Python Interview Questions

Question 1

*args vs **kwargs

Answer 1

*args

• when we don’t know 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.

**kwargs

• 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.

Question 2

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

Answer 2

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.

Question 3

Abstraction

Answer 3

Data Abstraction is providing only the required details and hiding the implementation from the world. It can be achieved in Python by using interfaces and abstract classes.

Question 4

Access specifiers

Answer 4

Python does not deprive access to an instance variable or function.

Python lays down the concept of prefixing the name of the variable, function or method with a single or double underscore to imitate the behavior of protected and private access specifiers.

Question 5

add values to array

Answer 5

append(), extend() and the insert (i,x) functions.

Example:

a=arr.array(‘d’, [1.1 , 2.1 ,3.1] )

a.append(3.4)

print(a)

a.extend([4.5,6.3,6.8])

print(a)

a.insert(2,3.8)

print(a)

Output:

array(‘d’, [1.1, 2.1, 3.1, 3.4])

array(‘d’, [1.1, 2.1, 3.1, 3.4, 4.5, 6.3, 6.8])

array(‘d’, [1.1, 2.1, 3.8, 3.1, 3.4, 4.5, 6.3, 6.8])

Question 6

remove values from array

Answer 6

pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.

Example:

a=arr.array(‘d’, [1.1, 2.2, 3.8, 3.1, 3.7, 1.2, 4.6])

print(a.pop())

print(a.pop(3))

a.remove(1.1)

print(a)

Output:

4. 6
5. 1

array(‘d’, [2.2, 3.8, 3.7, 1.2])

Question 7

Arrays vs Lists

Answer 7

Arrays and lists, in Python, have the same way of storing data.

But, arrays can hold only a single data type elements whereas lists can hold any data type elements.

ALso an array runs faster than a list because it alocates a block of memory.

Question 8

Break, Continue and Pass

Answer 8

Break

• Allows loop termination when some condition is met and the control is transferred to the next statement.

Continue

• Allows skipping some part of a loop when some specific condition is met and the control is transferred to the beginning of the loop

Pass

• Used when you need some block of code syntactically, but you want to skip its execution. This is basically a null operation. Nothing happens when this is executed.

Question 9

Built-in types

Answer 9

• Integers
• Floating-point
• Complex numbers
• Strings
• Boolean
• Built-in functions

Question 10

Capitalize the 1st letter of string

Answer 10

• Capitalize() method capitalizes the first letter of a string.
• If the string already consists of a capital letter at the beginning, then, it returns the original string.

Question 11

Case sensitive

Answer 11

Python is a case sensitive language.

Question 12

create an empty class

Answer 12

An empty class is a class that does not have any code defined within its block. It can be created using the pass keyword. However, you can create objects of this class outside the class itself. IN PYTHON THE PASS command does nothing when its executed. it’s a null statement.

For example-

class a:

pass

obj=a()

obj.name=”xyz”

print(“Name = “,obj.name)

Output:

Name = xyz

Question 13

Classes

Answer 13

Class in Python is created using the class keyword.

Example:

class Employee:

def __init__(self, name):

self.name = name

E1=Employee(“abc”)

print(E1.name)

Output:

abc

Question 14

Comments

Answer 14

Comments in Python start with a # character. However, alternatively at times, commenting is done using docstrings(strings enclosed within triple quotes).

Question 15

Comment multiple lines

Answer 15

Multi-line comments appear in more than one line.

• All the lines to be commented are to be prefixed by a #.
• block of string can be use for multi-lines comments, also called docstrings:
  
  • ”"”block
    of
    comment”””

Question 16

Compilation and Linking

Answer 16

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 are required in this as:

1. 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
2. Place this file in the Modules/ directory of the distribution which is getting used.
3. Add a line in the file Setup.local that is present in the Modules/ directory.
4. Run the file using spam file.o
5. After a successful run of this rebuild the interpreter by using the make command on the top-level directory.
6. If the file is changed then run rebuildMakefile by using the command as ‘make Makefile’.

Question 17

Type Conversion (casting)

Answer 17

Type conversion refers to the conversion of one data type iinto another (casting).

