Course Content

Python Advanced Concepts

1. Mastering Python Modules and Imports

Python Project Structure: Organizing Code for Scalability Understanding Python Modules Importing Modules in Python: Best Practices for Code Reusability Exploring Python Libraries: Built-in and Third-Party Solutions Python Standard Library: Essential Modules for Everyday Tasks Managing Python Packages

2. Mastering Error Handling in Python

Fundamentals of Error Handling in Python: Preventing Code Crashes Raising Exceptions in Python: Triggering and Managing Errors Executing Code After Exceptions: Handling Cleanup Operations Using Assertions in Python: Debugging and Ensuring Code Integrity Best Practices for Exception Handling

3. Mastering File Handling in Python

Introduction to File Handling in Python Reading and Writing Files: Handling Text and Data Efficiently Advanced File Operations: Modifying and Managing File Content Working with File Paths in Python Using Context Managers for File Handling in Python Creating Custom Context Managers Handling Binary Files in Python

4. Mastering Pytest Framework

Introduction to Testing in Python Pytest Framework: Setting Up and Running Automated Tests Using the Assert Statement in Pytest: Validating Test Conditions Test Parametrization in Pytest: Running Tests with Multiple Inputs

5. Mastering Unittest Framework

Introduction to Unittest in Python: Writing Structured Unit Tests Using Assert Methods in Unittest: Validating Test Results SetUp and TearDown in Unittest: Managing Test Environments Mocking Techniques in Unittest: Simulating Dependencies Advanced Mocking in Unittest

6. Mastering Iterators and Generators in Python

Python Iterables: Understanding Sequences and Data Structures Understanding Iterators in Python Lazy Evaluation in Python: Optimizing Memory and Performance Introduction to Generators: Yielding Values for Efficient Iteration Practical Applications of Generators: Real-World Use Cases

Practical Applications of Generators: Real-World Use Cases

Generators can be used as lightweight context managers to manage resources efficiently, such as database connections, file operations, or locking mechanisms. With the contextlib module, generators can handle resource allocation and cleanup seamlessly.


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from contextlib import contextmanager

@contextmanager
def database_connection():
    print("Opening database connection")
    connection = "Database Connection"  # Simulated connection
    try:
        yield connection
    finally:
        print("Closing database connection")

# Using the generator as a context manager
with database_connection() as conn:
    print(f"Using {conn}")

Processing Large Data Efficiently

Generators are ideal for building data pipelines that process large datasets lazily. Each stage of the pipeline can be implemented as a generator, enabling efficient, memory-friendly processing.


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import re

# Stage 1: Read lines lazily
def read_lines(text):
    for line in text.split("\n"):
        yield line

# Stage 2: Filter non-empty lines
def filter_lines(lines):
    for line in lines:
        if line.strip():
            yield line

# Stage 3: Extract words lazily
def extract_words(lines):
    for line in lines:
        for word in re.findall(r'\w+', line):
            yield word

# Stage 4: Transform words to lowercase
def lowercase_words(words):
    for word in words:
        yield word.lower()

# Input text
text = """Generators are powerful tools
They allow efficient data processing
This pipeline demonstrates their usage"""

# Build the pipeline
lines = read_lines(text)
filtered = filter_lines(lines)
words = extract_words(filtered)
lowercased = lowercase_words(words)

# Process the data
print("Processed words:")
for word in lowercased:
    print(word)