Top 80 Interview Questions on Python for Data Science | Tutort - Best Data Science-AI Course
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The document is a compilation of 80 questions about Python programming, covering topics such as data types, control flow, functions, and object-oriented programming. It explains key concepts such as compiled and interpreted languages, mutable and immutable data types, and the use of decorators, iterators, and generators. Additionally, the document addresses practical applications and techniques for debugging, memory management, and functional programming in Python.
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Question 8
Question 9
What is List Comprehension? Give an
Example.
What is a lambda function?
List comprehension is a syntax construction to ease the
creation of a list based on an existing iterable.
A lambda function is an anonymous function. This function
can have any number of parameters but can have just one
statement.
For example -
For example -
my_list = [i for i in range(1, 10)]
a = lambda x, y : x*y
print(a(7, 19))](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-6-320.jpg)
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Question 39
How to delete a file using Python?
Question 40
What is slicing in Python?
We can delete a file using Python by following
approaches:
Python Slicing is a string operation for extracting a part
of the string, or some part of a list. With this operator,
one can specify where to start the slicing, where to end,
and specify the step. List slicing returns a new list from
the existing list.
os.remove()
os.unlink()
Lst[ Initial : End : IndexJump ]](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-23-320.jpg)
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Question 54
What is the improvement in the enumerate()
function of Python?
Question 55
uses and benefits of python
In Python, enumerate() function is an improvement
over regular iteration.
The enumerate() function returns an iterator that gives
(0, item[0]).
>>> thelist=['a','b']
>>> for i,j in enumerate(thelist):
... print i,j
...
0 a
1 b
High readability
reduces cost of program maintenance
open source
support third party packages modularity
code reverse.](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-32-320.jpg)
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Question 58
What is comprehension in Python?
It provide us with the short and a concise way to
construct a new sequences
[such as a list set dictionary etc]
using sequences which have been already defined
Python supports four types of a comprehension
A. List comprehension-
it provide an elegant way to create a new list.The
following is the basic structure of a list comprehension
output list= [ output_execution for var in input_list if
(var satisfy this condition)]
Note - list comprehension may or may not contain and if
condition list comprehension can contain multiple for
nested list conference comprehension
list comprehension
set comprehension
dictionary comprehension
generator comprehension](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-35-320.jpg)
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1. example one suppose we want to create an output list
which contains only the even number which are present
in the input list let's see how to do this using for loops and
list comprehension and decide which method suits better
2. Suppose we want to create an output list which
contains squares of all the numbers from 1 to 9 let's see
how to do this using for loops and list comprehension
Output = output list using for loop : [2 4 4 6]
output = output list using for loop : [1 4 9 16 25 36 49 64 81]
#without using list comprehension
Input_list = [1, 2, 3, 4, 4, 5, 6, 7,7]
Output_list=[ ]
#look for constructing output
for var in input_list:
If var % 2 ==0:
Output_list.append(var)
print(“Output list using for loop, output_list)
# Constructing output list using for loop
output list is equal to[]
for war in range(1, 10):
output_list.append(var ** 2):
print(“Output list using for loops: “,output_list )](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-36-320.jpg)
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output = output list using for comprehension:
[1 4 9 16 25 36 49 64 81]
#using list comprehension
list_using_comprehension=[ var * 2 for var in
range(1,10)]
print(“Output list using for loops: “,
list_using_comprehension )
output_dict = {key:value for (key, value) in
iterable if (key, value satisfy this condition)}
B. Dictionary comprehension
Extending the idea of list comprehensions, we can also
create a dictionary using dictionary comprehensions.
The basic structure of a dictionary comprehension looks
like below.
Example #1: Suppose we want to create an output
dictionary which contains only the odd numbers that
are present in the input list as keys and their cubes as
values. Let’s see how to do this using for loops and
dictionary comprehension.](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-37-320.jpg)
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input_list = [1, 2, 3, 4, 5, 6, 7]
output_dict = {}
# Using loop for constructing output dictionary
for var in input_list:
if var % 2 != 0:
output_dict[var] = var**3
print("Output Dictionary using for loop:", output_dict )
# Using Dictionary comprehensions
# for constructing output dictionary
input_list = [1,2,3,4,5,6,7]
dict_using_comp = {var:var ** 3 for var in input_list if
var % 2 != 0}
print("Output Dictionary using dictionary
comprehensions:", dict_using_comp)
Output: Output Dictionary using for loop:
{1: 1, 3: 27, 5: 125, 7: 343}
Output: Output Dictionary using dictionary
comprehensions: {1: 1, 3: 27, 5: 125, 7: 343}
Example #2: Given two lists containing the names of
states and their corresponding capitals, construct a
dictionary which maps the states with their respective
capitals. Let’s see how to do this using for loops and
dictionary comprehension.](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-38-320.jpg)
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state = ['Gujarat', 'Maharashtra', 'Rajasthan']
capital = ['Gandhinagar', 'Mumbai', 'Jaipur']
output_dict = {}
# Using loop for constructing output dictionary
for (key, value) in zip(state, capital):
output_dict[key] = value
print("Output Dictionary using for loop:", output_dict)
# Using Dictionary comprehensions
# for constructing output dictionary
state = ['Gujarat', 'Maharashtra', 'Rajasthan']
capital = ['Gandhinagar', 'Mumbai', 'Jaipur']
dict_using_comp = {key:value for (key, value) in
zip(state, capital)}
print("Output Dictionary using dictionary
comprehensions:", dict_using_comp)
Output Output Dictionary using for loop: {'Gujarat':
'Gandhinagar', 'Maharashtra': 'Mumbai', 'Rajasthan': 'Jaipur'}
Output: Output Dictionary using dictionary
comprehensions: {'Rajasthan': 'Jaipur', 'Maharashtra':
'Mumbai', 'Gujarat': 'Gandhinagar'}](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-39-320.jpg)
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C. set comprehension:
Set comprehensions are pretty similar to list
comprehensions. The only difference between them is that
set comprehensions use curly brackets { }. Let’s look at the
following example to understand set comprehensions.
