Intermediate Python

Understanding the Intermediate Python concepts

Duration

3 Day

Level

Intermediate Level

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This course is designed to demonstrate an intermediate understanding of Python and increase your skills and techniques as a developer.

The course begins with an examination into advanced data types in Python and functional programming features. Then, we will discuss the specifics of testing Python programs with logging. After discussing Python libraries for advanced computation and data integration, we will cover multithreaded programming with Python. We will end with developing network aware applications through socket programming and Uniform Resource Locators and creating functional web application using the Django Web development framework.

During this instructor-led, hands-on course, you will learn to:

Work with Python’s support for resource management and collection iteration
Apply TDD concepts with Python using unit testing
Create concurrent programs using Threads
Build a simple client-server solution
Understand the basics of the Django Framework for web application development

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Software Developers
Data Scientists
Data Engineers
Web Developers

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Advanced Data Types and Functional Programming

Data Types: namedtuple, defaultdict, ordereddict, deque, etc
Functional programming: map/filter/reduce, lambda, list.sort, operator modules
Functional closures and decorators

Generators and Iterators

List comprehensions, generator expressions, and generator functions
Iteration: iter(), iter, next(), itertools, chain, groupby, etc
Understanding the yield statement

Context Managers

What is a context manager?
The ‘with’ statement
Work with a context manager: contextlib.closing, enter and exit
Utilize contextlib.contextmanager

Testing Python

Review of key testing concepts: unit, functional, integration, etc.
Overview of test-driven development concepts
Writing test: Unit test with a test-first approach
Unit testing with the unittest module
Creating test suites with nosetests as a test collector
Performing simple code coverage analysis with coverage

Introducing Logging

Importance of logging
Basic logging concepts within Python
Using basicConﬁg to set up loggers, handlers, formatters
Using ﬁleConﬁg for more complex logger configurations

NumPy/SciPy (Numerical and Computational Processing)

Open-source modules to Python for common mathematical and numerical routines
Demonstration of common pre-compiled and fast functions (use cases)
Vector and matrix mathematics
Special functions in SciPy related to vectorization (vq, weave)

Pandas and Data Integration

Data frames
Aggregation and grouping of data
Reshaping, transforming, and cleaning of data
Scraping data: web API, parsing HTML and XML, JSON (encoder/decoder)

Python Multithreading and Multiprocessing

Process class and using the multiprocessing module
Synchronization between processes
Exchanging objects between processes: pipes, queues, and circular buffers
Sharing state between processes (shared memory and server process)
Python Semaphores and Mutex
Synchronization events

Network Programming

Review of network programming concepts and protocol layers
Network programming support within Python
Fetching web resources with urllib/urllib2
Sending email with smtplib
Creating client-server like applications using sockets
Creating a simple JSON-REST client

Introduction to Web application Development with Django

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Basic understanding of Python

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