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Python is the most sought-after skill in the programming domain. In this Python Interview Questions blog, I will introduce you to the most frequently asked questions in Python interviews for the year 2025. We have 110+ questions on Python programming basics which will help you with different expertise levels to reap the maximum benefit from our blog.
Top 50 Python Code Interview Questions | Python Interview Questions And Answers | Edureka
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Q1. What is the difference between list and tuples in Python?
Q2. What are the key features of Python?
Q3. What type of language is python?
Q4. How is Python an interpreted language?
Q5. What is pep 8?
Q6. How is memory managed in Python?
Q7. What is name space in Python?
Q8. What is PYTHON PATH?
Q9. What are python modules?
Q10. What are local variables and global variables in Python?
LIST | TUPLES |
---|---|
Lists are mutable i.e., they can be edited. | Tuples are immutable, meaning they cannot be edited after creation. |
Lists are slower than tuples. | Tuples are faster than lists. |
Syntax: list_1 = [10, ‘Chelsea’, 20] | Syntax: tup_1 = (10, ‘Chelsea’, 20) |
x=111
and then x="I'm a string"
without errorpublic
, private
).Ans: Python is capable of scripting, but in general sense, it is considered as a general-purpose programming language. To know more about Scripting, you can refer to the Python scripting tutorial.
Ans: An interpreted language is any programming language which is not in machine-level code before runtime. Therefore, Python is an interpreted language.
Ans: PEP stands for Python Enhancement Proposal. It is a set of rules that specify how to format Python code for maximum readability.
Q6.What are the benefits of using Python?
Ans: The benefits of using python are-
Q7.What are Python namespaces?
Ans: A namespace in python refers to the name which is assigned to each object in python. The objects are variables and functions. As each object is created, its name along with space(the address of the outer function in which the object is), gets created. The namespaces are maintained in python like a dictionary where the key is the namespace and value is the address of the object. There 4 types of namespace in python-
Q8.What are decorators in Python?
Ans: Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
Q9.What are Dict and List comprehensions?
Ans: Dictionary and list comprehensions are just another concise way to define dictionaries and lists.
Example of list comprehension is-
1 | x = [i for i in range ( 5 )] |
The above code creates a list as below-
1 2 | 4 [ 0 , 1 , 2 , 3 , 4 ] |
Example of dictionary comprehension is-
1 | x = [i : i + 2 for i in range ( 5 )] |
The above code creates a list as below-
1 | [ 0 : 2 , 1 : 3 , 2 : 4 , 3 : 5 , 4 : 6 ] |
Q10.What are the common built-in data types in Python?
Ans: The common built-in data types in python are-
Numbers– They include integers, floating-point numbers, and complex numbers. eg. 1, 7.9,3+4i
List– An ordered sequence of items is called a list. The elements of a list may belong to different data types. Eg. [5,’market’,2.4]
Tuple– It is also an ordered sequence of elements. Unlike lists , tuples are immutable, which means they can’t be changed. Eg. (3,’tool’,1)
String– A sequence of characters is called a string. They are declared within single or double-quotes. Eg. “Sana”
, ‘She is going to the market’
, etc.
Set– Sets are a collection of unique items that are not in order. Eg. {7,6,8}
Dictionary– A dictionary stores values in key and value pairs where each value can be accessed through its key. The order of items is not important. Eg. {1:’apple’,2:’mango}
Boolean– There are 2 boolean values- True and False.
Q11.What is the difference between .py and .pyc files?
Ans: The .py files are the python source code files. While the .pyc files contain the bytecode of the python files. .pyc files are created when the code is imported from some other source. The interpreter converts the source .py files to .pyc files which helps by saving time.
Q12.What is slicing in Python?
Ans: Slicing is used to access parts of sequences like lists, tuples, and strings. The syntax of slicing is-[start:end:step]
. The step can be omitted as well. When we write [start:end]
this returns all the elements of the sequence from the start (inclusive) till the end-1 element. If the start or end element is negative i, it means the ith element from the end. The step indicates the jump or how many elements have to be skipped. Eg. if there is a list- [1,2,3,4,5,6,7,8]
. Then [-1:2:2]
will return elements starting from the last element till the third element by printing every second element.i.e. [8,6,4]
.
Q13.What are Keywords in Python?
Ans: Keywords in python are reserved words that have special meaning.They are generally used to define type of variables. Keywords cannot be used for variable or function names. There are following 33 keywords in python-
Q14.What are Literals in Python and explain about different Literals
Ans: A literal in python source code represents a fixed value for primitive data types. There are 5 types of literals in python-
name=”Tanya”
a=’t’
a=50
a) List collections-Eg. a=[1,2,3,’Amit’]
b) Tuple literals- Eg. a=(5,6,7,8)
c) Dictionary literals- Eg. dict={1: ’apple’, 2: ’mango, 3: ’banana`’}
d) Set literals- Eg. {“Tanya”, “Rohit”, “Mohan”}
6. Special literal- Python has 1 special literal None which is used to return a null variable.
Q15.What are the new features added in Python 3.9.0.0 version?
