PythonCheatSheet

Quick reference to a tremendously accessible high-level language —executable pseudocode!

The listing sheet, as PDF, can be found here, or as a single column portrait, while below is an unruly html rendition.

This reference sheet is built from a CheatSheets with Org-mode system.

• Hello, World!
  • This is how we do it
  • Exploring built-in modules with dir and ~help~
• Arithmetic
• Conditionals
  • Boolean connectives
  • Relational operations
  • If expressions and statements
• Iterables
  • Comprehensions
  • Generators
  • Unpacking operation
  • Loops
  • For/while via iter/next
  • Methods on Iterables
  • Sum is monoidal
  • Methods for sequences only
  • Sequences are Ordered
• Tuples
• Strings
  • String Comprehensions
  • String methods
  • f-strings: Formatted String Literals
• Lists
• Sets
• Dictionaries
  • Dictionaries implement hierarchical tree structures and case statements
  • Dictionary Methods
• Splicing
• Functions
  • Functions have attributes
  • On the nature of λ
  • Default & keyword argument values
  • Dictionary arguments
  • Type Annotations
  • Currying
  • Decorators
• Object-Oriented Programming
  • Person: Empty Class
  • A More Complex Person Class
  • Making People
  • Syntax Overloading: Dunder Methods
  • Extension Methods
  • Inheritance
• Decorators and Classes
  • Curry Decorator
• Named Expressions
• Modules
• Reads

Hello, World!

⇒ Python is object oriented and dynamically type checked.

⇒ With dunder methods, every syntactic construct extends to user-defined datatypes, classes! —Including loops, comprehensions, and even function call notation!

⇒ Children block fragments are realised by consistent indentation, usually 4 spaces. No annoying semicolons or braces.

⇒ τ(x) to try to coerce x into a τ type element, crashes if conversion is not possible. Usual types: src_python[:exports code]{bool, str, list, tuple, int, float, dict, set}. Use src_python[:exports code]{type(x)} to get the type of an object x.

⇒ Identifier names are case sensitive; some unicode such as “α” is okay but not “⇒”.

⇒ If obj is an instance of type τ, then we may invoke an instance method f in two ways: obj.f() or τ.f(obj). The latter hints at why src_python[:exports code]{“self”} is the usual name of the first argument to instance methods. The former τ.f is the name proper.

⇒ Function and class definitions are the only way to introduce new, local, scope.

⇒ src_python[:exports code]{del x} deletes the object x, thereby removing the name x from scope.

⇒ src_python[:exports code]{print(x, end = e)} outputs x as a string followed by e; end is optional and defaults to a newline. print(x₁, …, xₙ) prints a tuple without parentheses or commas.

⇒ The src_python[:exports code]{NoneType} has only one value, src_python[:exports code]{None}. It’s used as the return type of functions that only perform a side-effect, like printing to the screen. Use src_python[:exports code]{type(None)} to refer to it.

This is how we do it

Everything here works using Python3.

import sys
assert '3.8.1' == sys.version.split(' ')[0]

We’ll use assert y == f(x) to show that the output of f(x) is y.

• Assertions are essentially “machine checked comments”.

Exploring built-in modules with dir and help

Explore built-in modules with ~dir~ and ~help~
dir(M)	List of string names of all elements in module M
help(M.f)	Documentation string of function f in module M

⇒ Print alphabetically all regular expression utilities that mention find.

help can be called directly on a name; no need for quotes.

Arithmetic

Besides the usual operators +, *, **, /, //, %, abs, declare src_python[:exports code]{from math import *} to obtain sqrt, loq10, factorial, … —use dir to learn more, as mentioned above.

• Augmented assignments: x ⊕= y ≡ x = x ⊕ y for any operator ⊕.
• Floating point numbers are numbers with a decimal point.
• ** for exponentiation and % for the remainder after division.
• //, floor division, discards the fractional part, whereas / keeps it.
• Numeric addition and sequence catenation are both denoted by +.
  • However: 1 + 'a' ⇒ error!.

# Scientific notation: 𝓍e𝓎 ≈ 𝓍 * (10 ** 𝓎)
assert 250e-2 == 2.5 == 1 + 2 * 3 / 4.0

from math import *   # See below on imports
assert 2 == sqrt(4)
assert -inf < 123 < +inf

Conditionals

abs(x) ≈ x * (x > 0) - x * (x < 0)

Every “empty” collection is considered false! Non-empty values are truthy!

• src_python[:exports code]{bool(x)} ⇒ Interpret object x as either true or false.
• E.g. 0, None, and empty tuples/lists/strings/dictionaries are falsey.

In Boolean contexts:

“x is empty”	≡	not bool(x)
len(e) != 0	≡	bool(e)
bool(e)	≡	e
x != 0	≡	x

User-defined types need to implement dunder methods __bool__ or __len__ .

Boolean connectives

Usual infix operations src_python[:exports code]{and, or, not} for control flow whereas &, | are for Booleans only.

