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How To Learn Python From Scratch: A Comprehensive Guide?

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Learning Python from scratch can feel daunting, but with the right approach, it’s entirely achievable. At learns.edu.vn, we provide structured learning paths and expert guidance to help you master Python. We’ll break down the process into manageable steps, making your learning journey efficient and enjoyable. Unlock the power of Python programming and embark on an exciting journey towards a fulfilling career, enhanced problem-solving abilities, and a world of endless possibilities with our guidance and resources tailored to beginners.

1. Understanding the Basics of Python

Before diving into complex code, it’s crucial to grasp the fundamentals of Python. This involves understanding what Python is, its key features, and its diverse applications.

1.1. What is Python?

Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability, using significant indentation. Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented, and functional programming.

Key characteristics:

  • Interpreted: Python code is executed line by line, making it easier to debug.
  • Dynamically Typed: Variable types are checked during runtime, reducing the need for explicit declarations.
  • High-Level: Python abstracts away many low-level details, allowing developers to focus on problem-solving.
  • Versatile: Suitable for web development, data science, scripting, and more.

1.2. Key Features of Python

Python boasts several features that make it a favorite among developers:

  • Simple Syntax: Easy to read and understand, even for beginners.
  • Large Standard Library: Includes modules and functions for various tasks.
  • Cross-Platform Compatibility: Runs on Windows, macOS, and Linux.
  • Extensive Community Support: A vast community contributes to libraries, frameworks, and resources.
  • Object-Oriented: Supports object-oriented programming concepts.

1.3. Applications of Python

Python’s versatility shines through its wide range of applications:

  • Web Development: Frameworks like Django and Flask enable robust web applications.
  • Data Science: Libraries like NumPy, Pandas, and Scikit-learn are essential for data analysis and machine learning.
  • Scripting: Automating tasks and creating small utilities.
  • Scientific Computing: Used in research and simulations.
  • Game Development: Libraries like Pygame facilitate game creation.

2. Setting Up Your Python Environment

Before you can start coding, you need to set up your development environment. This involves installing Python and choosing an Integrated Development Environment (IDE).

2.1. Installing Python

  1. Download Python: Go to the official Python website (https://www.python.org) and download the latest version for your operating system.
  2. Run the Installer:
    • Windows: Check the “Add Python to PATH” box during installation to make Python accessible from the command line.
    • macOS: The installer may prompt you to install additional tools. Follow the instructions.
    • Linux: Python is often pre-installed. If not, use your distribution’s package manager (e.g., apt for Ubuntu, yum for Fedora).
  3. Verify Installation: Open a command prompt or terminal and type python --version or python3 --version. This should display the installed Python version.

2.2. Choosing an IDE

An IDE enhances your coding experience with features like code completion, debugging tools, and project management. Here are some popular options:

  • Visual Studio Code (VS Code): A lightweight but powerful editor with excellent Python support through extensions.
  • PyCharm: A dedicated Python IDE with advanced features for professional development.
  • Jupyter Notebook: Ideal for data science and interactive computing.
  • Sublime Text: A customizable text editor with Python syntax highlighting and plugins.
  • IDLE: Python’s Integrated Development and Learning Environment, which is included with Python.

Setting up VS Code for Python:

  1. Install VS Code: Download from the official website (https://code.visualstudio.com).
  2. Install the Python Extension: Open VS Code, go to the Extensions view (Ctrl+Shift+X), and search for “Python” by Microsoft. Install the extension.
  3. Configure Python Interpreter: VS Code will automatically detect your Python installation. If not, you can manually set the interpreter path in the settings.

2.3. Understanding Virtual Environments

Virtual environments isolate Python projects and their dependencies. This prevents conflicts between different projects.

Creating a virtual environment:

  1. Install virtualenv: Open a command prompt or terminal and type pip install virtualenv.
  2. Create a new environment: Navigate to your project directory and type virtualenv venv (where venv is the name of your environment).
  3. Activate the environment:
    • Windows: venvScriptsactivate
    • macOS/Linux: source venv/bin/activate

When the virtual environment is active, your terminal prompt will be prefixed with the environment name (e.g., (venv)).

3. Learning Basic Python Syntax

With your environment set up, it’s time to learn the basic syntax of Python. This includes variables, data types, operators, and control structures.

