Introduction: Why Choose Python for Your Coding Journey?

So, you want to learn how to code? Excellent choice! Coding opens up a universe of possibilities, from building websites and automating tasks to analyzing data and exploring artificial intelligence. If you're wondering where to begin, Python is consistently recommended as one of the best starting points, and for good reason. It's the engine behind giants like Instagram, Spotify, and Netflix, yet it's renowned for its readability and relatively gentle learning curve. This makes it an ideal candidate if you're looking for how to start coding python from scratch.

Python's philosophy emphasizes code readability and simplicity. Its syntax often resembles plain English, reducing the initial intimidation factor that comes with many other programming languages. This allows beginners to focus more on understanding programming *concepts* rather than wrestling with complex syntax rules. Furthermore, Python boasts a massive and supportive community, along with an extensive collection of pre-built code libraries (modules) that simplify complex tasks. Whether you dream of web development, data science, machine learning, scripting, or game development, Python has the tools and community support to help you get there.

This comprehensive guide is designed as a python programming basics tutorial for beginners. We'll walk through the essential first steps, from setting up your environment to understanding fundamental building blocks like variables, data types, operators, control structures, functions, and basic error handling. Think of this as your friendly launchpad into the exciting world of Python programming. No prior coding experience is assumed – just curiosity and a willingness to learn!

Phase 1: Getting Your Python Environment Ready

Before you can write Python code, you need Python itself! This involves installing the Python interpreter (the program that understands and executes your code) and choosing a place to write your code. This covers the essentials of setting up python development environment windows mac linux.

1.1 Installing Python

The official source for Python is python.org. The installation process varies slightly by operating system:

• Windows: Go to the Python downloads page for Windows. Download the latest stable installer (usually marked "Recommended"). During installation, **make sure to check the box that says "Add Python X.X to PATH"**. This makes it easier to run Python from your command prompt. Follow the installer prompts.
• macOS: macOS often comes with an older version of Python pre-installed. It's highly recommended to install the latest version from python.org. Go to the Python downloads page for macOS, download the installer, and run it. The installer usually handles PATH configuration automatically.
• Linux: Most Linux distributions come with Python pre-installed. You can check your version by opening a terminal and typing python3 --version. If you need a newer version or it's not installed, use your distribution's package manager (e.g., sudo apt update && sudo apt install python3 on Debian/Ubuntu, or sudo yum update && sudo yum install python3 on Fedora/CentOS).

To verify the installation, open your command prompt (Windows) or terminal (macOS/Linux) and type python --version or python3 --version. You should see the version number you installed.

1.2 Choosing a Code Editor or IDE

While you *can* write Python in a simple text editor like Notepad or TextEdit, using a dedicated code editor or Integrated Development Environment (IDE) makes life much easier. They offer features like syntax highlighting (coloring keywords), code completion (suggestions), debugging tools, and project management.

Excellent beginner-friendly options include:

• Visual Studio Code (VS Code): Free, highly popular, extensible with a vast marketplace of extensions (install the official Python extension!). Runs on Windows, macOS, and Linux.
• Thonny: Specifically designed for Python beginners. Simple interface, built-in debugger. Great for learning.
• PyCharm Edu / Community Edition: A powerful professional IDE with excellent features. The Community Edition is free, and the Edu version has learning aids. Can feel a bit more complex initially than VS Code or Thonny.
• Sublime Text: Lightweight, fast, and extensible, though might require more initial configuration than VS Code.

Install one of these. For this guide, we'll assume you have an editor ready, but we'll start with something even simpler.

1.3 Your First Interaction: The Python Interactive Shell (REPL)

Python comes with an interactive shell, also known as a REPL (Read-Eval-Print Loop). It allows you to type Python commands one at a time and see the results immediately. It's fantastic for experimenting and quick tests. This is a core part of the python interactive shell usage guide.

To start it:

1. Open your command prompt or terminal.
2. Type python or python3 (depending on your system) and press Enter.
3. You should see something like:

   Python 3.11.4 (main, Jun  7 2023, 10:13:09) [GCC 13.1.1 20230429] on linux
   Type "help", "copyright", "credits" or "license" for more information.
   >>>

The >>> is the Python prompt, waiting for your command. Let's try the traditional first program:

>>> print("Hello, Python World!")
Hello, Python World!
>>>

You typed a command (print(...)), Python evaluated it, printed the result, and gave you a new prompt. Try some simple math:

>>> 2 + 2
4
>>> 10 / 2
5.0
>>>

Experiment freely here! To exit the shell, type exit() or press `Ctrl+Z` then Enter (Windows) or `Ctrl+D` (macOS/Linux).

