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  • Lesson 1: Introduction to Flask

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    Flask is a lightweight and flexible web framework for Python that enables developers to build web applications quickly and efficiently. It is known for its simplicity and minimalism, making it a great choice for beginners and small to medium-sized projects.

    Lesson Outline:

    1. Setting Up a Flask Project
    2. Creating Routes and Views
    3. Handling Forms and Templates

    1. Setting Up a Flask Project

    Installing Flask:

    • Use pip to install Flask:
      pip install Flask
    • Verify the installation:
      python -m flask –version

    Creating a Basic Project Structure:

    • py: Main application file
    • templates/: Directory for HTML templates
    • static/: Directory for CSS, JavaScript, images

    Basic Flask App Example:

    from flask import Flask

    app = Flask(__name__)

     

    @app.route(‘/’)

    def home():

    return “Hello, Flask!”

     

    if __name__ == ‘__main__’:

    app.run(debug=True)

    • Run the app:
      python app.py
    • Visit http://127.0.0.1:5000/to see the output.

    2. Creating Routes and Views

    Understanding Routes:

    • Routes define the URL patterns for your web application.
    • The @app.route()decorator binds a URL to a function (view).

    Example:

    @app.route(‘/about’)

    def about():

    return “This is the About page.”

    • Access this view via http://127.0.0.1:5000/about

    Dynamic Routes:

    @app.route(‘/user/<username>’)

    def show_user(username):

    return f”Hello, {username}!”

    • This handles dynamic URLs like /user/John

    3. Handling Forms and Templates

    Using HTML Templates:

    • Create a templates

    Add an index.html file:

    <!DOCTYPE html>

    <html>

    <head><title>Flask App</title></head>

    <body>

    <h1>Welcome to Flask!</h1>

    </body>

    • </html>

    Rendering Templates in Flask:

    from flask import render_template

     

    @app.route(‘/’)

    def home():

    return render_template(‘index.html’)

    Handling Forms:

    python
    Create a simple form in form.html:
    <form method=”POST” action=”/submit”>

    <input type=”text” name=”username” placeholder=”Enter your name”>

    <input type=”submit” value=”Submit”>

    • </form>

    Processing Form Data:

    python
    from flask import request

     

    @app.route(‘/submit’, methods=[‘POST’])

    def submit():

    username = request.form[‘username’]

    return f”Hello, {username}!”

    Conclusion:

    • Flask provides a simple yet powerful framework to build web applications.
    • You’ve learned how to set up a project, create routes, and handle forms with templates.

    Next, we’ll dive deeper into advanced Flask features like blueprints, REST APIs, and database integration.

  • Lesson 2: SQLAlchemy ORM

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    SQLAlchemy is a powerful SQL toolkit and Object-Relational Mapping (ORM) library for Python. It allows developers to interact with databases using Python objects instead of writing raw SQL queries. This abstraction makes code cleaner, more maintainable, and easier to scale.

    Lesson Outline:

    1. Introduction to SQLAlchemy
    2. Setting Up the Database Model
    3. Querying with ORM

    1. Introduction to SQLAlchemy

    What is SQLAlchemy?

    • SQLAlchemy is a library that provides tools for working with relational databases in Python.
    • It supports both Core (SQL Expression Language)for raw SQL queries and ORM for working with Python objects.
    • SQLAlchemy supports multiple databases like SQLite, MySQL, PostgreSQL, etc.

    Why Use SQLAlchemy ORM?

    • Simplifies database interactions by allowing developers to work with Python classes and objects.
    • Enhances code readability and maintainability.
    • Supports complex database operations with less boilerplate code.

    Installing SQLAlchemy:

    pip install SQLAlchemy

    2. Setting Up the Database Model

    SQLAlchemy ORM maps Python classes to database tables. Here’s how to define a database model:

    Step 1: Import Required Modules

    from sqlalchemy import create_engine, Column, Integer, String

    from sqlalchemy.ext.declarative import declarative_base

    from sqlalchemy.orm import sessionmaker

    Step 2: Create an Engine and Base Class

    # Connecting to SQLite database

    engine = create_engine(‘sqlite:///students.db’, echo=True)

    Base = declarative_base()

    Step 3: Define a Model (Table)

    class Student(Base):

    __tablename__ = ‘students’

    id = Column(Integer, primary_key=True)

    name = Column(String)

    age = Column(Integer)

    grade = Column(String)

     

    def __repr__(self):

    return f”<Student(name='{self.name}’, age={self.age}, grade='{self.grade}’)>”

    Step 4: Create the Table

    Base.metadata.create_all(engine)

    This command generates the students table in the students.db database.

    3. Querying with ORM

    Creating a Session:
    Before querying, we need a session to interact with the database.

    Session = sessionmaker(bind=engine)

    session = Session()

    Inserting Data:

    new_student = Student(name=’Alice’, age=20, grade=’A’)

    session.add(new_student)

    session.commit()

    Querying Data:

    • Retrieve All Records:

    students = session.query(Student).all()

    for student in students:

    print(student)

    • Filter Records:

    student = session.query(Student).filter_by(name=’Alice’).first()

    print(student)

    Updating Records:

    student.age = 21

    session.commit()

    Deleting Records:

    session.delete(student)

    session.commit()

    Key Takeaways:

    • SQLAlchemy ORM helps manage database operations using Python classes.
    • The Sessionobject is crucial for adding, querying, updating, and deleting records.
    • Using SQLAlchemy ORM improves code readability and efficiency, especially for complex applications.

    Next Steps: Explore relationships between tables using foreign keys and advanced querying techniques.

  • Lesson 1: SQLite in Python

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    SQLite is a lightweight, embedded database that comes pre-installed with Python. It is widely used for applications that require simple, fast, and reliable database solutions without the need for a separate server. In this lesson, we’ll learn how to interact with SQLite databases using Python’s built-in sqlite3 module.

