Python Training in Chennai

Python Introduction, Installation

• Python Introduction
• Download Python
• Installing Python
• Verify the Installation
• Install a Text Editor or IDE (Optional)

Data Types

In Python, there are several built-in data types used to represent different kinds of values. Here are some of the fundamental data types.

• Numeric Types
• Text Type
• Sequence Types
• Set Types
• Mapping Type
• Boolean Type
• None Type

Operators

In Python, operators are symbols or keywords that perform operations on operands, which can be variables, values, or expressions. Here are some common types of operators.

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Assignment Operators
• Membership Operators
• Identity Operators
• Bitwise Operators

Flow Control Statements

In Python, flow control statements are used to manage the flow of execution in a program. They enable you to make decisions, create loops, and determine the order in which statements are executed. Here are the main flow control statements in Python.

• Conditional Statements
• Looping Statements
• Control Statements
• Exception Handling
• Pass Statement

List

In Python, a list is a versatile and widely used data type that represents an ordered, mutable collection of elements. Lists can contain elements of different data types, and you can easily modify, add, or remove elements from a list. Lists are created using square brackets [ ]. Here’s an overview of working with lists

• Creating a List
• Accessing Elements
• Slicing
• Modifying Elements
• Adding Elements
• Removing Elements

Tuple

In Python, a tuple is another sequence data type that is similar to a list. However, unlike lists, tuples are immutable, meaning their elements cannot be changed or modified after the tuple is created. Tuples are created using parentheses ( ) and can contain elements of different data types. Here’s an overview of working with tuples

• Creating a Tuple
• Accessing Elements
• Slicing
• Tuple Packing and Unpacking
• Immutable Nature

Set

In Python, a set is an unordered and mutable collection of unique elements. Sets are used to store multiple items in a single variable and are defined by enclosing the elements in curly braces { }. Here’s an overview of working with sets

• Creating a Set
• Accessing Elements
• Adding Elements
• Removing Elements
• Set Operations
• Other Set Operations

Dictionary

In Python, a dictionary is an unordered and mutable collection of key-value pairs. Dictionaries are defined using curly braces { } and consist of keys and their associated values separated by colons. Each key in a dictionary must be unique. Here’s an overview of working with dictionaries

• Creating a Dictionary
• Accessing Values
• Modifying Values
• Adding New Key-Value Pairs
• Removing Key-Value Pairs
• Dictionary Operations
• Nested Dictionaries

Functions

Function Definition

• Function Call
• Return Statement
• Default Parameters
• Variable-Length Argument Lists
• Lambda Functions
• Recursion

Module:

In Python, a module is a file containing Python definitions and statements. The file name is the module name with the suffix .py. Modules allow you to organize your code into separate files, making it more modular, maintainable, and reusable

Creating a Module

• Using a Module
• Importing Specific Functions or Variables
• Renaming Modules or Imported Items
• Using the if __name__ == “__main__”: Guard

Package

In Python, a package is a way of organizing related modules into a single directory hierarchy. It provides a mechanism for organizing large codebases and avoids naming conflicts between modules. A package is essentially a directory that contains a special file called __init__.py, which can be empty or contain Python code. This file is executed when the package is imported.

• Creating a Package
• Importing Modules from a Package
• Importing the Whole Package
• Subpackages
• Using Packages in Larger Projects

Python – OOPs Introduction:

In Python Object-Oriented Programming (OOP) is a programming paradigm that uses objects-collections of data and methods that operate on that data-to design and organize code. Python is an object-oriented programming language that supports the core principles of OOP. Here’s an introduction to OOP concepts in Python

• Class
• Object
• Attributes and Methods
• Encapsulation
• Inheritance
• Polymorphism
• Abstraction

Type of Methods:

In object-oriented programming (OOP), methods are functions that are associated with objects. They define the behavior of the objects and how they interact with each other. In Python, there are several types of methods, each serving a different purpose. Here are the main types of methods in Python

• Instance Methods
• Class Methods
• Static Methods
• Special Methods (Magic Methods or Dunder Methods)

Exception Handling:

Exception handling in Python allows you to handle runtime errors gracefully, preventing your program from crashing and providing a way to recover from unexpected situations. Python uses a try-except block to catch and handle exceptions. Here’s an overview of exception handling in Python.

• Try-Except Block
• Handling Specific Exceptions
• Else and Finally Blocks
• Raising Exceptions
• Custom Exceptions

File Handling

File handling in Python allows you to work with files on your computer. You can read from and write to files using various methods and modes. Here’s a basic overview of file handling in Python

• Opening a File
• Reading from a File
• Writing from a File
• Appending to a File
• Using with Statements
• File Modes
• Exception Handling for File Operations
• Working with Paths

Multithreading

Multithreading in Python allows you to execute multiple threads (smaller units of a process) concurrently, improving the performance of certain types of programs. However, due to the Global Interpreter Lock (GIL) in CPython, the benefits of multithreading for CPU-bound tasks are limited. Multithreading is more effective for I/O-bound tasks where threads can wait for I/O operations to complete.

• Creating Threads
• Thread Synchronization
• Thread Communication
• Daemon Threads

Python DB Programming

Python provides various libraries and modules for database programming, allowing you to interact with different Database Management Systems (DBMS). Two commonly used modules are SQLite3 (for SQLite databases) and Psycopg2 (for PostgreSQL databases). Here, I’ll provide a basic introduction to using SQLite3 and Psycopg2 for database programming in Python.

Using SQLite as an Example

• Install SQLite
• Import the SQLite Library
• Connect to a Database
• Create a Cursor Object
• Execute SQL Queries
• Querying Data
• Closing the Connection

Regular Expression

Regular expressions (regex or regexp) are sequences of characters that define a search pattern. They are used for pattern matching within strings, making them a powerful tool for text processing and manipulation. Here’s a brief overview of common regular expression concepts and usage in Python

• Basics of Regular Expressions
• Using Regular Expressions in Python

Decorator, Generator Functions

A decorator in Python is a design pattern that allows you to extend or modify the behavior of a function without modifying its actual code. Decorators are implemented using functions, and they are commonly used for tasks such as logging, timing, and authentication.

• Generator Functions

Flask

Flask is a lightweight and easy-to-extend web framework for Python. It is designed to be simple and modular, allowing developers to build web applications quickly and with minimal boilerplate code. Flask is widely used for creating small to medium-sized web applications and APIs. Below is an overview of Flask and its key features

• Installation
• Creating a Simple Flask App
• Key Concepts
• Extended Example
• Flask Extensions