• int() – converts any data type into integer type
• float() – converts any data type into float type
• ord() – converts characters into integer
• hex() – converts integers to hexadecimal
• oct() – converts integer to octal
• tuple() – This function is used to convert to a tuple.
• set() – This function returns the type after converting to set.
• list() – This function is used to convert any data type to a list type.
• dict() – This function is used to convert a tuple of order (key,value) into a dictionary.
• str() – Used to convert integer into a string.
• complex(real,imag) – This functionconverts real numbers to complex(real,imag) number.

Question 18

Deep vs Shallow copy

Answer 18

Shallow copy is used when a new instance type gets created and it keeps the values that are copied in the new instance. 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. Shallow copy allows faster execution of the program and it depends on the size of the data that is used.

Deep copy is used to store the values that are already copied. 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. Deep copy makes execution of the program slower due to making certain copies for each object that is been called.

Question 19

Delete files

Answer 19

import os

os.remove(“xyz.txt”)

Question 20

Dictionary

Answer 20

• built-in datatype. It defines one-to-one relationship between
• keys and values pair
• it is indexed by keys
• mutable
• iterable
• curly brackets

Question 21

Django vs Flask

Answer 21

Django and Flask map the URL’s or addresses typed in the web browsers to functions in Python.

Flask is much simpler compared to Django but, Flask does not do a lot for you meaning you will need to specify the details, whereas Django does a lot for you wherein you would not need to do much work.

Django consists of prewritten code, which the user will need to analyze whereas Flask gives the users to create their own code, therefore, making it simpler to understand the code. Technically both are equally good and both contain their own pros and cons.

Question 22

Django, Pyramid and Flask.

Answer 22

• 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 be used for larger applications just like Pyramid. It includes an ORM (object-relational-mapping) - like database object in the code.

Question 23

Django architecture

Answer 23

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.

Question 24

set up Database in Django

Answer 24

setting.py file for dababase details, 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. 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.

• Engines: you can change the 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 the file name of that file.
• If you are not choosing SQLite as your database then settings like Password, Host, User, etc. must be added.

Question 25

Inheritance styles in Django

Answer 25

In Django, there are three possible inheritance styles: * **Abstract Base Classes**: This style is used when you only want 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.

Question 26

Session in Django

Answer 26

* Django provides a 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. * The data itself is not stored client side. This is nice from a security perspective.

Question 27

Django templates

Answer 27

* 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 control the logic of the template.

Question 28

VIEW in Django

Answer 28

**from** django.http **import** HttpResponse **import** datetime **def** Current\_datetime(request): now = datetime.datetime.now() html = "It is now %s % now **return** HttpResponse(html) *Returns the current date and time, as an HTML document*

Question 29

Docstrings

Answer 29

* Docstrings are not actually comments, but, they are **documentation strings**. * These docstrings are within **triple quotes**. * They are not assigned to any variable and therefore, at times, serve the purpose of comments as well. * """ Using docstring as a comment. This code divides 2 numbers """

Question 30

Encapsulation

Answer 30

Encapsulation means **binding the code and the data together**. A Python *class is an example of encapsulation*.

Question 31

Flask

Answer 31

Flask is a **web microframework** for Python based on “Werkzeug, Jinja2 and good intentions” BSD license. Werkzeug and Jinja2 are two of its dependencies. * This means it will have little to no dependencies on external libraries. * It makes the framework light while there is a little dependency to update and fewer security bugs. * A session basically allows you to remember information from one request to another. In a flask, a session uses a signed cookie so the user can look at the session contents and modify. * The user can modify the session if only it has the secret key Flask.secret\_key.

Question 32

Functions

Answer 32

* A function is a **block of code** which is *executed only when it is called*. * To define a Python function, the **def** keyword is used.