3.1. Variables and Data Types

In Python, a variable is a named storage location that holds a value. Python supports several built-in data types:

  • Integers (int): Whole numbers (e.g., 10, -5).
  • Floating-Point Numbers (float): Numbers with a decimal point (e.g., 3.14, -0.5).
  • Strings (str): Sequences of characters (e.g., "Hello", 'Python').
  • Booleans (bool): True or False.
  • Lists (list): Ordered collections of items (e.g., [1, 2, 3], ['a', 'b', 'c']).
  • Tuples (tuple): Ordered, immutable collections of items (e.g., (1, 2, 3), ('a', 'b', 'c')).
  • Dictionaries (dict): Key-value pairs (e.g., {'name': 'Alice', 'age': 30}).
  • Sets (set): Unordered collections of unique items (e.g., {1, 2, 3}).

Example:

 # Integer
 age = 30


 # Float
 height = 5.9


 # String
 name = "Alice"


 # Boolean
 is_student = False


 # List
 numbers = [1, 2, 3, 4, 5]


 # Tuple
 coordinates = (10, 20)


 # Dictionary
 person = {'name': 'Alice', 'age': 30}


 # Set
 unique_numbers = {1, 2, 3, 4, 5}

3.2. Operators

Operators are symbols that perform operations on variables and values. Python supports various types of operators:

  • Arithmetic Operators: + (addition), - (subtraction), * (multiplication), / (division), // (floor division), % (modulus), ** (exponentiation).
  • Comparison Operators: == (equal), != (not equal), > (greater than), < (less than), >= (greater than or equal), <= (less than or equal).
  • Logical Operators: and, or, not.
  • Assignment Operators: =, +=, -=, *=, /=, //=, %=, **=.
  • Bitwise Operators: & (AND), | (OR), ^ (XOR), ~ (NOT), << (left shift), >> (right shift).
  • Membership Operators: in, not in.
  • Identity Operators: is, is not.

Example:

 x = 10
 y = 5


 # Arithmetic Operators
 print(x + y)  # Output: 15
 print(x - y)  # Output: 5
 print(x * y)  # Output: 50
 print(x / y)  # Output: 2.0
 print(x // y) # Output: 2
 print(x % y)  # Output: 0
 print(x ** y) # Output: 100000


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


 # Logical Operators
 a = True
 b = False
 print(a and b) # Output: False
 print(a or b)  # Output: True
 print(not a)  # Output: False


 # Assignment Operators
 x += y # x = x + y
 print(x)  # Output: 15


 # Membership Operators
 numbers = [1, 2, 3, 4, 5]
 print(3 in numbers)    # Output: True
 print(6 not in numbers) # Output: True

3.3. Control Structures

Control structures allow you to control the flow of your program. Python supports conditional statements and loops:

  • Conditional Statements: if, elif, else.
  • Loops: for, while.

Example:

 # Conditional Statements
 age = 20
 if age >= 18:
  print("You are an adult")
 elif age >= 13:
  print("You are a teenager")
 else:
  print("You are a child")


 # For Loop
 numbers = [1, 2, 3, 4, 5]
 for number in numbers:
  print(number)


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

4. Working with Functions

Functions are reusable blocks of code that perform a specific task. They help organize your code and make it more readable.

4.1. Defining Functions

You can define a function using the def keyword, followed by the function name, parentheses (), and a colon :. The function body is indented.

 def greet(name):
  """This function greets the person passed in as a parameter."""
  print(f"Hello, {name}!")


 # Calling the function
 greet("Alice") # Output: Hello, Alice!

4.2. Function Arguments and Parameters

Functions can accept arguments, which are values passed to the function when it is called. The function defines parameters, which are the variables that receive the arguments.

 def add(x, y):
  """This function adds two numbers."""
  return x + y


 # Calling the function with arguments
 result = add(5, 3)
 print(result) # Output: 8

4.3. Return Values

Functions can return values using the return statement. If a function does not have a return statement, it returns None.

 def multiply(x, y):
  """This function multiplies two numbers and returns the result."""
  return x * y


 # Calling the function and using the return value
 product = multiply(4, 6)
 print(product) # Output: 24

5. Understanding Data Structures

Data structures are ways to organize and store data. Python has several built-in data structures, each with its own characteristics and use cases.