    Lesson Outline:

    1. Connecting to an SQLite Database
    2. Creating Tables and Inserting Data
    3. Querying and Updating Records

    1. Connecting to an SQLite Database

    Introduction to SQLite:

    • SQLiteis a self-contained, serverless SQL database engine.
    • Unlike other SQL databases, it stores the entire database as a single file on disk.

    Connecting to a Database:

    To interact with an SQLite database in Python, we use the sqlite3 module.

    python
    import sqlite3

     

    # Connect to a database (or create it if it doesn’t exist)

    conn = sqlite3.connect(‘my_database.db’)

     

    # Create a cursor object to execute SQL commands

    cursor = conn.cursor()

     

    • connect(): Creates a connection to the database file. If the file doesn’t exist, it will be created automatically.
    • Cursor: Acts as a control structure to interact with the database (execute SQL commands).

    2. Creating Tables and Inserting Data

    Creating a Table:

    You can create tables using SQL CREATE TABLE statements.

    python
    # Create a table named ‘students’

    cursor.execute(“””

    CREATE TABLE IF NOT EXISTS students (

    id INTEGER PRIMARY KEY,

    name TEXT NOT NULL,

    age INTEGER,

    grade TEXT

    )

    “””)

     

    # Save (commit) the changes

    conn.commit()

     

    • SQL Syntax:Standard SQL is used to define table structure.
    • IF NOT EXISTS: Prevents errors if the table already exists.
    • commit(): Saves changes to the database.

    Inserting Data:

    python
    # Insert a record into the table

    cursor.execute(“INSERT INTO students (name, age, grade) VALUES (?, ?, ?)”,

    (‘Alice’, 20, ‘A’))

     

    # Commit the transaction

    conn.commit()

     

    • Parameterized Queries (?placeholders): Help prevent SQL injection attacks.
    • VALUES (?, ?, ?): Maps the data values to the table columns.

    3. Querying and Updating Records

    Querying Data (SELECT Statement):

    python

    # Retrieve all records from the ‘students’ table

    cursor.execute(“SELECT * FROM students”)

    rows = cursor.fetchall()

     

    for row in rows:

    print(row)

     

    • SELECT *: Retrieves all columns from the table.
    • fetchall(): Returns all results from the query as a list of tuples.

    Filtering Results:

    python
    # Retrieve students with grade ‘A’

    cursor.execute(“SELECT name, grade FROM students WHERE grade = ‘A’”)

    for row in cursor.fetchall():

    print(row)

     

    • WHEREClause: Filters records based on specific conditions.

    Updating Records:

    python
    # Update a student’s grade

    cursor.execute(“UPDATE students SET grade = ? WHERE name = ?”, (‘B’, ‘Alice’))

    conn.commit()

     

    • UPDATEStatement: Modifies existing records.
    • Condition:Ensures only the intended records are updated.

    Deleting Records:

    python
    # Delete a student record

    cursor.execute(“DELETE FROM students WHERE name = ‘Alice’”)

    conn.commit()

     

    • DELETEStatement: Removes records that meet specified conditions.

    Closing the Connection

    Always close the connection after completing database operations to free up resources.

    python
    conn.close()

     

    Key Takeaways:

    • SQLiteis ideal for lightweight database applications.
    • Use sqlite3in Python to connect, create tables, insert, query, update, and delete records.
    • Always commit changesafter insert/update/delete operations.
    • Use parameterized queriesto prevent SQL injection.
    • Close the connection with close()after database interactions.

    This foundational knowledge of SQLite will help you work efficiently with databases in Python applications.

  • Lesson 3: Context Managers

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    Context managers are an essential feature in Python used to manage resources efficiently, such as files, database connections, or network sockets. They help ensure that resources are properly acquired and released, preventing memory leaks or resource locks. The most common way to use context managers is with the with statement, which simplifies resource management and makes code cleaner and more readable.

    In this lesson, we’ll cover:

    1. Using the withStatement
    2. Writing Custom Context Managers

    1. Using the with Statement

    What is a Context Manager?

    A context manager is responsible for setting up and cleaning up resources. When you enter a code block using a context manager, it ensures that the resource is properly initialized, and when the block is exited (even if an error occurs), it automatically releases the resource.

    The with statement is Python’s built-in way to work with context managers.

    Basic Syntax:

    python
    with open(‘example.txt’, ‘r’) as file:

    content = file.read()

    print(content)

     

    In this example:

    • open(‘example.txt’, ‘r’)is the context manager that opens the file.
    • as fileassigns the opened file to the variable file.
    • When the block inside withis done, Python automatically closes the file, even if an error occurs inside the block.

    Why Use the with Statement?

    • Automatic Resource Management:No need to explicitly close files or connections.
    • Error Handling:Even if an exception occurs, the resource is safely released.
    • Cleaner Code:Reduces the need for try-finally

    2. Writing Custom Context Managers

    While Python provides built-in context managers (like for files), you can create your own for custom resource management.

    Using Classes to Create a Context Manager

    You can define a class with two special methods:

    • __enter__()– Code that runs when entering the with
    • __exit__()– Code that runs when exiting the with block (handles cleanup).
    Example: Custom Context Manager Using a Class
    python
    class ManagedResource:

    def __enter__(self):

    print(“Acquiring resource…”)

    return self  # This is assigned to the variable after ‘as’

     

    def __exit__(self, exc_type, exc_value, traceback):

    print(“Releasing resource…”)

     

    # Using the custom context manager

    with ManagedResource() as resource:

    print(“Using the resource inside the with block.”)

     

    Output:

    scss

    CopyEdit

    Acquiring resource…

    Using the resource inside the with block.

    Releasing resource…

     

    • __enter__initializes the resource.
    • __exit__cleans up, even if an error occurs inside the with
    • The exc_type, exc_value, and tracebackarguments in __exit__ help in handling exceptions if needed.