Output: Output Set using for loop: {2, 4, 6}
Example #1 : Suppose we want to create an output set
which contains only the even numbers that are present in
the input list. Note that set will discard all the duplicate
values. Let’s see how we can do this using for loops and set
comprehension.
input_list = [1, 2, 3, 4, 4, 5, 6, 6, 6, 7, 7]
output_set = set()
# Using loop for constructing output set
for var in input_list:
if var % 2 == 0:
output_set.add(var)
print("Output Set using for loop:", output_set)](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-40-320.jpg)
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# Using Set comprehensions
# for constructing output set
input_list = [1, 2, 3, 4, 4, 5, 6, 6, 6, 7, 7]
set_using_comp = {var for var in input_list if var % 2
== 0}
print("Output Set using set
comprehensions:",set_using_comp)
Output: Output Set using set comprehensions: {2, 4, 6}
D. generator comprehension
Generator Comprehensions are very similar to list
comprehensions. One difference between them is that
generator comprehensions use circular brackets
whereas list comprehensions use square brackets. The
major difference between them is that generators don’t
allocate memory for the whole list. Instead, they generate
each value one by one which is why they are memory
efficient.
Let’s look at the following example to understand
generator comprehension:](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-41-320.jpg)
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input_list = [1, 2, 3, 4, 4, 5, 6, 7, 7]
output_gen = (var for var in input_list if var % 2 ==
0)
print("Output values using generator
comprehensions:", end = ' ')
for var in output_gen:
print(var, end = ' ')
Output: Output values using generator comprehensions:
2 4 4 6
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However, Python provides a much easier way for us to apply
decorators. We simply use the @ symbol before the function
we'd like to decorate. Let's show that in practice below.
We can use multiple decorators to a single function.
However, the decorators will be applied in the order that
we've called them. Below we'll define another decorator
that splits the sentence into a list. We'll then apply the
uppercase_decorator and split_string decorator to a
single function.
● Applying Multiple Decorators to a Single Function
Output 'HELLO THERE'
@uppercase_decorator
def say_hi():
return 'hello there'
say_hi()
def split_string(function):
def wrapper():
func = function()
splitted_string = func.split()
return splitted_string
return wrapper
@split_string
@uppercase_decorator
def say_hi():
return 'hello there'
say_hi()
['HELLO', 'THERE']](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-47-320.jpg)
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Question 68
What is the difference between split() and
slicing in Python?
Question 69
How can you randomize the items of a list in
place in Python?
Both split() function and slicing work on a String object.
By using split() function, we can get the list of words
from a String.
E.g. 'a b c '.split() returns [‘a’, ‘b’, ‘c’]
Slicing is a way of getting substring from a String. It
returns another String.
E.g. >>> 'a b c'[2:3] returns b
This can be easily achieved by using the Shuffle()
function from the random library as shown below:
Output: [‘Loves’,’He’ ,’To ,’In’, ‘Python’,’Code’]
from random import shuffle
List = ['He', 'Loves', 'To', 'Code', 'In', 'Python']
shuffle(List)
print(List)](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-58-320.jpg)
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Similarly, a binary file can be opened by appending ‘b’
with them as follows:
To append the content in the files, we can use the
append mode (a):
For text files, the mode would be ‘at’
For binary files, it would be ‘ab’
To remove duplicate elements from the list we use the
set() function.
Consider the below example:
read-only mode (rb)
write-only mode (wb)
read–write mode (rwb)
Question 74
How will you remove duplicate elements
from a list?
demo_list=[5,4,4,6,8,12,12,1,5]
unique_list = list(set(demo_list))
output:[1,5,6,8,12]](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-63-320.jpg)
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Question 77
How can you generate random numbers in
Python?
Question 78
What is slicing in Python?
This is achieved with importing the random module,it is
the module that is used to generate
random numbers.
Syntax:
Slicing is a process used to select a range of elements
from sequence data type like list, string and tuple.
Slicing is beneficial and easy to extract out the
elements. It requires a : (colon) which separates the
start index and end index of the field. All the data
sequence types List or tuple allows us to use slicing to
get the needed elements. Although we can get
elements by specifying an index, we get only a single
element whereas using slicing we can get a group or
appropriate range of needed elements.
Syntax - List_name[start:stop]
import random
random.random # returns the floating point
random number between the range of [0,1].](https://image.slidesharecdn.com/python-80-question-241003100134-d45d66e5/85/Top-80-Interview-Questions-on-Python-for-Data-Science-Tutort-Best-Data-Science-AI-Course-65-320.jpg)
Top 80 Interview Questions on Python for Data Science | Tutort - Best Data Science-AI Course
- 2. Curated by 1 Question What is Python? List some popular applications of Python in the world of technology. Python is a widely-used general-purpose, high-level programming language. It was created by Guido van Rossum in 1991 and further developed by the Python Software Foundation. It was designed with an emphasis on code readability, and its syntax allows programmers to express their concepts in fewer lines of code. System Scripting Web Development Game Development Software Development Complex Mathematics It is used for:
- 3. Curated by 2 Question What are the benefits of using Python language as a tool in the present scenario? The following are the benefits of using Python language System Scripting Web Development Game Development Software Development Complex Mathematics Complex Mathematics Complex Mathematics Complex Mathematics Tutort Provides 24x7 Live 1:1 Video based doubt support
- 4. Curated by Question 3 Question 4 Question 5 Is Python a compiled language or an interpreted language? What does the ‘#’ Symbol in Python? What is the difference between a Mutable data type and an Immutable data type? Actually, Python is a partially compiled language and partially interpreted language. The compilation part is done first when we execute our code and this will generate byte code internally. This byte code gets converted by the Python virtual machine(p.v.m) according to the underlying platform(machine+operating system). ‘#’ is used to comment on everything that comes after on the line. Mutable data types can be edited i.e., they can change at runtime. Eg – List, Dictionary, etc. Immutable data types can not be edited i.e., they can not change at runtime. Eg – String, Tuple, etc.
- 5. Curated by Question 6 Question 7 How are arguments passed by value or by reference in Python? What is the difference between a Set and Dictionary? Everything in Python is an object and all variables hold references to the objects. The reference values are according to the functions; as a result, you cannot change the value of the references. However, you can change the objects if they are mutable. The set is an unordered collection of data types that is iterable, mutable and has no duplicate elements. A dictionary in Python is an unordered collection of data values, used to store data values like a map.