• src_python[:exports code]{None or 4 ≈ 4} but None | 4 crashes due to a type error.

s₁ and ⋯ and sₙ	⇒	Do sₙ only if all sᵢ “succeed”
s₁ or ⋯ or sₙ	⇒	Do sₙ only if all sᵢ “fail”

• src_python[:exports code]{x = y or z} ⇒ assign x to be y if y is “non-empty” otherwise assign it z.
• Precedence: src_python[:exports code]{A and not B or C ≈ (A and (not B)) or C}.

Relational operations

Value equality ==, discrepancy !=; Chained comparisons are conjunctive; e.g.,

x < y <= z	≡	x < y and y <= z
p == q == r	≡	p == q and q == r

If expressions and statements

Iterables

An iterable is an object which can return its members one at a time; this includes the (finite and ordered) sequence types —lists, strings, tuples— and non-sequence types —generators, sets, dictionaries. An iterable is any class implementing __iter__ and __next__; an example is shown later.

• Zero-based indexing, x[i], applies to sequence types only.
• We must have src_python[:exports code]{-len(x) < i < len(x)} and src_python[:exports code]{xs[-i] ≈ xs[len(x) - i]}.

We shall cover the general iterable interface, then cover lists, strings, tuples, etc.

Comprehensions

Comprehensions provide a concise way to create iterables; they consist of brackets —() for generators, [] for lists, {} for sets and dictionaries— containing an expression followed by a for clause, then zero or more for or if clauses.

src_python[:exports code]{(f(x) for x in xs if p(x))}

⇒ A new iterable obtained by applying f to the elements of xs that satisfy p ⇐

E.g., the following prints a list of distinct pairs.

print ([(x, y) for x in [1,2,3] for y in (3,1,4) if x != y])

Generators are “sequences whose elements are generated when needed”; i.e., are lazy lists.

If [,] are used in defining evens, the program will take forever to make a list out of the infinitly many even numbers!

Comprehensions are known as monadic do-notation in Haskell and Linq syntax in C#.

Generators

Generators are functions which act as a lazy streams of data: Once a yield is encountered, control-flow goes back to the caller and the function’s state is persisted until another value is required.

xs = evens()
print (next (xs)) # ⇒ 0
print (next (xs)) # ⇒ 2
print (next (xs)) # ⇒ 4

# Print first 5 even numbers
for _, x in zip(range(5),evens()):
    print x

Notice that evens is just count(0, 2) from the itertools module.

Unpacking operation

Unpacking operation

• Iterables are “unpacked” with * and dictionaries are “unpacked” with **.
• Unpacking syntactically removes the outermost parenthesis ()/[]/{}.
• E.g., if f needs 3 arguments, then f(*[x₁, x₂, x₃]) ≈ f(x₁, x₂, x₃).
• E.g., printing a number of rows: print(*rows, sep = '\n').
• E.g., coercing iterable it:

  set(it) ≈ {*it}, list(it) ≈ [*it], tuple(it) ≈ (*it,)
        

Iterable unpacking syntax may also be used for assignments, where * yields lists.

x, *y, z = it	≡	x = it[0]; z = it[-1]; y = list(it[1:len(it)-1])
	⇒	[x] + ys + [z] = list(it)

E.g., head , *tail = xs to split a sequence.

In particular, since tuples only need parenthesis within expressions, we may write x , y = e₁, e₂ thereby obtaining simultaneous assignment.

E.g., x, y = y , x to swap two values.

Loops

For/while via iter/next

Any user-defined class implementing __iter__ and __next__ can use loop syntax.

  for x in xs: f(x)
≈ it = iter(xs); while True: try: f(next(it)) except StopIteration: break

• iter(x) ⇒ Get an iterable for object x.
• next(it) ⇒ Get the current element and advance the iterable it to its next state.
  • Raise StopIteration exception when there are no more elements.

Methods on Iterables

Methods on Iterables

• src_python[:exports code]{len} gives the length of (finite) iterables
  • len ((1, 2)) ⇒ 2; the extra parentheses make it clear we’re giving one tuple argument, not two integer arguments.
• src_python[:exports code]{x in xs} ⇒ check whether value x is a member of xs
  • src_python[:exports code]{x in y ≡ any(x == e for e in y)}, provided y is a finite iterable.
  • src_python[:exports code]{x in y ≡ y.__contains__(x)}, provided y’s class defines the method.
  • src_python[:exports code]{x not in y ≡ not x in y}
• src_python[:exports code]{range(start, stop, step)} ⇒ An iterator of integers from start up to stop-1, skipping every other step-1 number.
  • Associated forms: range(stop) and range(start, stop).
• src_python[:exports code]{reversed(xs)} returns a reversed iterator for xs; likewise src_python[:exports code]{sorted(xs)}.
• src_python[:exports code]{enumerate(xs) ≈ zip(xs, range(len(xs)))}
  • Pair elements with their indices.
• src_python[:exports code]{zip(xs₁, …, xsₙ)} is the iterator of tuples (x₁, …, xₙ) where xᵢ is from xsᵢ.
  • Useful for looping over multiple iterables at the same time.
  • src_python[:exports code]{zip(xs, ys) ≈ ((x, y) for x in xs for y in ys)}
  • src_python[:exports code]{xs₁ , …, xsₙ = zip(*𝓍𝓈)} ⇒ “unzip” 𝓍𝓈, an iterable of tuples, into a tuple of (abstract) iterables xsᵢ, using the unpacking operation *.

