TRAINING PYTHONINTRODUCTION TO PYTHON (BASIC LEVEL)Editor: Nguyễn Đức Minh Khôi@HCMC University of Technology, September 2011
9/2/2011 Training Python Chapte 0: Introduction to Python 1 TRAINING PYTHON Chapter 0: INTRODUCTION TO PYTHON
9/2/2011 Training Python Chapte 0: Introduction to Python 2CONTENTS Python in general How Python program runs? How to run Python?
9/2/2011 Training Python Chapte 0: Introduction to Python 3Python in general• What is python? • High level programming language • Emphasize on code readability • Very clear syntax + large and comprehensive standard library • Use of indentation for block delimiters • Multiprogramming paradigm: OO, imperative, functional, procedural, reflective • A fully dynamic type system and automatic memory management • Scripting language + standalone executable program + interpreter • Can run on many platform: Windows, Linux, Mactonish• Updates: • Newest version: 3.2.2 (CPython, JPython, IronPython) • Website: www.python.org
9/2/2011 Training Python Chapte 0: Introduction to Python 4Python in general (Cont’)• Advantages: • Software quality • Developer productivity • Program portability • Support libraries • Component integration • Enjoyment• Disadvantages: • not always be as fast as that of compiled languages such as C and C++
9/2/2011 Training Python Chapte 0: Introduction to Python 5Python in general (Cont’)• Applications of python:
9/2/2011 Training Python Chapte 0: Introduction to Python 6Python in general (Cont’)• Python’s Capability: • System Programming • GUI • Internet Scripting • Component Integration • Database Programming • Rapid Prototyping • Numeric and Scientific Programming • Gaming, Images, Serial Ports, XML, Robots, and More
9/2/2011 Training Python Chapte 0: Introduction to Python 7How Python program runs? Notice: pure Python code runs at speeds somewhere between those of a traditional compiled language and a traditional interpreted language
9/2/2011 Training Python Chapte 0: Introduction to Python 8 How to run Python?• Install Python 3.2.2: • Go to website: http://www.python.org/download/ and download the latest version of Python • Run and install follow the instructions of the .msi file • If you successfully install, you will see this picture:• Coding Python: • Not IDE support: use notepad++ http://notepad-plus-plus.org/ • Use IDE support: Eclipse (3.7) or Netbeans (7.0)
9/2/2011 Training Python Chapte 0: Introduction to Python 9 How to run Python? (Cont’)• Install Eclipse: follow the instructions from this website: http://wiki.eclipse.org/FAQ_Where_do_I_get_and_install_Eclipse%3F (you should download the Eclipse Classics version)• Install Pydev plugin for eclipse: follow this instruction: http://pydev.org/manual_101_install.html
9/2/2011 Training Python Chapte 0: Introduction to Python 10 THANKS FOR LISTENING Editor: Nguyễn Đức Minh Khôi Contact: email@example.com Main reference: Part I – Getting Started Learning Python – O’reilly
5/22/2011 Training Python 3Lists• Ordered collections of arbitrary objects• Accessed by offset• Variable-length, heterogeneous, and arbitrarily nestable• Of the category “mutable sequence”• Arrays of object references
5/22/2011 Training Python 4Lists literals and operations
5/22/2011 Training Python 5Lists literals and operations (cont’)
5/22/2011 Training Python 6Dictionaries• Accessed by key, not offset• Accessed by key, not offset• Variable-length, heterogeneous, and arbitrarily nestable• Of the category “mutable mapping”• Tables of object references (hash tables)
5/22/2011 Training Python 7Dictionaries literals and operations
5/22/2011 Training Python 8Dictionaries literals and operations (c)
5/22/2011 Training Python 9Tuples• Ordered collections of arbitrary objects• Accessed by offset• Of the category “immutable sequence”• Fixed-length, heterogeneous, and arbitrarily nestable• Arrays of object references
5/22/2011 Training Python 10Tuples literals and operations
5/22/2011 Training Python 11Tuples literals and operations (con’t)
5/22/2011 Training Python 12Files – common operations
2NUMERIC TYPES• Integers and floating-point numbers• Complex numbers• Fixed-precision decimal numbers• Rational fraction numbers• Sets• Booleans• Unlimited integer precision• A variety of numeric built-ins and modules
3NUMERIC TYPES (Cont’)
4NUMERIC TYPES (Cont’)
Dynamic Typing• Variables, Objects, References: • Variables are entries in a system table, with spaces for links to objects. • Objects are pieces of allocated memory, with enough space to represent the values for which they stand. • References are automatically followed pointers from variables to objects.
