Python's dynamic nature (rough slides, November 2004)
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The document explores Python's dynamic nature, detailing how it supports scaling from simple scripts to complex applications. It describes Python's interpreted and compiled aspects, object-oriented features, namespaces, operator overloading, and the evolution of types and classes, particularly highlighting changes in version 2.2. Additionally, it discusses the implications of these features on code scalability, maintenance, and the development of frameworks like Zope.
![Controlling Attribute
Access
• An object can take control of the way its
attributes are accessed.
• The __getitem__ function for list or
dictionary access. Example: object[‘foo’]
• The __getattr__ function for named
access. Example: using object.foo will call
object.__getattr__(‘foo’)
• __setitem__ and __setattr__ for editing.](https://image.slidesharecdn.com/pythonsdynamicnature-roughslides-140305101303-phpapp01/85/Python-s-dynamic-nature-rough-slides-November-2004-8-320.jpg)
Python's dynamic nature (rough slides, November 2004)
- 1. Python’s Dynamic Nature An investigation of how Python’s dynamic nature allows it to scale up from one-line scripts to massive applications. Kiran Jonnalagadda <[email protected]> These are rough slides. The finals will make heavy use of diagrams in place of text.
- 2. What Does Interpreted Mean? • Python is not line interpreted like shell script. • Python is byte-code compiled like Java, but compilation is on-the-fly. • However, Python in some ways behaves like a line-interpreted language, making the learning curve easy.
- 3. Loading a Module • When a module (file) is loaded, it is first compiled into byte-code. • Python then executes all the code in the module that is not in a function. • This code can modify the module as it loads, making for the line-interpreted feel. • Code in functions is only executed on call.
- 4. Example of Loading # Example code print “Top level executes on load.” def somefunc(): print “Executes only when called.” class foo: print “Class level also executes on load.” def bar(self): print “Executes only when called.”
- 5. Bags of Attributes • Python is a fully object oriented language. • All symbols are objects with attributes. • Objects contain other objects. Therefore, Python’s namespace is nested. • A module is an object, a string is an object, an integer is an object, everything is an object.
- 6. __attributes__ • Python inserts several special attributes into objects. These attributes are all named in the __attribute__ style. • Example 1: __name__ indicates the name given to the object in code. • Example 2: __class__ refers to the class this object is an instance of.
- 7. Operator Overloading • Operator overloading is also achieved via functions with special attribute names. • Examples: • __eq__ and __ne__ for == and != • __add__ to add two objects • __cmp__ to compare, for sorting
- 8. Controlling Attribute Access • An object can take control of the way its attributes are accessed. • The __getitem__ function for list or dictionary access. Example: object[‘foo’] • The __getattr__ function for named access. Example: using object.foo will call object.__getattr__(‘foo’) • __setitem__ and __setattr__ for editing.
- 9. Documentation • Python code is self-documenting. • Documentation can be placed in a string, which must be the first non-remark declaration in a module, class or function. • This documentation is accessible as the __doc__ attribute of the object. • Example: class foo: “This is the documentation.” pass
- 10. Namespaces • In Python, everything is contained within something else. • The top-level, invisible container is called __builtins__. Contains internal functions. • Top-level symbols not from __builtins__ are local symbols. • Local symbols are not private. They can be accessed via the namespace.
- 11. Namespaces #2 • The top-level namespace is not crowded; an app can have several modules with conflicting names and not have a conflict. • “module1.cname” is different from “module2.cname” • Using “name = module1.cname” makes “name” a local alias for “module1.cname”
- 12. Symbols and Objects • Symbols are not the same as objects. • Symbols are only references to objects. • An object can have multiple references. • Objects are garbage collected when they have zero references. GC algorithms used are both reference counting and markand-sweep.
- 13. Multiple References • An module can consist of nothing but references to symbols in other modules. • Hence the term, “bags of attributes.” • This allows a large package to have a single API module that links to the rest of the package internally.
- 14. References Example module1.py: def foo(bar, baz): return bar + baz module2.py: import module1 new_name = module1.foo module3.py: from module2 import new_name print new_name(1, 2)
- 15. Runtime Editing • Python code can edit itself at run-time. • The only way to define variables in a class is by editing the instance at runtime. • Editing involves simply changing the object a symbol refers to. • This only works when all code refers to the object via the symbol, which is the case almost all the time.
- 16. How Classes Work • {To insert code examples and diagram here, showing a complex class definition and how it works when different parts are accessed. This will be across several slides.}
- 17. Scalability • Because of the clear namespace separation, a Python application can contain thousands of modules without conflicts. • Advanced class definition ability helps avoid repetitive code. • This makes Python code easy to maintain and scale, while still remaining simple.
- 18. The Type/Class Dichotomy • Prior to version 2.2, Python had two types of objects: Types and Classes. • Types could only be defined in C code. • Classes could only be defined in Python. • Classes could not be derived from Types. • This meant a base-class could not be defined externally in C/C++ code.
- 19. Zope’s Solution • Jim Fulton, the creator of Zope, created a new Type called ExtensionClass. • ExtensionClass can be derived from, like a regular Python Class, but is a Type. • Achieved by patching Python internals. • ExtensionClass made a lot of extensions possible that could not be done in Python.
- 20. Type/Class Merger • Python 2.2 merges Types and Classes, so they are all one type of object. • But changes are incompatible with ExtensionClass, which Zope depends on. • So Zope still uses the old Type/Class divide, even with Python 2.2 and 2.3. • Zope will upgrade with Zope 3.