Q&A for Python interview

Important topics in Q&A format is based on my experience. Feel free to update me in case of any observation or you want to add anything.

1. How to debug pip installation error?

For debugging purpose, pip displays some installation message on console. It also offers ways to control console level log by -v,–verbose, -q and –quiet. Full log can be saved by providing –log option with file path, it will have complete installation log. There are cases where currupt download also creates issues at the time of installation, in that case try –no-cache-dir. If that also does not work then you can always try the installation from source file.

2. Difference between list and dictionary?

List

• List in Python is a heterogeneous container for items.
• Elements present in List maintain their order.
• The elements present in list can be of any type (int, float, string, tuple etc.).
• Elements are accessed through their index values.
• If you have a collection of data that does not need random access, use List.
• Where you have to deal with values which can be changed, use List.

  mylist=[1,2,3,'4','sample']
  

Dictionary

• Dictionary is an unordered collection of key-value pairs.
• Dictionaries are used to handle large amount of data.
• Every element is having a key-value pair.
• Elements are accessed by using it’s key value.
• When you are dealing with unique keys and you are mapping values to the keys, use Dictionary.

  mydict = {1:'Python'}
  

3. How to read 10 characters from a file?

f = open('<file name>','r')
print(f.read(10))

4. What are membership operators in Python? Write an example to explain both.

There are 2 types of membership operators in Python:
in: If the value is found in a sequence, then the result becomes true else false
not in: If the value is not found in a sequence, then the result becomes true else false

a=15
b=30
list = [3,6,15,20,25]

if (a in list):
	print('a is available in given list')
else:
	print('a is not available in given list')

 
if ( b not in list ):
	print('b is not available in given list')
else:
	print('b is available in given list')

5. Command to get all keys from the dictionary.

mydict = {1:'Raj',2:'Som'}
print(mydict.keys())

6. What is monkey patching?

Monkey patching means adding a new variable or method to a class or module after it’s been defined.
Example: We define a class A as

class A(object):
    def __init__(self, num):
        self.num = num

    def __add__(self, other):
        return A(self.num + other.num)

But now we want to add another function later in the code like below.

def get_num(self):
    return self.num	

To do this as method of class A at runtime, we assign the function to class like below.

A.get_num = get_num

At runtime, assign the new method and use new methos like below.

foo = A(42)
A.get_num = get_num
bar = A(6);
foo.get_num() # 42
bar.get_num() # 6

7. What are *args and **kwargs?

The special syntax, args and *kwargs in function definitions is used to pass a variable number of arguments to a function. The single asterisk form (*args) is used to pass a non-keyworded, variable-length argument list, and the double asterisk form is used to pass a keyworded, variable-length argument list.
*args example :

def test_var_args(farg, *args):
    print('formal arg:{}'.format(farg))
    for arg in args:
        print('another arg:{}'.format(arg)

test_var_args(1, "two", 3)

Output

formal arg: 1
another arg: two
another arg: 3

**kwargs example

def test_var_kwargs(farg, **kwargs):
    print('formal arg:{}'.format(farg))
    for key in kwargs:
        print('another keyword arg: {}: {}'.format(key, kwargs[key]))

test_var_kwargs(farg=1, myarg2="two", myarg3=3)

formal arg: 1
another keyword arg: myarg2: two
another keyword arg: myarg3: 3

8. What is Iterables in python?

When you create a list, you can read its items one by one. Reading its items one by one is called iteration:

mylist = [1, 2, 3]
for i in mylist:
	print(i)

Output

1
2
3

mylist is an iterable. When you use a list comprehension, you create a list, and so an iterable:

mylist = [x*x for x in range(3)]
for i in mylist:
	print(i)

Output

0
1
4

Everything you can use “for… in…” on is an iterable; lists, strings, files…
These iterables are handy because you can read them as much as you wish, but you store all the values in memory and this is not always what you want when you have a lot of values.

9. What is Generators in python?

Generators are iterators, a kind of iterable you can only iterate over once. Generators do not store all the values in memory, they generate the values on the fly:

mygenerator = (x*x for x in range(3))
for i in mygenerator:
	print(i)

Output

0
1
4

It is just the same except you used () instead of []. BUT, you cannot perform for i in mygenerator a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.

10. What does the “yield” keyword do?

yield is a keyword that is used like return, except the function will return a generator.
Example:

def createGenerator():
  mylist = range(3)
  for i in mylist:
    yield i*i

# create a generator
mygenerator = createGenerator()
# print generator object
print(mygenerator)
# print value
for i in mygenerator:
  print(i)

Output

0
1
4

This is a simple example, but it’s handy when you know your function will return a huge set of values that you will only need to read once. To master yield, you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object. Then, your code will continue from where it left off each time for uses the generator.

