If you are appearing for a technical round of interview for Python, here’s a list of the top 60 interview questions with answers to help you prepare.
Last updated: Aug 23, 2021
Python is a general-purpose programming language that has simple, easy-to-learn syntax which emphasizes readability and therefore reduces the cost of program maintenance. Moreover, the language is capable of scripting, completely open-source and supports third-party packages encouraging modularity and code-reuse.
It's high-level data structures, combined with dynamic typing and dynamic binding, attract a huge community of developers for Rapid Application Development and deployment.
Before we understand what a dynamically typed language, we should learn about what typing is. Typing refers to type-checking in programming languages. In a strongly-typed language, such as Python, "1" + 2 will result in a type error, since these languages don't allow for "type-coercion" (implicit conversion of data types). On the other hand, a weakly-typed language, such as Javascript, will simply output "12" as result.
Type-checking can be done at two stages -
Python being an interpreted language, executes each statement line by line and thus type-checking is done on the fly, during execution. Hence, Python is a Dynamically Typed language.
PEP stands for Python Enhancement Proposal. A PEP is an official design document providing information to the Python Community, or describing a new feature for Python or its processes. PEP 8 is especially important since it documents the style guidelines for Python Code. Apparently contributing in the Python open-source community requires you to follow these style guidelines sincerely and strictly.
Memory management in Python is handled by the Python Memory Manager. The memory allocated by the manager is in form of a private heap space dedicated for Python. All Python objects are stored in this heap and being private, it is inaccessible to the programmer. Though, python does provide some core API functions to work upon the private heap space. Additionally, Python has an in-built garbage collection to recycle the unused memory for the private heap space.
A namespace in Python ensures that object names in a program are unique and can be used without any conflict. Python implements these namespaces as dictionaries with 'name as key' mapped to a corresponding 'object as value'. This allows for multiple namespaces to use the same name and map it to a separate object. A few examples of namespaces are as follows:
Local Namespace includes local names inside a function. the namespace is temporarily created for a function call and gets cleared when the function returns.
Global Namespace includes names from various imported packages/ modules that is being used in the current project. This namespace is created when the package is imported in the script and lasts until the execution of the script.
Built-in Namespace includes built-in functions of core Python and built-in names for various types of exceptions.
Lifecycle of a namespace depends upon the scope of objects they are mapped to. If the scope of an object ends, the lifecycle of that namespace comes to an end. Hence, it isn't possible to access inner namespace objects from an outer namespace.
Every object in Python functions within a scope. A scope is a block of code where an object in Python remains relevant. Namespaces uniquely identify all the objects inside a program. However, these namespaces also have a scope defined for them where you could use their objects without any prefix. A few examples of scope created during code execution in Python are as follows:
A local scope refers to the local objects available in the current function.
A global scope refers to the objects available throught the code execution since their inception.
A module-level scope refers to the global objects of the current module accessible in the program.
An outermost scope refers to all the built-in names callable in the program. The objects in this scope are searched last to find the name referenced.
Sometimes objects within the same scope have the same name but function differently. In such cases, scope resolution comes into play in Python automatically. A few examples of such behaviour are:
Python modules namely 'math' and 'cmath' have a lot of functions that are common to both of them - log10(), acos(), exp() etc. To resolve this amiguity, it is necessary to prefix them with their respective module, like math.exp() and cmath.exp().
Consider the code below, an object temp has been initialized to 10 globally and then to 20 on function call. However, the function call didn't change the value of the temp globally. Here, we can observe that Python draws a clear line between global and local variables treating both their namespaces as separate identities.