    xs , τ = [ {1,2} , [3, 4] ] , list
    assert τ(map(tuple, xs)) == τ(zip(*(zip(*xs)))) == [(1,2) , (3,4)]
    # I claim the first “==” above is true for any xs with:
    assert len({len(x) for x in xs}) == 1
            

• src_python[:exports code]{map(f, xs₁, …, xₙ)} is the iterable of values f x₁ … xₙ where xᵢ is from xsᵢ.
  • This is also known as zip with f, since it generalises the built-in zip.
  • src_python[:exports code]{zip(xs, ys) ≈ map(lambda x, y: (x, y), xs, ys)}
  • src_python[:exports code]{map(f, xs) ≈ (f(x) for x in xs)}
• src_python[:exports code]{filter(p, xs) ≈ (x for x in xs if p(x))}
• src_python[:exports code]{reduce(⊕, [x₀, …, xₙ], e) ≈ e ⊕ x₀ ⊕ ⋯ ⊕ eₙ}; the initial value e may be omitted if the list is non-empty.

  from functools import reduce
  
  assert 'ABC' == reduce(lambda x, y: x + chr(ord(y) - 32), 'abc', '')
      

  These are all instances of src_python[:exports code]{reduce}:

  • src_python[:exports code]{sum, min/max, any/all} —remember “empty” values are falsey!

    # Sum of first 10 evens
    assert 90 == (sum(2*i for i in range(10)))
            

  • Use prod from the numpy module for the product of elements in an iterable.

Sum is monoidal

Flattening

Since, src_python[:exports code]{sum(xs, e = 0) ≈ e + xs[0] + ⋯ + xs[len(xs)-1]} We can use sum as a generic “list of τ → τ” operation by providing a value for e. E.g., lists of lists are catenated via:

assert [1, 2, 3, 4] == sum([[1], [2, 3], [4]], [])
assert (1, 2, 3, 4) == sum([(1,), (2, 3), (4,)], ())
# List of numbers where each number is repeated as many times as its value
assert [1, 2, 2, 3, 3, 3, 4, 4, 4, 4] == sum([i * [i] for i in range(5)], [])

Methods for sequences only

Methods for sequences only

Sequences are Ordered

Sequences are Ordered

Sequences of the same type are compared lexicographically: Where k = min(n, m), [x₀, …, xₙ] < [y₀, …, yₘ] ≡ x₀ < y₀ or ⋯ or xₖ < yₖ —recalling that Python’s or is lazy; i.e., later arguments are checked only if earlier arguments fail to be true. Equality is component-wise.

assert [2, {}] != [3] # ⇒ Different lengths!
assert [2, {}] < [3]  # ⇒ True since 2 < 3.
assert (1, 'b', [2, {}]) < (1, 'b', [3])

Tuples

A tuple consists of a number of values separated by commas —parenthesis are only required when the tuples appear in complex expressions.

Simultaneous assignment is really just tuple unpacking on the left and tuple packing on the right.

Strings

Strings are both ~”~-enclosed and =’=-enclosed literals; the former easily allows us to include apostrophes, but otherwise they are the same.

• There is no separate character type; a character is simply a string of size one.
  • src_python[:exports code]{assert ‘hello’ == ‘he’ + ‘l’ + ‘lo’ == ‘he’ ‘l’ ‘lo’}
  • String literals separated by a space are automatically catenated.
• String characters can be accessed with [], but cannot be updated since strings are immutable. E.g., src_python[:exports code]{assert ‘i’ == ‘hi’[1]}.
• src_python[:exports code]{str(x)} returns a (pretty-printed) string representation of an object.

String Comprehensions

String comprehensions are formed by joining all the strings in the resulting iterable —we may join using any separator, but the empty string is common.

assert '5 ≤ 25 ≤ 125' == (' ≤ '.join(str(5 ** i) for i in [1, 2, 3]))

• s.join(xs).split(s) ≈ xs
• xs.split(s) ⇒ split string xs into a list every time s is encountered

String methods

Useful string operations:

s.startswith(⋯)	s.endswith(⋯)
s.upper()	s.lower()

• src_python[:exports code]{ord/chr} to convert between characters and integers.
• src_python[:exports code]{input(x)} asks user for input with optional prompt x.
• E.g., src_python[:exports code]{i = int(input(“Enter int: “))} ⇒ gets an integer from user

f-strings: Formatted String Literals

f-strings are string literals that have an f before the starting quote and may contain curly braces surrounding expressions that should be replaced by their values.

name, age = "Abbas", 33.1
print(f"{name} is {age:.2f} years {'young' if age > 50 else 'old'}!")
# ⇒ Abbas is 33.10 years old!

F-strings are expressions that are evaluated at runtime, and are generally faster than traditional formatted strings —which Python also supports.

The brace syntax is {expression:width.precision}, only the first is mandatory and the last is either 𝓃f or 𝓃e to denote 𝓃-many decimal points or scientific notation, respectively.