Phase 2: Python's Fundamental Building Blocks

Now that you're set up, let's dive into the core concepts you'll use constantly. This covers the introduction to python syntax and variables and basic data types.

2.1 Variables: Naming Things

Variables are like labeled boxes where you can store information (data). You give a box a name (the variable name) and put something inside it using the assignment operator (=).

• Variable names must start with a letter or underscore (_).
• They can contain letters, numbers, and underscores.
• They are case-sensitive (message is different from Message).
• Choose descriptive names (e.g., user_age instead of x).

# Assigning values to variables
message = "Welcome to Python!"
user_age = 30
price = 19.99

# Using variables
print(message)
print("User's age:", user_age)
print("The price is:", price)

You can change what's inside a variable box:

user_age = 30
print("Initial age:", user_age)

user_age = 31 # Update the value
print("New age:", user_age)

2.2 Data Types: Kinds of Information

Python needs to know what *kind* of data you're storing. Common basic types include:

• Integers (int): Whole numbers (e.g., -5, 0, 100).
• Floating-Point Numbers (float): Numbers with a decimal point (e.g., 3.14, -0.5, 2.0).
• Strings (str): Sequences of characters (text), enclosed in single ('...') or double quotes ("...").
• Booleans (bool): Represent truth values, either True or False (note the capitalization).

Python is dynamically typed, meaning you don't have to explicitly declare the type; Python figures it out based on the value assigned. Here are python data types explained simple examples:

count = 10         # int
pi_approx = 3.14   # float
greeting = "Hello" # str
is_active = True   # bool

print(type(count))      # Output: 
print(type(pi_approx))  # Output: 
print(type(greeting))   # Output: 
print(type(is_active))  # Output: 

Strings have useful built-in capabilities (methods):

my_string = "learning python"
print(my_string.upper())       # Output: LEARNING PYTHON
print(my_string.capitalize())  # Output: Learning python
print(my_string.replace("python", "coding")) # Output: learning coding
print(len(my_string))          # Output: 15 (length of the string)

2.3 Operators: Performing Actions

Operators are special symbols used to perform operations on values (operands). Let's cover the basics for understanding python operators for beginners:

2.3.1 Arithmetic Operators

Used for mathematical calculations:

• + (Addition)
• - (Subtraction)
• * (Multiplication)
• / (Division - always results in a float)
• // (Floor Division - discards the remainder, results in int if both operands are int)
• % (Modulus - returns the remainder of division)
• ** (Exponentiation - raise to the power)

a = 10
b = 3

print(a + b)  # Output: 13
print(a - b)  # Output: 7
print(a * b)  # Output: 30
print(a / b)  # Output: 3.333...
print(a // b) # Output: 3
print(a % b)  # Output: 1 (10 divided by 3 is 3 remainder 1)
print(a ** b) # Output: 1000 (10 to the power of 3)

2.3.2 Comparison Operators

Used to compare values; they result in a Boolean (True or False):

• == (Equal to)
• != (Not equal to)
• < (Less than)
• > (Greater than)
• <= (Less than or equal to)
• >= (Greater than or equal to)

x = 5
y = 8

print(x == y)  # Output: False
print(x != y)  # Output: True
print(x < y)   # Output: True
print(x >= 5)  # Output: True

2.3.3 Logical Operators

Used to combine Boolean values:

• and: Returns True if both sides are true.
• or: Returns True if at least one side is true.
• not: Inverts the truth value ( not True is False).

has_key = True
door_locked = False

print(has_key and not door_locked) # Output: True (True and not False -> True and True -> True)
print(has_key or door_locked)    # Output: True
print(not has_key)               # Output: False

2.4 Basic Input and Output (I/O)

Programs need to interact with the user.

• Output (print()): Displays values or text to the console. We've used this already!
• Input (input()): Reads a line of text typed by the user from the console. **Important:** input() always returns the user's input as a string, even if they type numbers!

