Python [Advanced] Interview Questions — 2022

Interview Questions are a great source to test your knowledge about any specific field. But remember whenever you are adding java as your strength in your resume do mention the details of the domain you have worked on that language. Otherwise the interviewer can question you from any of the fields in Python

Following are some Advanced Python Interview questions and answers for Freshers.

1. How is memory managed in Python?

• Memory management in Python is handled by the Python Memory Manager. The memory allocated by the manager is in form of a private heap space dedicated to Python. All Python objects are stored in this heap and being private, it is inaccessible to the programmer. Though, python does provide some core API functions to work upon the private heap space.
• Additionally, Python has an in-built garbage collection to recycle the unused memory for the private heap space.

2. How will you access the dataset of a publicly shared spreadsheet in CSV format stored in Google Drive?

We can use the StringIO module from the io module to read from the Google Drive link and then we can use the pandas library using the obtained data source.

from io import StringIO

import pandas

csv_link = "https://docs.google.com/spreadsheets/d/..."
data_source = StringIO.StringIO(requests.get(csv_link).content))
dataframe = pd.read_csv(data_source)
print(dataframe.head())

3. Write a program to check and return the pairs of a given array A whose sum value is equal to a target value N.

This can be done easily by using the phenomenon of hashing. We can use a hash map to check for the current value of the array, x. If the map has the value of (N-x), then there is our pair.

def print_pairs(arr, N):
   # hash set
   hash_set = set()
    
   for i in range(0, len(arr)):
       val = N-arr[i]
       if (val in hash_set):    #check if N-x is there in set, print the pair
           print("Pairs " + str(arr[i]) + ", " + str(val))
       hash_set.add(arr[i])

# driver code
arr = [1, 2, 40, 3, 9, 4]
N = 3
print_pairs(arr, N)

 4. Write a Program to add two integers >0 without using the plus operator.

We can use bitwise operators to achieve this.

def sum(n1, n2):
   while num2 != 0:
       data = n1 & n2
       n1 = n1 ^ n2
       n2 = data << 1
   return n1
print(sum(2, 10))

5.What are the steps to create 1D, 2D and 3D arrays?

• 1D array creation:

import numpy as np
one_dimensional_list = [1,2,4]
one_dimensional_arr = np.array(one_dimensional_list)
print("1D array is : ",one_dimensional_arr)

• 2D array creation:

import numpy as np
twoD_list=[[1,2,3],[4,5,6]]
twoD_arr = np.array(twoD_list)
print("2D array is : ",twoD_arr)

• 3D array creation:

import numpy as np
threeD_list=[[[1,2,3],[4,5,6],[7,8,9]]]
threeD_arr = np.array(threeD_list)
print("3D array is : ",threeD_arr)

• ND array creation: This can be achieved by giving the ndmin attribute. The below example demonstrates the creation of a 6D array:

import numpy as np
ndArr = np.array([1, 2, 3, 4], ndmin=6)
print(ndArr)
print('Dimensions of array:', ndArr.ndim)

Question From Python Library:

1. How will you distinguish between NumPy and SciPy?

 Typically, NumPy contains nothing but the array data type and the most basic operations, such as basic element-wise functions, indexing, reshaping, and sorting. All the numerical code resides in SciPy.

As one of NumPy’s most important goals is compatibility, the library tries to retain all features supported by either of its predecessors. Hence, NumPy contains a few linear algebra functions despite the fact that these more appropriately belong to the SciPy library.

SciPy contains fully-featured versions of the linear algebra modules available to NumPy in addition to several other numerical algorithms.

2. What is Django?

Django is an advanced python web framework that supports agile growth and clean pragmatic design, built through experienced developers, this cares much about the trouble of web development, so you can concentrate on writing your app without wanting to reinvent that wheel.

Features of Django:

• Excellent documentation
• Python web framework
• SEO optimised
• High scalability
• Versatile in nature
• Offers high security
• Thoroughly tested
• Provides rapid Development

Reasons to use Django:

The main goal to designing Django is to make it simple to its users, to do this   Django uses:

• The principles concerning rapid development, which implies developers can complete more than one iteration at a time without beginning the full schedule from scratch;
• DRY philosophy — Do not Replicate Yourself — that means developers can reuse surviving code and also focus on the individual one.

Advantage of Django:

• One of the important advantages of Django is it is a framework of python language which is very simple to learn
• Django is a multifaceted framework
• When it comes to security Django is the best framework
• Scalability is added advantage of Django

 Disadvantage of Django

• Uses routing pattern specify its URL.
• Django is too monolithic.
• Everything is based on Django ORM.
• Components get deployed together.
• Knowledge of full system is required to work.

3. Explain how can we build or set up the database in Django?

we can make use of the edit mysite/setting.py command, which is a simple Python module that consists of levels for presenting or displaying Django settings.

By default Django uses SQLite; this also makes it easy for Django users in case of any other type of installations. For example, if your database choice is different then you need to follow certain keys in the DATABASE like default items to match database connection settings.

• Engines: By these engines you change the database by using commands such as ‘django.db.backends.postgresql_psycopg2’, ‘django.db.backends.sqlite3’, ‘django.db.backends.oracle’, ‘django.db.backends.mysql’, and so on.
• Name: This represents the name of your own database. If you are familiar with using SQLite as your database, in such cases database is available as a file on your particular system. Moreover, the name should be as a fully absolute or exact path along with the file name of the particular file.
• Suppose if we are not using SQLite as your database then additional settings such as password, user, the host must be added.