Dynamic Typing (Cont’) - Shared references • Mutable types: • Notices: • It’s also just the default: if you don’t want such behavior, you can request that Python copy objects instead of making references.
Dynamic Typing (Cont’) - Shared references• Notices (next): • “is” function returns False if the names point to equivalent but different objects, as is the case when we run two different literal expressions. • Small integers and strings are cached and reused, though, is tells us they reference the same single object.
5/22/2011 Training Python 13Summary• Object just classification
5/22/2011 Training Python 14Summary (con’t)• Object Flexibility • Lists, dictionaries, and tuples can hold any kind of object. • Lists, dictionaries, and tuples can be arbitrarily nested. • Lists and dictionaries can dynamically grow and shrink.• Object copy • Slice expressions with empty limits (L[:]) copy sequences. • The dictionary and set copy method (X.copy()) copies a dictionary or set. • Some built-in functions, such as list, make copies (list(L)). • The copy standard library module makes full copies.
9/2/2011 Learning Python Chapter 1 1 THANKS FOR LISTENING Editor: Nguy n Đ c Minh Khôi Contact: firstname.lastname@example.org Main reference: Part II – Types and Operations Learning Python – O’reilly
9/2/2011 Learning Python Chapter 2 1 TRAINING PYTHON STATEMENTS AND SYNTAX
9/2/2011 Learning Python Chapter 2 7Assignment StatementsAssignment Properties: • Assignments create object references • Names are created when first assigned • Names must be assigned before being referenced • Some operations perform assignments implicitlyAssignment Statement Forms:
9/2/2011 Learning Python Chapter 2 8Variable name rules (opt)• Syntax: (underscore or letter) + (any number of letters, digits, or underscores)• Case matters: SPAM is not the same as spam• Reserved words are off-limits
9/2/2011 Learning Python Chapter 2 11Conditional Statements - IF• General Format:• The if/else ternary expression: • Example:
9/2/2011 Learning Python Chapter 2 12IF Statements - Truth tests (opt)Conditional expression:• Any nonzero number or nonempty object is true.• Zero numbers, empty objects, and the special object None are considered false.• Comparisons and equality tests are applied recursively to data structures.• Comparisons and equality tests return True or False (custom versions of 1 and 0).• Boolean “and” and “or” operators return a true or false operand object.
9/2/2011 Learning Python Chapter 2 13IF Statements - Truth tests (opt) (Cont) • “and” and “or” operands:
9/2/2011 Learning Python Chapter 2 14Loop Statements – while statements• General while format:• Notice:
9/2/2011 Learning Python Chapter 2 15Loop Statements – for statements• General Format:• Loop Coding Techniques: • The built-in range function produces a series of successively higher integers, which can be used as indexes in a for. • The built-in zip function returns a series of parallel-item tuples, which can be used to traverse multiple sequences in a for.• Notice: for loops typically run quicker than while-based counter loops, it’s to your advantage to use tools like these that allow you to use for when possible.
9/2/2011 Learning Python Chapter 2 17Iterations and comprehensions• Iterable: • an object is considered iterable if it is either a physically stored sequence or an object that produces one result at a time in the context of an iteration tool like a for loop. • iterable objects include both physical sequences and virtual sequences computed on demand.• Iterations: • Any object with a __next__ method to advance to a next result, which raises StopIteration at the end of the series of results, is considered iterable in Python.• Example:
9/2/2011 Learning Python Chapter 2 18List comprehension• Example: • (x + 10): arbitrary expression • (for x in L): iterable object• Extend List Comprehension:
9/2/2011 Learning Python Chapter 2 21 THANKS FOR LISTENING Editor: Nguyễn Đức Minh Khôi Contact: email@example.com Main reference: Part III – Statements and Syntax Learning Python – O’reilly
9/6/2011 Training Python Chapter 3 1 TRAINING PYTHON Chapter 3: FUNCTION
9/6/2011 Training Python Chapter 3 2CONTENTS Function Basics Scope Arguments Function Advanced Iterations and Comprehension Advanced
9/6/2011 Training Python Chapter 3 3Function Basics• Function: A function is a device that groups a set of statements so they can be run more than once in a program.• Why use?: • Maximizing code reuse and minimizing redundancy • Procedural decomposition
9/6/2011 Training Python Chapter 3 4Function Basics – def Statements• General format:• Use “def” statements:
9/6/2011 Training Python Chapter 3 5Function Basics – Examples
9/6/2011 Training Python Chapter 3 6Scopes• Three different scopes • If a variable is assigned inside a def, it is local to that function. • If a variable is assigned in an enclosing def, it is nonlocal to nested functions. • If a variable is assigned outside all defs, it is global to the entire file.• Notice: • All names assigned inside a function def statement (or a lambda, an expression we’ll meet later) are locals by default. • Functions can freely use names as-signed in syntactically enclosing functions and the global scope, but they must declare such nonlocals and globals in order to change them.
