Python Advanced – Building on the foundation

Python Advanced
Building on the foundation

course = {
'title':
'Python Advanced',
'chapters': [
'Introduction',
'Special Methods & Attributes',
'Unit Testing',
'Function, Object & Class Customisation',
'Object Thinking',
'Containers',
'Iterators & Generators',
'Functional Thinking',
'Concurrency',
'Modules & Packages',
'Outroduction',
'Labs & Homework'
],
...
}

course = {
...
'licence':
('Creative Commons Attribution 4.0, '
'https://creativecommons.org/licenses/by/4.0/'),
'contributors': [
'Kevlin Henney, kevlin@curbralan.com',
],
'date': '2015-11-05'
}

Introduction
Everything has a beginning...

Course goals
 Increase knowledge of Python 3
 This course builds on the themes and
concepts of the Python Foundation
 Attendance on the Python Foundation
course is assumed
 The course focus is on the language,
the standard library and techniques
 Using slides, discussion and hands-on
experiments and labs

Coding experiments
 Small (or large) coding experiments to
explore concepts in practice
 Use the IDLE environment, the default
python shell or any Python environment
and editor combination of your choice!
 Some experiments are described in the
slides, but others may be suggested
 You can carry out experiments
individually or in pairs

Programming labs
 Labs implemented using Cyber-Dojo
 http://cyber-dojo.org/
 Labs involve writing both code and tests
 Labs are undertaken in pairs
 But pairing is not marriage — you will
move around
 Oh, and there's homework too
 Individual rather than paired, and not in
Cyber-Dojo

Questions, answers & intros
 The best way to have your questions
answered is to ask them
 Mindreading is not (yet) supported
 And now, please introduce yourself!
 Briefly...

Integrating new types with
operators & built-in functions
Special Methods &
Attributes

Facts at a glance
 Special methods take the place of
operator overloading in Python
 Built-in functions also build on special
methods and special attributes
 The set of special names Python is
aware of is predefined
 Context managers allow new classes
to take advantage of with statements

Operator overloading
 It is not possible to overload operators
in Python directly
 There is no syntax for this
 But classes can support operators and
global built-ins using special methods
 They have reserved names, meanings
and predefined relationships, and some
cannot be redefined, e.g., is and id
 Often referred to as magic methods

Comparisons
__eq__(self, other) self == other
__ne__(self, other) self != other
Equality operations
If not defined for a class, == supports reference equality
__lt__(self, other) self < other
__gt__(self, other) self > other
__le__(self, other) self <= other
__ge__(self, other) self >= other
Ordering operations
If not defined for a class, != is defined as not self == other
These methods return NotImplemented if not provided for a class, but
whether TypeError is raised when an operator form is used depends on
whether a reversed comparison is also defined, e.g., if __lt__ is defined
but __gt__ is not, a > b will be executed as b < a
Note that the __cmp__
special method is not
supported in Python 3,
so consider using the
functools.total_ordering
decorator to help define
the rich comparisons

Arithmetic operations
__neg__(self) -self
__pos__(self) +self
__invert__(self) ~self
__abs__(self) abs(self)
Unary operators and built-in functions
__add__(self, other) self + other
__sub__(self, other) self - other
__mul__(self, other) self * other
__truediv__(self, other) self / other
__floordiv__(self, other) self // other
__mod__(self, other) self % other
__pow__(self, other) self**other
...
Binary arithmetic operators
Reflected forms also
exist, e.g., __radd__,
where method
dispatch should be
on the right-hand
rather than left-hand
operand

Augmented assignment
__iadd__(self, other) self += other
__isub__(self, other) self -= other
__imul__(self, other) self *= other
__itruediv__(self, other) self /= other
__ifloordiv__(self, other) self //= other
__imod__(self, other) self %= other
__ipow__(self, other) self **= other
...
Augmented assignment operators
Each method should return the result of the in-place operation (usually self) as the result is
used for the actual assignment, i.e., lhs += rhs is equivalent to lhs = lhs.__iadd__(rhs)
Note that if these special methods are not defined, but the
corresponding binary operations are, these assignment forms
are still valid, e.g., if __add__ is defined, then lhs += rhs
becomes lhs = lhs.__add__(rhs)

Conversions & representation
__int__(self)
__float__(self)
__round__(self)
__complex__(self)
__bool__(self)
__hash__(self)
Numeric type conversions
__str__(self)
__format__(self, format)
__repr__(self)
__bytes__(self)
String type conversions
And also __round__(self, n) to round to n digits
after the decimal point (or before if negative)
Official string representation, that
should ideally be a valid Python
expression that constructs the value
If __str__ is not defined, __repr__
is used by str if present
Should only define if __eq__ is also defined, such that
objects comparing equal have the same hash code

Containers & iteration
__len__(self) len(self)
__contains__(self, item) item in self
Content query
__getitem__(self, key) self[key]
__setitem__(self, key, item) self[key] = item
__delitem__(self, key) del self[key]
__missing__(self, key)
Subscripting
If __contains__ not defined,
in uses linear search based
on __iter__ or indexing from
0 of __getitem__
Hook method used by dict for its subclasses
__iter__(self) iter(self)
__next__(self) next(self)
Iteration
Most commonly invoked
indirectly in the context
of a for loop

Interception & lifecycle
__call__(self, ...)
__getattr__(self, name)
__getattribute__(self, name)
__setattr__(self, name, value)
__delattr__(self, name)
__dir__(self)
Support for function calls and attribute access
__init__(self, ...)
__del__(self)
Object lifecycle
__enter__(self)
__exit__(self, exception, value, traceback)
Context management

with & context managers
 The with statement provides exception
safety for paired actions
 I.e., where an initial acquire/open/lock
action must be paired with a final
release/close/unlock action, regardless
of exceptions
 A context manager is an object that
can be used in this way in a with
 It is defined by a simple protocol

Context manager anatomy
with Resource() as resource:
...
Called on entry to with
class Resource:
def __enter__(self):
...
return self
def __exit__(self, exception, value, trace):
...
Result bound to the target of the with
Exception class or None if no exception
Exception string or None if no exception
Traceback or None if no exception
Note that resource still accessible after the with statement

Experiment
from time import time
class Timing:
...
def __exit__(self, exception, value, trace):
...
with Timing():
task_to_time()
Record current time
using time
Print elapsed time,
optionally indicating
whether an exception
was thrown
Code to time the
execution of
Write a context manager to time the
execution of code within a with statement

Special attributes
 A number of special attributes can
also be defined, including...
 __doc__ is the documentation string and is
used by the built-in help function
 __name__ is used to hold the name for
functions and classes
 __dict__ refers to the dictionary of values
held by any object
 __module__ names the defining module

Unit Testing
Programming unittest with GUTs

Facts at a glance
 Python has many testing frameworks
available, including unittest
 unittest is derived from JUnit and has a
rich vocabulary of assertions
 Good Unit Tests (GUTs) are structured
around explanation and intention
 The outcome of a unit test depends
solely on the code and the tests

unittest
 The unittest framework is a standard
JUnit-inspired framework
 It is organised around test case classes,
which contain test case methods
 Naming follows Java camelCase
 Runnable in other frameworks, e.g., pytest
 Python has no shortage of available
testing frameworks!
 E.g., doctest, nose, pytest

