Unit –I Introduction

1. Explain History of Python?

v Python laid its foundation in the late 1980s.

v The implementation of Python was started in December 1989 by Guido Van Rossum at CWI in Netherland.

v In February 1991, Guido Van Rossum published the code (labeled version 0.9.0) to alt.sources.

v In 1994, Python 1.0 was released with new features like lambda, map, filter, and reduce.

v Python 2.0 added new features such as list comprehensions, garbage collection systems.

v On December 3, 2008, Python 3.0 (also called "Py3K") was released. It was designed to rectify the fundamental flaw of the language.

v ABC programming language is said to be the predecessor of Python language, which was capable of Exception Handling and interfacing with the Amoeba Operating System.

v The following programming languages influence Python:

o ABC language.

o Modula-3

Why the Name Python?

There is a fact behind choosing the name Python

. Guido van Rossum was reading the script of a popular BBC comedy series "Monty Python's Flying Circus". It was late on-air 1970s.

Van Rossum wanted to select a name which unique, sort, and little-bit mysterious. So he decided to select naming Python after the "Monty Python's Flying Circus" for their newly created programming language.

Python is also versatile and widely used in every technical field, such as Machine Learning

v Artificial Intelligence

v Web Development, Mobile Application

v Desktop Application, Scientific Calculation, etc.

v Latest Version of Python is Python 3.8 in October 14, 2019.

2. What is the Difference Between Python and C Language?

Python vs C Language
Python is a multi-paradigm. It mainly supports Object-oriented programming, Procedural programming, Functional programming.	C is a Structured programming language.
Language Type
Python is an interpreter based language. The interpreter reads the code line by line.	C is a compiled language. The complete source code is converted into machine language.
Memory Management
Python use automatic garbage collector for memory management.	In C, Programmer has to do memory management on his own.
Applications
Python is a General-Purpose programming language.	C is mainly used for hardware related applications.
Speed
Python is slow.	C is fast.
Variable Declaration
In Python, no need to declare variable type.	In C, it is compulsory to declare variable type.
Complexity
Python programs are easier to learn, write and read.	C program syntax is harder than Python.
Testing and Debugging
Testing and debugging is easier in Python.	Testing and debugging is harder in C.

1. Differences Between Java vs Python

A. programming language is a language that describes a set of rules that can be used to give various types of output. Python, Java, C, C++, Fortran, Perl all are high-level languages. Java is high level, an object-oriented programming language developed by Sun Microsystem in 1995. Java runs on many platforms like Windows, Mac OS, Linux. Java is object-oriented, platform independent, simple, secure, Robust, interpreted, multithread, high performance and dynamic. Python is an object-oriented, high-level and all in one programming language. It was developed by Guido Van Rossum during 1985- 1990. Python is highly readable and easily understandable. python uses simple English keywords very often whereas other languages use punctuation. Python is easy to learn, easy to read, interactive mode, portable, extendable, databases, scalable.

Below are the lists of points, describe the comparisons Between Java vs Python:

Basis for Comparison

Java

Python

Code

Longer lines of code as compared to Python

public class EduCba

{

public static void main (String [] args)

{

System.out.println(“Hello EduCBA”);

}

print (“Hello EduCBA”)

Syntax

At the end of the statement if you miss semicolon it throws an error.

In Java you must define particular block using curly braces without it code won’t work.

In python, statement do not need a semicolon to end.

In python, you have never seen a sight of curly braces but indentation is mandatory in python. Indentation also improves readability of code.

Dynamic

In java you must declare type of the data.

class Example

{

public static void main (String [] args)

{

int x=10;

System.out.println(x);

}

Python codes are dynamic typed. This means that you don’t need to declare a type of the variable this is known as duck typing.

X = 45

site = “educba.com”

Speed

In terms of speed, Java is faster. Whenever in projects speed matters the java is best.

It is slower because python is an interpreter and also it determines the type of data at runtime.

portability

Due to the high popularity of Java, JVM (Java Virtual Machine) is available almost everywhere.

Python is also portable but in front of java, python is not popular.

Databases

(JDBC)Java Database Connectivity is most popular and widely used to connect with database.

Python’s database access layers are weaker than Java’s JDBC. This is why it rarely used in enterprises.

Easy to use

Java is not easy to use as compared to python because there is no dynamic programming concept and codes are longer than python.

Python codes are shorter than java. python follows dynamic programming python codes not only easy to use but also easy to understand because of indentation.

Practical Dexterity

Java enjoys more undeviating refactoring support than python thanks to its static type system and universality of IDE’s in development.

Python has always had an existence in the talent space and has the popularity for many reasons including Data Science and DevOps movement.

Legacy

Java’s history in the enterprise and its slightly more verbose coding style mean that Java legacy systems are typically larger and more numerous than python’s.

Python has less legacy problem so organization finds difficulty for the script to copy and paste codes.

2. Explain Python? What are the features of Python?

A. Python is an interpreted, object-oriented programming language similar to PERL, that has gained popularity because of its clear syntax and readability. Python is said to be relatively easy to learn and portable, meaning is statements can be interpreted in a number of operating systems, including UNIX-based systems, Mac OS, MS-DOS, OS/2, and various versions of Microsoft Windows 98. Python was created by Guido van Rossum, a former resident of the Netherlands, whose favorite comedy group at the time was Monty Python's Flying Circus. The source code is freely available and open for modification and reuse. Python has a significant number of users.

A notable feature of Python is its indenting of source statements to make the code easier toread. Pythonoffers dynamic data type, ready-made class, and interfaces to many system calls and libraries. It can be extended, using the C or C++ language.Python can be used as the script in Microsoft's Active Server Page (ASP) technology. The scoreboard system for the Melbourne (Australia) Cricket Ground is written in Python. Z Object Publishing Environment, a popular Web application server, is also written in the Python language.Python is a multiparadigm, general-purpose, interpreted, high-level programming language.Python allows programmers to use different programming styles to create simple or complex programs, get quicker results and write code almost as if speaking in a human language. Some of the popular systems and applications that have employed Python during development include Google Search, YouTube, BitTorrent, Google App Engine, Eve Online, Maya and iRobot machines.

Python Features: Python provides lots of features that are listed below.

1) Easy to Learn and Use: Python is easy to learn and use. It is developer-friendly and high level programming language.

2) Expressive Language: Python language is more expressive means that it is more understandable and readable.

3) Interpreted Language: Python is an interpreted language i.e. interpreter executes the code line by line at a time. This makes debugging easy and thus suitable for beginners.

4) Cross-platform Language: Python can run equally on different platforms such as Windows, Linux, Unix and Macintosh etc. So, we can say that Python is a portable language.

5) Free and Open Source: Python language is freely available at offical web address.The source-code is also available. Therefore it is open source.

6) Object-Oriented Language: Python supports object oriented language and concepts of classes and objects come into existence.

7) Extensible: It implies that other languages such as C/C++ can be used to compile the code and thus it can be used further in our python code.

8) Large Standard Library: Python has a large and broad library and provides rich set of module and functions for rapid application development.

9) GUI Programming Support: Graphical user interfaces can be developed using Python.

10) Integrated: It can be easily integrated with languages like C, C++, JAVA etc.

3. How to Working with Python?

A. Python is a cross-platform programming language, which means that it can run on multiple platforms like Windows, macOS, Linux, and has even been ported to the Java and .NET virtual machines. It is free and open-source.

Even though most of today's Linux and Mac have Python pre-installed in it, the version might be out-of-date. So, it is always a good idea to install the most current version.

The Easiest Way to Run Python:

The easiest way to run Python is by using Thonny IDE.

The Thonny IDE comes with the latest version of Python bundled in it. So you don't have to install Python separately.

Follow the following steps to run Python on your computer.

v Download Thonny IDE.

v Run the installer to install Thonny on your computer.

v Go to: File > New. Then save the file with .py extension.

v For example, hello.py, example.py, etc.
You can give any name to the file. However, the file name should end with .py

Write Python code in the file and save it.

4. Explain Basic Fundamentals in Python?

A. This section contains the basic fundamentals of Python like :

i)Tokens and their types.

ii) Comments

a)Tokens:

v Tokens can be defined as a punctuator mark, reserved words and each individual word in a statement.

v Token is the smallest unit inside the given program.

There are following tokens in Python:

Keywords.
Identifiers.
Literals.
Operators.

1. Keywords:

v Keywords are the reserved words in Python.

v We cannot use a keyword as a variable name, function name or any other identifier. They are used to define the syntax and structure of the Python language.

v In Python, keywords are case sensitive.

v There are 33 keywords in Python 3.7. This number can vary slightly in the course of time.

v All the keywords except True, False and None are in lowercase and they must be written as it is. The list of all the keywords is given below:

false, class, finally, is, return, none, continue, for, lambda, try, true, while etc.

2. Identifiers:

An identifier is a name given to entities like class, functions, variables, etc. It helps to differentiate one entity from another.

Rules for writing identifiers:

1. Identifiers can be a combination of letters in lowercase (a to z) or uppercase (A to Z) or digits (0 to 9) or an underscore _. Names like myClass, var_1 and print_this_to_screen, all are valid example.

2. An identifier cannot start with a digit. 1variable is invalid, but variable1 is perfectly fine.

3. Keywords cannot be used as identifiers.

4. We cannot use special symbols like !, @, #, $, % etc. in our identifier.

5. Identifier can be of any length.

3. Literals:

Literal is a raw data given in a variable or constant. In Python, there are various types of literals they are as follows:

v Numeric Literals:

Numeric Literals are immutable (unchangeable). Numeric literals can belong to 3 different numerical types Integer, Float and Complex.

Example :

a = 0b1010 #Binary Literals

b = 100 #Decimal Literal

c = 0o310 #Octal Literal

d = 0x12c #Hexadecimal Literal

#Float Literal

float_1 = 10.5

float_2=13.5

#Complex Literal

x = 3.14j

print(a, b, c, d)

print(float_1, float_2)

print(x, x.imag, x.real)

v String literals:

A string literal is a sequence of characters surrounded by quotes. We can use both single, double or triple quotes for a string. And, a character literal is a single character surrounded by single or double quotes.

Example:

strings = "This is Python"

char = "C"

multiline_str = """This is a multiline string with more than one line code."""

unicode = u"\u00dcnic\u00f6de"

raw_str = r"raw \n string"

print(strings)

print(char)

print(multiline_str)

print(unicode)

print(raw_str)

v Boolean literals:

A Boolean literal can have any of the two values: True or False.

Example:

x = (1 == True)

y = (1 == False)

a = True + 4

b = False + 10

print("x is", x)

print("y is", y)

print("a:", a)

print("b:", b)

v Literal Collections:

There are four different literal collections List literals, Tuple literals, Dict literals, and Set literals.

Example :

fruits = ["apple", "mango", "orange"] #list

numbers = (1, 2, 3) #tuple

alphabets = {'a':'apple', 'b':'ball', 'c':'cat'} #dictionary

vowels = {'a', 'e', 'i' , 'o', 'u'} #set

print(fruits)

print(numbers)

print(alphabets)

print(vowels)

4. Operators:

The operator can be defined as a symbol which is responsible for a particular operation between two operands. Operators are the pillars of a program on which the logic is built in a particular programming language. Python provides a variety of operators described as follows.

v Arithmetic operators

v Comparison operators

v Assignment Operators

v Logical Operators

v Bitwise Operators

v Membership Operators

v Identity Operators

ii) Comments: Comments in Python can be used to explain any program code. It can also be used to hide the code as well.

Comments are the most helpful stuff of any program. It enables us to understand the way, a program works. In python, any statement written along with # symbol is known as a comment. The interpreter does not interpret the comment.

Comment is not a part of the program, but it enhances the interactivity of the program and makes the program readable.

Python supports two types of comments:

1) Single Line Comment:

In case user wants to specify a single line comment, then comment must start with ?#?

Eg:

1. # This is single line comment.

2. print ("Hello Python")

Output:

Hello Python

2) Multi Line Comment:

Multi lined comment can be given inside triple quotes.

Example: ''''' This

Multipline comment'''

Example:#single line comment

print "Hello Python" #Hello Python

'''''This is

multiline comment'''

5. Explain Python Variables?

A. Variable is a name which is used to refer memory location. Variable also known as identifier and used to hold value.In Python, we don't need to specify the type of variable because Python is a type infer language and smart enough to get variable type.

Variable names can be a group of both letters and digits, but they have to begin with a letter or an underscore.It is recomended to use lowercase letters for variable name. Rahul and rahul both are two different variables.

Identifier Naming:Variables are the example of identifiers. An Identifier is used to identify the literals used in the program. The rules to name an identifier are given below.

v The first character of the variable must be an alphabet or underscore ( _ ).

v All the characters except the first character may be an alphabet of lower-case(a-z), upper-case (A-Z), underscore or digit (0-9).

v Identifier name must not contain any white-space, or special character (!, @, #, %, ^, &, *).

v Identifier name must not be similar to any keyword defined in the language.

v Identifier names are case sensitive for example my name, and MyName is not the same.

v Examples of valid identifiers : a123, _n, n_9, etc.

v Examples of invalid identifiers: 1a, n%4, n 9, etc.

Declaring Variable and Assigning Values:

Python does not bound us to declare variable before using in the application. It allows us to create variable at required time.

We don't need to declare explicitly variable in Python. When we assign any value to the variable that variable is declared automatically.

The equal (=) operator is used to assign value to a variable.

Eg: a=10

print(a) #10

a=”ravi”

print(a) #ravi

a=20000.67

print(a) #20000.67

Multiple Assignment

Python allows us to assign a value to multiple variables in a single statement which is also known as multiple assignment.

We can apply multiple assignments in two ways either by assigning a single value to multiple variables or assigning multiple values to multiple variables. Lets see given examples.

1. Assigning single value to multiple variables

Eg: x=y=z=50

print x #50

print y #50

print z #50

2.Assigning multiple values to multiple variables:

Eg: a,b,c=5,10,15

print a #5

print b #10

print c #15

The values will be assigned in the order in which variables appears.

6. What are the datatypes in Python?

A. Python Data Types: Variables can hold values of different data types. Python is a dynamically typed language hence we need not define the type of the variable while declaring it. The interpreter implicitly binds the value with its type.

Python enables us to check the type of the variable used in the program. Python provides us

the type() function which returns the type of the variable passed.Consider the following example to define the values of different data types and checking its type.

A=10

b="Hi Python"

c = 10.5

print(type(a));                      #<type 'int'>

print(type(b));                      #<type 'str'>

print(type(c));                       #<type 'float'>

Standard data types:A variable can hold different types of values. For example, a person?s name must be stored as a string whereas its id must be stored as an integer.

Python provides various standard data types that define the storage method on each of them. The data types defined in Python are given below.

1. Numbers:Number stores numeric values. Python creates Number objects when a number is assigned to a variable. For example;

a = 3 , b = 5 #a and b are number objects

Python supports 4 types of numeric data.

1. int(signed integers like 10, 2, 29, etc.)

2. long(long integers used for a higher range of values like 908090800L, -0x1929292L, etc.)

3. float(float is used to store floating point numbers like 1.9, 9.902, 15.2, etc.)

4. complex (complex numbers like 2.14j, 2.0 + 2.3j, etc.)

Python allows us to use a lower-case L to be used with long integers. However, we must always use an upper-case L to avoid confusion.

A complex number contains an ordered pair, i.e., x + iy where x and y denote the real and imaginary parts respectively).

2. String:

The string can be defined as the sequence of characters represented in the quotation marks. In python, we can use single, double, or triple quotes to define a string.

String handling in python is a straightforward task since there are various inbuilt functions and operators provided.

In the case of string handling, the operator + is used to concatenate two strings as the operation "hello"+" python" returns "hello python".

The operator * is known as repetition operator as the operation "Python " *2 returns "Python Python ".The following example illustrates the string handling in python.

str1 = 'hello javatpoint' #string str1

str2 = ' how are you' #string str2

print (str1[0:2]) #printing first two character using slice operator

print (str1[4]) #printing 4th character of the string

print (str1*2) #printing the string twice

print (str1 + str2) #printing the concatenation of str1 and str2

Output:

he

hellojavatpointhellojavatpoint

hellojavatpoint how are you

3.List:

Lists are similar to arrays in C. However; the list can contain data of different types. The items stored in the list are separated with a comma (,) and enclosed within square brackets [].

We can use slice [:] operators to access the data of the list. The concatenation operator (+) and repetition operator (*) works with the list in the same way as they were working with the strings.

Consider the following example.

l = [1, "hi", "python", 2]

print (l[3:]);

print (l[0:2]);

print (l);

print (l + l);

print (l * 3);

Output:

[2]

[1, 'hi']

[1, 'hi', 'python', 2]

[1, 'hi', 'python', 2, 1, 'hi', 'python', 2]

[1, 'hi', 'python', 2, 1, 'hi', 'python', 2, 1, 'hi', 'python', 2]

4. Tuple:

Ø Tuple is another form of collection where different type of data can be stored.