- 6. Curated by Question 8 Question 9 What is List Comprehension? Give an Example. What is a lambda function? List comprehension is a syntax construction to ease the creation of a list based on an existing iterable. A lambda function is an anonymous function. This function can have any number of parameters but can have just one statement. For example - For example - my_list = [i for i in range(1, 10)] a = lambda x, y : x*y print(a(7, 19))
- 7. Curated by Question 10 What is a pass in Python? Pass means performing no operation or in other words, it is a placeholder in the compound statement, where there should be a blank left and nothing has to be written there. // represents floor division whereas / represents precise division. Question 11 What is the difference between / and // in Python? 5//2 = 2 5/2 = 2.5 Guaranteed Job Referrals Highest CTC 100% Hiring Partners 250+ 2.1CR One of the best institutes for getting started with DSA and System Design. It also assisted me in launching my technical career and in honing my problem-solving and coding abilities. I was placed in more than 6+ product based companies because of their constant support. Avishkar Dalvi
- 8. Curated by The beauty of the final block is to execute the code after trying for an 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. Question 12 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 that is monitored for errors. Except the block gets executed when an error occurs. It is a string’s function that converts all uppercase characters into lowercase and vice versa. It is used to alter the existing case of the string. This method creates a copy of the string which contains all the characters in the swap case. Question 13 What is a swapcase function in Python? string = "Hello Pune" string.swapcase() ---> "hELLO pUNE"
- 9. Curated by The “while” loop is the actual looping feature that is used in any other programming language. Programmers use a Python while loop where they just have the end conditions. Question 14 Difference between for loop and while loop in Python The “for” Loop is generally used to iterate through the elements of various collection types such as List, Tuple, Set, and Dictionary. Developers use a “for” loop where they have both the conditions start and the end. Question 15 Can we Pass a function as an argument in Python? Yes, Several arguments can be passed to a function, including objects, variables (of the same or distinct data types), and functions. Functions can be passed as parameters to other functions because they are objects. Higher-order functions are functions that can take other functions as arguments.
- 10. Curated by Question 16 Question 17 What are *args and *kwargs? Is Indentation Required in Python? To pass a variable number of arguments to a function in Python, use the special syntax *args and **kwargs in the function specification. It is used to pass a variable- length, keyword-free argument list. By using the *, the variable we associate with the * becomes iterable, allowing you to do operations on it such as iterating over it and using higher-order operations like map and filter. Yes, indentation is required in Python. A Python interpreter can be informed that a group of statements belongs to a specific block of code by using Python indentation. Indentations make the code easy to read for developers in all programming languages but in Python, it is very important to indent the code in a specific order.
- 11. Curated by Global variables: Global variables are the ones that are defined and declared outside any function and are not specified to any function. Module-level scope: It refers to the global objects of the current module accessible in the program. Outermost scope: It refers to any built-in names that the program can call. The name referenced is located last among the objects in this scope. Question 18 What is Scope in Python? Local variable: Local variables are those that are initialized within a function and are unique to that function. It cannot be accessed outside of the function. The location where we can find a variable and also access it if required is called the scope of a variable.
- 12. Curated by Question 19 What is docstring in Python? Question 20 What is a dynamically typed language? Python documentation strings (or docstrings) provide a convenient way of associating documentation with Python modules, functions, classes, and methods. Statically typed languages: In this type of language, the data type of a variable is known at the compile time which means the programmer has to specify the data type of a variable at the time of its declaration. Declaring Docstrings: The docstrings are declared using ”’triple single quotes”’ or “””triple double quotes””” just below the class, method, or function declaration. All functions should have a docstring. Accessing Docstrings: The docstrings can be accessed using the __doc__ method of the object or using the help function. Typed languages are the languages in which we define the type of data type and it will be known by the machine at the compile-time or at runtime. Typed languages can be classified into two categories:
- 13. Curated by Question 21 What is a break, continue, and pass in Python? The break statement is used to terminate the loop or statement in which it is present. After that, the control will pass to the statements that are present after the break statement, if available. Continue is also a loop control statement just like the break statement. The continue statement is opposite to that of the break statement, instead of terminating the loop, it forces the execution of the next iteration of the loop. Pass means performing no operation or in other words, it is a placeholder in the compound statement, where there should be a blank left and nothing has to be written there. Dynamically typed languages: These are the languages that do not require any pre-defined data type for any variable as it is interpreted at runtime by the machine itself. In these languages, interpreters assign the data type to a variable at runtime depending on its value.
- 14. Curated by Question 22 What are Built-in data types in Python? Numeric: The numeric data type in Python represents the data that has a numeric value. A numeric value can be an integer, a floating number, a Boolean, or even a complex number. Sequence Type: The sequence Data Type in Python is the ordered collection of similar or different data types. There are several sequence types in Python: String List Tuple Dictionary Range Mapping Types: In Python, hashable data can be mapped to random objects using a mapping object. There is currently only one common mapping type, the dictionary, and mapping objects are mutable. Set: In Python, a Set is an unordered collection of data types that is iterable, mutable, and has no duplicate elements. The order of elements in a set is undefined though it may consist of various elements. The following are the standard or built-in data types in Python:
- 15. Curated by Question 23 Question 24 How do you floor a number in Python? What is the difference between xrange and range functions? The Python math module includes a method that can be used to calculate the floor of a number. floor() method in Python returns the floor of x i.e., the largest integer not greater than x. ceil(x) in Python returns a ceiling value of x i.e., the smallest integer greater than or equal to x. range() and xrange() are two functions that could be used to iterate a certain number of times in for loops in Python. In Python 3, there is no xrange, but the range function behaves like xrange in Python 2. range() – This returns a list of numbers created using the range() function. xrange() – This function returns the generator object that can be used to display numbers only by looping. The only particular range is displayed on demand and hence called lazy evaluation.
