Introduction of Python

Python3
1. Python laid its foundation in the late 1980s.
2. The implementation of Python was started in the December 1989 by Guido Van
Rossum at CWI in Netherland.
3. In February 1991, van Rossum published the code.
4. In 1994, Python 1.0 was released with new features like: lambda, map, filter, and
reduce.
5. Python 2.0 added new features like: list comprehensions, garbage collection
system was introduced in 2000.
6. On December 3, 2008, Python 3.0 (also called "Py3K") was released. It was
designed to rectify fundamental flaw of the language.
7. Python is influenced by the programming languages like ABC language and
Modula-3.

Why we need Python?
Python has top the charts in the recent years over other programming
languages like C, C++ and Java and is widely used by the programmers.
The software development companies prefer Python language because of its
versatile features and fewer programming codes.
The language has extensive support libraries and clean object-oriented designs
that increase two to tenfold of programmer’s productivity while using the
languages like Java, VB, Perl, C, C++ and C#.
The Python language has diversified application in the software development
companies such as in gaming, web frameworks and applications, language
development, prototyping, graphic design applications, etc.
It provides large standard libraries that include the areas like string operations,
Internet, web service tools, operating system interfaces and protocols.

Interactive Shell
The interactive shell is also interactive in the way that it stands between the
commands or actions and their execution. This means the Shell waits for commands
from the user, which it executes and returns the result of the execution. After this
the shell waits for the next input.
Applications of Python
GUI-Based Desktop Applications
Image Processing and Graphic Design Applications
Scientific and Computational Applications
Games
Web Frameworks and Web Applications
Enterprise and Business Applications
Operating Systems
Language Development
Prototyping

Advantages of Python Programming Language
Extensive Libraries
Python libraries contain code for various purposes like regular expressions,
documentation-generation, unit-testing, web browsers, threading, databases, CGI,
email, image manipulation, and more.
Extensible
Python can be extended to other languages. You can write some of your code in
languages like C++ or C. This comes in handy, especially in projects.
Improved Productivity
The language’s simplicity and extensive libraries render programmers more
productive than languages like Java and C++ do. Also, the fact that you need to
write less lets more get done.

IOT Opportunities
Since Python forms the basis of new platforms like Raspberry Pi, it finds the
future bright for Internet Of Things. This is a way to connect the language with
the real world.
Simple and Easy
When working with Java, you may have to create a class to print ‘Hello World’.
But in Python, just a print statement will do. It is also quite easy to learn,
understand, and code. This is why when people pick up Python, they have a
hard time adjusting to other more verbose languages like Java.
Readable
Because it is not such a verbose language, reading Python is much like reading
English. This is also why it is so easy to learn, understand, and code. It also
does not need curly braces to define blocks, and indentation is mandatory.
This further aids the readability of the code.
Object-Oriented
This language supports both the procedural and object-oriented programming
paradigms. While functions help us with code reusability, classes and objects
let us model the real world. A class allows the encapsulation of data and
functions into one.

Free and Open-Source
Python is freely available. But not only can you download python for free, but
you can also download its source code, make changes to it, and even
distribute it. It downloads with an extensive collection of libraries to help you
with your tasks.
Portable
When you code your project in a language like C++, you may need to make
some changes to it if you want to run it on another platform. But it isn’t the
same with Python. Here, you need to code only once, and you can run it
anywhere. This is called Write Once Run Anywhere (WORA). However, you
need to be careful enough not to include any system-dependent features.
Interpreted
Python is an interpreted language. Since statements are executed one by one,
debugging is easier than in compiled languages.

Disadvantages of Python Programming Language
Speed Limitations
We have seen that Python code is executed line by line. But since Python is
interpreted, it often results in slow execution. This, however, isn’t a problem
unless speed is a focal point for the project. In other words, unless high speed is a
requirement, the benefits offered by Python are enough to distract us from its
speed limitations.
Weak in Mobile Computing and Browsers
While it serves as an excellent server-side language, Python is much rarely seen on
the client-side. Besides that, it is rarely ever used to implement smartphone-based
applications.
Design Restrictions
As you know, Python is dynamically-typed. This means that you don’t need to
declare the type of variable while writing the code.

Underdeveloped Database Access Layers
Compared to more widely used technologies like JDBC (Java DataBase
Connectivity) and ODBC (Open DataBase Connectivity), Python’s database access
layers are a bit underdeveloped. Consequently, it is less often applied in huge
enterprises.
Interactive vs Normal Mode
Python has two basic modes normal and interactive mode. The normal mode is the
mode where the scripted and finished .py files are run in the Python interpreter.
Interactive mode is a command line shell which gives immediate feedback for each
statement, while running previously fed statements in active memory. As new lines
are fed into the interpreter, the fed program is evaluated both in part and in whole.