Ø It is similar to list where data is separated by commas. Only the difference is that list uses square bracket and tuple uses parenthesis.

Ø Tuples are enclosed in parenthesis and cannot be changed.

Ø A tuple is a read-only data structure as we can't modify the size and value of the items of a tuple.

Let's see a simple example of the tuple.

t = ("hi", "python", 2)

print (t[1:]);

print (t[0:1]);

print (t);

print (t + t);

print (t * 3);

print (type(t))

t[2] = "hi";

Output:

('python', 2)

('hi',)

('hi', 'python', 2)

('hi', 'python', 2, 'hi', 'python', 2)

('hi', 'python', 2, 'hi', 'python', 2, 'hi', 'python', 2)

<type 'tuple'>

TypeError: 'tuple' object does not support item assignment

>>> tuple=('rahul',100,60.4,'deepak')

>>> tuple1=('sanjay',10)

>>> tuple

('rahul', 100, 60.4, 'deepak')

>>> tuple[2:]

(60.4, 'deepak')

>>> tuple1[0]

'sanjay'

>>> tuple+tuple1

('rahul', 100, 60.4, 'deepak', 'sanjay', 10)

5. Dictionary:

Ø Dictionary is a collection which works on a key-value pair.

Ø It works like an associated array where no two keys can be same.

Ø Dictionaries are enclosed by curly braces ({}) and values can be retrieved by square bracket([]).

Ø It is like an associative array or a hash table where each key stores a specific value. Key can hold any primitive data type whereas value is an arbitrary Python object.

Consider the following example.

d = {1:'Jimmy', 2:'Alex', 3:'john', 4:'mike'};

print("1st name is "+d[1]);

print("2nd name is "+ d[4]);

print (d);

print (d.keys());

print (d.values());

Output:

1st name is Jimmy

2nd name is mike

{1: 'Jimmy', 2: 'Alex', 3: 'john', 4: 'mike'}

[1, 2, 3, 4]

['Jimmy', 'Alex', 'john', 'mike']

6. Set:

It is an unordered collection of unique items. Set is defined by values separated by comma inside braces { }. Items in a set are not ordered.

a = {5,2,3,1,4}

# printing set variable

print("a = ", a)

# data type of variable a

print(type(a))

7. What is an Operator? Explain different types of Operators?

A. Python Operators: The operator can be defined as a symbol which is responsible for a particular operation between two operands. Operators are the pillars of a program on which the logic is built in a particular programming language. Python provides a variety of operators described as follows.

v Arithmetic operators

v Comparison operators

v Assignment Operators

v Logical Operators

v Bitwise Operators

v Membership Operators

v Identity Operators

1. Arithmetic operators: Arithmetic operators are used to perform arithmetic operations between two operands. It includes +(addition), - (subtraction), *(multiplication), /(divide), %(reminder), //(floor division), and exponent (**).

Operator	Meaning	Example
+	Add two operands or unary plus	x + y
-	Subtract right operand from the left or unary minus	x - y
*	Multiply two operands	x * y
/	Divide left operand by the right one (always results into float)	x / y
%	Modulus - remainder of the division of left operand by the right	x % y
//	Floor division - division that results into whole number adjusted to the left in the number line	x // y
**	Exponent - left operand raised to the power of right	x**y (x to the power y)

Example #1: Arithmetic operators in Python

x = 15

y = 4

# Output: x + y = 19

print('x + y =',x+y)

# Output: x - y = 11

print('x - y =',x-y)

# Output: x * y = 60

print('x * y =',x*y)

# Output: x / y = 3.75

print('x / y =',x/y)

# Output: x // y = 3

print('x // y =',x//y)

# Output: x ** y = 50625

print('x ** y =',x**y)

2. Comparison operator: (Relational Operator):

Comparison operators are used to comparing the value of the two operands and returns boolean true or false accordingly. The comparison operators are described in the following table.

Operator	Meaning	Example
>	Greater that - True if left operand is greater than the right	x > y
<	Less that - True if left operand is less than the right	x < y
==	Equal to - True if both operands are equal	x == y
!= or <>	Not equal to - True if operands are not equal	x != y x<>y
>=	Greater than or equal to - True if left operand is greater than or equal to the right	x >= y
<=	Less than or equal to - True if left operand is less than or equal to the right	x <= y

Example #2: Comparison operators in Python

x = 10

y = 12

# Output: x > y is False

print('x > y is',x>y)

# Output: x < y is True

print('x < y is',x<y)

# Output: x == y is False

print('x == y is',x==y)

# Output: x != y is True

print('x != y is',x!=y)

# Output: x >= y is False

print('x >= y is',x>=y)

# Output: x <= y is true

print('x <= y is',x<=y)

3. Assignment operators:

The assignment operators are used to assign the value of the right expression to the left operand. The assignment operators are described in the following table.

Operator	Example	Equivatent to
=	x = 5	x = 5
+=	x += 5	x = x + 5
-=	x -= 5	x = x – 5
*=	x *= 5	x = x * 5
/=	x /= 5	x = x / 5
%=	x %= 5	x = x % 5
//=	x //= 5	x = x // 5
**=	x **= 5	x = x ** 5
&=	x &= 5	x = x & 5
\|=	x \|= 5	x = x \| 5
^=	x ^= 5	x = x ^ 5
>>=	x >>= 5	x = x >> 5
<<=	x <<= 5	x = x << 5

4. Bitwise operator:

The bitwise operators perform bit by bit operation on the values of the two operands.

For example, 2 is 10 in binary and 7 is 111.

In the table below: Let x = 10 (0000 1010 in binary) and y = 4 (0000 0100 in binary)

Bitwise operators in Python
Operator	Meaning	Example
&	Bitwise AND	x& y = 0 (0000 0000)
\|	Bitwise OR	x \| y = 14 (0000 1110)
~	Bitwise NOT	~x = -11 (1111 0101)
^	Bitwise XOR	x ^ y = 14 (0000 1110)
>>	Bitwise right shift	x>> 2 = 2 (0000 0010)
<<	Bitwise left shift	x<< 2 = 40 (0010 1000)

5. Logical Operators:

The logical operators are used primarily in the expression evaluation to make a decision. Python supports the following logical operators.

Operator	Meaning	Example
and	True if both the operands are true	x and y
or	True if either of the operands is true	x or y
not	True if operand is false (complements the operand)	not x

Example #3: Logical Operators in Python

x = True

y = False

# Output: x and y is False

print('x and y is',xand y)

# Output: x or y is True

print('x or y is',xor y)

# Output: not x is False

print('not x is',not x)

6. Membership Operators:

Python membership operators are used to check the membership of value inside a data structure. If the value is present in the data structure, then the resulting value is true otherwise it returns false.

Operator	Meaning	Example
in	True if value/variable is found in the sequence	5 in x
not in	True if value/variable is not found in the sequence	5 not in x

Example : Membership operators in Python

x = 'Hello world'

y = {1:'a',2:'b'}

# Output: True

print('H' in x)

# Output: True

print('hello' notin x)

# Output: True

print(1 in y)

# Output: False

print('a' in y)

Here, 'H' is in x but 'hello' is not present in x (remember, Python is case sensitive). Similary, 1 is key and 'a' is the value in dictionary y. Hence, 'a' in y returns False.

7. Identity Operators:

Operator	Description
is	It is evaluated to be true if the reference present at both sides point to the same object.
is not	It is evaluated to be true if the reference present at both side do not point to the same object.

Example: Identity operators in Python

x1 = 5

y1 = 5

x2 = 'Hello'

y2=’Hello’

x3 = [1,2,3]

y3 = [1,2,3]

# Output: False

print(x1 isnot y1)

# Output: True

print(x2 is y2)

# Output: True

print(x3 is y3)

8. Explain Decision Control Statements? (Or) Explain Branching Statements.

A. Decision making is required when we want to execute a code only if a certain condition is

satisfied.

a. Simple if Statement

if test expression:

statement(s)

Here, the program evaluates the test expression and will execute statement(s) only if the text expression is True.If the text expression is False, the statement(s) is not executed.

In Python, the body of the if statement is indicated by the indentation. Body starts with an indentation and the first unindented line marks the end.

Python interprets non-zero values as True. None and 0 are interpreted as False.

Example:If the number is positive, we print an appropriate message

num = 3

ifnum> 0:

print(num, "is a positive number.")

print("This is always printed.")

num = -1

ifnum< 0:

print(num, "is a negative number.")

print("This is also always printed.")

When you run the program, the output will be:

3 is a positive number

This is always printed

This is also always printed.

b. if...else Statement

if test expression:

Body of if

else:

Body of else

The if..else statement evaluates test expression and will execute body of ifonly when test condition is True.

If the condition is False, body of else is executed. Indentation is used to separate the blocks.

Example: Program checks if the number is positive or negative and displays an appropriate message

num = 3

# num = -5

# num = 0

ifnum>= 0:

print("Positive or Zero")

else:

print("Negative number")

c. Python if...elif...else Statement

if test expression:

Body of if

elif test expression:

Body of elif

else:

Body of else

The elif is short for else if. It allows us to check for multiple expressions.

If the condition for if is False, it checks the condition of the next elif block and so on.

Example:To check number is positive or negative or zero and display an appropriate message

num = 3.4

# Try these two variations as well:

# num = 0

# num = -4.5

ifnum> 0:

print("Positive number")

elifnum == 0:

print("Zero")

else:

print("Negative number")

Output:

When variable num is positive, Positive number is printed.

If num is equal to 0, Zero is printed.

If num is negative, Negative number is printed

d.Nestedif statements: We can have a if...elif...else statementinsideanother if...elif...elsestatement. This is called nesting in computer programming.

Any number of these statements can be nested inside one another. Indentation is the only way to figure out the level of nesting. This can get confusing, so must be avoided if we can.

Example:input a number check if the number is positive or negative or zero and display an appropriate message

num = float(input("Enter a number: "))

ifnum>= 0:

ifnum == 0:

print("Zero")

else:

print("Positive number")

else:

print("Negative number")

Output 1

Enter a number: 5

Positive number

Output 2

Enter a number: -1

Negative number

Output 3

Enter a number: 0

Zero

9.Explain Looping Statements?

A. Looping Statements is also called as Iterative Control Statements or Repetitive Control statements in Python it can be divided into six types.

1. for loop in Python: The for loop in Python is used to iterate over a sequence (list, tuple, string) or other iterable objects. Iterating over a sequence is called traversal.

Syntax of for Loop

forval in sequence:

Body of for

Here, val is the variable that takes the value of the item inside the sequence on each iteration.

Loop continues until we reach the last item in the sequence. The body of for loop is separated from the rest of the code using indentation.

Example:

# Program to find the sum of all numbers stored in a list

# List of numbers

numbers = [6, 5, 3, 8, 4, 2, 5, 4, 11]

sum = 0

forval in numbers:

sum = sum+val

print("The sum is", sum)

when you run the program, the output will be:

The sum is 48

2. while loop in Python: The while loop in Python is used to iterate over a block of code as long as the test expression (condition) is true.

We generally use this loop when we don't know beforehand, the number of times to iterate.

Syntax of while Loop in Python

whiletest_expression:

Body of while

In while loop, test expression is checked first. The body of the loop is entered only if the test_expression evaluates to True. After one iteration, the test expression is checked again. This process continues until the test_expressionevaluates to False.

Flowchart of while Loop

Example: Python while Loop

# Program to add natural numbersupto

# sum = 1+2+3+...+n To take input from the user,

# n = int(input("Enter n: "))

n = 10

# initialize sum and counter

sum = 0

i = 1

while i <= n:

sum = sum + i

i = i+1 # update counter

# print the sum

print("The sum is", sum)

When you run the program, the output will be:

Enter n: 10

The sum is 55

3. The infinite loop: We can create an infinite loop using while statement. If the condition of while loop is always True, we get an infinite loop.

Example: # Infinite loop using while

# An example of infinite loop press Ctrl + c to exit from the loop

while True:

num = int(input("Enter an integer: "))

print("The double of",num,"is",2 * num)

Output

Enter an integer: 3

The double of 3 is 6

Enter an integer: 5

The double of 5 is 10

Enter an integer: 6

The double of 6 is 12

Enter an integer:

Traceback (most recent call last):

4. Loop with condition at the top:This is a normal while loop without break statements. The condition of the while loop is at the top and the loop terminates when this condition is False.

Example: # Loop with condition at the top

# Program to illustrate a loop with condition at the top

# Try different numbers

n = 10

# Uncomment to get user input

#n = int(input("Enter n: "))

# initialize sum and counter

sum = 0

i = 1

while i <= n:

sum = sum + i

i = i+1 # update counter

# print the sum

print("The sum is",sum)

When you run the program, the output will be:

The sum is 55

5. Loop with condition in the middle: This kind of loop can be implemented using an infinite loop along with a conditional break in between the body of the loop.

Flowchart of Loop with Condition in Middle

Example #3: Loop with condition in the middle

# Program to illustrate a loop with condition in the middle.

# Take input from the user untill a vowel is entered

vowels = "aeiouAEIOU"

# infinite loop

vowels = "aeiouAEIOU"

while True:

v = input("Enter a vowel: ")

# condition in the middle

if v in vowels:

break

print("That is not a vowel. Try again!")

print("Thank you!")

Output

Enter a vowel: r

That is not a vowel. Try again!

Enter a vowel: 6

That is not a vowel. Try again!

Enter a vowel: ,

That is not a vowel. Try again!

Enter a vowel: u

Thank you!

6. Loop with condition at the bottom: This kind of loop ensures that the body of the loop is executed at least once. It can be implemented using an infinite loop along with a conditional break at the end. This is similar to the do...while loop in C.

Example : #Loop with condition at the bottom

# Python program to illustrate a loop with condition at the bottom Roll a dice untill user chooses to exit

import random

while True:

input("Press enter to roll the dice")

num = random.randint(1,6)

print("You got",num)

option = input("Roll again?(y/n) ")

if option == 'n':

break

Output

Press enter to roll the dice

You got 1

Roll again?(y/n) y

Press enter to roll the dice

You got 5

Roll again?(y/n) n

10. What is the use of break and continue in Python?

A. In Python, break and continue statements can alter the flow of a normal loop.Loops iterate over a block of code until test expression is false, but sometimes we wish to terminate the current iteration or even the whole loop without checking test expression.

1. Python break statement: The break statement terminates the loop containing it. Control of the program flows to the statement immediately after the body of the loop.

If break statement is inside a nested loop (loop inside another loop), break will terminate the innermost loop. Flowchart of break

Syntax of break

break

Example: Python break

# Use of break statement inside loop

forval in "string":

ifval == "i":

break

print(val)

print("The end")

Output

The end

In this program, we iterate through the "string" sequence. We check if the letter is "i", upon which we break from the loop. Hence, we see in our output that all the letters up till "i" gets printed. After that, the loop terminates.

2. Python continue statement: The continue statement is used to skip the rest of the code inside a loop for the current iteration only. Loop does not terminate but continues on with the next iteration. Flowchart of continue

Syntax of Continue

continue

The working of continue statement in for and while loop is shown below.

Example: Python continue

# Program to show the use of continue statement inside loops

forval in "string":

ifval == "i":

continue

print(val)

print("The end")

Output

The end

11. What is pass statement in Python?

In Python programming, pass is a null statement. The difference between a comment and

pass statement in Python is that, while the interpreter ignores a comment entirely, pass is not ignored.However, nothing happens when pass is executed. It results into no operation (NOP).

Syntax of pass:

pass

We generally use it as a placeholder.

Suppose we have a loop or a function that is not implemented yet, but we want to implement it in the future. They cannot have an empty body. The interpreter would complain. So, we use the pass statement to construct a body that does nothing.

Example: pass Statement

# pass is just a placeholder for

# functionality to be added later.

sequence = {'p', 'a', 's', 's'}

forval in sequence:

pass

We can do the same thing in an empty function or class as well.

def function(args):

pass

class example:

pass

12. Explain Input & Output Statements in Python.

A.Input Statement:Up till now, our programs were static. The value of variables were defined or hard coded into the source code.

To allow flexibility we might want to take the input from the user. In Python, we have the input() function to allow this. The syntax for input() is

input([prompt])

where prompt is the string we wish to display on the screen. It is optional.

>>>num = input('Enter a number: ')

Enter a number: 10

>>>num

'10'

Output Statement:We use the print() function to output data to the standard output device (screen).We can also output data to a file, but this will be discussed later. An example use is given below.

print('This sentence is output to the screen')

# Output: This sentence is output to the screen

a = 5

print('The value of a is', a)

# Output: The value of a is 5

print(1,2,3,4)

# Output: 1 2 3 4

print (1,2,3,4,sep='*')

# Output: 1*2*3*4

print(1,2,3,4,sep='#',end='&')

# Output: 1#2#3#4&

We can even use keyword arguments to format the string.