- 16. Curated by Question 25 What is Dictionary Comprehension? Give an Example Question 26 Is Tuple Comprehension? If yes, how, and if not why? Dictionary Comprehension is a syntax construction to ease the creation of a dictionary based on the existing iterable. Tuple comprehension is not possible in Python because it will end up in a generator, not a tuple comprehension. my_dict = {i:1+7 for i in range(1, 10)} (i for i in (1, 2, 3))
- 17. Curated by Question 27 Differentiate between List and Tuple? Let’s analyze the differences between List and Tuple: Lists are Mutable data types. LISTS TUPLES Tuples are Immutable data types. Lists consume more memory Tuple consumes less memory as compared to the list The implication of iterations is comparatively Faster A Tuple data type is appropriate for accessing the elements The implication of iterations is Time-consuming The list is better for performing operations, such as insertion and deletion. Question 28 What is the difference between a shallow copy and a deep copy? Shallow copy is used when a new instance type gets created and it keeps values that are copied whereas deep copy stores values that are already copied. A shallow copy has faster program execution whereas a deep copy makes it slow.
- 18. Curated by Question 29 Which sorting technique is used by sort() and sorted() functions of python? Question 30 What are Decorators? Python uses the Tim Sort algorithm for sorting. It’s a stable sorting whose worst case is O(N log N). It’s a hybrid sorting algorithm, derived from merge sort and insertion sort, designed to perform well on many kinds of real-world data. Decorators in simple terms is the specific change that we make in Python syntax to alter functions easily. Tutort Academy is always ready to provide job assistance in various organizations such as MNCs and Startups, as well as help you with resume writing, mock interviews, LinkedIn profile creation, and everything else related to job search. They make you industry-ready. Disha Patil
- 19. Curated by Question 31 How do you debug a Python program? In Python, we can use the debugger pdb for debugging the code. To start debugging we have to enter the following lines on the top of a Python script. import pdb pdb.set_trace() After adding these lines, our code runs in debug mode. Now we can usecommands like breakpoint, step through, step into etc for debugging. By using this command we can debug a Python program: $ python -m pdb python-script.py Question 32 What are Iterators in Python? In Python, iterators are used to iterate a group of elements, containers like a list. Iterators are collections of items, and they can be a list, tuples, or a dictionary. Python iterator implements __itr__ and the next() method to iterate the stored elements. We generally use loops to iterate over the collections (list, tuple) in Python.
- 20. Curated by Question 33 What are Generators in Python? In Python, the generator is a way that specifies how to implement iterators. It is a normal function except that it yields expression in the function. It does not implement __itr__ and next() method and reduces other overheads as well. If a function contains at least a yield statement, it becomes a generator. The yield keyword pauses the current execution by saving its states and then resumes from the same when required. Question 34 Does Python support multiple Inheritance? Python does support multiple inheritances, unlike Java. Multiple inheritances mean that a class can be derived from more than one parent class.
- 21. Curated by Question 35 What is Polymorphism and encapsulation 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 polymorphism. Encapsulation means binding the code and the data together. A Python class is an example of encapsulation. Question 36 What is garbage collection in Python? A garbage collection in Python manages the memory automatically and heap allocation. In simpler terms, the process of automatic deletion of unwanted or unused objects to free the memory is called garbage collection in Python.
- 22. Curated by Question 37 How do you do data abstraction in Python? Question 38 How is memory management done in Python? Data Abstraction is providing only the required details and hides the implementation from the world. It can be achieved in Python by using interfaces and abstract classes. Python uses its private heap space to manage the memory. Basically, all the objects and data structures are stored in the private heap space. Even the programmer can not access this private space as the interpreter takes care of this space. Python also has an inbuilt garbage collector, which recycles all the unused memory and frees the memory and makes it available to the heap space.
- 23. Curated by Question 39 How to delete a file using Python? Question 40 What is slicing in Python? We can delete a file using Python by following approaches: Python Slicing is a string operation for extracting a part of the string, or some part of a list. With this operator, one can specify where to start the slicing, where to end, and specify the step. List slicing returns a new list from the existing list. os.remove() os.unlink() Lst[ Initial : End : IndexJump ]
- 24. Curated by Question 41 What is a namespace in Python? Question 42 What is PIP? Question 43 What is a zip function? A namespace is a naming system used to make sure that names are unique to avoid naming conflicts. PIP is an acronym for Python Installer Package which provides a seamless interface to install various Python modules. It is a command-line tool that can search for packages over the internet and install them without any user interaction. Python zip() function returns a zip object, which maps a similar index of multiple containers. It takes an iterable, converts it into an iterator and aggregates the elements based on iterables passed. It returns an iterator of tuples.
- 25. Curated by Question 44 What are Pickling and Unpickling? Question 45 How can we do Functional programming in Python? The Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using the dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling. Python has a module named pickle. This module has the implementation of a powerful algorithm for serialization and deserialization of Python object structure. In Functional Programming, we decompose a program into functions. These functions take input and after processing give an output. The function does not maintain any state. Python provides built-in functions that can be used for Functional programming.
- 26. Curated by Map() reduce() filter() Question 46 What is __init__() in Python? Question 47 Write a code to display the current time? Equivalent to constructors in OOP terminology, __init__ is a reserved method in Python classes. The __init__ method is called automatically whenever a new object is initiated. This method allocates memory to the new object as soon as it is created. This method can also be used to initialize variables. Some of these functions are: Event iterators and generators can be used for Functional programming in Python. current_time= time.localtime() print (“Current time is”, current_time)
- 27. Curated by Question 48 What are Access Specifiers in Python? Public Access Modifier: The members of a class that are declared public are easily accessible from any part of the program. All data members and member functions of a class are public by default. Protected Access Modifier: The members of a class that are declared public are easily accessible from any part of the program. All data members and member functions of a class are public by default. Private Access Modifier: The members of a class that are declared private are accessible within the class only, the private access modifier is the most secure access modifier. Data members of a class are declared private by adding a double underscore ‘__’ symbol before the data member of that class. Python uses the ‘_’ symbol to determine the access control for a specific data member or a member function of a class. A Class in Python has three types of Python access modifiers:
- 28. Curated by Question 49 What are unit tests in Python? Question 50 Python Global Interpreter Lock (GIL)? The Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using the dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling. Python has a module named pickle. This module has the implementation of a powerful algorithm for serialization and deserialization of Python object structure. Python Global Interpreter Lock (GIL) is a type of process lock that is used by Python whenever it deals with processes. Generally, Python only uses only one thread to execute the set of written statements. The performance of the single-threaded process and the multi-threaded process will be the same in Python and this is because of GIL in Python. We can not achieve multithreading in Python because we have a global interpreter lock that restricts the threads and works as a single thread.
