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1. What is Python?

Python is a high-level, interpreted, general-purpose programming language. Being a general-purpose language, it can be used to build almost any type of application with the right tools/libraries. Additionally, python supports objects, modules, threads, exception-handling and automatic memory management which help in modelling real-world problems and building applications to solve these problems.

2. What are the benefits of using Python?

Python is a general-purpose programming language that has simple, easy-to-learn syntax which emphasizes readability and therefore reduces the cost of program maintenance. Moreover, the language is capable of scripting, completely open-source and supports third-party packages encouraging modularity and code-reuse.

Its high-level data structures, combined with dynamic typing and dynamic binding, attract a huge community of developers for Rapid Application Development and deployment.

3. What is a dynamically typed language?

Before we understand what a dynamically typed language, we should learn about what typing is. Typing refers to type-checking in programming languages. In a strongly-typed language, such as Python, "1" + 2 will result in a type error, since these languages don't allow for "type-coercion" (implicit conversion of data types). On the other hand, a weakly-typed language, such as Javascript, will simply output "12" as result.

Type-checking can be done at two stages -

Static - Data Types are checked before execution.

Data Types are checked before execution. Dynamic - Data Types are checked during execution.

4. What is an Interpreted language?

Python being an interpreted language, executes each statement line by line and thus type-checking is done on the fly, during execution. Hence, Python is a Dynamically Typed language.

An Interpreted language executes its statements line by line. Languages such as Python, Javascript, R, PHP and Ruby are prime examples of Interpreted languages. Programs written in an interpreted language runs directly from the source code, with no intermediary compilation step.

5. What is PEP 8 and why is it important?

PEP stands for Python Enhancement Proposal. A PEP is an official design document providing information to the Python Community, or describing a new feature for Python or its processes. PEP 8 is especially important since it documents the style guidelines for Python Code. Apparently contributing in the Python open-source community requires you to follow these style guidelines sincerely and strictly.

6. How is memory managed in Python?

Memory management in Python is handled by the Python Memory Manager. The memory allocated by the manager is in form of a private heap space dedicated for Python. All Python objects are stored in this heap and being private, it is inaccessible to the programmer. Though, python does provide some core API functions to work upon the private heap space.

Additionally, Python has an in-built garbage collection to recycle the unused memory for the private heap space.

7. What are Python namespaces? Why are they used?

A namespace in Python ensures that object names in a program are unique and can be used without any conflict. Python implements these namespaces as dictionaries with 'name as key' mapped to a corresponding 'object as value'. This allows for multiple namespaces to use the same name and map it to a separate object. A few examples of namespaces are as follows:

Local Namespace includes local names inside a function. the namespace is temporarily created for a function call and gets cleared when the function returns.

includes local names inside a function. the namespace is temporarily created for a function call and gets cleared when the function returns. Global Namespace includes names from various imported packages/ modules that is being used in the current project. This namespace is created when the package is imported in the script and lasts until the execution of the script.

includes names from various imported packages/ modules that is being used in the current project. This namespace is created when the package is imported in the script and lasts until the execution of the script. Built-in Namespace includes built-in functions of core Python and built-in names for various types of exceptions.

8. What is Scope in Python?

depends upon the scope of objects they are mapped to. If the scope of an object ends, the lifecycle of that namespace comes to an end. Hence, it isn't possible to access inner namespace objects from an outer namespace.

Every object in Python functions within a scope. A scope is a block of code where an object in Python remains relevant. Namespaces uniquely identify all the objects inside a program. However, these namespaces also have a scope defined for them where you could use their objects without any prefix. A few examples of scope created during code execution in Python are as follows:

A local scope refers to the local objects available in the current function.

refers to the local objects available in the current function. A global scope refers to the objects available throught the code execution since their inception.

refers to the objects available throught the code execution since their inception. A module-level scope refers to the global objects of the current module accessible in the program.

refers to the global objects of the current module accessible in the program. An outermost scope refers to all the built-in names callable in the program. The objects in this scope are searched last to find the name referenced.