>>>print('Hello {name}, {greeting}'.format(greeting ='Goodmorning', name ='John'))

HelloJohn,Goodmorning

We can even format strings like the old sprintf() style used in C programming language. We use the % operator to accomplish this.

>>> x =12.3456789

>>>print('The value of x is %3.2f'%x)

The value of x is12.35

>>>print('The value of x is %3.4f'%x)

The value of x is12.3457

13. What is a String? How to Access and its operqations by using Strings?

A. Strings: Strings in python are surrounded by either single quotation marks, or double quotation marks. 'hello' is the same as "hello". You can display a string literal with the print() function:

Example:

print("Hello")
print('Hello')

v Assign String to a Variable: Assigning a string to a variable is done with the variable name followed by an equal sign and the string:

Example:

a = "Hello"
print(a)

v Multiline Strings: You can assign a multiline string to a variable by using three quotes:

Example:

a = """Loremipsumdolor sit amet, consecteturadipiscingelit, sed do eiusmodtemporincididuntutlabore et dolore magna aliqua."""
print(a)

v Strings are Arrays: Like many other popular programming languages, strings in Python are arrays of bytes representing unicode characters. However, Python does not have a character data type, a single character is simply a string with a length of 1.

Example:

a = "Hello, World!"
print(a[1])

v Looping Through a String: Since strings are arrays, we can loop through the characters in a string, with a `for` loop.

Example:

for x in "banana":
print(x)

v String Length:To get the length of a string, use the `len()` function.

a = "Hello, World!"
print(len(a))

v Check String: To check if a certain phrase or character is present in a string, we can use the keyword `in`.

txt = "The best things in life are free!"
print("free" in txt)

v Check if NOT: To check if a certain phrase or character is NOT present in a string, we can use the keyword `not in`.

txt = "The best things in life are free!"
print("expensive" not in txt)

v Slicing:You can return a range of characters by using the slice syntax.

Specify the start index and the end index, separated by a colon, to return a part of the string.

Example:Get the characters from position 2 to position 5 (not included):

b = "Hello, World!"
print(b[2:5])

Example: Get the characters from the start to position 5 (not included):

b = "Hello, World!"
print(b[:5])

Example:Get the characters from position 2, and all the way to the end:

b = "Hello, World!"
print(b[2:])

Example: Get the characters:

From: "o" in "World!" (position -5)

To, but not included: "d" in "World!" (position -2):

b = "Hello, World!"
print(b[-5:-2])

v Upper Case: The `upper()` method returns the string in upper case:

Example:

a = "Hello, World!"
print(a.upper())

v Lower Case:The `lower()` method returns the string in lower case:

a = "Hello, World!"
print(a.lower())

v Replace String:The `replace()` method replaces a string with another string:

a = "Hello, World!"
print(a.replace("H", "J"))

v Split String: The `split()` method returns a list where the text between the specified separator becomes the list items.

Example: The `split()` method splits the string into substrings if it finds instances of the separator:

a = "Hello, World!"
print(a.split(",")) # returns ['Hello', ' World!']

v String Concatenation: To concatenate, or combine, two strings you can use the + operator.

Example:Merge variable `a` with variable `b` into variable `c`:

     a = "Hello"
     b = "World"
     c = a + b
     print(c)

Example:To add a space between them, add a `" "`:

     a = "Hello"
     b = "World"
     c = a + " " + b
     print(c)

v String Format: As we learned in the Python Variables chapter, we cannot combine strings and numbers like this:

Example:

age = 36
txt = "My name is John, I am " + age
print(txt)

Example: Use the `format()` method to insert numbers into strings:

age = 36
txt = "My name is John, and I am {}"
print(txt.format(age))

Example:

quantity = 3
itemno = 567
price = 49.95
myorder = "I want to pay {2} dollars for {0} pieces of item {1}."
print(myorder.format(quantity, itemno, price))

Python Lists

The list is a most versatile datatype available in Python which can be written as a list of comma-separated values (items) between square brackets. Important thing about a list is that items in a list need not be of the same type.

Creating a list is as simple as putting different comma-separated values between square brackets. For Example:

list1 = ['physics', 'chemistry', 1997, 2000];

list2 = [1, 2, 3, 4, 5 ];

list3 = ["a", "b", "c", "d"]

Similar to string indices, list indices start at 0, and lists can be sliced, concatenated and so on.

Accessing Values in Lists:

To access values in lists, use the square brackets for slicing along with the index or indices to obtain value available at that index. For Example:

list1 =['physics','chemistry',1997,2000];

list2 =[1,2,3,4,5,6,7];

print"list1[0]: ", list1[0]

print"list2[1:5]: ", list2[1:5]

When the above code is executed, it produces the following result −

list1[0]:  physics

list2[1:5]:  [2, 3, 4, 5]

Updating Lists:

You can update single or multiple elements of lists by giving the slice on the left-hand side of the assignment operator, and you can add to elements in a list with the append() method. For Example:

list =['physics','chemistry',1997,2000];

print"Value available at index 2 : "

print list[2]

list[2]=2001;

print"New value available at index 2 : "

print list[2]

Note − append() method is discussed in subsequent section.

When the above code is executed, it produces the following result:

Value available at index 2 :

New value available at index 2 :

Delete List Elements:

To remove a list element, you can use either the del statement if you know exactly which element(s) you are deleting or the remove() method if you do not know. For Example:

list1 =['physics','chemistry',1997,2000];

print list1

del list1[2];

print"After deleting value at index 2 : "

print list1

When the above code is executed, it produces following result:

['physics', 'chemistry', 1997, 2000]

After deleting value at index 2 :

['physics', 'chemistry', 2000]

Note − remove() method is discussed in subsequent section.

Basic List Operations:

Lists respond to the + and * operators much like strings; they mean concatenation and repetition here too, except that the result is a new list, not a string.

In fact, lists respond to all of the general sequence operations we used on strings in the prior chapter.

Python Expression	Results	Description
len([1, 2, 3])	3	Length
[1, 2, 3] + [4, 5, 6]	[1, 2, 3, 4, 5, 6]	Concatenation
['Hi!'] * 4	['Hi!', 'Hi!', 'Hi!', 'Hi!']	Repetition
3 in [1, 2, 3]	True	Membership
for x in [1, 2, 3]: print x,	1 2 3	Iteration

Indexing, Slicing, and Matrixes:

Because lists are sequences, indexing and slicing work the same way for lists as they do for strings.

Assuming following input: L = ['spam', 'Spam', 'SPAM!']

Python Expression	Results	Description
L[2]	SPAM!	Offsets start at zero
L[-2]	Spam	Negative: count from the right
L[1:]	['Spam', 'SPAM!']	Slicing fetches sections

Built-in List Functions & Methods

Functions: Python includes the following list functions −

SNo	Function with Description
1	cmp(list1, list2) Compares elements of both lists. Example: list1, list2 =[123,'xyz'],[456,'abc'] print cmp(list1, list2) print cmp(list2, list1) list3 = list2 +[786]; print cmp(list2, list3) Output: -1 1 -1
2	len(list) Gives the total length of the list. Example: list1, list2 =[123,'xyz','zara'],[456,'abc'] print"First list length : ", len(list1) print"Second list length : ", len(list2) Output: First list length : 3 Second list length : 2
3	max(list) Returns item from the list with max value. Example: list1 =[123,'xyz','zara','abc'] list2 =[456,700,200] print"Max value element : ", max(list1) print"Max value element : ", max(list2) Output: Max value element : zara Max value element : 700
4	min(list) Returns item from the list with min value. Example: list1 =[123,'xyz','zara','abc'] list2 = [456,700,200] print"min value element : ", min(list1) print"min value element : ", min(list2) Output: min value element : 123 min value element : 200
5	list(seq) Converts a tuple into list. Example: aTuple =(123,'xyz','zara','abc'); aList = list(aTuple) print"List elements : ", aList Output: List elements : [123, 'xyz', 'zara', 'abc']

Methods: Python includes following list methods

SNo	Methods with Description
1	list.append(obj) Appends object obj to list Example: aList =[123,'xyz','zara','abc']; aList.append(2009); print"Updated List : ", aList Output: Updated List : [123, 'xyz', 'zara', 'abc', 2009]
2	list.count(obj) Returns count of how many times obj occurs in list Example: aList =[123,'xyz','zara','abc',123]; print"Count for 123 : ", aList.count(123) print"Count for zara : ", aList.count('zara') Output: Count for 123 : 2 Count for zara : 1
3	list.extend(seq) Appends the contents of seq to list Example: aList =[123,'xyz','zara','abc',123]; bList =[2009,'manni']; aList.extend(bList) print"Extended List : ", aList Output: Extended List : [123, 'xyz', 'zara', 'abc', 123, 2009, 'manni']
4	list.index(obj) Returns the lowest index in list that obj appears Example: aList =[123,'xyz','zara','abc']; print"Index for xyz : ", aList.index('xyz') print"Index for zara : ", aList.index('zara') Output: Index for xyz : 1 Index for zara : 2
5	list.insert(index, obj) Inserts object obj into list at offset index Example: aList =[123,'xyz','zara','abc'] aList.insert(3,2009) print"Final List : ", aList Output: Final List : [123, 'xyz', 'zara', 2009, 'abc']
6	list.pop(obj=list[-1]) Removes and returns last object or obj from list Example: aList =[123,'xyz','zara','abc']; print"A List : ", aList.pop() print"B List : ", aList.pop(2) Output: A List : abc B List : zara
7	list.remove(obj) Removes object obj from list Example: aList =[123,'xyz','zara','abc','xyz']; aList.remove('xyz'); print"List : ", aList aList.remove('abc'); print"List : ", aList Output: List : [123, 'zara', 'abc', 'xyz'] List : [123, 'zara', 'xyz']
8	list.reverse() Reverses objects of list in place Example: aList =[123,'xyz','zara','abc','xyz']; aList.reverse(); print"List : ", aList Output: List : ['xyz', 'abc', 'zara', 'xyz', 123]
9	list.sort([func]) Sorts objects of list, use compare func if given Example: aList =[123,'xyz','zara','abc','xyz']; aList.sort(); print"List : ", aList Output: List : [123, 'abc', 'xyz', 'xyz', 'zara']

Python - Tuples

A tuple is a collection of objects which ordered and immutable. Tuples are sequences, just like lists. The differences between tuples and lists are, the tuples cannot be changed unlike lists and tuples use parentheses, whereas lists use square brackets.

Creating a tuple is as simple as putting different comma-separated values. Optionally you can put these comma-separated values between parentheses also. For example:

tup1 = ('physics', 'chemistry', 1997, 2000);

tup2 = (1, 2, 3, 4, 5 );

tup3 = "a", "b", "c", "d";

The empty tuple is written as two parentheses containing nothing:

tup1 = ();

To write a tuple containing a single value you have to include a comma, even though there is only one value:

tup1 = (50,);

Like string indices, tuple indices start at 0, and they can be sliced, concatenated, and so on.

Accessing Values in Tuples:

To access values in tuple, use the square brackets for slicing along with the index or indices to obtain value available at that index. For example −

tup1 =('physics','chemistry',1997,2000);

tup2 =(1,2,3,4,5,6,7);

print"tup1[0]: ", tup1[0];

print"tup2[1:5]: ", tup2[1:5];

When the above code is executed, it produces the following result −

tup1[0]:  physics

tup2[1:5]:  [2, 3, 4, 5]

Updating Tuples:

Tuples are immutable which means you cannot update or change the values of tuple elements. You are able to take portions of existing tuples to create new tuples as the following example demonstrates −

tup1 =(12,34.56);

tup2 =('abc','xyz');

# Following action is not valid for tuples

# tup1[0] = 100;

# So let's create a new tuple as follows

tup3 = tup1 + tup2;

print tup3;

When the above code is executed, it produces the following result −

(12, 34.56, 'abc', 'xyz')

Delete Tuple Elements:

Removing individual tuple elements is not possible. There is, of course, nothing wrong with putting together another tuple with the undesired elements discarded.

To explicitly remove an entire tuple, just use the del statement. For example −

tup =('physics','chemistry',1997,2000);

print tup;

del tup;

print"After deleting tup : ";

print tup;

This produces the following result. Note an exception raised, this is because after del tup tuple does not exist any more −

('physics', 'chemistry', 1997, 2000)

After deleting tup :

Traceback (most recent call last):

   File "test.py", line 9, in <module>

      print tup;

NameError: name 'tup' is not defined

Basic Tuples Operations:

Tuples respond to the + and * operators much like strings; they mean concatenation and repetition here too, except that the result is a new tuple, not a string.

In fact, tuples respond to all of the general sequence operations we used on strings in the prior:

Python Expression	Results	Description
len((1, 2, 3))	3	Length
(1, 2, 3) + (4, 5, 6)	(1, 2, 3, 4, 5, 6)	Concatenation
('Hi!',) * 4	('Hi!', 'Hi!', 'Hi!', 'Hi!')	Repetition
3 in (1, 2, 3)	True	Membership
for x in (1, 2, 3): print x,	1 2 3	Iteration

Indexing, Slicing, and Matrixes:

Because tuples are sequences, indexing and slicing work the same way for tuples as they do for strings. Assuming following input −

L =('spam','Spam','SPAM!')

Python Expression	Results	Description
L[2]	'SPAM!'	Offsets start at zero
L[-2]	'Spam'	Negative: count from the right
L[1:]	['Spam', 'SPAM!']	Slicing fetches sections

No Enclosing Delimiters:

Any set of multiple objects, comma-separated, written without identifying symbols, i.e., brackets for lists, parentheses for tuples, etc., default to tuples, as indicated in these short examples:

print'abc',-4.24e93,18+6.6j,'xyz';

x, y =1,2;

print"Value of x , y : ", x,y;

When the above code is executed, it produces the following result −

abc -4.24e+93 (18+6.6j) xyz

Value of x , y : 1 2

Built-in Tuple Functions:

Python includes the following tuple functions :


tuple1, tuple2 =(123,'xyz'),(456,'abc')
tuple1, tuple2 =(123,'xyz','zara'),(456,'abc')
tuple1 =(123,'xyz','zara','abc')
tuple1 =(123,'xyz','zara','abc')
aList =[123,'xyz','zara','abc']

Python – Dictionary

Each key is separated from its value by a colon (:), the items are separated by commas, and the whole thing is enclosed in curly braces. An empty dictionary without any items is written with just two curly braces, like this: {}.

Keys are unique within a dictionary while values may not be. The values of a dictionary can be of any type, but the keys must be of an immutable data type such as strings, numbers, or tuples.

Accessing Values in Dictionary:

To access dictionary elements, you can use the familiar square brackets along with the key to obtain its value. Following is a simple example −

dict ={'Name':'Zara','Age':7,'Class':'First'}

print"dict['Name']: ", dict['Name']

print"dict['Age']: ", dict['Age']

When the above code is executed, it produces the following result:

dict['Name']:  Zara

dict['Age']:  7

If we attempt to access a data item with a key, which is not part of the dictionary, we get an error as follows −

dict ={'Name':'Zara','Age':7,'Class':'First'}

print"dict['Alice']: ", dict['Alice']

When the above code is executed, it produces the following result:

dict['Alice']:

Traceback (most recent call last):

   File "test.py", line 4, in <module>

      print "dict['Alice']: ", dict['Alice'];

KeyError: 'Alice'

Updating Dictionary:

You can update a dictionary by adding a new entry or a key-value pair, modifying an existing entry, or deleting an existing entry as shown below in the simple example −

dict ={'Name':'Zara','Age':7,'Class':'First'}

dict['Age']=8;# update existing entry

dict['School']="DPS School";# Add new entry

print"dict['Age']: ", dict['Age']

print"dict['School']: ", dict['School']

When the above code is executed, it produces the following result:

dict['Age']:  8

dict['School']:  DPS School

Delete Dictionary Elements:

You can either remove individual dictionary elements or clear the entire contents of a dictionary. You can also delete entire dictionary in a single operation.

To explicitly remove an entire dictionary, just use the del statement. Following is a simple Example :

dict ={'Name':'Zara','Age':7,'Class':'First'}

del dict['Name'];# remove entry with key 'Name'

dict.clear();# remove all entries in dict

del dict ;# delete entire dictionary

print"dict['Age']: ", dict['Age']

print"dict['School']: ", dict['School']

This produces the following result. Note that an exception is raised because after del dict dictionary does not exist any more:

dict['Age']:

Traceback (most recent call last):

   File "test.py", line 8, in <module>

      print "dict['Age']: ", dict['Age'];

TypeError: 'type' object is unsubscriptable

Note − del() method is discussed in subsequent section.