- 29. Curated by Question 51 What are Function Annotations in Python? Function Annotation is a feature that allows you to add metadata to function parameters and return values. This way you can specify the input type of the function parameters and the return type of the value the function returns. Function annotations are arbitrary Python expressions that are associated with various parts of functions. These expressions are evaluated at compile time and have no life in Python’s runtime environment. Python does not attach any meaning to these annotations. They take life when interpreted by third-party libraries, for example, mypy. Courses Offered by Tutort Academy Full Stack with MERN Learn more DSA with System Design Learn more
- 30. Curated by Question 52 What are Exception Groups in Python? The ExceptionGroup can be handled using a new except* syntax. The * symbol indicates that multiple exceptions can be handled by each except* clause. ExceptionGroup is a collection/group of different kinds of Exception. Without creating Multiple Exceptions we can group together different Exceptions which we can later fetch one by one whenever necessary, the order in which the Exceptions are stored in the Exception Group doesn’t matter while calling them. try: raise ExceptionGroup('Example ExceptionGroup', ( TypeError('Example TypeError'), ValueError('Example ValueError'), KeyError('Example KeyError'), AttributeError('Example AttributeError') )) except* TypeError: ... except* ValueError as e: ... except* (KeyError, AttributeError) as e: ...
- 31. Curated by Question 53 What is Python Switch Statement Python has implemented a switch case feature called “structural pattern matching”. You can implement this feature with the match and case keywords. Note that the underscore symbol is what you use to define a default case for the switch statement in Python. Note: Before Python 3.10 Python didn't support match Statements. match term: case pattern-1: action-1 case pattern-2: action-2 case pattern-3: action-3 case _: action-default
- 32. Curated by Question 54 What is the improvement in the enumerate() function of Python? Question 55 uses and benefits of python In Python, enumerate() function is an improvement over regular iteration. The enumerate() function returns an iterator that gives (0, item[0]). >>> thelist=['a','b'] >>> for i,j in enumerate(thelist): ... print i,j ... 0 a 1 b High readability reduces cost of program maintenance open source support third party packages modularity code reverse.
- 33. Curated by Question 56 Dynamically typed language? Typing refers to type checking in programming language storing type language such as a Python 1 + 2 will result in type error since this language don't allow for “type coercion” (implicit conversion of data types) on the other hand a weekly type data weakly typed language such as JS will simply output 12 to a result Two stage of a Typing-checking Static- data types are checked before execution. dynamic- data type are checked during execution Tutort Benefits 24x7 Live 1:1 Video based doubt support 1:1 Mentorship from Industry experts Resume building & Mock Interview Preparations Special support for foreign students
- 34. Curated by Question 57 What is self in code? self represents the instances of class . These handy keywords allow you to access the variables, attributes and methods of a defined class in Python. Self parameter doesn't have to be you named ‘self’ as you can call it by any other home however the self parameter must always be the first parameter of any class function regardless of a name choose chosen so instead of selfie you call you ‘mine’ or ‘ours’ or anything else Class address: def __init__(mine,street,number): Mine.street = street Mine.number = number Def myfunc(abc): print(“my address is “ abc,street) P1 = address(“Albert street”,20) p1.myfunc()
- 35. Curated by Question 58 What is comprehension in Python? It provide us with the short and a concise way to construct a new sequences [such as a list set dictionary etc] using sequences which have been already defined Python supports four types of a comprehension A. List comprehension- it provide an elegant way to create a new list.The following is the basic structure of a list comprehension output list= [ output_execution for var in input_list if (var satisfy this condition)] Note - list comprehension may or may not contain and if condition list comprehension can contain multiple for nested list conference comprehension list comprehension set comprehension dictionary comprehension generator comprehension
- 36. Curated by 1. example one suppose we want to create an output list which contains only the even number which are present in the input list let's see how to do this using for loops and list comprehension and decide which method suits better 2. Suppose we want to create an output list which contains squares of all the numbers from 1 to 9 let's see how to do this using for loops and list comprehension Output = output list using for loop : [2 4 4 6] output = output list using for loop : [1 4 9 16 25 36 49 64 81] #without using list comprehension Input_list = [1, 2, 3, 4, 4, 5, 6, 7,7] Output_list=[ ] #look for constructing output for var in input_list: If var % 2 ==0: Output_list.append(var) print(“Output list using for loop, output_list) # Constructing output list using for loop output list is equal to[] for war in range(1, 10): output_list.append(var ** 2): print(“Output list using for loops: “,output_list )
- 37. Curated by output = output list using for comprehension: [1 4 9 16 25 36 49 64 81] #using list comprehension list_using_comprehension=[ var * 2 for var in range(1,10)] print(“Output list using for loops: “, list_using_comprehension ) output_dict = {key:value for (key, value) in iterable if (key, value satisfy this condition)} B. Dictionary comprehension Extending the idea of list comprehensions, we can also create a dictionary using dictionary comprehensions. The basic structure of a dictionary comprehension looks like below. Example #1: Suppose we want to create an output dictionary which contains only the odd numbers that are present in the input list as keys and their cubes as values. Let’s see how to do this using for loops and dictionary comprehension.
- 38. Curated by input_list = [1, 2, 3, 4, 5, 6, 7] output_dict = {} # Using loop for constructing output dictionary for var in input_list: if var % 2 != 0: output_dict[var] = var**3 print("Output Dictionary using for loop:", output_dict ) # Using Dictionary comprehensions # for constructing output dictionary input_list = [1,2,3,4,5,6,7] dict_using_comp = {var:var ** 3 for var in input_list if var % 2 != 0} print("Output Dictionary using dictionary comprehensions:", dict_using_comp) Output: Output Dictionary using for loop: {1: 1, 3: 27, 5: 125, 7: 343} Output: Output Dictionary using dictionary comprehensions: {1: 1, 3: 27, 5: 125, 7: 343} Example #2: Given two lists containing the names of states and their corresponding capitals, construct a dictionary which maps the states with their respective capitals. Let’s see how to do this using for loops and dictionary comprehension.