Lists

Besides all of the iterable methods above, for lists we have:

• src_python[:exports code]{list(cs)} ⇒ turns a string/tuple into the list of its characters/components
• xs.remove(x) ⇒ remove the first item from the list whose value is x.
• xs.index(x) ⇒ get first index where x occurs, or error if it’s not there.
• xs.pop(i) ≈ (x := xs[i], xs := xs[:i] + xs[i+1:])[0]
  • Named Expressions are covered below; if i is omitted, it defaults to len(xs)-1.
  • Lists are thus stacks with interface append/pop.
• For a list-like container with fast appends and pops on either end, see the deque collection type.

Sets

Dictionaries

Note that {} denotes the empty dictionary, not the empty set.

A dictionary is like a list but indexed by user-chosen keys, which are members of any immutable type. It’s really a set of “key:value” pairs.

E.g., a dictionary of numbers along with their squares can be written explicitly (below left) or using a comprehension (below right).

assert {2: 4, 4: 16, 6: 36} == {x: x**2 for x in (2, 4, 6)}

Dictionaries implement hierarchical tree structures and case statements

“Case Statements”

i, default = 'k' , "Dec"
x = { 'a': "Jan"
    , 'k': "Feb"
    , 'p': "Mar"
    }.get(i, default)
assert x == 'Feb'

Alternatively: Start with you = {} then later add key-value pairs: you[key] = value.

assert 'Bobert' == you["child2"]['child1'] # access via indices
del you['child2']['child2']                # Remove a key and its value
assert 'Mary' not in you['child2'].values()

Dictionary Methods

• src_python[:exports code]{list(d)} ⇒ list of keys in dictionary d.
• d.keys(), d.values() ⇒ get an iterable of the keys or the values.
• src_python[:exports code]{k in d} ⇒ Check if key k is in dictionary d.
• src_python[:exports code]{del d[k]} ⇒ Remove the key-value pair at key k from dictionary d.
• d[k] = v ⇒ Add a new key-value pair to d, or update the value at key k if there is one.
• src_python[:exports code]{dict(xs)} ⇒ Get a dictionary from a list of key-value tuples.

  When the keys are strings, we can specify pairs using keyword arguments:

  src_python[:exports code]{dict(me = 12, you = 41, them = 98)}.

  Conversely, d.items() gives a list of key-value pairs; which is useful to have when looping over dictionaries.

In dictionary literals, later values will always override earlier ones:

assert dict(x = 2) == {'x':1, 'x':2}

Dictionary update: d = {**d, key₁:value₁, …, keyₙ:valueₙ}.

Splicing

xs[start:stop:step] ≈ the subsequence of xs from start to stop-1 skipping every step-1 element. All are optional, with start, stop, and step defaulting to 0, len(xs), and 1; respectively.

• The start is always included and the end always excluded.
• start may be negative: -𝓃 means the 𝓃-th item from the end.
• All slice operations return a new sequence containing the requested elements.
• One colon variant: xs[start:stop], both start and stop being optional.
• Slicing applies to sequence types only —i.e., types implementing __getitem__.

assert "ola"     == "hola"[1:]
assert (3, 2, 1) == (1, 2, 3)[::-1]

assert xs[-1::] == [55]

n, N = 10, len(xs)
assert xs[-n::] == xs[max(0, N - n)::]

Assignment to slices is possible, resulting in sequences with possibly different sizes.

xs      = list(range(10)) # ⇒ xs ≈ [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
xs[3:7] = ['a', 'b']      # ⇒ xs ≈ [0, 1, 2, 'a', 'b',   7, 8, 9]

Other operations via splicing:

• 0 == s.find(s[::-1]) ⇒ string s is a palindrome
• src_python[:exports code]{inits xs ≈ [xs[0:i] for i in range(1 + len(xs))]}
• src_python[:exports code]{segs xs ≈ [xs[i:j] for i in range(len(xs)) for j in range(i, len(xs))]}

Functions

Functions are first-class citizens: Python has one namespace for functions and variables, and so there is no special syntax to pass functions around or to use them anywhere, such as storing them in lists.

• src_python[:exports code]{return} clauses are optional; if there are none, a function returns src_python[:exports code]{None}.
• Function application always requires parentheses, even when there are no arguments.
• Any object x can be treated like a function, and use the x(⋯) application syntax, if it implements the __call__ method: x(⋯) ≈ x.__call__(⋯). The src_python[:exports code]{callable} predicate indicates whether an object is callable or not.
• Functions, and classes, can be nested without any special syntax; the nested functions are just new local values that happen to be functions. Indeed, nested functions can be done with src_python[:exports code]{def} or with assignment and src_python[:exports code]{lambda}.
• Functions can receive a variable number of arguments using *.

def compose(*fs):
    """Given many functions f₀,…,fₙ return a new one: λ x. f₀(⋯(fₙ(x))⋯)"""
    def seq(x):
        seq.parts = [f.__name__ for f in fs]
        for f in reversed(fs):
            x = f(x)
        return x
    return seq

print (help(compose))  # ⇒ Shows the docstring with function type
compose.__doc__ = "Dynamically changing docstrings!"