5.1. Lists

Lists are ordered, mutable collections of items. They are defined using square brackets [].

 # Creating a list
 numbers = [1, 2, 3, 4, 5]
 fruits = ['apple', 'banana', 'cherry']


 # Accessing elements
 print(numbers[0])  # Output: 1
 print(fruits[1])   # Output: banana


 # Modifying elements
 numbers[0] = 10
 print(numbers)  # Output: [10, 2, 3, 4, 5]


 # List methods
 numbers.append(6)
 print(numbers)  # Output: [10, 2, 3, 4, 5, 6]


 numbers.insert(2, 15)
 print(numbers)  # Output: [10, 2, 15, 3, 4, 5, 6]


 numbers.remove(4)
 print(numbers)  # Output: [10, 2, 15, 3, 5, 6]


 print(len(numbers)) # Output: 6

5.2. Tuples

Tuples are ordered, immutable collections of items. They are defined using parentheses ().

 # Creating a tuple
 coordinates = (10, 20)
 colors = ('red', 'green', 'blue')


 # Accessing elements
 print(coordinates[0]) # Output: 10
 print(colors[1])  # Output: green


 # Tuples are immutable, so you cannot modify them
 # coordinates[0] = 15 # This will raise an error


 print(len(coordinates)) # Output: 2

5.3. Dictionaries

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

 # Creating a dictionary
 person = {'name': 'Alice', 'age': 30, 'city': 'New York'}


 # Accessing values
 print(person['name']) # Output: Alice
 print(person['age'])  # Output: 30


 # Modifying values
 person['age'] = 31
 print(person)  # Output: {'name': 'Alice', 'age': 31, 'city': 'New York'}


 # Adding new key-value pairs
 person['occupation'] = 'Engineer'
 print(person)  # Output: {'name': 'Alice', 'age': 31, 'city': 'New York', 'occupation': 'Engineer'}


 # Dictionary methods
 print(person.keys())  # Output: dict_keys(['name', 'age', 'city', 'occupation'])
 print(person.values()) # Output: dict_values(['Alice', 31, 'New York', 'Engineer'])
 print(len(person))  # Output: 4

5.4. Sets

Sets are unordered collections of unique items. They are defined using curly braces {} or the set() constructor.

 # Creating a set
 numbers = {1, 2, 3, 4, 5}
 fruits = set(['apple', 'banana', 'cherry'])


 # Adding elements
 numbers.add(6)
 print(numbers)  # Output: {1, 2, 3, 4, 5, 6}


 # Removing elements
 numbers.remove(3)
 print(numbers)  # Output: {1, 2, 4, 5, 6}


 # Set operations
 set1 = {1, 2, 3}
 set2 = {3, 4, 5}


 print(set1.union(set2))      # Output: {1, 2, 3, 4, 5}
 print(set1.intersection(set2)) # Output: {3}
 print(set1.difference(set2))   # Output: {1, 2}

6. Object-Oriented Programming (OOP) in Python

Object-oriented programming is a programming paradigm that uses objects to design applications and computer programs. Python supports OOP concepts, making it a powerful tool for building complex systems.

6.1. Classes and Objects

A class is a blueprint for creating objects. An object is an instance of a class.

 class Dog:
  """A simple class for dogs."""


  def __init__(self, name, breed):
  """Initializes the dog's name and breed."""
  self.name = name
  self.breed = breed


  def bark(self):
  """Simulates the dog barking."""
  print("Woof!")


 # Creating objects
 my_dog = Dog("Buddy", "Golden Retriever")
 your_dog = Dog("Lucy", "Labrador")


 # Accessing attributes
 print(my_dog.name)  # Output: Buddy
 print(your_dog.breed) # Output: Labrador


 # Calling methods
 my_dog.bark()  # Output: Woof!

6.2. Inheritance

Inheritance allows a class to inherit attributes and methods from another class. This promotes code reuse and creates a hierarchy of classes.

 class Animal:
  """Base class for animals."""


  def __init__(self, name):
  self.name = name


  def speak(self):
  print("Generic animal sound")


 class Dog(Animal):
  """Dog class inheriting from Animal."""


  def __init__(self, name, breed):
  super().__init__(name)  # Call the parent class's constructor
  self.breed = breed


  def speak(self):
  print("Woof!")