The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield, then it’ll return the first value of the loop. Then, each other call will run the loop you have written in the function one more time, and return the next value until there is no value to return.

The generator is considered empty once the function runs, but does not hit yield anymore. It can be because the loop had come to an end, or because you do not satisfy an “if/else” anymore.

11. What is class in Python?

Like any other language classes in Python are also a piece of code that descibe how to produce an object. That is true for python as well.

class ObjectCreator(object):
  pass

my_object = ObjectCreator()
print(my_object)

Output

<__main__.ObjectCreator object at 0x7f3d86c186d0>

But classes are more than that in Python. Classes are objects too. As soon as you use the keyword class, Python executes it and creates an OBJECT. The instruction

class ObjectCreator(object):
  pass

creates in memory an object with the name “ObjectCreator”.
This object (the class) is itself capable of creating objects (the instances), and this is why it’s a class. But still, it’s an object, and therefore:

• you can assign it to a variable
• you can copy it
• you can add attributes to it
• you can pass it as a function parameter
  Example:
  ```python class ObjectCreator(object): pass

you can print a class because it’s an object

print(ObjectCreator)

def echo(o): print(o)

you can pass a class as a parameter

echo(ObjectCreator)

print(hasattr(ObjectCreator, ‘new_attribute’))

you can add attributes to a class

ObjectCreator.new_attribute = ‘foo’

print(hasattr(ObjectCreator, ‘new_attribute’))

print(ObjectCreator.new_attribute)

you can assign a class to a variable

ObjectCreatorMirror = ObjectCreator print(ObjectCreatorMirror.new_attribute)

print(ObjectCreatorMirror())

Output
```python
<class '__main__.ObjectCreator'>
<class '__main__.ObjectCreator'>
False
True
foo
foo
<__main__.ObjectCreator object at 0x7f455006b810>

Since classes are objects, you can create them on the fly, like any object. But it’s not so dynamic, since you still have to write the whole class yourself. For more details, Please refer link.

12. What are metaclasses in Python?

Metaclasses are the ‘stuff’ that creates classes, i.e. metaclasses are what create these objects. They are the classes’ classes.

MyClass = MetaClass()
my_object = MyClass()

type is the metaclass Python uses to create all classes behind the scenes.
Everything is an object in Python. That includes ints, strings, functions and classes. All of them are objects. And all of them have been created from a class:

>>> age = 35
>>> age.__class__
<type 'int'>
>>> name = 'bob'
>>> name.__class__
<type 'str'>
>>> def foo(): pass
>>> foo.__class__
<type 'function'>
>>> class Bar(object): pass
>>> b = Bar()
>>> b.__class__
<class '__main__.Bar'>

Now, what is the class of any class ?

>>> age.__class__.__class__
<type 'type'>
>>> name.__class__.__class__
<type 'type'>
>>> foo.__class__.__class__
<type 'type'>
>>> b.__class__.__class__
<type 'type'>

type is the built-in metaclass Python uses, but there is a way to create own metaclasses by defining metaclass attribute.
Python 2 syntax for creating metaclass:

class Foo(object):
    __metaclass__ = something...
    [...]

Python 3 syntax for creating metaclass:

class Foo(object, metaclass=something):
    ...    

You write class Foo(object) first, but the class object Foo is not created in memory yet.
Python will look for metaclass in the class definition. If it finds it, it will use it to create the object class Foo. If it doesn’t, it will use type to create the class.
For detail explaination please refer stackoverflow link.

13. How to merge two dictionaries in single expression?

In Python 3.5 and above to mearge two dictionaries x and y below syntax is used.

z = {**x, **y}

In Python 2, (or 3.4 or lower) write a function:

def merge_two_dicts(x, y):
    z = x.copy()   # start with x's keys and values
    z.update(y)    # modifies z with y's keys and values & returns None
    return z
    
z = merge_two_dicts(x, y)   

For dictionaries x and y, z becomes a shallowly merged dictionary with values from y replacing those from x.

14. How to print without new line?

print('.', end='')

15. What is Decorators in Python?

Decorators are functions which modify the functionality of other functions. In another word decorators wrap a function, modifying its behavior. Let us start with simple example:

def my_decorator(func):
    def wrapper():
        print("Something is happening before the function is called.")
        func()
        print("Something is happening after the function is called.")
    return wrapper

def say_whee():
    print("Whee!")

say_whee = my_decorator(say_whee)

say_whee()

Output

Something is happening before the function is called.
Whee!
Something is happening after the function is called.

