import m
def monkey_f(self):
print("monkey_f()")
m.MyClass.f = monkey_f
obj = m.MyClass()
obj.f()
def monkey_f(self):
print "monkey_f()"
m.MyClass.f = monkey_f
obj = m.MyClass()
obj.f()
The output will be as below:
monkey_f()
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.
Q13. What does this mean: *args, **kwargs? And why would we use it?
Ans: We use *args when we aren’t sure how many arguments are going to be passed to a function, or if we want to pass a stored list or tuple of arguments to a function. **kwargsis used when we don’t know how many keyword arguments will be passed to a function, or it can be used to pass the values of a dictionary as keyword arguments. The identifiers args and kwargs are a convention, you could also use *bob and **billy but that would not be wise.
Q14. 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.
Ans: Let us first write a multiple line solution and then convert it to one liner code.
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with open(SOME_LARGE_FILE) as fh:
count = 0
text = fh.read()
for character in text:
if character.isupper():
count += 1
We will now try to transform this into a single line.
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count sum(1 for line in fh for character in line if character.isupper())
Q15. What are negative indexes and why are they used?
Ans: The sequences in Python are indexed and it consists of the positive as well as negative numbers. The numbers that are positive uses ‘0’ that is uses as first index and ‘1’ as the second index and the process goes on like that.
The index for the negative number starts from ‘-1’ that represents the last index in the sequence and ‘-2’ as the penultimate index and the sequence carries forward like the positive number.
The negative index is used to remove any new-line spaces from the string and allow the string to except the last character that is given as S[:-1]. The negative index is also used to show the index to represent the string in correct order.
Q16. How can you randomize the items of a list in place in Python?
Ans: Consider the example shown below:
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from random import shuffle
x = ['Keep', 'The', 'Blue', 'Flag', 'Flying', 'High']
shuffle(x)
print(x)
The output of the following code is as below.
['Flying', 'Keep', 'Blue', 'High', 'The', 'Flag']
Q17. What is the process of compilation and linking in python?
Ans: The compiling and linking allows the new extensions to be compiled properly without any error and the linking can be done only when it passes the compiled procedure. If the dynamic loading is used then it depends on the style that is being provided with the system. The python interpreter can be used to provide the dynamic loading of the configuration setup files and will rebuild the interpreter.
The steps that is required in this as:
Create a file with any name and in any language that is supported by the compiler of your system. For example file.c or file.cpp
Place this file in the Modules/ directory of the distribution which is getting used.
Add a line in the file Setup.local that is present in the Modules/ directory.
Run the file using spam file.o
After successful run of this rebuild the interpreter by using the make command on the top-level directory.
If the file is changed then run rebuildMakefile by using the command as ‘make Makefile’.
Q18. Write a sorting algorithm for a numerical dataset in Python.
Ans: The following code can be used to sort a list in Python:
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list = ["1", "4", "0", "6", "9"]
list = [int(i) for i in list]
list.sort()
print (list)
Q19. Looking at the below code, write down the final values of A0, A1, …An.
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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)
Ans: The following will be the final outputs of A0, A1, … 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]]
Q20. Explain split(), sub(), subn() methods of “re” module in Python.
Ans: 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.
Q21. How can you generate random numbers in Python?
Ans: Random module is the standard module that is used to generate the random number. The method is defined as:
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import random
random.random
The statement random.random() method return the floating point number that is in the range of [0, 1). The function generates the random float numbers. The methods that are used with the random class are the bound methods of the hidden instances. The instances of the Random can be done to show the multi-threading programs that creates different instance of individual threads. The other random generators that are used in this are:
randrange(a, b): it chooses an integer and define the range in-between [a, b). It returns the elements by selecting it randomly from the range that is specified. It doesn’t build a range object.
uniform(a, b): it chooses a floating point number that is defined in the range of [a,b).Iyt returns the floating point number
normalvariate(mean, sdev): it is used for the normal distribution where the mu is a mean and the sdev is a sigma that is used for standard deviation.
The Random class that is used and instantiated creates an independent multiple random number generators.
Q22. What is the difference between range & xrange?
Ans: For the most part, xrange and range are the exact same in terms of functionality. They both provide a way to generate a list of integers for you to use, however you please. The only difference is that range returns a Python list object and x range returns an xrange object.
