python.md

Python Interview Questions

Below are some set of questions that can be considered for evaluation.

Basics

Is python interprted or compiled? Or both?

In the simple model of the world, “compile” means to convert a program in a high-level language into a binary executable full of machine code (CPU instructions). When you compile a C program, this is what happens. The result is a file that your operating system can run for you. it has machine level instructions . On the other hand , “interpreted” means to executing a program/source code line by line , one line at a time .

Considering from a general perspective , compiling is a taking a program in one language and converting it to another language , not necessarily machine code . In python , the source code is compiled to a much simpler form called bytecode . The bytecode instructions are similar to the CPU instructions but are executed by a virtual machine . The dis module in the Python standard library is the disassembler that can show you Python bytecode.

The catch is although the source code gets “compiled” to the bytecode but the process is entirely implicit . One never needs to invoke compiler to run a .py file. Another feature which points to the interpretable nature of python is the interactive prompt. One can run any code statement directly without explicitly converting it to the bytecode.

There are some building tools which can convert the python source code to executables , that automatically sets things up so that modules are imported from the ZIP archive containing PY or PYC(or PYO) files and use a small bootstrap script to add the ZIP archive to PATH . Examples include : Gordon McMillan’s installer (alternative download) (cross-platform), Thomas Heller’s py2exe (Windows), Anthony Tuininga’s cx_Freeze (cross-platform), or Bob Ippolito’s py2app (Mac).

Another explaination

During compilation, the entire source code is converted into machine code. Machine code is understandable by the CPU. The program will be executed only if the entire code is compiled successfully.

In interpretation, the code is executed line by line. Each line of source code in Python is translated into machine code during the execution. There is a Virtual Machine available to interpret the python code. The compilation and interpretation runtime structure is given in the above diagram.

Simple Explanation

• In various books of python programming, it is mentioned that python language is interpreted. But that is half correct the python program is first compiled and then interpreted. The compilation part is hidden from the programmer thus, many programmers believe that it is an interpreted language.
• The compilation part is done first when we execute our code and this will generate byte code and internally this byte code gets converted by the python virtual machine(p.v.m) according to the underlying platform(machine+operating system).
• Now the question is – if there is any proof that python first compiles the program internally and then run the code via interpreter? The answer is yes! and note this compiled part is get deleted by the python(as soon as you execute your code) just it does not want programmers to get into complexity

What is meant by dynamicaly typed language?

We need not define what type of value a variable will hold. The type will be assigned on runtime by checking what value it stores, like int, str, list, tuple, etc

It is one of the behaviors of high level programming language to check the type of data stored in a variable during the execution. In programming languages such as C, we must declare the type of data before using it.

What is string slicing?

String slicing is the process of making subsets from strings. We can pass a range in the following syntax to slice the string.

Syntax: string[start:end:step]

Consider the string “Hello World”. We can slice the string using the range values. To print ‘Hello’ from the string. Our range should be 0 to 5. The slicing creates sub string from start index to end-1 th index.

string = "Hello World"   
print(string[0:5:1])   
print(string[6:11:1])

#Hello
#world

What is python virtual environment?

Used create a separate runtime environment so that we can keep dependancies of different project separated. Some project might need different python version of might need different version of libraries. Also helps to keep gloabal installation clean.

python -m venv venv
source venv/bin/activate/
pip install flask
pip freeze > requirements.txt
pip install requirements.txt
deactivate

How is memory managed in Python?

• 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.

Explain different data types in python?

int, float, str, list, tuple, dict, sets

What is a lambda expression in python

Lambda is an anonymous function that helps us to create functions in single line of code. This is an important thing in functional programming. The lambda function should contains only one expressions in it.

Syntax: lambda arguments : expression

y = lambda x : x * 2
print(y(7))
#14

Explain difference between list and tuple

• basic syntax [] and ()
• mutable and non-mutable
• can ask questions like t= (1,2,3) and t[1] =5. Will fail because tuple does not support item re-assignment

What is range builtin function?

In Python you can use the range built-in to generate a sequence of numbers

# range = iterable / lazy 
>>> range(1, 11) 
range(1, 11) 
# so casting to list 
# also note upper bound is exclusive 
>>> list(range(1, 11)) 
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 
# 3rd arg = stepping 
>>> list(range(1, 11, 2)) 
[1, 3, 5, 7, 9] 
# reverse 
>>> list(range(11, 1)) 
[] 
# use negative step 
>>> list(range(11, 1, -1)) 
[11, 10, 9, 8, 7, 6, 5, 4, 3, 2]

What is enumerate in python? How can you print index inside a loop?