Django mainly uses SQLite as its default database to store entire information in a single file of the filesystem. If you want to use different database servers rather than SQLite, then make use of database administration tools to create a new database for the Django project. Another way is by using your own database in that place, and the remaining is to explain Django about how to use it. This is the place in which Python’s project settings.py file comes into the picture. 

We need to add the below code to the setting.py file:

DATABASE  = {
‘Default’ : {
‘ENGINE’ :  ‘django.db.backends.sqlite3’,
‘NAME’ : os.path.join(BASE_DIR, ‘db.sqlite3’),
}
}

4. How do I test a Python program or component?

Python comes with two testing frameworks: The documentation test module finds examples in the documentation strings for a module and runs them, comparing the output with the expected output given in the documentation string.

The unittest module is a fancier testing framework modelled on Java and Smalltalk testing frameworks.

For testing, it helps to write the program so that it may be easily tested by using good modular design. Your program should have almost all functionality encapsulated in either functions or class methods. And this sometimes has the surprising and delightful effect of making the program run faster because local variable accesses are faster than global accesses.

Furthermore the program should avoid depending on mutating global variables, since this makes testing much more difficult to do. The "global main logic" of your program may be as simple as:

if name=="main":
main_logic()

at the bottom of the main module of your program. Once your program is organised as a tractable collection of functions and class behaviours, you should write test functions that exercise the behaviours.

A test suite can be associated with each module which automates a sequence of tests.

You can make coding much more pleasant by writing your test functions in parallel with the "production code", since this makes it easy to find bugs and even design flaws earlier.

"Support modules" that are not intended to be the main module of a program may include a self-test of the module.

if name == "main": self_test()

Even programs that interact with complex external interfaces may be tested when the external interfaces are unavailable by using "fake" interfaces implemented in Python.

5. What is Flask & its benefits?

Python Flask, as we've previously discussed, is a web microframework for Python. It is based on the 'Werkzeug, Jinja 2 and good intentions' BSD licence. Two of its dependencies are Werkzeug and Jinja2. This means that it has around no dependencies on external libraries. Due to this, we can call it a light framework. A session uses a signed cookie to allow the user to look at and modify session contents. It will remember information from one request to another. However, to modify a session, the user must have the secret key Flask.secret_key.

Flask is a web micro framework for Python based on "Werkzeug, Jinja 2 and good intentions" BSD licensed. Werkzeug and jinja are two of its dependencies.

Flask is part of the micro-framework. Which means it will have little to no dependencies on external libraries. It makes the framework light while there is little dependency to update and less security bugs.

6. Why would you use NumPy arrays instead of lists in Python?

• NumPy arrays provide users with three main advantages as shown below:
• NumPy arrays consume a lot less memory, thereby making the code more efficient.
• NumPy arrays execute faster and do not add heavy processing to the runtime.
• NumPy has a highly readable syntax, making it easy and convenient for programmers.

7. Define the different ways a DataFrame can be created in pandas?

We can create a DataFrame using following ways:

• Lists
• Dict of ndarrays

Example-1: Create a DataFrame using List:

import pandas as pd    
# a list of strings    
a = ['Python', 'Pandas']    
# Calling DataFrame constructor on list    
info = pd.DataFrame(a)    
print(info)    
Output:
 
	0
0   Python
1   Pandas

Example-2: Create a DataFrame from dict of ndarrays:

import pandas as pd    
info = {'ID' :[101, 102, 103],'Department' :['B.Sc','B.Tech','M.Tech',]}    
info = pd.DataFrame(info)    
print (info)   
Output:
 
       ID      Department
0      101        B.Sc
1      102        B.Tech
2      103        M.Tech

8. What is array slicing and how do you do it in NumPy?

Slicing means extracting a portion of the given array to generate a new view of the same array without copying it. This is done by specifying the following:

start: specifies where to start slicing from aka the lower limit

stop: specifies where to stop slicing aka the upper limit

step: determines the increment between each index for slicing

#Slicing a 1D array
arr = np.array([2, 3, 7, 11, 13, 17, 19, 23, 29, 31, 37]) 
arr[1:7:2] 
array slicing

Bonus:

Top Python Tips and Tricks 

1. In-place swapping of two numbers

p, q = 20, 40
print(p, q)
p, q = q, p
print(p, q)

Output:
20 40
40 20

2. Reversing a String in Python

x = "PepHubTraining"
print( "Reverse is" , x[: : -1]
Output:
Reverse is gniniarTbuHpeP

3. Create a single string from all the elements in the list

x = ["PepHub", "Online", "Training"]
print(" ".join(x))
Output:
PepHub Online Training

4. Return multiple values from functions

def fun():
return 4, 5, 6, 7
p, q, r, s = fun()  
print(p, q, r, s)
Output:
4, 5, 6, 7

5. Check the memory usage of an object

import sys
x = 10
print(sys.getsizeof(x))
Output:
28

6. Find the most frequent value in a list

max_freq = [1, 2, 3, 9, 2, 7, 3, 5, 9, 9, 9]
print(max(set(test), key = max_freq.count))
Output:
9

7. Print the file path of imported modules

import os;
import socket; 
print(os)
print(socket)
Output:
<module 'os' from '/usr/lib/python3.5/os.py'>
<module 'socket' from '/usr/lib/python3.5/socket.py'>