temp = 10 # global-scope variable
def func():
temp = 20 # local-scope variable
print(temp)
print(temp) # output => 10
func() # output => 20
print(temp) # output => 10
This behaviour can be overriden using the global keyword inside the function, as shown in the following example:
temp = 10 # global-scope variable def func(): global temp temp = 20 # local-scope variable print(temp) print(temp) # output => 10 func() # output => 20 print(temp) # output => 20
Decorators in Python are essentially functions that add functionality to an existing function in Python without changing the structure of the function itself. They are represented by the @decorator_name in Python and are called in bottom-up fashion. For example:
# decorator function to convert to lowercase
def lowercase_decorator(function):
def wrapper():
func = function()
string_lowercase = func.lower()
return string_lowercase
return wrapper
# decorator function to split words
def splitter_decorator(function):
def wrapper():
func = function()
string_split = func.split()
return string_split
return wrapper
@splitter_decorator # this is executed next
@lowercase_decorator # this is executed first
def hello():
return 'Hello World'
hello() # output => [ 'hello' , 'world' ]
Lists and Tuples are both sequence data types that can store a collection of objects in Python. The objects stored in both sequences can have different data types. Lists are represented with square brackets ['sara', 6, 0.19], while tuples are represented with parantheses ('ansh', 5, 0.97).
But what is the real difference between the two? The key difference between the two is that while lists are mutable, tuples on the other hand are immutable objects. This means that lists can be modified, appended or sliced on-the-go but tuples remain constant and cannot be modified in any manner. You can run the following example on Python IDLE to confirm the difference:
my_tuple = ('sara', 6, 5, 0.97)
my_list = ['sara', 6, 5, 0.97]
print(my_tuple[0]) # output => 'sara'
print(my_list[0]) # output => 'sara'
my_tuple[0] = 'ansh' # modifying tuple => throws an error
my_list[0] = 'ansh' # modifying list => list modified
print(my_tuple[0]) # output => 'sara'
print(my_list[0]) # output => 'ansh'
Python comprehensions, like decorators, are syntactic sugar constructs that help build altered and filtered lists, dictionaries or sets from a given list, dictionary or set. Using comprehensions, saves a lot of time and code that might be considerably more verbose (containing more lines of code). Let's check out some examples, where comprehensions can be truly beneficial:
Performing mathematical operations on the entire list
my_list = [2, 3, 5, 7, 11]
squared_list = [x**2 for x in my_list] # list comprehension
# output => [4 , 9 , 25 , 49 , 121]
squared_dict = {x:x**2 for x in my_list} # dict comprehension
# output => {11: 121, 2: 4 , 3: 9 , 5: 25 , 7: 49}
Performing conditional filtering operations on the entire list
my_list = [2, 3, 5, 7, 11]
squared_list = [x**2 for x in my_list if x%2 != 0] # list comprehension
# output => [9 , 25 , 49 , 121]
squared_dict = {x:x**2 for x in my_list if x%2 != 0} # dict comprehension
# output => {11: 121, 3: 9 , 5: 25 , 7: 49}
Combining multiple lists into one
Comprehensions allow for multiple iterators and hence, can be used to combine multiple lists into one.
a = [1, 2, 3] b = [7, 8, 9] [(x + y) for (x,y) in zip(a,b)] # parallel iterators # output => [8, 10, 12] [(x,y) for x in a for y in b] # nested iterators # output => [(1, 7), (1, 8), (1, 9), (2, 7), (2, 8), (2, 9), (3, 7), (3, 8), (3, 9)]
Flattening a multi-dimensional list
A similar approach of nested iterators (as above) can be applied to flatten a multi-dimensional list or work upon its inner elements.
my_list = [[10,20,30],[40,50,60],[70,80,90]] flattened = [x for temp in my_list for x in temp] # output => [10, 20, 30, 40, 50, 60, 70, 80, 90]
Lambda is an anonymous function in Python, that can accept any number of arguments, but can only have a single expression. It is generally used in situations requiring an anonymous function for a short time period. Lambda functions can be used in either of the two ways:
Assigning lambda functions to a variable
mul = lambda a, b : a * b print(mul(2, 5)) # output => 10
Wrapping lambda functions inside another function
def myWrapper(n):
return lambda a : a * n
mulFive = myWrapper(5)
print(mulFive(2)) # output => 10
The pass keyword represents a null operation in Python. It is generally used for the purpose of filling up empty blocks of code which may execute during runtime but has yet to be written. Without the pass statement in the following code, we may run into some errors during code execution.
def myEmptyFunc():
# do nothing
pass
myEmptyFunc() # nothing happens
## Without the pass keyword
# File "<stdin>", line 3
# IndentationError: expected an indented block
In Python, the assignment statement (= operator) does not copy objects. Instead, it creates a binding between the existing object and the target variable name. To create copies of an object in Python, we need to use the copy module. Moreover, there are two ways of creating copies for the given object using the copy module -
Shallow Copy is a bit-wise copy of an object. The copied object created has an exact copy of the values in the original object. If either of the values are references to other objects, just the reference addresses for the same are copied.