In the above code, decoration happened at line.

say_whee = my_decorator(say_whee)

The way you decorated say_whee() above is a little clunky.Python allows you to use decorators in a simpler way with the @ symbol.
Same code can be re-written.

def my_decorator(func):
    def wrapper():
        print("Something is happening before the function is called.")
        func()
        print("Something is happening after the function is called.")
    return wrapper

@my_decorator
def say_whee():
    print("Whee!")

Output

Something is happening before the function is called.
Whee!
Something is happening after the function is called.

For more details, Please refer the link.

16. What does if name == “main”: do in Python?

Whenever the Python interpreter reads a source file, it does two things:

• it sets a few special variables like name, and then
• it executes all of the code found in the file. For detailed explaination, please refer link

17. What is the meaning of a single and a double underscore before an object name?

• Single Underscore
  Names, in a class, with a leading underscore are simply to indicate to other programmers that the attribute or method is intended to be private.
• Double Underscore
  Any identifier of the form __spam (at least two leading underscores, at most one trailing underscore) is textually replaced with _classname__spam, where classname is the current class name with leading underscore(s) stripped. This mangling is done without regard to the syntactic position of the identifier, so it can be used to define class-private instance and class variables, methods, variables stored in globals, and even variables stored in instances. private to this class on instances of other classes.
  Example:

  >>> class MyClass():
  ...     def __init__(self):
  ...             self.__superprivate = "Hello"
  ...             self._semiprivate = ", world!"
  ...
  >>> mc = MyClass()
  >>> print mc.__superprivate
  Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  AttributeError: myClass instance has no attribute '__superprivate'
  >>> print mc._semiprivate
  , world!
  >>> print mc.__dict__
  {'_MyClass__superprivate': 'Hello', '_semiprivate': ', world!'}
  

18. What is NumPy?

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more. For more details please refer numpy.

19. What is pandas?

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.
pandas is well suited for many different kinds of data:

• Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
• Ordered and unordered (not necessarily fixed-frequency) time series data.
• Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
• Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure

For getting started with basic pandas, Please refer 10 minutes to pandas

20. How to iterate over pandas dataframe rows?

Using DataFrame.iterrows (generator) which yields both index and row.

import pandas as pd

df = pd.DataFrame([{'c1':10, 'c2':100}, {'c1':11,'c2':110}, {'c1':12,'c2':120}])

for index, row in df.iterrows():
    print(row['c1'], row['c2'])

Output: 
   10 100
   11 110
   12 120

21. How to handle large data files in pandas?

Data files which does not fit into memory can be ready in chunk sizes. Pandas has option to define chunk size while reading the data file.

• Option 1: Spefify chunksize to read_csv, doing so will return an iterable object of type TextFileReader ```python reader = pd.read_csv(‘tmp.sv’, sep=’|’, chunksize=4)

for chunk in reader: print(chunk)