 # Creating objects
 my_animal = Animal("Generic Animal")
 my_dog = Dog("Buddy", "Golden Retriever")


 # Calling methods
 my_animal.speak() # Output: Generic animal sound
 my_dog.speak()  # Output: Woof!
 print(my_dog.name) # Output: Buddy

6.3. Polymorphism

Polymorphism allows objects of different classes to be treated as objects of a common type. This is often achieved through inheritance and method overriding.

 class Animal:
  """Base class for animals."""


  def __init__(self, name):
  self.name = name


  def speak(self):
  print("Generic animal sound")


 class Dog(Animal):
  """Dog class inheriting from Animal."""


  def speak(self):
  print("Woof!")


 class Cat(Animal):
  """Cat class inheriting from Animal."""


  def speak(self):
  print("Meow!")


 # Polymorphism example
 animals = [Animal("Generic Animal"), Dog("Buddy", "Golden Retriever"), Cat("Lucy")]


 for animal in animals:
  animal.speak()
  # Output:
  # Generic animal sound
  # Woof!
  # Meow!

6.4. Encapsulation

Encapsulation is the bundling of data and methods that operate on that data within a class. It helps protect the data from being accessed directly and ensures that it is accessed through well-defined methods.

 class BankAccount:
  """A simple class for bank accounts."""


  def __init__(self, account_number, balance):
  self.account_number = account_number
  self.__balance = balance  # Private attribute


  def deposit(self, amount):
  if amount > 0:
  self.__balance += amount
  print(f"Deposited ${amount}. New balance: ${self.__balance}")
  else:
  print("Invalid deposit amount.")


  def withdraw(self, amount):
  if 0 < amount <= self.__balance:
  self.__balance -= amount
  print(f"Withdrew ${amount}. New balance: ${self.__balance}")
  else:
  print("Insufficient funds or invalid withdrawal amount.")


  def get_balance(self):
  return self.__balance


 # Creating an object
 my_account = BankAccount("123456789", 1000)


 # Accessing methods
 my_account.deposit(500)
 my_account.withdraw(200)
 print(my_account.get_balance())
 # Output:
 # Deposited $500. New balance: $1500
 # Withdrew $200. New balance: $1300
 # 1300


 # Attempting to access the private attribute directly will raise an error
 # print(my_account.__balance) # This will raise an AttributeError

7. Working with Modules and Packages

Modules and packages are ways to organize and reuse code. A module is a single file containing Python code, while a package is a directory containing multiple modules.

7.1. Importing Modules

You can import modules using the import statement.

 # Importing the math module
 import math


 # Using functions from the math module
 print(math.sqrt(25)) # Output: 5.0
 print(math.pi)  # Output: 3.141592653589793


 # Importing specific functions
 from math import sqrt, pi


 print(sqrt(16)) # Output: 4.0
 print(pi)  # Output: 3.141592653589793


 # Renaming a module or function
 import math as m
 print(m.sqrt(9)) # Output: 3.0

7.2. Creating Modules

You can create your own modules by saving Python code in a .py file.

Example:

Create a file named my_module.py with the following content:

 def greet(name):
  """This function greets the person passed in as a parameter."""
  print(f"Hello, {name}!")


 def add(x, y):
  """This function adds two numbers."""
  return x + y

Then, you can import and use this module in another Python file:

 import my_module


 my_module.greet("Alice") # Output: Hello, Alice!
 result = my_module.add(5, 3)
 print(result)  # Output: 8

7.3. Packages

A package is a directory containing multiple modules and an __init__.py file, which indicates that the directory should be treated as a package.

Example:

Create a directory named my_package with the following structure:

 my_package/
  __init__.py
  module1.py
  module2.py

__init__.py (can be empty or contain initialization code):

 # You can leave this file empty or add initialization code

module1.py:

 def greet(name):
  """This function greets the person passed in as a parameter."""
  print(f"Hello from module1, {name}!")

module2.py:

 def add(x, y):
  """This function adds two numbers."""
  return x + y

Then, you can import and use the package in another Python file:

 from my_package import module1, module2


 module1.greet("Alice") # Output: Hello from module1, Alice!
 result = module2.add(5, 3)
 print(result)  # Output: 8

8. Handling Exceptions

Exceptions are errors that occur during the execution of a program. Handling exceptions allows you to gracefully deal with these errors and prevent your program from crashing.