If you need the index inside a loop in Python use enumerate:

>>> names = 'bob julian tim sara'.split() 
>>> for i, name in enumerate(names, start=1): 
...     print(i, name) 
1 bob 
2 julian 
3 tim 
4 sara

Sorted / min / max key argument

In Python, the sorted / min / max builtin functions take an optional "key" keyword argument that receives a callable:

>>> ages = {'bob': 23, 'julian': 11, 'tim': 7, 'sara': 37} 
>>> sorted(ages.items()) 
[('bob', 23), ('julian', 11), ('sara', 37), ('tim', 7)] 
>>> sorted(ages.items(), key=lambda x: x[1]) 
[('tim', 7), ('julian', 11), ('bob', 23), ('sara', 37)] 
>>> sorted(ages.items(), key=lambda x: x[1], reverse=True) 
[('sara', 37), ('bob', 23), ('julian', 11), ('tim', 7)]

ages = {'bob': 23, 'julian': 11, 'tim': 7, 'sara': 37}
sorted(ages, key=lambda x: ages[x], reverse=True)
['sara', 'bob', 'julian', 'tim']

max(ages)
Out[24]: 'tim'
max(ages, key=lambda x : ages[x])
Out[25]: 'sara'

>>> names = 'bob Julian tim sara'.split() 
>>> sorted(names) 
['Julian', 'bob', 'sara', 'tim'] 
>>> sorted(names, key=str.lower) 
['bob', 'Julian', 'sara', 'tim']

Is vs == (object equality)

The difference in Python between comparing objects and their values:

>>> a = [1, 2, 3]
>>> b = [1, 2, 3]
>>> c = a
>>> a == b # same content
True
>>> a == c # also same content
True
>>> a is c # same object
True
>>> a is b # not the same object
False
# to check for equal objects you can check their
# identity using id()
>>> id(a), id(b), id(c)
(140611808855040, 140611819909632, 140611808855040)

F-strings

Starting Python 3.6 you can use f-strings. F-strings let you embed variables and even expressions. More explicit and concise.

for i in range(5):
    print(f'{i} is {"even" if  i %2==0 else "odd"}')
    
0 is even
1 is odd
2 is even
3 is odd
4 is even

Shallow copy and deep copy

• A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.
• A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original
• making a shallow copy of a nested object with copy (or using slicing: [:]) inner objects are references, so updating one updates all copies. Not so with deepcopy.

items = { 'id': 1, 'name':'laptop'}
items_copy = items
items_copy['name'] = 'Desktop'
items
#output oops it should have stayed intact
{'id': 1, 'name': 'Desktop'}
#using deepcopy
items = { 'id': 1, 'name':'laptop'}
items_copy = deepcopy(items)
items_copy['name'] = 'Desktop'
items
{'id': 1, 'name': 'laptop'}

Collections.Counter

For counting in Python look no further than collections.Counter. Counter() can receive an iterable, a mapping or keyword args (nice!) most_common is useful to get, well, the most common elements.

>>> from collections import Counter 
>>> languages = 'Python Java Perl Python JS C++ JS Python'.split() 
>>> Counter(languages) 
Counter({'Python': 3, 'JS': 2, 'Java': 1, 'Perl': 1, 'C++': 1}) 
>>> Counter(languages).most_common(2) 
[('Python', 3), ('JS', 2)]

from collections import Counter
balls = ['blue','black','red','blue','black', 'red', 'red', 'red', 'black']
Counter(balls)
Out[31]: Counter({'blue': 2, 'black': 3, 'red': 4})
Counter(balls).most_common(2)
Out[32]: [('red', 4), ('black', 3)]

Deduplicate values using a set

Need unique values from a list in Python? Use a set. Python has a basic data type called set which is an unordered collection with no duplicate elements. As they are implemented using hash tables, membership checking is fast. The other use case is eliminating duplicate entries.

 >>> languages = 'Python Java Perl PHP Python JS C++ JS Python Ruby'.split() 
 >>> set(languages) 
 {'Perl', 'JS', 'Python', 'Ruby', 'Java', 'PHP', 'C++'}

List comprehension to filter out values

Use a list comprehension in Python to filter a list:

>>> languages = ['Python', 'Java', 'Perl', 'PHP', 'Python', 'JS', 'C++', 'JS', 'Python', 'Ruby'] 
>>> [l for l in languages if l.lower().startswith('p')] 
['Python', 'Perl', 'PHP', 'Python', 'Python'] # alternative but less readable 
>>> list(filter(lambda l: l.lower().startswith('p'), languages)) 
['Python', 'Perl', 'PHP', 'Python', 'Python']

Any / all built-ins

A Pythonic way to check if any / all elements of an iterable are true.