Ans: The new features in Python 3.9.0.0 version are-
Ans: Memory is managed in Python in the following ways:
Ans: A namespace is a naming system used to make sure that names are unique to avoid naming conflicts.
Ans: 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.
Ans: 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:
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.
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.
Example:
1 2 3 4 5 6 | a = 2 def add(): b = 3 c = a + b print (c) add() |
Output: 5
When you try to access the local variable outside the function add(), it will throw an error.
Ans: Yes. Python is a case sensitive language.
Ans: Type conversion refers to the conversion of one data type into another.
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 function converts real numbers to complex(real,imag) number.
Q23. How to install Python on Windows and set path variable?
Ans: To install Python on Windows, follow the below steps:
Ans: 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.
Ans: 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.
Example:
1 2 3 4 5 | import array as arr My_Array = arr.array( 'i' ,[ 1 , 2 , 3 , 4 ]) My_list = [ 1 , 'abc' , 1.20 ] print (My_Array) print (My_list) |
Output:
array(‘i’, [1, 2, 3, 4])
[1, ‘abc’, 1.2]
Ans: 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.
Example:
1 2 3 | def Newfunc(): print ( "Hi, Welcome to Edureka" ) Newfunc(); #calling the function |
Output: Hi, Welcome to Edureka
Ans: __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.
Here is an example of how to use it.
1 2 3 4 5 6 7 8 9 10 11 | class Employee: def __init__( self , name, age,salary): self .name = name self .age = age self .salary = 20000 E1 = Employee( "XYZ" , 23 , 20000 ) # E1 is the instance of class Employee. #__init__ allocates memory for E1. print (E1.name) print (E1.age) print (E1.salary) |
Output:
XYZ
23
20000
Ans: An anonymous function is known as a lambda function. This function can have any number of parameters but, can have just one statement.
Example:
1 2 | a = lambda x,y : x + y print (a( 5 , 6 )) |
Output: 11
Ans: 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.
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. |
1 2 3 | import array as arr My_Array = arr.array( 'i' ,[ 1 , 2 , 3 , 4 , 5 ]) My_Array[:: - 1 ] |
Output: array(‘i’, [5, 4, 3, 2, 1])
Ans:Consider the example shown below:
1 2 3 4 | from random import shuffle x = [ 'Keep' , 'The' , 'Blue' , 'Flag' , 'Flying' , 'High' ] shuffle(x) print (x) |
The output of the following code is as below.
['Flying', 'Keep', 'Blue', 'High', 'The', 'Flag']
Ans: Iterators are objects which can be traversed though or iterated upon.
Ans:Random module is the standard module that is used to generate a random number. The method is defined as:
1 2 | 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:
Ans:For the most part, xrange and range are the exact same in terms of functionality. They both provide a way to generate a list of integers for you to use, however you please. The only difference is that range returns a Python list object and x range returns an xrange object.
This means that xrange doesn’t actually generate a static list at run-time like range does. It creates the values as you need them with a special technique called yielding. This technique is used with a type of object known as generators. That means that if you have a really gigantic range you’d like to generate a list for, say one billion, xrange is the function to use.
This is especially true if you have a really memory sensitive system such as a cell phone that you are working with, as range will use as much memory as it can to create your array of integers, which can result in a Memory Error and crash your program. It’s a memory hungry beast.
Ans: Comments in Python start with a # character. However, alternatively at times, commenting is done using docstrings(strings enclosed within triple quotes).
Example:
1 2 | #Comments in Python start like this print ( "Comments in Python start with a #" ) |
Output: Comments in Python start with a #
Ans:Pickle module accepts any Python object and converts
it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
Ans: Functions that return an iterable set of items are called generators.
Ans: In Python, the 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.
Ans: To convert a string to lowercase, lower() function can be used.
Example:
1 2 | stg = 'ABCD' print (stg.lower()) |
Output: abcd
Ans: Multi-line comments appear in more than one line. All the lines to be commented are to be prefixed by a #
. You can also a very good shortcut method to comment multiple lines. All you need to do is hold the ctrl key and left click in every place wherever you want to include a # character and type a # just once. This will comment all the lines where you introduced your cursor.
Ans: 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.
Example:
1 2 3 4 5 6 7 8 | """ Using docstring as a comment. This code divides 2 numbers """ x = 8 y = 4 z = x / y print (z) |
Output: 2.0
Ans: Operators are special functions. They take one or more values and produce a corresponding result.
is: returns true when 2 operands are true (Example: “a” is ‘a’)
not: returns the inverse of the boolean value
in: checks if some element is present in some sequence
Ans:Help() and dir() both functions are accessible from the Python interpreter and used for viewing a consolidated dump of built-in functions.