# Apply the “compose” function;
# first define two argument functions in two ways.

g = lambda x: x + 1
def f(x): print(x)

h = compose(f, g, int)
h('3')                         # ⇒ Prints 4
print(h.parts)                 # ⇒ ['f', '<lambda>', 'int']
print (h.__code__.co_argcount) # ⇒ 1; h takes 1 argument!

# Redefine “f” from being a function to being an integer.
f = 3
# f(1) # ⇒ Error: “f” is not a function anymore!

Note that compose() is just the identity function lambda x∶ x.

Functions have attributes

The first statement of a function body can optionally be a ‘docstring’, a string enclosed in three double quotes. You can easily query such documentation with help(functionName). In particular, f.__code__.co_argcount to obtain the number of arguments f accepts.

That functions have attributes —state that could alter their behaviour— is not at all unexpected: Functions are objects; Python objects have attributes like __doc__ and can have arbitrary attributes (dynamically) attached to them.

On the nature of λ

A src_python[:exports code]{lambda} is a single line expression; you are prohibited from writing statements like src_python[:exports code]{return}, but the semantics is to do the src_python[:exports code]{return}.

src_python[:exports code]{lambda args: (x₀ := e₀, …, xₙ := eₙ)[k]} is a way to perform n-many stateful operations and return the value of the k-th one. See pop above for lists; Named Expressions are covered below.

For fresh name x, a let-clause “let x = e in ⋯” can be simulated with x = e; …; del x. However, in combination with Named Expressions, lambda’s ensure a new local name: src_python[:exports code]{(lambda x = e: ⋯)()}.

Default & keyword argument values

Default & keyword argument values are possible

def go(a, b=1, c='two'):
    """Required 'a', optional 'b' and 'c'"""
    print(a, b, c)

Keyword arguments must follow positional arguments; order of keyword arguments (even required ones) is not important.

• Keywords cannot be repeated.

go('a')          # ⇒ a 1 two ;; only required, positional
go(a='a')        # ⇒ a 1 two ;; only required, keyword
go('a', c='c')   # ⇒ a 1 c   ;; out of order, keyword based
go('a', 'b')     # ⇒ a b two ;; positional based
go(c='c', a='a') # ⇒ a 1 c   ;; very out of order

Dictionary arguments

Dictionary arguments

After the required positional arguments, we can have an arbitrary number of optional/ positional arguments (a tuple) with the syntax *args, after that we may have an arbitrary number of optional keyword-arguments (a dictionary) with the syntax **args.

The reverse situation is when arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. Recall, from above, that we do so using the * operator; likewise ** is used to unpack dictionaries.

• E.g., if f needs 3 arguments, then f(*[x₁, x₂, x₃]) ≈ f(x₁, x₂, x₃).

def go(a, *more, this='∞', **kwds):
    print (a)
    for m in more: print(m)
    print (this)
    for k in kwds: print(f'{k} ↦ {kwds[k]}')
    return kwds['neato'] if 'neato' in kwds else -1

# Elementary usage
go(0)                            # ⇒ 0 ∞
go(0, 1, 2, 3)                   # ⇒ 0 1 2 3 ∞
go(0, 1, 2, this = 8, three = 3) # ⇒ 0 1 2 8 three ↦ 3
go(0, 1, 2, three=3, four = 4)   # ⇒ 0 1 2 ∞ three ↦ 3 four ↦ 4

# Using “**”
args = {'three': 3, 'four': 4}
go(0, 1, 2, **args) # ⇒ 0 1 2 ∞ three ↦ 3 four ↦ 4

# Making use of a return value
assert 5 == go (0, neato = 5)

Type Annotations

Type Annotations

We can annotate functions by expressions —these are essentially useful comments, and not enforced at all— e.g., to provide type hints. They’re useful to document to human readers the intended types, or used by third-party tools.

# A function taking two ints and returning a bool
def f(x:int, y : str = 'neat') -> bool:
    return str(x) # Clearly not enforced!

print (f('hi')) # ⇒ hi; Typing clearly not enforced

print(f.__annotations__) # ⇒ Dictionary of annotations

Currying

Currying: Fixing some arguments ahead of time.

from functools import partial

multiply  = lambda x, y, z: z * y + x
twice     = partial(multiply, 0, 2)
assert 10 == twice(5)

Using decorators and classes, we can make an ‘improved’ partial application mechanism —see the final page.

Decorators

The decoration syntax @ d f is a convenient syntax that emphasises code acting on code.

Decorators can be helpful for functions we did not write, but we wish to advise their behaviour; e.g., math.factorial = my_decorator(math.factorial) to make the standard library’s factorial work in new ways.

When decorating, we may use *args and **kwargs in the inner wrapper function so that it will accept an arbitrary number of positional and keyword arguments. See typed below, whose inner function accepts any number of arguments and passes them on to the function it decorates.

