Python Int/Adv 

This course builds upon your foundational Python knowledge, delving into advanced concepts, libraries, and development practices.

Target Audience:

• Programmers with basic Python experience (comfortable with core syntax, data structures, and control flow)

Prerequisites:

• Completion of an Introduction to Python course or equivalent experience

Course Objectives:

By the end of this course, you'll be able to:

• Apply advanced data types and functional programming paradigms in Python.

• Utilize generators and iterators for efficient memory management and iteration.

• Implement context managers using the with statement for cleaner code and resource handling.

• Write unit tests using the unittest and nosetest frameworks for effective test-driven development (TDD).

• Integrate logging functionalities for improved application monitoring and debugging.

• Leverage NumPy and SciPy libraries for numerical computing and scientific data analysis.

• Work with Pandas for data manipulation, analysis, and integration tasks.

Course Length: 3 Days

Course Outline:

Python Syntax Review

• Refreshing core Python syntax (data types, operators, control flow)

Advanced Data Types and Functional Programming

• Exploring advanced data types (namedtuple, defaultdict, etc.)

• Introducing functional programming concepts (map/filter/reduce, lambda functions)

• Understanding closures and decorators

Generators and Iterators

• Implementing list comprehensions, generator expressions, and generator functions

• Mastering iteration concepts (iter(), iter, next(), itertools)

• Utilizing the yield statement for generator creation

Context Managers

• Understanding context managers and their benefits

• Leveraging the with statement for context management

• Working with contextlib modules (closing, contextmanager)

Testing Python

• Reviewing testing concepts (unit, functional, integration)

• Applying test-driven development (TDD) principles

• Writing unit tests with the unittest module

• Creating test suites with nosetests and analyzing code coverage

Introducing Logging

• Recognizing the importance of logging in applications

• Exploring basic logging concepts within Python

• Configuring loggers, handlers, and formatters with basicConfig

• Setting up more complex configurations using fileConfig

NumPy and SciPy (Numerical and Computational Processing)

• Introduction to NumPy and SciPy for numerical computing

• Demonstrating common pre-compiled functions and their use cases

• Performing vector and matrix operations with NumPy

• Exploring SciPy's special functions for vectorization

Pandas and Data Integration

• Working with data frames, the core data structure in Pandas

• Learning data aggregation, grouping, reshaping, and transformation techniques

• Performing data cleaning tasks and exploring web scraping techniques (APIs, HTML/XML parsing, JSON)

Additional Notes:

• Hands-on exercises and labs will be incorporated throughout the course to solidify your learning.

• This course assumes a basic understanding of Python programming.