Ans:
Ans:The built-in datatypes in Python is called dictionary. It defines one-to-one relationship between keys and values. Dictionaries contain pair of keys and their corresponding values. Dictionaries are indexed by keys.
Let’s take an example:
The following example contains some keys. Country, Capital & PM. Their corresponding values are India, Delhi and Modi respectively.
1 | dict = { 'Country' : 'India' , 'Capital' : 'Delhi' , 'PM' : 'Modi' } |
1 | print dict [Country] |
Output:India
1 | print dict [Capital] |
Output:Delhi
1 | print dict [PM] |
Output:Modi
Ans:The Ternary operator is the operator that is used to show the conditional statements. This consists of the true or false values with a statement that has to be evaluated for it.
Syntax:
The Ternary operator will be given as:
[on_true] if [expression] else [on_false]x, y = 25, 50big = x if x < y else y
Example:
The expression gets evaluated like if x<y else y, in this case if x<y is true then the value is returned as big=x and if it is incorrect then big=y will be sent as a result.
Ans:We use *args when we aren’t sure how many arguments are going to be passed to a function, or if we want to pass a stored list or tuple of arguments to a function. **kwargs is used when we don’t know how many keyword arguments will be passed to a function, or it can be used to pass the values of a dictionary as keyword arguments. The identifiers args and kwargs are a convention, you could also use *bob and **billy but that would not be wise.
Ans: It is used to determine the length of a string, a list, an array, etc.
Example:
1 2 | stg = 'ABCD' len (stg) |
Output:4
Ans:To modify the strings, Python’s “re” module is providing 3 methods. They are:
Ans:The sequences in Python are indexed and it consists of the positive as well as negative numbers. The numbers that are positive uses ‘0’ that is uses as first index and ‘1’ as the second index and the process goes on like that.
The index for the negative number starts from ‘-1’ that represents the last index in the sequence and ‘-2’ as the penultimate index and the sequence carries forward like the positive number.
The negative index is used to remove any new-line spaces from the string and allow the string to except the last character that is given as S[:-1]. The negative index is also used to show the index to represent the string in correct order.
Ans: Python packages are namespaces containing multiple modules.
Ans: To delete a file in Python, you need to import the OS Module. After that, you need to use the os.remove() function.
Example:
1 2 | import os os.remove( "xyz.txt" ) |
Ans: Variables can be used to store data of different types. Python has the following data types built-in by default, in these categories:
Text Type: str
Numeric Types: int, float, complex
Sequence Types: list, tuple, range
Mapping Type: dict
Set Types: set, frozenset
Boolean Type: bool
Binary Types: bytes, bytearray, memoryview
None Type: NoneType
You can get the data type of any object by using the type() function.
Ans:
Ans: Elements can be added to an array using the append(), extend() and the insert (i,x) functions.
Example:
1 2 3 4 5 6 7 | 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])
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
Example:
1 2 3 4 5 | 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
3.1
array(‘d’, [2.2, 3.8, 3.7, 1.2])
Ans: 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 a procedural as well as structural language.
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Ans: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.
Ans:
Ans:The compiling and linking allow 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:
Ans. Python libraries are a collection of Python packages. Some of the majorly used python libraries are – Numpy, Pandas, Matplotlib, Scikit-learn and many more.
Ans. The split() method is used to separate a given String in Python.
Example:
1 2 | a = "edureka python" print (a.split()) |
Output: [‘edureka’, ‘python’]
Ans. Data types in Python are categorized into mutable and immutable data types.
Mutable | Immutable | |
Definition | Data type whose values can be changed after creation. | Data types whose values can’t be changed or altered. |
Memory Location | Retains the same memory location even after the content is modified. | Any modification results in a new object and new memory location |
Performance | It is memory-efficient, as no new objects are created for frequent changes. | It might be faster in some scenarios as there’s no need to track changes. |
Use-cases | When you need to modify, add, or remove existing data frequently. | When you want to ensure data remains consistent and unaltered. |
Example | List, Dictionaries, Set | Strings, Types, Integer |
Ans. The try block is used to check some code for errors i.e the code inside the try block will execute when there is no error in the program. Whereas the code inside the except block will execute whenever the program encounters some error in the preceding try block.
Syntax:
1 2 3 4 5 6 7 | try : #Code 1 except : #Code 2 |
The try clause is executed first i.e. the code between try. If there is no exception, then only the try clause will run, except clause is finished. If any exception occurs, the try clause will be skipped and except clause will run. If any exception occurs, but the except clause within the code doesn’t handle it, it is passed on to the outer try statements. If the exception is left unhandled, then the execution stops. A try statement can have more than one except clause.