We can also use decorators to add a bit of type checking at runtime:

import functools

# “typed” makes decorators; “typed(s₁, …, sₙ, t)” is an actual decorator.
def typed(*types):
    *inTys, outT = types
    def decorator(fun):
        @functools.wraps(fun)
        def new(*args, **kwdargs):
            # (1) Preprocessing stage
            if any(type(𝓌 := arg) != ty for (arg, ty) in zip(args, inTys)):
                nom = fun.__name__
                raise TypeError (f"{nom}: Wrong input type for {𝓌!r}.")
            # (2) Call original function
            result = fun(*args, **kwdargs) # Not checking keyword args
            # (3) Postprocessing stage
            if type(result) != outT:
                raise TypeError ("Wrong output type!")
            return result
        return new
    return decorator

After being decorated, function attributes such as __name__ and __doc__ refer to the decorator’s resulting function. In order to have it’s attributes preserved, we copy them over using @functools.wraps decorator —or by declaring functools.update_wrapper(newFun, oldFun).

# doit : str × list × bool → NoneType
@typed(str, list, bool, type(None))
def doit(x, y, z = False, *more):
    print ((ord(x) + sum(y)) * z, *more)

Notice we only typecheck as many positions as given, and the output; other arguments are not typechecked.

# ⇒ TypeError: doit: Wrong input type for 'bye'!
doit('a', [1, 2], 'bye')

# ⇒ 100 n i ;; typechecking succeeds
doit('a', [1, 2], True, 'n', 'i')

# ⇒ 0; Works with defaults too ;-)
doit(x, y)

# ⇒ 194; Backdoor: No typechecking on keyword arguments!
doit('a', z = 2, y = {})

The implementation above matches the typed specification, but the one below does not and so always crashes.

# This always crashes since
# the result is not a string.
@typed(int, str)
def always_crashes(x):
    return 2 + x

Note that typed could instead enforce type annotations, as shown before, at run time ;-)

An easier way to define a family of decorators is to define a decorator-making-decorator!

Object-Oriented Programming

Classes bundle up data and functions into a single entity; Objects are just values, or instances, of class types. That is, a class is a record-type and an object is a tuple value.

1. A Python object is just like a real-world object: It’s an entity which has attributes —/a thing which has features/.
2. We classify objects according to the features they share.
3. A class specifies properties and behaviour, an implementation of which is called an object. Think class is a cookie cutter, and an actual cookie is an object.
4. Classes are also known as “bundled up data”, structures, and records.

   They let us treat a collection of data, including methods, as one semantic entity. E.g., rather than speak of name-age-address tuples, we might call them person objects.

   Rather than “acting on” tuples of data, an object “knows how to act”; we shift from doit(x₁, …, xₙ) to 𝓍.doit(). We abstract away the n-many details into 1 self-contained idea.

Person: Empty Class

What can we learn from an empty class?

src_python[:exports code]{pass} is the “do nothing” statement. It’s useful for writing empty functions/classes that will be filled in later or for explicitly indicating do-nothing cases in complex conditionals.

# We defined a new type!
assert isinstance(Person, type)

# View information of the class
print (help(Person))
# Or use: Person.__name__,
# Person.__doc__, Person.__dict__

# Let's make a Person object
jasim = Person()
assert Person == type(jasim)
assert isinstance(jasim, Person)

Instance (reference) equality is compared with src_python[:exports code]{is}.

src_python[:exports code]{x is y ≡ id(x) == id(y)}

id(x) is a unique number identifying object x; usually its address in memory.

jason = jasim
qasim = Person ()
assert jason is jasim and jasim is jason
assert qasim is not jasim

# Check attributes exist before use
assert not hasattr(jasim, 'work')

# Dynamically add new (instance) attributes
jasim.work = 'farmer'
jasim.nick = 'jay'

# Delete a property
del jasim.nick

# View all attribute-values of an object
print(jasim.__dict__) # {'work': 'farmer'}

Look at that, classes are just fancy dictionary types!

The converse is also true: src_python[:exports code]{class X: a = 1 ≈ X = type(‘X’, (object,), dict(a = 1))}

A More Complex Person Class

Let’s add more features!

src_python[:exports code]{# [0]} An __init__ method is called whenever a new object is created via Person(name, age). It constructs the object by initialising its necessary features.

src_python[:exports code]{# [1]} The argument src_python[:exports code]{self} refers to the object instance being created and src_python[:exports code]{self.x = y} is the creation of an attribute x with value y for the newly created object instance. Compare src_python[:exports code]{self} with jasim above and src_python[:exports code]{self.work} with jasim.work. It is convention to use the name src_python[:exports code]{self} to refer to the current instance, you can use whatever you want but it must be the first argument.

src_python[:exports code]{# [2]} Each Person instance has their own name and work features, but they universally share the Person.__world feature. Attributes starting with two underscores are private; they can only be altered within the definition of the class. Names starting with no underscores are public and can be accessed and altered using dot-notation. Names starting with one underscore are protected; they can only be used and altered by children classes.

class Person:
    __world = 0  # [2]

    def __init__(self, name, work): # [0]
        self.name = name
        self.work = work
        Person.__world += 1

    def speak(me): # [1] Note, not using “self”
        print (f"I, {me.name}, have the world at my feet!")