Deep Copy copies all values recursively from source to target object, i.e. it even duplicates the objects referenced by the source object.
from copy import copy, deepcopy list_1 = [1, 2, [3, 5], 4] ## shallow copy list_2 = copy(list_1) list_2[3] = 7 list_2[2].append(6) list_2 # output => [1, 2, [3, 5, 6], 7] list_1 # output => [1, 2, [3, 5, 6], 4] ## deep copy list_3 = deepcopy(list_1) list_3[3] = 8 list_3[2].append(7) list_3 # output => [1, 2, [3, 5, 6, 7], 8] list_1 # output => [1, 2, [3, 5, 6], 4]
xrange() and range() are quite similar in terms of functionality. They both generate a sequence of integers, with the only difference that range() returns a Python list, whereas, xrange() returns an xrange object.
So how does that make a difference? It sure does, because unlike range(), xrange() doesn't generate a static list, it creates the value on the go. This technique is commonly used with an object type generators and has been termed as "yielding".
Yielding is crucial in applications where memory is a constraint. Creating a static list as in range() can lead to a Memory Error in such conditions, while, xrange() can handle it optimally by using just enough memory for the generator (significantly less in comparison).
for i in xrange(10): # numbers from o to 9
print i # output => 0 1 2 3 4 5 6 7 8 9
for i in xrange(1,10): # numbers from 1 to 9
print i # output => 1 2 3 4 5 6 7 8 9
for i in xrange(1, 10, 2): # skip by two for next
print i # output => 1 3 5 7 9
Modules, in general, are simply Python files with a .py extension and can have a set of functions, classes or variables defined and implemented. They can be imported and initialized once using the import statement. If partial functionality is needed, import the requisite classes or functions using from foo import bar.
Packages allow for hierarchial structuring of the module namespace using dot notation. As, modules help avoid clashes between global variable names, in a similary manner, packages help avoid clashes between module names.
Creating a package is easy since it makes use of the system's inherent file structure. So just stuff the modules into a folder and there you have it, the folder name as the package name. Importing a module or its contents from this package requires the package name as prefix to the module name joined by a dot.
Global variables are public variables that are defined in the global scope. To use the variable in the global scope inside a function, we use the global keyword.
Protected attributes are attributes defined with a underscore prefixed to their identifier eg. _foo. They can still be accessed and modified from outside the class they are defined in but a responsible developer should refrain from doing so.
Private attributes are attributes with double underscore prefixed to their identifier eg. __bar. They cannot be accessed or modified from the outside directly and will result in an AttributeError if such an attempt is made.
Self is a keyword in Python used to define an instance or an object of a class. In Python, it is explicity used as the first paramter, unlike in Java where it is optional. It helps in disinguishing between the methods and attributes of a class from its local variables.
__init__ is a contructor method in Python and is automatically called to allocate memory when a new object/instance is created. All classes have a __init__ method associated with them. It helps in distinguishing methods and attributes of a class from local variables.
# class definition
class Student:
def __init__(self, fname, lname, age, section):
self.firstname = fname
self.lastname = lname
self.age = age
self.section = section
# creating a new object
stu1 = Student("Anil", "Kumar", 22, "A2")
Python library offers a feature - serialization out of the box. Serializing a object refers to transforming it into a format that can be stored, so as to be able to deserialize it later on, to obtain the original object. Here, the pickle module comes into play.
Pickling
Pickling is the name of the serialization process in Python. Any object in Python can be serialized into a byte stream and dumped as a file in the memory. The process of pickling is compact but pickle objects can be compressed further. Moreover, pickle keeps track of the objects it has serialized and the serialization is portable across versions.
The function used for the above process is pickle.dump().
Unpickling
Unpickling is the complete inverse of pickling. It deserializes the byte stream to recreate the objects stored in the file, and loads the object to memory.
The function used for the above process is pickle.load().