Ans. OrderedDict() is used to maintains the sequence in which keys are added, ensuring that the order is preserved during iteration. In contrast, a standard dictionary does not guarantee any specific order when iterated, providing values in an arbitrary sequence. OrderedDict() distinguishes itself by retaining the original insertion order of items.
Ans. In Python, ‘return’ sends a value and terminates a function, while ‘yield’ produces a value but retains the function’s state, allowing it to resume from where it left off.
YIELD | RETURN |
It replace the return of a function to suspend its execution without destroying local variables. | It exits from a function and handing back a value to its caller. |
It is used when the generator returns an intermediate result to the caller. | It is used when a function is ready to send a value. |
Code written after yield statement execute in next function call. | while, code written after return statement wont execute. |
It can run multiple times. | It only runs single time. |
Yield statement function is executed from the last state from where the function get paused. | Every function calls run the function from the start. |
Ans. Set and frozenset are two built-in collection data types in Python that are used to store a collection of unique elements. While set is mutable, meaning that we can add, remove, or change elements in a set, frozenset is immutable and cannot be modified after creation.
Q69. What are the ways to swap the values of two elements?
Ans. The below program can be used to swap the value in a List:
1 2 3 4 5 6 7 8 | # Swap function def swapPositions( list , pos1, pos2) list [pos1], list [pos2] = list [pos2], list [pos1] return list # Driver function List = [ 23 , 65 , 19 , 90 ] pos1, pos2 = 1 , 3 print (swapPositions( List , pos1 - 1 , pos2 - 1 )) |
Output: [19, 65, 23, 90]
Q70. How to import modules in python?
Ans. 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 |
Next, in this Interview Questions blog, let’s have a look at Object Oriented Concepts in Python.
These Python Interview Questions and Answers will help you prepare for Python job interviews. Start your preparation by going through the most frequently asked questions on Python.
Ans: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:
Ans: Class in Python is created using the class keyword.
Example:
1 2 3 4 5 | class Employee: def __init__(self, name): self.name = name E1=Employee( "abc" ) print(E1.name) |
Output: abc
Ans:In Python, the term monkey patch only refers to dynamic modifications of a class or module at run-time.
Consider the below example:
1 2 3 4 | # m.py class MyClass: def f( self ): print "f()" |
We can then run the monkey-patch testing like this:
1 2 3 4 5 6 7 | 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.
Ans: Multiple inheritance means that a class can be derived from more than one parent classes. Python does support multiple inheritance, unlike Java.
Ans: 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.
Ans: Encapsulation means binding the code and the data together. A Python class in an example of encapsulation.
Ans: 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.
1 2 3 4 5 | class a: pass obj = a() obj.name = "xyz" print ( "Name = " ,obj.name) |
Output:
Name = xyz
Python Pandas Interview Questions
Ans. Pandas is a data manipulation package in Python for tabular data. That is, data in the form of rows and columns, also known as DataFrames. Intuitively, you can think of a DataFrame as an Excel sheet. Pandas’ functionality includes data transformations, like sorting rows and taking subsets, to calculating summary statistics such as the mean, reshaping DataFrames, and joining DataFrames together. pandas works well with other popular Python data science packages, often called the PyData ecosystem, including
pandas is used throughout the data analysis workflow. With pandas, you can:
pandas also contains functionality for time series analysis and analyzing text data.
[Python]
import pandas as pd
data = {“Fruits”: [“Apple”, “Mango”, “Orange”],”Quantity”: [4, 8, 7]}
df = pd.DataFrame(data)
print(df)
[/python]
Fruits Quantity
0 Apple 4
1 Mango 8
2 Orange 7
Ans: The dataframes in Python can be combined in the following ways-
The concat() function is used to concatenate two dataframes. Its syntax is- pd.concat([dataframe1, dataframe2])
.
Dataframes are joined together on a common column called a key. When we combine all the rows in dataframe it is a union and the join used is outer join. While, when we combine the common rows or intersection, the join used is the inner join. Its syntax is- pd.concat([dataframe1, dataframe2], axis=’axis’, join=’type_of_join)
1 2 3 4 5 6 7 8 9 10 11 12 | < / div> <div> import pandas as pd # Create the data of the series as a dictionary ser_data = { 'A' : 5 , 'B' : 10 , 'C' : 15 , 'D' : 20 , 'E' : 25 } # Create the series ser = pd.Series(ser_data) print (ser) |
Output:
A 5
B 10
C 15
D 20
E 25
Ans. To check for missing values in a Pandas dataframe, we use isnull() and notnull() functions. Both the functions help in checking whether a value is NaN or not. These functions can also be used with Panda series to identify null value in the series.
Ans. You can modify a DataFrame’s row and column index using reindexing in Pandas. Indexes can be used with reference to many index DataStructure associated with several pandas series or pandas DataFrame.