This means that xrange doesn’t actually generate a static list at run-time like range does. It creates the values as you need them with a special technique called yielding. This technique is used with a type of object known as generators. That means that if you have a really gigantic range you’d like to generate a list for, say one billion, xrange is the function to use.
This is especially true if you have a really memory sensitive system such as a cell phone that you are working with, as range will use as much memory as it can to create your array of integers, which can result in a Memory Error and crash your program. It’s a memory hungry beast.
Q23. What is pickling and unpickling?
Ans: Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
Django – Python Interview Questions
Q24. Mention the differences between Django, Pyramid and Flask.
Ans:
Flask is a “microframework” primarily build for a small application with simpler requirements. In flask, you have to use external libraries. Flask is ready to use.
Pyramid is built for larger applications. It provides flexibility and lets the developer use the right tools for their project. The developer can choose the database, URL structure, templating style and more. Pyramid is heavy configurable.
Django can also used for larger applications just like Pyramid. It includes an ORM.
Q25. Discuss the Django architecture.
Ans: Django MVT Pattern:
Django Architecture - Python Interview Questions - EdurekaFigure: Python Interview Questions – Django Architecture
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.
Q26. Explain how you can set up the Database in Django.
Ans: 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 settings like Password, Host, User, etc. must be added.
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.
We will add the following lines of code to the setting.py file:
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DATABASES = {
'default': {
'ENGINE' : 'django.db.backends.sqlite3',
'NAME' : os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
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Q27. Give an example how you can write a VIEW in Django?
Ans: This is how we can use write a view in Django:
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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
Q28. Mention what the Django templates consists of.
Ans: 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.
Django Template - Python Interview Questions - EdurekaFigure: Python Interview Questions – Django Template
Q29. Explain the use of session in Django framework?
Ans: 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.
Django Framework - Python Interview Questions - EdurekaFigure: Python Interview Questions – Django Framework
So the data itself is not stored client side. This is nice from a security perspective.
Q30. List out the inheritance styles in Django.
Ans: In Django, there is three possible inheritance styles:
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.
Web Scraping – Python Interview Questions
Q31. How To Save An Image Locally Using Python Whose URL Address I Already Know?
Ans: We will use the following code to save an image locally from an URL address
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import urllib.request
urllib.request.urlretrieve("URL", "local-filename.jpg")
Q32. How can you Get the Google cache age of any URL or web page?
Ans: 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
Q33. You are required to scrap data from IMDb top 250 movies page. It should only have fields movie name, year, and rating.
Ans: We will use the following lines of code:
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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)
The above code will help scrap data from IMDb’s top 250 list
Data Analysis – Python Interview Questions
Q34. What is map function in Python?
Ans: map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given. #Follow the link to know more similar functions.
Q35. How to get indices of N maximum values in a NumPy array?
Ans: We can get the indices of N maximum values in a NumPy array using the below code:
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import numpy as np
arr = np.array([1, 3, 2, 4, 5])
print(arr.argsort()[-3:][::-1])
Output
[ 4 3 1 ]
Q36. How do you calculate percentiles with Python/ NumPy?
Ans: We can calculate percentiles with the following code
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import numpy as np
a = np.array([1,2,3,4,5])
p = np.percentile(a, 50) #Returns 50th percentile, e.g. median
print(p)
Output
3
Q37. What advantages do NumPy arrays offer over (nested) Python lists?
Ans:
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.
Q38. Explain the use of decorators.
Ans: Decorators in Python are used to modify or inject code in functions or classes. Using decorators, you can wrap a class or function method call so that a piece of code can be executed before or after the execution of the original code. Decorators can be used to check for permissions, modify or track the arguments passed to a method, logging the calls to a specific method, etc.
Q39. What is the difference between NumPy and SciPy?
Ans:
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.
Q40. How do you make 3D plots/visualizations using NumPy/SciPy?
Ans: 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.
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Multiple Choice Questions
Q41. Which of the following statements create a dictionary? (Multiple Correct Answers Possible)
a) d = {}
b) d = {“john”:40, “peter”:45}
c) d = {40:”john”, 45:”peter”}
d) d = (40:”john”, 45:”50”)