9. What is Scope Resolution in Python?

Sometimes objects within the same scope have the same name but function differently. In such cases, scope resolution comes into play in Python automatically. A few examples of such behaviour are:

Python modules namely 'math' and 'cmath' have a lot of functions that are common to both of them - log10() , acos() , exp() etc. To resolve this amiguity, it is necessary to prefix them with their respective module, like math.exp() and cmath.exp() .

, , etc. To resolve this amiguity, it is necessary to prefix them with their respective module, like and . Consider the code below, an object temp has been initialized to 10 globally and then to 20 on function call. However, the function call didn't change the value of the temp globally. Here, we can observe that Python draws a clear line between global and local variables treating both their namespaces as separate identities.

temp = 10 # global-scope variable def func (): temp = 20 # local-scope variable print (temp) print (temp) # output => 10 func () # output => 20 print (temp) # output => 10

This behaviour can be overriden using the global keyword inside the function, as shown in the following example:

temp = 10 # global-scope variable def func (): global temp temp = 20 # local-scope variable print (temp) print (temp) # output => 10 func () # output => 20 print (temp) # output => 20

10. What are decorators in Python?

Decorators in Python are essentially functions that add functionality to an existing function in Python without changing the structure of the function itself. They are represented by the @decorator_name in Python and are called in bottom-up fashion. For example:

# decorator function to convert to lowercase def lowercase_decorator (function): def wrapper (): func = function() string_lowercase = func . lower() return string_lowercase return wrapper # decorator function to split words def splitter_decorator (function): def wrapper (): func = function() string_split = func . split() return string_split return wrapper @splitter_decorator # this is executed next @lowercase_decorator # this is executed first def hello (): return 'Hello World' hello () # output => [ 'hello' , 'world' ]

The beauty of the decorators lies in the fact that besides adding functionality to the output of the method, they can even accept arguments for functions and can further modify those arguments before passing it to the function itself. The inner nested function, i.e. 'wrapper' function, plays a significant role here. It is implemented to enforce encapsulation and thus, keep itself hidden from the global scope.

# decorator function to capitalize names def names_decorator (function): def wrapper (arg1, arg2): arg1 = arg1 . capitalize() arg2 = arg2 . capitalize() string_hello = function(arg1, arg2) return string_hello return wrapper @names_decorator def say_hello (name1, name2): return 'Hello ' + name1 + '! Hello ' + name2 + '!' say_hello ( 'sara' , 'ansh' ) # output => 'Hello Sara! Hello Ansh!'

11. What are lists and tuples? What is the key difference between the two?

Lists and Tuples are both sequence data types that can store a collection of objects in Python. The objects stored in both sequences can have different data types. Lists are represented with square brackets ['sara', 6, 0.19] , while tuples are represented with parantheses ('ansh', 5, 0.97) .

But what is the real difference between the two? The key difference between the two is that while lists are mutable, tuples on the other hand are immutable objects. This means that lists can be modified, appended or sliced on-the-go but tuples remain constant and cannot be modified in any manner. You can run the following example on Python IDLE to confirm the difference:

my_tuple = ( 'sara' , 6 , 5 , 0.97 ) my_list = [ 'sara' , 6 , 5 , 0.97 ] print (my_tuple[ 0 ]) # output => 'sara' print (my_list[ 0 ]) # output => 'sara' my_tuple[ 0 ] = 'ansh' # modifying tuple => throws an error my_list[ 0 ] = 'ansh' # modifying list => list modified print (my_tuple[ 0 ]) # output => 'sara' print (my_list[ 0 ]) # output => 'ansh'

12. What are Dict and List comprehensions?

Python comprehensions, like decorators, are syntactic sugar constructs that help build altered and filtered lists, dictionaries or sets from a given list, dictionary or set. Using comprehensions, saves a lot of time and code that might be considerably more verbose (containing more lines of code). Let's check out some examples, where comprehensions can be truly beneficial:

Performing mathematical operations on the entire list my_list = [ 2 , 3 , 5 , 7 , 11 ] squared_list = [x ** 2 for x in my_list] # list comprehension # output => [4 , 9 , 25 , 49 , 121] squared_dict = {x:x ** 2 for x in my_list} # dict comprehension # output => {11: 121, 2: 4 , 3: 9 , 5: 25 , 7: 49}