Ans. A Pandas dataframe is a two dimensional data structure which allows you to store data in rows and columns. To drop a row or column in a dataframe, you need to use the drop() method available in the dataframe.
Syntax: DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)
Parameters:
Return type: Dataframe with dropped values.
Ans. To add a new column to an existing dataframe, we can do that using Dataframe.insert(). The below code is an example of adding a column to an existing dataframe:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | import pandas as pd # Define a dictionary containing Students data data = { 'Name' : [ 'John' , 'Sam' , 'Michel' , 'Adam' , 'Justin' ], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 'Age' : [ 24 , 22 , 25 , 24 , 26 ], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 'Qualification' : [ 'BE' , 'MBA' , 'Msc' , 'Msc' , 'MBA' ]} # Convert the dictionary into DataFrame df = pd.DataFrame(data) print (df) # Using DataFrame.insert() to add a column df.insert( 2 , "GPA" , [ 4 , 3.8 , 3 , 3.5 , 3.6 ], True ) print ( 'nDataframe after insertionn' ) # Observe the result print (df) |
Output:
Name Age Qualification
0 John 24 BE
1 Sam 22 MBA
2 Michel 25 Msc
3 Adam 24 Msc
4 Justin 26 MBA
Dataframe after insertion
Name Age GPA Qualification
0 John 24 4.0 BE
1 Sam 22 3.8 MBA
2 Michel 25 3.0 Msc
3 Adam 24 3.5 Msc
4 Justin 26 3.6 MBA
Ans. The below program will help you in identifying items in series A but no in series B:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | import pandas as pd # Creating 2 pandas Series ps1 = pd.Series([ 2 , 4 , 8 , 20 , 10 , 47 , 99 ]) ps2 = pd.Series([ 1 , 3 , 6 , 4 , 10 , 99 , 50 ]) print ( "Series 1:" ) print (ps1) print ( "nSeries 2:" ) print (ps2) # Using Bitwise NOT operator along # with pandas.isin() print ( "nItems of Series 1 not present in Series 2:" ) res = ps1[~ps1.isin(ps2)] print (res) |
Output:
Series 1:
0 2
1 4
2 8
3 20
4 10
5 47
6 99
Series 2:
0 1
1 3
2 6
3 4
4 10
5 99
6 50
Items of Series 1 not present in Series 2:
0 2
2 8
3 20
5 47
Ans. The below program is to identify the elements which are not common in both series:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | import pandas as pd import numpy as np sr1 = pd.Series([ 1 , 2 , 3 , 4 , 5 ]) sr2 = pd.Series([ 2 , 4 , 6 , 8 , 10 ]) print ( "Original Series:" ) print ( "sr1:" ) print (sr1) print ( "sr2:" ) print (sr2) print ( "nItems of a given series not present in another given series:" ) sr11 = pd.Series(np.union1d(sr1, sr2)) sr22 = pd.Series(np.intersect1d(sr1, sr2)) result = sr11[~sr11.isin(sr22)] print (result) |
Output:
Original Series:
sr1:
0 1
1 2
2 3
3 4
4 5
sr2:
0 2
1 4
2 6
3 8
4 10
Items of a given series not present in another given series:
0 1
2 3
4 5
5 6
6 8
7 10
1 2 3 4 5 6 7 8 9 10 | def bs(a): # a = name of list b = len (a) - 1nbsp ; # minus 1 because we always compare 2 adjacent values for x in range (b): for y in range (b - x): a[y] = a[y + 1 ] a = [ 32 , 5 , 3 , 6 , 7 , 54 , 87 ] bs(a) |
Output: [3, 5, 6, 7, 32, 54, 87]
1 2 3 4 | def pyfunc(r): for x in range (r): print ( ' ' * (r - x - 1 ) + '*' * ( 2 * x + 1 )) pyfunc( 9 ) |
Output:
*
***
*****
*******
*********
***********
*************
***************
*****************
1 2 3 4 5 6 7 8 9 10 11 12 | # Enter number of terms needednbsp;#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): print ( next ,end = " " ) f = s s = next |
Output: Enter the terms 5 0 1 1 2 3
1 2 3 4 5 6 7 8 9 10 | 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
1 2 3 4 5 6 | a = input ( "enter sequence" ) b = a[:: - 1 ] if a = = b: print ( "palindrome" ) else : print ( "Not a Palindrome" ) |
Output:
enter sequence 323 palindrome
Ans:Let us first write a multiple line solution and then convert it to one-liner code.
1 2 3 4 5 6 | 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.