9/6/2011 Training Python Chapter 3 7Scopes – the LEGB rules
9/6/2011 Training Python Chapter 3 8Scopes – examples Global names: X, func Local names: Y, Z # The Built – in Scopes
9/6/2011 Training Python Chapter 3 9Scopes – Global statements• Global Statement:• Other ways to access Globals:
9/6/2011 Training Python Chapter 3 10Scopes – Global statements(Cont’)
9/6/2011 Training Python Chapter 3 11Scopes – Nested functions• Factory function • These terms refer to a function object that remembers values in enclosing scopes regardless of whether those scopes are still present in memory.
9/6/2011 Training Python Chapter 3 12Scopes – Nested scope (Cont’)• Nested scope and lambda:
9/6/2011 Training Python Chapter 3 13Scopes – Nonlocal statements• The nonlocal statement: • Is a close cousin to global • Like global: nonlocal declares that a name will be changed in an enclosing scope. • Unlike global: • nonlocal applies to a name in an enclosing function’s scope, not the global module scope outside all defs. • nonlocal names must already exist in the enclosing function’s scope when declared• Format:
9/6/2011 Training Python Chapter 3 14Scopes – Nonlocal statements (Con’t)
9/6/2011 Training Python Chapter 3 15Arguments – Passing Basics• Arguments are passed by automatically assigning objects to local variable names.• Assigning to argument names inside a function does not affect the caller.• Changing a mutable object argument in a function may impact the caller.• Immutable arguments are effectively passed “by value.”• Mutable arguments are effectively passed “by pointer.”
9/6/2011 Training Python Chapter 3 16Arguments – Matching Modes• Keyword-only arguments: arguments that must be passed by keyword only and will never be filled in by a positional argument.
9/6/2011 Training Python Chapter 3 17Arguments - Examples
9/6/2011 Training Python Chapter 3 18Arguments – Examples (Cont’)
9/6/2011 Training Python Chapter 3 19Arguments – Bonus Points
9/6/2011 Training Python Chapter 3 20Function Advanced• General guidelines: • Coupling: use arguments for inputs and return for outputs. • Coupling: use global variables only when truly necessary. • Coupling: don’t change mutable arguments unless the caller expects it. • Cohesion: each function should have a single, unified purpose. • Size: each function should be relatively small. • Coupling: avoid changing variables in another module file directly.
9/6/2011 Training Python Chapter 3 21 Function Advanced - Recursions• Examples:• Alternatives:
9/6/2011 Training Python Chapter 3 22Function Advanced – Lambda Expression • Lambda format: • Use lambda for: • inline a function definition • defer execution of a piece of code • Notices: • lambda is an expression, not a statement • lambda’s body is a single expression, not a block of statements. • If you have larger logic to code, use def; lambda is for small pieces of inline code. On the other hand, you may find these techniques useful in moderation • Examples:
9/6/2011 Training Python Chapter 3 23Lambda Expression (Cont’)• Logic within lambda function:• Nested lambda:• Used with map function:• Used with filter function:• Used with reduce function:
9/6/2011 Training Python Chapter 3 24Iterations and Comprehension Part 2• List Comprehension: • Vs. Map: • Vs. filter: • Vs. Nested for:
9/6/2011 Training Python Chapter 3 25Iterations and Comprehension Part 2• Generators: • Generator functions: are coded as normal def statements but use yield statements to return results one at a time, suspending and resuming their state between each. • Generator expressions: are similar to the list comprehensions of the prior section, but they return an object that produces results on demand instead of building a result list.• Generator functions:
9/6/2011 Training Python Chapter 3 26Iterations and Comprehension Part 2• Generator Expression:
9/6/2011 Training Python Chapter 3 273.0 Comprehension Syntax
9/6/2011 Training Python Chapter 3 28Function Pitfall• “List comprehensions were nearly twice as fast as equivalent for loop statements, and map was slightly quicker than list comprehensions when mapping a built-in function such as abs (absolute value)”• Python detects locals statically, when it compiles the def’s code, rather than by noticing assignments as they happen at runtime.
