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
    The 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 object oriented programming 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