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PART 1

NOVEMBER 23, 2023

JK Edu corp Avenue #1, New York

Course Content

Chapter 1: Introduction

 Brief overview of Python  Why learn Python?  Setting up Python (installation)

Chapter 2: Basics of Python

2.1 Variables and Data Types  What are variables?  Common data types (integers, floats, strings, booleans) 2.2 Operators  Arithmetic operators  Comparison operators  Logical operators 2.3 Control Flow  if statements  else statements  elif statements  while loops  for loops

Chapter 3: Data Structures

3.1 Lists  Creating lists  Indexing and slicing  Modifying lists 3.2 Dictionaries  Creating dictionaries  Accessing and modifying dictionary elements 3.3 Sets  Creating sets  Set operations 3.4 Tuples

 What are generators?  Creating and using generators 9.3 Virtual Environments  Why use virtual environments?  Creating and managing virtual environments

Chapter 10: Introduction to Libraries and Frameworks

 Overview of popular Python libraries (NumPy, Pandas, Matplotlib)  Introduction to popular frameworks (Django, Flask)

Chapter 11: Best Practices and Tips

 Writing clean and readable code  PEP 8 and coding conventions  Debugging techniques

Chapter 12: Final Projects and Further Learning

 Small projects to apply knowledge  Additional resources for further learning

CHAPTER 1:

INTRODUCTION

1. Download Python:  Visit python.org to download the latest version of Python.  Choose the appropriate version for your operating system (Windows, macOS, or Linux).
2. Installation:  Run the installer and follow the installation instructions.  During installation, you may have the option to add Python to your system's PATH. Select this option to make it easier to run Python from the command line.
3. Verification:  Open a terminal or command prompt and type the following command: python --version  This should display the installed Python version.
4. Interactive Mode:  Python comes with an interactive mode where you can type and execute Python code directly. Type python in the terminal to enter interactive mode. Congratulations! You've successfully set up Python on your system. Now, you're ready to explore the world of Python programming. In the following chapters, we'll dive into the basics of Python, covering variables, data types, control flow, and more. Each chapter will build upon the previous ones, providing a comprehensive guide for both beginners and those looking to deepen their Python knowledge. Enjoy the journey of learning Python!

CHAPTER 2: BASICS OF

PYTHON

Comparison operators

is_equal = (10 == 5) # Equal to is_not_equal = (10 != 5) # Not equal to is_greater = (10 > 5) # Greater than is_less = (10 < 5) # Less than is_greater_equal = (10 >= 5) # Greater than or equal to

Logical operators

and_result = True and False # Logical AND or_result = True or False # Logical OR not_result = not True # Logical NOT2.3 Control Flow Control flow statements allow you to control the flow of your program based on certain conditions. This includes if , else , elif statements for decision-making and while and for loops for iteration. Example: Control Flow pythonCopy code

if statement

age = 18 if age >= 18: print("You are an adult.") else: print("You are a minor.")

while loop

count = 0 while count < 5: print(count) count += 1

for loop

for i in range(3):

print(i)  if Statements: Used for decision-making. If the condition is true, the code inside the if block is executed; otherwise, the else block is executed.  while Loop: Executes a block of code as long as a specified condition is true.  for Loop: Iterates over a sequence (e.g., range of numbers) and executes a block of code for each item in the sequence. In the upcoming chapters, we'll explore more advanced topics, including data structures, functions, and object-oriented programming. These foundational concepts lay the groundwork for building more complex and sophisticated Python applications. Enjoy coding!

Chapter 3: Data Structures

In this chapter, we'll explore essential data structures in Python. These structures are fundamental for organizing and storing data efficiently. We'll cover lists, dictionaries, sets, and tuples, examining how each serves a unique purpose in Python programming. 3.1 Lists Lists are versatile and mutable collections in Python that can hold elements of different data types. They are defined using square brackets []. Example: Lists

Creating a list

fruits = ["apple", "banana", "orange"]

Indexing and slicing

first_fruit = fruits[0] print(first_fruit) # Output: "apple"

Modifying lists

fruits.append("grape") print(fruits) # Output: ["apple", "banana", "orange", "grape"]  Indexing and Slicing: Lists are zero-indexed, meaning the first element is at index 0. You can use negative indices to access elements from the end of the list. Slicing allows you to create sublists by specifying a range of indices.

Negative indexing

last_fruit = fruits[-1] print(last_fruit) # Output: "grape"

Slicing

subset = fruits[1:3] print(subset) # Output: ["banana", "orange"]

3.2 Dictionaries Dictionaries are unordered collections of key-value pairs. They are defined using curly braces {}. Example: Dictionaries

Creating a dictionary

person = {"name": "John", "age": 30, "city": "New York"}

Accessing and modifying dictionary elements

print(person["age"]) # Output: 30 person["age"] = 31 print(person) # Output: {"name": "John", "age": 31, "city": "New York"}  Accessing and Modifying: Dictionary elements are accessed using keys. You can add, modify, or delete key-value pairs.

Adding a new key-value pair

person["gender"] = "Male" print(person) # Output: {"name": "John", "age": 31, "city": "New York", "gender": "Male"}

Deleting a key-value pair

del person["city"] print(person) # Output: {"name": "John", "age": 31, "gender": "Male"} 3.3 Sets Sets are unordered collections of unique elements. They are defined using curly braces {}. Example: Sets

Creating a set

colors = {"red", "green", "blue"}

Set operations

Unpacking a tuple

x, y = coordinates print(x, y) # Output: 3 5 Understanding these data structures is crucial for effective Python programming. In the following chapters, we'll explore more advanced topics such as functions, object-oriented programming, and advanced data structures to enhance your Python skills. Practice working with lists, dictionaries, sets, and tuples to gain confidence in using these fundamental building blocks. Happy coding!

CHAPTER 4:

FUNCTIONS

print(sum_result) # Output: 8  Default Parameters: You can provide default values for parameters, making them optional when calling the function.

Function with default parameter value

def greet_with_default(name="Stranger"): print(f"Hello, {name}!") greet_with_default() # Output: "Hello, Stranger!" greet_with_default("Bob") # Output: "Hello, Bob!"  Return Values: Functions can return values using the return statement. Multiple values can be returned as a tuple.

Function returning multiple values

def get_quotient_and_remainder(dividend, divisor): quotient = dividend // divisor remainder = dividend % divisor return quotient, remainder

Unpacking the returned tuple

result = get_quotient_and_remainder(10, 3) print(result) # Output: (3, 1) 4.3 Scope and Lifetime of Variables Understanding scope is crucial when working with functions. Variables defined inside a function have local scope and are separate from variables in the global scope. Example: Scope of Variables

Global variable

global_var = 10

def function_with_local_var():

Local variable

local_var = 5 print(local_var)

Attempting to access local_var outside the function will result in an error

print(local_var)

Accessing the global variable

print(global_var) # Output: 10  Global vs. Local Scope: Variables declared inside a function are local to that function and are not accessible outside it. Global variables, on the other hand, are accessible from anywhere in the code. 4.4 Lambda Functions Lambda functions, or anonymous functions, are concise and often used for short-term operations. They are defined using the lambda keyword. Example: Lambda Function

Lambda function to calculate the square of a number

square = lambda x: x ** 2 print(square(5)) # Output: 25  Use Cases: Lambda functions are handy for one-time operations and can be passed as arguments to other functions.

Using lambda function as an argument

numbers = [1, 2, 3, 4, 5] squared_numbers = list(map(lambda x: x ** 2, numbers)) print(squared_numbers) # Output: [1, 4, 9, 16, 25]