Python Interview Questions And Answers 2019 | Edureka

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Python Interview Questions

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Agenda
Basic Python
Questions
1
Django Questions
2
Web Scraping
Questions
3
Data Analysis Using
Python Questions
4

Basic Python Questions

Data Analysis Using Python Questions
Shallow copy is used when a
new instance type gets created
and it keeps the values that are
copied in the new instance.
Deep copy is used to store the
values that are already copied.
Shallow copy is used to copy the reference
pointers just like it copies the values. These
references point to the original objects and
the changes made in any member of the
class will also affect the original copy of it.
Deep copy doesn’t copy the reference pointers to the
objects. It makes the reference to an object and the
new object that is pointed by some other object gets
stored. The changes made in the original copy won’t
affect any other copy that uses the object.
Shallow copy allows faster
execution of the program and it
depends on the size of the data
that is used.
Deep copy makes execution of
the program slower due to
making certain copies for each
object that is been called.
Q1. What is the difference between deep and shallow copy?

Q2. What is the difference between list and tuples?
Lists Tuples
Lists are mutable i.e it can be edited Tuples are immutable (tuples are lists
which can't be edited)
Syntax: list_1 = [10, ‘Chelsea’, 20] Syntax: tup_1 = (10, ‘Chelsea’ , 20)
Consider the program shown below:

Q3. How Multithreading is achieved in Python?
❑ Python has a multi-threading package but if you want to multi-thread to speed your code up.
❑ Python has a construct called the Global Interpreter Lock (GIL). The GIL makes sure that only one of your
'threads' can execute at any one time. A thread acquires the GIL, does a little work, then passes the GIL onto
the next thread.
❑ This happens very quickly so to the human eye 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.

Q4. How can the ternary operators be used in python?
The Ternary operator is the
operator that is used to
show the conditional
statements. This consists
of the true or false values
with a statement that has
to be evaluated for it.
The Ternary operator will be given as:
[on_true] if [expression] else [on_false]
x, y = 25, 50
big = x if x < y else y
Syntax and example
The expression gets evaluated like if
x<y else y, in this case if x<y is true
then the value is returned as big=x and
if it is incorrect then big=y will be sent
as a result.

Q5. What is monkey patching in Python?
In Python, the term monkey patch only refers to dynamic modifications of a class or module at runtime. Consider
the below example:
Then, if we run the monkey-
patch testing like this:
As we can see, we did make some changes in the behavior
of f() in MyClass using the function we
defined, monkey_f(), outside of the module m.

Q6. How can you randomize the items of a list in place in Python?
from random import shuffle
x = ['Keep', 'The', 'Blue', 'Flag', 'Flying', 'High']
shuffle(x)
print(x)
Consider the example shown below:
['Flying', 'Keep', 'Blue', 'High', 'The', 'Flag']
Output:

Q7. Write a sorting algorithm for a numerical dataset in Python.

Q8. Looking at the below code, write down the final values of A0, A1, ...An.
A0 = dict(zip(('a','b','c','d','e'),(1,2,3,4,5)))
A1 = range(10)
A2 = sorted([i for i in A1 if i in A0])
A3 = sorted([A0[s] for s in A0])
A4 = [i for i in A1 if i in A3]
A5 = {i:i*i for i in A1}
A6 = [[i,i*i] for i in A1]
print(A0,A1,A2,A3,A4,A5,A6)
A0 = {'a': 1, 'c': 3, 'b': 2, 'e': 5, 'd': 4} # the order may vary
A1 = range(0, 10)
A2 = []
A3 = [1, 2, 3, 4, 5]
A4 = [1, 2, 3, 4, 5]
A5 = {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
A6 = [[0, 0], [1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36], [7, 49], [8, 64], [9, 81]]

Q9. Explain split(), sub(), subn() methods of “re” module in Python.
To modify the strings, Python’s “re” module is providing 3 methods. They are:
❑ split() – uses a regex pattern to “split” a given string into a list.
❑ sub() – finds all substrings where the regex pattern matches and then replace them with a different string
❑ subn() – it is similar to sub() and also returns the new string along with the no. of replacements.