    Using contextlib to Simplify Custom Context Managers

    Python’s contextlib module allows you to create context managers using generator functions, which is simpler than defining a class.

    Example: Using contextlib.contextmanager
    python
    from contextlib import contextmanager

     

    @contextmanager

    def managed_resource():

    print(“Acquiring resource…”)

    yield  # Code before yield runs when entering the block

    print(“Releasing resource…”)  # Runs after the with block

     

    with managed_resource():

    print(“Using the resource inside the with block.”)

     

    Output:

    scss

    CopyEdit

    Acquiring resource…

    Using the resource inside the with block.

    Releasing resource…

     

    Here, everything before yield happens when the context starts, and everything after yield happens when it ends.

    Key Takeaways:

    • The withstatement simplifies resource management in Python.
    • Context managers automatically handle resource allocation and cleanup.
    • You can create custom context managers using classes (__enter__and __exit__) or with contextlib.
    • They are widely used in file operations, database connections, network sockets, and more.

    Practice Problems:

    1. Create a context manager using a class to manage a simple database connection simulation.
    2. Use contextmanagerto create a timer that measures how long a code block takes to execute.
    3. Modify a file-handling context manager to handle exceptions gracefully and log any errors.

    These exercises will help solidify your understanding of context managers in real-world scenarios.

  • Lesson 2: Generators

    Module 1 Introduction to Python
    Lesson 2: Writing Your First Python Program
    Lesson 3: Python’s Interactive Shell
    Module 2 Variables and Data Types
    Lesson 1: Variables and Constants
    Lesson 2: Data Types
    Lesson 3: Basic Input and Output
    Module 3 Control Structures
    Lesson 1: Conditional Statements
    Lesson 2: Loops
    Module 4 Functions
    Lesson 1: Defining Functions
    Lesson 2: Lambda Functions
    Lesson 3: Recursion
    Module 5 Data Structures
    Lesson 1: Lists
    Lesson 2: Tuples
    Lesson 3: Dictionaries
    Lesson 4: Sets
    Module 6 Object-Oriented Programming
    Lesson 1: Classes and Objects
    Lesson 2: Inheritance
    Lesson 3: Polymorphism and Encapsulation
    Module 7 File Handling
    Lesson 1: Reading Files
    Lesson 2: Writing to Files
    Module 8 Error Handling and Exceptions
    Lesson 1: Introduction to Exceptions
    Lesson 2: Handling Multiple Exceptions
    Module 9 Libraries and Modules
    Lesson 1: Standard Python Library
    Lesson 2: External Libraries and Packages
    Module 10 Working with Data
    Lesson 1: Working with JSON
    Lesson 2: Working with CSV Files
    Lesson 3: Web Scraping with BeautifulSoup
    Module 11 Advanced Python Topics
    Lesson 1: Decorators
    Lesson 2: Generators
    Lesson 3: Context Managers
    Module 12 Working with Databases
    Lesson 1: SQLite in Python
    Lesson 2: SQLAlchemy ORM
    Module 13 Python for Web Development (Optional)
    Lesson 1: Introduction to Flask
    Lesson 2: Introduction to Django
    Module 14 Final Project and Challenges
    Lesson 1: Designing Your First Python Project
    Lesson 2: Python Challenges

    Generators are a powerful feature in Python that allow you to create iterators in a more memory-efficient and readable way. Instead of returning all values at once like regular functions, generators produce items one at a time using the yield keyword. This makes them especially useful when dealing with large datasets or infinite sequences.

    In this lesson, we’ll cover:

    1. Using yieldto Create Generators
    2. Differences Between Generators and Functions

    1. Using yield to Create Generators

    What is a Generator?

    A generator is a special type of function that returns an iterator. Instead of using the return statement, a generator uses the yield keyword to produce a value and pause the function’s state. This means the function can be resumed later from where it left off.

    Why Use Generators?

    • Memory Efficient:They don’t store the entire sequence in memory.
    • Lazy Evaluation:Values are generated only when needed.
    • Readable Code:Cleaner syntax compared to managing state manually.

    Creating a Simple Generator:

    python
    def simple_generator():

    yield 1

    yield 2

    yield 3

     

    # Using the generator

    for value in simple_generator():

    print(value)

     

    Explanation:

    • The function simple_generatoryields three values: 1, 2, and 3.
    • Each time yieldis encountered, the function pauses, saving its current state.
    • The next iteration resumes from where it left off.

    Generator with Loop:

    python
    def count_up_to(n):

    count = 1

    while count <= n:

    yield count

    count += 1

     

    for num in count_up_to(5):

    print(num)

     

    Key Points:

    • The generator count_up_toyields numbers from 1 to n.
    • No list is created in memory, making it efficient for large ranges.

    2. Differences Between Generators and Functions

    Aspect Regular Functions Generators
    Keyword Used Uses return to send back a value. Uses yield to produce values one at a time.
    Memory Usage Stores entire data in memory. Generates values on the fly (memory efficient).
    Execution Runs to completion when called. Pauses at yield and resumes later.
    Return Value Returns a single value or data structure. Returns a generator object (iterator).
    State Retention Does not retain state between calls. Retains state between yield calls.

    Example: Function vs. Generator

    • Regular Function:
    python
    def get_numbers():

    return [1, 2, 3, 4, 5]

     

    for num in get_numbers():

    print(num)

     

    • Generator:
    python
    def generate_numbers():

    for i in range(1, 6):

    yield i

     

    for num in generate_numbers():

    print(num)

     

    Memory Usage:

    • The function get_numbers()creates and stores the entire list in memory.
    • The generator generate_numbers()yields one number at a time, reducing memory usage.

    Real-World Use Cases for Generators:

    1. Large Data Processing:Reading large files without loading the entire file into memory.
    2. Streaming Data:Processing data streams like logs, API responses, or real-time feeds.
    3. Infinite Sequences:Generating endless series (e.g., Fibonacci numbers) without memory overflow.