Properties of Dictionary Keys:

Dictionary values have no restrictions. They can be any arbitrary Python object, either standard objects or user-defined objects. However, same is not true for the keys.

There are two important points to remember about dictionary keys −

(a) More than one entry per key not allowed. Which means no duplicate key is allowed. When duplicate keys encountered during assignment, the last assignment wins. For Example:

dict ={'Name':'Zara','Age':7,'Name':'Manni'}

print"dict['Name']: ", dict['Name']

When the above code is executed, it produces the following result −

dict['Name']:  Manni

(b) Keys must be immutable. Which means you can use strings, numbers or tuples as dictionary keys but something like ['key'] is not allowed. Following is a simple Example:

dict ={['Name']:'Zara','Age':7}

print"dict['Name']: ", dict['Name']

When the above code is executed, it produces the following result −

Traceback (most recent call last):

   File "test.py", line 3, in <module>

      dict = {['Name']: 'Zara', 'Age': 7};

TypeError: unhashable type: 'list'

Built-in Dictionary Functions & Methods

Python includes the following dictionary functions:

Sr.No.	Function with Description
1	cmp(dict1, dict2) Compares elements of both dict. Example: dict1 = {'Name': 'Zara', 'Age': 7}; dict2 = {'Name': 'Mahnaz', 'Age': 27}; dict3 = {'Name': 'Abid', 'Age': 27}; dict4 = {'Name': 'Zara', 'Age': 7}; print "Return Value : %d" % cmp (dict1, dict2) print "Return Value : %d" % cmp (dict2, dict3) print "Return Value : %d" % cmp (dict1, dict4) Output: Return Value : -1 Return Value : 1 Return Value : 0
2	len(dict) Gives the total length of the dictionary. This would be equal to the number of items in the dictionary. Example: dict = {'Name': 'Zara', 'Age': 7}; print "Length : %d" % len (dict) Output: Length : 2
3	str(dict) Produces a printable string representation of a dictionary Example: dict = {'Name': 'Zara', 'Age': 7}; print "Equivalent String : %s" % str (dict) Output: Equivalent String : {'Age': 7, 'Name': 'Zara'}
4	type(variable) Returns the type of the passed variable. If passed variable is dictionary, then it would return a dictionary type. Example: dict = {'Name': 'Zara', 'Age': 7}; print "Variable Type : %s" % type (dict) Output: Variable Type : <type 'dict'>

Methods:

Python includes following dictionary methods −

Sr.No.	Methods with Description
1	dict.clear() Removes all elements of dictionary dict Example: dict = {'Name': 'Zara', 'Age': 7}; print "Start Len : %d" % len(dict) dict.clear() print "End Len : %d" % len(dict) Output: Start Len : 2 End Len : 0
2	dict.copy() Returns a shallow copy of dictionary dict Example: dict1 = {'Name': 'Zara', 'Age': 7}; dict2 = dict1.copy() print "New Dictionary : %s" % str(dict2) Output: New Dictionary : {'Age': 7, 'Name': 'Zara'}
3	dict.fromkeys() Create a new dictionary with keys from seq and values set to value. Example: seq = ('name', 'age', 'Gen') dict = dict.fromkeys(seq) print "New Dictionary : %s" % str(dict) dict = dict.fromkeys(seq, 10) print "New Dictionary : %s" % str(dict) Output: New Dictionary : {'age': None, 'name': None, 'Gen': None} New Dictionary : {'age': 10, 'name': 10, 'Gen': 10}
4	dict.get(key, default=None) For key key, returns value or default if key not in dictionary Example: dict = {'Name': 'Zabra', 'Age': 7} print "Value : %s" % dict.get('Age') print "Value : %s" % dict.get('Education', "Never") Output: Value : 7 Value : Never
5	dict.has_key(key) Returns true if key in dictionary dict, false otherwise Example: dict = {'Name': 'Zara', 'Age': 7} print "Value : %s" % dict.has_key('Age') print "Value : %s" % dict.has_key('Gen') Output: Value : True Value : False
6	dict.items() Returns a list of dict's (key, value) tuple pairs Example: dict = {'Name': 'Zara', 'Age': 7} print "Value : %s" % dict.items() Output: Value : [('Age', 7), ('Name', 'Zara')]
7	dict.keys() Returns list of dictionary dict's keys Example: dict = {'Name': 'Zara', 'Age': 7} print "Value : %s" % dict.keys() Output: Value : ['Age', 'Name']
8	dict.setdefault(key, default=None) Similar to get(), but will set dict[key]=default if key is not already in dict Example: dict = {'Name': 'Zara', 'Age': 7} print "Value : %s" % dict.setdefault('Age', None) print "Value : %s" % dict.setdefault('Gen', None) Output: Value : 7 Value : None
9	dict.update(dict2) Adds dictionary dict2's key-values pairs to dict Example: dict = {'Name': 'Zara', 'Age': 7} dict2 = {'Gen': 'female' } dict.update(dict2) print "Value : %s" % dict Output: Value : {'Age': 7, 'Name': 'Zara', 'Sex': 'female'}
10	dict.values() Returns list of dictionary dict's values Example: dict = {'Name': 'Zara', 'Age': 7} print "Value : %s" % dict.values() Output: Value : [7, 'Zara']

Unit - III

Functions, Modules & Packages

1. What is a function in Python?

A. In Python, function is a group of related statements that perform a specific task. Functions help break our program into smaller and modular chunks. As our program grows larger and larger, functions make it more organized and manageable. Furthermore, it avoids repetition and makes code reusable.

Uses of Functions:

1. Code re-usability: Lets say we are writing an application in Python where we need to perform a specific task in several places of our code, assume that we need to write 10 lines of code to do that specific task. It would be better to write those 10 lines of code in a function and just call the function wherever needed, because writing those 10 lines every time you perform that task is tedious, it would make your code lengthy, less-readable and increase the chances of human errors.

2. Improves Readability: By using functions for frequent tasks you make your code structured and readable. It would be easier for anyone to look at the code and be able to understand the flow and purpose of the code.

3. Avoid redundancy: When you no longer repeat the same lines of code throughout the code and use functions in places of those, you actually avoiding the redundancy that you may have created by not using functions.

Advantage of Functions in Python: There are the following advantages of Python functions.

v Using functions, we can avoid rewriting the same logic/code again and again in a program.

v We can call Python functions multiple times in a program and anywhere in a program.

v We can track a large Python program easily when it is divided into multiple functions.

v Reusability is the main achievement of Python functions.

v However, Function calling is always overhead in a Python program.

Types of functions:There are two types of functions in Python:

v User-define functions - The user-defined functions are those define by the user to perform the specific task.

v Built-in functions - The built-in functions are those functions that are pre-defined in Python.

Syntax of functions in Python:

Function declaration:

def function_name(function_parameters):

function_body # Set of Python statements

return # optional return statement

Calling the function:

# when function doesn't return anything

function_name(parameters)

# when function returns something

# variable is to store the returned value

variable = function_name(parameters)

How Function works in Python?

Python Function Example:

Here we have a function add() that adds two numbers passed to it as parameters. Later after function declaration we are calling the function twice in our program to perform the addition.

def add(num1, num2):

return num1 + num2

sum1 = add(100, 200)

sum2 = add(8, 9)

print(sum1) #300

print(sum2) #17

Default arguments in Function: Now that we know how to declare and call a function, lets see how can we use the default arguments. By using default arguments we can avoid the errors that may arise while calling a function without passing all the parameters. Lets take an example to understand this:

In this example we have provided the default argument for the second parameter, this default argument would be used when we do not provide the second parameter while calling this function.

# default argument for second parameter

def add(num1, num2=1):

return num1 + num2

sum1 = add(100, 200)

sum2 = add(8) # used default argument for second param

sum3 = add(100) # used default argument for second param

print(sum1) #300

print(sum2) #9

print(sum3) #101

The return statement

The return statement is used to exit a function and go back to the place from where it was called.

Syntax of return

return [expression_list]

This statement can contain expression which gets evaluated and the value is returned. If there is no expression in the statement or the return statement itself is not present inside a function, then the function will return the Noneobject.

For example:

print(greet("May"))

Hello, May. Good morning!

None

Here, None is the returned value.

Example of return

def absolute_value(num):

"""This function returns the absolutevalue of the entered number"""

if num >= 0:

return num

else:

return -num

# Output: 2

print(absolute_value(2))

# Output: 4

print(absolute_value(-4))

2. Explain types of User defined Functions in Python?

A. Types of User Defined Functions in Python:

In real-time, a Python function may define with or without parameters, and a function may or may not return a value. It entirely depends upon the user requirement. In this article, we explain to you the types of functions in Python Programming language with examples.

In Python programming, as per our requirement, We can define the User defined functions in multiple ways. The following are the list of available types of functions in Python.

Function without argument and without return value
Function without argument and with return value
Function with argument and without return value
Function with argument and with return value

1. Function without argument and without return value:In this type of function in Python, while defining, declaring, or calling the function, We won’t pass any arguments to the function. This type of Python function won’t return any value when we call the function.

# Function without argument and without return value

def Adding():   #Called Function

    a = 20

    b = 30

print("Sum of Two Nums:", a+b)

Adding() #Calling Function

Output: Sum of Two Nums: 50

2.Function without argument and with return value:In this type of function in the

Python program, we are going to calculate the multiplication of 2 integer values using the user defined function without arguments and return keyword.

# Function without argument and with return value

def Multiplication():

    a = 10

    b = 25

return a*b

print("Product of Two Nums: ", Multiplication())

Output: Product of Two Nums: 250

3. Function with argument and without return value: If you observe the above 2 types of functions, No matter how many times you executive, Python gives the same output. We don’t have any control over the variable values (a, b) because they are fixed values. In real-time, we mostly deal with dynamic data means we have to allow the user to enter his own values rather than fixed ones.

This type of function in Python allows us to pass the arguments to the function while calling the function. But, This type of function in Python won’t return any value when we call the function.

# Function with argument and without return value

def Multiplications(a, b):

print("Product of Two Nums:", a*b)

Multiplications(10, 20)

Output: Product of Two Nums: 200

4. Function with argument and with return value:This type of python function allows us to pass the arguments to the function while calling the function. This type of functions in Python returns some value when we call the function. This type of user defined function called a fully dynamic function means it provides maximum control to the end-user.

# Function with argument and with return value

def Addition(a, b):

    Sum = a + b

return Sum

print("Sum of Two Nums:", Addition(25, 45))

Output: Sum of Two Nums: 70

3. Write an example for all User Defined Functions Examples?

A. Example: All User Defined Functions to Shows as follows:

def add(a, b): #fun with arg with return value

return a + b

def mul(): #fun without arg without return value

a=int(input("Enter A Value:"))

b=int(input("Enter B Value:"))

print("Product of Two Nums:",a*b)

def subt(x,y): #fun with arg without return value

print("Subtract of two Nums:",x-y)

def div(): #fun without arg without return value

a=int(input("Enter A Value:"))

b=int(input("Enter B Value:"))

return a/b

print("Addition of Two Nums:",add(100, 200));#Calling Functions

mul()

m=int(input("Enter M Value:"))

n=int(input("Enter N Value:"))

subt(m,n)

print("Division of Two Nums:",div())

4. Explain Anonymous/Lambda Function in Python?

A. The anonymous function, also known as lambda functions. You'll learn what they are, their syntax and how to use them (with examples).

What are lambda functions in Python?

In Python, an anonymous function is a function that is defined without a name.

While normal functions are defined using the def keyword in Python, anonymous functions are defined using the lambda keyword.Hence, anonymous functions are also called lambda functions.

Syntax of Lambda Function in python

lambda arguments: expression

Lambda functions can have any number of arguments but only one expression. The

expression is evaluated and returned. Lambda functions can be used wherever function

objects are required.

Example of Lambda Function in python

Here is an example of lambda function that doubles the input value.

#Program to show the use of lambda functions:

double=lambda x: x*2

print(double(5))

Output:10

In the above program, lambda x: x * 2 is the lambda function. Here x is the argument and x * 2 is the expression that gets evaluated and returned.

This function has no name. It returns a function object which is assigned to the identifier double. We can now call it as a normal function. The statement

double = lambda x: x * 2

is nearly the same as:

def double(x):

return x * 2

Use of Lambda Function in python

We use lambda functions when we require a nameless function for a short period of time.

In Python, we generally use it as an argument to a higher-order function (a function that takes in other functions as arguments). Lambda functions are used along with built-in functions like filter(), map() etc.

v Example use with filter(): The `filter()` function in Python takes in a function and a

list as arguments.The function is called with all the items in the list and a new list is returned which contains items for which the function evaluates to `True`.

Here is an example use of filter() function to filter out only even numbers from a list.

#Program to filter out only the even items from a list

my_list=[1,5,4,6,8,11,3,12]

new_list=list(filter(lambda( x: (x%2==0), my_list))

print(new_list)

Output:

[4, 6, 8, 12]

v Example use with map(): The `map()` function in Python takes in a function and a

list. The function is called with all the items in the list and a new list is returned which contains items returned by that function for each item.

Here is an example use of map() function to double all the items in a list.

#Program to double each item in a list using map()

my_list=[1,5,4,6,8,11,3,12];

new_list=list(map(lambda x: x*2, my_list))

print(new_list)

Output:

[2, 10, 8, 12, 16, 22, 6, 24]

5. Explain Scope and Lifetime of variables

A. Scope of a variable is the portion of a program where the variable is recognized. Parameters and variables defined inside a function is not visible from outside. Hence, they have a local scope.

Lifetime of a variable is the period throughout which the variable exits in the memory. The lifetime of variables inside a function is as long as the function executes.

They are destroyed once we return from the function. Hence, a function does not remember the value of a variable from its previous calls.

Here is an example to illustrate the scope of a variable inside a function.

def my_func(): #called function

x = 10

print("Value inside function:",x)

x = 20

my_func() #calling function

print("Value outside function:",x)

Output:

Value inside function: 10

Value outside function: 20

6. Explain Recursive Function?

A. A function is said to be a recursive if it calls itself. For example, lets say we have a function abc()and in the body of abc() there is a call to the abc().

In this example we are defining a user-defined function factorial(). This function finds the factorial of a number by calling itself repeatedly until the base case is reached.

We use base case in recursive function so that the function stops calling itself when the base case is reached. Without the base case, the function would keep calling itself indefinitely.

Why use recursion in programming?

We use recursion to break a big problem in small problems and those small problems into further smaller problems and so on. At the end the solutions of all the smaller subproblems are collectively helps in finding the solution of the big main problem.

Advantages of recursion:
1. Easier to write.
2. Readable – Code is easier to read and understand.
3. Reduce the lines of code – It takes less lines of code to solve a problem using recursion.

Disadvantages of recursion

1. Not all problems can be solved using recursion.
2. If you don’t define the base case then the code would run indefinitely.
3. Debugging is difficult in recursive functions as the function is calling itself in a loop and it is hard to understand which call is causing the issue.
4. Memory overhead – Call to the recursive function is not memory efficient.

# Example for recursion function to find the factorial of a given number

def factorial(num):

if num ==0:

return1

else:

return(num * factorial(num -1))

num =int(input(“Enter N Value:”))

print("Factorial of", num,"is: ", factorial(num))

Output:

Factorial of 5is:120

Lets see what happens in the above example:

factorial(5) returns 5* factorial(5-1)

    i.e.5* factorial(4)

|__5*4*factorial(3)

|__5*4*3*factorial(2)

|__5*4*3*2*factorial(1)

#Example for Recursive function to calculate sum of N series

defsum(num):

if num ==0:

return0

else:

return(num +sum(num -1))

num =int(input(“Enter N Value:”))

print("Sum of N Series:",sum(num))

Output:

Enter N Value: 5

Sum of N Series:15

7. What are Python modules?

A. Python module is a Python file containing a set of functions and variables to be used in an application. The variables can be of any type (arrays, dictionaries, objects, etc.)

Modules can be either:

Benefits of modules in Python:

There are a couple of key benefits of creating and using a module in Python:

Ø Structured Code

Code is logically organized by being grouped into one Python file which makes development easier and less error-prone.
Code is easier to understand and use.

Ø Reusability

Functionality defined in a single module can be easily reused by other parts of the application. This eliminates the need to recreate duplicate code.

Modules can be divided into two types namely:

1. Built in Module

2. User-defined Module

1. Built-in Modules

There are several built-in modules in Python, which you can import whenever you like.

Call a built-in Module

To call a built-in Module and use the function of that module write:

import moduleName #call a module

moduleName.function()#use module function

import math

print("The value of cosine is", math.cos(30))

print("The value of sine is", math.sin(45))

print("The value of tangent is", math.tan(90))

print("The SquareRoot Value is", math.sqrt(25))

print("The Power of a given value is", math.pow(2,4))

print("The value of pi is", math.pi)

2. User-defined Modules

Create a Module

To create a module, create a Python file with a .py extension.