Good Unit Tests (GUTs)
Very many people say "TDD" when they really
mean, "I have good unit tests" ("I have GUTs"?).
Ron Jeffries tried for years to explain what this
was, but we never got a catch-phrase for it, and
now TDD is being watered down to mean GUTs.
Alistair Cockburn
http://alistair.cockburn.us/The+modern+programming+professional+has+GUTs

GUTs in practice
 Good unit tests (GUTs)...
 Are fully automated, i.e., write code to test
code
 Offer good coverage of the code under
test, including boundary cases and error-
handling paths
 Are easy to read and to maintain
 Express the intent of the code under test
— they do more than just check it

Not-so-good unit tests
 Problematic test styles include...
 Monolithic tests: all test cases in a single
function, e.g., test
 Ad hoc tests: test cases arbitrarily
scattered across test functions, e.g., test1,
test2, ...
 Procedural tests: test cases bundled into a
test method that correspond to target
method, e.g., test_foo tests foo

A unittest example
from leap_year import is_leap_year
import unittest
class LeapYearTests(unittest.TestCase):
def test_that_years_not_divisible_by_4_are_not_leap_years(self):
self.assertFalse(is_leap_year(2015))
def test_that_years_divisible_by_4_but_not_by_100_are_leap_years(self):
self.assertTrue(is_leap_year(2016))
def test_that_years_divisible_by_100_but_not_by_400_are_not_leap_years(self):
self.assertFalse(is_leap_year(1900))
def test_that_years_divisible_by_400_are_leap_years(self):
self.assertTrue(is_leap_year(2000))
if __name__ == '__main__':
unittest.main()
Derivation from base class is necessary
Test case method names must start with test

Assertions
assertEqual(lhs, rhs)
assertNotEqual(lhs, rhs)
assertTrue(result)
assertFalse(result)
assertIs(lhs, rhs)
assertIsNot(lhs, rhs)
assertIsNone(result)
assertIsNotNone(result)
assertIn(lhs, rhs)
assertNotIn(lhs, rhs)
assertIsInstance(lhs, rhs)
assertNotIsInstance(lhs, rhs)
...
All assertions also take an optional msg argument

More assertions
assertLess(lhs, rhs)
assertLessEqual(lhs, rhs)
assertGreater(lhs, rhs)
assertGreaterEqual(lhs, rhs)
assertRegex(result)
assertNotRegex(result)
assertRaises(exception)
assertRaises(exception, callable, *args, *kwargs)
fail()
assertAlmostEqual(lhs, rhs)
assertAlmostNotEqual(lhs, rhs)
...
By default, approximate equality is established to within 7
decimal places — this can be changed using the places
keyword argument or by providing a delta keyword argument

assertRaises
 assertRaises can be used as an
ordinary assertion call or with with
 If a callable argument is not provided,
assertRaises returns a context manager
 As a context manager, assertRaises fails if
associated with body does not raise
self.assertRaises(ValueError, lambda: is_leap_year(-1))
with self.assertRaises(ValueError) as context:
is_leap_year(-1)
Optional, but can be used to access raised exception details

assertRaisesRegex
 assertRaisesRegex also matches string
representation of raised exception
 Equivalent to regex search
 Like assertRaises, assertRaisesRegex can
be used as function or context manager
self.assertRaisesRegex(
ValueError, 'invalid', lambda: is_leap_year(-1))
with self.assertRaisesRegex(ValueError, 'invalid') :
is_leap_year(-1)

Tests as explanation
So who should you be writing the tests for? For the person trying to
understand your code.
Good tests act as documentation for the code they are testing. They
describe how the code works. For each usage scenario, the test(s):
 Describe the context, starting point, or preconditions that must
be satisfied
 Illustrate how the software is invoked
 Describe the expected results or postconditions to be verified
Different usage scenarios will have slightly different versions of
each of these.
Gerard Meszaros
"Write Tests for People"

Test anatomy
 Example-based tests ideally have a
simple linear flow: arrange, act, assert
 Given: set up data
 When: perform the action to be tested
 Then: assert desired outcome
 Tests should be short and focused
 A single objective or outcome — but not
necessarily a single assertion — that is
reflected in the name

Common fixture code
 Common test fixture code can be
factored out...
 By defining setUp and tearDown methods
that are called automatically before and
after each test case execution
 By factoring out common initialisation
code, housekeeping code or assertion
support code into its own methods, local
to the test class

Tests as specifications
Tests that are not written with their role as
specifications in mind can be very confusing to
read. The difficulty in understanding what they
are testing can greatly reduce the velocity at
which a codebase can be changed.
Nat Pryce & Steve Freeman
"Are your tests really driving your development"

What is a unit test?
A test is not a unit test if:
 It talks to the database
 It communicates across the network
 It touches the file system
 It can't run at the same time as any of your other unit tests
 You have to do special things to your environment (such as editing
config files) to run it.
Tests that do these things aren't bad. Often they are worth writing, and
they can be written in a unit test harness. However, it is important to be
able to separate them from true unit tests so that we can keep a set of
tests that we can run fast whenever we make our changes.
Michael Feathers
http://www.artima.com/weblogs/viewpost.jsp?thread=126923

External dependencies
 External resources are a common
source of non-unit dependencies
 Registries and environment variables
 Network connections and databases
 Files and directories
 Current date and time
 Hardware devices
 Externals can often be replaced by
test doubles or pre-evaluated objects

A taxonomy of test doubles
 It's not just about mocks...
 A test stub is used to substitute input and
can be used for fault injection
 A test spy offers input and captures output
behaviour
 A mock object validates expectations
 A fake object offers a usable alternative
to a real dependency
 A dummy object fulfils a dependency

Doubles in practice
 Mocking and related techniques are
easier in Python than many languages
 Duck typing means dependencies can be
substituted without changing class design
 Passing functions as objects allows for
simple pluggability
 It is easy to write interception code for
new classes and to add interception code
on existing objects or use existing
libraries, e.g., unittest.mock

Function, Object & Class
Customisation
Ad hoc objects, metaclasses,
monkey patching & decorators

Facts at a glance
 Functions are objects with state
 Classes are mutable after definition, so
methods can be added and removed
 Objects can have attributes added
and removed after creation
 Decorators wrap functions on definition
 A metaclass is the class of a class,
defining how the class behaves

It's objects all the way down
 Everything in Python is expressed as
objects, from functions to modules
 Functions are callable objects — with
associated state and metadata — and
are instantiated with a def statement
 And Python is a very dynamic
language, not just dynamically typed
 Python's object model can be fully
accessed at runtime

Object creation
 Objects are normally created based
on calling their class name
 But the class name does not have to
be present, only the class object
 E.g., type(parrot)() creates a new object
with the same type as parrot
 This eliminates the need for some of the
factory patterns and convoluted tricks
employed in other languages