Performing conditional filtering operations on the entire list my_list = [ 2 , 3 , 5 , 7 , 11 ] squared_list = [x ** 2 for x in my_list if x % 2 != 0 ] # list comprehension # output => [9 , 25 , 49 , 121] squared_dict = {x:x ** 2 for x in my_list if x % 2 != 0 } # dict comprehension # output => {11: 121, 3: 9 , 5: 25 , 7: 49}

Combining multiple lists into one

Comprehensions allow for multiple iterators and hence, can be used to combine multiple lists into one. a = [ 1 , 2 , 3 ] b = [ 7 , 8 , 9 ] [(x + y) for (x,y) in zip (a,b)] # parallel iterators # output => [8, 10, 12] [(x,y) for x in a for y in b] # nested iterators # output => [(1, 7), (1, 8), (1, 9), (2, 7), (2, 8), (2, 9), (3, 7), (3, 8), (3, 9)]

Comprehensions allow for multiple iterators and hence, can be used to combine multiple lists into one. Flattening a multi-dimensional list

A similar approach of nested iterators (as above) can be applied to flatten a multi-dimensional list or work upon its inner elements. my_list = [[ 10 , 20 , 30 ],[ 40 , 50 , 60 ],[ 70 , 80 , 90 ]] flattened = [x for temp in my_list for x in temp] # output => [10, 20, 30, 40, 50, 60, 70, 80, 90]

Note: List comprehensions have the same effect as the map method in other languages. They follow the mathematical set builder notation rather than map and filter functions in Python.

13. What are the common built-in data types in Python?

There are several built-in data types in Python. Although, Python doesn't require data types to be defined explicitly during variable declarations but type errors are likely to occur if the knowledge of data types and their compatibility with each other are neglected. Python provides type() and isinstance() functions to check the type of these variables. These data types can be grouped into the following catetgories-

None Type

None keyword represents the null values in Python. Boolean equality operation can be performed using these NoneType objects. Class Name Description NoneType Represents the NULL values in Python



keyword represents the null values in Python. Boolean equality operation can be performed using these NoneType objects. Numeric Types

There are three distint numeric types - integers , floating-point numbers , and complex numbers . Additionally, booleans are a sub-type of integers. Class Name Description int Stores integer literals including hex, octal and binary numbers as integers float Stores literals containing decimal values and/or exponent sign as floating-point numbers complex Stores complex number in the form (A + Bj) and has attributes: real and imag bool Stores boolean value (True or False) Note: The standard library also includes fractions to store rational numbers and decimal to store floating-point numbers with user-defined precision.





There are three distint numeric types - , , and . Additionally, are a sub-type of integers. Sequence Types

According to Python Docs, there are three basic Sequence Types - lists , tuples , and range objects . Sequence types have the in and not in operators defined for their traversing their elements. These operators share the same priority as the comparison operations. Class Name Description list Mutable sequence used to store collection of items. tuple Immutable sequence used to store collection of items. range Represents an immutable sequence of numbers generated during execution. str Immutable sequence of Unicode code points to store textual data. Note: The standard library also includes additional types for processing:

1. Binary data such as bytearray bytes memoryview , and

2. Text strings such as str .





According to Python Docs, there are three basic Sequence Types - , , and . Sequence types have the and operators defined for their traversing their elements. These operators share the same priority as the comparison operations. Mapping Types

A mapping object can map hashable values to random objects in Python. Mappings objects are mutable and there is currently only one standard mapping type, the dictionary . Class Name Description dict Stores comma-separated list of key: value pairs



A mapping object can map to random objects in Python. Mappings objects are mutable and there is currently only one standard mapping type, the . Set Types

Currently, Python has two built-in set types - set and frozenset . set type is mutable and supports methods like add() and remove() . frozenset type is immutable and can't be modified after creation. Class Name Description set Mutable unordered collection of distinct hashable objects frozenset Immutable collection of distinct hashable objects Note: set is mutable and thus cannot be used as key for a dictionary. On the other hand, frozenset is immutable and thus, hashable, and can be used as a dictionary key or as an element of another set.