Generators are functions that return an iterable collection of items, one at a time, in a set manner. Generators, in general, are used to create iterators with a different approach. They employ the use of yield keyword rather than return to return a generator object.
Let's try and build a generator for fibonacci numbers -
## generate fibonacci numbers upto n
def fib(n):
p, q = 0, 1
while(p < n):
yield p
p, q = q, p + q
x = fib(10) # create generator object
## iterating using __next__(), for Python2, use next()
x.__next__() # output => 0
x.__next__() # output => 1
x.__next__() # output => 1
x.__next__() # output => 2
x.__next__() # output => 3
x.__next__() # output => 5
x.__next__() # output => 8
x.__next__() # error
## iterating using loop
for i in fib(10):
print(i) # output => 0 1 1 2 3 5 8
PYTHONPATH is an environment variable which you can set to add additional directories where Python will look for modules and packages. This is especially useful in maintaining Python libraries that you do not wish to install in the global default location.
help() function in Python is used to display the documentation of modules, classes, functions, keywords, etc. If no parameter is passed to the help() function, then an interactive help utility is launched on the console.
dir() function tries to return a valid list of attributes and methods of the object it is called upon. It behaves differently with different objects, as it aims to produce the most relevant data, rather than the complete information.
For Modules/Library objects, it returns a list of all attributes, contained in that module.
For Class Objects, it returns a list of all valid attributes and base attributes.
With no arguments passed, it returns a list of attributes in the current scope.
.py files contain the source code of a program. Whereas, .pyc file contains the bytecode of your program. We get bytecode after compilation of .py file (source code). .pyc files are not created for all the files that you run. It is only created for the files that you import.
Before executing a python program python interpreter checks for the compiled files. If the file is present, the virtual machine executes it. If not found, it checks for .py file. If found, compiles it to .pyc file and then python virtual machine executes it.
Having .pyc file saves you the compilation time.
Python as a language is not interpreted or compiled. Interpreted or compiled is the property of the implementation. Python is a bytecode(set of interpreter readable instructions) interpreted generally.
Source code is a file with .py extension.
Python compiles the source code to a set of instructions for a virtual machine. The Python interpreter is an implementation of that virtual machine. This intermediate format is called "bytecode".
.py source code is first compiled to give .pyc which is bytecode. This bytecode can be then interpreted by official CPython, or JIT(Just in Time compiler) compiled by PyPy.
Pass by value: Copy of the actual object is passed. Changing the value of the copy of the object will not change the value of the original object.
Pass by reference: Reference to the actual object is passed. Changing the value of the new object will change the value of the original object.
In Python, arguments are passed by reference, i.e., reference to the actual object is passed.
def appendNumber(arr):
arr.append(4)
arr = [1, 2, 3]
print(arr) #Output: => [1, 2, 3]
appendNumber(arr)
print(arr) #Output: => [1, 2, 3, 4]
Iterator is an object.
It remembers its state i.e., where it is during iteration (see code below to see how)
__iter__() method initializes an iterator.
It has a __next__() method which returns the next item in iteration and points to the next element. Upon reaching the end of iterable object __next__() must return StopIteration exception.
It is also self iterable.
Iterators are objects with which we can iterate over iterable objects like lists, strings, etc.
class ArrayList:
def __init__(self, number_list):
self.numbers = number_list
def __iter__(self):
self.pos = 0
return self
def __next__(self):
if(self.pos < len(self.numbers)):
self.pos += 1
return self.numbers[self.pos - 1]
else:
raise StopIteration
array_obj = ArrayList([1, 2, 3])
it = iter(array_obj)
print(next(it)) #output: 2
print(next(it)) #output: 3
print(next(it))
#Throws Exception
#Traceback (most recent call last):
#...
#StopIteration
As the name suggests, ‘slicing’ is taking parts of.
Syntax for slicing is [start : stop : step]
Default value for start is 0, stop is number of items, step is 1.
Slicing can be done on strings, arrays, lists, and tuples.
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] print(numbers[1 : : 2]) #output : [2, 4, 6, 8, 10]
Use command os.remove(file_name)
import os
os.remove('ChangedFile.csv')
print('File Removed!')