Call a Module

Modules created with a .py extension can be used in another Python source file, using the import statement.

To call this Python module myModule.py, create another Python file callModule.py file and use the import statement.

import myModule.py

myModule.myFunction(“Python Programming”)

When the above code is executed, the following output is produced:

callModule.py

myModule.py

def myFunction( parameter ): #define a function in myModule.py

print("Course : ", parameter)

Example for User defined Module:

module1.py #save as module1.py filename

def add(num1, num2): #fun with arg with return value

return num1 + num2

def mul(): #fun without arg without return value

a=int(input("Enter A Value:"))

b=int(input("Enter B Value:"))

print("Product of Two Nums:",a*b)

def subt(x,y): #fun with arg without return value

print("Subtract of two Nums:",x-y)

def div(): #fun without arg without return value

a=int(input("Enter A Value:"))

b=int(input("Enter B Value:"))

return a/b

import module1

print("Addition of Two Nums:",module1.add(100, 200));

module1.mul()

m=int(input("Enter M Value:"))

n=int(input("Enter N Value:"))

module1.subt(m,n)

print("Division of Two Nums:",module1.div())

Output:

Addition of Two Nums: 300

Enter A Value:5

Enter B Value:6

Product of Two Nums: 30

Enter M Value:7

Enter N Value:8

Subtract of two Nums: -1

Enter A Value:2

Enter B Value:1

Division of Two Nums: 2.0

8. Explain how to importing Built in modules and its usage?

A.Python module is a Python file containing a set of functions and variables to be used in an application. The variables can be of any type (arrays, dictionaries, objects, etc.)

Modules can be either:

To import modules either may be User defined or System defined by using import statement.

import modulename.functionname

(or)

import modulename.*

Build in modules like:

1. Python - OS Module: It is possible to automatically perform many operating system tasks. The OS module in Python provides functions for creating and removing a directory (folder), fetching its contents, changing and identifying the current directory, etc.

Getting Current Working Directory

v The getcwd(): function confirms returns the current working directory.

Example: Get Current Working Directory

importos

os.getcwd()

v Creating a Directory:We can create a new directory using the `os.mkdir()` function,

as shown below.Example: Create a Physical Directory

importos

os.mkdir("C:\MyPython")

v Changing the Current Working Directory: We must first change the current

working directory to a newly created one before doing any operations in it. This is done using the `chdir()` function. The following change current working directory to `C:\MyPython`.Example: Change Working Directory

importos

os.chdir("C:\MyPython")# changing current working directory

os.getcwd()

Example: Change Directory to Drive

os.chdir("C:\\")

os.getcwd()            #'C:\\'

In order to set the current directory to the parent directory use ".." as the argument in the chdir() function.

Example: Change CWD to Parent

os.chdir("C:\\MyPython")

os.getcwd()            #'C:\\MyPython'

os.chdir("..")

os.getcwd()            #'C:\\'

v Removing a Directory: The `rmdir()` function in the OS module removes the

specified directory either with an absolute or relative path. Note that, for a directory to be removed, it should be empty.Example: Remove Directory

import`os`

os.rmdir("C:\\MyPython")

However, you can not remove the current working directory. To remove it, you must change the current working directory, as shown below.

v List Files and Sub-directories: The `listdir()` function returns the list of all files and

directories in the specified directory.Example: List Directories

importos

os.listdir("c:\python37")

If we don't specify any directory, then list of files and directories in the current working directory will be returned.

2. Python - Math Module:Some of the most popular mathematical functions are defined in the math module. These include trigonometric functions, representation functions, logarithmic functions, angle conversion functions, etc. In addition, two mathematical constants are also defined in this module.

v Piand eis a well-known mathematical constant, which is defined as the ratio of

thecircumference to the diameter of a circle and its value is 3.141592653589793.e is

called Euler's number and it is a base of the natural logarithm. Its value is

2.718281828459045.

Example: Getting Pi Value and e Value

import math

math.pi

math.e

v The math module contains functions for calculating various trigonometric ratios for

a given angle. The functions (sin, cos, tan, etc.) need the angle in radians as an argument.

Examples:

import math

math.radians(30)

math.degrees(math.pi/6)

math.sin(0.5235987755982988)

math.cos(0.5235987755982988)

math.tan(0.5235987755982988)

math.log(10)

math.exp(10)

math.pow(2,4)

math.sqrt(100)

math.ceil(4.5867)

math.floor(4.5687)

3. Python - Statistics Module:The statistics module provides functions to mathematical statistics of numeric data. The following popular statistical functions are defined in this module.

v Mean: It calculates the arithmetic mean of the numbers in a list.Example:

import statistics

statistics.mean([2,5,6,9])   #5.5

v Median: It returns the middle value of numeric data in a list.Example:

import statistics

statistics.median([1,2,3,8,9])       #3

statistics.median([1,2,3,7,8,9])             #5.0

v Mode: It returns the most common data point in the list.Example:

import statistics

statistics.mode([2,5,3,2,8,3,9,4,2,5,6]) #2

v Standard Deviation: The `stdev()` method calculates the standard deviation on a given sample in the form of a list.Example:

import statistics

statistics.stdev([1,1.5,2,2.5,3,3.5,4,4.5,5])

1.3693063937629153

4.Python - Random Module: The random module is a built-in module to generate the pseudo-random variables. It can be used perform some action randomly such as to get a random number, selecting a random elements from a list, shuffle elements randomly, etc.

Generate Random Floats

v The random.random() method returns a random float number between 0.0 to 1.0.

The function doesn't need any arguments.Example: random()

import random

random.random()   #0.645173684807533

v Generate Random Integers: It returns a randominteger between the specified

integers.Example: randint()

import random

random.randint(1,100)          #95

random.randint(1,100)          #49

v Generate Random Numbers within Range: It returns a randomly selected element

from the range created by the start, stop and step arguments. The value of start is 0 by default. Similarly, the value of step is 1 by default.Example:

random.randrange(1,10)       #2

random.randrange(1,10,2)    #5

v Select Random Elements: It returns a randomly selected element from a non-

empty sequence. An empty sequence as argument raises an IndexError.Example:

import random

random.choice('computer')   #'t'

random.choice([12,23,45,67,65,43])    #45

random.choice((12,23,45,67,65,43))    #67

Shuffle Elements Randomly:It randomly reorders the elements in a list.Example:

numbers=[12,23,45,67,65,43]

random.shuffle(numbers)

numbers                 #[23,12,43,65,67,45]

random.shuffle(numbers)

numbers                 #[23,43,65,45,12,67]

9. What is a Package? Explain its usage in python.

A. Create and Access a Python Package: Packages are a way of structuring many packages and modules which helps in a well-organized hierarchy of data set, making the directories and modules easy to access. Just like there are different drives and folders in an OS to help us store files, similarly packages help us in storing other sub-packages and modules, so that it can be used by the user when necessary.

Creating and Exploring Packages:

To tell Python that a particular directory is a package, we create a file named __init__.py

inside it and then it is considered as a package and we may create other modules and sub-packages within it. This __init__.py file can be left blank or can be coded with the initialization code for the package.
To create a package in Python, we need to follow these three simple steps:

1. First, we create a directory and give it a package name, preferably related to its operation.

2. Then we put the classes and the required functions in it.

3. Finally we create an __init__.py file inside the directory, to let Python know that the directory is a package.

Example of Creating Package

Let’s look at this example and see how a package is created. Let’s create a package named Cars and build three modules in it namely, Bmw, Audi and Nissan.

1. First we create a directory and name it Cars.

2. Then we need to create modules. To do this we need to create a file with the name Bmw.py and create its content by putting this code into it.

# Python code to illustrate the Modules

class Bmw:

# First we create a constructor for this class and add members to it, here models

def __init__(self):

self.models = ['i8', 'x1', 'x5', 'x6']

# A normal print function

def outModels(self):

print('These are the available models for BMW')

for model in self.models:

print('\t%s ' % model)

3. Then we create another file with the name Audi.py and add the similar type of code to it with different members.

# Python code to illustrate the Module

class Audi:

# First we create a constructor for this class and add members to it, here models

def __init__(self):

self.models = ['q7', 'a6', 'a8', 'a3']

# A normal print function

def outModels(self):

print('These are the available models for Audi')

for model in self.models:

print('\t%s ' % model)

4. Then we create another file with the name Nissan.py and add the similar type of code to it with different members.

# Python code to illustrate the Module

class Nissan:

# First we create a constructor for this class and add members to it, here models

def __init__(self):

self.models = ['altima', '370z', 'cube', 'rogue']

# A normal print function

def outModels(self):

print('These are the available models for Nissan')

for model in self.models:

print('\t%s ' % model)

5. Finally we create the __init__.py file. This file will be placed inside Cars directory and can be left blank or we can put this initialisation code into it.

from Bmw import Bmw

from Audi import Audi

from Nissan import Nissan

Now, let’s use the package that we created. To do this make a sample.py file in the same directory where Cars package is located and add the following code to it:

# Import classes from your brand new package

from Cars import Bmw

from Cars import Audi

from Cars import Nissan

# Create an object of Bmw class & call its method

ModBMW = Bmw()

ModBMW.outModels()

# Create an object of Audi class & call its method

ModAudi = Audi()

ModAudi.outModels()

# Create an object of Nissan class & call its method

ModNissan = Nissan()

ModNissan.outModels()

Various ways of Accessing the Packages

Let’s look at this example and try to relate packages with it and how can we access it.

import in Packages
Suppose the cars and the brand directories are packages. For them to be a package they all must contain __init__.py file in them, either blank or with some initialization code. Let’s assume that all the models of the cars to be modules. Use of packages helps importing any modules, individually or whole.
Suppose we want to get Bmw i5. The syntax for that would be:

'import' Cars.Bmw.x5

While importing a package or sub packages or modules, Python searches the whole tree

of directories looking for the particular package and proceeds systematically as programmed by the dot operator.
If any module contains a function and we want to import that. For e.g., a8 has a function get_buy(1) and we want to import that, the syntax would be:

1. import Cars.Audi.a8

2. Cars.Audi.a8.get_buy(1)

While using just the import syntax, one must keep in mind that the last attribute must be a

subpackage or a module, it should not be any function or class name.

‘from…import’ in Packages
Now, whenever we require using such function we would need to write the whole long line after importing the parent package. To get through this in a simpler way we use ‘from’ keyword. For this we first need to bring in the module using ‘from’ and ‘import’:

from Cars.Audi import a8

Now we can call the function anywhere using

a8.get_buy(1)

There’s also another way which is less lengthy. We can directly import the function and use

it wherever necessary. First import it using:

from Cars.Audi.a8 import get_buy

Now call the function from anywhere:

get_buy(1)

‘from…import *’ in Packages
While using the from…import syntax, we can import anything from submodules to class or function or variable, defined in the same module. If the mentioned attribute in the import part is not defined in the package then the compiler throws an ImportError exception.
Importing sub-modules might cause unwanted side-effects that happens while importing sub-modules explicitly. Thus we can import various modules at a single time using * syntax. The syntax is:

from Cars.Chevrolet import *

This will import everything i.e., modules, sub-modules, function, classes, from the sub-package.

10. Explain Composition in Python.

A. It is one of the important concepts of Object-oriented programming (OOPs). Composition basically enables us for creating complex types objects by combining other types of objects in the program. Composition represents ‘has a relationship’ type or we can call composition as ‘has a relationship’ in the OOPs concept. It means that a composite class present in the program can contains the objects from the other class components and this type of relationship model is called as has a relationship.

Give an example of Composition in Python

In composition one of the classes is composed of one or more instance of other classes. In other words one class is container and other class is content and if you delete the container object then all of its contents objects are also deleted.

Note:In the above figure Classes are represented as boxes with the class name Composite and Component representing Has-A relation between both of them.

class Salary:

def __init__(self, pay):

self.pay = pay

def get_total(self):

return (self.pay*12)

class Employee:

def __init__(self, pay, bonus):

self.pay = pay

self.bonus = bonus

self.obj_salary = Salary(self.pay)

def annual_salary(self):

return "Total: " + str(self.obj_salary.get_total() + self.bonus)

obj_emp = Employee(600, 500)

print(obj_emp.annual_salary())

Output: Total: 7700

Unit –IV

Input-Output & Exception Handling

1. Explain Input and Output in Python

A. Input Statement:Up till now, our programs were static. The value of variables were defined or hard coded into the source code.

To allow flexibility we might want to take the input from the user. In Python, we have the input() function to allow this. The syntax for input() is

input([prompt])

where prompt is the string we wish to display on the screen. It is optional.

>>>num = input('Enter a number: ')

Enter a number: 10

>>>num

'10'

How to Take Input from User in Python: Sometimes a developer might want to take user input at some point in the program. To do this Python provides an input() function.

Syntax:input('prompt')

where prompt is an optional string that is displayed on the string at the time of taking input.

Example 1:# Taking input from the user

name =input("Enter your name: ")      #Enter your name: Rama

print("Hello, "+name)                          #Hello, Rama

print(type(name)) #<class 'str'>

Note: Python takes all the input as a string input by default. To convert it to any other data type we have to convert the input explicitly. For example, to convert the input to int or float we have to use the int() and float() method respectively.

Example 2: Integer input in Python

num =int(input("Enter a number: "))                  #Enter a number: 25

add =num +1

print(add) #26

Output Statement:We use the print() function to output data to the standard output device (screen).We can also output data to a file, An example use is given below.

print('This sentence is output to the screen')

Example1:

a = 5

print('The value of a is', a)# Output: The value of a is 5

Example 2:

print(1,2,3,4) # Output: 1 2 3 4

Example 3:

print (1,2,3,4,sep='*') # Output: 1*2*3*4

Example 4:

print(1,2,3,4,sep='#',end='&') # Output: 1#2#3#4&

Example 5:

We can even use keyword arguments to format the string.

>>>print('Hello {name}, {greeting}'.format(greeting ='Goodmorning', name ='John'))

HelloJohn,Goodmorning

Example 6:

We can even format strings like the old printf() style used in C programming language. We use the % operator to accomplish this.

>>> x =12.3456789

>>>print('The value of x is %3.2f'%x)

The value of x is12.35

>>>print('The value of x is %3.4f'%x)

The value of x is12.3457

Using % Operator

We can use ‘%’ operator. % values are replaced with zero or more value of elements. The formatting using % is similar to that of ‘printf’ in the C programming language.

· %d – integer

· %f – float

· %s – string

· %x – hexadecimal

· %o – octal

Example:

# Taking input from the user

num =int(input("Enter a value: "))

add =num +5

print("The sum is %d"%add)

Output:

Enter a value: 50

The sum is 55

2. Explain File Handling in Python

A. Till now, we were taking the input from the console and writing it back to the console to interact with the user.

Sometimes, it is not enough to only display the data on the console. The data to be displayed may be very large, and only a limited amount of data can be displayed on the console, and since the memory is volatile, it is impossible to recover the programmatically generated data again and again.

However, if we need to do so, we may store it onto the local file system which is volatile and can be accessed every time. Here, comes the need of file handling.

In this section of the tutorial, we will learn all about file handling in python including, creating a file, opening a file, closing a file, writing and appending the file, etc.

1. Opening a file: Python provides the open() function which accepts two arguments, file name and access mode in which the file is accessed. The function returns a file object which can be used to perform various operations like reading, writing, etc.

file object = open(<file-name>, <access-mode>, <buffering>)

The files can be accessed using various modes like read, write, or append. The following are the details about the access mode to open a file.

Sno	Access mode	Description
1	r	It opens the file to read-only. The file pointer exists at the beginning.
2	rb	It opens the file to read only in binary format.
3	r+	It opens the file to read and write both.
4	rb+	It opens the file to read and write both in binary format.
5	w	It opens the file to write only. It overwrites the file if previously exists or creates a new one if no file exists with the same name.
6	wb	It opens the file to write only in binary format. It overwrites the file if it exists previously or creates a new one if no file exists with the same name.
7	w+	It opens the file to write and read both. It is different from r+ in the sense that it overwrites the previous file if one exists whereas r+ doesn't overwrite the previously written file.
8	wb+	It opens the file to write and read both in binary format.
9	a	It opens the file in the append mode. The file pointer exists at the end of the previously written file if exists any. It creates a new file if no file exists with the same name.
10	ab	It opens the file in the append mode in binary format. The pointer exists at the end of the previously written file. It creates a new file in binary format if no file exists with the same name.
11	a+	It opens a file to append and read both. The file pointer remains at the end of the file if a file exists.
12	ab+	It opens a file to append and read both in binary format. The file pointer remains at the end of the file.