Ad hoc data structures
 Keyword arguments make dicts easy
to use as ad hoc data structures
 Keyword arguments are passed as dicts,
with keywords becoming keys
 See also collections.namedtuple
sputnik_1 = dict(year=1957, month=10, day=4)
{'day': 4, 'month': 10, 'year': 1957}
origin = dict(x=0, y=0)
{'y': 0, 'x': 0}

Dynamic object attributes
 It is possible to add attributes to an
object after it has been created
 Object attributes are dynamic and
implemented as a dictionary
 An attribute can be removed using del
class Spam:
pass
meal = Spam()
meal.egg = True
meal.bacon = False
Ad hoc data structures with named
attributes can be created easily

Monkey patching
A monkey patch is a way for a program to extend or
modify supporting system software locally (affecting
only the running instance of the program). This process
has also been termed duck punching.
The definition of the term varies depending upon the
community using it. In Ruby, Python, and many other
dynamic programming languages, the term monkey
patch only refers to dynamic modifications of a class or
module at runtime.
http://en.wikipedia.org/wiki/Monkey_patch

Modifying existing classes
 Methods and attributes can be
attached to existing classes
 But be careful doing so!
class Menu:
pass
menu = Menu()
Menu.options = {'spam', 'egg', 'bacon', 'sausage'}
def order(self, *choice):
return set(choice) <= self.options
Menu.order = order
assert menu.order('egg')
assert not menu.order('toast')

Exploring the object model
 A number of functions help to explore
the runtime object model
 dir: a sorted list of strings of names in the
current or specified scope
 locals: dict of current scope's variables
 globals: dict of current scope's global
variables
 vars: dict of an object's state, i.e., __dict__,
or locals if no object specified

Object attribute operations
 Object attributes can be accessed
using hasattr, getattr and setattr
 These global functions are preferred to
manipulating an object's __dict__ directly
 An object can provide special
methods to intercept attribute access
 Query is via __getattr__ (if not present)
and __getattribute__ (unconditional)
 Modify via __setattr__ and __delattr__

Decorators
 A function definition may be wrapped
in decorator expressions
 A decorator is a callable object that
results in a callable object given a
callable object or specified arguments
 On definition the function is passed to the
decorator and the decorator's result is
used in place of the function
 This forms a chain of wrapping if more
than one decorator is specified

Basic decoration
def non_zero_args(wrapped):
def wrapper(*args):
if all(args):
return wrapped(*args)
else:
raise ValueError
return wrapper
@non_zero_args
def date(year, month, day):
return year, month, day
date(1957, 10, 4)
date(2000, 0, 0)
A nested function that performs
the validation, executing the
wrapped function with
arguments if successful
(Note: for brevity, keyword
arguments are not handled)
Decorator
Returns (1957, 10, 4)
Raises ValueError

Refined decoration
from functools import wraps
def non_zero_args(wrapped):
@wraps(wrapped)
def wrapper(*args):
if all(args):
else:
raise ValueError
return wrapper
@non_zero_args
Ensures the resulting wrapper
has the same principal attributes
as the wrapped function, e.g.,
__name__ and __doc__

Parameterised decoration
def check_args(checker):
def decorator(wrapped):
def wrapper(*args):
if checker(args):
else:
raise ValueError
return wrapper
return decorator
@check_args(all)
date(1957, 10, 4)
date(2000, 0, 0)
Returns (1957, 10, 4)
Raises ValueError
Nested nested function that
performs the validation
Nested function that curries
the checker argument

Metaclasses
 A metaclass can be considered the
class of class
 A class defines how objects behaves; a
metaclass defines how classes behaves
 Metaclasses are most commonly used
as class factories
 Customise class creation and execution
 A class is created from a triple of name,
base classes and a dictionary of attributes

abc.ABCMeta
from abc import ABCMeta
from abc import abstractmethod
class Visitor(metaclass=ABCMeta):
@abstractmethod
def enter(self, visited):
pass
@abstractmethod
def exit(self, visited):
pass
@abstractmethod
def visit(self, value):
pass
Instantiation for Visitor
is disallowed because of
ABCMeta and the
presence of at least one
@abstractmethod
Use of the ABCMeta
metaclass to define
abstract classes is one
of the more common
metaclass examples
@abstractmethod only
has effect if ABCMeta is
the metaclass

type
 type is the default metaclass in Python
 And type is its own class, i.e., type(type) is
an identity operation
 type is most often used as a query, but it
can also be used to create a new type
def init(self, options):
self.__options = options
def options(self):
return self.__options
Menu = type('Menu', (),
{'__init__': init, 'options': options})
menu = Menu({'spam', 'egg', 'bacon', 'sausage'})
The base
defaults to
object

Adding new code at runtime
 New code, from source, can be
added to the Python runtime
 The compile function returns a code
object from a module, statement or
expression
 The exec function executes a code object
 The eval function evaluates source code
and returns the resulting value
 Here be dragons!

Object Thinking
Protocols, polymorphism,
patterns & practical advice

Facts at a glance
 Object usage defined more by
protocols than by class relationships
 Substitutability is strong conformance
 Polymorphism is a fundamental
consideration (but inheritance is not)
 Values can be expressed as objects
following particular conventions
 Enumeration types have library support

Not all objects are equal
 Be careful how you generalise your
experience!
 Patterns of practice are context sensitive
 Python's type system means that...
 Some OO practices from other languages
and design approaches travel well
 Some do not translate easily or well — or
even at all
 And some practices are specific to Python

Protocols and conformance
 An expected set of method calls can
be considered to form a protocol
 Protocols may be named informally, but
they are not part of the language
 Conformance to a protocol does not
depend on inheritance
 Dynamic binding means polymorphism
and subclassing are orthogonal
 Think duck types not declared types

Liskov Substitution Principle
A type hierarchy is composed of subtypes and
supertypes. The intuitive idea of a subtype is one whose
objects provide all the behavior of objects of another
type (the supertype) plus something extra. What is
wanted here is something like the following substitution
property: If for each object o1 of type S there is an
object o2 of type T such that for all programs P defined
in terms of T, the behavior of P is unchanged when o1 is
substituted for o2, then S is a subtype of T.
Barbara Liskov
"Data Abstraction and Hierarchy"

Contracts & consequences
 Substitutability is the strongest form of
conformance to a protocol
 A contract of behavioural equivalence
 Following the contract means...
 An overridden method cannot result in a
wider range or cover a narrower domain
 Concrete classes should not be bases
 A derived class should support the same
initialisation protocol as its base

Inheritance & composition
 A class can respect logical invariants
 Assertions true of its methods and state
 A derived class should respect the
invariants of its base classes
 And may strengthen them
 Consider composition and forwarding
 Especially if derivation would break
invariants and lead to method spam from
the base classes

Object structure & traversal
 The following patterns combine and
complement one another:
 Composite: recursive–whole part structure
 Visitor: dispatch based on argument type
 Enumeration Method: iteration based on
inversion of control flow
 Lifecycle Callback: define actions for
object lifecycle events as callbacks

Composite pattern
class Primitive(Node):
def __init__(self, name):
self._name = name
def children(self):
return ()
...
*
class Node(metaclass=ABCMeta):
@abstractmethod
def children(self):
pass
def name(self):
return self._name
...
class Group(Node):
def __init__(self, name, children):
self._name = name
self._children = tuple(children)
def children(self):
return self._children
...

