Enterprise Python Training in Canada

Students should have experience writing Python scripts, as well as a user-level knowledge of Unix/Linux, Mac, or Windows.

• Common practices for writing scalable and legible Python code
• Effective data processing practices and features like generators and decorators to improve complex computations on large datasets
• Pythonic design patterns for secure, large-scale applications.
• Enhancements to classes
• Advanced Python metaprogramming concepts
• Leveraging OS services
• Interacting with network services
• Coding graphical interfaces for applications
• Designing professional scripts
• Querying databases
• Processing XML, CSV, and JSON data

Day 1: Pythonic Python

1. Pythonic Programming
   
   • The Zen of Python
   • Comments, docstrings, annotations
   • String Handling
   • List and Array optimization
   • Tuple tricks
   • Control structures in Python – effective use of Lambda functions, List comprehensions
   • Creating efficient dictionaries
   • Advanced unpacking
   • Essential conventions for Pythonic classes
   • Clean Python code patterns
   • Code quality enforcement tools – Style and Logic guides
   • Pythonic class conventions
   • Effective handling of import
   • Handling Decorators – Classes, Functions, accepting arguments, Multi decorators, singleton
   • Decorators and the DRY principle
   • Context Manager
   • Iterators
   • Generators for performance improvement
   • Coroutines – yield from, async def, await
   • Descriptors – types, chains, accessing attributes, benefits

Day 2: Python System Scripting

1. OS Services
   
   • The os and os.path modules
   • Environment variables
   • Launching external commands with subprocess
   • Walking directory trees
   • Paths, directories, and filenames
   • Working with file systems
2. Network Programming
   
   • Built-in classes
   • Using requests
   • Grabbing web pages
   • Sending email
   • Working with binary data
   • Remote access (SSH)
   • Using FTP
3. Scripting for System Administration
   
   • Running external programs
   • Parsing arguments
   • Creating filters to read text files
   • Logging

Day 3: Python Data Processing

1. Serializing data – XML and JSON
   
   • Working with XML
   • XML modules in Python
   • Getting started with ElementTree
   • Parsing XML
   • Updating an XML tree
   • Creating a new document
   • About JSON
   • Reading JSON
   • Writing JSON
   • Translating JSON to Pythonic data
   • Validating JSON data
   • Reading/writing CSV files
   • YAML, other formats as time permits
   • Time Permitting Sessions
2. Database access
   
   • The DB API
   • Available Interfaces
   • Connecting to a server
   • Creating and executing a cursor
   • Fetching data
   • Parameterized statements
   • Using Metadata
   • Transaction control
   • ORMs and NoSQL overview

Day 4: Beyond Python

1. Pythonic Architecture
   
   • Python Design Patterns
   • Architecture of Python applications
   • Event driven programming architecture
   • Microservices architecture
   • Scaling Python applications
   • Differentiate the features of event-based, microservice, and API architectures.
   • Application security guidelines
2. PyQt
   
   • Overview
   • Qt Architecture
   • Using designer
   • Standard widgets
   • Event handling
   • Creating a simple application
3. Type hinting
   
   • Annotate variables
   • Learn what type hinting does NOT do
   • Use the typing module for detailed type hints
   • Understand union and optional types
   • Write stub interfaces
4. Writing real-life applications
   
   • Reading input files in Unix
   • Parsing command-line options
   • Detecting the current platform
   • Implementing logging
   • Creating a calculator application in PyQt
   • Perform CRUD operations by connecting to DB