- 39. Curated by state = ['Gujarat', 'Maharashtra', 'Rajasthan'] capital = ['Gandhinagar', 'Mumbai', 'Jaipur'] output_dict = {} # Using loop for constructing output dictionary for (key, value) in zip(state, capital): output_dict[key] = value print("Output Dictionary using for loop:", output_dict) # Using Dictionary comprehensions # for constructing output dictionary state = ['Gujarat', 'Maharashtra', 'Rajasthan'] capital = ['Gandhinagar', 'Mumbai', 'Jaipur'] dict_using_comp = {key:value for (key, value) in zip(state, capital)} print("Output Dictionary using dictionary comprehensions:", dict_using_comp) Output Output Dictionary using for loop: {'Gujarat': 'Gandhinagar', 'Maharashtra': 'Mumbai', 'Rajasthan': 'Jaipur'} Output: Output Dictionary using dictionary comprehensions: {'Rajasthan': 'Jaipur', 'Maharashtra': 'Mumbai', 'Gujarat': 'Gandhinagar'}
- 40. Curated by C. set comprehension: Set comprehensions are pretty similar to list comprehensions. The only difference between them is that set comprehensions use curly brackets { }. Let’s look at the following example to understand set comprehensions. Output: Output Set using for loop: {2, 4, 6} Example #1 : Suppose we want to create an output set which contains only the even numbers that are present in the input list. Note that set will discard all the duplicate values. Let’s see how we can do this using for loops and set comprehension. input_list = [1, 2, 3, 4, 4, 5, 6, 6, 6, 7, 7] output_set = set() # Using loop for constructing output set for var in input_list: if var % 2 == 0: output_set.add(var) print("Output Set using for loop:", output_set)
- 41. Curated by # Using Set comprehensions # for constructing output set input_list = [1, 2, 3, 4, 4, 5, 6, 6, 6, 7, 7] set_using_comp = {var for var in input_list if var % 2 == 0} print("Output Set using set comprehensions:",set_using_comp) Output: Output Set using set comprehensions: {2, 4, 6} D. generator comprehension Generator Comprehensions are very similar to list comprehensions. One difference between them is that generator comprehensions use circular brackets whereas list comprehensions use square brackets. The major difference between them is that generators don’t allocate memory for the whole list. Instead, they generate each value one by one which is why they are memory efficient. Let’s look at the following example to understand generator comprehension:
- 42. Curated by input_list = [1, 2, 3, 4, 4, 5, 6, 7, 7] output_gen = (var for var in input_list if var % 2 == 0) print("Output values using generator comprehensions:", end = ' ') for var in output_gen: print(var, end = ' ') Output: Output values using generator comprehensions: 2 4 4 6 Courses Offered by Tutort Academy Data Science and Artificial Intelligence Program Data Analytics and Business Analytics Program Learn more Learn more Data Analytics and Business Analytics Program Learn more Data Science and Artificial Intelligence Program Learn more
- 43. Curated by Question 59 What is a decorator? A decorator is a design pattern in Python that allows a user to add new functionality to an existing object without modifying its structure. Decorators are usually called before the definition of a function you want to decorate. ● Assigning Functions to Variables To kick us off we create a function that will add one to a number whenever it is called. We'll then assign the function to a variable and use this variable to call the function. Output:- 6 def plus_one(number): return number + 1 add_one = plus_one add_one(5)
- 44. Curated by ● Defining Functions Inside other Functions Next, we'll illustrate how you can define a function inside another function in Python. Stay with me, we'll soon find out how all this is relevant in creating and understanding decorators in Python. ● Passing Functions as Arguments to other Functions Functions can also be passed as parameters to other functions. Let's illustrate that below. Output:- 5 Output:- 5 def plus_one(number): def add_one(number): return number + 1 result = add_one(number) return result plus_one(4) def plus_one(number): return number + 1 def function_call(function): number_to_add = 5 return function(number_to_add) function_call(plus_one)
- 45. Curated by ● Functions Returning other Functions A function can also generate another function. We'll show that below using an example. ● Nested Functions have access to the Enclosing Function's Variable Scope Python allows a nested function to access the outer scope of the enclosing function. This is a critical concept in decorators -- this pattern is known as a Closure. Output:- ‘hi’ Output:- Some random message def hello_function(): def say_hi(): return "Hi" return say_hi hello = hello_function() hello() def print_message(message): "Enclosing Function" def message_sender(): "Nested Function" print(message) message_sender() print_message("Some random message")
- 46. Curated by ● Creating Decorators With these prerequisites out of the way, let's go ahead and create a simple decorator that will convert a sentence to uppercase. We do this by defining a wrapper inside an enclosed function. As you can see it is very similar to the function inside another function that we created earlier. Our decorator function takes a function as an argument, and we shall, therefore, define a function and pass it to our decorator. We learned earlier that we could assign a function to a variable. We'll use that trick to call our decorator function. Output 'HELLO THERE' def uppercase_decorator(function): def wrapper(): func = function() make_uppercase = func.upper() return make_uppercase return wrapper def say_hi(): return 'hello there' decorate = uppercase_decorator(say_hi) decorate()
- 47. Curated by However, Python provides a much easier way for us to apply decorators. We simply use the @ symbol before the function we'd like to decorate. Let's show that in practice below. We can use multiple decorators to a single function. However, the decorators will be applied in the order that we've called them. Below we'll define another decorator that splits the sentence into a list. We'll then apply the uppercase_decorator and split_string decorator to a single function. ● Applying Multiple Decorators to a Single Function Output 'HELLO THERE' @uppercase_decorator def say_hi(): return 'hello there' say_hi() def split_string(function): def wrapper(): func = function() splitted_string = func.split() return splitted_string return wrapper @split_string @uppercase_decorator def say_hi(): return 'hello there' say_hi() ['HELLO', 'THERE']