* Option 2: Specify iterator=True to read_csv, it also return an iterable object of type TextFileReader.
```sql
reader = pd.read_csv('tmp.sv', sep='|', iterator=True)
reader.get_chunk(5)

22. What is intance, class and static method in python?

Instance method: Method associated with instance, such method first parameter is self. It is used for handling instance related variables.
Class method: Method associate with class and declared by using classmethod decorator. It is used for handling class related variables.
Static method: Method that is part of class but not related to instance or class variable, it is declared using staticmethod decorator.

import datetime

class Employee:

    # class variable
    num_of_emps = 0
    raise_amt = 1.04
    # instantiate employee class
    def __init__(self, first, last, pay):
        self.first = first
        self.last = last        
        self.pay = pay

        Employee.num_of_emps += 1

    # instance method
    def fullname(self):
        return '{} {}'.format(self.first, self.last)

    # instance method
    def apply_raise(self):
        self.pay = int(self.pay * self.raise_amt)
    
    # instance method
    def apply_raise2(self, amt):
        self.pay = int(self.pay * amt)

    # class method
    @classmethod
    def set_raise_amt(cls, amount):
        cls.raise_amt = amount

    # class method
    @classmethod
    def from_string(cls, emp_str):
        first, last, pay = emp_str.split('-')
        return cls(first, last, pay)

    # static method
    @staticmethod
    def is_workday(day):
        if day.weekday() == 5 or day.weekday() == 6:
            return False
        return True

# create employee class instance with value
emp_1 = Employee('Raj', 'Nath', 100)
emp_2 = Employee('Som', 'Nath', 200)

# print employee 1 name
print(emp_1.fullname())
print(emp_1.pay)
# print employee 2 name
print(emp_2.fullname())
print(emp_2.pay)

# raise employee 2 pay by 10% using instance method
emp_2.apply_raise2(1.10)

# print employee pay
print(emp_1.pay)
print(emp_2.pay)

# set employee raise pay to 5% using class method
Employee.set_raise_amt(1.05)

print(Employee.raise_amt)
print(emp_1.raise_amt)
print(emp_2.raise_amt)

emp_str_3 = 'Sonia-Dhawan-300'

emp_3 = Employee.from_string(emp_str_3)

print(emp_3.first)
print(emp_3.pay)

my_date = datetime.date(2016, 7, 11)

print(Employee.is_workday(my_date))

23. Difference between list and tuple?

List is mutable, i.e. list can be modified after declaring it.

mylist =[1,2,3]
print(mylist)
# prints [1,2,3]
mylist.append(4)
print(mylist)
# prints [1,2,3,4]

Tuple is immutable, i.e. tuple can not be modified after declaring it.

mytuple = (1,2,3)
print(mytuple)

24. What is module and package in python?

Module : File containing Python definition and statements. For more details refer module.
Package : Packages are way of structuring Python’s module namespace by using “dotted module name”. For more details refer package.

25. How to declare global variable to used accross module?

By defining variable in config.py file and importing it accross different modules.

26. What is the return type of range function?

range function return a range object.

r = range(1,3)
print(r)
# print range(1, 4)
print(type(r))
# print <class 'range'>

27. How to show all files recursively of a directory?

Using os.walk, all directory, sub directory and files can be listed.

import os

rootdir = os.getcwd()

for folder, subs, files in os.walk(rootdir):    
    print(files)

28. What is deepcopy, shallow copy and reference copy?

deep copy and shallow copy can be created using copy class method. Reference copy can be created by assign reference to another variable.

import copy

lst = [1,2,3]

print('List: {}'.format(lst))

# deepcopy which will create another copy
d_copy = copy.deepcopy(lst)
print('New copy: {}'.format(d_copy))

# any operation on this copy will not change original list
d_copy[1] = 123
print('After update (new_copy): {}'.format(d_copy))
print('Original list: {}'.format(lst))

#shallow copy
s_copy = copy.copy(lst)
print('After shallow copy: {}'.format(s_copy))

# operation on this copy data will not change original
s_copy.append(123)
print('Shallo copy after update: {}'.format(s_copy))
print('Original list : {}'.format(lst))

# reference copy
r_copy = lst
print('reference copy: {}'.format(r_copy))
r_copy[1]=123
print('reference copy: {}'.format(r_copy))
print('original copy: {}'.format(lst))

Output

List: [1, 2, 3]
New copy: [1, 2, 3]
After update (new_copy): [1, 123, 3]
Original list: [1, 2, 3]
After shallow copy: [1, 2, 3]
Shallo copy after update: [1, 2, 3, 123]
Original list : [1, 2, 3]
reference copy: [1, 2, 3]
reference copy: [1, 123, 3]
original copy: [1, 123, 3]

29. How to perform operaton on list and tuple values?

Operation can be performed on list and tuple, but it behaves differently when we try normally. In below example when multiplied by 2, it prints same list 2 times, which is not intended. In order to get intended result, try list comprehension or other option such map function.

# operation of list
lst = [1,2,3,4] # list
tpl = (1,2,3,4) # tuple

lm = lambda x : x*2 # lambda function

print('Original list: {}'.format(lst))
print('After multiplication with 2: {}'.format(lst*2))
print('Multiplication using list comprehension')
print([i*2 for i in lst])
print('Original list: {}'.format(lst))
print('Multiplication using list comprehension with lmbda function')
print([lm(x) for x in lst])
for i in map(lm,lst):
    print(i)

# operation over tuple
print('Original tuple: {}'.format(tpl))