8.1. Try-Except Blocks

You can handle exceptions using try-except blocks.

 try:
  # Code that may raise an exception
  x = 10 / 0
 except ZeroDivisionError:
  # Code to handle the exception
  print("Cannot divide by zero!")

8.2. Multiple Except Blocks

You can use multiple except blocks to handle different types of exceptions.

 try:
  # Code that may raise an exception
  x = int(input("Enter a number: "))
  y = 10 / x
  print(y)
 except ZeroDivisionError:
  print("Cannot divide by zero!")
 except ValueError:
  print("Invalid input. Please enter a number.")

8.3. Finally Block

The finally block is executed regardless of whether an exception is raised or not. It is often used to clean up resources.

 try:
  # Code that may raise an exception
  file = open("my_file.txt", "r")
  content = file.read()
  print(content)
 except FileNotFoundError:
  print("File not found!")
 finally:
  # Code to close the file
  file.close()

9. Working with Files

Python provides functions for reading from and writing to files.

9.1. Opening Files

You can open a file using the open() function.

 # Opening a file for reading
 file = open("my_file.txt", "r")


 # Opening a file for writing
 file = open("my_file.txt", "w")


 # Opening a file for appending
 file = open("my_file.txt", "a")

9.2. Reading from Files

You can read from a file using the read(), readline(), and readlines() methods.

 # Opening a file for reading
 file = open("my_file.txt", "r")


 # Reading the entire file
 content = file.read()
 print(content)


 # Reading a single line
 line = file.readline()
 print(line)


 # Reading all lines into a list
 lines = file.readlines()
 print(lines)


 # Closing the file
 file.close()

9.3. Writing to Files

You can write to a file using the write() and writelines() methods.

 # Opening a file for writing
 file = open("my_file.txt", "w")


 # Writing a string to the file
 file.write("Hello, world!n")


 # Writing multiple lines to the file
 lines = ["This is line 1n", "This is line 2n"]
 file.writelines(lines)


 # Closing the file
 file.close()

9.4. Using with Statement

The with statement automatically closes the file when the block is exited, even if an exception is raised.

 with open("my_file.txt", "r") as file:
  content = file.read()
  print(content)


 # The file is automatically closed after the with block

10. Regular Expressions

Regular expressions are patterns used to match character combinations in strings. Python’s re module provides support for regular expressions.

10.1. Basic Patterns

  • .: Matches any character except a newline.
  • ^: Matches the beginning of the string.
  • $: Matches the end of the string.
  • *: Matches zero or more occurrences of the preceding character.
  • +: Matches one or more occurrences of the preceding character.
  • ?: Matches zero or one occurrence of the preceding character.
  • []: Matches any character within the brackets.
  • [^]: Matches any character not within the brackets.
  • d: Matches a digit.
  • D: Matches a non-digit.
  • s: Matches a whitespace character.
  • S: Matches a non-whitespace character.
  • w: Matches a word character (alphanumeric and underscore).
  • W: Matches a non-word character.

10.2. Using the re Module

 import re


 # Searching for a pattern
 text = "The quick brown fox jumps over the lazy dog."
 pattern = "fox"
 match = re.search(pattern, text)


 if match:
  print("Pattern found!")
  print(f"Start index: {match.start()}")
  print(f"End index: {match.end()}")
 else:
  print("Pattern not found.")


 # Finding all occurrences of a pattern
 text = "The cat and the hat."
 pattern = "at"
 matches = re.findall(pattern, text)
 print(matches) # Output: ['at', 'at', 'at']


 # Substituting a pattern
 text = "The quick brown fox."
 pattern = "fox"
 replacement = "dog"
 new_text = re.sub(pattern, replacement, text)
 print(new_text) # Output: The quick brown dog.

10.3. Common Regular Expression Functions

  • re.search(pattern, string): Searches for the first occurrence of the pattern in the string.
  • re.findall(pattern, string): Finds all occurrences of the pattern in the string and returns them as a list.
  • re.sub(pattern, replacement, string): Substitutes all occurrences of the pattern in the string with the replacement.
  • re.split(pattern, string): Splits the string by the occurrences of the pattern.