Let's look at the simple example to open a file named "file.txt" (stored in the same directory) in read mode and printing its content on the console.

Example

#opens the file file.txt in read mode

fileptr = open("file.txt","r")

if fileptr:

print("file is opened successfully")

Output:file is opened successfully

2.The close(): Once all the operations are done on the file, we must close it through our python script using the close() method. Any unwritten information gets destroyed once the close() method is called on a file object.

We can perform any operation on the file externally in the file system is the file is opened in python, hence it is good practice to close the file once all the operations are done.

Example

# opens the file file.txt in read mode

fileptr = open("file.txt","r")

if fileptr:

print("file is opened successfully")

#closes the opened file

fileptr.close()

3. Reading the file: To read a file using the python script, the python provides us the read() method. The read() method reads a string from the file. It can read the data in the text as well as binary format.The syntax of the read() method is given below.

fileobj.read(<count>)

Here, the count is the number of bytes to be read from the file starting from the beginning of the file. If the count is not specified, then it may read the content of the file until the end.

Consider the following

Example:

#open the file.txt in read mode. causes error if no such file exists.

fileptr = open("file.txt","r");

#stores all the data of the file into the variable content

content = fileptr.read(9);

# prints the type of the data stored in the file

print(type(content))

#prints the content of the file

print(content)

#closes the opened file

fileptr.close()

Output:

<class 'str'>

Hi, I am

4. Read Lines of the file:Python facilitates us to read the file line by line by using a function readline(). The readline() method reads the lines of the file from the beginning, i.e., if we use the readline() method two times, then we can get the first two lines of the file.

Consider the following example which contains a function readline() that reads the first line of our file "file.txt" containing three lines.

Example:

.close()#open the file.txt in read mode. causes error if no such file exists.

fileptr = open("file.txt","r");

#stores all the data of the file into the variable content

content = fileptr.readline();

# prints the type of the data stored in the file

print(type(content))

#prints the content of the file

print(content)

#closes the opened file

fileptr

Output:

<class 'str'>

Hi, I am the file and being used as

5. Looping through the file:By looping through the lines of the file, we can read the whole file.

Example:

#open the file.txt in read mode. causes an error if no such file exists.

fileptr = open("file.txt","r");

#running a for loop

for i in fileptr:

print(i) # i contains each line of the file

Output:

Hi, I am the file and being used as

an example to read a

file in python.

6. Writing the file:To write some text to a file, we need to open the file using the open method with one of the following access modes.

Ø a: It will append the existing file. The file pointer is at the end of the file. It creates a new file if no file exists.

Ø w: It will overwrite the file if any file exists. The file pointer is at the beginning of the file.

Consider the following example.

Example 1

#open the file.txt in append mode. Creates a new file if no such file exists.

fileptr = open("file.txt","a");

#appending the content to the file

fileptr.write("Python is the modern day language. It makes things so simple.")

#closing the opened file

fileptr.close();

Now, we can see that the content of the file is modified.

File.txt:

Hi, I am the file and being used as

an example to read a

file in python.

Python is the modern day language. It makes things so simple.

Example 2

#open the file.txt in write mode.

fileptr = open("file.txt","w");

#overwriting the content of the file

fileptr.write("Python is the modern day language. It makes things so simple.")

#closing the opened file

fileptr.close();

Now, we can check that all the previously written content of the file is overwritten with the new text we have passed.

File.txt:

Python is the modern day language. It makes things so simple.

7. Creating a new file:The new file can be created by using one of the following access modes with the function open().

Ø x: it creates a new file with the specified name. It causes an error a file exists with the same name.

Ø a: It creates a new file with the specified name if no such file exists. It appends the content to the file if the file already exists with the specified name.

Ø w: It creates a new file with the specified name if no such file exists. It overwrites the existing file.

Consider the following example.

Example

#open the file.txt in read mode. causes error if no such file exists.

fileptr = open("file2.txt","x");

print(fileptr)

if fileptr:

print("File created successfully");

Output:

File created successfully

8. Using with statement with files: The with statement was introduced in python 2.5. The with statement is useful in the case of manipulating the files. The with statement is used in the scenario where a pair of statements is to be executed with a block of code in between.

The syntax to open a file using with statement is given below.

with open(<file name>, <access mode>) as <file-pointer>:

#statement suite

The advantage of using with statement is that it provides the guarantee to close the file regardless of how the nested block exits.

Cnsider the following example.

Example:

with open("file.txt",'r') as f:

content = f.read();

print(content)

Output:

Python is the modern day language. It makes things so simple.

9. File Pointer positions: Python provides the tell() method which is used to print the byte number at which the file pointer exists. Consider the following example.

Example:

# open the file file2.txt in read mode

fileptr = open("file2.txt","r")

#initially the filepointer is at 0

print("The filepointer is at byte :",fileptr.tell())

#reading the content of the file

content = fileptr.read();

#after the read operation file pointer modifies. tell() returns the location of the fileptr.

print("After reading, the filepointer is at:",fileptr.tell())

Output:

The filepointer is at byte : 0

After reading, the filepointer is at 26

10. Modifying file pointer position: In the real world applications, sometimes we need to change the file pointer location externally since we may need to read or write the content at various locations.For this purpose, the python provides us the seek() method which enables us to modify the file pointer position externally.

The syntax to use the seek() method is given below.

<file-ptr>.seek(offset[, from)

The seek() method accepts two parameters:

offset: It refers to the new position of the file pointer within the file.

from: It indicates the reference position from where the bytes are to be moved. If it is set to 0, the beginning of the file is used as the reference position. If it is set to 1, the current position of the file pointer is used as the reference position. If it is set to 2, the end of the file pointer is used as the reference position.

Consider the following example.

Example:

# open the file file2.txt in read mode

fileptr = open("file2.txt","r")

#initially the filepointer is at 0

print("The filepointer is at byte :",fileptr.tell())

#changing the file pointer location to 10.

fileptr.seek(10);

#tell() returns the location of the fileptr.

print("After reading, the filepointer is at:",fileptr.tell())

Output:

The filepointer is at byte : 0

After reading, the filepointer is at 10

The file related methods:

The file object provides the following methods to manipulate the files on various operating systems.

SN	Method	Description
1	file.close()	It closes the opened file. The file once closed, it can't be read or write any more.
2	File.flush()	It flushes the internal buffer.
3	File.fileno()	It returns the file descriptor used by the underlying implementation to request I/O from the OS.
4	File.isatty()	It returns true if the file is connected to a TTY device, otherwise returns false.
5	File.next()	It returns the next line from the file.
6	File.read([size])	It reads the file for the specified size.
7	File.readline([size])	It reads one line from the file and places the file pointer to the beginning of the new line.
8	File.readlines([sizehint])	It returns a list containing all the lines of the file. It reads the file until the EOF occurs using readline() function.
9	File.seek(offset[,from)	It modifies the position of the file pointer to a specified offset with the specified reference.
10	File.tell()	It returns the current position of the file pointer within the file.
11	File.truncate([size])	It truncates the file to the optional specified size.
12	File.write(str)	It writes the specified string to a file
13	File.writelines(seq)	It writes a sequence of the strings to a file.

3. What is an Exception Handling in Python?

An exception is an error which happens at the time of execution of a program. However, while running a program, Python generates an exception that should be handled to avoid your program to crash. In Python language, exceptions trigger automatically on errors, or they can be triggered and intercepted by your code.

The exception indicates that, although the event can occur, this type of event happens infrequently. When the method is not able to handle the exception, it is thrown to its caller function. Eventually, when an exception is thrown out of the main function, the program is terminated abruptly.

Common Examples of Exception:

v Division by Zero

v Accessing a file which does not exist.

v Addition of two incompatible types

v Trying to access a nonexistent index of a sequence

v Removing the table from the disconnected database server.

v ATM withdrawal of more than the available amount

Why should you use Exceptions?

Here are the reasons for using exceptions in Python:

v Exception handling allows you to separate error-handling code from normal code.

v An exception is a Python object which represents an error.

v As with code comments, exceptions helps you to remind yourself of what the program expects.

v It clarifies the code and enhances readability.

v Allows you to stimulate consequences as the error-handling takes place at one place and in one manner.

v An exception is a convenient method for handling error messages.

v In Python, you can raise an exception in the program by using the raise exception method.

v Raising an exception helps you to break the current code execution and returns the exception back to exception until it is handled.

v Processing exceptions for components which can’t handle them directly.

Rules of Exceptions

Here are some essential rules of Python exception handling:

v Exceptions must be class objects

v For class exceptions, you can use try statement with an except clause which mentions a particular class.

v Even if a statement or expression is syntactically correct, it may display an error when an attempt is made to execute it.

v Errors found during execution are called exceptions, and they are not unconditionally fatal.

Python Exception Handling Mechanism

Exception handling is managed by the following 5 keywords:

try
catch
finally
throw

1. Python Try Statement: A try statement includes keyword try, followed by a colon (:) and a suite of code in which exceptions may occur. It has one or more clauses.

During the execution of the try statement, if no exceptions occurred then, the interpreter ignores the exception handlers for that specific try statement.

In case, if any exception occurs in a try suite, the try suite expires and program control transfers to the matching except handler following the try suite.

Syntax:

try:

statement(s)

2. The catch Statement: Catch blocks take one argument at a time, which is the type of exception that it is likely to catch. These arguments may range from a specific type of exception which can be varied to a catch-all category of exceptions.

Rules for catch block:

v You can define a catch block by using the keyword catch

v Catch Exception parameter is always enclosed in parentheses

v It always represents the type of exception that catch block handles.

v An exception handling code is written between two {} curly braces.

v You can place multiple catch block within a single try block.

v You can use a catch block only after the try block.

v All the catch block should be ordered from subclass to superclass exception.

Example:

try

{

}

catch (ArrayIndexOutOfBoundsException e)

{

System.err.printin("Caught first " + e.getMessage());

}

catch (IOException e)

{

System.err.printin("Caught second " + e.getMessage());

}

3. Finally Statement in Python: Finally block always executes irrespective of an exception being thrown or not. The final keyword allows you to create a block of code that follows a try-catch block.Finally, clause is optional. It is intended to define clean-up actions which should be that executed in all conditions.

try:

raise KeyboardInterrupt

finally:

print 'welcome, world!'

Output:

Welcome, world!

KeyboardInterrupt

Finally, clause is executed before try statement.

4. Raise Statement in Python: The raise statement specifies an argument which initializes the exception object. Here, a comma follows the exception name, and argument or tuple of the argument that follows the comma.

Syntax:raise [Exception [, args [, traceback]]]

List of Standard Exceptions:

Sr.No.	Exception Name & Description
1	Exception: Base class for all exceptions
2	StopIteration: Raised when the next() method of an iterator does not point to any object.
3	SystemExit: Raised by the sys.exit() function.
4	ArithmeticError: Base class for all errors that occur for numeric calculation.
5	OverflowError: Raised when a calculation exceeds maximum limit for a numeric type.
6	FloatingPointError: Raised when a floating point calculation fails.
7	ZeroDivisionError: Raised when division or modulo by zero takes place for all numeric types.
8	EOFError: Raised when there is no input from either the raw_input() or input() function and the end of file is reached.
9	IndexError: Raised when an index is not found in a sequence.
10	lIOError: Raised when an input/ output operation fails, such as the print statement or the open() function when trying to open a file that does not exist.

#import module sys to get the type of exception

import sys

randomList=[‘a’,0,2]

for entry in randomList:

try:

print(“The entry is”,entry)

r=1/int(entry)

break

except:

print(“Oops!”,sys.exce_info()[0],”Occurred”)

print(“Next entry”)

print()

print(“The reciprocal of”,entry,”is”,r)

Output:

The entry is a

Oops! <class 'ValueError'> occurred.

Next entry.

The entry is 0

Oops! <class 'ZeroDivisionError'> occured.

Next entry.

The entry is 2

The reciprocal of 2 is 0.5

Example:

defdivide(x, y):

... try:

... result = x / y

... exceptZeroDivisionError:

... print("division by zero!")

... else:

... print("result is", result)

... finally:

... print("executing finally clause")

Unit –V

1. Explain Basic Oops Concepts in Python?

A.Python OOPs Concepts: Like other general-purpose programming languages, Python is also an object-oriented language since its beginning. It allows us to develop applications using an Object-Oriented approach. In Python, we can easily create and use classes and objects.

An object-oriented paradigm is to design the program using classes and objects. The object is related to real-word entities such as book, house, pencil, etc. The Oops concept focuses on writing the reusable code. It is a widespread technique to solve the problem by creating objects.

Major principles of object-oriented programming system are given below.

1. Class: The class can be defined as a collection of objects. It is a logical entity that has some specific attributes and methods. For example: if you have an employee class, then it should contain an attribute and method, i.e. an email id, name, age, salary, etc.

Syntax:

class ClassName:

<statement-1>

<statement-N>

2. Object:The object is an entity that has state and behavior. It may be any real-world object like the mouse, keyboard, chair, table, pen, etc.

Everything in Python is an object, and almost everything has attributes and methods. All functions have a built-in attribute __doc__, which returns the docstring defined in the function source code.

When we define a class, it needs to create an object to allocate the memory. Consider the following example.

Example:

class car:

def __init__(self,modelname, year):

self.modelname = modelname

self.year = year

def display(self):

print(self.modelname,self.year)

c1 = car("Toyota", 2016)

c1.display()

3. Method:The method is a function that is associated with an object. In Python, a method is not unique to class instances. Any object type can have methods.

4. Inheritance:Inheritance is the most important aspect of object-oriented programming, which simulates the real-world concept of inheritance. It specifies that the child object acquires all the properties and behaviours of the parent object.

By using inheritance, we can create a class which uses all the properties and behaviour of another class. The new class is known as a derived class or child class, and the one whose properties are acquired is known as a base class or parent class.

It provides the re-usability of the code.

5. Polymorphism:Polymorphism contains two words "poly" and "morphs". Poly means many, and morph means shape. By polymorphism, we understand that one task can be performed in different ways. For example - you have a class animal, and all animals speak. But they speak differently. Here, the "speak" behaviour is polymorphic in a sense and depends on the animal. So, the abstract "animal" concept does not actually "speak", but specific animals (like dogs and cats) have a concrete implementation of the action "speak".

6. Encapsulation:Encapsulation is also an essential aspect of object-oriented programming. It is used to restrict access to methods and variables. In encapsulation, code and data are wrapped together within a single unit from being modified by accident.

7. Data Abstraction:Data abstraction and encapsulation both are often used as synonyms. Both are nearly synonyms because data abstraction is achieved through encapsulation.

Abstraction is used to hide internal details and show only functionalities. Abstracting something means to give names to things so that the name captures the core of what a function or a whole program does.

2. Differences between Oops and Pops

A.Object-oriented vs. Procedure-oriented Programming languages

The difference between object-oriented and procedure-oriented programming is given below:

Sno	Object-oriented Programming	Procedural Programming
1.	Object-oriented programming is the problem-solving approach and used where computation is done by using objects.	Procedural programming uses a list of instructions to do computation step by step.
2.	It makes the development and maintenance easier.	In procedural programming, It is not easy to maintain the codes when the project becomes lengthy.
3.	It simulates the real world entity. So real-world problems can be easily solved through oops.	It doesn't simulate the real world. It works on step by step instructions divided into small parts called functions.
4.	It provides data hiding. So it is more secure than procedural languages. You cannot access private data from anywhere.	Procedural language doesn't provide any proper way for data binding, so it is less secure.
5.	Example of object-oriented programming languages is C++, Java, .Net, Python, C#, etc.	Example of procedural languages are: C, Fortran, Pascal, VB etc.

3. Explain Class and Object in Python with Example?

A. Python Classes/Objects: Python is an object oriented programming language.Almost everything in Python is an object, with its properties and methods.A Class is like an object constructor, or a "blueprint" for creating objects.