print('After muplication with 2: {}'.format(tpl*2))
print('Multiplication using list comprehension')
print([i*2 for i in tpl])
print('Multiplication using list comprehension with lmbda function')
for i in (lm(x) for x in tpl):
    print(i)

Output

Original list: [1, 2, 3, 4]
After multiplication with 2: [1, 2, 3, 4, 1, 2, 3, 4]
Multiplication using list comprehension
[2, 4, 6, 8]
Original list: [1, 2, 3, 4]
Multiplication using list comprehension with lmbda function
[2, 4, 6, 8]
2
4
6
8
Original tuple: (1, 2, 3, 4)
After muplication with 2: (1, 2, 3, 4, 1, 2, 3, 4)
Multiplication using list comprehension
[2, 4, 6, 8]
Multiplication using list comprehension with lmbda function
2
4
6
8

30. How to make python script executable from anywhere in linux?

Adding below line as first link in python script, makes it executable from anywhere.
#!/usr/bin/env python3

31. How to calculate total unique duration given a list of tupple passed as input?

Let us take a case of YouTube, a user logged in and started watching video and watched from 0-10 min, later logged in again and watched same video from 15-20 min and 18-25 min. In this case total duration of viewer is 10+6+8 = 24 min but if we remove the timing where user watched same content, then total duration would be 10+6+5 = 21 min.

# Input
input_list = [(1, 10), (10,16)]

# Output
output_list = []
for lst in input_list:
    output_list = output_list + list(range(lst[0], lst[1]))

# Total duration after removing duplicates
total_duration = len(list(set(output_list)))

# Total time
print(total_duration)

32. How to check if a string is palindrome?

# Input data
str1 = 'raR'

if str1.lower() == str1[::-1].lower():
    print('Palindrome.')
else:
    print('Not a Palindrome.')

33. What is context manager?

Context manager is used to manage resources correctly. Using context manager one can eliminate the chances of keeping resources such a file and database connections opened when it is no longer needed. Although as programmer it is expected to write code, taking care of releasing resources once that is no longer needed. Using context manager, it can be automated and resources can be released automatically. Most common use of inbuilt context manager is the function to open file.
Let us understand with example by creating our own custom function for handling file.

from contextlib import contextmanager


@contextmanager
def open_file(file_name, mode):
    try:
        f = open(file_name, mode)
        yield f
    finally:
        f.close()


with open_file('log_a.txt', 'w') as f:
    f.append('Hello')
    print(f.closed)

print(f.closed)

On running above code sniplets, it will open file and write a word and print status of file as False (file is not closed) and then when code is out of context, it print status as True (file is closed).

There is another option of using classes.

class File():

    def __init__(self, filename, mode):
        self.filename = filename
        self.mode = mode

    def __enter__(self):
        self.open_file = open(self.filename, self.mode)
        return self.open_file

    def __exit__(self, *args):
        self.open_file.close()

files = []
for _ in range(10000):
    with File('foo.txt', 'w') as infile:
        infile.write('foo')
        files.append(infile)
print(f.closed)

Above also does same, when code is out of context, it print status of file as closed.

34. How to pass parameter to remote python code execution over ssh?

In case of distributed system where different systems are involved in executing different parts, we often execute piece of code on different server. In such cases if you want to return some complex data structure like dictionary or list on the fly which is really needed in case of small data sets, thing become complex with no details around it. I found very good peice of code online to solve this problem.

# Parse data for sending as argument
import urllib.parse
import json
dict1_j = urllib.parse.quote(json.dumps(dict1))

# Unparse at receiving end
import json 
import urllib.parse 
dict1 = json.loads(urllib.parse.unquote(sys.argv[1]))

35. Complete the code sniplet to generate true random password using alphabet, number and symbols.

#Password Generator
letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['!', '#', '$', '%', '&', '(', ')', '*', '+']

print("Welcome to the PyPassword Generator!")
nr_letters= int(input("How many letters would you like in your password?\n")) 
nr_numbers = int(input(f"How many numbers would you like?\n"))
nr_symbols = int(input(f"How many symbols would you like?\n"))

Using python random module choices and shuffle method, random password can be generated easily.

password_list = random.choices(letters,k=nr_letters) + random.choices(symbols,k=nr_symbols) + random.choices(numbers,k=nr_numbers)
print(password_list)
random.shuffle(password_list)
password = ''.join(password_list)

print('Random generated password :{}'.format(new_password))

36. Restriction on dictionary key data type.

Python dictionary key can be of any data type but it can not have mutable object/data type as key. In other a dictionary can not have a list or dict as key.

37. What is patch in rest api?

38. Difference between filter and reduce.

39. Data caching in flask api.

40. Ashnchronous request in flask.

41. How to create singleton class in Python?

42. Describe SOLID coding priciple with example.

42. Find sum of n numbers without using any loop or in-built functions.

def sum(n):
    if n <= 1:
        return n
    else:
        return n + sum(n-1)

43. Check balanced parentheses in Python.