9/6/2011 Learning Python Chapter 2 29 THANKS FOR LISTENING Editor: Nguyễn Đức Minh Khôi Contact: firstname.lastname@example.org Main reference: Part IV – Functions Learning Python 4th Edition – O’reilly 2010
TRAINING PYTHONChapter 4: MODULES
9/15/2011 Training Python Chapter 4 2Contents Modules Basics Modules Package Modules in advance
9/15/2011 Training Python Chapter 4 3Modules Basics• Modules are process with: • import: Lets a client (importer) fetch a module as a whole • from: Allows clients to fetch particular names from a module • imp.reload: Provides a way to reload a module’s code without stopping Python• Why use Modules? • Code reuse • System namespace partitioning • Implementing service or data
9/15/2011 Training Python Chapter 4 4Modules Basics – import statements• How imports work? 1. Find the module’s file. 2. Compile it to byte code (if needed). 3. Run the module’s code to build the objects it defines.• The Module Search Path: 1. The home directory of the program 2. PYTHONPATH directories (if set) 3. Standard library directories 4. The contents of any .pth files (if present)
9/15/2011 Training Python Chapter 4 5Modules Basics – create Modules• In fact, both the names of module files and the names of directories used in package must conform to the rules for variable names: • They may, for instance, contain only letters, digits, and underscores. • Package directories also cannot contain platform-specific syntax such as spaces in their names.• Modules in Python can be written in external languages such as C/C++ in Cpython, Java in Jython, .net languages in IronPython
9/15/2011 Training Python Chapter 4 6Modules Basics - Usages• The import statement:• The from statement:• The from * statement• The import happens only once
9/15/2011 Training Python Chapter 4 7Modules Basics – Usages (Con’t) • Import assigns an entire module object to a single name. • From assigns one or more names to objects of the same names in another module. Be careful:
9/15/2011 Training Python Chapter 4 8 Modules Basics - namespaces• Files generate Namespaces: • Module statements run on the first import. • Top-level assignments create module attributes. • Module namespaces can be accessed via the attribute__dict__or dir(M) • Modules are a single scope (local is global)• Namespace nesting: • In mod3.py: • In mod2.py: • In mod1.py:
9/15/2011 Training Python Chapter 4 9Modules Basics – reloading function• Unlike import and from: • reload is a function in Python, not a statement. • reload is passed an existing module object, not a name. • reload lives in a module in Python 3.0 and must be imported itself.• How to use:
9/15/2011 Training Python Chapter 4 10Modules Basics – reload example • In changer.py: • Change global message variable: •
9/15/2011 Training Python Chapter 4 11Modules package• Package __init__.py files: • Directory: dir0dir1dir2mod.py • Import statement: import dir1.dir2.mod • Rules: • dir1 and dir2 both must contain an __init__.py file. • dir0, the container, does not require an __init__.py file; this file will simply be ignored if present. • dir0, not dir0dir1, must be listed on the module search path (i.e., it must be the home directory, or be listed in your PYTHONPATH, etc.). • Present in tree mode:
9/15/2011 Training Python Chapter 4 12Modules package• Relative import: • instructs Python to import a module named spam located in the same package directory as the file in which this statement appears.• Sibling import:
9/15/2011 Training Python Chapter 4 13Modules In Advance – Data Hiding• Minimizing from * Damage: _X and __all__ • you can prefix names with a single underscore (e.g., _X) to prevent them from being copied out when a client imports a module’s names with a from * statement.• Enabling future language features• Mixed Usage Modes: __name__ and __main__ • If the file is being run as a top-level program file, __name__ is set to the string "__main__" when it starts. • If the file is being imported instead, __name__ is set to the module’s name as known by its clients
9/15/2011 Training Python Chapter 4 14Modules in Advance (Cont’)• In runme.py:• Unit Tests with __name__: • we can wrap up the self-test call in a __name__ check, so that it will be launched only when the file is run as a top-level script, not when it is imported
9/15/2011 Training Python Chapter 4 15Modules in Advance (Cont’)• The as Extension for import and from:
9/15/2011 Training Python Chapter 4 16Module Gotchas• Statement Order Matters in Top-Level Code• from Copies Names but Doesn’t Link• from * Can Obscure the Meaning of Variables• Recursive from Imports May Not Work • You can usually eliminate import cycles like this by careful design— maximizing cohesion and minimizing coupling are good first steps.