Question 33

Generators

Answer 33

Functions that return an iterable set of items are called generators. example: function that returns a list

Question 34

Get Google cache age of any URL or web page

Answer 34

http://webcache.googleusercontent.com/search?q=cache:URLGOESHERE Example: http://webcache.googleusercontent.com/search?q=cache:billygustave.com

Question 35

**help()** vs **dir()**

Answer 35

* help() and dir() both functions are accessible from the Python interpreter and used for viewing a consolidated dump of built-in functions. **help()** * returns/displays the docstring for a function/class/module * help([object]) **dir()** * list all functions and variable names in a module

Question 36

import modules

Answer 36

Modules can be imported using the **import** keyword. You can import modules in three ways- Example: 1 2 3 **import** array #importing using the original module name **import** array **as** arr # importing using an alias name **from** array **import** \* #imports everything present in the array module

Question 37

Indentation

Answer 37

* *Indentation is necessary* for Python. **It specifies a block of code.** * All code within loops, classes, functions, etc is specified within an indented block. It is usually done using *four space characters*. * If your code is not indented necessarily, it will not execute accurately and will throw errors as well.

Question 38

negative indexes

Answer 38

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.

Question 39

Inheritance

Answer 39

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: 1. Single Inheritance – where a derived class acquires the members of a single super class. 2. Multi-level inheritance – a derived class d1 in inherited from base class base1, and d2 are inherited from base2. 3. Hierarchical inheritance – from one base class you can inherit any number of child classes 4. Multiple inheritance – a derived class is inherited from more than one base class.

Question 40

Iterators

Answer 40

objects which can be traversed though or iterated upon.

Question 41

Lambda

Answer 41

* An anonymous function is known as a lambda function. * This function can have any number of parameters but, can have just one statement.

Question 42

len()

Answer 42

* returns count or length or how many elements in an object It is used to determine the length of a string, a list, an array, etc. Example: 1 2 stg='ABCD' len(stg)

Question 43

Libraries

Answer 43

Python libraries are a collection of Python packages. Some of the majorly used python libraries are – **Numpy**, **Pandas**, **Matplotlib**, **Scikit-learn** and many more.

Question 44

LIST vs TUPLES

Answer 44

LIST * Lists are mutable i.e they can be edited. * Lists are slower than tuples. * Syntax: list\_1 = [10, ‘Chelsea’, 20] TUPLES * Tuples are immutable (tuples are lists which can’t be edited). * Tuples are faster than list. * Syntax: tup\_1 = (10, ‘Chelsea’ , 20)

Question 45

Lowercase

Answer 45

To convert a string to lowercase, lower() function can be used. "string"**.lower()**

Question 46

map()

Answer 46

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.

Question 47

Why isn’t all the **memory de-allocated** upon **exit**?

Answer 47

* Whenever Python exits, especially those Python modules which are having circular references to other objects or the objects that are referenced from the global namespaces are not always de-allocated or freed. * It is impossible to de-allocate those portions of memory that are reserved by the C library. * On exit, because of having its own efficient clean up mechanism, Python would try to de-allocate/destroy every other object.

Question 48

Modules

Answer 48

* Python modules are files containing Python code. This code can either be functions classes or variables. * A Python module is a .py file containing executable code. * Some of the commonly used built-in modules are: * os * sys * math * random * datatime * JSON

Question 49

monkey patching

Answer 49

In Python, the term monkey patch only refers to **dynamic modifications of a class** or module **at run-time**. Consider the below example: m.py class MyClass: def f(self): print "f()" We can then run the monkey-patch testing like this: . import m 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.

Question 50

Which of the following statements create a dictionary?

Answer 50

a) d = {} b) d = {“john”:40, “peter”:45} c) d = {40:”john”, 45:”peter”} d) d = (40:”john”, 45:”50”) Answer: a, b & c. Dictionaries are created by specifying keys and values.

Question 51

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

Answer 51

a) always b) when an exception occurs c) when no exception occurs d) when an exception occurs into except block Answer: c) when no exception occurs The else part is executed when no exception occurs.

Question 52

Which one of these is floor division?

Answer 52

a) / b) // c) % d) None of the mentioned Answer: b) //

Question 53

What is the maximum possible length of an identifier?

Answer 53

a) 31 characters b) 63 characters c) 79 characters d) None of the above Answer: d) None of the above Identifiers can be of any length.