1 | count sum ( 1 for line in fh for character in line if character.isupper()) |
Ans:The following code can be used to sort a list in Python:
1 2 3 4 | list = [ "1" , "4" , "0" , "6" , "9" ] list = [ int (i) for i in list ] list .sort() print ( list ) |
1 2 3 4 5 6 7 | A0 = dict ( zip (( 'a' , 'b' , 'c' , 'd' , 'e' ),( 1 , 2 , 3 , 4 , 5 ))) A1 = range ( 10 )A2 = sorted ([i for i in A1 if i in A0]) A3 = sorted ([A0[s] for s in A0]) A4 = [i for i in A1 if i in A3] A5 = {i:i * i for i in A1} A6 = [[i,i * i] for i in A1] print (A0,A1,A2,A3,A4,A5,A6) |
Ans:The following will be the final outputs of A0, A1, … A6
A0 = {'a': 1, 'c': 3, 'b': 2, 'e': 5, 'd': 4} # the order may vary
A1 = range(0, 10)
A2 = []
A3 = [1, 2, 3, 4, 5]
A4 = [1, 2, 3, 4, 5]
A5 = {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}
A6 = [[0, 0], [1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36], [7, 49], [8, 64], [9, 81]]
1 2 3 4 5 6 7 8 9 10 11 12 13 | < / pre> def check_odd_even(number): if number % 2 = = 0 : print (f "{number} is even." ) else : print (f "{number} is odd." ) # Input from the user num = int ( input ( "Enter a number: " )) # Checking if the number is odd or even check_odd_even(num) <pre> |
In this program a function named check_odd_even()
is defined which takes a number as input and prints whether it’s odd or even. After the program is executed, it will prompt the user to enter a number and calls this function to determine if the entered number is odd or even.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | < / pre> def is_armstrong(num): # Calculate the number of digits in the number num_digits = len ( str (num)) # Initialize sum to store the result sum = 0 # Temporary variable to store the original number temp = num # Calculate Armstrong sum while temp > 0 : digit = temp % 10 sum + = digit * * num_digits temp / / = 10 # Check if the number is Armstrong or not if num = = sum : return True else : return False # Input from the user number = int ( input ( "Enter a number: " )) # Check if the number is Armstrong or not if is_armstrong(number): print (f "{number} is an Armstrong number." ) else : print (f "{number} is not an Armstrong number." ) <pre> |
In this program the function is_armstrong()
which takes a number as input and returns True if it’s an Armstrong number, otherwise False. It will prompt the user to enter a number and calls this function to determine if the entered number is an Armstrong number or not when executed.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | def calculate_simple_interest(principal, rate, time): # Simple interest formula: SI = (P * R * T) / 100 simple_interest = (principal * rate * time) / 100 return simple_interest # Input from the user principal = float ( input ( "Enter the principal amount: " )) rate = float ( input ( "Enter the annual interest rate (in percentage): " )) time = float ( input ( "Enter the time period (in years): " )) # Calculate simple interest interest = calculate_simple_interest(principal, rate, time) # Display the result print (f "The simple interest for the principal amount ${principal}, annual interest rate of {rate}%, and time period of {time} years is ${interest}." ) |
The function calculate_simple_interest()
takes the principal amount, annual interest rate, and time period as input and returns the simple interest. Then, it prompts the user to enter these values and calls the function to calculate the simple interest, finally displaying the result.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | < / div> <div> def is_symmetrical(input_string): # Check if the string is symmetrical return input_string = = input_string[:: - 1 ] def is_palindrome(input_string): # Remove spaces and convert to lowercase input_string = input_string.replace( " " , "").lower() # Check if the string is a palindrome return input_string = = input_string[:: - 1 ] # Input from the user string = input ( "Enter a string: " ) # Check if the string is symmetrical if is_symmetrical(string): print (f "{string} is symmetrical." ) else : print (f "{string} is not symmetrical." ) # Check if the string is a palindrome if is_palindrome(string): print (f "{string} is a palindrome." ) else : print (f "{string} is not a palindrome." ) < / div> <div> |
This program defines two functions is_symmetrical()
and is_palindrome()
. The is_symmetrical() function checks if the string is symmetrical, and the is_palindrome() function checks if the string is a palindrome. Then, it prompts the user to enter a string and calls these functions to determine whether the entered string is symmetrical or a palindrome, and prints the result accordingly.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | from datetime import datetime, timedelta def get_dates(): # Get today's date today = datetime.now().date() # Calculate yesterday's and tomorrow's date yesterday = today - timedelta(days = 1 ) tomorrow = today + timedelta(days = 1 ) return yesterday, today, tomorrow # Get the dates yesterday_date, today_date, tomorrow_date = get_dates() # Display the dates print ( "Yesterday's date:" , yesterday_date) print ( "Today's date:" , today_date) print ( "Tomorrow's date:" , tomorrow_date) |
This program uses the datetime
module to work with dates. It defines a function get_dates()
to calculate yesterday’s, today’s, and tomorrow’s dates using timedelta objects. Then, it calls this function and prints the dates accordingly.
Next, in this Python Programming Interview Questions let’s have a look at some Python Libraries.