>>> languages = ['Java', 'Perl', 'PHP', 'Python', 'JS', 'C++', 'JS', 'Ruby'] 
>>> all(len(l) >= 2 for l in languages) 
True 
>>> any('++' in l for l in languages) 
True

Join strings

Use join in Python to combine a list of multiple strings:

>>> tweets = ("I was happy with the book", "this is awful", ... "Python is object oriented", "Python is awesome") 
>>> print('\n'.join(tweets)) I was happy with the book this is awful Python is object oriented Python is awesome 
>>> print(' >> '.join(tweets)) I was happy with the book >> this is awful >> Python is object oriented >> Python is awesome 

can be good question to ask

# make sure all elements are strings 
>>> '-'.join([100, 'pybites', 'tips', 4, 'you']) 
Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: sequence item 0: expected str instance, int found 
>>> '-'.join(str(w) for w in [100, 'pybites', 'tips', 4, 'you']) 
'100-pybites-tips-4-you'

join is a string method, so you call it on the string you want to use to concatenate the elements in the iterable you pass into it. Make sure all these elements are strings or you'll hit a TypeError. Also note that this can be significantly faster than ordinary string concatenation if you have a lot of strings!

Reverse a list

Different ways to reverse a list in Python:

The first example modifies numbers in place, reversed returns a list_reverseiterator iterator which we cast to a list to show in the REPL (the original list remains unchanged). Lastly, using a negative step in the list slicing syntax returns a new object as well.

>>> numbers = [1, 2, 3, 4, 5] 
# in-place 
>>> numbers.reverse() 
>>> numbers [5, 4, 3, 2, 1] 
>>> list(reversed(numbers)) 
[1, 2, 3, 4, 5] 
>>> numbers 
[5, 4, 3, 2, 1] 
>>> numbers[::-1] 
[1, 2, 3, 4, 5]

Which python version do you use? How to check it?

python -V

Open a file sample.txt for wirting and write "Hello world" in it or Prevent overwriting files?

with open('sample.txt','w') as f:
    f.write('Hello world')

#Python's open built-in has the 'x' switch which opens the file for "exclusive creation", failing if the file already exists.
>>> with open('hello', 'x') as f: 
...     f.write('spam') 
... Traceback (most recent call last): File "<stdin>", line 1, in <module> FileExistsError: [Errno 17] File exists: 'hello'

Is it faster to search a list or a dictionary in Python?

Searching in a list can be considerably slow as compared to a dictionary. The effect is even more pronounced , when the size of elements to search increases . As a fact, using a dictionary is over 100,000x faster than using a list if the search is on 10 million items. So , why is list slow as compared to a dictionary ? The reason is simple , the list uses iterative method to search for an element while a dictionary uses a hash lookup

What are Iterators in Python?

The objects in python that implements the iterator protocols are called iterators. iter is the keyword used to create the iterable objects. It iterates throough various iterable objects such as List, dictionary.

my_list = [1, 2, 3, 4, 5]   
my_iterator = iter(my_list)
print(next(my_iterator))   
print(next(my_iterator))   
print(next(my_iterator))
#output
1   
2   
3

How to generate random numbers in Python?

There is no in-bulit function in python to generate random numbers We can use random module for this purpose. We can create random number from a range or list. randint is the simple method used to create a random number between two numbers.

import random   
print( random.randint(0,9))

How to create private data in Python?

Protected members can be accessed only by the class and its sub classes. The following code examples shows that how the private and public members works in python.

class emp:   
    def __init__(self, s):   
        self.__salary = s   
t = emp(25000)   
print(t.__salary)
#output
# Traceback (most recent call last):   
#   File "main.py", line 5, in <module>   
#     print(t.__salary)   
# AttributeError: 'emp' object has no attribute '__salary'

What is the meaning of this commonly used statement in Python If __name__ == "___main__"

It's used at the end of a script to write code that ONLY executes if the module (script) is called directly. Code in this if does NOT run when the module gets imported into another module or in the REPL.