Question 54

Which of the following is an invalid statement?

Answer 54

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 Answer: b) a b c = 1000 2000 3000 Spaces are not allowed in variable names.

Question 55

Why are local variable names beginning with an underscore discouraged?

Answer 55

a) 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 Answer: a) they are used to indicate a private variable of a class As Python has no concept of private variables, leading underscores are used to indicate variables that must not be accessed from outside the class.

Question 56

To open a file c:scores.txt for writing, we use

Answer 56

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”) Answer: b) The location contains double slashes ( ) and w is used to indicate that file is being written to.

Question 57

Suppose list1 is [2, 33, 222, 14, 25], What is list1[-1] ?

Answer 57

a) Error b) None c) 25 d) 2 Answer: c) 25 The index -1 corresponds to the last index in the list.

Question 58

What is the output of the following?

Answer 58

try: if '1' != 1: raise "someError" else: print("someError has not occured") except "someError": print ("someError has occured") a) someError has occured b) someError has not occured c) invalid code d) none of the above Answer: c) invalid code A new exception class must inherit from a BaseException. There is no such inheritance here.

Question 59

What is the output of the following?

Answer 59

f = None for i in range (5): with open("data.txt", "w") as f: if i \> 2: break print f.closed a) True b) False c) None d) Error Answer: a) True The WITH statement when used with open file guarantees that the file object is closed when the with block exits.

Question 60

Multiple Inheritance

Answer 60

Multiple inheritance means that a class can be derived from more than one parent classes. Python does support multiple inheritance, unlike Java.

Question 61

Multithreading

Answer 61

1. Python has a multi-threading package but if you want to multi-thread to speed your code up, then it’s usually not a good idea to use it. 2. 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. 3. 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. 4. 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.

Question 62

Namespace

Answer 62

* A namespace is a *naming system* used to make sure that *names are unique* to avoid naming conflicts.

Question 63

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

Answer 63

We can get the indices of N maximum values in a NumPy array using the below code: import numpy as np arr = np.array([1, 3, 2, 4, 5]) print(arr.argsort()[-3:][::-1]) Output [4 3 1]

Question 64

NumPy **arrays** vs **lists**

Answer 64

1. 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. 2. 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. 3. 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. 4. **NumPy array is faster** and You get a lot built in with NumPy, FFTs, convolutions, fast searching, basic statistics, linear algebra, histograms, etc.

Question 65

What is the difference between NumPy and SciPy?

Answer 65

1. In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, etc. 2. 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. 3. 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. 4. 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.

Question 66

Is **Numpy** better than **lists**?

Answer 66

We use python numpy array instead of a list because of the below three reasons: * Less Memory * Fast * Convenient

Question 67

How do you calculate percentiles with Python/ NumPy?

Answer 67

We can calculate percentiles with the following code 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

Question 68

object()

Answer 68

* It returns a featureless object that is a **base for all classes**. * Also, *it does not take any parameters.*

Question 69

OOps concepts

Answer 69

Python is an **object-oriented programming** language. This means that any program can be solved in *python by creating an object model*. However, Python can be treated as procedural as well as structural language.

Question 70

**IS****,****IN, NOT** operators

Answer 70

**IS/IS NOT** * Identity operator * **IS** - compares 2 object and see if the are the same * returns true is they are the same: **if** x **is** y: * **IS NOT** - compares 2 objects and see if they are different * returns true is they are not same: **if** x **is not** y: **IN/NOT IN** * Membership operator * **IN** - check to see if object is in a sequence * returns true is it is: **if** x **in** y: * **NOT IN** - check to see if object is not in a sequence * returns true is it is not: **if** x **not in** y: **NOT** * returns reverse/opposite of any conditional statement

Question 71

Ternary operators

Answer 71

* [on\_true] if [expression] else [on\_false] * it's a represention of: if [expression]: [on\_true] else [on\_false]

Question 72

Packages

Answer 72

* namespaces containing *multiple modules*

Question 73

PEP

Answer 73

PEP stands for **Python Enhancement Proposal**. It is a set of rules that specify *how to format Python* code for maximum readability.