Visitor pattern
...
def accept(self, visitor):
visitor.visit_primitive(self)
...
...
@abstractmethod
pass
...
class Group(Node):
...
visitor.visit_group(self)
...
class Visitor:
def visit_primitive(self, visited):
...
def visit_group(self, visited):
...

Enumeration Method pattern
...
def map(self, callback):
pass
...
*
@abstractmethod
pass
...
class Group(Node):
def __init__(self, name, children):
self._name = name
self._children = tuple(children)
for child in self._children:
callback(child)
...

Lifecycle Callback pattern
class Visitor:
def enter(self, group):
...
def leave(self, group):
...
def visit(self, primitive):
...
class Printer:
def __init__(self):
self.__depth = 0
def enter(self, group):
print(self.__depth * ' ' + '<group>')
self.__depth += 1
def leave(self, group):
self.__depth -= 1
print(self.__depth * ' ' + '</group>')
def visit(self, primitive):
print(self.__depth * ' ' + '<primitive/>')

Patterns in combination
...
def for_all(self, visitor):
visitor.visit(self)
...
@abstractmethod
pass
...
class Group(Node):
...
visitor.enter(self)
for child in self._children:
child.for_all(visitor)
visitor.leave(self)
...
*

Object forwarding
 Many object forwarding patterns have
a simple and general form in Python
 Proxy: offer transparent forwarding by one
object to another, supporting the target
object's protocol
 Null Object: represent absence of an
object with an object that realises the
object protocol with do-nothing or return-
default implementations for its methods

Proxy pattern
class TimingProxy:
def __init__(self, target, report=print):
self.__target = target
self.__report = report
def __getattr__(self, name):
attr = getattr(self.__target, name)
def wrapper(*args, **kwargs):
start = time()
try:
return attr(*args, **kwargs)
finally:
end = time()
self.__report(name, end – start)
return wrapper if callable(attr) else attr

Null Object pattern
class NullObject:
def __call__(self, *args, **kwargs):
return self
def __getattr__(self, name):
return self
A generic Null Object implementation useful, for example, as for test dummy
objects, particularly if no specific return values are expected or tested
Method calls on NullObject are chainable as NullObject is callable

Realising values as objects
 It is worth differentiating between
objects that represent mechanisms...
 And may therefore have changing state
 And objects that represent values
 Their focus is information (e.g., quantities),
rather than being strongly behavioural
(e.g., tasks), or entity-like (e.g., users)
 They should be easily shared and
consistent, hence immutable (i.e., like str)

Immutable Value pattern
 For a class of value objects...
 Provide for rich construction to ensure
well-formed objects are easy to create
 Provide query but not modifier methods
 Consider @property for zero-parameter
query methods so they look like attributes
 Define __eq__ and __hash__ methods
 Provide relational operators if values are
ordered (see functools.total_ordering)

In the money
@total_ordering
class Money:
def __init__(self, units, hundredths):
self.__units = units
self.__hundredths = hundredths
@property
def units(self):
return self.__units
@property
def hundredths(self):
return self.__hundredths
def __eq__(self, other):
return (self.units == other.units and
self.hundredths == other.hundredths)
def __lt__(self, other):
return ((self.units, self.hundredths) <
(other.units, other.hundredths))
...

On the money
@total_ordering
class Money:
def __init__(self, units, hundredths):
self.__total_hundredths = units * 100 + hundredths
@property
def units(self):
return self.__total_hundredths // 100
@property
def hundredths(self):
return self.__total_hundredths % 100
def __eq__(self, other):
return (self.units == other.units and
self.hundredths == other.hundredths)
def __lt__(self, other):
return ((self.units, self.hundredths) <
(other.units, other.hundredths))
...

Enumeration types
 Python (as of 3.4) supports enum types
 They are similar — but also quite different
— to enum types in other languages
 Support comes from the enum module
 An enumeration type must inherit from
either Enum or IntEnum
 Enum is the base for pure enumerations
 IntEnum is the base class for integer-
comparable enumerations

Enumerated
from enum import Enum
class Suit(Enum):
spades = 1
hearts = 2
diamonds = 3
clubs = 4
values = ['A'] + list(range(2, 11)) + ['J', 'Q', 'K']
[(value, suit.name) for suit in Suit for value in values]
Use IntEnum if easily comparable
enumerations are needed
Enumeration names are accessible as
strings are accessible via the name
property and the integer via value
Enumeration types are iterable

Ad hoc enums
 Enum and IntEnum can be used to
create enums on the fly
 They are both callable
Colour = Enum('Colour', 'red green blue')
Colour = Enum('Colour', ('red', 'green', 'blue'))
Colour = Enum('Colour', {'red': 1, 'green': 2, 'blue': 3})
class Colour(Enum):
red = 1
green = 2
blue = 3

Containers
Built-in containers, library
collections, tips & tricks

Facts at a glance
 Built-in containers address most needs
 There are some common iteration
patterns to follow (and avoid)
 Comprehensions address many
common container iteration needs
 collections module offers variations on
standard sequence and lookup types
 collections.abc supports container
usage and definition

Container guidance
 Although everything is, at one level, a
dict, dict is not always the best choice
 Nor is list
 Choose containers based on usage
patterns and mutability
 E.g., tuple is immutable whereas list is not
 Iteration is the most common container
activity, so favour the most direct and
efficient iteration style

Built-in sequence types
list list()
[]
[0, 0x33, 0xCC]
['Albert', 'Einstein', [1879, 3, 14]]
[random() for _ in range(42)]
tuple tuple()
()
(42,)
('red', 'green', 'blue')
((0, 0), (3, 4))
range range(42)
range(1, 100)
range(100, 0, -1)

From str to bytes & back
 bytes and str are immutable sequence
types used for holding strings
 str is Unicode and bytes holds, well, bytes
 By default, encoding and decoding
between str and bytes is based on UTF-8
str
encode
bytes
decode

Built-in lookup types
dict dict()
{}
{'Bohr': True, 'Einstein': False}
{n: n**2 for n in range(0, 100)}
set set()
{'1st', '2nd', '3rd'}
{n**2 for n in range(1, 100)}
frozenset frozenset()
frozenset({15, 30, 40})
frozenset(n**2 for n in range(0, 100))

How not to iterate
currencies = {
'EUR': 'Euro',
'GBP': 'British pound',
'NOK': 'Norwegian krone',
}
for code in currencies:
print(code, currencies[code])
ordinals = ['first', 'second', 'third']
for index in range(0, len(ordinals)):
print(ordinals[index])
for index in range(0, len(ordinals)):
print(index + 1, ordinals[index])

How to iterate
currencies = {
'EUR': 'Euro',
'GBP': 'British pound',
'NOK': 'Norwegian krone',
}
for code, name in currencies.items():
print(code, name)
ordinals = ['first', 'second', 'third']
for ordinal in ordinals:
print(ordinal)
for index, ordinal in enumerate(ordinals, 1):
print(index, ordinal)

