Python training course

Python Features

•     Ease and economy of development
•     Scalability
•     Extensibility
•     Adoption by major users

Introduction to Python

•     Python history
•     Interactive and scripted execution
•     Dynamic typing examples and uses

Basic Data Types         

•     Arithmetic on integers and longs
•     Overflow-free arithmetic
•     Using floating point for fractional values
•     Using Decimal for precise decimal calculations
•     Strings: indexing, slicing and formatting

Python aggregated types

•     Lists and tuples: accessing information by position
•     Modifying and appending to lists by index or slice
•     Operations on lists: comparison and sorting
•     List comprehensions for more compact code
•     Managing large data sets with generators

Flow Control    

•     Making decisions with the if statement
•     Python code layout and clarity
•     Iterating with the for and while constructs
•     Writing your own iterators and generators

 

Functions       

•     Parameters: positional, named and default arguments
•     Variable length argument lists
•     Functional programming: functions as arguments and return values
•     Using lambda functions to simplify code

Larger Programs and Modularisation

•     Writing Python modules to modularise code
•     Using the import statement to use Python modules
•     Customising the import search path
•     Grouping modules into packages

Improving code robustness by handling exceptions 

•     The importance of avoiding unhandled errors
•     Using the try/except/else and finally construct
•     Raising exceptions
•     Using custom exceptions for a better user experience

File handling    

•     Opening files for read and/or write
•     Managing file handles correctly
•     Reading and writing text and binary files
•     Performing random access

 

Agile and Test Driven development   

•     Improving code quality and delivery with unit testing
•     Th Python unit testing libraries
•     Using unittest, PyTest, Doctest
•     Using umbrella test classes to integrate different testing approaches

Powerful text processing with regular expressions    
        

•     Expressing powerful abstract text patterns with metacharacters
•     Using capturing to extract patterns from text
•     Substituting text patterns with fixed or dynamic replacement patterns

Object oriented programming with classes    

•     Understanding the power of OOP using abstract data types
•     Defining abstract data types using classes
•     Writing class member and static functions
•     Understanding the class and object structure
•     Exploiting Python’s dynamic class and object behaviour

More on classes    

•     Using inheritance for code reusability
•     Further enhancing reusability through polymorphism
•     Using Python dynamic typing to change types at run time