science. 

3. It is the largest machine learning library supporting complex tasks like dynamic computational graphs design and fast tensor computations with GPU acceleration. 

4. For applications calling for neural network algorithms, PyTorch offers a rich API. It supports a cloud-based ecosystem for scaling of resources used in deployment and testing.

5. PyTorch allows you to define your computational graph dynamically and transition in graph mode for optimization. 

6. It is a great library for your deep learning research projects as it provides great flexibility and native support for establishing P2P communication.

LightGBM:

1. It is another important concept that is being used in Python.

2. Using the Light Gradient Boosting Machine model to find important features in a dataset with many features.

3. If you look in the lightgbm docs for the feature_importance function, you will see that it has a parameter importance_type. 

4. The two valid values for these parameters are split(default one) and gain. 

5. It is not necessarily important that both split and gain produce the same feature importance. There is a new library for feature importance shape. 

6. Here you should use verbose_eval and early_stopping_rounds to track the actual performance of the model upon training. 

Eli5:

1. For sklearn-compatible estimators, eli5 provides PermutationImportance wrapper.

2. This method can be useful not only for introspection but also for feature selection - one can compute feature importances using PermutationImportance, then drop unimportant features using e.g. sklearn’s SelectFromModel or RFE.

3.  Here the permutation importance should be used for feature selection with care (like many other feature importance measures). 

4. For example, if several features are correlated, and the estimator uses them all equally, permutation importance can be low for all of these features: 

5. Dropping one of the features may not affect the result, as estimator still has access to the same information from other features. 

6. So if features are dropped based on importance threshold, such correlated features could be dropped all at the same time, regardless of their usefulness.

7. The eli5 provides a way to compute feature importances for any black-box estimator by measuring how the score decreases when a feature is not available; the method is also known as “permutation importance” or “Mean Decrease Accuracy (MDA)”.

Theano:

1. Theano is a Python library that allows us to evaluate mathematical operations including multi-dimensional arrays so efficiently. 

2. It is mostly used in building Deep Learning Projects. 

3. It works away faster on a Graphics Processing Unit (GPU) rather than on the CPU. 

4. Theano attains high speeds that give tough competition to C implementations for problems involving large amounts of data. 

5. It can take advantage of GPUs which makes it perform better than C on a CPU by considerable orders of magnitude under certain circumstances.

6. It is mainly designed to handle the types of computation required for large neural network algorithms used in Deep Learning.

Scope @ NareshIT:

1. At NareshIT’s Python application Development program, you will be able to get extensive hands-on training in front-end, middleware, and back-end technology.

2. It skilled you along with phase-end and capstone projects based on real business scenarios.

3. Here you learn the concepts from leading industry experts with content structured to ensure industrial relevance.

4. An end-to-end application with exciting features

5. Earn an industry-recognized course completion certificate.