Python Libraries – Python Interview Questions
Ans:Flask is a web microframework for Python based on “Werkzeug, Jinja2 and good intentions” BSD license. Werkzeug and Jinja2 are two of their 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 them. The user can modify the session if only it has the secret key Flask.secret_key.
Ans: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.
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 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. Q108. Discuss Django architecture.
Ans:Django MVT Pattern:
Figure:Django Architecture
The developer provides the Model, the view and the template then just maps it to a URL and Django does the magic to serve it to the user.
Ans:You can use the command edit mysite/setting.py, it is a normal python module with module level representing Django settings.
Django uses SQLite by default; it is easy for Django users as such it won’t require any other type of installation. In the case your database choice is different that you have to the following keys in the DATABASE ‘default’ item to match your database connection settings.
Engines: you can change 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.
Django uses SQLite as a default database, it stores data as a single file in the filesystem. If you do have a database server—PostgreSQL, MySQL, Oracle, MSSQL—and want to use it rather than SQLite, then use your database’s administration tools to create a new database for your Django project. Either way, with your (empty) database in place, all that remains is to tell Django how to use it. This is where your project’s settings.py file comes in.
We will add the following lines of code to the setting.py file:
1 2 3 4 5 6 | DATABASES = { 'default' : { 'ENGINE' : 'django.db.backends.sqlite3' , 'NAME' : os.path.join(BASE_DIR, 'db.sqlite3' ), } } |
Ans:This is how we can use write a view in Django:
1 2 3 4 5 6 7 | from django.http import HttpResponse import datetime def Current_datetime(request): now = datetime.datetime.now() html = "It is now % s / body / html % now return HttpResponse(html) |
Returns the current date and time, as an HTML document
Ans:The template is a simple text file. It can create any text-based format like XML, CSV, HTML, etc. A template contains variables that get replaced with values when the template is evaluated and tags (% tag %) that control the logic of the template.
Figure: Django Template
Ans: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.
Figure: Django Framework
So the data itself is not stored client side. This is nice from a security perspective.
Ans: In Django, there are three possible inheritance styles:
Next, in this Python Interview Questions blog, let’s have a look at NumPy Concepts in Python.
Ans. 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.
Ans. Below code helps you create 1D array:
1 2 3 4 5 6 7 8 9 10 11 12 13 | import numpy as np &nbsp; # creating the list list = [ 100 , 200 , 300 , 400 ] # creating 1-d array n = np.array( list ) print (n) |
Output: [100 200 300 400] Below code helps you create a 2D array:
1 2 3 4 5 6 7 8 9 | import numpy as np # Create a 2-dimensional array with 3 rows and 4 columns arr = np.array([[ 1 , 2 , 3 , 4 ], [ 5 , 6 , 7 , 8 ], [ 9 , 10 , 11 , 12 ]]) # Print the array print (arr) |
Output: [[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] Below code helps you create a 3D array:
1 2 3 4 5 6 7 8 | import numpy as np # Create a 3D array with shape (2, 3, 4) nested_list = [[[ 1 , 2 , 3 , 4 ], [ 5 , 6 , 7 , 8 ], [ 9 , 10 , 11 , 12 ]], &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [[ 13 , 14 , 15 , 16 ], [ 17 , 18 , 19 , 20 ], [ 21 , 22 , 23 , 24 ]]] arr = np.array(nested_list) print (arr) |
Output: [[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] [[13 14 15 16] [17 18 19 20] [21 22 23 24]]]
Ans. To import Text files into Numpy Arrays, we can use the functions numpy.loadtxt( ) in Numpy. Syntax: numpy.loadtxt(fname, dtype = float, comments=’#’, delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding=’bytes’, max_rows=None, *, like= None) Example: The following ‘example.txt’ text file is considered in this example. It contains the following data:
1 2 3 4 5 6 7 | import numpy as np # Text file data converted to integer data type File_data = np.loadtxt( "example1.txt" , dtype = int ) print (File_data) |
Output: [[1 2 3 4] [5 6 7 8] [9 10 11 12]]
Ans. To read a csv file, we can use the numpy.loadtxt() function. Syntax: numpy.loadtxt(fname, dtype=<class ‘float’>, comments=’#’, delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding=’bytes’, max_rows=None, *, quotechar=None, like=None) Example:
1 2 3 4 5 6 7 | import numpy as np # using loadtxt() arr = np.loadtxt( "sample_data.csv" , delimiter = "," , dtype = str ) display(arr) |
Ans. To reverse a numpy array, we can use the flip() function in NumPy.