Currently, Python has two built-in set types - and . type is mutable and supports methods like and . type is immutable and can't be modified after creation. Modules

Module is an additional built-in type supported by the Python Interpreter. It supports one special operation, i.e., attribute access : mymod.myobj , where mymod is a module and myobj references a name defined in m's symbol table. The module's symbol table resides in a very special attribute of the module __dict__ , but direct assignment to this module is neither possible nor recommended.





Module is an additional built-in type supported by the Python Interpreter. It supports one special operation, i.e., : , where is a module and references a name defined in m's symbol table. The module's symbol table resides in a very special attribute of the module , but direct assignment to this module is neither possible nor recommended. Callable Types

Callable types are the types to which function call can be applied. They can be user-defined functions, instance methods, generator functions, and some other built-in functions, methods and classes.

Refer the documentation at docs.python.org for a detailed view into the callable types.





14. What is lambda in Python? Why is it used?

Lambda is an anonymous function in Python, that can accept any number of arguments, but can only have a single expression. It is generally used in situations requiring an anonymous function for a short time period. Lambda functions can be used in either of the two ways:

Assigning lambda functions to a variable mul = lambda a, b : a * b print ( mul ( 2 , 5 )) # output => 10

Wrapping lambda functions inside another function def myWrapper (n): return lambda a : a * n mulFive = myWrapper ( 5 ) print ( mulFive ( 2 )) # output => 10

15. What is pass in Python?

The pass keyword represents a null operation in Python. It is generally used for the purpose of filling up empty blocks of code which may execute during runtime but has yet to be written. Without the pass statement in the following code, we may run into some errors during code execution.

def myEmptyFunc (): # do nothing pass myEmptyFunc () # nothing happens ## Without the pass keyword # File "<stdin>", line 3 # IndentationError: expected an indented block

16. How do you copy an object in Python?

In Python, the assignment statement ( = operator) does not copy objects. Instead, it creates a binding between the existing object and the target variable name. To create copies of an object in Python, we need to use the copy module. Moreover, there are two ways of creating copies for the given object using the copy module -

Shallow Copy is a bit-wise copy of an object. The copied object created has an exact copy of the values in the original object. If either of the values are references to other objects, just the reference addresses for the same are copied.

is a bit-wise copy of an object. The copied object created has an exact copy of the values in the original object. If either of the values are references to other objects, just the reference addresses for the same are copied. Deep Copy copies all values recursively from source to target object, i.e. it even duplicates the objects referenced by the source object.

from copy import copy, deepcopy list_1 = [ 1 , 2 , [ 3 , 5 ], 4 ] ## shallow copy list_2 = copy (list_1) list_2[ 3 ] = 7 list_2[ 2 ] . append( 6 ) list_2 # output => [1, 2, [3, 5, 6], 7] list_1 # output => [1, 2, [3, 5, 6], 4] ## deep copy list_3 = deepcopy (list_1) list_3[ 3 ] = 8 list_3[ 2 ] . append( 7 ) list_3 # output => [1, 2, [3, 5, 6, 7], 8] list_1 # output => [1, 2, [3, 5, 6], 4]

17. What is the difference between xrange and range in Python?

xrange() and range() are quite similar in terms of functionality. They both generate a sequence of integers, with the only difference that range() returns a Python list, whereas, xrange() returns an xrange object.

So how does that make a difference? It sure does, because unlike range(), xrange() doesn't generate a static list, it creates the value on the go. This technique is commonly used with an object type generators and has been termed as "yielding".