# Using print with multiple items
name = "Alice"
age = 25
print("Name:", name, "- Age:", age) # print automatically adds spaces

# Getting user input
user_name = input("Please enter your name: ")
print("Hello,", user_name + "!") # String concatenation

# Getting numerical input (requires conversion)
age_str = input("Enter your age: ")
age_num = int(age_str) # Convert string to integer
print("Next year you will be:", age_num + 1)

# Or combine input and conversion
height_str = input("Enter your height in meters (e.g., 1.75): ")
height_float = float(height_str) # Convert string to float
print("Your height:", height_float, "meters")

Phase 3: Controlling the Flow of Your Program

Programs rarely execute instructions straight from top to bottom. Control flow statements allow you to make decisions and repeat actions based on conditions. This section covers python control flow if else loops explained.

Crucial Concept: Indentation! Unlike many languages that use curly braces {}, Python uses whitespace (specifically, indentation - typically 4 spaces) to define blocks of code. This is not just for style; it's mandatory syntax. Code that is executed under an if, else, for, or while statement *must* be indented.

3.1 Conditional Statements: Making Decisions (if, elif, else)

These statements allow your program to execute different code blocks based on whether a condition is true or false.

• if condition:: Executes the indented block *only if* the condition is True.
• elif condition: (short for "else if"): Checked *only if* the preceding if (or elif) conditions were False. Executes its block if its condition is True. You can have multiple elif statements.
• else:: Executes its indented block *only if* all preceding if and elif conditions were False. It's optional.

temperature = 25

if temperature > 30:
    print("It's hot outside!") # Indented block for 'if'
elif temperature > 20:
    print("It's warm and pleasant.") # Indented block for 'elif'
elif temperature < 10:
    print("It's quite cold.") # Another 'elif'
else:
    print("It's moderate weather.") # Indented block for 'else'

print("Weather check complete.") # This line is NOT indented, so it runs after the if/elif/else structure

3.2 Loops: Repeating Actions (for and while)

Loops are used to execute a block of code multiple times.

3.2.1 for Loops: Iterating Over Sequences

The for loop is typically used to iterate over items in a sequence (like a string, a list, or a range of numbers).

# Looping through characters in a string
for char in "Python":
    print(char) # Prints P, y, t, h, o, n on separate lines

# Looping through a range of numbers
# range(5) generates numbers from 0 up to (but not including) 5
print("Numbers 0 to 4:")
for i in range(5):
    print(i)

# range(start, stop, step)
print("Even numbers 2 to 8:")
for num in range(2, 9, 2): # Start at 2, stop before 9, step by 2
    print(num)

3.2.2 while Loops: Repeating While a Condition is True

The while loop continues executing its block as long as its condition remains True. It's essential to ensure the condition eventually becomes false, otherwise you create an infinite loop!

count = 0
print("Counting up to 3:")
while count < 3:
    print("Count is:", count)
    count = count + 1 # Crucial: Update the variable controlling the condition!

print("Loop finished.")

# Example: Simple validation loop
user_input = ""
while user_input != "quit":
    user_input = input("Enter text (or 'quit' to exit): ")
    print("You entered:", user_input)

print("Exiting program.")

3.2.3 Loop Control: break and continue

• break: Immediately exits the current loop (the innermost one if nested).
• continue: Skips the rest of the current iteration and proceeds to the next iteration of the loop.

print("Finding first number divisible by 3:")
for number in range(1, 10):
    if number % 3 == 0:
        print("Found it:", number)
        break # Exit the loop once found
    print("Checking:", number)

print("\nPrinting odd numbers up to 10:")
for number in range(1, 11):
    if number % 2 == 0: # If number is even...
        continue # ...skip the print statement for this iteration
    print(number)

Phase 4: Organizing Code with Functions

As programs grow, repeating code becomes messy and hard to maintain. Functions allow you to group a block of code, give it a name, and reuse it whenever needed. This is fundamental to writing clean, efficient code and key to the writing basic python functions tutorial.

4.1 Defining and Calling Functions

• Use the def keyword to define a function, followed by the function name, parentheses (), and a colon :.
• The code block within the function must be indented.
• To execute the function, you "call" it by using its name followed by parentheses.

# Define a simple function
def greet_user():
    """This is a docstring: It explains what the function does."""
    print("Hello there!")
    print("Welcome to the function section.")

# Call the function multiple times
print("Calling the function now:")
greet_user()
print("Calling it again:")
greet_user()

The text within triple quotes ("""...""") right after the def line is called a **docstring**. It's good practice to include one to explain what your function does.