- 48. Curated by From the above output, we notice that the application of decorators is from the bottom up. Had we interchanged the order, we'd have seen an error since lists don't have an upper attribute. The sentence has first been converted to uppercase and then split into a list. Output My arguments are: Nairobi, Accra Cities I love are Nairobi and Accra def decorator_with_arguments(function): def wrapper_accepting_arguments(arg1, arg2): print("My arguments are: {0}, {1}".format(arg1,arg2)) function(arg1, arg2) return wrapper_accepting_arguments @decorator_with_arguments def cities(city_one, city_two): print("Cities I love are {0} and {1}".format(city_one, city_two)) cities("Nairobi", "Accra") Sometimes we might need to define a decorator that accepts arguments. We achieve this by passing the arguments to the wrapper function. The arguments will then be passed to the function that is being decorated at call time. ● Accepting Arguments in Decorator Functions
- 49. Curated by Let's see how we'd use the decorator using positional arguments. def a_decorator_passing_arbitrary_arguments(function _to_decorate): def a_wrapper_accepting_arbitrary_arguments(*args,** kwargs): print('The positional arguments are', args) print('The keyword arguments are', kwargs) function_to_decorate(*args) return a_wrapper_accepting_arbitrary_arguments @a_decorator_passing_arbitrary_arguments def function_with_no_argument(): print("No arguments here.") function_with_no_argument() The positional arguments are () The keyword arguments are {} No arguments here. To define a general purpose decorator that can be applied to any function we use args and **kwargs. args and **kwargs collect all positional and keyword arguments and store them in the args and kwargs variables. args and kwargs allow us to pass as many arguments as we would like during function calls. ● Defining General Purpose Decorators
- 50. Curated by Keyword arguments are passed using keywords. An illustration of this is shown below. The positional arguments are () The keyword arguments are {'first_name': 'Derrick', 'last_name': 'Mwiti'} This has shown keyword arguments ● Passing Arguments to the Decorator Now let's see how we'd pass arguments to the decorator itself. In order to achieve this, we define a decorator maker that accepts arguments then defines a decorator inside it. We then define a wrapper function inside the decorator as we did earlier. @a_decorator_passing_arbitrary_arguments def function_with_arguments(a, b, c): print(a, b, c) function_with_arguments(1,2,3) The positional arguments are (1, 2, 3) The keyword arguments are {} 1 2 3 @a_decorator_passing_arbitrary_arguments def function_with_keyword_arguments(): print("This has shown keyword arguments") function_with_keyword_arguments(first_name="Der rick", last_name="Mwiti")
- 51. Curated by def decorator_maker_with_arguments(decorator_arg1, decorator_arg2, decorator_arg3): def decorator(func): def wrapper(function_arg1, function_arg2, function_arg3) : "This is the wrapper function" print("The wrapper can access all the variablesn" "t- from the decorator maker: {0} {1} {2}n" "t- from the function call: {3} {4} {5}n" "and pass them to the decorated function" .format(decorator_arg1, decorator_arg2,decorator_arg3, function_arg1, function_arg2,function_arg3)) return func(function_arg1, function_arg2,function_arg3) return wrapper return decorator pandas = "Pandas" @decorator_maker_with_arguments(pandas, "Numpy","Scikit-learn") def decorated_function_with_arguments(function_arg1, function_arg2,function_arg3): print("This is the decorated function and it only knows about its arguments: {0}" " {1}" " {2}".format(function_arg1, function_arg2,function_arg3)) decorated_function_with_arguments(pandas, "Science", "Tools")
- 52. Curated by The wrapper can access all the variables - from the decorator maker: Pandas Numpy Scikit-learn - from the function call: Pandas Science Tools and pass them to the decorated function This is the decorated function, and it only knows about its arguments: Pandas Science Tools In order to solve this challenge Python provides a functools.wraps decorator. This decorator copies the lost metadata from the undecorated function to the decorated closure. Let's show how we'd do that. ● Debugging Decorators As we have noticed, decorators wrap functions. The original function name, its docstring, and parameter list are all hidden by the wrapper closure: For example, when we try to access the decorated_function_with_arguments metadata, we'll see the wrapper closure's metadata. This presents a challenge when debugging. decorated_function_with_arguments.__name__ 'wrapper' decorated_function_with_arguments.__doc__ 'This is the wrapper function'
- 53. Curated by When we check the say_hi metadata, we notice that it is now referring to the function's metadata and not the wrapper's metadata. say_hi.__name__ 'say_hi' say_hi.__doc__ 'This will say hi' It is advisable and good practice to always use functools.wraps when defining decorators. It will save you a lot of headache in debugging. import functools def uppercase_decorator(func): @functools.wraps(func) def wrapper(): return func().upper() return wrapper @uppercase_decorator def say_hi(): "This will say hi" return 'hello there' say_hi() 'HELLO THERE'
- 54. Curated by ● Python Decorators Summary Decorators dynamically alter the functionality of a function, method, or class without having to directly use subclasses or change the source code of the function being decorated. Using decorators in Python also ensures that your code is DRY(Don't Repeat Yourself). Decorators have several use cases such as: Authorization in Python frameworks such as Flask and Django Logging Measuring execution time Synchronization Question 60 What are Python Modules? Files containing Python codes are referred to as Python Modules. This code can either be classes, functions, or variables and saves the programmer time by providing the predefined functionalities when needed. It is a file with “.py” extension containing an executable code. Commonly used built modules are listed below: os sys data time math random JSON
- 55. Curated by Question 61 What is pep 8? Question 62 What are Python packages? Question 63 What are the popular Python libraries used in Data analysis? PEP in Python stands for Python Enhancement Proposal. It is a set of rules that specify how to write and design Python code for maximum readability. A Python package refers to the collection of different sub-packages and modules based on the similarities of the function. Some of the popular libraries of Python used for Data analysis are: Pandas: Powerful Python Data Analysis Toolkit Seaborn: This is a statistical data visualization library in Python. SciKit: This is a machine learning library in Python. SciPy: This is an open source system for science, mathematics and engineering implemented in Python.
