Python Introduction

• Python is a powerful and beginner-friendly programming language created by Guido van Rossum in 1989 and officially released in 1991.
• Guido developed Python during his free time over the holidays while working at a research institute in the Netherlands.
• The name "Python" doesn't come from the snake but from Guido's favorite comedy show, "Monty Python's Flying Circus".
• He wanted the language to be fun and easy to use, just like the show.

Version :

• Python became popular because of its simple and readable syntax, which looks a lot like regular English.
• Over time, it has evolved through various versions. The first version, Python 1.0, was released with basic features like functions and exception handling.
• In 2000, Python 2.0 introduced advanced tools like list comprehensions and automatic memory cleanup.
• Later, in 2008, Python 3.0 made the language cleaner and more efficient, though it wasn’t backward-compatible with older versions.
• Today, Python is one of the most widely used programming languages in the world

Applications of Python:

• Python is incredibly versatile and is used in many fields.
• It is popular for web development, where frameworks like Django and Flask help build websites.
• In data science and machine learning, Python is the top choice, with powerful libraries like Pandas and TensorFlow.
• It is also used for automation, helping to write scripts that can save time by performing repetitive tasks automatically.
• Python is even used in game development, desktop applications, and IoT (Internet of Things), making it a tool that fits almost every need.

IDE Tools to use Python:

• IDE is Integrated Development Environment.
• It is a software application that provides tools like a code editor, debugger, and compiler/interpreter in one interface, making programming easier and more efficient.

Python IDE Tools:

• PyCharm
• Visual Studio Code (VS Code)
• Jupyter Notebook
• IDLE
• Thonny
• Spyder
• Atom
• Sublime Text
• Anaconda
• Wing IDE

Python Scripting

• Python scripting refers to writing Python code that automates tasks, processes data, or performs specific actions.
• Unlike full-fledged software development, scripting focuses on creating scripts—short programs meant to execute a series of instructions or automate a task efficiently.

Common Applications of Python Scripting

• System Administration: Automating file operations, running system commands, or managing servers. Example: Renaming multiple files in a folder.
• Data Processing: Cleaning, transforming, and analyzing data. Example: Reading CSV files, applying calculations, and saving results.
• Web Scraping: Extracting data from websites. Example: Scraping product prices from e-commerce sites.
• API Interaction: Automating API calls for data retrieval or updates. Example: Fetching weather data from an API.
• Testing and Debugging: Writing test cases for software and debugging issues.
• Task Automation: Scheduling and running periodic tasks. Example: Sending automated email reminders.
• Machine Learning and AI: Running training scripts for models. Example: Automating data preprocessing and model evaluation.

Python Interactive Mode (REPL)

• R: Read – It reads the code or input you type.
• E: Evaluate – It evaluates the code or expression.
• P: Print – It displays the result of the evaluation.
• L: Loop – It goes back to accept more input in a loop.

use windows powershell: code to check python version

python --version

numeric calculations

variables

Variable are the containers which holds the data.

ways of writting variables:

1. snake case : This is the most commonly used convention in Python. Words are separated by underscores, and all letters are lowercase. Ex: my_variable_name = 10 first_name = "Eshwari"

2. Camel Case: This is less common in Python but still used by some developers. The first word is lowercase, and each subsequent word starts with an uppercase letter. Ex: myVariableName = 10 firstName = "Eshwari"

3. Pascal Case: Similar to camel case, but the first word also starts with an uppercase letter. This is usually used for class names rather than variables. Ex: MyVariableName = 10 FirstName = "Eshwari"

4. Upper Case (Screaming Snake Case): Ex: MY_VARIABLE_NAME = "Esh" USER_AGE = 45

5. Global vs Local Variables A global variable is a variable that is declared outside of any function, class, or method and can be accessed and modified from anywhere in the program. A local variable, on the other hand, is a variable that is declared within a function or block of code and is only accessible within that function or block.

Variable = 10 #global variable

Ex:

def function():

   value=5 #local variable
   Global variable # using the global variable
   variable += value
   print(f"local variable:{value}")
   print(f"global variable: {variable}")
function()

Rules for Naming Variables:

• Start with a Letter or Underscore
• Followed by Letters, Digits, or Underscores
• Case-Sensitive
• No Spaces
• No Reserved Keywords

Data type

Primitive Data Types:

Primitive data types are the most basic data types available within a programming language. These types are predefined by the language and are used to represent simple values.

• Integer (int): Represents whole numbers without a fractional part.
• Floating-point (float): Represents numbers with a fractional part (decimals).
• Character (char): Represents a single character (e.g., 'a', 'b', '1', '$').
• Boolean (bool): Represents true or false values (True or False in Python)

In Python, these include:

• int: For integers.
• float: For floating-point numbers.
• bool: For boolean values.
• str: For strings (though in some languages, str is not considered a primitive type).

Non-Primitive Data Types:

Non-primitive data types are more complex and are derived from primitive data types. They are used to store multiple values or collections of values.

In Python, non-primitive data types include:

• list: Ordered and mutable sequences of elements.
• tuple: Ordered and immutable sequences of elements.
• set: Unordered and mutable collections of unique elements.
• frozenset: Unordered and immutable collections of unique elements.
• dict: Collections of key-value pairs.

Comment Lines in Python

Comment lines are lines of text in a code file that the Python interpreter ignores. They are used to explain and annotate the code, making it more understandable for anyone reading it, including your future self.

Why Use Comments?

• Clarify Code Functionality: Explain what specific parts of the code are doing.
• Provide Context: Offer background information or the purpose of certain sections of the code.
• Improve Readability: Make the code more readable and easier to follow.
• Debugging: Temporarily disable certain parts of the code without deleting them.

Types of Comments in Python

• Single-line Comments = #

• Multi-line comments = """ or '''

Arithmetic Operators in Python:

• Addition (+) : Adds two operands. a=1

b=2

result = a+b

print(result)

• Subtraction (-) : Subtracts the second operand from the first. a=1

b=2

result = a-b

print(result)

• Multiplication (*) : Multiplies two operands

a=1

b=2

result = a*b

print(result)

• Division (/) : Divides the first operand by the second and returns a float.

a=1

b=2

result = a/b

print(result)

• Floor Division (//) : Divides the first operand by the second and returns the largest integer less than or equal to the result.

a=1

b=2

result = a//b

print(result)

• Modulus (%) : Returns the remainder of the division of the first operand by the second.

a=1

b=2

result = a%b

print(result)

• Exponentiation (**) : Raises the first operand to the power of the second.

a=1

b=2

result = a**b

print(result)

Logical Functions in Python

• Logical AND (and): Returns True if both operands are true. a = True

b = False

result = a and b # result is False

• Logical OR (or): Returns True if at least one of the operands is true. a = True

b = False

result = a or b

• Logical NOT (not): Returns True if the operand is false and vice versa.

a = True

result = not a # result is False