4.2 Functions with Parameters and Arguments

Functions can accept input values, called **parameters** (defined in the def line), which allow them to be more flexible. When you call the function, you provide **arguments** for those parameters.

# Function with one parameter
def greet_by_name(name):
    """Greets a user by their provided name."""
    print("Hello,", name + "!")

# Calling with different arguments
greet_by_name("Alice")
greet_by_name("Bob")

# Function with multiple parameters
def add_numbers(num1, num2):
    """Calculates and prints the sum of two numbers."""
    total = num1 + num2
    print(f"{num1} + {num2} = {total}") # Using an f-string for formatted output

add_numbers(5, 3)
add_numbers(100, -20)

(f"..." is an f-string, a convenient way to embed variable values directly inside a string.)

4.3 Functions That Return Values

Functions can also perform calculations or operations and send a result back to the part of the program that called them using the return statement.

def multiply_numbers(num1, num2):
    """Calculates and returns the product of two numbers."""
    product = num1 * num2
    return product # Send the result back

# Call the function and store the returned value
result = multiply_numbers(6, 7)
print("The result of multiplication is:", result)

# You can use the returned value directly
print("Another multiplication:", multiply_numbers(10, 5))

def check_if_even(number):
    """Returns True if the number is even, False otherwise."""
    if number % 2 == 0:
        return True
    else:
        return False

is_10_even = check_if_even(10)
print("Is 10 even?", is_10_even) # Output: True
print("Is 7 even?", check_if_even(7)) # Output: False

Functions help break down complex problems into smaller, manageable, reusable pieces – a core principle of good programming (often called DRY: Don't Repeat Yourself).

Phase 5: Introduction to Basic Data Structures

Variables hold single pieces of data. Often, you need to store and manage collections of data. Python provides several built-in data structures for this. Here's a basic overview, part of the python lists tuples dictionaries basics guide.

5.1 Lists: Ordered, Mutable Collections

Lists store an ordered sequence of items, enclosed in square brackets []. Lists are **mutable**, meaning you can change their contents (add, remove, or modify items) after they are created.

• Items are accessed by their **index** (position), starting from 0.
• Can contain items of different data types.

# Creating a list
fruits = ["apple", "banana", "cherry"]
numbers = [1, 5, 2, 8, 3]
mixed = [10, "hello", True, 3.14]

# Accessing items by index (0-based)
print("First fruit:", fruits[0])    # Output: apple
print("Second number:", numbers[1]) # Output: 5

# Modifying a list item
fruits[1] = "blueberry"
print("Updated fruits:", fruits)    # Output: ['apple', 'blueberry', 'cherry']

# Adding items
fruits.append("orange") # Adds to the end
print("Appended fruits:", fruits)   # Output: ['apple', 'blueberry', 'cherry', 'orange']

# Removing items
removed_fruit = fruits.pop(2) # Removes item at index 2 ('cherry') and returns it
print("Removed:", removed_fruit)
print("List after pop:", fruits)    # Output: ['apple', 'blueberry', 'orange']

# Length of a list
print("Number of fruits:", len(fruits)) # Output: 3

# Looping through a list
print("My favorite fruits:")
for fruit in fruits:
    print(fruit)

5.2 Tuples: Ordered, Immutable Collections

Tuples are similar to lists (ordered sequences) but are enclosed in parentheses (). The key difference is that tuples are **immutable** – once created, you cannot change their contents (no adding, removing, or modifying items).

Tuples are often used for data that shouldn't change, like coordinates or fixed configuration settings.

# Creating a tuple
point = (10, 20) # x, y coordinates
colors = ("red", "green", "blue")

# Accessing items (same as lists)
print("X coordinate:", point[0]) # Output: 10
print("Second color:", colors[1])# Output: green

# Trying to change a tuple causes an error!
# point[0] = 15 # This would raise a TypeError

# Looping through a tuple
for color in colors:
    print(color)

# Single-item tuples need a trailing comma
single_tuple = (99,) # The comma makes it a tuple, not just parentheses around a number
print(type(single_tuple)) # Output: 

5.3 Dictionaries: Unordered Key-Value Pairs

Dictionaries store collections of items as **key-value pairs**, enclosed in curly braces {}. Think of them like real-world dictionaries where you look up a word (the key) to find its definition (the value).