Create a Class: To create a class, use the keyword class:

Example: Create a class named MyClass, with a property named x:

class MyClass:
x = 5

The init() Function: The examples above are classes and objects in their simplest form, and are not really useful in real life applications.To understand the meaning of classes we have to understand the built-in init() function.All classes have a function called init(), which is always executed when the class is being initiated.Use the init() function to assign values to object properties, or other operations that are necessary to do when the object is being created:

Example: Create a class named Person, use the init() function to assign values for name and age:

class Person:
  def __init__(self, name, age):
    self.name = name
    self.age = age

p1 = Person("SriRama", 36)
print(p1.name)
print(p1.age)

Object Methods: Objects can also contain methods. Methods in objects are functions that belongs to the object.Let us create a method in the Person class:

Example: Insert a function that prints a greeting, and execute it on the p1 object:

class Person:
  def __init__(self, name, age):
    self.name = name
    self.age = age
  def myfunc(self):
    print("Hello my name is " + self.name)

p1 = Person("John", 36)
p1.myfunc()

The self-Parameter: The `self`-parameter is a reference to the current instance of the class, and is used to access variables that belong to the class.It does not have to be named `self` , you can call it whatever you like, but it has to be the first parameter of any function in the class:

Example: Use the words mysillyobject and abc instead of self:

class Person:
  def __init__(mysillyobject, name, age):
    mysillyobject.name = name
    mysillyobject.age = age

  def myfunc(abc):
    print("Hello my name is " + abc.name)

p1 = Person("John", 36)
p1.myfunc()

Modify Object Properties: You can modify properties on objects like this:

Example:

Set the age of p1 to 40:

p1.age = 40

Delete Object Properties: You can delete properties on objects by using the `del` keyword:

Example: Delete the age property from the p1 object:

del p1.age

Delete Objects: You can delete objects by using the `del` keyword:

Example: Delete the p1 object:

del p1

4. Explain built in class Attributes?

A. Python In-built class functions: The in-built functions defined in the class are described in the following table.

SN	Function	Description
1	getattr(obj,name,default)	It is used to access the attribute of the object.
2	setattr(obj, name,value)	It is used to set a particular value to the specific attribute of an object.
3	delattr(obj, name)	It is used to delete a specific attribute.
4	hasattr(obj, name)	It returns true if the object contains some specific attribute.

Example:

class Student:

def __init__(self,name,id,age):

self.name = name;

self.id = id;

self.age = age

#creates the object of the class Student

s = Student("John",101,22)

#prints the attribute name of the object s

print(getattr(s,'name'))           # John

# reset the value of attribute age to 23

setattr(s,"age",23)

# prints the modified value of age

print(getattr(s,'age'))                  #23

# prints true if the student contains the attribute with name id

  print(hasattr(s,'id'))                  # True

# deletes the attribute age

delattr(s,'age')

# this will give an error since the attribute age has been deleted

print(s.age)                 #AttributeError: 'Student' object has no attribute 'age'

Built-in class attributes: Along with the other attributes, a python class also contains some built-in class attributes which provide information about the class.The built-in class attributes are given

in the below table.

SN	Attribute	Description
1	__dict__	It provides the dictionary containing the information about the class namespace.
2	__doc__	It contains a string which has the class documentation
3	__name__	It is used to access the class name.
4	__module__	It is used to access the module in which, this class is defined.
5	__bases__	It contains a tuple including all base classes.

Example:

class Student:

def __init__(self,name,id,age):

self.name = name;

self.id = id;

self.age = age

def display_details(self):

print("Name:%s, ID:%d, age:%d"%(self.name,self.id,self.age))

s = Student("John",101,22)

print(s.__doc__) #None

print(s.__dict__) #{'name': 'John', 'id': 101, 'age': 22}

print(s.__module__) #__main__

5. What is Inheritance? Explain types of Inheritance.

A. Inheritance is an important aspect of the object-oriented paradigm. Inheritance provides code reusability to the program because we can use an existing class to create a new class instead of creating it from scratch.

In inheritance, the child class acquires the properties and can access all the data members and functions defined in the parent class. A child class can also provide its specific implementation to the functions of the parent class. In this section of the tutorial, we will discuss inheritance in detail.

In python, a derived class can inherit base class by just mentioning the base in the bracket after the derived class name. Consider the following syntax to inherit a base class into the derived class.

Syntax:

class derived-class(base class):

<class-suite>

A class can inherit multiple classes by mentioning all of them inside the bracket. Consider the following syntax.

Syntax:

class derive-class(<base class 1>, <base class 2>, ..... <base class n>):

Example (Single Inheritance)

class Animal:

def speak(self):

print("Animal Speaking")

#child class Dog inherits the base class Animal

class Dog(Animal):

def bark(self):

print("dog barking")

d = Dog()

d.bark()

d.speak()

Output:

dog barking

Animal Speaking

Python Multi-Level inheritance: Multi-Level inheritance is possible in python like other object-oriented languages. Multi-level inheritance is archived when a derived class inherits another derived class. There is no limit on the number of levels up to which, the multi-level inheritance is archived in python.The syntax of multi-level inheritance is given below.

Syntax

class class1:

<class-suite>

class class2(class1):

class class3(class2):

Example (Multilevel inheritance)

class Animal:

def speak(self):

print("Animal Speaking")

#The child class Dog inherits the base class Animal

class Dog(Animal):

def bark(self):

print("dog barking")

#The child class Dogchild inherits another child class Dog

class DogChild(Dog):

def eat(self):

print("Eating bread...")

d = DogChild()

d.bark()                  # dog barking

d.speak()                # Animal Speaking

d.eat() # Eating bread...

Python Multiple inheritance: Python provides us the flexibility to inherit multiple base classes in the child class.The syntax to perform multiple inheritances is given below.

Syntax:

class Base1:

<class-suite>

class Base2:

<class-suite>

class BaseN:

<class-suite>

class Derived(Base1, Base2, ...... BaseN):

<class-suite>

Example (Multiple Inheritance)

class Calculation1:

def Summation(self,a,b):

return a+b;

class Calculation2:

def Multiplication(self,a,b):

return a*b;

class Derived(Calculation1,Calculation2):

def Divide(self,a,b):

return a/b;

d = Derived()

print(d.Summation(10,20)) #30

print(d.Multiplication(10,20)) #200

print(d.Divide(10,20)) #0.5

6. What is method overloading in Python?

A. Methods in Python can be called with zero, one, or more parameters. This process of calling the same method in different ways is called method overloading. It is one of the important concepts in OOP. Two methods cannot have the same name in Python; hence method overloading is a feature that allows the same operator to have different meanings.

Method overloading…

v reduces complexities

v improves the quality of the code

v is also used for reusability and easy accessibility

Example:

class Compute:

# area method

def area(self, x = None, y = None):

if x != None and y != None:

return x * y

elif x != None:

return x * x

else:

return 0

obj = Compute() # object

print("Area Value:", obj.area()) # zero argumentà0

print("Area Value:", obj.area(4)) # one argumentà16

print("Area Value:", obj.area(3, 5)) # two argument à15

7. Explain Method Overriding with Example.

A. We can provide some specific implementation of the parent class method in our child class. When the parent class method is defined in the child class with some specific implementation, then the concept is called method overriding. We may need to perform method overriding in the scenario where the different definition of a parent class method is needed in the child class.

Consider the following example to perform method overriding in python.

Example:

class Animal:

def speak(self):

print("speaking")

class Dog(Animal):

def speak(self):

print("Barking")

d = Dog()

d.speak() #Output: Barking

Real Life Example of method overriding

Example:

class Bank:

def getroi(self):

return 10;

class SBI(Bank):

def getroi(self):

return 7;

class ICICI(Bank):

def getroi(self):

return 8;

b1 = Bank()

b2 = SBI()

b3 = ICICI()

print("Bank Rate of interest:",b1.getroi());                        # Bank Rate of interest: 10

print("SBI Rate of interest:",b2.getroi());                          # SBI Rate of interest: 7

print("ICICI Rate of interest:",b3.getroi());                       # ICICI Rate of interest: 8

8. What is Data Hiding give with an Example?

A. Data hiding is a part of object-oriented programming, which is generally used to hide the data information from the user. It includes internal object details such as data members, internal working. It maintained the data integrity and restricted access to the class member. The main working of data hiding is that it combines the data and functions into a single unit to conceal data within a class. We cannot directly access the data from outside the class.

This process is also known as the data encapsulation. It is done by hiding the working information to user. In the process, we declare class members as private so that no other class can access these data members. It is accessible only within the class.

We can perform data hiding in Python using the __ double underscore before prefix. This makes the class members private and inaccessible to the other classes.

Example -

class CounterClass:

__privateCount = 0

def count(self):

self.__privateCount += 1

print(self.__privateCount)

counter = CounterClass()

counter.count()

print(counter.__privateCount)

However we can access the private member using the class name.

print(counter._CounterClass__privatecounter)

Output:

Advantages of Data Hiding

Below are the main advantages of the data hiding.

v The class objects are disconnected from the irrelevant data.

v It enhances the security against hackers that are unable to access important data.

v It isolates object as the basic concept of OOP.

v It helps programmer from incorrect linking to the corrupt data.

v We can isolate the object from the basic concept of OOP.

v It provides the high security which stops damage to violate data by hiding it from the public.

Disadvantages of Data Hiding

Every coin has two sides if there are advantages then there will be disadvantage as well. Here are the some disadvantages are given below.

v Sometimes programmers need to write the extra lien of the code.

v The data hiding prevents linkage that act as link between visible and invisible data makes the object faster.

v It forces the programmers to write extra code to hide the important data from the common users.

Regular Expression

: Match function, Search function, Matching VS Searching, Modifiers, patterns

9. Explain Regular Expressions in Python?

A. The term Regular Expression is popularly shortened as regex. A regex is a sequence of characters that defines a search pattern, used mainly for performing find and replace operations in search engines and text processors.

Python offers regex capabilities through the re module bundled as a part of the standard library.

v Raw strings: Different functions in Python's re module use raw string as an

argument. A normal string, when prefixed with 'r' or 'R' becomes a raw string.

Example: Raw String

rawstr=r'Hello! How are you?'

print(rawstr)                          #Hello! How are you?

The difference between a normal string and a raw string is that the normal string in print() function translates escape characters (such as \n, \t etc.) if any, while those in a raw string are not.

Example: String vs Raw String

str1 ="Hello!\nHow are you?"

print("normal string:", str1)                 # normal string: Hello!

str2 =r"Hello!\nHow are you?"           # How are you?

print("raw string:",str2)                        # raw string: Hello!\nHow are you?

In the above example, \n inside str1 (normal string) has translated as a newline being printed in the next line. But, it is printed as \n in str2 - a raw string.

v meta characters: Some characters carry a special meaning when they appear as a

part pattern matching string. In Windows or Linux DOS commands, we use * and ? - they are similar to meta characters. Python's re module uses the following characters as meta characters:

. ^ $ * + ? [ ] \ | ( )

When a set of alpha-numeric characters are placed inside square brackets [], the target string is matched with these characters. A range of characters or individual characters can be listed in the square bracket. For example:

Pattern	Description
[abc]	match any of the characters a, b, or c
[a-c]	which uses a range to express the same set of characters.
[a-z]	match only lowercase letters.
[0-9]	match only digits.

The following specific characters carry certain specific meaning.

Pattern	Description
\d	Matches any decimal digit; this is equivalent to the class [0-9].
\D	Matches any non-digit character
\s	Matches any whitespace character
\S	Matches any non-whitespace character
\w	Matches any alphanumeric character
\W	Matches any non-alphanumeric character.
.	Matches with any single character except newline ‘\n’.
?	match 0 or 1 occurrence of the pattern to its left
+	1 or more occurrences of the pattern to its left
*	0 or more occurrences of the pattern to its left
\b	boundary between word and non-word. /B is opposite of /b
[..]	Matches any single character in a square bracket
\	It is used for special meaning characters like .to match a period or + for plus sign.
{n,m}	Matches at least n and at most m occurrences of preceding
a\| b	Matches either a or b

v re.match() function: This function in `re` module tries to find if the specified pattern

is present at the beginning of the given string.

Syntax: re.match(pattern, string)

The function returns None, if the given pattern is not in the beginning, and a match objects if found.

Example-1: re.match()

from re import match

mystr="Welcome to TutorialsTeacher"

obj1 =match("We",mystr)

print(obj1)

obj2 =match("teacher",mystr)

print(obj2)

Output:

<re.Match object; span=(0, 2), match='We'>

None

The match object has start and end properties.

Example-2:

print("start:",obj.start(),"end:",obj.end()) #start: 0 end: 2

The following example demonstrates the use of the range of characters to find out if a string starts with 'W' and is followed by an alphabet.

Example-3: match()

from re import match

strings=["Welcome to TutorialsTeacher","weather forecast","WinstonChurchill","W.G.Grace","Wonders of India","Water park"]

for string in strings:

       obj= match("W[a-z]",string)

       print(obj)

Output

<re.Match object; span=(0, 2), match='We'>

None

<re.Match object; span=(0, 2), match='Wi'>

None

<re.Match object; span=(0, 2), match='Wo'>

<re.Match object; span=(0, 2), match='Wa'>

v re.search() function: The `re.search()` function searches for a specified pattern

anywhere in the given string and stops the search on the first occurrence.

Example: re.search()

from re import search

string="Try to earn while you learn"

obj= search("earn", string)

print(obj)

print(obj.start(),obj.end(),obj.group())

711 earn

Output:

<re.Match object; span=(7, 11), match='earn'>

This function also returns the Match object with start and end attributes. It also gives a group of characters of which the pattern is a part of.

v re.findall() Function: As against the `search()` function, the `findall()` continues to

search for the pattern till the target string is exhausted. The object returns a list of all occurrences.

Example-1: re.findall()

from re importfindall

string="Try to earn while you learn"

obj=findall("earn", string)

print(obj)

Output:

['earn', 'earn']

This function can be used to get the list of words in a sentence. We shall use \W* pattern for the purpose. We also check which of the words do not have any vowels in them.

Example-2: re.findall()

obj=findall(r"\w*","Fly in the sky.")

print(obj)

for word inobj:

      obj= search(r"[aeiou]",word)

             if word!=''andobj==None:

                    print(word)

Output:

['Fly', '', 'in', '', 'the', '', 'sky', '', '']

Fly

sky

v re.finditer() function: The `re.finditer()` function returns an iterator object of all

matches in the target string. For each matched group, start and end positions can be obtained by span() attribute.

Example: re.finditer()

from re importfinditer

string="Try to earn while you learn"

it=finditer("earn", string)

for match in it:

               print(match.span())

Output:

(7, 11)

(23, 27)

v re.split() function: The `re.split()` function works similar to the split() method

of `str` object in Python. It splits the given string every time a white space is found. In the above example of the `findall()` to get all words, the list also contains each occurrence of white space as a word. That is eliminated by the `split()` function in `re` module.

Example: re.split()

from re import split

string="Flat is better than nested. Sparse is better than dense."

words= split(r' ', string)

print(words)

Output:

['Flat', 'is', 'better', 'than', 'nested.', 'Sparse', 'is', 'better', 'than', 'dense.']

v re.compile() Function: The `re.compile()` function returns a pattern object which

can be repeatedly used in different regex functions. In the following example, a string 'is' is compiled to get a pattern object and is subjected to the `search()` method.

Example: re.compile()

from re import*

pattern=compile(r'[aeiou]')

string="Flat is better than nested. Sparse is better than dense."

words= split(r' ', string)

for word in words:

               print(word,pattern.match(word))

Output:

Flat None

is<re.Match object; span=(0, 1), match='i'>

better None

than None

nested. None

Sparse None

is<re.Match object; span=(0, 1), match='i'>

better None

than None

dense. None

The same pattern object can be reused in searching for words having vowels, as shown below.

Example: search()

for word in words:

print(word,pattern.search(word))

Output:

Flat <re.Match object; span=(2, 3), match='a'>

is<re.Match object; span=(0, 1), match='i'>

better<re.Match object; span=(1, 2), match='e'>

than<re.Match object; span=(2, 3), match='a'>

nested. <re.Match object; span=(1, 2), match='e'>

Sparse <re.Match object; span=(2, 3), match='a'>

is<re.Match object; span=(0, 1), match='i'>

better<re.Match object; span=(1, 2), match='e'>

than<re.Match object; span=(2, 3), match='a'>

dense. <re.Match object; span=(1, 2), match='e'>

Multithreading

10. Explain Multithreading in Python?

A. A thread is the smallest unit of a program or process executed independently or scheduled by the Operating System. In the computer system, an Operating System achieves multitasking by dividing the process into threads. A thread is a lightweight process that ensures the execution of the process separately on the system. In Python 3, when multiple processors are running on a program, each processor runs simultaneously to execute its tasks separately.

Python Multithreading: Multithreading is a threading technique in Python programming to run multiple threads concurrently by rapidly switching between threads with a CPU help (called context switching). Besides, it allows sharing of its data space with the main threads inside a process that share information and communication with other threads easier than individual processes. Multithreading aims to perform multiple tasks simultaneously, which increases performance, speed and improves the rendering of the application.

Benefits of Multithreading in Python:

Following are the benefits to create a multithreaded application in Python, as follows:

It ensures effective utilization of computer system resources.
Multithreaded applications are more responsive.
It shares resources and its state with sub-threads (child) which makes it more economical.
It makes the multiprocessor architecture more effective due to similarity.
It saves time by executing multiple threads at the same time.
The system does not require too much memory to store multiple threads.

How to achieve multithreading in Python?

There are two main modules of multithreading used to handle threads in Python

1. The thread module

2. The threading module

1. Thread modules: It is started with Python 3, designated as obsolete, and can only be accessed with _thread that supports backward compatibility.