    Example – Reading Large Files Efficiently:

    python
    def read_large_file(file_path):

    with open(file_path, ‘r’) as file:

    for line in file:

    yield line.strip()

     

    for line in read_large_file(‘bigdata.txt’):

    print(line)

     

    Why It’s Efficient:

    • Only one line is loaded into memory at a time, making it scalable for large datasets.

    Summary

    • Generatorsallow you to write memory-efficient code using the yield
    • They differ from regular functions by pausing and resuming execution without losing state.
    • Generators are ideal for large data processing, streaming applications, and infinite sequences.

    In the next lesson, we’ll explore Context Managers to handle resource management more effectively.

  • Lesson 1: Decorators

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    Decorators are a powerful and advanced feature in Python that allows you to modify or enhance the behavior of functions or classes without changing their actual code. They are widely used in real-world applications, especially in frameworks like Flask, Django, and even in Python’s standard library.

    In this lesson, we will cover:

    1. Understanding Function Decorators
    2. Creating and Using Decorators

    1. Understanding Function Decorators

    What is a Decorator?
    A decorator is essentially a function that takes another function as an argument, adds some functionality to it, and returns the modified function. This is based on the concept of higher-order functions, which can accept other functions as arguments or return them.

    Think of a decorator like adding toppings to a pizza. The base (original function) remains the same, but you can enhance it by adding extra ingredients (decorator).

    Basic Syntax of a Decorator:

    python
    def decorator_function(original_function):

    def wrapper_function():

    print(“Wrapper executed before the original function.”)

    original_function()

    print(“Wrapper executed after the original function.”)

    return wrapper_function

     

    @decorator_function

    def display():

    print(“Hello from the original function!”)

     

    display()

     

    Explanation:

    • decorator_functionis the decorator.
    • original_functionis the function being decorated.
    • The @decorator_functionsyntax is a shorthand for display = decorator_function(display).
    • When display()is called, it’s actually invoking the wrapper_function, which adds functionality before and after the original display

    2. Creating and Using Decorators

    Creating a Simple Decorator:

    Let’s create a decorator that logs the execution of functions:

    python
    def log_decorator(func):

    def wrapper(*args, **kwargs):

    print(f”Function ‘{func.__name__}’ is about to run.”)

    result = func(*args, **kwargs)

    print(f”Function ‘{func.__name__}’ has finished running.”)

    return result

    return wrapper

     

    @log_decorator

    def add(a, b):

    return a + b

     

    print(add(5, 3))

     

    Key Points:

    • *argsand **kwargs are used to handle any number of positional and keyword arguments.
    • The decorator logs messages before and after the function execution.

    Decorators with Arguments:

    Sometimes, you may want to pass arguments to your decorator. This requires an extra layer of functions:

    python
    def repeat(times):

    def decorator(func):

    def wrapper(*args, **kwargs):

    for _ in range(times):

    func(*args, **kwargs)

    return wrapper

    return decorator

     

    @repeat(3)

    def greet(name):

    print(f”Hello, {name}!”)

     

    greet(“Alice”)

     

    Here, the greet function will be executed three times because of the @repeat(3) decorator.

    Using Built-in Decorators:

    Python provides several built-in decorators like:

    • @staticmethod
    • @classmethod
    • @property

    Example using @property:

    python
    class Circle:

    def __init__(self, radius):

    self._radius = radius

     

    @property

    def area(self):

    return 3.14159 * self._radius ** 2

     

    circle = Circle(5)

    print(circle.area)  # Accessing area like an attribute

     

    When to Use Decorators?

    • Logging: Track function calls and execution details.
    • Authorization: Check user permissions before allowing access.
    • Validation: Validate input data before processing.
    • Caching: Store results of expensive function calls for faster future access.

    ✅ Summary:

    • Decorators modify or enhance functions without changing their core code.
    • They use higher-order functions to wrap additional functionality around the original function.
    • Decorators can take arguments, handle function metadata, and even stack on top of each other.

    In the next lesson, we’ll dive deeper into Generators, another powerful feature for efficient data processing in Python.

  • Lesson 3: Web Scraping with BeautifulSoup

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    Web scraping is the process of extracting data from websites. It’s widely used for data collection, market analysis, competitive research, and more. In Python, BeautifulSoup is one of the most popular libraries for parsing HTML and XML documents, making web scraping easier. Combined with the requests library, it allows you to fetch and extract data from web pages effortlessly.

    In this lesson, we’ll cover:

    1. Introduction to Web Scraping
    2. Installing beautifulsoup4and requests
    3. Scraping Data from HTML Pages

    1. Introduction to Web Scraping

    What is Web Scraping?
    Web scraping involves automating the process of visiting web pages, retrieving their content, and extracting specific information such as text, images, links, etc. It’s commonly used for:

    • Gathering data from e-commerce sites
    • Extracting news headlines
    • Collecting social media trends
    • Aggregating job listings

    Is Web Scraping Legal?
    While web scraping is technically possible for most websites, it’s important to follow legal and ethical guidelines:

    • Check the website’s txtfile to see if scraping is allowed.
    • Always comply with the website’s Terms of Service.
    • Avoid sending too many requests quickly, which can overwhelm servers.

    2. Installing beautifulsoup4 and requests

    Before starting with web scraping, you need to install two key Python libraries:

    • requests: To send HTTP requests and retrieve web page content.
    • beautifulsoup4: To parse and extract data from HTML.

    Installation with pip:

    bash

    pip install beautifulsoup4

    pip install requests

     

    You can verify the installation in Python:

    python
    import requests

    from bs4 import BeautifulSoup

     

    3. Scraping Data from HTML Pages

    Let’s walk through a simple web scraping example.