• Keys must be unique and immutable (strings, numbers, or tuples are common keys).
• Values can be any data type and can be duplicated.
• Items are accessed using their keys, not numerical indices.
• Dictionaries are **mutable**.

# Creating a dictionary
student_grades = {
    "Alice": 85,
    "Bob": 92,
    "Charlie": 78
}

# Accessing values using keys
print("Bob's grade:", student_grades["Bob"]) # Output: 92

# Adding a new key-value pair
student_grades["David"] = 88
print(student_grades) # Output: {'Alice': 85, 'Bob': 92, 'Charlie': 78, 'David': 88}

# Modifying an existing value
student_grades["Alice"] = 87
print("Alice's updated grade:", student_grades["Alice"]) # Output: 87

# Removing a key-value pair
del student_grades["Charlie"]
print("After removing Charlie:", student_grades)

# Checking if a key exists
if "Alice" in student_grades:
    print("Alice's grade is available.")

# Looping through keys
print("\nStudents:")
for student_name in student_grades.keys():
    print(student_name)

# Looping through values
print("\nGrades:")
for grade in student_grades.values():
    print(grade)

# Looping through key-value pairs
print("\nStudent Grades:")
for student, grade in student_grades.items():
    print(f"{student}: {grade}")

Lists, tuples, and dictionaries are fundamental tools for structuring data in Python.

Phase 6: Using Modules and Libraries - Borrowing Code

One of Python's greatest strengths is its vast ecosystem of **modules** and **libraries**. A module is simply a Python file containing definitions and statements (functions, variables, classes). A library is typically a collection of related modules. Using them allows you to leverage code written by others, saving you time and effort.

6.1 Importing Modules (import)

The import statement is used to bring code from a module into your current script. This is how python modules importing explained works at its core.

Python comes with a rich **Standard Library** – a collection of modules included with every Python installation. Let's use the math module as an example:

import math # Import the entire math module

# To use functions/constants from the module, prefix with the module name and a dot (.)
print("Value of Pi:", math.pi)
print("Square root of 16:", math.sqrt(16))
print("Ceiling of 4.3 (rounds up):", math.ceil(4.3))

# Another useful standard library module: random
import random

print("Random integer between 1 and 10:", random.randint(1, 10))
choices = ["rock", "paper", "scissors"]
print("Random choice:", random.choice(choices))

6.2 Importing Specific Parts (from...import)

You can also import specific functions or variables directly, avoiding the need to use the module name prefix.

from math import pi, sqrt # Import only pi and sqrt from math

print("Value of Pi directly:", pi)
print("Square root of 25 directly:", sqrt(25))

# You can import everything (generally discouraged for clarity, except maybe for small scripts)
# from random import *
# print(randint(1, 100)) # Can call randint directly now

6.3 Using Aliases (import...as)

If a module name is long or conflicts with your own variable names, you can give it a shorter alias.

import math as m # Import math and call it 'm'

print("Cosine of 0:", m.cos(0))

from random import randint as get_random_int # Give randint a different name

print("Another random int:", get_random_int(100, 200))

Beyond the standard library, you can install countless third-party packages using `pip` (e.g., `pip install requests` for web requests, `pip install pandas` for data analysis, `pip install flask` for web development).

Phase 7: Basic Error Handling (try...except)

Things don't always go as planned. Users might enter invalid input, files might be missing, or network connections might fail. If your program encounters an error (called an **exception**), it will normally crash. The try...except block allows you to gracefully handle potential errors. This is a brief look at python error handling try except basics.

• The code that *might* cause an error is placed inside the try block.
• If an error occurs within the try block, Python jumps to the corresponding except block.
• If no error occurs, the except block is skipped.

try:
    numerator = int(input("Enter a numerator: "))
    denominator = int(input("Enter a denominator: "))
    result = numerator / denominator
    print(f"The result is: {result}")

except ZeroDivisionError: # Specifically handle division by zero
    print("Error: Cannot divide by zero!")

except ValueError: # Specifically handle invalid number input
    print("Error: Please enter valid numbers!")

except Exception as e: # Catch any other general exception (use sparingly)
    print(f"An unexpected error occurred: {e}")

finally: # Optional: This block *always* executes, error or not
    print("Calculation attempt finished.")

print("Program continues after try-except block...")

Handling potential errors makes your programs more robust and user-friendly.