- 56. Curated by Question 64 What is the difference between append() and extend() methods? Both append() and extend() methods are methods used to add elements at the end of a list. append(element): Adds the given element at the end of the list that called this append() method extend(another-list): Adds the elements of another list at the end of the list that called this extend() method Python lets users include a description (or quick notes) for their methods using documentation strings or docstrings. Docstrings are different from regular comments in Python as, rather than being completely ignored by the Python Interpreter like in the case of comments, these are defined within triple quotes. Question 65 What is docstring in Python? Using docstring as a comment. This code add two numbers """ x=7 y=9 z=x+y print(z)
- 57. Curated by Question 66 How is Multithreading achieved in Python? Question 67 What are the common built-in data types in Python? Python has a multi-threading package ,but commonly not considered as good practice to use it as it will result in increased code execution time. Python has a constructor called the Global Interpreter Lock (GIL). The GIL ensures that only one of your ‘threads’ can execute at one time.The process makes sure that a thread acquires the GIL, does a little work, then passes the GIL onto the next thread. This happens at a very Quick instance of time and that’s why to the human eye it seems like your threads are executing parallely, but in reality they are executing one by one by just taking turns using the same CPU core. Python supports the below-mentioned built-in data types: Immutable data types: Number , String, Tuple Mutable data types: List, Dictionary, Set
- 58. Curated by Question 68 What is the difference between split() and slicing in Python? Question 69 How can you randomize the items of a list in place in Python? Both split() function and slicing work on a String object. By using split() function, we can get the list of words from a String. E.g. 'a b c '.split() returns [‘a’, ‘b’, ‘c’] Slicing is a way of getting substring from a String. It returns another String. E.g. >>> 'a b c'[2:3] returns b This can be easily achieved by using the Shuffle() function from the random library as shown below: Output: [‘Loves’,’He’ ,’To ,’In’, ‘Python’,’Code’] from random import shuffle List = ['He', 'Loves', 'To', 'Code', 'In', 'Python'] shuffle(List) print(List)
- 59. Curated by Question 70 What are negative indexes and why are they used? To access an element from ordered sequences, we simply use the index of the element, which is the position number of that particular element. The index usually starts from 0, i.e., the first element has index 0, the second has 1, and so on. Python Indexing When we use the index to access elements from the end of a list, it’s called reverse indexing. In reverse indexing, the indexing of elements starts from the last element with the index number ‘−1’. The second last element has index ‘−2’, and so on. These indexes used in reverse indexing are called negative indexes. Guaranteed Job Referrals Highest CTC 100% Hiring Partners 250+ 2.1CR Why Tutort Academy?
- 60. Curated by Question 71 Explain split(), sub(), subn() methods of “re” module in Python? These methods belong to the Python RegEx or ‘re’ module and are used to modify strings. split(): This method is used to split a given string into a list. sub(): This method is used to find a substring where a regex pattern matches, and then it replaces the matched substring with a different string. subn(): This method is similar to the sub() method, but it returns the new string, along with the number of replacements. Tutort Provides Special support for foreign students
- 61. Curated by Question 72 What is a map function in Python? The map() function in Python has two parameters, function and iterable. The map() function takes a function as an argument and then applies that function to all the elements of an iterable, passed to it as another argument. It returns an object list of results. def calculateSq(n): return n*n numbers = (2, 3, 4, 5) result = map( calculateSq, numbers) print(result)
- 62. Curated by Question 73 Explain all file processing modes supported in Python? Python has various file processing modes. For opening files, there are three modes: For opening a text file using the above modes, we will have to append ‘t’ with them as follows: read-only mode (r) write-only mode (w) read–write mode (rw) read-only mode (rt) write-only mode (wt) read–write mode (rwt)
- 63. Curated by Similarly, a binary file can be opened by appending ‘b’ with them as follows: To append the content in the files, we can use the append mode (a): For text files, the mode would be ‘at’ For binary files, it would be ‘ab’ To remove duplicate elements from the list we use the set() function. Consider the below example: read-only mode (rb) write-only mode (wb) read–write mode (rwb) Question 74 How will you remove duplicate elements from a list? demo_list=[5,4,4,6,8,12,12,1,5] unique_list = list(set(demo_list)) output:[1,5,6,8,12]
- 64. Curated by Question 75 How will you read a random line in a file? Question 76 How can files be deleted in Python? We can read a random line in a file using the random module. For example: You need to import the OS Module and use os.remove() function for deleting a file in python. consider the code below: import os os.remove("file_name.txt") import random def read_random(fname): lines = open(fname).read().splitlines() return random.choice(lines) print(read_random (‘hello.txt’))
- 65. Curated by Question 77 How can you generate random numbers in Python? Question 78 What is slicing in Python? This is achieved with importing the random module,it is the module that is used to generate random numbers. Syntax: Slicing is a process used to select a range of elements from sequence data type like list, string and tuple. Slicing is beneficial and easy to extract out the elements. It requires a : (colon) which separates the start index and end index of the field. All the data sequence types List or tuple allows us to use slicing to get the needed elements. Although we can get elements by specifying an index, we get only a single element whereas using slicing we can get a group or appropriate range of needed elements. Syntax - List_name[start:stop] import random random.random # returns the floating point random number between the range of [0,1].
- 66. Curated by Question 79 Define Constructor in Python? Constructor is a special type of method with a block of code to initialize the state of instance members of the class. A constructor is called only when the instance of the object is created. It is also used to verify that they are sufficient resources for objects to perform a specific task. There are two types of constructors in Python, and they are: ● Parameterized constructor ● Non-parameterized constructor Courses Offered by Tutort Academy Full Stack Data Science (AI & ML) Data Science & Machine Learning Learn more Learn more
- 67. Curated by Question 80 How can we create a constructor in Python programming? The _init_ method in Python stimulates the constructor of the class. Creating a constructor in Python can be explained clearly in the below example. Output: ID: 1 Name: nirvi ID: 106 Name: Tanvi class Student: def __init__(self,name,id): self.id = id; self.name = name; def display (self): print("ID: %d nName: %s"%(self.id,self.name)) stu1 =Student("nirvi",105) stu2 = Student("tanvi",106) #accessing display() method to print employee 1 information stu1.display(); #accessing display() method to print employee 2 information stu2.display();
- 68. StartYour withus Upskilling www.tutort.net Follow us on Watch us on Youtube Read more on Quora Explore our courses Advance AI and ML Master's Program Data Science and Artificial Intelligence Program