$ more script.py 
def func(): 
    print("Hello from function") 
if __name__ == "__main__": 
    func() 
# main block gets invoked 
$ python script.py 
# Hello from function 
$ python 
>>> import script # main block does not get invoked 
>>> script.func() Hello from function

Also "__name__" has value of __main__ when the script is invoked directly otherwise it will have the name of the module.

Not to be run in cae of imports.

Decorators

• syntax
• use cases
• give me a decorator to print all args before running or time it function

from functools import wraps

def my_decorator(func):
    @wraps(func)
    def wrapper(*args,**kwargs):
        print('Before function call ',func.__name__)
        func(*args,**kwargs)
        print('After function call ', func.__name__)
    return wrapper

Generators

Unit Testing

Logging module, configuration etc

The logging module is quite easy to setup and use. Here we log various messages to an example.log. Notice how logging.exception logs the Traceback alongside the message we gave it (note it should only be called from an exception handler)

>>> import logging 
>>> FORMAT = '%(asctime)s %(levelname)s %(module)s %(funcName)s %(message)s' 
>>> logging.basicConfig(filename='example.log', encoding='utf-8', level=logging.INFO, format=FORMAT)
>>> logging.debug('not logged') 
>>> logging.info('info is logged')

Types of argument positional, keyword, *args, **kwargs

Create a function which can add any number of arguments

def add_unknown(*args):
...     sum=0
...     for arg in args:
...         sum+=arg
...     return sum    
... 
add_unknown(1,4,2,5,8)
20

How is Exceptional handling done in Python

There are 3 main keywords i.e. try, except, and finally which are used to catch exceptions and handle the recovering mechanism accordingly. Try is the block of a code which is monitored for errors. Except block gets executed when an error occurs.

The beauty of the final block is to execute the code after trying for error. This block gets executed irrespective of whether an error occurred or not. Finally block is used to do the required cleanup activities of objects/variables.

List comprehensions

nums = [1,2,3,4,5]

#square
[ num * num for num in nums]

#cube of even nums

[n*n*n for n in nums if n%2==0]

#dictionary
{n:n*n for n in nums if n%2==0}

Use of with statement

• exception handling
• make code clean and readable
• e.g file streams and databases

WAP to check if number is palindrome

How would you parse user argument in a python script

# groups.py
import argparse

my_parser = argparse.ArgumentParser()
my_group = my_parser.add_mutually_exclusive_group(required=True)

my_group.add_argument('-v', '--verbose', action='store_true')
my_group.add_argument('-s', '--silent', action='store_true')
my_group.add_argument('-d', '--debug', action='store_true')

my_group_2 = my_parser.add_argument_group()
my_group_2.add_argument('-r', '--rerun',action='store')
my_group_2.add_argument('-t','--run-history',action='store')

args = my_parser.parse_args()

print(vars(args))

have you used any third party libraries, name them and how they were used.

How can you use regular expression in python

import re what are re, metacharacters

`^$?+*.[]{}

Explain inheritance in python

• example
• super()
• types
• instance method, class method, static method

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 are 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.

What is polymorphism in Python?

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 for polymorphism.

WAP to find largest word in a text file

• words = read.split()
• max (word, key=len(word))

WAP to count frequency of words ina file

• Counter(f.read().split())

What is the difference between __str__ and __repr__ in Python?

__repr__ is to be unambiguous and __str__ is to be readable. Or as Ned Batchelder succinctly said: "__repr__ is for developers, __str__ is for customers." As we can see above, the datetime module follows this advice nicely.

>>> from datetime import date 
>>> today = date.today() 
>>> str(today) 
'2020-10-31' 
>>> repr(today) 
'datetime.date(2020, 10, 31)'

How do you achieve multithreading in Python?

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. 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

Convert a dict to json

json.dumps to convert a dict into JSON This is handy when you need to pass in JSON when calling an API endpoint like we'll see in the next tip. Note that when a dictionary is converted into JSON, all the keys of the dictionary are coerced to strings. So converting it back to a dict you might not end up with the same initial data.

payload = {
... "description": "the description for this gist",
... "public": True,
... "files": {
... "file1.txt": {
... "content": "String file contents"
... }
... }
... }
>>> type(payload)
<class 'dict'>
>>> import json
>>> payload_json = json.dumps(payload, indent=4)
>>> type(payload_json)
<class 'str'>
>>> print(payload_json)
{
"description": "the description for this gist",
"public": true,
"files": {
"file1.txt": {
"content": "String file contents"
}
}
}