    Step 1: Sending an HTTP Request

    Use the requests library to get the content of a web page:

    python
    import requests

     

    url = ‘https://example.com’

    response = requests.get(url)

    print(response.text)  # Displays the raw HTML content

     

    The response.text contains the entire HTML of the page.

    Step 2: Parsing HTML with BeautifulSoup

    Now, use BeautifulSoup to parse the HTML content:

    python
    from bs4 import BeautifulSoup

     

    soup = BeautifulSoup(response.text, ‘html.parser’)

    print(soup.prettify())  # Prints the formatted HTML structure

     

    Step 3: Extracting Specific Data

    To extract specific elements like headings, links, or paragraphs:

    python
    # Extract all headings (h1 tags)

    headings = soup.find_all(‘h1’)

    for heading in headings:

    print(heading.text)

     

    # Extract all links

    links = soup.find_all(‘a’)

    for link in links:

    print(link.get(‘href’))

     

    • find_all()searches for all occurrences of the specified tag.
    • .textretrieves the text content inside an HTML element.
    • .get(‘href’)fetches the URL from anchor (<a>) tags.

    Handling Complex Web Pages

    Web pages often have nested HTML elements. You can target specific sections using:

    CSS Selectors with select():

    python
    articles = soup.select(‘div.article > h2’)

    for article in articles:

    print(article.text)

    Attributes Filtering:
    python
    CopyEdit
    images = soup.find_all(‘img’, {‘class’: ‘featured-image’})

    for img in images:

    print(img[‘src’])

    Error Handling and Best Practices

    Handle Missing Elements Gracefully:

    python
    title = soup.find(‘h1’)

    if title:

    print(title.text)

    else:

    print(“Title not found.”)

    Avoid Overloading Servers: Use delays between requests:

    python
    import time

    time.sleep(2)  # Sleep for 2 seconds before the next request

    Respect Robots.txt: Check if scraping is allowed:

    python
    import urllib.robotparser

     

    rp = urllib.robotparser.RobotFileParser()

    rp.set_url(‘https://example.com/robots.txt’)

    rp.read()

    print(rp.can_fetch(‘*’, url))  # Returns True if scraping is allowed

    Real-World Example: Scraping Quotes from a Website

    python
    import requests

    from bs4 import BeautifulSoup

     

    url = ‘http://quotes.toscrape.com’

    response = requests.get(url)

    soup = BeautifulSoup(response.text, ‘html.parser’)

     

    quotes = soup.find_all(‘span’, class_=’text’)

    authors = soup.find_all(‘small’, class_=’author’)

     

    for quote, author in zip(quotes, authors):

    print(f'{quote.text} — {author.text}’)

     

    Key Takeaways

    • Web scrapingautomates data extraction from websites.
    • Use requeststo fetch web pages and BeautifulSoup to parse and extract data.
    • Always respect the website’s rules (robots.txt) and Terms of Service.
    • For dynamic websites (JavaScript-heavy), consider advanced tools like Selenium.

    In the next lessons, we’ll dive deeper into handling dynamic content, pagination, and working with APIs, which are often a cleaner alternative to web scraping.

  • Lesson 2: Working with CSV Files

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    CSV (Comma Separated Values) files are widely used for storing tabular data in a simple, text-based format. They are easy to read and edit with various applications, including spreadsheet software like Microsoft Excel and Google Sheets. In Python, working with CSV files is very straightforward thanks to the built-in csv module. In this lesson, we’ll cover:

    1. Reading CSV Files: How to read data from CSV files.
    2. Writing to CSV Files: How to write data to CSV files.
    3. Handling Headers and Rows: Managing headers and row data in CSV files.
    4. Using the csvModule: The tools and functions provided by Python’s csv

    1. Reading CSV Files

    When you need to read data from a CSV file in Python, the csv.reader() function is used. This function allows you to read each row of data and convert it into a Python list or dictionary.

    Example: Reading CSV Data

    Let’s start with a simple example. Suppose you have a CSV file named students.csv that contains the following data:

    css
    name,age,grade

    Alice,23,A

    Bob,25,B

    Charlie,22,A

     

    To read this CSV file and access the data in Python, you can use the following code:

    python
    import csv

     

    # Open the CSV file

    with open(‘students.csv’, ‘r’) as file:

    # Create a CSV reader object

    csv_reader = csv.reader(file)

     

    # Loop through each row in the CSV file

    for row in csv_reader:

    print(row)

     

    Output:

    css

    CopyEdit

    [‘name’, ‘age’, ‘grade’]

    [‘Alice’, ’23’, ‘A’]

    [‘Bob’, ’25’, ‘B’]

    [‘Charlie’, ’22’, ‘A’]

     

    In this example:

    • reader(file)reads the CSV file and returns each row as a list of strings.
    • The forloop iterates over each row, printing it.

    2. Writing to CSV Files

    Writing data to a CSV file is just as simple. You can use the csv.writer() function to write rows of data to a file. Each row is typically a list of values.

    Example: Writing to CSV

    Suppose you want to write the following data into a CSV file:

    python
    data = [

    [‘name’, ‘age’, ‘grade’],

    [‘Alice’, 23, ‘A’],

    [‘Bob’, 25, ‘B’],

    [‘Charlie’, 22, ‘A’]

    ]

     

    # Open a file in write mode

    with open(‘output.csv’, ‘w’, newline=”) as file:

    # Create a CSV writer object

    csv_writer = csv.writer(file)

     

    # Write all rows to the CSV file

    csv_writer.writerows(data)

     

    In this example:

    • The writer(file)creates a writer object that will write to output.csv.
    • writerows(data)writes all the rows (each row is a list) into the CSV file.

    The newline=” argument ensures that no extra blank lines are added when writing to the CSV file (this is platform-dependent behavior).

    3. Handling Headers and Rows

    In CSV files, headers are the first row, often used to define the column names, while subsequent rows contain the data. When working with CSV files, it is common to handle headers and rows separately, especially when processing data.

