Our Python training course will show you why, in programming terms, Python has every angle covered. It is used extensively in the cloud, and is one of the first languages to support Google App Engine. Python is popular among scientific communities through the SciPy package. It's simplicity makes it easy enough for beginners who use a Raspberry Pi, which was originally targeted to run Python. System administrators, looking for more than shell scripts, also take to Python, given the extensive library support available.
Python is a dynamic language, object-oriented and has features enabling its use as a functional language. It also supports meta-programming structures and aspects of Lisp and Haskell.
Python can be used very effectively for rapid Test Script development and through hands-on practicals, you will see why Python is simple enough to be used to teach young children to program, advanced enough to be used by M.I.T. to teach computer science and is perhaps the most widely used dynamic language with many high quality, open source libraries and frameworks.
Quants, Data Scientists, Data Analysts, Mathematicians, System Testers and Shell Scripters who are new to Python
Python In action : We all know YouTube as the place to upload cat videos and fails. As one of the most popular websites in existence, it provides us with endless hours of video entertainment. The Python programming language powers it and the features we love.
Ease and economy of development
Adoption by major users
Introduction to Python
Interactive and scripted execution
Dynamic typing examples and uses
Basic Data Types
Arithmetic on integers and longs
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
Making decisions with the if statement
Python code layout and clarity
Iterating with the for and while constructs
Writing your own iterators and generators
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
Using custom exceptions for a better user experience
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
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