Comprehensive containers
[n for n in range(2, 100)
if n not in
{m for l in range(2, 10)
for m in range(l*2, 100, l)}]
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31,
37, 41, 43, 47, 53, 59, 61, 67, 71, 73,
79, 83, 89, 97]
List comprehension
Set comprehension

collections
 Built-in container types meet many
common and initial needs
 But sometimes a different data structure
will cut down the amount of code written
or the runtime resources used
 Built-in container types supplemented
by the standard collections module
 Container types in collections are not built
in, so they do not have display forms

defaultdict
 defaultdict offers on-demand creation
for keys that are not already present
 Derives from dict
 Uses a given factory function, such as a
class name, to create default values
def histogram(data):
result = defaultdict(str)
for item in data:
result[word] += '#'
return result
Inserts a value of str()
for each key looked
up that is not already
present

Counter
 Counter tracks occurrences of a key
 It derives from dict but behaves a lot like
a bag or a multiset
 Counts can be looked up for a key
 Each key 'occurrence' can be iterated
with open(filename) as input:
words = input.read().split()
counted = Counter(votes)
most_common = counted.most_common(1)
most_to_least_common = counted.most_common()

OrderedDict
 OrderedDict preserves the order in
which keys were inserted
 Derives from dict (with some LSP issues...)
 One of the most useful applications is to
create sorted dictionaries, i.e., initialise
from result of sorted on a dict
def word_counts(words):
counts = OrderedDict()
for word in words:
counts[word] = 1+ counts.setdefault(word, 0)
return counts

ChainMap
 ChainMap provides a view over a
number of mapping objects
 Lookups are in sequence along the chain
 Updates apply only to the first mapping —
avoid side effects by supplying {}
defaults = {'drink': 'tea', 'snack': 'biscuit'}
preferences = {'drink': 'coffee'}
options = ChainMap(preferences, defaults)
for key, value in options.items():
print(key, value, sep=': ')
drink: coffee
snack: biscuit

deque
 A deque is a double-ended queue
 Supports efficient append and pop and
appendleft and popleft operations
 Can be bounded, with overflow causing a
pop from the opposite end to the append
def tail(filename, lines=10):
with open(filename) as source:
return deque(source, lines)

namedtuple
 namedtuple allows the creation of a
tuple type with named attributes
 Can still be indexed and iterated
 namedtuple is subclass of tuple
Date = namedtuple('Date', 'year month day')
Date = namedtuple('Date', ('year', 'month', 'day'))
sputnik_1 = Date(1957, 10, 4)
sputnik_1 = Date(year=1957, month=10, day=4)
year, month, day = sputnik_1
year = sputnik_1.year
month = sputnik_1[1]

collections.abc
 The collections.abc module provides
abstract container classes
 Using isinstance, these can be used to
check whether an object conforms to a
protocol, e.g., Container, Hashable,
Iterable, MutableSequence, Set
 These abstract classes can also be used
as mixin base classes for new container
classes

Iterators & Generators
Iteration abstractions, iterators,
generators & wrapped control

Facts at a glance
 Iterators enjoy direct protocol and
control structure support in Python
 Iterators can be defined as classes
 Generators are functions that
automatically create iterators
 Generator expressions support a
simple way of generating generators
 Generators can abstract with logic

Iteration
 An iterator is an object that iterates,
following the iterator protocol
 An iterable object can be used with for
 Iterators and generators are lazy,
returning values on demand
 You don't need to resolve everything into
a list in order to use a series of values
 Yield values in sequence, so can linearise
complex traversals, e.g., tree structures

Iterables & iterators
class Iterable:
...
def __iter__(self):
return Iterator(...)
...
class Iterator:
...
def __iter__(self):
return self
def __next__(self):
...
raise StopIteration
...
...
All iterators are iterable, so
the __iter__ method is an
identity operation
An object is iterable if it
supports the __iter__
special method, which is
called by the iter function
The __next__ special
method, called by next,
advances the iterator
Iteration is terminated by
raising StopIteration

Defining iterators
 There are many ways to provide an
iterator...
 Define a class that supports the iterator
protocol directly
 Return an iterator from another object
 Compose an iterator with iter, using an
action and a termination value
 Define a generator function
 Write a generator expression

iter
 Use iter to create an iterator from a
callable object and a sentinel value
 Or to create an iterator from an iterable
def repl():
for line in iter(lambda: input('> '), 'exit'):
print(evaluate(line))
def pop_until(stack, end):
return iter(stack.pop, end)
for popped in pop_until(history, None):
print(popped)

next
 Iterators can be advanced manually
using next
 Calls the __next__ method
 Watch out for StopIteration at the end...
def repl():
try:
lines = iter(lambda: input('> '), 'exit')
while True:
line = next(lines)
print(evaluate(line))
except StopIteration:
pass

Generator expressions
 A comprehension-based expression
that results in an iterator object
 Does not result in a container of values
 Must be surrounded by parentheses
unless it is the sole argument of a function
 May be returned as the result of a function
numbers = (random() for _ in range(42))
sum(numbers)
sum(random() for _ in range(42))

Generator functions
 A generator is an ordinary function that
returns an iterator as its result
 The presence of a yield or yield from
makes a function a generator, and can
only be used within a function
 yield returns a single value
 yield from takes values from another
iterator, advancing by one on each call
 return in a generator raises StopIteration,
passing it any return value specified

Yielding a winner
def medals():
yield 'Gold'
yield 'Silver'
yield 'Bronze'
def medals():
for result in 'Gold', 'Silver', 'Bronze':
yield result
for medal in medals():
print(medal)
def medals():
yield from ['Gold', 'Silver', 'Bronze']

@contextmanager
 The contextmanager decorator offers
simple definition of context managers
 Defined in the standard contextlib module
 Decorate a generator that holds a
single yield statement
 The entry action comes before the yield
 The exit action follows the yield
 If yield has a value it will be bound to the
target of the with

It's all about timing
class timing:
def __init__(self, report=print):
self.__report = report
self.__start = time()
def __exit__(self, *args):
self.__report(time() – self.__start)
with timing():
task_to_time()
@contextmanager
def timing(report=print):
start = time()
yield
report(time() – start)

Trading places
print(os.getcwd())
with pushd('/tmp'):
print(os.getcwd())
task()
print(os.getcwd())
@contextmanager
def pushd(new):
old = os.getcwd()
os.chdir(new)
try:
yield
finally:
os.chdir(old)
Should print the same directory
before and after the with
Should print '/tmp'
Without the finally, any exception
would propagate out from the yield,
terminating the generator and
bypassing the directory restore action

Experiment
 Define a generator that returns
successive days from a start date
 Use date and timedelta from datetime
 Consider using date.today() as default
 Use it to explore different ways of
selecting dates that fall on Friday 13th
 Use a generator expression
 Use a container comprehension
 Use filter