Phase 8: Practice Makes Perfect - Your Next Steps

Congratulations! You've covered the absolute essentials of Python programming. But learning to code is like learning a musical instrument – reading about it isn't enough; you need to practice consistently.

8.1 Practice, Practice, Practice!

• Revisit Examples:** Go back through the code examples in this guide. Type them out yourself (don't just copy-paste). Experiment by changing values and observing the results.
• Use the Interactive Shell:** Keep using the REPL for quick tests and exploring how functions and operators work.
• Solve Simple Problems:** Find beginner coding exercises online (sites like HackerRank, Codewars, or project-based tutorial sites often have easy sections).

8.2 Easy Python Projects for Absolute Beginners

Applying your knowledge to small projects is the best way to solidify understanding. Try these easy python projects for absolute beginners:

• Simple Calculator:** Takes two numbers and an operator (+, -, *, /) as input and prints the result. (Practices input, type conversion, if/elif/else).
• Number Guessing Game:** The program chooses a random number, and the user tries to guess it. The program gives hints ("higher" or "lower"). (Practices random module, loops, input, conditionals).
• Basic To-Do List:** Allows users to add tasks, view tasks, and maybe mark them as done (initially just printing to the console). (Practices lists, loops, input, functions).
• Simple Text Analyzer:** Takes a string as input and counts the number of words or characters. (Practices strings, loops).

Start simple and gradually increase complexity.

8.3 Embrace Best Practices Early

Even as a beginner, start building good habits. Follow these best practices python coding beginners should know:

• Write Clear, Readable Code:** Use descriptive variable and function names.
• Use Comments Sparingly:** Explain the *why*, not the *what*. Good code should be largely self-explanatory. Use docstrings for functions.
• Keep Functions Small:** Functions should ideally do one thing well.
• Don't Repeat Yourself (DRY):** If you find yourself writing the same code block multiple times, turn it into a function.
• Test Your Code:** Even simple `print` statements can help verify things are working as expected.

8.4 Resources for Further Learning

• Official Python Tutorial:** The official documentation includes a thorough tutorial (docs.python.org/3/tutorial/).
• Online Courses:** Platforms like Coursera, edX, Udemy, Codecademy offer interactive Python courses.
• Books:** Many excellent beginner Python books are available (e.g., "Python Crash Course," "Automate the Boring Stuff with Python").
• Community:** Engage with forums (Stack Overflow, Reddit's r/learnpython) – ask questions (after searching first!) and learn from others.

Conclusion: Your Python Adventure Has Begun!

You've taken the crucial first steps on your Python programming journey! We've covered setting up your environment, explored fundamental syntax like variables, data types, and operators, learned how to control program flow with conditionals and loops, organized code with functions, touched upon basic data structures, understood the power of modules, and even dipped into error handling. This foundational knowledge is your springboard into the vast possibilities Python offers.

Remember, learning to code is a marathon, not a sprint. Be patient with yourself, celebrate small victories, embrace challenges as learning opportunities, and most importantly, keep practicing. The more you code, the more intuitive these concepts will become. The Python community is vast and welcoming, so don't hesitate to seek help when you get stuck. Welcome to the world of Python – happy coding!

Simulated References & Further Learning (Python Basics)

Continue your learning journey with these types of resources:

• Official Python Resources:**
  • The Python Tutorial (docs.python.org/3/tutorial/) - The definitive guide.
  • Python Standard Library Reference (docs.python.org/3/library/) - Explore built-in modules.
  • Python Language Reference (docs.python.org/3/reference/) - For deep dives into syntax.
• Beginner-Friendly Books:**
  • "Python Crash Course" by Eric Matthes
  • "Automate the Boring Stuff with Python" by Al Sweigart (Practical focus)
  • "Head First Python" by Paul Barry (Visual learning style)
• Interactive Platforms:**
  • Codecademy (Interactive Python course)
  • freeCodeCamp (Python certifications)
  • HackerRank / Codewars (Practice problems)
  • LeetCode (More algorithm-focused practice)
• Online Courses (Various Platforms):**
  • Coursera (e.g., "Python for Everybody" by University of Michigan)
  • edX
  • Udemy
• Community Forums:**
  • Stack Overflow (For specific questions - search first!)
  • Reddit: r/learnpython, r/Python
• Development Environment Guides:**
  • Visual Studio Code Python Tutorial
  • PyCharm Documentation
  • Thonny Website