Question 74

**Pickling** vs **Unpickling**

Answer 74

* import pickle **Pickling** * Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function. * the python object is converted into a byte stream * serialization * pickled\_string = pickle.dumps(python\_object) **Unpickling** * Process of retrieving original Python objects from the stored string representation. * the byte stream is converted back into a python object * de-serialization * python\_object = pickle.loads(pickled\_string)

Question 75

Polymorphism

Answer 75

Polymorphism means the **ability to take multiple forms**. So, for instance, if the parent class has a method named ABC then the child class also can have a method with the same name ABC having its own parameters and variables. Python allows polymorphism.

Question 76

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

Answer 76

**def** bs(a): *# a = name of list* b=*len*(a)-1 *# minus 1 because we always compare 2 adjacent values* **for** x **in** *range*(b): **for** y **in** *range*(b-x): **if** a[y]\>a[y+1]: a[y],a[y+1]=a[y+1],a[y] **return** a a=[32,5,3,6,7,54,87] bs(a) Output: [3, 5, 6, 7, 32, 54, 87]

Question 77

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

Answer 77

a=int(input("enter number")) if a\>1: for x in range(2,a): if(a%x)==0: print("not prime") break else: print("Prime") else: print("not prime") Output: enter number 3 Prime

Question 78

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

Answer 78

a=input("enter sequence") b=a[::-1] if a==b: print("palindrome") else: print("Not a Palindrome") Output: enter sequence 323 palindrome

Question 79

Write a program to produce Fibonacci series in Python.

Answer 79

Enter number of terms needed #0,1,1,2,3,5.... a=int(input("Enter the terms")) f=0 #first element of series s=1 #second element of series if a\<=0: print("The requested series is ",f) else: print(f,s,end=" ") for x in range(2,a): next=f+s print(next,end=" ") f=s s=next Output: Enter the terms 5 0 1 1 2 3

Question 80

Write a sorting algorithm for a numerical dataset in Python.

Answer 80

The following code can be used to sort a list in Python: list = ["1", "4", "0", "6", "9"] list = [int(i) for i in list] list.sort() print (list)

Question 81

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.

Answer 81

Let us first write a multiple line solution and then convert it to one-liner code. 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. count sum(1 for line in fh for character in line if character.isupper()) ```

Question 82

Write a program in Python to produce Star triangle.

Answer 82

``` def pyfunc(r): for x in range(r): print(' '\*(r-x-1)+'\*'\*(2\*x+1)) pyfunc(9) ``` Output: \* \*\*\* \*\*\*\*\* \*\*\*\*\*\*\* \*\*\*\*\*\*\*\*\* \*\*\*\*\*\*\*\*\*\*\* \*\*\*\*\*\*\*\*\*\*\*\*\* \*\*\*\*\*\*\*\*\*\*\*\*\*\*\* \*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*

Question 83

Web Scrapping example

Answer 83

from bs4 import BeautifulSoup import requests import sys url = *'[http://www.imdb.com/chart/top](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)

Question 84

Looking at the below code, write down the final values of A0, A1, …An.

Answer 84

``` 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,

Question 85

PYTHON features

Answer 85

* Python is an **interpreted language**. That means that, unlike languages like C and its variants, Python *does not need to be compiled* before it is run. Other interpreted languages include PHP and Ruby. * Python is **dynamically typed**, this means that you *don’t need to state the types of variables* when you declare them or anything like that. You can do things like x=111 and then x="I'm a string" without error * Python is well suited to **object orientated programming** in that it allows the definition of *classes* along with composition and *inheritance*. Python does not have access specifiers (like C++’s public, private). * In Python, **functions are first-class objects**. This means that they can be *assigned to variables*, *returned from other functions* and *passed into functions*. Classes are also first class objects * **Writing Python code is quick** but running it is often slower than compiled languages. Fortunately，Python *allows the inclusion of C based extensions* so bottlenecks can be optimized away and often are. The *numpy* package is a good example of this, it’s really quite quick because a lot of the number crunching it does isn’t actually done by Python * Python finds **use in many spheres** – *web applications, automation, scientific modeling, big data applications* and many more. It’s also often used as “glue” code to get other languages and components to play nice.