Functional Thinking
Functional programming
techniques, tips & tricks

Facts at a glance
 Functions are the principal units of
composition
 Functions are pure, i.e., no side effects
 Immutability, i.e., query and create
state rather than modify it
 Declarative over imperative style, e.g.,
use of comprehensions over loops
 Deferred evaluation, i.e., be lazy

Functional guidance
 Immutability...
 Prefer to return new state rather than
modifying arguments and attributes
 Resist the temptation to match every
query or get method with a modifier or set
 Expressiveness...
 Consider where loops and intermediate
variables can be replaced with
comprehensions and existing functions

Mutability pitfalls
 Python is reference-based language,
so objects are shared by default
 Easily accessible data or state-modifying
methods can give aliasing surprises
 Note that true and full object immutability
is not possible in Python
 Default arguments should be of
immutable type, e.g., use tuple not list
 Changes persist between function calls

Fewer explicit loops
 A common feature of functional
programming is fewer explicit loops
 Recursion, but note that Python does not
support tail recursion optimisation
 Comprehensions — very declarative, very
Pythonic
 Higher-order functions, e.g., map applies
a function over an iterable sequence
 Existing algorithmic functions, e.g., min,
str.split, str.join

Recursion
 Recursion may be a consequence of
data structure traversal or algorithm
 E.g., iterating over a tree structure
 E.g., quicksort
 But it can be used as an alternative to
looping in many simple situations
 But beware of efficiency concerns and
Python's limits (see sys.getrecursionlimit)

Reducing modifiable state
def factorial(n):
result = 1
while n > 0:
result *= n
n -= 1
return result
def factorial(n):
result = 1
for i in range(1, n + 1):
result *= i
return result
def factorial(n):
if n > 0:
return n * factorial(n – 1)
else:
return 1
Two variables being
modified explicitly
No variables modified

Expressions versus statements
def factorial(n):
if n > 0:
return n * factorial(n – 1)
else:
return 1
def factorial(n):
return n * factorial(n – 1) if n > 0 else 1
factorial = lambda n: n * factorial(n – 1) if n > 0 else 1
There is a tendency in functional programming to favour expressions...
But using a lambda bound to a variable instead of a single-statement function is not considered
Pythonic and means factorial lacks some metadata of a function, e.g., a good __name__.

Comprehensions
 A comprehension is used to define a
sequence of values declaratively
 Sequence of values defined by intension
as an expression, rather than procedurally
in terms of loops and modifiable state
 They have a select...from...where structure
 Many common container-based looping
patterns are captured in the form of
container comprehensions

Imperative versus declarative
leap_years = []
for year in range(2000, 2030):
if is_leap_year(year):
leap_years.append(year)
[2000, 2004, 2008, 2012, 2016, 2020, 2024, 2028]
leap_years = [year for year in range(2000, 2030) if is_leap_year(year)]
[2000, 2004, 2008, 2012, 2016, 2020, 2024, 2028]
Imperative list initialisation
List comprehension
def is_leap_year(year):
return year % 4 == 0 and year % 100 != 0 or year % 400 == 0

Higher-order functions
 A higher-order function...
 Takes one or more functions as arguments
 Returns one or more function as its result
 Takes and returns functions
 Higher-order functions often used to
abstract common iteration operations
 Hides the mechanics of repetition
 Comprehensions are often an alternative
to such higher-order functions

map
 map applies a function over an
iterable to produce a new iterable
 Can sometimes be replaced with a
comprehension or generator expression
that has no predicate
 Often shorter if no lambdas are involved
map(len, 'The cat sat on the mat'.split())
(len(word) for word in 'The cat sat on the mat'.split())

filter
 filter includes only values that satisfy a
given predicate in its generated result
 Can sometimes be replaced with a
comprehension or generator expression
that has a predicate
 Often shorter if no lambdas are involved
filter(lambda score: score > 50, scores)
(score for score in scores if score > 50)

reduce
 functools.reduce implements what is
know as a fold left operation
 Reduces a sequence of values to a single
value, left to right, with the accumulated
value on the left and the other on the right
def factorial(n):
return reduce(operator.mul, range(1, n+1), 1)
def factorial(n):
return reduce(lambda l, r: l*r, range(1, n+1), 1)
int.__mul__ would be a less general alternative

The operator module
eq(a, b) a == b
ne(a, b) a != b
lt(a, b) a < b
le(a, b) a <= b
gt(a, b) a > b
ge(a, b) a >= n
is_(a, b) a is b
is_not(a, b) a is not b
contains(a, b) a in b
add(a, b) a + b
sub(a, b) a - b
mul(a, b) a * b
truediv(a, b) a / b
floordiv(a, b) a // b
mod(a, b) a % b
pow(a, b) a**b
Unary operations
Comparison operations
Binary arithmetic operations
pos(a) +a
neg(a) -a
invert(a) ~a
operator exports named functions
corresponding to operators

Experiment
 Use reduce to implement your own
version of map
 Can be written as a single-line method
 Return the result as a list
def my_map(func, iterable):
...
my_map(lambda n: n*2, [3, 1, 4, 1, 5, 9])
[6, 2, 8, 2, 10, 18]

Currying
In mathematics and computer science, currying is
the technique of translating the evaluation of a
function that takes multiple arguments (or a tuple
of arguments) into evaluating a sequence of
functions, each with a single argument (partial
application). It was introduced by Moses
Schönfinkel and later developed by Haskell Curry.
http://en.wikipedia.org/wiki/Currying

Nested functions & lambdas
 Nested functions and lambdas bind to
their surrounding scope
 I.e., they are closures
def curry(function, first):
def curried(second):
return function(first, second)
return curried
def curry(function, first):
return lambda second: function(first, second)
hello = curry(print, 'Hello')
hello('World')

Function wrapping
def timed_function(function, *, report=print):
from time import time
def wrapper(*args):
start = time()
try:
function(*args)
finally:
report(time() – start)
return wrapper
Force non-positional use of
subsequent parameters
wrapped = timed_function(long_running)
wrapped(arg_for_long_running)

partial application
 Values can be bound to a function's
parameters using functools.partial
 Bound positional and keyword arguments
are supplied on calling the resulting
callable, other arguments are appended
quoted = partial(print, '>')
quoted()
print('>')
quoted('Hello, World!')
print('>', 'Hello, World!')
quoted('Hello, World!', end=' <n')
print('>', 'Hello, World!', end=' <n')

Built-in algorithmic functions
min(iterable)
min(iterable, default=value)
min(iterable, key=function)
max(iterable)
max(iterable, default=value)
max(iterable, key=function)
sum(iterable)
sum(iterable, start)
any(iterable)
all(iterable)
sorted(iterable)
sorted(iterable, key=function)
sorted(iterable, reverse=True)
zip(iterable, ...)
...
Default used if iterable is empty
The function to transform the
values before determining the
lowest one
One or more iterables can be
zipped together as tuples, i.e.,
effectively converting rows to
columns, but zipping two
iterables is most common

Functions from itertools
chain(iterable, ...)
compress(iterable, selection)
dropwhile(predicate, iterable)
takewhile(predicate, iterable)
count()
count(start)
count(start, step)
islice(iterable, stop)
islice(iterable, start, stop)
islice(iterable, start, stop, step)
cycle(iterable)
repeat(value)
repeat(value, times)
zip_longest(iterable, ...)
zip_longest(iterable, ..., fillvalue=fill)
...