Yielding is crucial in applications where memory is a constraint. Creating a static list as in range() can lead to a Memory Error in such conditions, while, xrange() can handle it optimally by using just enough memory for the generator (significantly less in comparison).

for i in xrange ( 10 ): # numbers from o to 9 print i # output => 0 1 2 3 4 5 6 7 8 9 for i in xrange ( 1 , 10 ): # numbers from 1 to 9 print i # output => 1 2 3 4 5 6 7 8 9 for i in xrange ( 1 , 10 , 2 ): # skip by two for next print i # output => 1 3 5 7 9

range

xrange

18. What are modules and packages in Python?

Python packages and Python modules are two mechanisms that allow for modular programming in Python. Modularizing ahs several advantages -

Simplicity: Working on a single module helps you focus on a relatively small portion of the problem at hand. This makes development easier and less error-prone.

Working on a single module helps you focus on a relatively small portion of the problem at hand. This makes development easier and less error-prone. Maintainability: Modules are designed to enforce logical boundaries between different problem domains. If they are written in a manner that reduces interdependency, it is less likely that modifications in a module might impact other parts of the program.

Modules are designed to enforce logical boundaries between different problem domains. If they are written in a manner that reduces interdependency, it is less likely that modifications in a module might impact other parts of the program. Reusability: Functions defined in a module can be easily reused by other parts of the application.

Functions defined in a module can be easily reused by other parts of the application. Scoping: Modules typically define a separate namespace, which helps avoid confusion between identifiers from other parts of the program.

Modules, in general, are simply Python files with a .py extension and can have a set of functions, classes or variables defined and implemented. They can be imported and initialized once using the import statement. If partial functionality is needed, import the requisite classes or functions using from foo import bar .

Packages allow for hierarchial structuring of the module namespace using dot notation. As, modules help avoid clashes between global variable names, in a similary manner, packages help avoid clashes between module names.

Creating a package is easy since it makes use of the system's inherent file structure. So just stuff the modules into a folder and there you have it, the folder name as the package name. Importing a module or its contents from this package requires the package name as prefix to the module name joined by a dot.

19. What are global, protected and private attributes in Python?

Global variables are public variables that are defined in the global scope. To use the variable in the global scope inside a function, we use the global keyword.

variables are public variables that are defined in the global scope. To use the variable in the global scope inside a function, we use the keyword. Protected attributes are attributes defined with a underscore prefixed to their identifier eg. _sara . They can still be accessed and modified from outside the class they are defined in but a responsible developer should refrain from doing so.

attributes are attributes defined with a underscore prefixed to their identifier eg. . They can still be accessed and modified from outside the class they are defined in but a responsible developer should refrain from doing so. Private attributes are attributes with double underscore prefixed to their identifier eg. __ansh. They cannot be accessed or modified from the outside directly and will result in an AttributeError if such an attempt is made.

20. What is self in Python?

Self is a keyword in Python used to define an instance or an object of a class. In Python, it is explicity used as the first paramter, unlike in Java where it is optional. It helps in disinguishing between the methods and attributes of a class from its local variables.

21. What is __init__?

__init__ is a contructor method in Python and is automatically called to allocate memory when a new object/instance is created. All classes have a __init__ method associated with them. It helps in distinguishing methods and attributes of a class from local variables.

# class definition class Student : def __init__ ( self , fname, lname, age, section): self . firstname = fname self . lastname = lname self . age = age self . section = section # creating a new object stu1 = Student( "Sara" , "Ansh" , 22 , "A2" )

22. What is break, continue and pass in Python?

Break The break statement terminates the loop immediately

and the control flows to the statement after the body of the loop. Continue The continue statement terminates the current iteration of the statement,

skips the rest of the code in the current iteration and the control flows to the next iteration of the loop. Pass As explained above, pass keyword in Python is generally used to fill-up empty blocks

and is similar to an empty statement represented by a semi-colon in languages such as Java, C++, Javascript etc.

pat = [ 1 , 3 , 2 , 1 , 2 , 3 , 1 , 0 , 1 , 3 ] for p in pat: pass if (p == 0 ): current = p break elif (p % 2 == 0 ): continue print (p) # output => 1 3 1 3 1 print (current) # output => 0

23. What is pickling and unpickling?

Python library offers a feature - serialization out of the box. Serializing a object refers to transforming it into a format that can be stored, so as to be able to deserialize it later on, to obtain the original object. Here, the pickle module comes into play.