    Reading with Headers

    If the CSV file contains headers, you can use csv.DictReader() instead of csv.reader(). This allows each row to be accessed as a dictionary, where the keys are the column names (headers) and the values are the corresponding data.

    Example: Reading CSV with Headers

    Given a CSV file students.csv like this:

    css
    name,age,grade

    Alice,23,A

    Bob,25,B

    Charlie,22,A

     

    You can read it and access each row as a dictionary:

    python
    import csv

     

    # Open the CSV file

    with open(‘students.csv’, ‘r’) as file:

    # Create a CSV dictionary reader object

    csv_reader = csv.DictReader(file)

     

    # Loop through each row

    for row in csv_reader:

    print(row)

     

    Output:

    bash

    {‘name’: ‘Alice’, ‘age’: ’23’, ‘grade’: ‘A’}

    {‘name’: ‘Bob’, ‘age’: ’25’, ‘grade’: ‘B’}

    {‘name’: ‘Charlie’, ‘age’: ’22’, ‘grade’: ‘A’}

     

    In this example:

    • DictReader(file)reads the CSV file into a dictionary, where the header names (e.g., name, age, grade) become the keys.
    • Each row is represented as a dictionary, which makes it easier to access specific columns by their header names.

    Writing with Headers

    If you want to include headers when writing data to a CSV file, you can use csv.DictWriter() to write the data as a dictionary.

    Example: Writing CSV with Headers

    python
    import csv

     

    # Data to be written

    data = [

    {‘name’: ‘Alice’, ‘age’: 23, ‘grade’: ‘A’},

    {‘name’: ‘Bob’, ‘age’: 25, ‘grade’: ‘B’},

    {‘name’: ‘Charlie’, ‘age’: 22, ‘grade’: ‘A’}

    ]

     

    # Open the file in write mode

    with open(‘output_with_headers.csv’, ‘w’, newline=”) as file:

    # Define the column headers

    fieldnames = [‘name’, ‘age’, ‘grade’]

     

    # Create a CSV writer object

    csv_writer = csv.DictWriter(file, fieldnames=fieldnames)

     

    # Write the header row

    csv_writer.writeheader()

     

    # Write all rows

    csv_writer.writerows(data)

     

    In this example:

    • DictWriter(file, fieldnames=fieldnames)creates a writer object that writes dictionaries to the CSV file.
    • writeheader()writes the headers first.
    • writerows(data)writes the dictionary rows.

    4. Using the csv Module

    Python’s csv module provides several helpful functions to work with CSV files. Below is an overview of some useful functions in the csv module:

    Common Functions in csv Module:

    • reader(file): Reads a CSV file and returns an iterator that yields each row as a list.
    • writer(file): Creates a writer object that writes rows to a CSV file.
    • DictReader(file): Reads a CSV file and returns each row as a dictionary, with the header as the keys.
    • DictWriter(file, fieldnames): Writes data to a CSV file as dictionaries, with specified fieldnames as headers.
    • field_size_limit(): Returns or sets the maximum field size allowed in a CSV file.
    • QUOTE_MINIMAL, csv.QUOTE_ALL, csv.QUOTE_NONNUMERIC, csv.QUOTE_NONE: Constants for controlling how quotes are handled in CSV files.

    5. Conclusion

    In this lesson, we learned how to work with CSV files using Python’s csv module:

    • Reading CSV files: We used reader()and csv.DictReader() to read and process data from CSV files.
    • Writing to CSV files: We used writer()and csv.DictWriter() to write data to CSV files, with and without headers.
    • Handling headers and rows: We explored how to handle CSV headers and rows efficiently, making it easy to manipulate data.
    • Using the csvmodule: We covered various functions in the csv module that simplify working with CSV files in Python.

    CSV files are an essential part of data exchange in many domains, and Python provides a clean and simple way to read, write, and process them. With these tools, you can easily handle data stored in CSV format, whether it’s from files or external sources like APIs.

  • Lesson 1: Working with JSON

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    JSON (JavaScript Object Notation) is a widely used format for data exchange, especially in web development. It is lightweight, easy for humans to read and write, and easy for machines to parse and generate. In Python, working with JSON is straightforward, thanks to the built-in json module, which allows for easy parsing, writing, and conversion of JSON data.

    In this lesson, we’ll cover:

    1. Parsing JSON Data: How to convert JSON strings into Python objects.
    2. Writing JSON to Files: Saving Python objects in JSON format.
    3. Use Cases for JSON in APIs: How JSON is used in web APIs to exchange data.

    1. Parsing JSON Data

    Parsing refers to the process of converting a JSON string into a format that can be used in Python, such as dictionaries or lists. Python provides the json module to parse JSON data into Python objects.

    Example: Parsing JSON String into Python Dictionary

    Let’s say you have a JSON string representing some data, like a list of users. To use that data in Python, we need to parse it:

    python
    import json

     

    # JSON string

    json_string = ‘{“name”: “John”, “age”: 30, “city”: “New York”}’

     

    # Parse JSON string into a Python dictionary

    data = json.loads(json_string)

     

    # Accessing values from the parsed data

    print(data[‘name’])  # Output: John

    print(data[‘age’])   # Output: 30

     

    In this example:

    • loads()is used to parse the JSON string into a Python dictionary.
    • The parsed dictionary (data) can now be accessed like any other Python dictionary.

    Parsing JSON from a File

    In real-world scenarios, JSON data often comes from files. Python’s json.load() function reads and parses a JSON file:

    python
    import json

     

    # Open and parse the JSON file

    with open(‘data.json’, ‘r’) as file:

    data = json.load(file)

     

    print(data)

     

    In this case:

    • load(file)reads the contents of data.json and parses it into a Python dictionary.
    • The with open()statement ensures that the file is properly closed after reading.

    2. Writing JSON to Files

    Sometimes you need to save Python objects into JSON format, especially when you want to share data or store it in a structured format.