Q11. Explain Inheritance in Python with example?
Inheritance allows One class to gain all the members(say attributes and methods) of
another class. Inheritance provides code reusability, makes it easier to create and
maintain an application. The class from which we are inheriting is called super-class
and the class that is inherited is called a derived / child class. They are different
types of inheritance supported by Python:
❑ Single Inheritance – where a derived class acquires the members of a single
super class.
❑ Multi-level inheritance – a derived class d1 in inherited from base class base1,
and d2 is inherited from base2.
❑ Hierarchical inheritance – from one base class you can inherit any number of
child classes
❑ Multiple inheritance – a derived class is inherited from more than one base class.
class ParentClass:
v1 = "from ParentClass - v1"
v2 = "from ParentClass - v2"
class ChildClass(ParentClass):
pass
print(ChildClass.v1)
print(ChildClass.v2)

Django Questions

Django Questions
Q12. Mention the Django architecture?
Django MVT Pattern:
The developer provides the Model, the view and the template then just maps it to a URL and Django does the
magic to serve it to the user.
ViewURL
Model
TemplateDjangoUser

Django Questions
Q13. Explain how you can set up the Database in Django?
You can use the command edit mysite/setting.py , it is a normal python module with module level representing
Django settings.
Django uses SQLite by default; it is easy for Django users as such it won’t require any other type of installation. In
the case your database choice is different that you have to the following keys in the DATABASE ‘default’ item to
match your database connection settings
❑ Engines: you can change database by using ‘django.db.backends.sqlite3’ , ‘django.db.backeneds.mysql’,
‘django.db.backends.postgresql_psycopg2’, ‘django.db.backends.oracle’ and so on
❑ Name: The name of your database. In the case if you are using SQLite as your database, in that case database
will be a file on your computer, Name should be a full absolute path, including file name of that file.
❑ If you are not choosing SQLite as your database then setting like Password, Host, User, etc. must be added.

Django Questions
Q13. Explain how you can set up the Database in Django?
Django uses SQLite as default database, it stores data as a single file in the filesystem.
If you do have a database server—PostgreSQL, MySQL, Oracle, MSSQL—and want to use it rather than SQLite,
then use your database’s administration tools to create a new database for your Django project.
Either way, with your (empty) database in place, all that remains is to tell Django how to use it. This is where your
project’s settings.py file comes in.
setting.py

Django Questions
Q14. Give an example how you can write a VIEW in Django?
from django.http import HttpResponse
import datetime
def Current_datetime(request):
now = datetime.datetime.now()
html = "<html><body>It is now %s.</body></html>" % now
return HttpResponse(html)
Returns the current date and time, as an HTML document

Django Questions
Q15. Mention what does the Django templates consists of?
The template is a simple text file. It can create any text-based format like XML, CSV, HTML, etc. A template
contains variables that get replaced with values when the template is evaluated and tags (% tag %) that
controls the logic of the template.
DataBase ViewsModels
Templates
URL
Browser

Django Questions
Q16. Explain the use of session in Django framework?
Django provides session that lets you store and retrieve data on a per-site-visitor basis. Django abstracts the
process of sending and receiving cookies, by placing a session ID cookie on the client side, and storing all the
related data on the server side. So the data itself is not stored client side. This is nice from a security perspective.
Client-Side
# Session
ID Cookie
Website
Server-Side

Django Questions
Q17. List out the inheritance styles in Django?
Abstract base classes:
This style is used when
you only wants parent’s
class to hold information
that you don’t want to
type out for each child
model
Multi-table Inheritance:
This style is used If you
are sub-classing an
existing model and need
each model to have its
own database table
Proxy models:
You can use this model, If
you only want to modify
the Python level behavior
of the model, without
changing the model’s
fields
In Django, there is three possible inheritance styles:

Django Questions
Q18. Mention what does the Django field class types?
Field
Class
The database column type
The default HTML widget to avail while rendering a
form field
The minimal validation requirements used in Django
admin and in automatically generated forms

Web Scraping Using Python Questions

Q19. How To Save An Image Locally Using Python Whose URL Address I Already Know?
import urllib.request
urllib.request.urlretrieve("URL", "local-filename.jpg")

Q20. How Can I Get The Google Cache Age Of Any URL Or Web Page?
Use the following URL format:
http://webcache.googleusercontent.com/search?q=cache:URLGOESHERE
Be sure to replace “URLGOESHERE” with the proper web address of the page or site whose
cache you want to retrieve and see the time for. For example, to check the Google Webcache
age of edureka.co you’d use the following URL:
http://webcache.googleusercontent.com/search?q=cache:edureka.co

Q21. You are required to scrap data from IMDB top 250 movies page. It should only have fields movie name,
year, and rating.
from bs4 import BeautifulSoup
import requests
import sys
url = 'http://www.imdb.com/chart/top'
response = requests.get(url)
soup = BeautifulSoup(response.text)
tr = soup.findChildren("tr")
tr = iter(tr)
next(tr)
for movie in tr:
title = movie.find('td', {'class': 'titleColumn'} ).find('a').contents[0]
year = movie.find('td', {'class': 'titleColumn'} ).find('span', {'class': 'secondaryInfo'}).contents[0]
rating = movie.find('td', {'class': 'ratingColumn imdbRating'} ).find('strong').contents[0]
row = title + ' - ' + year + ' ' + ' ' + rating
print(row)

