Python Advanced Concepts

1. Modules and Imports

If you find yourself frustrated with import statements, the difference between modules and libraries, or managing your Python project structure, this section is your key to clarity.

Python Project Structure

Understanding Modules

Import Statements

Understanding Libraries in Python

Exploring Standard Libraries

Package Management

2. Error Handling

Want to debug your code effectively and understand the power of leveraging various errors? This section demystifies error handling in Python, showcasing why it’s crucial for developers to master these skills for robust applications.

Basics of Error Handling

Raising Exceptions

Handling Code After Exceptions

Using Assertions

Best Practices in Exception Handling

3. File Handling

Eager to automate file operations in your projects? This section delves into the essentials of file handling in Python, equipping you with the tools to read, write, and manage files efficiently for smoother, more powerful applications.

Introduction to File Handling

Reading and Writing Files

Advanced File Operations

Working with File Paths

Context Managers

Custom Context Managers

Handling Binary Files

4. Pytest Framework

You will learn the ins and outs of testing with Pytest and the fundamentals of Test-Driven Development (TDD). This section guides you through creating robust tests that enhance your code's reliability and streamline your development process.

Introduction to Testing in Python

Pytest Framework

Assert Statement

Test Parametrization

5. Unittest Framework

Master automation testing with Unittest. Learn how to cover various test cases effectively using setup, teardown, and mocking.

Introduction to Unittest

Assert Methods

SetUp and TearDown Steps

Mocking Technique

Mocking in Details

6. Iterators and Generators

In Python, iterators and generators are key concepts that enable efficient and memory-friendly data processing. While iterators follow a specific protocol for accessing data sequentially, generators simplify iteration by producing values on demand with the yield keyword. This section explores these concepts and their practical applications, helping you master memory-efficient programming.

Python Iterables

Understanding Iterators

Lazy Evaluation

Introduction to Generators

Practical Examples of Generators

Contenido del Curso

Python Advanced Concepts

1. Modules and Imports

Python Project Structure Understanding Modules Import Statements Understanding Libraries in Python Exploring Standard Libraries Package Management

2. Error Handling

Basics of Error Handling Raising Exceptions Handling Code After Exceptions Using Assertions Best Practices in Exception Handling

3. File Handling

Introduction to File Handling Reading and Writing Files Advanced File Operations Working with File Paths Context Managers Custom Context Managers Handling Binary Files

4. Pytest Framework

Introduction to Testing in Python Pytest Framework Assert Statement Test Parametrization

5. Unittest Framework

Introduction to Unittest Assert Methods SetUp and TearDown Steps Mocking Technique Mocking in Details

6. Iterators and Generators

Python Iterables Understanding Iterators Lazy Evaluation Introduction to Generators Practical Examples of Generators

Practical Examples of Generators

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)

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Sección 6. Capítulo 5