Concurrency
Coroutines, threading,
processes & futures

Facts at a glance
 Generators can be used as coroutines
 Threading is based on native threads
 Python's threading is subject to GIL
constraints on many implementations
 Multiprocessing has API-compatible
support plus IPC mechanisms
 Executors and futures offer a simple
task-based concurrency model

Concurrency
 Concurrency can...
 Be implicit in the problem domain
 Be a by-product of the implementation
 Improve performance
 Worsen performance
 Simplify and decouple code
 Complicate and couple code
 It's all a question of implementation
mechanisms and choices!

Coroutines
Coroutines are computer program components that
generalize subroutines for nonpreemptive
multitasking, by allowing multiple entry points for
suspending and resuming execution at certain
locations. Coroutines are well-suited for
implementing more familiar program components
such as cooperative tasks, exceptions, event loop,
iterators, infinite lists and pipes.
https://en.wikipedia.org/wiki/Coroutine

Generators as coroutines
 yield and yield from cause suspension
of the current function execution
 On resumption, e.g., via next, execution
continues where it left off, so that local
variable state is retained
 The alternative to using coroutines is
often more complex
 E.g., a state machine to remember where
a task was last time it was executed

Reducing complexity
Symmetry is a complexity-reducing
concept (co-routines include
subroutines); seek it everywhere.
Alan Perlis
"Epigrams in Programming"

Coroutine communication
 Can communicate with a generator
 send passes a value to it
 throw raises an exception within it
 close terminates it
 send and throw return next yielded value
 Although often used as statements,
yield and yield from are expressions
 When invoked by next, they return None
 When send is used, value sent is returned

Coroutine action
def evaluate():
input = ''
for n in count():
input = (yield str(n) + ' ' + evaluated(input))
def repl():
evaluator = evaluate()
next(evaluator)
try:
print(evaluator.send(line))
finally:
evaluator.close()

Multithreading
Multithreading is just one
damn thing before, after, or
simultaneous with another.
Andrei Alexandrescu

threading
 The threading module offers constructs
for creating threads and locking
 Threads are built on native OS threads
 Locking primitives are based on binary
semaphores, which are in turn based on
OS mutexes
 Other synchronisation primitives, such as
events, and thread-based constructs,
such as timers and thread-local data, are
also defined

Thread definition
class Task(Thread):
def run():
...
task = Task()
task.start()
...
task.join()
def run():
...
task = Thread(target=run)
task.start()
...
task.join()
Inheritance-based approach Composition-based approach

Global Interpreter Lock (GIL)
 In CPython, the GIL introduces a
concurrency bottleneck
 It is a mutex that restricts system threads
from concurrently executing Python code
 The thread-unsafe reference-counted
memory management model is the
primary reason for the GIL's existence
 A feature of CPython but not necessarily
other implementations, e.g., Jython relies
on JVM GC and IronPython relies on .NET

Synchronisation
 Python offers a number of different
types of synchronisation primitive
 E.g., Lock, RLock, Condition, Semaphore,
BoundedSemaphore, Event, Barrier
 All the locking-related primitives are
context managers
 Use with rather than acquire and release
unless for non-blocked acquisition or a
timeout is required

Sharing & synchronisation
Shared memory is like a canvas where threads
collaborate in painting images, except that they
stand on the opposite sides of the canvas and
use guns rather than brushes. The only way they
can avoid killing each other is if they shout
"duck!" before opening fire.
Bartosz Milewski
http://bartoszmilewski.com/2013/12/10/functional-data-structures-and-concurrency-in-c/

Multiprocessing
 An alternative to threads is to use OS
processes to express concurrency
 A standard workaround for GIL issues
 Processes naturally give better
isolation and long-term concurrency
 But may not be effective for fine-grained
concurrency
 Any values exchanged between
processes must be picklable

multiprocessing
 The multiprocessing module has similar
API to threading
 Including locking primitives
 Also includes IPC mechanisms
 Different types of inter-process queues
and Pipe, which results in a pair of
Connection objects
 Value and Array can be used to create
and access objects in shared memory

Process action
def evaluate(client):
try:
for n in count(1):
input = client.recv()
client.send(str(n) + ' ' + evaluated(input))
except EOFError:
pass
def repl():
here, there = Pipe()
evaluator = Process(target=evaluate, args=(there,))
evaluator.start()
here.send(line)
print(here.recv())
here.close()
evaluator.join()

Asynchronous functions
 Asynchronous function approaches
simplify use of threads and processes
 Instead of blocking on a function call, a
function is executed concurrently
 A future — a virtual proxy also known as
an IOU or a deferred — is a queryable
place holder for the function's result
 asyncio and concurrent.futures are
based on this model

Executors & futures
 Executors simplify use of concurrency
execution resources
 One or more tasks can be submitted to an
executor for execution
 Execution mechanics and management
are hidden from the submitter
 The caller uses a future to synchronise
with and collect execution results
 Reduces need for other synchronisation

concurrent.futures
 An executor is a pools of workers
 Either threads, in the case of
ThreadPoolExecutor, or processes, in the
case of ProcessPoolExecutor
 Executors are context managers
 To submit work...
 Call submit to submit an individual item
 Call map to submit multiple items — a
returned generator plays the future role

In the future...
with ThreadPoolExecutor(max_workers=1) as executor:
future = executor.submit(task, data)
print(future.result())
futures = [
executor.submit(task, data) for data in work
]
for future in futures:
print(future.result())
for result in executor.map(task, work):
print(result)

Synchronisation quadrant
Mutable
Immutable
Unshared Shared
Unshared mutable
data needs no
synchronisation
Unshared immutable
data needs no
synchronisation
Mutable data
shared between
threads needs
synchronisation
Shared immutable
data needs no
synchronisation

Modules & Packages
The mechanics of organising
source code

Facts at a glance
 A package hierarchically organises
modules and other packages
 Where a module corresponds to a file,
a regular package corresponds to a
directory
 Modules and packages define global
namespaces
 Extension modules allow extension of
Python itself

Modules
 A module in Python corresponds to a
file with a .py extension
 import is used to access features in
another module
 A module's __name__ corresponds to
its file name without the extension
 When run as a script (e.g., using the –m
option), the root module has '__main__' as
its __name__

Extensions & embedding
 The Python interpreter can be
extended using extension modules
 Python has a C API that allows
programmatic access in C (or C++) and,
therefore, any language callable from C
 Python can also be embedded in an
application as a scripting language
 E.g., allow an application's features to be
scripted in Python

Packages
 Packages are a way of structuring the
module namespace
 A submodule bar within a package foo
represents the module foo.bar
 A regular package corresponds to a
directory of modules (and packages)
 A regular package directory must have
an __init__.py file (even if it's empty)
 The package name is the directory name