You can use split() function to split a string based on a delimiter to a list of strings.
You can use join() function to join a list of strings based on a delimiter to give a single string.
string = 'This is a string.'
string_list = string.split(' ') #delimiter is 'space' character or ' '
print(string_list) #output: ['This', 'is', 'a', 'string.']
print(' '.join(string_list)) #output: This is a string.
Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.
import array
a = array.array('i', [1, 2, 3])
for i in a:
print(i, end=' ') #OUTPUT: 1 2 3
a = array.array('i', [1, 2, 'string']) #OUTPUT: TypeError: an integer is required (got type str)
a = [1, 2, 'string']
for i in a:
print(i, end=' ') #OUTPUT: 1 2 string
*args
*args is a special syntax used in function definition to pass variable-length argument.
"*" means variable length and "args" is the name used by convention. You can use any other.
def multiply(a, b, *args):
mul = a * b
for num in args:
mul *= num
return mul
print(multiply(1, 2, 3, 4, 5)) #output: 120
**kwargs
**kwargs is a special syntax used in function definition to pass variable-length keyworded argument.
Here, also, “kwargs” is used just by convention. You can use any other name.
Keyworded argument means a variable which has a name when passed to a function.
It is actually a dictionary of variable name and its value.
def tellArguments(**kwargs):
for key, value in kwargs.items():
print(key + ": " + value)
tellArguments(arg1 = "argument 1", arg2 = "argument 2", arg3 = "argument 3")
#output:
# arg1: argument 1
# arg2: argument 2
# arg3: argument 3
Negative indexes are the indexes from the end of the list or tuple or string.
Arr[-1] means last element of array Arr[]
arr = [1, 2, 3, 4, 5, 6] #get the last element print(arr[-1]) #output 6 #get the second last element print(arr[-2]) #output 5
PYTHONSTARTUP - It contains the path of an initialization file containing Python source code. It is executed every time you start the interpreter. It is named as .pythonrc.py in Unix and it contains commands that load utilities or modify PYTHONPATH.
PYTHONCASEOK − It is used in Windows to instruct Python to find the first case-insensitive match in an import statement. Set this variable to any value to activate it.
PYTHONHOME − It is an alternative module search path. It is usually embedded in the PYTHONSTARTUP or PYTHONPATH directories to make switching module libraries easy.
A unit testing framework in Python is known as unittest. It supports sharing of setups, automation testing, shutdown code for tests, aggregation of tests into collections etc.
A Python documentation string is known as docstring, it is a way of documenting Python functions, modules and classes.
In order to convert a number into a string, use the inbuilt function str(). If you want a octal or hexadecimal representation, use the inbuilt function oct() or hex().
To generate random numbers in Python, you need to import command as
import random random.random()
This returns a random floating point number in the range [0,1)
You can access a module written in Python from C by following method,
Module = PyImport_ImportModule("<modulename>")
It is a Floor Divisionoperator , which is used for dividing two operands with the result as quotient showing only digits before the decimal point. For instance, 10//5 = 2 and 10.0//5.0 = 2.0.
Here is the list of most commonly used built-in types that Python supports:
Immutable built-in datatypes of Python
Mutable built-in datatypes of Python
We can create a Python function in the following manner.
Step-1: to begin the function, start writing with the keyword def and then mention the function name.
Step-2: We can now pass the arguments and enclose them using the parentheses. A colon, in the end, marks the end of the function header.
Step-3: After pressing an enter, we can add the desired Python statements for execution.
A function in Python gets treated as a callable object. It can allow some arguments and also return a value or multiple values in the form of a tuple. Apart from the function, Python has other constructs, such as classes or the class instances which fits in the same category.
The Python trunc() function performs a mathematical operation to remove the decimal values from a particular expression and provides an integer value as its output.
The id() is one of the built-in functions in Python.
Signature: id(object)
It accepts one parameter and returns a unique identifier associated with the input object.
The __name__ is a unique variable. Since Python doesn't expose the main() function, so when its interpreter gets to run the script, it first executes the code which is at level 0 indentation.
To see whether the main() gets called, we can use the __name__ variable in an if clause compares with the value "__main__."