Pickling

Pickling is the name of the serialization process in Python. Any object in Python can be serialized into a byte stream and dumped as a file in the memory. The process of pickling is compact but pickle objects can be compressed further. Moreover, pickle keeps track of the objects it has serialized and the serialization is portable across versions.

The function used for the above process is pickle.dump() .

Unpickling

Unpickling is the complete inverse of pickling. It deserializes the byte stream to recreate the objects stored in the file, and loads the object to memory.

The function used for the above process is pickle.load() .

24. What are generators in Python?

Generators are functions that return an iterable collection of items, one at a time, in a set manner. Generators, in general, are used to create iterators with a different approach. They employ the use of yield keyword rather than return to return a generator object.

Let's try and build a generator for fibonacci numbers -

## generate fibonacci numbers upto n def fib (n): p, q = 0 , 1 while (p < n): yield p p, q = q, p + q x = fib ( 10 ) # create generator object ## iterating using __next__(), for Python2, use next() x . __next__() # output => 0 x . __next__() # output => 1 x . __next__() # output => 1 x . __next__() # output => 2 x . __next__() # output => 3 x . __next__() # output => 5 x . __next__() # output => 8 x . __next__() # error ## iterating using loop for i in fib ( 10 ): print (i) # output => 0 1 1 2 3 5 8

25. What is PYTHONPATH in Python?

PYTHONPATH is an environment variable which you can set to add additional directories where Python will look for modules and packages. This is especially useful in maintaining Python libraries that you do not wish to install in the global default location.

26. What is the use of help() and dir() functions?

help() function in Python is used to display the documentation of modules, classes, functions, keywords, etc. If no parameter is passed to the help() function, then an interactive help utility is launched on the console.

dir() function tries to return a valid list of attributes and methods of the object it is called upon. It behaves differently with different objects, as it aims to produce the most relevant data, rather than the complete information.

For Modules/Library objects, it returns a list of all attributes, contained in that module.

For Class Objects, it returns a list of all valid attributes and base attributes.

With no arguments passed, it returns a list of attributes in the current scope.

27. What is the difference between .py and .pyc files?

.py files contain the source code of a program. Whereas, .pyc file contains the bytecode of your program. We get bytecode after compilation of .py file (source code). .pyc files are not created for all the files that you run. It is only created for the files that you import.

Before executing a python program python interpreter checks for the compiled files. If the file is present, the virtual machine executes it. If not found, it checks for .py file. If found, compiles it to .pyc file and then python virtual machine executes it.

Having .pyc file saves you the compilation time.

28. How Python is interpreted?

Python as a language is not interpreted or compiled. Interpreted or compiled is the property of the implementation. Python is a bytecode(set of interpreter readable instructions) interpreted generally.

Source code is a file with .py extension.

Python compiles the source code to a set of instructions for a virtual machine. The Python interpreter is an implementation of that virtual machine. This intermediate format is called “bytecode”.

.py source code is first compiled to give .pyc which is bytecode. This bytecode can be then interpreted by official CPython, or JIT(Just in Time compiler) compiled by PyPy.

29. What are unittests in Python?

unittest is a unit testing framework of Python.

Unit testing means testing different components of software separately. Can you think why unit testing is important? Imagine a scenario, you are building software which uses three components namely A, B, and C. Now, suppose your software breaks at a point time. How will you find which component was responsible for breaking the software? Maybe it was component A that failed, which in turn failed component B, and this actually failed the software. There can be many such combinations.

This is why it is necessary to test each and every component properly so that we know which component might be highly responsible for the failure of the software.

30. What is docstring in Python?

Documentation string or docstring is a multiline string used to document a specific code segment.

The docstring should describe what the function or method does.

31. How are arguments passed by value or by reference in python?

Pass by value: Copy of the actual object is passed. Changing the value of the copy of the object will not change the value of the original object.

Copy of the actual object is passed. Changing the value of the copy of the object will not change the value of the original object. Pass by reference: Reference to the actual object is passed. Changing the value of the new object will change the value of the original object.