Namespaces
 A namespace is a mapping from
names to objects, e.g., variables
 A namespace is implemented as a dict
 Global, local, built-in and nested
 Each module gets its own global
namespace, as does each package
 import pulls names into a namespace
 Within a scope, names in a namespace
can be accessed without qualification

Packages & import
 A submodule can be imported with
respect to its package
 E.g., import package.submodule
 Relative naming can be used to navigate
within a package
 Main modules must use absolute imports
 Submodules seen by wildcard import can
be specified by assigning a list of module
names (as strings) to __all__ in __init__.py

Packages, modules & imports
root/
__init__.py
first/
__init__.py
foo.py
bar.py
second/
__init__.py
foo.py
bar.py
Top-level regular package
Subpackage
# empty, but necessary
__all__ = ['foo']
from . import bar
from .. import first
from ..first import foo

Experiment
 Write a simple script that lists the
package and module hierarchy
 Use current directory if no path supplied
 Use os.listdir, os.walk or pathlib for
traversal
 No need to open and load modules, just
detect packages by matching __init__.py
and modules by matching *.py
 List the results using indentation and/or in
some XML-like form

Outroduction
All good things...

The end?
 The foundation is all there
 Python Foundation course and this course
 But a foundation means little unless
you build on it
 There is more to the language...
 There is more to the library...
 There are more libraries...
 Enjoy!

Guidance
 Labs should be undertaken in pairs
using Cyber-Dojo
 Swap between driving and navigator roles
within the same pair
 Homework is carried out individually in
your own environment
 Do not spend too much time on this — an
hour or so — and try not to overengineer
the solution!

Lab: Day 1
Money
Write a class (and unit tests) for a class that represents a
monetary value:
 It should hold an amount that can be queried as units
and hundredths (e.g., pounds and pence, euros and
cents, dollars and cents).
 It should be possible to compare money values for
equality and for ordering using relational operators.
 It should support non-lossy addition and subtraction, i.e.,
don't use float to hold the amount.
 Negative money is allowed.
 Money values should not offer modifier methods.
 Don't worry about different currencies.

Lab: Day 1
Option 1: Incompatible currencies
Refine your money class and tests so that instances have a
currency code associated with them, e.g., 'NOK' for
Norwegian krone, 'EUR' for Euro:
 Only money values with the same currency code can be
added or subtracted from one another.
 Money values of different currencies can be compared
for equality, but always return False.
 Money values of different currencies cannot be ordered.

Lab: Day 1
Option 2: Compatible currencies
Allow money values of different currencies to be added
together, but so that they result in composite type, i.e., a
money bag:
 They support addition, but do not resolve down to a single
currency, e.g., 100 NOK + 10 EUR + 200 NOK + 5 EUR results
in a money bag of 300 NOK + 15 EUR.
 Money bags can be compared for equality, but cannot
be ordered.

Homework
A simple testing framework
Write a small testing framework that executes all the tests
defined within a given class:
 Provide a run function that takes a class of test cases as its
argument.
 For each test method in the given class, create an
instance of the class and execute the test method
against it, reporting back the result to the console, and
then reporting a summary of the result at the end of the
test run.
 A test method is one whose name begins with test.
 Use the built-in assert statement for assertions within test
methods.

Homework
An example of use
For the following function, which determines whether a year is a
leap year or not:
def is_leap_year(year): ...
The test framework should support the following test code:
class LeapYearTests:
def test_years_not_divisible_by_4_are_not_leap_years(self):
assert not is_leap_year(2015)
def test_years_divisible_by_4_but_not_by_100_are_leap_years(self):
assert is_leap_year(2016)
def test_years_divisible_by_100_but_not_by_400_are_not_leap_years(self):
def test_years_divisible_by_400_are_leap_years(self):
Executed as follows:
run(LeapYearTests)

Homework
Option 1: Using decorators to mark test methods
If you have enough time, consider changing the test runner so that
instead of relying on a method prefix, a decorator is used to
indicate that a method is a test method:
@test
def years_not_divisible_by_4_are_not_leap_years(self):
@test
def years_divisible_by_4_but_not_by_100_are_leap_years(self):
...
This is syntactically similar to the annotations used in JUnit, but in
Python it will work by a different effect. Define a test decorator that
adds an attribute to the test method; it is this attribute that the run
function will use to select the test methods for execution.

Homework
Option 2: Using decorators to pass data to a test method
If you have enough time, consider refining your decorator design so
that data can optionally be passed into the test case:
@test
@data(2015, 1999, 1)
def years_not_divisible_by_4_are_not_leap_years(self, year):
assert not is_leap_year(year)
@test
@data(2016)
@data(1984)
@data(4)
def years_divisible_by_4_but_not_by_100_are_leap_years(self, year):
assert is_leap_year(year)
@test
def years_divisible_by_100_but_not_by_400_are_not_leap_years(self):
@test
@data(*range(400, 2401, 400))
def years_divisible_by_400_are_leap_years(self, year):
assert is_leap_year(year)

Homework
Further variations
If you still have enough time, or would prefer something different to
the previous two options, here are some more ideas to play with:
 Use a keyword argument to specify the output for test results, with
the sys.stdout as default.
 Use an Enumeration Method callback from the test runner
function to communicate test execution results (default to print).
 Use multiple callbacks to indicate different events in a test run's
lifecycle, i.e., the Lifecycle Callback pattern. Either pass in
callables via keyword arguments or use the Visitor pattern.
 Instead of instantiating a test object, consider using module-level
functions or static methods as the basis for test cases.
 Define a decorator that checks that an exception has been
thrown from a test case, failing if no or the wrong type is thrown.
 Execute test cases concurrently instead of sequentially.

Lab: Day 2
Recently used list
Write a class (and unit tests) for a container that represents a
recently used list, i.e., a sequence ordered by reverse
insertion order and restricted by uniqueness:
 A recently used list is initially empty.
 Strings can be added to the list, but non-string values
(including None) are disallowed.
 Items can be indexed, which counts from zero.
 Additions are retained in stack ordering (i.e., LIFO), so the
most recently added item is first, the least recently added
item is last, and so on.
 Items in the list are unique, so duplicate insertions are
moved to the head rather than added, i.e., the length will
not increase.

Lab: Day 2
Option 1: Support for non-string items
Generalise the implementation so that addition of non-string
values is supported. This is not, however, simply a case of
removing the type restriction in add:
 Because the list is set-like — i.e., items are unique — items
also play the role of keys, which means they should be
immutable.
 There is no guarantee of a type's immutability, but a
common convention is that hashable types are
immutable and, therefore, non-hashable types are not.
 It should therefore be possible to add tuples and integers
to a recently used list, but not lists and sets.
 But note that addition of None is still disallowed.

Lab: Day 2
Option 2: Additional container operations
Add methods to improve support for a more typical
container protocol:
 A clear method to empty the list.
 A pop method to remove the oldest item in the list.
 A remove method to remove an item by value.
 Support common lookup operations such as in, not in and
index.
 Support comparison for equality and inequality.
 Support the iterator protocol.
 Allow items to be deleted by index.

Python Advanced – Building on the foundation

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