    # Implementing __str__ allows our class to be coerced as string
    # and, in particular, to be printed.
    def __str__(self):
        return (f"In a world of {Person.__world} people, "
                f"{self.name} works at {self.work}")

    # [3] Any class implementing methods __eq__ or __lt__
    # can use syntactic sugar == or <, respectively.
    def __eq__(self, other):
        return self.work == other.work

    # We can loop over this class by defining __iter__,
    # to setup iteration, and __next__ to obtain subsequent elements.
    def __iter__(self):
        self.x = -1
        return self

    def __next__(self):
        self.x += 1
        if self.x < len(self.name): return self.name[self.x]
        else: raise StopIteration

Making People

Making People

jason = Person('Jasim', "the old farm")
kathy = Person('Kalthum', "Jasim's farm")
print(kathy) # ⇒ In a world of 2 people, Kalthum works at Jasim's farm
# Two ways to use instance methods
jason.speak()       # ⇒ I, Jasim, have the world at my feet!
Person.speak(jason)

The following code creates a new public feature that happens to have the same name as the private one. This has no influence on the private feature of the same name! See src_python[:exports code]{# [2]} above.

Person.__world = -10
# Check that our world still has two people:
print(jason) # ⇒ In a world of 2 people, Jasim works at the old farm

Syntax Overloading: Dunder Methods

Syntax Overloading: Dunder Methods

src_python[:exports code]{# [3]} Even though jasim and kathy are distinct people, in a dystopian world where people are unique up to contribution, they are considered “the same”.

kathy.work = "the old farm"
assert jason is not kathy
assert jason == kathy

We can use any Python syntactic construct for new types by implementing the dunder —“d”ouble “under”score— methods that they invoke. This way new types become indistinguishable from built-in types. E.g., implementing __call__ makes an object behave like a function whereas implementing __iter__ and __next__ make it iterable —possibly also implementing __getitem__ to use the slicing syntax obj[start:stop] to get a ‘subsegment’ of an instance. Implementing __eq__ and __lt__ lets us use ==, < which are enough to get <=, > if we decorate the class by the @functools.total_ordering decorator. Reflected operators __rℴ𝓅__ are used for arguments of different types: x ⊕ y ≈ y.__r⊕__(x) if x.__⊕__(y) is not implemented.

# Loop over the “jason” object; which just loops over the name's letters.
for e in jason:
    print (e)  # ⇒ J \n a \n s \n i \n m

# Other iterable methods all apply.
print(list(enumerate(jason)) # ⇒ [(0, 'J'), (1, 'a'), (2, 's'), …]

One should not have attributes named such as __attribute__; the dunder naming convention is for the Python implementation team.

• Here is a list of possible dunder methods.
• __add__ so we can use + to merge instances —then use sum to ‘add’ a list of elements.
• Note: 𝒽(x) ≈ x.__𝒽__ for 𝒽: src_python[:exports code]{len, iter, next, bool, str}.

Extension Methods

Extension Methods

# “speak” is a public name, so we can assign to it:
# (1) Alter it for “jason” only
jason.speak =  lambda: print(f"{jason.name}: Hola!")
# (2) Alter it for ALL Person instances
Person.speak = lambda p: print(f"{p.name}: Salam!")
jason.speak() # ⇒ Jasim: Hola!
kathy.speak() # ⇒ Kalthum: Salam!

Notice how speak() above was altered. In general, we can “mix-in new methods” either at the class level or at the instance level in the same way.

This ability to extend classes with new functions does not work with the builtin types like src_python[:exports code]{str} and src_python[:exports code]{int}; neither at the class level nor at the instance level. If we want to inject functionality, we can simply make an empty class like the first incarnation of Person above. An example, PartiallyAppliedFunction, for altering how function calls work is shown on the right column ⇒

Inheritance

Inheritance

A class may inherit the features of another class; this is essentially automatic copy-pasting of code. This gives rise to polymorphism, the ability to “use the same function on different objects”: If class A has method f(), and classes B and C are children of A, then we can call f on B- and on C-instances; moreover B and C might redefine f, thereby ‘overloading’ the name, to specialise it further.

class Teacher(Person):
    # Overriding the inherited __init__ method.
    def __init__(self, name, subject):
        super().__init__(name, f'the university teaching {subject}')
        self.subject = subject

assert isinstance(Teacher, type)
assert issubclass(Teacher, Person)
assert issubclass(Person, object)
# The greatest-grandparent of all classes is called “object”.

moe = Teacher('Ali', 'Logic')
assert isinstance(moe, Teacher) # By construction.
assert isinstance(moe, Person)  # By inheritance.
print(moe)
# ⇒ In a world of 3 people, Ali works at the university teaching Logic

Decorators and Classes

Since @C f stands for f = C(f), we can decorate via classes C whose __init__ method takes a function. Then @C f will be a class! If the class implements __call__ then we can continue to treat @C f as if it were a (stateful) function.

In turn, we can also decorate class methods in the usual way. E.g., when a method 𝓍(self) is decorated @property , we may attach logic to its setter obj.𝓍 = ⋯ and to its getter obj.𝓍!

We can decorate an entire class C as usual; @dec C still behaves as update via function application: C = dec(C). This is one way to change the definition of a class dynamically.

• E.g., to implement design patterns like the singleton pattern.

A class decorator is a function from classes to classes; if we apply a function decorator, then only the class’ constructor is decorated —which makes sense, since the constructor and class share the same name.