In Python, arguments are passed by reference, i.e., reference to the actual object is passed.

def appendNumber (arr): arr . append( 4 ) arr = [ 1 , 2 , 3 ] print (arr) #Output: => [1, 2, 3] appendNumber(arr) print (arr) #Output: => [1, 2, 3, 4]

32. What are iterators in Python?

Iterator is an object.

is an object. It remembers its state i.e., where it is during iteration (see code below to see how)

__iter__() method initializes an iterator.

method initializes an iterator. It has a __next__() method which returns the next item in iteration and points to the next element. Upon reaching the end of iterable object __next__() must return StopIteration exception.

method which returns the next item in iteration and points to the next element. Upon reaching the end of iterable object must return exception. It is also self iterable.

Iterators are objects with which we can iterate over iterable objects like lists, strings, etc.

class ArrayList : def __init__ ( self , number_list): self .numbers = number_list def __iter__ ( self ): self . pos = 0 return self def __next__ ( self ): if ( self . pos < len ( self . numbers)): self . pos += 1 return self . numbers[self . pos - 1 ] else : raise StopIteration array_obj = ArrayList ([ 1 , 2 , 3 ]) it = iter (array_obj) print ( next (it)) #output: 2 print ( next (it)) #output: 3 print ( next (it)) #Throws Exception #Traceback (most recent call last): #... #StopIteration

33. What is slicing in Python?

As the name suggests, ‘slicing’ is taking parts of.

Syntax for slicing is [start : stop : step]

start is the starting index from where to slice a list or tuple

is the starting index from where to slice a list or tuple stop is the ending index or where to sop.

is the ending index or where to sop. step is the number of steps to jump.

is the number of steps to jump. Default value for start is 0, stop is number of items, step is 1.

is 0, is number of items, is 1. Slicing can be done on strings, arrays, lists, and tuples.

numbers = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] print (numbers[ 1 : : 2 ]) #output : [2, 4, 6, 8, 10]

34. Explain how can you make a Python Script executable on Unix?

Script file must begin with #!/usr/bin/env python

35. Explain how to delete a file in Python?

Use command os.remove(file_name)

import os os . remove ( "ChangedFile.csv" ) print ( "File Removed!" )

36. Explain split() and join() functions in Python?

You can use split() function to split a string based on a delimiter to a list of strings.

function to split a string based on a delimiter to a list of strings. You can use join() function to join a list of strings based on a delimiter to give a single string.

string = "This is a string." string_list = string . split ( ' ' ) #delimiter is ‘space’ character or ‘ ‘ print (string_list) #output: ['This', 'is', 'a', 'string.'] print ( ' ' . join (string_list)) #output: This is a string.

37. What is the difference between Python Arrays and lists?

Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.

Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.

import array a = array . array ( 'i' , [ 1 , 2 , 3 ]) for i in a: print (i, end = ' ' ) #OUTPUT: 1 2 3 a = array . array ( 'i' , [ 1 , 2 , 'string' ]) #OUTPUT: TypeError: an integer is required (got type str) a = [ 1 , 2 , 'string' ] for i in a: print (i, end = ' ' ) #OUTPUT: 1 2 string

38. What does *args and **kwargs mean?

*args

*args is a special syntax used in function definition to pass variable-length argument.

“*” means variable length and “args” is the name used by convention. You can use any other.

def multiply (a, b, *argv): mul = a * b for num in argv: mul *= num return mul print ( multiply ( 1 , 2 , 3 , 4 , 5 )) #output: 120

**kwargs

**kwargs is a special syntax used in function definition to pass variable-length keyworded argument.

Here, also, “kwargs” is used just by convention. You can use any other name.

Keyworded argument means a variable which has a name when passed to a function.

It is actually a dictionary of variable name and its value.

def tellArguments (**kwargs): for key, value in kwargs . items (): print (key + ": " + value) tellArguments (arg1 = "argument 1" , arg2 = "argument 2" , arg3 = "argument 3" ) #output: # arg1: argument 1 # arg2: argument 2 # arg3: argument 3

39. What are negative indexes and why are they used?

Negative indexes are the indexes from the end of the list or tuple or string.

Arr[-1] means last element of array Arr[]