Python's print() function always prints a newline in the end. The print() function accepts an optional parameter known as the 'end'. Its value is ' ' by default. We can change the end character in a print statement with the value of our choice using this parameter.
# Example: Print a instead of the new line in the end.
print("Let's learn" , end = ' ')
print("Python")
# Printing a dot in the end.
print("Learn to code from techbeamers" , end = '.')
print("com", end = ' ')
In Python, the len() is a primary string function. It determines the length of an input string.
>>> some_string = 'techbeamers'
>>> len(some_string)
11
The chr() function got re-added in Python 3.2. In version 3.0, it got removed.
It returns the string denoting a character whose Unicode code point is an integer.
For example, the chr(122) returns the string 'z' whereas the chr(1212) returns the string 'Ҽ'.
The ord(char) in Python takes a string of size one and returns an integer denoting the Unicode code format of the character in case of a Unicode type object, or the value of the byte if the argument is of 8-bit string type.
>>> ord("z")
122
Python provides the rstrip() method which duplicates the string but leaves out the whitespace characters from the end.
The rstrip() escapes the characters from the right end based on the argument value, i.e., a string mentioning the group of characters to get excluded.
The signature of the rstrip() is:
str.rstrip([char sequence])
Example:
test_str = 'Programming ' # The trailing whitespaces are excluded print(test_str.rstrip())
Python provides this built-in isalpha() function for the string handling purpose.
It returns True if all characters in the string are of alphabet type, else it returns False.
Python provides the title() method to convert the first letter in each word to capital format while the rest turns to Lowercase.
Example:
str = 'lEaRn pYtHoN' print(str.title())
The output:
Learn Python
Python ensures safe access to threads. It uses the GIL mutex to set synchronization. If a thread loses the GIL lock at any time, then you have to make the code thread-safe.
For example, many of the Python operations execute as atomic such as calling the sort() method on a list.
Python closures are function objects returned by another function. We use them to eliminate code redundancy.
In the example below, we’ve written a simple closure for multiplying numbers.
def multiply_number(num):
def product(number):
'product() here is a closure'
return num * number
return product
num_2 = multiply_number(2)
print(num_2(11))
print(num_2(24))
num_6 = multiply_number(6)
print(num_6(1))
The output is:
22
48
6
The globals() function in Python returns the current global symbol table as a dictionary object.
Python maintains a symbol table to keep all necessary information about a program. This info includes the names of variables, methods, and classes used by the program.
All the information in this table remains in the global scope of the program and Python allows us to retrieve it using the globals() method.
Signature: globals()
Arguments: None
# Example: globals() function
x = 9
def fn():
y = 3
z = y + x
# Calling the globals() method
z = globals()['x'] = z
return z
# Test Code
ret = fn()
print(ret) # Output 12
The zip method lets us map the corresponding index of multiple containers so that we can use them using as a single unit.
Signature:
zip(*iterators)
Arguments:
Python iterables or collections (e.g., list, string, etc.)
Returns:
A single iterator object with combined mapped values
# Example: zip() function
emp = [ "tom", "john", "jerry", "jake" ]
age = [ 32, 28, 33, 44 ]
dept = [ 'HR', 'Accounts', 'R&D', 'IT' ]
# call zip() to map values
out = zip(emp, age, dept)
# convert all values for printing them as set
out = set(out)
# Displaying the final values
print ("The output of zip() is : ",end="")
print (out)
# output
# The output of zip() is : {('jerry', 33, 'R&D'), ('jake', 44, 'IT'), ('john', 28, 'Accounts'), ('tom', 32, 'HR')}
The ternary operator is an alternative for the conditional statements. It combines true or false values with a statement that you need to test.
The syntax would look like the one given below.
[onTrue] if [Condition] else [onFalse]
x, y = 35, 75 smaller = x if x < y else y print(smaller)
The yield keyword can turn any function into a generator. It works like a standard return keyword. But it’ll always return a generator object. Also, a method can have multiple calls to the yield keyword.
See the example below.
def testgen(index):
weekdays = ['sun','mon','tue','wed','thu','fri','sat']
yield weekdays[index]
yield weekdays[index+1]
day = testgen(0)
print next(day), next(day)
#output: sun mon