Python Character Set
A…….Z ASCII value 65 to 90
a……….z ASCII value 97 to 122
0……..9 ASCII value 48 to 57
Special Characters
Python Tokens
Each single unit which together combine to a statement is known as tokens.
Keywords
Python has a set of keywords that are reserved words that cannot be used as
variable names, function names, or any other identifiers. E.g. and, as, in etc.
Python 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
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.
An identifier cannot start with a digit. 1variable is invalid, but variable1 is
perfectly fine.
Keywords cannot be used as identifiers.
We cannot use special symbols like !, @, #, $, % etc. in our identifier.
Identifier can be of any length.
Python is case sensitive
Python is a case-sensitive language. This means, sum and Sum are not the same.
Always name identifiers that make sense.
Python Literals
Literals can be defined as a data that is given in a variable or constant.

String literals "Arjun" , '12345'
Numeric literals 100, -26.2
Boolean literals True or False
Special literals None is used to specify to that field
that is not created.
Literal Collections Collections such as tuples, lists and
Dictionary are used in Python
Python support the following literals
Python Operators
Python language supports the following types of operators.
Arithmetic Operators
Comparison (Relational) Operators
Assignment Operators
Logical Operators
Bitwise Operators
Membership Operators
Identity Operators

Python Arithmetic Operators
Operator Example
+ Addition 10 + 20 = 30
- Subtraction 10 – 20 = -10
* Multiplication 10 * 20 = 200
/ Division 5/ 2 = 2.5
% Modulus 10 % 2 = 0
** Exponent 2**3 =8
// Integer Division 5//2 = 2
Python Comparison Operators
Operator Example
== (2== 3) is not true.
!= (2!= 3) is true.
<> (2<> 3) is true. This is similar to !=
operator.
> (2 > 3) is not true.
< (2 < 3) is true.
>= (2 >= 3) is not true.
<= (2 <= 3) is true.

Python Assignment Operators
Operator Example
= c = a + b assigns value of a + b into c
+= Add AND c += a is equivalent to c = c + a
-= Subtract AND c -= a is equivalent to c = c - a
*= Multiply AND c *= a is equivalent to c = c * a
/= Divide AND c /= a is equivalent to c = c / ac /= a
is equivalent to c = c / a
%= Modulus AND c %= a is equivalent to c = c % a
**= Exponent AND c **= a is equivalent to c = c ** a
//= Floor Division c //= a is equivalent to c = c // a
Python Bitwise Operators
Python Bitwise Operators
Operator Example
& Binary AND (a & b) (means 0000 1100)
| Binary OR (a | b) = 61 (means 0011 1101)
^ Binary XOR (a ^ b) = 49 (means 0011 0001)
~ Binary Ones
Complement
(~a ) = -61 (means 1100 0011 in 2's complement
form due to a signed binary number.
<< Binary Left Shift a << 2 = 240 (means 1111 0000)
>> Binary Right Shift a >> 2 = 15 (means 0000 1111)

Python Logical Operators
Python Logical Operators
Operator Example
and Logical AND (a and b) is true.
or Logical OR (a or b) is true.
not Logical NOT Not(a and b) is false.
Python Membership Operators
Operato
r
Example
in x in y, here in results in a 1 if x is a member of sequence
y.
not in x not in y, here not in results in a 1 if x is not a member
of sequence y.
Python Identity Operators
Operator Example
is x is y, here is results in 1 if id(x) equals id(y).
is not x is not y, here is not results in 1 if id(x) is not equal to id(y).

Python Operators Precedence
The following table lists all operators from highest precedence to lowest.
Precedence Operator
1 ** Exponent
2 ~ + - Complement, unary plus and minus
3 * / % // Multiply, divide, modulo and floor division
4 + - Addition and subtraction
5 >> << Right and left bitwise shift
6 & Bitwise 'AND'
7 ^ | Bitwise exclusive `OR' and regular `OR'
8 <= < > >= Comparison operators
9 <> == != Equality operators
10 = %= /= //= -= += *= **= Assignment operators
11 is is not Identity operators
12 in not in Membership operators
13 not or and Logical operators

Python Data Types
Python provides various standard data types that define the storage method on
each of them. The data types defined in Python are given below.
Numbers
String
List
Tuple
Dictionary
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
int (signed integers like 10, 2, 29, etc.)
long (long integers used for a higher range of values like 908090800L, -
0x1929292L, etc.)
float (float is used to store floating point numbers like 1.9, 9.902, 15.2, etc.)
complex (complex numbers like 2.14j, 2.0 + 2.3j, etc.)

Strings
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.
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 [].
Tuple
A tuple is similar to the list in many ways. Like lists, tuples also contain the
collection of the items of different data types. The items of the tuple are separated
with a comma (,) and enclosed in parentheses ().
A tuple is a read-only data structure as we can't modify the size and value of the
items of a tuple.

Dictionary
Dictionary is an ordered set of a key-value pair of items. 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.
The items in the dictionary are separated with the comma and enclosed in the
curly braces {}.
Mutable and Immutable Data Types in Python
Since everything in Python is an Object, every variable holds an object
instance. When an object is initiated, it is assigned a unique object id. Its type
is defined at runtime and once set can never change, however its state can be
changed if it is mutable. A mutable object can be changed after it is created,
and an immutable object can’t.
Mutable Data Types: int, float, bool, str, tuple
Immutable Data Types: list, set, dict

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