THANKS FOR LISTENINGEditor: Nguyễn Đức Minh KhôiContact: email@example.comMain reference: Part V – ModulesLearning Python 4th Edition – O’reilly 2010
TRAINING PYTHONChapter 5: CLASSES AND OOP
9/18/2011 Training Python Chapter 5: Classes and OOP 2Contents Class Coding Basics Class Coding Detail Advanced Class topics
9/18/2011 Training Python Chapter 5: Classes and OOP 3Class Coding Basics• OOP program must show: • Abstraction (or sometimes called encapsulation) • Inheritance (vs. composition) • Polymorphism• Class vs. Instance Object: • Class: Serve as instance factories. Their attributes provide behavior—data and functions—that is inherited by all the instances generated from them. • Instance: Represent the concrete items in a program’s domain. Their attributes record data that varies per specific object
9/18/2011 Training Python Chapter 5: Classes and OOP 4 Class Coding Basics (Cont’)• Each class statement generates a new class object.• Each time a class is called, it generates a new instance object.• Instances are automatically linked to the classes from which they are created.• Classes are linked to their superclasses by listing them in parentheses in a classheader line; the left-to-right order there gives the order in the tree.
9/18/2011 Training Python Chapter 5: Classes and OOP 5Class Coding Basics – Class trees• Notice: • Python uses multiple inheritance: if there is more than one superclass listed in parentheses in a class statement (like C1’s here), their left-to-right order gives the order in which those superclasses will be searched for attributes. • Attributes are usually attached to classes by assignments made within class statements, and not nested inside function def statements. • Attributes are usually attached to instances by assignments to a special argument passed to functions inside classes, called self.
9/18/2011 Training Python Chapter 5: Classes and OOP 6Class Coding Basics - Class vs. Instance • Class Object: • The class statement creates a class object and assigns it a name. • Assignments inside class statements make class attributes. • Class attributes provide object state and behavior. • Instance Object: • Calling a class object like a function makes a new instance object. • Each instance object inherits class attributes and gets its own namespace. • Assignments to attributes of self in methods make per-instance attributes.
9/18/2011 Training Python Chapter 5: Classes and OOP 8Class Coding Basics - Inheritance• Attribute inheritance: • Superclasses are listed in parentheses in a class header. • Classes inherit attributes from their superclasses. • Instances inherit attributes from all accessible classes. • Each object.attribute reference invokes a new, independent search. • Logic changes are made by subclassing, not by changing superclasses.
9/18/2011 Training Python Chapter 5: Classes and OOP 10Class Coding Details • Class statement: Assigning names inside the class statement makes class attributes, and nested defs make class methods, but other assignments make attributes, too. • Examples:
9/18/2011 Training Python Chapter 5: Classes and OOP 21Advanced Class topics – Extending built in types• By embedding:
9/18/2011 Training Python Chapter 5: Classes and OOP 22Advanced Class topics – Extending built in types• By subclassing:
9/18/2011 Training Python Chapter 5: Classes and OOP 23Advanced Class topics – Diamond Inheritance • Old and new style inheritance:
9/18/2011 Training Python Chapter 5: Classes and OOP 24Advanced Class topics – Diamond Inheritance• Explicit Conflict Resolution:
9/18/2011 Training Python Chapter 5: Classes and OOP 25Advanced Class topics – static class method• Notice:
9/18/2011 Training Python Chapter 5: Classes and OOP 26Advanced Class topics – static and class method
9/18/2011 Training Python Chapter 5: Classes and OOP 27Advanced Class topics - Decorators • Function decorators provide a way to specify special operation modes for functions, by wrapping them in an extra layer of logic implemented as another function. • Syntax: • Example:
9/18/2011 Training Python Chapter 5: Classes and OOP 29Advanced Class topics – Class gotchas• Changing Class Attributes Can Have Side Effects• Changing Mutable Class Attributes Can Have Side Effects, Too• Multiple Inheritance: Order Matters • multiple inheritance works best when your mix-in classes are as self-contained as possible—because they may be used in a variety of contexts, they should not make assumptions about names related to other classes in a tree.
THANKS FOR LISTENINGEditor: Nguyễn Đức Minh KhôiContact: firstname.lastname@example.orgMain reference: Part VI – Classes and OOPLearning Python 4th Edition – O’reilly 2010