11. Working with Libraries and Frameworks

Python has a rich ecosystem of libraries and frameworks that extend its capabilities.

11.1. NumPy

NumPy is a library for numerical computing. It provides support for arrays, matrices, and mathematical functions.

 import numpy as np


 # Creating an array
 arr = np.array([1, 2, 3, 4, 5])
 print(arr) # Output: [1 2 3 4 5]


 # Array operations
 print(arr + 2) # Output: [3 4 5 6 7]
 print(arr * 2) # Output: [ 2  4  6  8 10]


 # Matrix operations
 matrix1 = np.array([[1, 2], [3, 4]])
 matrix2 = np.array([[5, 6], [7, 8]])


 print(matrix1 + matrix2)
 # Output:
 # [[ 6  8]
 #  [10 12]]

11.2. Pandas

Pandas is a library for data analysis. It provides data structures like DataFrames for working with structured data.

 import pandas as pd


 # Creating a DataFrame
 data = {'name': ['Alice', 'Bob', 'Charlie'],
  'age': [25, 30, 28],
  'city': ['New York', 'London', 'Paris']}


 df = pd.DataFrame(data)
 print(df)
 # Output:
 # name  age  city
 # 0  Alice  25  New York
 # 1  Bob  30  London
 # 2  Charlie  28  Paris


 # Accessing data
 print(df['name'])
 # Output:
 # 0  Alice
 # 1  Bob
 # 2  Charlie
 # Name: name, dtype: object


 # Filtering data
 print(df[df['age'] > 27])
 # Output:
 # name  age  city
 # 1  Bob  30  London
 # 2  Charlie  28  Paris

11.3. Matplotlib

Matplotlib is a library for creating visualizations.

 import matplotlib.pyplot as plt


 # Sample data
 x = [1, 2, 3, 4, 5]
 y = [2, 4, 6, 8, 10]


 # Creating a line plot
 plt.plot(x, y)


 # Adding labels and title
 plt.xlabel("X-axis")
 plt.ylabel("Y-axis")
 plt.title("Simple Line Plot")


 # Displaying the plot
 plt.show()

11.4. Django

Django is a high-level web framework that encourages rapid development and clean, pragmatic design.

Creating a Django project:

  1. Install Django: pip install Django
  2. Create a new project: django-admin startproject myproject
  3. Navigate to the project directory: cd myproject
  4. Start the development server: python manage.py runserver

11.5. Flask

Flask is a lightweight web framework that provides the essentials for building web applications.

Creating a Flask application:

  1. Install Flask: pip install Flask
  2. Create a file named app.py:
 from flask import Flask
 app = Flask(__name__)


 @app.route('/')
 def hello_world():
  return 'Hello, World!'


 if __name__ == '__main__':
  app.run(debug=True)
  1. Run the application: python app.py

12. Best Practices for Writing Python Code

Adhering to best practices ensures that your code is readable, maintainable, and efficient.

12.1. Code Style (PEP 8)

PEP 8 is the style guide for Python code. It provides guidelines on naming conventions, indentation, line length, and more.

  • Indentation: Use 4 spaces for indentation.
  • Line Length: Limit lines to 79 characters.
  • Naming Conventions:
    • snake_case for variables and functions.
    • CamelCase for classes.
    • UPPER_CASE for constants.
  • Comments: Write clear and concise comments to explain your code.

12.2. Writing Clear and Concise Code

  • Use Meaningful Names: Choose names that clearly indicate the purpose of variables, functions, and classes.
  • Keep Functions Short: Break down complex functions into smaller, more manageable units.
  • Avoid Code Duplication: Use functions and loops to reuse code.

12.3. Testing Your Code

  • Write Unit Tests: Test individual components of your code to ensure they work correctly.
  • Use Assertions: Use assertions to check for expected conditions in your code.
  • Test-Driven Development (TDD): Write tests before writing the code.

12.4. Version Control (Git)

  • Use Git: Track changes to your code and collaborate with others using Git.
  • Create Repositories: Store your code in Git repositories on platforms like GitHub, GitLab, or Bitbucket.
  • Use Branches: Create branches for new features or bug fixes.

13. Advanced Topics in Python

Once

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