Q1. What is the
difference between list and tuples?
LIST vs TUPLES
LIST TUPLES
Lists are mutable i.e they can be edited.
Tuples are immutable (tuples are lists which can’t be edited).
Lists are slower than tuples. Tuples
are faster than list.
Syntax: list_1 = [10, ‘Chelsea’, 20] Syntax: tup_1 = (10, ‘Chelsea’ , 20)
Q2. What are the key
features of Python?
Python is an interpreted language. That means that, unlike
languages like C and its variants, Python does not need to be compiled before
it is run. Other interpreted languages include PHP and Ruby.
Python is dynamically typed, this means that you don’t need
to state the types of variables when you declare them or anything like that.
You can do things like x=111 and then x="I'm a string" without error
Python is well suited to object orientated programming in
that it allows the definition of classes along with composition and
inheritance. Python does not have access specifiers (like C++’s public,
private), the justification for this point is given as “we are all adults here”
In Python, functions are first-class objects. This means
that they can be assigned to variables, returned from other functions and
passed into functions. Classes are also first class objects
Writing Python code is quick but running it is often slower
than compiled languages. Fortunately，Python allows the inclusion of C
based extensions so bottlenecks can be optimized away and often are. The numpy
package is a good example of this, it’s really quite quick because a lot of the
number crunching it does isn’t actually done by Python
Python finds use in many spheres – web applications,
automation, scientific modelling, big data applications and many more. It’s
also often used as “glue” code to get other languages and components to play
nice.
Q3. What is the
difference between deep and shallow copy?
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.
Q4. How is Multithreading
achieved in Python?
Ans:
Python has a multi-threading package but if you want to
multi-thread to speed your code up, then it’s usually not a good idea to use
it.
Python has a construct called the Global Interpreter Lock
(GIL). The GIL makes sure that only one of your ‘threads’ can execute at any
one time. A thread acquires the GIL, does a little work, then passes the GIL
onto the next thread.
This happens very quickly so to the human eye it may seem
like your threads are executing in parallel, but they are really just taking
turns using the same CPU core.
All this GIL passing adds overhead to execution. This means
that if you want to make your code run faster then using the threading package
often isn’t a good idea.
Q5. How can the
ternary operators be used in python?
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.
Q6. How is memory
managed in Python?
Ans:
Memory management in python is managed by Python private
heap space. All Python objects and data structures are located in a private
heap. The programmer does not have access to this private heap. The python
interpreter takes care of this instead.
The allocation of heap space for Python objects is done by
Python’s memory manager. The core API gives access to some tools for the programmer
to code.
Python also has an inbuilt garbage collector, which recycles
all the unused memory and so that it can
be made available to the heap space.
Q7. Explain
Inheritance in Python with an example.
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:
Single Inheritance – where a derived class acquires the
members of a single super class.
Multi-level inheritance – a derived class d1 in inherited
from base class base1, and d2 are inherited from base2.
Hierarchical inheritance – from one base class you can
inherit any number of child classes
Multiple inheritance – a derived class is inherited from
more than one base class.
Q8. Explain what
Flask is and its benefits?
Ans: Flask is a web microframework for Python based on
“Werkzeug, Jinja2 and good intentions” BSD license. Werkzeug and Jinja2 are two
of its 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. The user can modify the session if
only it has the secret key Flask.secret_key.
Q9. What is the usage
of help() and dir() function in Python?
Ans: Help() and dir() both functions are accessible from the
Python interpreter and used for viewing a consolidated dump of built-in
functions.
Help() function: The help() function is used to display the
documentation string and also facilitates you to see the help related to
modules, keywords, attributes, etc.
Dir() function: The dir() function is used to display the
defined symbols.
Q10. Whenever Python
exits, why isn’t all the memory de-allocated?
Ans:
Whenever Python exits, especially those Python modules which
are having circular references to other objects or the objects that are
referenced from the global namespaces are not always de-allocated or freed.
It is impossible to de-allocate those portions of memory
that are reserved by the C library.
On exit, because of having its own efficient clean up
mechanism, Python would try to de-allocate/destroy every other object.
No comments:
Post a Comment