Question 86

**Global** vs **Local** variables

Answer 86

**Global Variables**: * Variables declared outside a function or in global space are called global variables. These variables can be accessed by any function in the program. * declare a global variable inside a function using keyword *global* **Local Variables**: * Any variable declared inside a function is known as a local variable. This variable is present in the local space and not in the global space.

Question 87

Python an interpreted language

Answer 87

An **interpreted language** is any programming *language which is not in machine level code before runtime.*

Question 88

Memory Management in Python

Answer 88

* Memory management in python is managed by *Python private heap space*. All Python objects and data structures are located in a private heap. The *programmer does not have access to this private heap*. The python interpreter takes care of this instead. * The allocation of heap space for Python objects is done by Python’s memory manager. The core API gives access to some tools for the programmer to code. * Python also has an *inbuilt garbage collector*, which recycles all the unused memory and so that it can be made available to the heap space.

Question 89

Is Python a **Programming** or **Scripting** language?

Answer 89

Python is capable of scripting, but in general sense, it is considered as a general-purpose **programming language**.

Question 90

Install Python on Windows with path variable

Answer 90

To install Python on Windows, follow the below steps: * Download python from this link: [https://www.python.org/downloads/](https://www.python.org/downloads/) * After this, install it on your PC. Look for the location where PYTHON has been installed on your PC using the following command on your command prompt: cmd python. * Then go to advanced system settings and add a new variable and name it as PYTHON\_NAME and paste the copied path. * Look for the path variable, select its value and select ‘edit’. * Add a semicolon towards the end of the value if it’s not present and then type %PYTHON\_HOME%

Question 91

PYTHONPATH

Answer 91

It is an *environment variable* which is *used when a module is imported*. Whenever a module is imported, PYTHONPATH is also looked up to check for the presence of the imported modules in various directories. The interpreter uses it to determine which module to load.

Question 92

Generate **Random** numbers

Answer 92

* Random module is the standard module that is used to generate a random number. * The method is defined as: * *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 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 a different instance of individual threads. The other random generators that are used in this are: 1. 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. 2. uniform(a, b): it chooses a floating point number that is defined in the range of [a,b).Iyt returns the floating point number 3. 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. 4. The Random class that is used and instantiated creates an independent multiple random number generators.

Question 93

**randomize** the items of a **list in place**

Answer 93

from random import shuffle x = ['Keep', 'The', 'Blue', 'Flag', 'Flying', 'High'] **shuffle(**x**)**

Question 94

**Range** vs **xRange**

Answer 94

Both creates list or number to use/iterate through Range * range returns list of object * generates a static list at run-time which takes memory space * not ideal for big range list or memory sensitive systems * can run the risk of Memory Error if run out of memory xRange * xrange returns an xrange object * creates value when needed using yield technique which uses a generator object. * good for memory sensitive systems such as cellphone * ideal for big range list

Question 95

Save An Image Locally Using Python

Answer 95

**import** urllib.request *urllib.request.urlretrieve(*"URL", "local-filename.jpg"*)*

Question 96

self

Answer 96

* Self is an instance or an object of a class. * In Python, this is explicitly included as the first parameter. However, this is not the case in Java where it’s optional. * It helps to differentiate between the methods and attributes of a class with local variables. * The self variable in the init method refers to the newly created object while in other methods, it refers to the object whose method was called.

Question 97

split()

Answer 97

split() method is used to separate a given string in Python. Example: a=**"edureka python"** print(**a.split()**) Output: **[‘edureka’, ‘python’]**

Question 98

split(), sub(), subn()

Answer 98

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*.

Question 99

[::-1]

Answer 99

[::-1] is used to **reverse the order** *of* an array or *a sequence*.

Question 100

\_\_init\_\_

Answer 100

* \_\_init\_\_ is a **method or constructor** in Python. * This method is **automatically called to allocate memory** when a new object/ instance of a class is created. * **All classes have the \_\_init\_\_ method.**