    Writing Python Objects to JSON

    You can use json.dumps() to convert Python objects (like dictionaries or lists) into JSON strings, and then save them to a file.

    python
    import json

     

    # Data to be saved in JSON format

    data = {

    “name”: “Alice”,

    “age”: 25,

    “city”: “Los Angeles”

    }

     

    # Convert Python dictionary to JSON string

    json_string = json.dumps(data, indent=4)

     

    # Write the JSON string to a file

    with open(‘output.json’, ‘w’) as file:

    file.write(json_string)

     

    In this example:

    • dumps()converts the Python dictionary into a JSON string.
    • The indent=4argument makes the output more readable by adding indentation.
    • The write()function saves the JSON string to a file.

    Writing JSON with json.dump()

    You can also use json.dump() to directly write a Python object to a file in JSON format:

    python
    import json

     

    # Data to be written to file

    data = {

    “name”: “Bob”,

    “age”: 28,

    “city”: “Chicago”

    }

     

    # Write the Python dictionary as JSON to a file

    with open(‘output.json’, ‘w’) as file:

    json.dump(data, file, indent=4)

     

    Here, the json.dump() function does the conversion and file writing in a single step.

    3. Use Cases for JSON in APIs

    JSON is heavily used in APIs (Application Programming Interfaces) because it is both human-readable and easy for machines to parse. When working with web APIs, the data exchanged between the server and client is usually in JSON format. This is especially common in RESTful APIs, which use HTTP requests to send and receive data in JSON format.

    Example: Making a GET Request to an API and Parsing JSON

    Here’s how you can use the requests library to interact with an API and process the JSON response:

    python
    import requests

     

    # Send GET request to API

    response = requests.get(‘https://jsonplaceholder.typicode.com/posts’)

     

    # Parse JSON response into Python objects

    posts = response.json()

     

    # Print the first post

    print(posts[0])  # Output: {‘userId’: 1, ‘id’: 1, ‘title’: ‘…’, ‘body’: ‘…’}

     

    In this example:

    • get()sends a GET request to the API.
    • The json()method parses the JSON response and converts it into a Python object (usually a list of dictionaries).

    Example: Sending JSON Data to an API

    APIs also accept JSON data, often in POST requests. You can send data to an API by converting your Python dictionary into a JSON string and passing it in the request:

    python
    import requests

    import json

     

    # Data to be sent

    data = {

    “title”: “New Post”,

    “body”: “This is a new post”,

    “userId”: 1

    }

     

    # Convert Python dictionary to JSON string

    json_data = json.dumps(data)

     

    # Send POST request with JSON data

    response = requests.post(‘https://jsonplaceholder.typicode.com/posts’, data=json_data, headers={‘Content-Type’: ‘application/json’})

     

    # Print the response

    print(response.status_code)  # Output: 201 (Created)

    print(response.json())  # Output: The response data from the API

     

    In this example:

    • dumps()converts the Python dictionary into a JSON string.
    • The post()sends the JSON data to the API.
    • The Content-Type: application/jsonheader tells the server that the request body contains JSON data.

    4. Advanced JSON Operations

    Handling Nested JSON Objects

    Sometimes, JSON data may contain nested objects or lists. You can easily access these nested elements by chaining keys and indices:

    python

    import json

     

    # Nested JSON string

    json_string = ‘{“user”: {“name”: “Alice”, “age”: 25}, “city”: “New York”}’

     

    # Parse JSON string

    data = json.loads(json_string)

     

    # Access nested data

    print(data[‘user’][‘name’])  # Output: Alice

    print(data[‘user’][‘age’])   # Output: 25

     

    In this case:

    • The userkey contains another dictionary, which you can access by chaining keys (data[‘user’][‘name’]).

    Working with JSON Arrays

    JSON arrays are translated into Python lists. You can iterate over these arrays to process data:

    python
    import json

     

    # JSON array (list)

    json_string = ‘[{“name”: “Alice”, “age”: 25}, {“name”: “Bob”, “age”: 30}]’

     

    # Parse JSON string into Python list

    data = json.loads(json_string)

     

    # Access each item in the list

    for person in data:

    print(f”Name: {person[‘name’]}, Age: {person[‘age’]}”)

     

    This will output:

    yaml

    CopyEdit

    Name: Alice, Age: 25

    Name: Bob, Age: 30

     

    5. Conclusion

    In this lesson, we’ve explored how to work with JSON in Python:

    • Parsing JSON data: Converting JSON strings into Python objects (using loads()).
    • Writing JSON to files: Saving Python objects in JSON format (using dumps()and json.dump()).
    • Use cases for JSON in APIs: Sending and receiving data in JSON format when interacting with web APIs.

    JSON is a crucial part of web development and data exchange, and Python provides excellent tools to easily parse, generate, and manipulate JSON data. By mastering these techniques, you can interact with APIs, store and share data, and integrate your Python applications with external systems more efficiently.

  • Lesson 2: External Libraries and Packages

    Module 1 Introduction to Python
    Module 2 Variables and Data Types
    Module 3 Control Structures
    Module 4 Functions
    Module 5 Data Structures
    Module 6 Object-Oriented Programming
    Module 7 File Handling
    Module 8 Error Handling and Exceptions
    Module 9 Libraries and Modules
    Module 10 Working with Data
    Module 11 Advanced Python Topics
    Module 12 Working with Databases
    Module 13 Python for Web Development (Optional)
    Module 14 Final Project and Challenges

    In Python, while the standard library provides a lot of functionality, there are situations where you may need more specialized tools. These external libraries and packages extend Python’s capabilities, allowing you to perform tasks ranging from web requests, data manipulation, machine learning, and much more.

    In this lesson, we’ll explore how to install external libraries using pip, work with an external library (the popular requests library for HTTP requests), and understand the concept of virtual environments, which help manage dependencies for different projects.