Curry Decorator

Example: Currying via Class Decoration

Goal: We want to apply functions in many ways, such as f(x₁, …, xₙ) and f(x₁, …, xᵢ)(xᵢ₊₁, …, xₙ); i.e., all the calls on the right below are equivalent.

doit(1)(2)(3)
doit(1, 2)(3)
doit(1)(2, 3)
doit(1, 2, 3)
doit(1, 2, 3, 666, '∞') # Ignore extra args

The simplest thing to do is to transform src_python[:exports code]{f = lambda x₁, …, xₙ: body} into src_python[:exports code]{nestLambdas(f, [], f.__code__.co_argcount) = lambda x₁: …: lambda xₙ: body}.

def nestLambdas (func, args, remaining):
   if remaining == 0: return func(*args)
   else: return lambda x: nestLambdas(func, args + [x], remaining - 1)

However, the calls shift from f(v₁, …, vₖ) to f(v₁)(v₂)⋯(vₖ); so we need to change what it means to call a function. As already mentioned, we cannot extend built-in classes, so we’ll make a wrapper to slightly alter what it means to call a function on a smaller than necessary amount of arguments.

class PartiallyAppliedFunction():

    def __init__(self, func):
        self.value = nestLambdas(func, [], func.__code__.co_argcount)

    def __mul__ (self, other):
        return PartiallyAppliedFunction(lambda x: self(other(x)))

    apply = lambda self, other: other * self if callable(other) else self(other)
    def __rshift__(self, other):  return self.apply(other)
    def __rrshift__(self, other): return self.apply(other)

    def __call__(self, *args):
        value = self.value
        for a in args:
            if callable(value):
                value = value(a)
        return PartiallyAppliedFunction(value) if (callable(value)) else value

curry = PartiallyAppliedFunction   # Shorter convenience name

The above invocation styles, for doit, now all work ^_^

Multiplication now denotes function composition and the (‘r’eflected) ‘r’ight-shift denotes forward-composition/application:

(g * f(v₁, …, vₘ))(x₁, …, xₙ) = g(f (v₁, …, vₘ, x₁))(x₂, …, xₙ)

assert(   (g * f(3, 1))(9, 4)
       == (f(3, 1) >> g)(9, 4)
       == [13, 13, 13, 13])

assert (   ['a', 'a', 'b']
        == 2 >> g('a')
             >> curry(lambda x: x + ['b']))

Named Expressions

The value of a “walrus” expression x := e is the value of e, but it also introduces the name x into scope. The name x must be an atomic identifier; e.g., not an unpacked pattern or indexing; moreover x cannot be a for-bound name.

“if-let”

   x = e; if p(x): f(x)
≈ if p(x := e): f(x)

This can be useful to capture the value of a truthy item so as to use it in the body:

  if e: x = e; f(x)
≈ if (x := e): f(x)

“while-let”

  while True:
      x = input(); if p(x): break; f(x)
≈
  while p(x := input()): f(x)

“witness/counterexample capture”

if any(p(witness := x) for x in xs):
    print(f"{witness} satisfies p")

if not all(p(witness := x) for x in xs):
    print(f"{witness} falsifies p")

“Stateful Comprehensions”

   partial sums of xs
≈ [sum(xs[: i + 1]) for i in range(len(xs))]
≈ (total := 0, [total := total + x for x in xs])[1]

Walrus introduces new names, what if we wanted to check if a name already exists?

# alter x if it's defined else, use 7 as default value
x = x + 2 if 'x' in vars() else 7

Modules

⇒ Each Python file myfile.py determines a module whose contents can be used in other files, which declare src_python[:exports code]{import myfile}, in the form myfile.component.

⇒ To use a function f from myfile without qualifying it each time, we may use the from import declaration: src_python[:exports code]{from myfile import f}.

⇒ Moreover, src_python[:exports code]{from myfile import *} brings into scope all contents of myfile and so no qualification is necessary.

⇒ To use a smaller qualifier, or have conditional imports that alias the imported modules with the same qualifier, we may use src_python[:exports code]{import thefile as newNameHere}.

⇒ A Python package is a directory of files —i.e., Python modules— with a (possibly empty) file named __init__.py to declare the directory as a package.

⇒ If P is a package and M is a module in it, then we can use src_python[:exports code]{import P.M} or src_python[:exports code]{from P import M}, with the same behaviour as for modules. The init file can mark some modules as private and not for use by other packages.

Reads

• Dan Bader’s Python Tutorials —bite-sized lessons
  • Likewise: w3schools Python Tutorial
• www.learnpython.org —an interactive and tremendously accessible tutorial
• The Python Tutorial —really good introduction from python.org
• https://realpython.com/ —real-world Python tutorials
• Python for Lisp Programmers
• A gallery of interesting Jupyter Notebooks —interactive, ‘live’, Python tutorials
• How to think like a computer scientist —Python tutorial that covers turtle graphics as well as drag-and-drop interactive coding and interactive quizzes along the way to check your understanding; there are also videos too!
• Monads in Python —Colourful Python tutorials converted from Haskell
• Teach Yourself Programming in Ten Years