Next in this Python Interview Question blog, let’s have a look at questions related to Web Scraping
Ans:We will use the following code to save an image locally from an URL address
1 2 | import urllib.request urllib.request.urlretrieve( "URL" , "local-filename.jpg" ) |
Ans:Use the following URL format:
http://webcache.googleusercontent.com/search?q=cache:URLGOESHERE
Be sure to replace “URLGOESHERE” with the proper web address of the page or site whose cache you want to retrieve and see the time for. For example, to check the Google Webcache age of edureka.co you’d use the following URL:
http://webcache.googleusercontent.com/search?q=cache:edureka.co
Ans:We will use the following lines of code:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | from bs4 import BeautifulSoup import requests import sys response = requests.get(url) soup = BeautifulSoup(response.text) tr = soup.findChildren( "tr" ) tr = iter (tr) next (tr) for movie in tr: title = movie.find( 'td' , { 'class' : 'titleColumn' } ).find( 'a' ).contents[ 0 ] year = movie.find( 'td' , { 'class' : 'titleColumn' } ).find( 'span' , { 'class' : 'secondaryInfo' }).contents[ 0 ] rating = movie.find( 'td' , { 'class' : 'ratingColumn imdbRating' } ).find( 'strong' ).contents[ 0 ] row = title + ' - ' + year + ' ' + ' ' + rating print (row) |
The above code will help scrap data from IMDb’s top 250 list
Next, as part of the Python Interview Questions, lets explore some Data Analysis questions
Data Analysis – Python Interview Questions
Ans:map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given. #Follow the link to know more similar functions.
Ans:We use python numpy array instead of a list because of the below three reasons:
For more information on these parameters, you can refer to this section – Numpy Vs List.
Ans:We can get the indices of N maximum values in a NumPy array using the below code:
1 2 3 | import numpy as np arr = np.array([ 1 , 3 , 2 , 4 , 5 ]) print (arr.argsort()[ - 3 :][:: - 1 ]) |
Output
[ 4 3 1 ]
Ans:We can calculate percentiles with the following code
1 2 3 4 | 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
Ans:
NumPy | SciPy |
It refers to Numerical python. | It refers to Scientific python. |
It has fewer new scientific computing features. | Most new scientific computing features belong in SciPy. |
It contains less linear algebra functions. | It has more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms. |
NumPy has a faster processing speed. | SciPy on the other hand has slower computational speed. |
Ans:Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in the 2D case, packages exist that integrate with NumPy. Matplotlib provides basic 3D plotting in the mplot3d subpackage, whereas Mayavi provides a wide range of high-quality 3D visualization features, utilizing the powerful VTK engine.
Now lets explore some multiple choice quesions as part of this Python Interview Questions.
Answer: b, c & d.
Dictionaries are created by specifying keys and values.
Answer: b) //
When both of the operands are integer then python chops out the fraction part and gives you the round off value, to get the accurate answer use floor division. For ex, 5/2 = 2.5 but both of the operands are integer so answer of this expression in python is 2. To get the 2.5 as the answer, use floor division using //. So, 5//2 = 2.5
Answer: d) None of the above
Identifiers can be of any length.
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.
Answer: b) a b c = 1000 2000 3000
Spaces are not allowed in variable names.
1 2 3 4 5 6 7 | try : if '1' ! = 1 : raise "someError" else : print ( "someError has not occured" ) except "someError" : print ( "someError has occured" ) |
Answer: c) invalid code
A new exception class must inherit from a BaseException. There is no such inheritance here.
Answer: c) 25
The index -1 corresponds to the last index in the list.
Answer: b) The location contains double slashes ( ) and w is used to indicate that file is being written to.
1 2 3 4 5 6 7 8 | f = None for i in range ( 5 ): with open ( "data.txt" , "w" ) as f: if (i > 2 ): break print f.closed |
Answer: a) True
The WITH statement when used with open file guarantees that the file object is closed when the with block exits.
Answer: c) when no exception occurs
The else part is executed when no exception occurs.
I hope this set of Python Interview Questions will help you in preparing for your interviews. All the best!
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Edureka offers a Data science with Python course that will help you master the art of analytics and data science techniques employing Python. You will use libraries like Pandas, Numpy, Matplotlib, Scipy, Scikit, and Pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping, and big data analytics leveraging Apache Spark.
The questions were really helpful for clearing basics of Python.
Hello Aayushi, Thanks for sharing best python interview questions with us. by the way can we scrap data via python?
Very usefull post.
Wow
Edureka that question and solutions is very useful for give any Python interview .
Amazing absolutely great.
Should be worth noting: in python 3 Q42 actually behaves opposite as mentioned. The ‘/’ operator performs true division by default, so 5/2 = 2.5, and 5//2 = 2. The ‘//’ is used to truncate the decimal and round down the solution.
answer of second question reg access modifiers feature is very short/shallow.
The dir() method tries to return a list of valid attributes of the object. your answer is wrong.
This post is a good start but needs some serious editing! To copy an array, don’t use a list comprehension… Use list[:]
Also your floor operator details are incorrect.
Q41 is simply wrong, “{}” is correct literal for dict
option d is wrong.