    1. Installing External Libraries with pip

    Python has a package manager called pip (Pip Installs Packages), which is used to install, update, and manage external libraries and packages. The pip tool connects to the Python Package Index (PyPI), which is a repository of open-source Python libraries.

    Installing Packages with pip

    To install an external library, you use the following command in your terminal or command prompt:

    bash

    pip install library_name

     

    For example, to install the requests library (used for making HTTP requests), you would use:

    bash
    pip install requests

     

    Once installed, you can import and use the library in your Python script.

    Verifying Installation

    To verify if the installation was successful, you can try importing the library into your Python code:

    python
    import requests

     

    If no error is raised, the library has been installed successfully.

    Upgrading a Package

    If you want to upgrade an already installed package to the latest version, you can use the –upgrade flag:

    bash
    pip install –upgrade library_name

     

    Uninstalling a Package

    If you no longer need a package, you can uninstall it using:

    bash
    pip uninstall library_name

     

    Listing Installed Packages

    To list all the installed packages and their versions, use:

    bash
    pip list

     

    2. Example: Working with the requests Library

    The requests library is a simple, yet powerful tool used for making HTTP requests to interact with APIs or websites. It abstracts the complexities of making requests, allowing you to focus on the task at hand.

    Installing requests

    First, install the requests library using pip:

    bash
    pip install requests

     

    Making a Basic GET Request

    Once the requests library is installed, you can use it to make a simple HTTP GET request to a URL.

    Example:

    python
    import requests

     

    response = requests.get(‘https://jsonplaceholder.typicode.com/posts’)

    print(response.status_code)  # Status code of the response

    print(response.text)         # Content of the response

     

    In this example:

    • get()is used to send a GET request to the provided URL.
    • status_codegives the status code of the response, such as 200 for success.
    • textgives the content returned by the server.

    Handling JSON Data

    Often, the response from web services is in JSON format. requests makes it easy to parse this data:

    python
    import requests

     
    response = requests.get(‘https://jsonplaceholder.typicode.com/posts’)

    data = response.json()  # Converts JSON response to a Python dictionary

    print(data)

     

    In this case, response.json() automatically converts the JSON content into a Python dictionary, which you can then manipulate in your code.

    Making POST Requests

    The requests library can also handle POST requests, which are commonly used to send data to a server.

    Example of sending data with a POST request:

    python

    import requests

     

    url = ‘https://jsonplaceholder.typicode.com/posts’

    data = {

    ‘title’: ‘foo’,

    ‘body’: ‘bar’,

    ‘userId’: 1

    }

    response = requests.post(url, data=data)

    print(response.json())

     

    In this example:

    • post()sends data to the specified URL.
    • The datadictionary holds the information you want to send.
    • json()is used to parse the server’s response.

    Error Handling in requests

    When making HTTP requests, it’s important to handle possible errors, such as network issues or invalid responses.

    python
    import requests

     

    try:

    response = requests.get(‘https://jsonplaceholder.typicode.com/posts’)

    response.raise_for_status()  # Raises an HTTPError if the status code is not 200

    data = response.json()

    print(data)

    except requests.exceptions.HTTPError as err:

    print(f”HTTP error occurred: {err}”)

    except Exception as err:

    print(f”An error occurred: {err}”)

     

    • raise_for_status()raises an exception if the status code indicates an error (e.g., 404 or 500).
    • exceptions.HTTPErrorhandles errors that are HTTP-specific, while Exception is used to handle all other exceptions.

    3. Introduction to Virtual Environments

    As you develop different Python projects, you may encounter issues with library versions. One project may require a specific version of a library, while another project needs a different version. To solve this problem, Python uses virtual environments.

    What is a Virtual Environment?

    A virtual environment is an isolated environment in which you can install Python libraries without affecting the global Python installation. This is important when different projects require different versions of libraries.

    Creating a Virtual Environment

    Install virtualenv (if not already installed):

    bash
    pip install virtualenv

    Create a new virtual environment: In your terminal, navigate to the directory where you want the project to reside and run:

    bash
    virtualenv myenv
    1. This creates a folder named myenvthat contains a separate Python environment.
    2. Activate the Virtual Environment:

    On Windows, run:

    bash
    myenv\Scripts\activate

    On Mac/Linux, run:

    bash
    source myenv/bin/activate
    1. After activation, your terminal should display the name of the virtual environment at the beginning of the command prompt, indicating that it is active.

    Install Libraries within the Virtual Environment: Once the virtual environment is activated, you can install packages as usual using pip, and they will only affect that specific environment.

    bash
    pip install requests

    Deactivating the Virtual Environment: When you’re done, you can deactivate the virtual environment by running:

    bash
    deactivate

    4. Benefits of Using Virtual Environments

    • Isolation: Each project gets its own environment, preventing conflicts between dependencies.
    • Flexibility: You can manage dependencies per project without worrying about affecting global packages.
    • Reproducibility: You can easily recreate the environment on different machines using a txtfile.

    Using requirements.txt to Manage Dependencies

    Once you’ve installed all the necessary packages in your virtual environment, you can create a requirements.txt file to list them. This file can be shared with others or used to recreate the environment.

    To generate the requirements.txt file:

    bash
    pip freeze > requirements.txt

     

    To install the packages listed in requirements.txt on a new machine:

    bash
    pip install -r requirements.txt

     

    5. Conclusion

    In this lesson, we’ve learned how to:

    • Install and use external libraries in Python using pip.
    • Work with the requestslibrary to make HTTP requests and handle responses.
    • Understand the importance of virtual environments and how they help manage dependencies in Python projects.

    By utilizing external libraries, you can extend Python’s functionality to handle tasks like HTTP requests, file management, data manipulation, and more. Virtual environments ensure that each project has the necessary libraries installed without conflicts, allowing you to manage dependencies effectively across multiple projects.