Q22. How To Get Indices Of N Maximum Values In A Numpy Array?
import numpy as np
arr = np.array([1, 3, 2, 4, 5])
print(arr.argsort()[-3:][::-1])
Output:
[ 4 3 1 ]

Q23. How Do I Calculate Percentiles With Python/Numpy?
import numpy as np
a = np.array([1,2,3,4,5])
p = np.percentile(a, 50) # return 50th percentile, e.g median.
print(p)
Output:
3

Data Analysis Python Questions
Q24. What advantages does NumPy arrays offers over (nested) Python lists?
❑ Python’s lists are efficient general-purpose containers. They support (fairly) efficient insertion, deletion,
appending, and concatenation, and Python’s list comprehensions make them easy to construct and manipulate.
❑ They have certain limitations: they don’t support “vectorized” operations like elementwise addition and
multiplication, and the fact that they can contain objects of differing types mean that Python must store type
information for every element, and must execute type dispatching code when operating on each element.
❑ NumPy is not just more efficient; it is also more convenient. You get a lot of vector and matrix operations for
free, which sometimes allow one to avoid unnecessary work. And they are also efficiently implemented.
❑ NumPy array is faster and You get a lot built in with NumPy, FFTs, convolutions, fast searching, basic statistics,
linear algebra, histograms, etc.

Q25. What is the difference between NumPy and SciPy?
❑ In an ideal world, NumPy would contain nothing but the array data type and the most basic operations:
indexing, sorting, reshaping, basic elementwise functions, et cetera.
❑ All numerical code would reside in SciPy. However, one of NumPy’s important goals is compatibility, so
NumPy tries to retain all features supported by either of its predecessors.
❑ Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In
any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other
numerical algorithms.
❑ If you are doing scientific computing with python, you should probably install both NumPy and SciPy.
Most new features belong in SciPy rather than NumPy.

Q26. How Do I make 3D plots/visualizations using NumPy/SciPy?
Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in the 2D case, packages exist
that integrate with NumPy. Matplotlib provides basic 3D plotting in the mplot3d subpackage,
whereas Mayavi provides a wide range of high-quality 3D visualization features, utilizing the powerful VTK engine.

Q27. How do I find the indices of an array where some condition is true?
import numpy as np
a = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(a > 3 )
print(np.nonzero(a > 3))
Output:
[[False False False]
[ True True True]
[ True True True]]
(array([1, 1, 1, 2, 2, 2], dtype=int32), array([0, 1, 2, 0, 1, 2], dtype=int32))

Q28. How to rename column headers in Pandas DataFrame
import pandas as pd
data = {'Commander': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'Date': ['2012, 02, 08', '2012, 02, 08', '2012, 02, 08', '2012, 02, 08', '2012, 02, 08'],
'Score': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df.columns = ['Leader', 'Time', 'Score']
print(df)
Original DataFrame Updated DataFrame

Q29. What is the function to iterate through the values in a manner that one
also retrieves the index ? df.iteritems unfortunately only iterates column by
column.
The newest versions of pandas now include a built-in function for iterating over rows.
for index, row in df.iterrows(): # do some logic here

Q30. How to convert a list of Dictionaries into a Pandas DataFrame
Supposing d is your list of dicts, simply:
pd.DataFrame(d)

Q31. Lets say that I have the following Pandas DataFrame:
df = DataFrame({'A' : [5,6,3,4], 'B' : [1,2,3, 5]})
how can I subset based on a list of values? - something like this:
list_of_values = [3,6] y = df[df['A'] in list_of_values]
This is indeed a duplicate of how to filter the dataframe rows of pandas by "within"/"in"?.
translating the response to the example shown above:
df[df['A'].isin([3, 6])]
A B
1 6 2
2 3 3

Q32. I have a DataFrame:
STK_ID EPS cash
STK_ID RPT_Date
601166 20111231 601166 NaN NaN
600036 20111231 600036 NaN 12
600016 20111231 600016 4.3 NaN
601009 20111231 601009 NaN NaN
601939 20111231 601939 2.5 NaN
000001 20111231 000001 NaN NaN
I just want the records whose EPS is not NaN
Just take rows where EPS is finite:
df = df[np.isfinite(df['EPS'])]

Q33. Write a program to read and write the binary data using python?
The module that is used to write and read the binary data is known as struct. The program can read or write the
binary data is:
import struct
f = open(file-name, "rb")
# This Open() method allows the file to get opened in binary mode to make it portable for # use.
s = f.read(8)
f.write("Hello, World!n")

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Python Interview Questions And Answers 2019 | Edureka

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