Syntax: thread.start_new_thread ( function_name, args[, kwargs] )

To implement the thread module in Python, we need to import a thread module and then define a function that performs some action by setting the target with a variable.

#Thread.py

import thread # import the thread module

import time # import time module

def cal_sqre(num): # define the cal_sqre function

print(" Calculate the square root of the given number")

for n in num:

time.sleep(0.3) # at each iteration it waits for 0.3 time

print(' Square is : ', n * n)

def cal_cube(num): # define the cal_cube() function

print(" Calculate the cube of the given number")

for n in num:

time.sleep(0.3) # at each iteration it waits for 0.3 time

print(" Cube is : ", n * n *n)

arr = [4, 5, 6, 7, 2] # given array

t1 = time.time() # get total time to execute the functions

cal_sqre(arr) # call cal_sqre() function

cal_cube(arr) # call cal_cube() function

print(" Total time taken by threads is :", time.time() - t1) # print the total time

Output:

Calculate the square root of the given number

 Square is:  16

 Square is:  25

 Square is:  36

 Square is:  49

 Square is:  4

 Calculate the cube of the given number

 Cube is:  64

 Cube is:  125

 Cube is:  216

 Cube is:  343

 Cube is:  8

 Total time taken by threads is: 3.005793809890747

2. Threading Modules: The threading module is a high-level implementation of multithreading used to deploy an application in Python

. To use multithreading, we need to import the threading module in Python Program

Methods	Description
start()	A start() method is used to initiate the activity of a thread. And it calls only once for each thread so that the execution of the thread can begin.
run()	A run() method is used to define a thread's activity and can be overridden by a class that extends the threads class.
join()	A join() method is used to block the execution of another code until the thread terminates.

Follow the given below steps to implement the threading module in Python Multithreading:

1. Import the threading module

Create a new thread by importing the threading module, as shown.

Syntax: import threading

A threading module is made up of a Thread class, which is instantiated to create a Python thread.

2. Declaration of the thread parameters: It contains the target function, argument, and kwargs as the parameter in the Thread() class.

v Target: It defines the function name that is executed by the thread.

v Args: It defines the arguments that are passed to the target function name.

For example:

import threading

def print_hello(n):

print("Hello, how old are you ", n)

t1 = threading.Thread( target = print_hello, args =(18, ))

In the above code, we invoked the print_hello() function as the target parameter. The print_hello() contains one parameter n, which passed to the args parameter.

3. Start a new thread: To start a thread in Python multithreading, call the thread class's object. The start() method can be called once for each thread object; otherwise, it throws an exception error.

Syntax:

t1.start()

t2.start()

4. Join method: It is a join() method used in the thread class to halt the main thread's execution and waits till the complete execution of the thread object. When the thread object is completed, it starts the execution of the main thread in Python.

#Joinmethod.py

import threading

def print_hello(n):

Print("Hello, how old are you? ", n)

T1 = threading.Thread( target = print_hello, args = (20, ))

T1.start()

T1.join()

Print("Thank you")

Output:

Hello, how old are you? 20

Thank you

When the above program is executed, the join() method halts the execution of the main thread and waits until the thread t1 is completely executed. Once the t1 is successfully executed, the main thread starts its execution.

5. Synchronizing Threads in Python: It is a thread synchronization mechanism that ensures

no two threads can simultaneously execute a particular segment inside the program to access the shared resources. The situation may be termed as critical sections. We use a race condition to avoid the critical section condition, in which two threads do not access resources at the same time.

Let's write a program to use the threading module in Python Multithreading.

#Threading.py

import time # import time module

import threading

from threading import *

def cal_sqre(num): # define a square calculating function

print(" Calculate the square root of the given number")

for n in num: # Use for loop

time.sleep(0.3) # at each iteration it waits for 0.3 time

print(' Square is : ', n * n)

def cal_cube(num): # define a cube calculating function

print(" Calculate the cube of the given number")

for n in num: # for loop

time.sleep(0.3) # at each iteration it waits for 0.3 time

print(" Cube is : ", n * n *n)

ar = [4, 5, 6, 7, 2] # given array

t = time.time() # get total time to execute the functions

#cal_cube(ar)

#cal_sqre(ar)

th1 = threading.Thread(target=cal_sqre, args=(ar, ))

th2 = threading.Thread(target=cal_cube, args=(ar, ))

th1.start()

th2.start()

th1.join()

th2.join()

print(" Total time taking by threads is :", time.time() - t) # print the total time

print(" Again executing the main thread")

print(" Thread 1 and Thread 2 have finished their execution.")

Output:

Calculate the square root of the given number

 Calculate the cube of the given number

 Square is:  16

 Cube is:  64

 Square is:  25

 Cube is:  125

 Square is:  36

 Cube is:  216

 Square is:  49

 Cube is:  343

 Square is:  4

 Cube is:  8

 Total time taken by threads is: 1.5140972137451172

 Again executing the main thread

 Thread 1 and Thread 2 have finished their execution.

11. What is an Array in Python and its Operations?

A. The Array is an idea of storing multiple items of the same type together, making it easier to calculate the position of each element by simply adding an offset to the base value. A combination of the arrays could save a lot of time by reducing the overall size of the code. It is used to store multiple values in a single variable. If you have a list of items that are stored in their corresponding variables like this:

car1 = "Lamborghini"

car2 = "Bugatti"

car3 = "Koenigsegg"

If you want to loop through cars and find a specific one, you can use the Array. You can use an array to store more than one item in a specific variable.

The Array can be handled in Python by a module named Array. It is useful when we must manipulate only specific data values. The following are the terms to understand the concept of an array:

Element - Each item stored in an array is called an element.

Index - The location of an element in an array has a numerical index, which is used to identify the element's position. The index value is very much important in an Array.

Array Representation:

An array can be declared in various ways and in different languages. The important points that should be considered are as follows:

The index starts with 0.
We can easily find any elements within this Array using the Index value.
The length of the Array defines the capacity to store the elements. It is written like x[100], which means the length of array x is specified by 100.

v Array operations

Some of the basic operations supported by an array are as follows:

Traverse - It prints all the elements one by one.
Insertion - It adds an element at the given index.
Deletion - It deletes an element at the given index.
Search - It searches an element using the given index or by the value.
Update - It updates an element at the given index.

The Array can be created in Python by importing the array module to the python program.

from array import *

arrayName = array(typecode, [initializers])

Accessing array elements

We can access the array elements using the respective indices of those elements.

Program code:

Here we give an example of how we access the elements of an array using its index value in Python. The code is given below -

import array as arr

a = arr.array('i', [2, 4, 5, 6])

print("First element is:", a[0])

print("Second element is:", a[1])

print("Third element is:", a[2])

print("Forth element is:", a[3])

print("last element is:", a[-1])

print("Second last element is:", a[-2])

print("Third last element is:", a[-3])

print("Forth last element is:", a[-4])

print(a[0], a[1], a[2], a[3], a[-1],a[-2],a[-3],a[-4])

Output:

Now we compile the above code in python, and after successful compilation, we run it. Then the output is given below -

First element is: 2

Second element is: 4

Third element is: 5

Forth element is: 6

last element is: 6

Second last element is: 5

Third last element is: 4

Forth last element is: 2

2 4 5 6 6 5 4 2

v How to change or add elements?

Arrays are mutable, and their elements can be changed similarly to lists.

Program code:

Here in this example, we can change or add or replace any element from the Array in Python. The code is given below -

import array as arr

numbers = arr.array('i', [1, 2, 3, 5, 7, 10])

# changing first element 1 by the value 0.

numbers[0] = 0

print(numbers) # Output: array('i', [0, 2, 3, 5, 7, 10])

# changing last element 10 by the value 8.

numbers[5] = 8

print(numbers) # Output: array('i', [0, 2, 3, 5, 7, 10])

# replace the value of 3rd to 5th element by 4, 6 and 8

numbers[2:5] = arr.array('i', [4, 6, 8])

print(numbers) # Output: array('i', [0, 2, 4, 6, 8, 10])

Output:

Now we compile the above code in python, and after successful compilation, we run it. Then the output is given below -

array('i', [0, 2, 3, 5, 7, 10])

array('i', [0, 2, 3, 5, 7, 8])

array('i', [0, 2, 4, 6, 8, 8])

v Why use Arrays in Python?

A combination of arrays saves a lot of time. The Array can reduce the overall size of the code. Using an array, we can solve a problem quickly in any language. The Array is used for dynamic memory allocation.

How to Delete Elements from an Array?

The elements can be deleted from an array using Python's del statement. If we want to delete any value from the Array, we can use the indices of a particular element.

import array as arr

number = arr.array('i', [1, 2, 3, 3, 4])

del number[2] # removing third element

print(number) # Output: array('i', [1, 2, 3, 4])

Output:

Now we compile the above code in python, and after successful compilation, we run it. Then the output is given below -

array('i', [10, 20, 40, 60])

v Finding the length of an array

The length of an array is defined as the number of elements present in an array. It returns an integer value that is equal to the total number of the elements present in that array.

Syntax

By using the syntax below, we can easily find the length of the given Array. The syntax is -

len(array_name)

Array Concatenation

We can easily concatenate any two arrays using the + symbol.

Example 1:

a=arr.array('d',[1.1 , 2.1 ,3.1,2.6,7.8])

b=arr.array('d',[3.7,8.6])

c=arr.array('d')

c=a+b

print("Array c = ",c)

Output:

Now we compile the above code in python, and after successful compilation, we run it. Then the output is given below -

Array c= array('d', [1.1, 2.1, 3.1, 2.6, 7.8, 3.7, 8.6])

Example 2:

import array as arr

x = arr.array('i', [4, 7, 19, 22]) # Initialize the array elements

print("First element:", x[0])

print("Second element:", x[1])

print("Second last element:", x[-1])

Output:

First element: 4

Second element: 7

Second last element: 22

SNo	Function with Description
1	cmp(tuple1, tuple2) Compares elements of both tuples. Example: tuple1, tuple2 =(123,'xyz'),(456,'abc') print cmp(tuple1, tuple2) print cmp(tuple2, tuple1) tuple3 = tuple2 +(786,); print cmp(tuple2, tuple3) Output: -1 1 -1
2	len(tuple) Gives the total length of the tuple. Example: tuple1, tuple2 =(123,'xyz','zara'),(456,'abc') print"First tuple length : ", len(tuple1) print"Second tuple length : ", len(tuple2) Output: First tuple length : 3 Second tuple length : 2
3	max(tuple) Returns item from the tuple with max value. Example: tuple1 =(123,'xyz','zara','abc') tuple2 = (456,700, 200) print"Max value element : ", max(tuple1) print"Max value element : ", max(tuple2) Output: Max value element : zara Max value element : 700
4	min(tuple) Returns item from the tuple with min value. Example: tuple1 =(123,'xyz','zara','abc') tuple2 = (456,700, 200) print"min value element : ", min(tuple1) print"min value element : ", min(tuple2) Output: min value element : 123 min value element : 200
5	tuple(seq) Converts a list into tuple. Example: aList =[123,'xyz','zara','abc'] aTuple = tuple(aList) print"Tuple elements : ", aTuple Output: Tuple elements : (123, 'xyz', 'zara', 'abc')

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Python Material

Why the Name Python?

2. What is the Difference Between Python and C Language?

Python vs C Language

1. Differences Between Java vs Python

3. How to Working with Python?

The Easiest Way to Run Python:

Write Python code in the file and save it.

4. Explain Basic Fundamentals in Python?

v Numeric Literals:

Example :

v String literals:

Example:

v Boolean literals:

Example:

v Literal Collections:

Example :

The operator can be defined as a symbol which is responsible for a particular operation between two operands. Operators are the pillars of a program on which the logic is built in a particular programming language. Python provides a variety of operators described as follows.

5. Explain Python Variables?

A. Variable is a name which is used to refer memory location. Variable also known as identifier and used to hold value.In Python, we don't need to specify the type of variable because Python is a type infer language and smart enough to get variable type.

Identifier Naming:Variables are the example of identifiers. An Identifier is used to identify the literals used in the program. The rules to name an identifier are given below.

Declaring Variable and Assigning Values:

Multiple Assignment

6. What are the datatypes in Python?

A. Python Data Types: Variables can hold values of different data types. Python is a dynamically typed language hence we need not define the type of the variable while declaring it. The interpreter implicitly binds the value with its type.

1. Numbers:Number stores numeric values. Python creates Number objects when a number is assigned to a variable. For example;

2. String:

3.List:

4. Tuple:

5. Dictionary:

7. What is an Operator? Explain different types of Operators?

A. Python Operators: The operator can be defined as a symbol which is responsible for a particular operation between two operands. Operators are the pillars of a program on which the logic is built in a particular programming language. Python provides a variety of operators described as follows.

1. Arithmetic operators: Arithmetic operators are used to perform arithmetic operations between two operands. It includes +(addition), - (subtraction), *(multiplication), /(divide), %(reminder), //(floor division), and exponent (**).

2. Comparison operator: (Relational Operator):

3. Assignment operators:

4. Bitwise operator:

5. Logical Operators:

6. Membership Operators:

7. Identity Operators:

12. Explain Input & Output Statements in Python.

v Assign String to a Variable: Assigning a string to a variable is done with the variable name followed by an equal sign and the string:

v Multiline Strings: You can assign a multiline string to a variable by using three quotes:

v Strings are Arrays: Like many other popular programming languages, strings in Python are arrays of bytes representing unicode characters. However, Python does not have a character data type, a single character is simply a string with a length of 1.

v Looping Through a String: Since strings are arrays, we can loop through the characters in a string, with a for loop.

v String Length:To get the length of a string, use the len() function.

v Check String: To check if a certain phrase or character is present in a string, we can use the keyword in.

v Check if NOT: To check if a certain phrase or character is NOT present in a string, we can use the keyword not in.

v Slicing:You can return a range of characters by using the slice syntax.

Example:Get the characters from position 2 to position 5 (not included):

Example: Get the characters from the start to position 5 (not included):

Example:Get the characters from position 2, and all the way to the end:

Example: Get the characters:

v Upper Case: The upper() method returns the string in upper case:

v Lower Case:The lower() method returns the string in lower case:

v Replace String:The replace() method replaces a string with another string:

v Split String: The split() method returns a list where the text between the specified separator becomes the list items.

Example: The split() method splits the string into substrings if it finds instances of the separator:

v String Concatenation: To concatenate, or combine, two strings you can use the + operator.

Example:Merge variable a with variable b into variable c:

Example:To add a space between them, add a " ":

v String Format: As we learned in the Python Variables chapter, we cannot combine strings and numbers like this:

Example:

Example: Use the format() method to insert numbers into strings:

Example:

Python Lists

Accessing Values in Lists:

Updating Lists:

Delete List Elements:

Basic List Operations:

Indexing, Slicing, and Matrixes:

Built-in List Functions & Methods

Python - Tuples

Accessing Values in Tuples:

Updating Tuples:

Delete Tuple Elements:

Basic Tuples Operations:

Indexing, Slicing, and Matrixes:

No Enclosing Delimiters:

Built-in Tuple Functions:

v Looping Through a String: Since strings are arrays, we can loop through the characters in a string, with a `for` loop.

v String Length:To get the length of a string, use the `len()` function.

v Check String: To check if a certain phrase or character is present in a string, we can use the keyword `in`.

v Check if NOT: To check if a certain phrase or character is NOT present in a string, we can use the keyword `not in`.

v Upper Case: The `upper()` method returns the string in upper case:

v Lower Case:The `lower()` method returns the string in lower case:

v Replace String:The `replace()` method replaces a string with another string:

v Split String: The `split()` method returns a list where the text between the specified separator becomes the list items.

Example: The `split()` method splits the string into substrings if it finds instances of the separator:

Example:Merge variable `a` with variable `b` into variable `c`:

Example:To add a space between them, add a `" "`:

Example: Use the `format()` method to insert numbers into strings:

v Example use with filter(): The `filter()` function in Python takes in a function and a

list as arguments.The function is called with all the items in the list and a new list is returned which contains items for which the function evaluates to `True`.

v Example use with map(): The `map()` function in Python takes in a function and a

Advantages of recursion:
1. Easier to write.
2. Readable – Code is easier to read and understand.
3. Reduce the lines of code – It takes less lines of code to solve a problem using recursion.

v Creating a Directory:We can create a new directory using the `os.mkdir()` function,

working directory to a newly created one before doing any operations in it. This is done using the `chdir()` function. The following change current working directory to `C:\MyPython`.Example: Change Working Directory

v Removing a Directory: The `rmdir()` function in the OS module removes the

import`os`

v List Files and Sub-directories: The `listdir()` function returns the list of all files and

v Standard Deviation: The `stdev()` method calculates the standard deviation on a given sample in the form of a list.Example: