July 2, 2020 Jure Šorn

#Main if __name__ == '__main__' : main()

<list>.append(<el>) <list>.extend(<collection>)

<list>.sort() <list>.reverse() <list> = sorted(<collection>) <iter> = reversed(<list>)

sum_of_elements = sum(<collection>) elementwise_sum = [sum(pair) for pair in zip(list_a, list_b)] sorted_by_second = sorted(<collection>, key= lambda el: el[ 1 ]) sorted_by_both = sorted(<collection>, key= lambda el: (el[ 1 ], el[ 0 ])) flatter_list = list(itertools.chain.from_iterable(<list>)) product_of_elems = functools.reduce( lambda out, el: out * el, <collection>) list_of_chars = list(<str>)

Module operator provides functions itemgetter() and mul() that offer the same functionality as lambda expressions above.

<int> = <list>.count(<el>) index = <list>.index(<el>) <list>.insert(index, <el>) <el> = <list>.pop([index]) <list>.remove(<el>) <list>.clear()

value = <dict>.get(key, default= None ) value = <dict>.setdefault(key, default= None ) <dict> = collections.defaultdict(<type>) <dict> = collections.defaultdict( lambda : 1 )

<dict> = dict(<collection>) <dict> = dict(zip(keys, values)) <dict> = dict.fromkeys(keys [, value])

<dict>.update(<dict>) value = <dict>.pop(key) {k for k, v in <dict>.items() if v == value} {k: v for k, v in <dict>.items() if k in keys}

Counter from collections import Counter colors = [ 'blue' , 'blue' , 'blue' , 'red' , 'red' ] counter = Counter(colors) counter[ 'yellow' ] += 1 Counter({ 'blue' : 3 , 'red' : 2 , 'yellow' : 1 }) counter.most_common()[ 0 ] ( 'blue' , 3 )

<set>.add(<el>) <set>.update(<collection>)

<set> = <set>.union(<coll.>) <set> = <set>.intersection(<coll.>) <set> = <set>.difference(<coll.>) <set> = <set>.symmetric_difference(<coll.>) <bool> = <set>.issubset(<coll.>) <bool> = <set>.issuperset(<coll.>)

<el> = <set>.pop() <set>.remove(<el>) <set>.discard(<el>)

Frozen Set Is immutable and hashable.

That means it can be used as a key in a dictionary or as an element in a set. <frozenset> = frozenset(<collection>)

#Tuple Tuple is an immutable and hashable list. <tuple> = () <tuple> = (<el>, ) <tuple> = (<el_1>, <el_2> [, ...])

Named Tuple Tuple's subclass with named elements. from collections import namedtuple Point = namedtuple( 'Point' , 'x y' ) p = Point( 1 , y= 2 ) Point(x= 1 , y= 2 ) p[ 0 ] 1 p.x 1 getattr(p, 'y' ) 2 p._fields ( 'x' , 'y' )

from_inclusive = <range>.start to_exclusive = <range>.stop

#Enumerate for i, el in enumerate(<collection> [, i_start]): ...

from itertools import count, repeat, cycle, chain, islice

<iter> = count(start= 0 , step= 1 ) <iter> = repeat(<el> [, times]) <iter> = cycle(<collection>)

<iter> = chain(<coll_1>, <coll_2> [, ...]) <iter> = chain.from_iterable(<collection>)

<iter> = islice(<collection>, to_exclusive) <iter> = islice(<collection>, from_inclusive, to_exclusive [, +step_size])

#Generator Any function that contains a yield statement returns a generator.

Generators and iterators are interchangeable. def count (start, step) : while True : yield start start += step

counter = count( 10 , 2 ) next(counter), next(counter), next(counter) ( 10 , 12 , 14 )

#Type Everything is an object.

Every object has a type.

Type and class are synonymous. <type> = type(<el>) <bool> = isinstance(<el>, <type>)

type( 'a' ), 'a' .__class__, str (< class ' str '>, < class ' str '>, < class ' str '>)

Some types do not have built-in names, so they must be imported: from types import FunctionType, MethodType, LambdaType, GeneratorType

Abstract Base Classes Each abstract base class specifies a set of virtual subclasses. These classes are then recognized by isinstance() and issubclass() as subclasses of the ABC, although they are really not. from collections.abc import Sequence, Collection, Iterable isinstance([ 1 , 2 , 3 ], Iterable) True

┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━┓ ┃ │ Sequence │ Collection │ Iterable ┃ ┠──────────────────┼────────────┼────────────┼────────────┨ ┃ list, range, str │ ✓ │ ✓ │ ✓ ┃ ┃ dict, set │ │ ✓ │ ✓ ┃ ┃ iter │ │ │ ✓ ┃ ┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┛

from numbers import Integral, Rational, Real, Complex, Number isinstance( 123 , Number) True

┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━┓ ┃ │ Integral │ Rational │ Real │ Complex │ Number ┃ ┠────────────────────┼──────────┼──────────┼──────────┼──────────┼──────────┨ ┃ int │ ✓ │ ✓ │ ✓ │ ✓ │ ✓ ┃ ┃ fractions.Fraction │ │ ✓ │ ✓ │ ✓ │ ✓ ┃ ┃ float │ │ │ ✓ │ ✓ │ ✓ ┃ ┃ complex │ │ │ │ ✓ │ ✓ ┃ ┃ decimal.Decimal │ │ │ │ │ ✓ ┃ ┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━┛

<list> = <str>.split() <list> = <str>.split(sep= None , maxsplit= -1 ) <list> = <str>.splitlines(keepends= False ) <str> = <str>.join(<coll_of_strings>)

<bool> = <sub_str> in <str> <bool> = <str>.startswith(<sub_str>) <bool> = <str>.endswith(<sub_str>) <int> = <str>.find(<sub_str>) <int> = <str>.index(<sub_str>)

<str> = <str>.replace(old, new [, count]) <str> = <str>.translate(<table>)

<str> = chr(<int>) <int> = ord(<str>)

Also: 'lstrip()' , 'rstrip()' .

Also: 'lower()' , 'upper()' , 'capitalize()' and 'title()' .

Property Methods ┏━━━━━━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━┓ ┃ │ [ !#$%…] │ [a-zA-Z] │ [¼½¾] │ [²³¹] │ [0-9] ┃ ┠───────────────┼──────────┼──────────┼──────────┼──────────┼──────────┨ ┃ isprintable() │ ✓ │ ✓ │ ✓ │ ✓ │ ✓ ┃ ┃ isalnum() │ │ ✓ │ ✓ │ ✓ │ ✓ ┃ ┃ isnumeric() │ │ │ ✓ │ ✓ │ ✓ ┃ ┃ isdigit() │ │ │ │ ✓ │ ✓ ┃ ┃ isdecimal() │ │ │ │ │ ✓ ┃ ┗━━━━━━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━┛

Also: 'isspace()' checks for '[ \t

\r\f\v…]' .

#Regex import re <str> = re.sub(<regex>, new, text, count= 0 ) <list> = re.findall(<regex>, text) <list> = re.split(<regex>, text, maxsplit= 0 ) <Match> = re.search(<regex>, text) <Match> = re.match(<regex>, text) <iter> = re.finditer(<regex>, text)

Search() and match() return None if they can't find a match.

Argument 'flags=re.IGNORECASE' can be used with all functions.

Argument 'flags=re.MULTILINE' makes '^' and '$' match the start/end of each line.

Argument 'flags=re.DOTALL' makes dot also accept the '

' .

Use r'\1' or '\\1' for backreference.

Add '?' after an operator to make it non-greedy.

Match Object <str> = <Match>.group() <str> = <Match>.group( 1 ) <tuple> = <Match>.groups() <int> = <Match>.start() <int> = <Match>.end()

Special Sequences By default digits, alphanumerics and whitespaces from all alphabets are matched, unless 'flags=re.ASCII' argument is used.

Use a capital letter for negation. '\d' == '[0-9]' '\w' == '[a-zA-Z0-9_]' '\s' == '[ \t

\r\f\v]'

Attributes from collections import namedtuple Person = namedtuple( 'Person' , 'name height' ) person = Person( 'Jean-Luc' , 187 ) f' {person.height} ' '187' '{p.height}' .format(p=person) '187'

General Options {<el>:< 10 } {<el>:^ 10 } {<el>:> 10 } {<el>:.< 10 } {<el>:< 0 }

Strings '!r' calls object's repr() method, instead of str(), to get a string. { 'abcde' !r: 10 } { 'abcde' : 10.3 } { 'abcde' : .3 }

Numbers { 123456 : 10 ,} { 123456 : 10 _} { 123456 :+ 10 } { -123456 := 10 } { 123456 : } { -123456 : }

Floats { 1.23456 : 10.3 } { 1.23456 : 10.3 f} { 1.23456 : 10.3 e} { 1.23456 : 10.3 %}

Comparison of presentation types: ┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┓ ┃ │ {<float>} │ {<float>:f} │ {<float>:e} │ {<float>:%} ┃ ┠───────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┨ ┃ 0.000056789 │ '5.6789e-05' │ '0.000057' │ '5.678900e-05' │ '0.005679%' ┃ ┃ 0.00056789 │ '0.00056789' │ '0.000568' │ '5.678900e-04' │ '0.056789%' ┃ ┃ 0.0056789 │ '0.0056789' │ '0.005679' │ '5.678900e-03' │ '0.567890%' ┃ ┃ 0.056789 │ '0.056789' │ '0.056789' │ '5.678900e-02' │ '5.678900%' ┃ ┃ 0.56789 │ '0.56789' │ '0.567890' │ '5.678900e-01' │ '56.789000%' ┃ ┃ 5.6789 │ '5.6789' │ '5.678900' │ '5.678900e+00' │ '567.890000%' ┃ ┃ 56.789 │ '56.789' │ '56.789000' │ '5.678900e+01' │ '5678.900000%' ┃ ┃ 567.89 │ '567.89' │ '567.890000' │ '5.678900e+02' │ '56789.000000%' ┃ ┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┛

┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┓ ┃ │ {<float>:.2} │ {<float>:.2f} │ {<float>:.2e} │ {<float>:.2%} ┃ ┠───────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┨ ┃ 0.000056789 │ '5.7e-05' │ '0.00' │ '5.68e-05' │ '0.01%' ┃ ┃ 0.00056789 │ '0.00057' │ '0.00' │ '5.68e-04' │ '0.06%' ┃ ┃ 0.0056789 │ '0.0057' │ '0.01' │ '5.68e-03' │ '0.57%' ┃ ┃ 0.056789 │ '0.057' │ '0.06' │ '5.68e-02' │ '5.68%' ┃ ┃ 0.56789 │ '0.57' │ '0.57' │ '5.68e-01' │ '56.79%' ┃ ┃ 5.6789 │ '5.7' │ '5.68' │ '5.68e+00' │ '567.89%' ┃ ┃ 56.789 │ '5.7e+01' │ '56.79' │ '5.68e+01' │ '5678.90%' ┃ ┃ 567.89 │ '5.7e+02' │ '567.89' │ '5.68e+02' │ '56789.00%' ┃ ┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┛

Ints { 90 :c} { 90 :b} { 90 :X}

#Numbers Types <int> = int(<float/str/bool>) <float> = float(<int/str/bool>) <complex> = complex(real= 0 , imag= 0 ) <Fraction> = fractions.Fraction( 0 , 1 ) <Decimal> = decimal.Decimal(<str/int>)

'int(<str>)' and 'float(<str>)' raise ValueError on malformed strings.

Decimal numbers can be represented exactly, unlike floats where '1.1 + 2.2 != 3.3' .

Precision of decimal operations is set with: 'decimal.getcontext().prec = <int>' .

Basic Functions <num> = pow(<num>, <num>) <num> = abs(<num>) <num> = round(<num> [, ±ndigits])

Math from math import e, pi, inf, nan, isinf, isnan from math import cos, acos, sin, asin, tan, atan, degrees, radians from math import log, log10, log2

Statistics from statistics import mean, median, variance, stdev, pvariance, pstdev

Random from random import random, randint, choice, shuffle <float> = random() <int> = randint(from_inclusive, to_inclusive) <el> = choice(<list>) shuffle(<list>)

Bin, Hex <int> = ± 0 b<bin> <int> = int( '±<bin>' , 2 ) <int> = int( '±0b<bin>' , 0 ) <str> = bin(<int>)

Bitwise Operators <int> = <int> & <int> <int> = <int> | <int> <int> = <int> ^ <int> <int> = <int> << n_bits <int> = ~<int>

#Combinatorics Every function returns an iterator.

If you want to print the iterator, you need to pass it to the list() function first! from itertools import product, combinations, combinations_with_replacement, permutations

product([ 0 , 1 ], repeat= 3 ) [( 0 , 0 , 0 ), ( 0 , 0 , 1 ), ( 0 , 1 , 0 ), ( 0 , 1 , 1 ), ( 1 , 0 , 0 ), ( 1 , 0 , 1 ), ( 1 , 1 , 0 ), ( 1 , 1 , 1 )]

product( 'ab' , '12' ) [( 'a' , '1' ), ( 'a' , '2' ), ( 'b' , '1' ), ( 'b' , '2' )]

combinations( 'abc' , 2 ) [( 'a' , 'b' ), ( 'a' , 'c' ), ( 'b' , 'c' )]

combinations_with_replacement( 'abc' , 2 ) [( 'a' , 'a' ), ( 'a' , 'b' ), ( 'a' , 'c' ), ( 'b' , 'b' ), ( 'b' , 'c' ), ( 'c' , 'c' )]

permutations( 'abc' , 2 ) [( 'a' , 'b' ), ( 'a' , 'c' ), ( 'b' , 'a' ), ( 'b' , 'c' ), ( 'c' , 'a' ), ( 'c' , 'b' )]

Module 'datetime' provides 'date' <D> , 'time' <T> , 'datetime' <DT> and 'timedelta' <TD> classes. All are immutable and hashable.

Time and datetime objects can be 'aware' <a> , meaning they have defined timezone, or 'naive' <n> , meaning they don't.

If object is naive, it is presumed to be in the system's timezone. from datetime import date, time, datetime, timedelta from dateutil.tz import UTC, tzlocal, gettz, resolve_imaginary

Constructors <D> = date(year, month, day) <T> = time(hour= 0 , minute= 0 , second= 0 , microsecond= 0 , tzinfo= None , fold= 0 ) <DT> = datetime(year, month, day, hour= 0 , minute= 0 , second= 0 , ...) <TD> = timedelta(days= 0 , seconds= 0 , microseconds= 0 , milliseconds= 0 , minutes= 0 , hours= 0 , weeks= 0 )

Use '<D/DT>.weekday()' to get the day of the week (Mon == 0).

'fold=1' means the second pass in case of time jumping back for one hour.

'<DTa> = resolve_imaginary(<DTa>)' fixes DTs that fall into the missing hour.

Now <D/DTn> = D/DT.today() <DTn> = DT.utcnow() <DTa> = DT.now(<tzinfo>)

To extract time use '<DTn>.time()' , '<DTa>.time()' or '<DTa>.timetz()' .

Timezone <tzinfo> = UTC <tzinfo> = tzlocal() <tzinfo> = gettz( '<Continent>/<City>' ) <DTa> = <DT>.astimezone(<tzinfo>) <Ta/DTa> = <T/DT>.replace(tzinfo=<tzinfo>)

Encode <D/T/DT> = D/T/DT.fromisoformat( '<iso>' ) <DT> = DT.strptime(<str>, '<format>' ) <D/DTn> = D/DT.fromordinal(<int>) <DTn> = DT.fromtimestamp(<real>) <DTa> = DT.fromtimestamp(<real>, <tz.>)

ISO strings come in following forms: 'YYYY-MM-DD' , 'HH:MM:SS.ffffff[±<offset>]' , or both separated by an arbitrary character. Offset is formatted as: 'HH:MM' .

Epoch on Unix systems is: '1970-01-01 00:00 UTC' , '1970-01-01 01:00 CET' , …

Decode <str> = <D/T/DT>.isoformat(sep= 'T' ) <str> = <D/T/DT>.strftime( '<format>' ) <int> = <D/DT>.toordinal() <float> = <DTn>.timestamp() <float> = <DTa>.timestamp()

Format from datetime import datetime dt = datetime.strptime( '2015-05-14 23:39:00.00 +0200' , '%Y-%m-%d %H:%M:%S.%f %z' ) dt.strftime( "%A, %dth of %B '%y, %I:%M%p %Z" ) "Thursday, 14th of May '15, 11:39PM UTC+02:00"

When parsing, '%z' also accepts '±HH:MM' .

For abbreviated weekday and month use '%a' and '%b' .

Arithmetics <D/DT> = <D/DT> ± <TD> <TD> = <D/DTn> - <D/DTn> <TD> = <DTa> - <DTa> <TD> = <DT_UTC> - <DT_UTC>

#Arguments Inside Function Call <function>(<positional_args>) <function>(<keyword_args>) <function>(<positional_args>, <keyword_args>)

Inside Function Definition def f (<nondefault_args>) : def f (<default_args>) : def f (<nondefault_args>, <default_args>) :

#Splat Operator Inside Function Call Splat expands a collection into positional arguments, while splatty-splat expands a dictionary into keyword arguments. args = ( 1 , 2 ) kwargs = { 'x' : 3 , 'y' : 4 , 'z' : 5 } func(*args, **kwargs)

Is the same as: func( 1 , 2 , x= 3 , y= 4 , z= 5 )

Inside Function Definition Splat combines zero or more positional arguments into a tuple, while splatty-splat combines zero or more keyword arguments into a dictionary. def add (*a) : return sum(a)

add( 1 , 2 , 3 ) 6

Legal argument combinations: def f (x, y, z) : def f (*, x, y, z) : def f (x, *, y, z) : def f (x, y, *, z) :

def f (*args) : def f (x, *args) : def f (*args, z) : def f (x, *args, z) :

def f (**kwargs) : def f (x, **kwargs) : def f (*, x, **kwargs) :

def f (*args, **kwargs) : def f (x, *args, **kwargs) : def f (*args, y, **kwargs) : def f (x, *args, z, **kwargs) :

Other Uses <list> = [*<collection> [, ...]] <set> = {*<collection> [, ...]} <tuple> = (*<collection>, [...]) <dict> = {**<dict> [, ...]}

head, *body, tail = <collection>

#Inline Lambda <function> = lambda : <return_value> <function> = lambda <argument_1>, <argument_2>: <return_value>

Comprehensions <list> = [i+ 1 for i in range( 10 )] <set> = {i for i in range( 10 ) if i > 5 } <iter> = (i+ 5 for i in range( 10 )) <dict> = {i: i* 2 for i in range( 10 )}

out = [i+j for i in range( 10 ) for j in range( 10 )]

Is the same as: out = [] for i in range( 10 ): for j in range( 10 ): out.append(i+j)

Map, Filter, Reduce from functools import reduce <iter> = map( lambda x: x + 1 , range( 10 )) <iter> = filter( lambda x: x > 5 , range( 10 )) <obj> = reduce( lambda out, x: out + x, range( 10 ))

Any, All <bool> = any(<collection>) <bool> = all(el[ 1 ] for el in <collection>)

If - Else <obj> = <expression_if_true> if <condition> else <expression_if_false>

[a if a else 'zero' for a in ( 0 , 1 , 2 , 3 )] [ 'zero' , 1 , 2 , 3 ]

Namedtuple, Enum, Dataclass from collections import namedtuple Point = namedtuple( 'Point' , 'x y' ) point = Point( 0 , 0 )

from enum import Enum Direction = Enum( 'Direction' , 'n e s w' ) direction = Direction.n

from dataclasses import make_dataclass Creature = make_dataclass( 'Creature' , [ 'location' , 'direction' ]) creature = Creature(Point( 0 , 0 ), Direction.n)

#Closure We have a closure in Python when: A nested function references a value of its enclosing function and then

the enclosing function returns the nested function. def get_multiplier (a) : def out (b) : return a * b return out

multiply_by_3 = get_multiplier( 3 ) multiply_by_3( 10 ) 30

If multiple nested functions within enclosing function reference the same value, that value gets shared.

To dynamically access function's first free variable use '<function>.__closure__[0].cell_contents' .

Partial from functools import partial <function> = partial(<function> [, <arg_1>, <arg_2>, ...])

import operator as op multiply_by_3 = partial(op.mul, 3 ) multiply_by_3( 10 ) 30

Partial is also useful in cases when function needs to be passed as an argument, because it enables us to set its arguments beforehand.

A few examples being: 'defaultdict(<function>)' , 'iter(<function>, to_exclusive)' and dataclass's 'field(default_factory=<function>)' .

Non-Local If variable is being assigned to anywhere in the scope, it is regarded as a local variable, unless it is declared as a 'global' or a 'nonlocal'. def get_counter () : i = 0 def out () : nonlocal i i += 1 return i return out

counter = get_counter() counter(), counter(), counter() ( 1 , 2 , 3 )

#Decorator A decorator takes a function, adds some functionality and returns it. def function_that_gets_passed_to_decorator () : ...

Debugger Example Decorator that prints function's name every time it gets called. from functools import wraps def debug (func) : def out (*args, **kwargs) : print(func.__name__) return func(*args, **kwargs) return out def add (x, y) : return x + y

Wraps is a helper decorator that copies the metadata of the passed function (func) to the function it is wrapping (out).

Without it 'add.__name__' would return 'out' .

LRU Cache Decorator that caches function's return values. All function's arguments must be hashable. from functools import lru_cache def fib (n) : return n if n < 2 else fib(n- 2 ) + fib(n- 1 )

CPython interpreter limits recursion depth to 1000 by default. To increase it use 'sys.setrecursionlimit(<depth>)' .

Parametrized Decorator A decorator that accepts arguments and returns a normal decorator that accepts a function. from functools import wraps def debug (print_result=False) : def decorator (func) : def out (*args, **kwargs) : result = func(*args, **kwargs) print(func.__name__, result if print_result else '' ) return result return out return decorator def add (x, y) : return x + y

#Class class < name >: def __init__ (self, a) : self.a = a def __repr__ (self) : class_name = self.__class__.__name__ return f' {class_name} ( {self.a!r} )' def __str__ (self) : return str(self.a) def get_class_name (cls) : return cls.__name__

Return value of repr() should be unambiguous and of str() readable.

If only repr() is defined, it will also be used for str().

Str() use cases: print(<el>) print( f' {<el>} ' ) raise Exception(<el>) loguru.logger.debug(<el>) csv.writer(<file>).writerow([<el>])

Repr() use cases: print([<el>]) print( f' {<el>!r} ' ) <el> loguru.logger.exception() Z = dataclasses.make_dataclass( 'Z' , [ 'a' ]); print(Z(<el>))

Constructor Overloading class < name >: def __init__ (self, a=None) : self.a = a

Inheritance class Person : def __init__ (self, name, age) : self.name = name self.age = age class Employee (Person) : def __init__ (self, name, age, staff_num) : super().__init__(name, age) self.staff_num = staff_num

Multiple Inheritance class A : pass class B : pass class C (A, B) : pass

MRO determines the order in which parent classes are traversed when searching for a method:

C.mro() [< class ' C '>, < class ' A '>, < class ' B '>, < class ' object '>]

Property Pythonic way of implementing getters and setters. class MyClass : def a (self) : return self._a def a (self, value) : self._a = value

el = MyClass() el.a = 123 el.a 123

Dataclass Decorator that automatically generates init(), repr() and eq() special methods. from dataclasses import dataclass, field class < class_name >: <attr_name_1>: <type> <attr_name_2>: <type> = <default_value> <attr_name_3>: list/dict/set = field(default_factory=list/dict/set)

Objects can be made sortable with 'order=True' and/or immutable and hashable with 'frozen=True' .

Function field() is needed because '<attr_name>: list = []' would make a list that is shared among all instances.

Default_factory can be any callable.

Inline: from dataclasses import make_dataclass <class> = make_dataclass( '<class_name>' , <coll_of_attribute_names>) <class> = make_dataclass( '<class_name>' , <coll_of_tuples>) <tuple> = ( '<attr_name>' , <type> [, <default_value>])

Slots Mechanism that restricts objects to attributes listed in 'slots' and significantly reduces their memory footprint. class MyClassWithSlots : __slots__ = [ 'a' ] def __init__ (self) : self.a = 1

Copy from copy import copy, deepcopy <object> = copy(<object>) <object> = deepcopy(<object>)

#Duck Types A duck type is an implicit type that prescribes a set of special methods. Any object that has those methods defined is considered a member of that duck type. Comparable If eq() method is not overridden, it returns 'id(self) == id(other)' , which is the same as 'self is other' .

That means all objects compare not equal by default.

Only the left side object has eq() method called, unless it returns NotImplemented, in which case the right object is consulted. class MyComparable : def __init__ (self, a) : self.a = a def __eq__ (self, other) : if isinstance(other, type(self)): return self.a == other.a return NotImplemented

Hashable Hashable object needs both hash() and eq() methods and its hash value should never change.

Hashable objects that compare equal must have the same hash value, meaning default hash() that returns 'id(self)' will not do.

That is why Python automatically makes classes unhashable if you only implement eq(). class MyHashable : def __init__ (self, a) : self._a = a def a (self) : return self._a def __eq__ (self, other) : if isinstance(other, type(self)): return self.a == other.a return NotImplemented def __hash__ (self) : return hash(self.a)

Sortable With total_ordering decorator, you only need to provide eq() and one of lt(), gt(), le() or ge() special methods. from functools import total_ordering class MySortable : def __init__ (self, a) : self.a = a def __eq__ (self, other) : if isinstance(other, type(self)): return self.a == other.a return NotImplemented def __lt__ (self, other) : if isinstance(other, type(self)): return self.a < other.a return NotImplemented

Iterator Any object that has methods next() and iter() is an iterator.

Next() should return next item or raise StopIteration.

Iter() should return 'self'. class Counter : def __init__ (self) : self.i = 0 def __next__ (self) : self.i += 1 return self.i def __iter__ (self) : return self

counter = Counter() next(counter), next(counter), next(counter) ( 1 , 2 , 3 )

Python has many different iterator objects: Iterators returned by the iter() function, such as list_iterator and set_iterator.

Objects returned by the itertools module, such as count, repeat and cycle.

Generators returned by the generator functions and generator expressions.

File objects returned by the open() function, etc. Callable All functions and classes have a call() method, hence are callable.

When this cheatsheet uses '<function>' as an argument, it actually means '<callable>' . class Counter : def __init__ (self) : self.i = 0 def __call__ (self) : self.i += 1 return self.i

counter = Counter() counter(), counter(), counter() ( 1 , 2 , 3 )

Context Manager Enter() should lock the resources and optionally return an object.

Exit() should release the resources.

Any exception that happens inside the with block is passed to the exit() method.

If it wishes to suppress the exception it must return a true value. class MyOpen : def __init__ (self, filename) : self.filename = filename def __enter__ (self) : self.file = open(self.filename) return self.file def __exit__ (self, exc_type, exception, traceback) : self.file.close()

with open( 'test.txt' , 'w' ) as file: file.write( 'Hello World!' ) with MyOpen( 'test.txt' ) as file: print(file.read()) Hello World!

#Iterable Duck Types Iterable Only required method is iter(). It should return an iterator of object's items.

Contains() automatically works on any object that has iter() defined. class MyIterable : def __init__ (self, a) : self.a = a def __iter__ (self) : return iter(self.a) def __contains__ (self, el) : return el in self.a

obj = MyIterable([ 1 , 2 , 3 ]) [el for el in obj] [ 1 , 2 , 3 ] 1 in obj True

Collection Only required methods are iter() and len().

This cheatsheet actually means '<iterable>' when it uses '<collection>' .

I chose not to use the name 'iterable' because it sounds scarier and more vague than 'collection'. class MyCollection : def __init__ (self, a) : self.a = a def __iter__ (self) : return iter(self.a) def __contains__ (self, el) : return el in self.a def __len__ (self) : return len(self.a)

Sequence Only required methods are len() and getitem().

Getitem() should return an item at index or raise IndexError.

Iter() and contains() automatically work on any object that has getitem() defined.

Reversed() automatically works on any object that has getitem() and len() defined. class MySequence : def __init__ (self, a) : self.a = a def __iter__ (self) : return iter(self.a) def __contains__ (self, el) : return el in self.a def __len__ (self) : return len(self.a) def __getitem__ (self, i) : return self.a[i] def __reversed__ (self) : return reversed(self.a)

ABC Sequence It's a richer interface than the basic sequence.

Extending it generates iter(), contains(), reversed(), index() and count().

Unlike 'abc.Iterable' and 'abc.Collection' , it is not a duck type. That is why 'issubclass(MySequence, abc.Sequence)' would return False even if MySequence had all the methods defined. from collections import abc class MyAbcSequence (abc.Sequence) : def __init__ (self, a) : self.a = a def __len__ (self) : return len(self.a) def __getitem__ (self, i) : return self.a[i]

Table of required and automatically available special methods: ┏━━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━━━┓ ┃ │ Iterable │ Collection │ Sequence │ abc.Sequence ┃ ┠────────────┼────────────┼────────────┼────────────┼──────────────┨ ┃ iter() │ ! │ ! │ ✓ │ ✓ ┃ ┃ contains() │ ✓ │ ✓ │ ✓ │ ✓ ┃ ┃ len() │ │ ! │ ! │ ! ┃ ┃ getitem() │ │ │ ! │ ! ┃ ┃ reversed() │ │ │ ✓ │ ✓ ┃ ┃ index() │ │ │ │ ✓ ┃ ┃ count() │ │ │ │ ✓ ┃ ┗━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━┛

Other ABCs that generate missing methods are: MutableSequence, Set, MutableSet, Mapping and MutableMapping.

Names of their required methods are stored in '<abc>.__abstractmethods__' .

#Enum from enum import Enum, auto class < enum_name > (Enum) : <member_name_1> = <value_1> <member_name_2> = <value_2_a>, <value_2_b> <member_name_3> = auto()

If there are no numeric values before auto(), it returns 1.

Otherwise it returns an increment of the last numeric value.

<member> = <enum>.<member_name> <member> = <enum>[ '<member_name>' ] <member> = <enum>(<value>) <str> = <member>.name <obj> = <member>.value

list_of_members = list(<enum>) member_names = [a.name for a in <enum>] member_values = [a.value for a in <enum>] random_member = random.choice(list(<enum>))

def get_next_member (member) : members = list(member.__class__) index = (members.index(member) + 1 ) % len(members) return members[index]

Inline Cutlery = Enum( 'Cutlery' , 'fork knife spoon' ) Cutlery = Enum( 'Cutlery' , [ 'fork' , 'knife' , 'spoon' ]) Cutlery = Enum( 'Cutlery' , { 'fork' : 1 , 'knife' : 2 , 'spoon' : 3 })

User-defined functions cannot be values, so they must be wrapped: from functools import partial LogicOp = Enum( 'LogicOp' , { 'AND' : partial( lambda l, r: l and r), 'OR' : partial( lambda l, r: l or r)})

Another solution in this particular case is to use built-in functions and_() and or_() from the module operator.

#Exceptions Basic Example try : <code> except <exception>: <code>

Complex Example try : <code_1> except <exception_a>: <code_2_a> except <exception_b>: <code_2_b> else : <code_2_c> finally : <code_3>

Code inside the 'else' block will only be executed if 'try' block had no exception.

Code inside the 'finally' block will always be executed.

Catching Exceptions except <exception>: except <exception> as <name>: except (<exception>, ...): except (<exception>, ...) as <name>:

Also catches subclasses of the exception.

Use 'traceback.print_exc()' to print the error message to stderr.

Raising Exceptions raise <exception> raise <exception>() raise <exception>(<el> [, ...])

Re-raising caught exception: except <exception> as <name>: ... raise

Exception Object arguments = <name>.args exc_type = <name>.__class__ filename = <name>.__traceback__.tb_frame.f_code.co_filename func_name = <name>.__traceback__.tb_frame.f_code.co_name line = linecache.getline(filename, <name>.__traceback__.tb_lineno) error_msg = traceback.format_exception(exc_type, <name>, <name>.__traceback__)

Built-in Exceptions BaseException ├── SystemExit ├── KeyboardInterrupt └── Exception ├── ArithmeticError │ └── ZeroDivisionError ├── AttributeError ├── EOFError ├── LookupError │ ├── IndexError │ └── KeyError ├── NameError ├── OSError │ └── FileNotFoundError ├── RuntimeError │ └── RecursionError ├── StopIteration ├── TypeError └── ValueError └── UnicodeError

Collections and their exceptions: ┏━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━┓ ┃ │ list │ dict │ set ┃ ┠───────────┼────────────┼────────────┼────────────┨ ┃ getitem() │ IndexError │ KeyError │ ┃ ┃ pop() │ IndexError │ KeyError │ KeyError ┃ ┃ remove() │ ValueError │ │ KeyError ┃ ┃ index() │ ValueError │ │ ┃ ┗━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┛

Useful built-in exceptions: raise TypeError( 'Argument is of wrong type!' ) raise ValueError( 'Argument is of right type but inappropriate value!' ) raise RuntimeError( 'None of above!' )

User-defined Exceptions class MyError (Exception) : pass class MyInputError (MyError) : pass

#Exit Exits the interpreter by raising SystemExit exception. import sys sys.exit() sys.exit(<el>) sys.exit(<int>)

print(<el_1>, ..., sep= ' ' , end= '

' , file=sys.stdout, flush= False )

Use 'file=sys.stderr' for messages about errors.

Use 'flush=True' to forcibly flush the stream.

Pretty Print from pprint import pprint pprint(<collection>, width= 80 , depth= None , compact= False , sort_dicts= True )

Levels deeper than 'depth' get replaced by '…'.

#Input Reads a line from user input or pipe if present. <str> = input(prompt= None )

Trailing newline gets stripped.

Prompt string is printed to the standard output before reading input.

Raises EOFError when user hits EOF (ctrl-d/z) or input stream gets exhausted.

#Command Line Arguments import sys script_name = sys.argv[ 0 ] arguments = sys.argv[ 1 :]

Argument Parser from argparse import ArgumentParser, FileType p = ArgumentParser(description=<str>) p.add_argument( '-<short_name>' , '--<name>' , action= 'store_true' ) p.add_argument( '-<short_name>' , '--<name>' , type=<type>) p.add_argument( '<name>' , type=<type>, nargs= 1 ) p.add_argument( '<name>' , type=<type>, nargs= '+' ) p.add_argument( '<name>' , type=<type>, nargs= '*' ) args = p.parse_args() value = args.<name>

Use 'help=<str>' to set argument description.

Use 'default=<el>' to set the default value.

Use 'type=FileType(<mode>)' for files.

#Open Opens the file and returns a corresponding file object. <file> = open( '<path>' , mode= 'r' , encoding= None , newline= None )

'encoding=None' means that the default encoding is used, which is platform dependent. Best practice is to use 'encoding="utf-8"' whenever possible.

'newline=None' means all different end of line combinations are converted to '

' on read, while on write all '

' characters are converted to system's default line separator.

'newline=""' means no conversions take place, but input is still broken into chunks by readline() and readlines() on either '

', '\r' or '\r

'.

Modes 'r' - Read (default).

'w' - Write (truncate).

'x' - Write or fail if the file already exists.

'a' - Append.

'w+' - Read and write (truncate).

'r+' - Read and write from the start.

'a+' - Read and write from the end.

't' - Text mode (default).

'b' - Binary mode. Exceptions 'FileNotFoundError' can be raised when reading with 'r' or 'r+' .

'FileExistsError' can be raised when writing with 'x' .

'IsADirectoryError' and 'PermissionError' can be raised by any.

'OSError' is the parent class of all listed exceptions. File Object <file>.seek( 0 ) <file>.seek(offset) <file>.seek( 0 , 2 ) <bin_file>.seek(±offset, <anchor>)

<str/bytes> = <file>.read(size= -1 ) <str/bytes> = <file>.readline() <list> = <file>.readlines() <str/bytes> = next(<file>)

<file>.write(<str/bytes>) <file>.writelines(<collection>) <file>.flush()

Methods do not add or strip trailing newlines, even writelines().

Read Text from File def read_file (filename) : with open(filename, encoding= 'utf-8' ) as file: return file.readlines()

Write Text to File def write_to_file (filename, text) : with open(filename, 'w' , encoding= 'utf-8' ) as file: file.write(text)

#Path from os import getcwd, path, listdir from glob import glob

<str> = getcwd() <str> = path.join(<path>, ...) <str> = path.abspath(<path>)

<str> = path.basename(<path>) <str> = path.dirname(<path>) <tup.> = path.splitext(<path>)

<list> = listdir(path= '.' ) <list> = glob( '<pattern>' )

<bool> = path.exists(<path>) <bool> = path.isfile(<path>) <bool> = path.isdir(<path>)

DirEntry Using scandir() instead of listdir() can significantly increase the performance of code that also needs file type information. from os import scandir

<iter> = scandir(path= '.' ) <str> = <DirEntry>.path <str> = <DirEntry>.name <file> = open(<DirEntry>)

Path Object from pathlib import Path

<Path> = Path(<path> [, ...]) <Path> = <path> / <path> [/ ...]

<Path> = Path() <Path> = Path.cwd() <Path> = <Path>.resolve()

<Path> = <Path>.parent <str> = <Path>.name <str> = <Path>.stem <str> = <Path>.suffix <tup.> = <Path>.parts

<iter> = <Path>.iterdir() <iter> = <Path>.glob( '<pattern>' )

<str> = str(<Path>) <file> = open(<Path>)

#OS Commands Files and Directories Paths can be either strings, Paths or DirEntry objects.

Functions report OS related errors by raising either OSError or one of its subclasses. import os, shutil

os.chdir(<path>) os.mkdir(<path>, mode= 0o777 )

shutil.copy(from, to) shutil.copytree(from, to)

os.rename(from, to) os.replace(from, to)

os.remove(<path>) os.rmdir(<path>) shutil.rmtree(<path>)

Shell Commands import os <str> = os.popen( '<shell_command>' ).read()

Sends '1 + 1' to the basic calculator and captures its output: from subprocess import run run( 'bc' , input= '1 + 1

' , capture_output= True , encoding= 'utf-8' ) CompletedProcess(args= 'bc' , returncode= 0 , stdout= '2

' , stderr= '' )

Sends test.in to the basic calculator running in standard mode and saves its output to test.out: from shlex import split os.popen( 'echo 1 + 1 > test.in' ) run(split( 'bc -s' ), stdin=open( 'test.in' ), stdout=open( 'test.out' , 'w' )) CompletedProcess(args=[ 'bc' , '-s' ], returncode= 0 ) open( 'test.out' ).read() '2

'

#JSON Text file format for storing collections of strings and numbers. import json <str> = json.dumps(<object>, ensure_ascii= True , indent= None ) <object> = json.loads(<str>)

Read Object from JSON File def read_json_file (filename) : with open(filename, encoding= 'utf-8' ) as file: return json.load(file)

Write Object to JSON File def write_to_json_file (filename, an_object) : with open(filename, 'w' , encoding= 'utf-8' ) as file: json.dump(an_object, file, ensure_ascii= False , indent= 2 )

#Pickle Binary file format for storing objects. import pickle <bytes> = pickle.dumps(<object>) <object> = pickle.loads(<bytes>)

Read Object from File def read_pickle_file (filename) : with open(filename, 'rb' ) as file: return pickle.load(file)

Write Object to File def write_to_pickle_file (filename, an_object) : with open(filename, 'wb' ) as file: pickle.dump(an_object, file)

#CSV Text file format for storing spreadsheets. import csv

Read <reader> = csv.reader(<file>) <list> = next(<reader>) <list> = list(<reader>)

File must be opened with 'newline=""' argument, or newlines embedded inside quoted fields will not be interpreted correctly!

Write <writer> = csv.writer(<file>) <writer>.writerow(<collection>) <writer>.writerows(<coll_of_coll>)

File must be opened with 'newline=""' argument, or '\r' will be added in front of every '

' on platforms that use '\r

' line endings!

Parameters 'dialect' - Master parameter that sets the default values.

'delimiter' - A one-character string used to separate fields.

'quotechar' - Character for quoting fields that contain special characters.

'doublequote' - Whether quotechars inside fields get doubled or escaped.

'skipinitialspace' - Whether whitespace after delimiter gets stripped.

'lineterminator' - Specifies how writer terminates rows.

'quoting' - Controls the amount of quoting: 0 - as necessary, 1 - all.

'escapechar' - Character for escaping 'quotechar' if 'doublequote' is False. Dialects ┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━┓ ┃ │ excel │ excel-tab │ unix ┃ ┠──────────────────┼──────────────┼──────────────┼──────────────┨ ┃ delimiter │ ',' │ '\t' │ ',' ┃ ┃ quotechar │ '"' │ '"' │ '"' ┃ ┃ doublequote │ True │ True │ True ┃ ┃ skipinitialspace │ False │ False │ False ┃ ┃ lineterminator │ '\r

' │ '\r

' │ '

' ┃ ┃ quoting │ 0 │ 0 │ 1 ┃ ┃ escapechar │ None │ None │ None ┃ ┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━┛

Read Rows from CSV File def read_csv_file (filename) : with open(filename, encoding= 'utf-8' , newline= '' ) as file: return list(csv.reader(file))

Write Rows to CSV File def write_to_csv_file (filename, rows) : with open(filename, 'w' , encoding= 'utf-8' , newline= '' ) as file: writer = csv.writer(file) writer.writerows(rows)

#SQLite Server-less database engine that stores each database into a separate file. Connect Opens a connection to the database file. Creates a new file if path doesn't exist. import sqlite3 <con> = sqlite3.connect( '<path>' ) <con>.close()

Read Returned values can be of type str, int, float, bytes or None. <cursor> = <con>.execute( '<query>' ) <tuple> = <cursor>.fetchone() <list> = <cursor>.fetchall()

Write <con>.execute( '<query>' ) <con>.commit()

Or: with <con>: <con>.execute( '<query>' )

Placeholders Passed values can be of type str, int, float, bytes, None, bool, datetime.date or datetime.datetme.

Bools will be stored and returned as ints and dates as ISO formatted strings. <con>.execute( '<query>' , <list/tuple>) <con>.execute( '<query>' , <dict/namedtuple>) <con>.executemany( '<query>' , <coll_of_above>)

Example In this example values are not actually saved because 'con.commit()' is omitted! con = sqlite3.connect( 'test.db' ) con.execute( 'create table person (person_id integer primary key, name, height)' ) con.execute( 'insert into person values (null, ?, ?)' , ( 'Jean-Luc' , 187 )).lastrowid 1 con.execute( 'select * from person' ).fetchall() [( 1 , 'Jean-Luc' , 187 )]

MySQL Has a very similar interface, with differences listed below. from mysql import connector <con> = connector.connect(host=<str>, …) <cursor> = <con>.cursor() <cursor>.execute( '<query>' ) <cursor>.execute( '<query>' , <list/tuple>) <cursor>.execute( '<query>' , <dict/namedtuple>)

#Bytes Bytes object is an immutable sequence of single bytes. Mutable version is called bytearray. <bytes> = b'<str>' <int> = <bytes>[<index>] <bytes> = <bytes>[<slice>] <bytes> = <bytes>.join(<coll_of_bytes>)

Encode <bytes> = bytes(<coll_of_ints>) <bytes> = bytes(<str>, 'utf-8' ) <bytes> = <int>.to_bytes(n_bytes, …) <bytes> = bytes.fromhex( '<hex>' )

Decode <list> = list(<bytes>) <str> = str(<bytes>, 'utf-8' ) <int> = int.from_bytes(<bytes>, …) '<hex>' = <bytes>.hex()

Read Bytes from File def read_bytes (filename) : with open(filename, 'rb' ) as file: return file.read()

Write Bytes to File def write_bytes (filename, bytes_obj) : with open(filename, 'wb' ) as file: file.write(bytes_obj)

#Struct Module that performs conversions between a sequence of numbers and a bytes object.

Machine’s native type sizes and byte order are used by default. from struct import pack, unpack, iter_unpack <bytes> = pack( '<format>' , <num_1> [, <num_2>, ...]) <tuple> = unpack( '<format>' , <bytes>) <tuples> = iter_unpack( '<format>' , <bytes>)

Example pack( '>hhl' , 1 , 2 , 3 ) b'\x00\x01\x00\x02\x00\x00\x00\x03' unpack( '>hhl' , b'\x00\x01\x00\x02\x00\x00\x00\x03' ) ( 1 , 2 , 3 )

Format For standard type sizes start format string with: '=' - native byte order

'<' - little-endian

'>' - big-endian (also '!' )

Integer types. Use a capital letter for unsigned type. Standard sizes are in brackets: 'x' - pad byte

'b' - char (1)

'h' - short (2)

'i' - int (4)

'l' - long (4)

'q' - long long (8)

Floating point types: 'f' - float (4)

'd' - double (8)

#Array List that can only hold numbers of a predefined type. Available types and their sizes in bytes are listed above. from array import array <array> = array( '<typecode>' , <collection>) <array> = array( '<typecode>' , <bytes>) <array> = array( '<typecode>' , <array>) <bytes> = bytes(<array>)

#Memory View A sequence object that points to the memory of another object.

Each element can reference a single or multiple consecutive bytes, depending on format.

Order and number of elements can be changed with slicing. <mview> = memoryview(<bytes/bytearray/array>) <real> = <mview>[<index>] <mview> = <mview>[<slice>] <mview> = <mview>.cast( '<typecode>' ) <mview>.release()

Decode <bin_file>.write(<mview>) <bytes> = bytes(<mview>) <bytes> = <bytes>.join(<coll_of_mviews>) <array> = array( '<typecode>' , <mview>)

<list> = list(<mview>) <str> = str(<mview>, 'utf-8' ) <int> = int.from_bytes(<mview>, …) '<hex>' = <mview>.hex()

#Deque A thread-safe list with efficient appends and pops from either side. Pronounced "deck". from collections import deque <deque> = deque(<collection>, maxlen= None )

<deque>.appendleft(<el>) <deque>.extendleft(<collection>) <el> = <deque>.popleft() <deque>.rotate(n= 1 )

#Threading CPython interpreter can only run a single thread at a time.

That is why using multiple threads won't result in a faster execution, unless at least one of the threads contains an I/O operation. from threading import Thread, RLock, Semaphore, Event, Barrier

Thread <Thread> = Thread(target=<function>) <Thread>.start() <bool> = <Thread>.is_alive() <Thread>.join()

Use 'kwargs=<dict>' to pass keyword arguments to the function.

Use 'daemon=True' , or the program will not be able to exit while the thread is alive.

Lock <lock> = RLock() <lock>.acquire() <lock>.release()

Or: lock = RLock() with lock: ...

Semaphore, Event, Barrier <Semaphore> = Semaphore(value= 1 ) <Event> = Event() <Barrier> = Barrier(n_times)

Thread Pool Executor from concurrent.futures import ThreadPoolExecutor with ThreadPoolExecutor(max_workers= None ) as executor: <iter> = executor.map( lambda x: x + 1 , range( 3 )) <iter> = executor.map( lambda x, y: x + y, 'abc' , '123' ) <Future> = executor.submit(<function> [, <arg_1>, ...])

Future: <bool> = <Future>.done() <obj> = <Future>.result()

Queue A thread-safe FIFO queue. For LIFO queue use LifoQueue. from queue import Queue <Queue> = Queue(maxsize= 0 )

<Queue>.put(<el>) <Queue>.put_nowait(<el>) <el> = <Queue>.get() <el> = <Queue>.get_nowait()

#Operator Module of functions that provide the functionality of operators. from operator import add, sub, mul, truediv, floordiv, mod, pow, neg, abs from operator import eq, ne, lt, le, gt, ge from operator import and_, or_, not_ from operator import itemgetter, attrgetter, methodcaller

import operator as op elementwise_sum = map(op.add, list_a, list_b) sorted_by_second = sorted(<collection>, key=op.itemgetter( 1 )) sorted_by_both = sorted(<collection>, key=op.itemgetter( 1 , 0 )) product_of_elems = functools.reduce(op.mul, <collection>) LogicOp = enum.Enum( 'LogicOp' , { 'AND' : op.and_, 'OR' : op.or_}) last_el = op.methodcaller( 'pop' )(<list>)

#Introspection Inspecting code at runtime. Variables <list> = dir() <dict> = vars() <dict> = globals()

Attributes <list> = dir(<object>) <dict> = vars(<object>) <bool> = hasattr(<object>, '<attr_name>' ) value = getattr(<object>, '<attr_name>' ) setattr(<object>, '<attr_name>' , value) delattr(<object>, '<attr_name>' )

Parameters from inspect import signature <sig> = signature(<function>) no_of_params = len(<sig>.parameters) param_names = list(<sig>.parameters.keys()) param_kinds = [a.kind for a in <sig>.parameters.values()]

#Metaprograming Code that generates code. Type Type is the root class. If only passed an object it returns its type (class). Otherwise it creates a new class. <class> = type( '<class_name>' , <parents_tuple>, <attributes_dict>)

Z = type( 'Z' , (), { 'a' : 'abcde' , 'b' : 12345 }) z = Z()

Meta Class A class that creates classes. def my_meta_class (name, parents, attrs) : attrs[ 'a' ] = 'abcde' return type(name, parents, attrs)

Or: class MyMetaClass (type) : def __new__ (cls, name, parents, attrs) : attrs[ 'a' ] = 'abcde' return type.__new__(cls, name, parents, attrs)

New() is a class method that gets called before init(). If it returns an instance of its class, then that instance gets passed to init() as a 'self' argument.

It receives the same arguments as init(), except for the first one that specifies the desired type of the returned instance (MyMetaClass in our case).

Like in our case, new() can also be called directly, usually from a new() method of a child class ( def __new__ (cls) : return super().__new__(cls) ).

The only difference between the examples above is that my_meta_class() returns a class of type type, while MyMetaClass() returns a class of type MyMetaClass.

Metaclass Attribute Right before a class is created it checks if it has the 'metaclass' attribute defined. If not, it recursively checks if any of his parents has it defined and eventually comes to type(). class MyClass (metaclass=MyMetaClass) : b = 12345

MyClass.a, MyClass.b ( 'abcde' , 12345 )

Type Diagram type(MyClass) == MyMetaClass type(MyMetaClass) == type

┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┓ ┃ Classes │ Metaclasses ┃ ┠─────────────┼─────────────┨ ┃ MyClass ──→ MyMetaClass ┃ ┃ │ ↓ ┃ ┃ object ─────→ type ←╮ ┃ ┃ │ ↑ ╰──╯ ┃ ┃ str ──────────╯ ┃ ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┛

Inheritance Diagram MyClass.__base__ == object MyMetaClass.__base__ == type

┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┓ ┃ Classes │ Metaclasses ┃ ┠─────────────┼─────────────┨ ┃ MyClass │ MyMetaClass ┃ ┃ ↓ │ ↓ ┃ ┃ object ←───── type ┃ ┃ ↑ │ ┃ ┃ str │ ┃ ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┛

#Eval from ast import literal_eval literal_eval( '1 + 2' ) 3 literal_eval( '[1, 2, 3]' ) [ 1 , 2 , 3 ] literal_eval( 'abs(1)' ) ValueError: malformed node or string

#Coroutines Coroutines have a lot in common with threads, but unlike threads, they only give up control when they call another coroutine and they don’t use as much memory.

Coroutine definition starts with 'async' and its call with 'await' .

'asyncio.run(<coroutine>)' is the main entry point for asynchronous programs.

Functions wait(), gather() and as_completed() can be used when multiple coroutines need to be started at the same time.

Asyncio module also provides its own Queue, Event, Lock and Semaphore classes. Runs a terminal game where you control an asterisk that must avoid numbers: import asyncio, collections, curses, enum, random P = collections.namedtuple( 'P' , 'x y' ) D = enum.Enum( 'D' , 'n e s w' ) def main (screen) : curses.curs_set( 0 ) screen.nodelay( True ) asyncio.run(main_coroutine(screen)) async def main_coroutine (screen) : state = { '*' : P( 0 , 0 ), **{id_: P( 30 , 10 ) for id_ in range( 10 )}} moves = asyncio.Queue() coros = (*(random_controller(id_, moves) for id_ in range( 10 )), human_controller(screen, moves), model(moves, state, *screen.getmaxyx()), view(state, screen)) await asyncio.wait(coros, return_when=asyncio.FIRST_COMPLETED) async def random_controller (id_, moves) : while True : moves.put_nowait((id_, random.choice(list(D)))) await asyncio.sleep(random.random() / 2 ) async def human_controller (screen, moves) : while True : ch = screen.getch() key_mappings = { 259 : D.n, 261 : D.e, 258 : D.s, 260 : D.w} if ch in key_mappings: moves.put_nowait(( '*' , key_mappings[ch])) await asyncio.sleep( 0.01 ) async def model (moves, state, height, width) : while state[ '*' ] not in {p for id_, p in state.items() if id_ != '*' }: id_, d = await moves.get() p = state[id_] deltas = {D.n: P( 0 , -1 ), D.e: P( 1 , 0 ), D.s: P( 0 , 1 ), D.w: P( -1 , 0 )} new_p = P(*[sum(a) for a in zip(p, deltas[d])]) if 0 <= new_p.x < width -1 and 0 <= new_p.y < height: state[id_] = new_p async def view (state, screen) : while True : screen.clear() for id_, p in state.items(): screen.addstr(p.y, p.x, str(id_)) await asyncio.sleep( 0.01 ) curses.wrapper(main)





Libraries #Progress Bar from tqdm import tqdm from time import sleep for el in tqdm([ 1 , 2 , 3 ]): sleep( 0.2 )

#Plot from matplotlib import pyplot pyplot.plot(<y_data> [, label=<str>]) pyplot.plot(<x_data>, <y_data>) pyplot.legend() pyplot.savefig( '<path>' ) pyplot.show() pyplot.clf()

#Table Prints a CSV file as an ASCII table: import csv, tabulate with open( 'test.csv' , encoding= 'utf-8' , newline= '' ) as file: rows = csv.reader(file) header = [a.title() for a in next(rows)] table = tabulate.tabulate(rows, header) print(table)

#Curses Clears the terminal, prints a message and waits for the ESC key press: from curses import wrapper, curs_set, ascii from curses import KEY_UP, KEY_RIGHT, KEY_DOWN, KEY_LEFT def main () : wrapper(draw) def draw (screen) : curs_set( 0 ) screen.nodelay( True ) screen.clear() screen.addstr( 0 , 0 , 'Press ESC to quit.' ) while screen.getch() != ascii.ESC: pass def get_border (screen) : from collections import namedtuple P = namedtuple( 'P' , 'x y' ) height, width = screen.getmaxyx() return P(width -1 , height -1 ) if __name__ == '__main__' : main()

#Logging from loguru import logger

logger.add( 'debug_{time}.log' , colorize= True ) logger.add( 'error_{time}.log' , level= 'ERROR' ) logger.<level>( 'A logging message.' )

Levels: 'debug' , 'info' , 'success' , 'warning' , 'error' , 'critical' .

Exceptions Exception description, stack trace and values of variables are appended automatically. try : ... except <exception>: logger.exception( 'An error happened.' )

Rotation Argument that sets a condition when a new log file is created. rotation=<int>|<datetime.timedelta>|<datetime.time>|<str>

'<int>' - Max file size in bytes.

'<timedelta>' - Max age of a file.

'<time>' - Time of day.

'<str>' - Any of above as a string: '100 MB' , '1 month' , 'monday at 12:00' , …

Retention Sets a condition which old log files get deleted. retention=<int>|<datetime.timedelta>|<str>

'<int>' - Max number of files.

'<timedelta>' - Max age of a file.

'<str>' - Max age as a string: '1 week, 3 days' , '2 months' , …

#Scraping Scrapes Python's URL, version number and logo from Wikipedia page: import requests, sys from bs4 import BeautifulSoup URL = 'https://en.wikipedia.org/wiki/Python_(programming_language)' try : html = requests.get(URL).text doc = BeautifulSoup(html, 'html.parser' ) table = doc.find( 'table' , class_= 'infobox vevent' ) rows = table.find_all( 'tr' ) link = rows[ 11 ].find( 'a' )[ 'href' ] ver = rows[ 6 ].find( 'div' ).text.split()[ 0 ] url_i = rows[ 0 ].find( 'img' )[ 'src' ] image = requests.get( f'https: {url_i} ' ).content with open( 'test.png' , 'wb' ) as file: file.write(image) print(link, ver) except requests.exceptions.ConnectionError: print( "You've got problems with connection." , file=sys.stderr)

#Web from bottle import run, route, static_file, template, post, request, response import json

Run run(host= 'localhost' , port= 8080 ) run(host= '0.0.0.0' , port= 80 )

Static Request def send_image (image) : return static_file(image, 'img_dir/' , mimetype= 'image/png' )

Dynamic Request def send_page (sport) : return template( '<h1>{{title}}</h1>' , title=sport)

REST Request def odds_handler (sport) : team = request.forms.get( 'team' ) home_odds, away_odds = 2.44 , 3.29 response.headers[ 'Content-Type' ] = 'application/json' response.headers[ 'Cache-Control' ] = 'no-cache' return json.dumps([team, home_odds, away_odds])

Test: import requests url = 'http://localhost:8080/odds/football' data = { 'team' : 'arsenal f.c.' } response = requests.post(url, data=data) response.json() [ 'arsenal f.c.' , 2.44 , 3.29 ]

#Profiling Stopwatch from time import time start_time = time() ... duration = time() - start_time

High performance: from time import perf_counter start_time = perf_counter() ... duration = perf_counter() - start_time

Timing a Snippet from timeit import timeit timeit( '"-".join(str(i) for i in range(100))' , number= 10000 , globals=globals(), setup= 'pass' ) 0.34986

def main () : a = [*range( 10000 )] b = {*range( 10000 )} main()

$ kernprof -lv test.py Line # Hits Time Per Hit % Time Line Contents ======================================================= 1 @profile 2 def main(): 3 1 1128.0 1128.0 27.4 a = [*range(10000)] 4 1 2994.0 2994.0 72.6 b = {*range(10000)}

$ python3 -m memory_profiler test.py Line # Mem usage Increment Line Contents ======================================================= 1 35.387 MiB 35.387 MiB @profile 2 def main(): 3 35.734 MiB 0.348 MiB a = [*range(10000)] 4 36.160 MiB 0.426 MiB b = {*range(10000)}

Call Graph Generates a PNG image of a call graph with highlighted bottlenecks: from pycallgraph import output, PyCallGraph from datetime import datetime time_str = datetime.now().strftime( '%Y%m%d%H%M%S' ) filename = f'profile- {time_str} .png' drawer = output.GraphvizOutput(output_file=filename) with PyCallGraph(drawer): <code_to_be_profiled>

#NumPy Array manipulation mini-language. It can run up to one hundred times faster than the equivalent Python code. import numpy as np

<array> = np.array(<list>) <array> = np.arange(from_inclusive, to_exclusive, ±step_size) <array> = np.ones(<shape>) <array> = np.random.randint(from_inclusive, to_exclusive, <shape>)

<array>.shape = <shape> <view> = <array>.reshape(<shape>) <view> = np.broadcast_to(<array>, <shape>)

<array> = <array>.sum(axis) indexes = <array>.argmin(axis)

Shape is a tuple of dimension sizes.

Axis is the index of a dimension that gets collapsed. The leftmost dimension has index 0.

Indexing <el> = <2d_array>[0, 0] <1d_view> = <2d_array>[0] <1d_view> = <2d_array>[:, 0] <3d_view> = <2d_array>[None, :, :]

<1d_array> = <2d_array>[<1d_row_indexes>, <1d_column_indexes>] <2d_array> = <2d_array>[<2d_row_indexes>, <2d_column_indexes>]

<2d_bools> = <2d_array> > 0 <1d_array> = <2d_array>[<2d_bools>]

If row and column indexes differ in shape, they are combined with broadcasting.

Broadcasting Broadcasting is a set of rules by which NumPy functions operate on arrays of different sizes and/or dimensions. left = [[ 0.1 ], [ 0.6 ], [ 0.8 ]] right = [ 0.1 , 0.6 , 0.8 ]

1. If array shapes differ in length, left-pad the shorter shape with ones: left = [[ 0.1 ], [ 0.6 ], [ 0.8 ]] right = [[ 0.1 , 0.6 , 0.8 ]]

2. If any dimensions differ in size, expand the ones that have size 1 by duplicating their elements: left = [[ 0.1 , 0.1 , 0.1 ], [ 0.6 , 0.6 , 0.6 ], [ 0.8 , 0.8 , 0.8 ]] right = [[ 0.1 , 0.6 , 0.8 ], [ 0.1 , 0.6 , 0.8 ], [ 0.1 , 0.6 , 0.8 ]]

3. If neither non-matching dimension has size 1, raise an error. Example For each point returns index of its nearest point ( [ 0.1 , 0.6 , 0.8 ] => [ 1 , 2 , 1 ] ): points = np.array([ 0.1 , 0.6 , 0.8 ]) [ 0.1 , 0.6 , 0.8 ] wrapped_points = points.reshape( 3 , 1 ) [[ 0.1 ], [ 0.6 ], [ 0.8 ]] distances = wrapped_points - points [[ 0. , -0.5 , -0.7 ], [ 0.5 , 0. , -0.2 ], [ 0.7 , 0.2 , 0. ]] distances = np.abs(distances) [[ 0. , 0.5 , 0.7 ], [ 0.5 , 0. , 0.2 ], [ 0.7 , 0.2 , 0. ]] i = np.arange( 3 ) [ 0 , 1 , 2 ] distances[i, i] = np.inf [[ inf, 0.5 , 0.7 ], [ 0.5 , inf, 0.2 ], [ 0.7 , 0.2 , inf]] distances.argmin( 1 ) [ 1 , 2 , 1 ]

#Image from PIL import Image

<Image> = Image.new( '<mode>' , (width, height)) <Image> = Image.open( '<path>' ) <Image> = <Image>.convert( '<mode>' ) <Image>.save( '<path>' ) <Image>.show()

<tuple/int> = <Image>.getpixel((x, y)) <Image>.putpixel((x, y), <tuple/int>) <ImagingCore> = <Image>.getdata() <Image>.putdata(<list/ImagingCore>) <Image>.paste(<Image>, (x, y))

<2d_array> = np.array(<Image>) <3d_array> = np.array(<Image>) <Image> = Image.fromarray(<array>)

Modes '1' - 1-bit pixels, black and white, stored with one pixel per byte.

'L' - 8-bit pixels, greyscale.

'RGB' - 3x8-bit pixels, true color.

'RGBA' - 4x8-bit pixels, true color with transparency mask.

'HSV' - 3x8-bit pixels, Hue, Saturation, Value color space. Examples Creates a PNG image of a rainbow gradient: WIDTH, HEIGHT = 100 , 100 size = WIDTH * HEIGHT hues = [ 255 * i/size for i in range(size)] img = Image.new( 'HSV' , (WIDTH, HEIGHT)) img.putdata([(int(h), 255 , 255 ) for h in hues]) img.convert( 'RGB' ).save( 'test.png' )

Adds noise to a PNG image: from random import randint add_noise = lambda value: max( 0 , min( 255 , value + randint( -20 , 20 ))) img = Image.open( 'test.png' ).convert( 'HSV' ) img.putdata([(add_noise(h), s, v) for h, s, v in img.getdata()]) img.convert( 'RGB' ).save( 'test.png' )

Drawing from PIL import ImageDraw

<ImageDraw> = ImageDraw.Draw(<Image>) <ImageDraw>.point((x, y), fill= None ) <ImageDraw>.line((x1, y1, x2, y2 [, ...]), fill= None , width= 0 , joint= None ) <ImageDraw>.arc((x1, y1, x2, y2), from_deg, to_deg, fill= None , width= 0 ) <ImageDraw>.rectangle((x1, y1, x2, y2), fill= None , outline= None , width= 0 ) <ImageDraw>.polygon((x1, y1, x2, y2 [, ...]), fill= None , outline= None ) <ImageDraw>.ellipse((x1, y1, x2, y2), fill= None , outline= None , width= 0 )

Use 'fill=<color>' to set the primary color.

Use 'outline=<color>' to set the secondary color.

Color can be specified as a tuple, int, '#rrggbb' string or a color name.

#Animation Creates a GIF of a bouncing ball: from PIL import Image, ImageDraw import imageio WIDTH, R = 126 , 10 frames = [] for velocity in range( 15 ): y = sum(range(velocity+ 1 )) frame = Image.new( 'L' , (WIDTH, WIDTH)) draw = ImageDraw.Draw(frame) draw.ellipse((WIDTH/ 2 -R, y, WIDTH/ 2 +R, y+R* 2 ), fill= 'white' ) frames.append(frame) frames += reversed(frames[ 1 : -1 ]) imageio.mimsave( 'test.gif' , frames, duration= 0.03 )

#Audio import wave

<Wave_read> = wave.open( '<path>' , 'rb' ) framerate = <Wave_read>.getframerate() nchannels = <Wave_read>.getnchannels() sampwidth = <Wave_read>.getsampwidth() nframes = <Wave_read>.getnframes() <params> = <Wave_read>.getparams() <bytes> = <Wave_read>.readframes(nframes)

<Wave_write> = wave.open( '<path>' , 'wb' ) <Wave_write>.setframerate(<int>) <Wave_write>.setnchannels(<int>) <Wave_write>.setsampwidth(<int>) <Wave_write>.setparams(<params>) <Wave_write>.writeframes(<bytes>)

Bytes object contains a sequence of frames, each consisting of one or more samples.

In a stereo signal, the first sample of a frame belongs to the left channel.

Each sample consists of one or more bytes that, when converted to an integer, indicate the displacement of a speaker membrane at a given moment.

If sample width is one, then the integer should be encoded unsigned.

For all other sizes, the integer should be encoded signed with little-endian byte order.

Sample Values ┏━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━┯━━━━━━━━━━━━━┓ ┃ sampwidth │ min │ zero │ max ┃ ┠───────────┼─────────────┼──────┼─────────────┨ ┃ 1 │ 0 │ 128 │ 255 ┃ ┃ 2 │ -32768 │ 0 │ 32767 ┃ ┃ 3 │ -8388608 │ 0 │ 8388607 ┃ ┃ 4 │ -2147483648 │ 0 │ 2147483647 ┃ ┗━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━┷━━━━━━━━━━━━━┛

Read Float Samples from WAV File def read_wav_file (filename) : def get_int (a_bytes) : an_int = int.from_bytes(a_bytes, 'little' , signed=width!= 1 ) return an_int - 128 * (width == 1 ) with wave.open(filename, 'rb' ) as file: width = file.getsampwidth() frames = file.readframes( -1 ) byte_samples = (frames[i: i + width] for i in range( 0 , len(frames), width)) return [get_int(b) / pow( 2 , width * 8 - 1 ) for b in byte_samples]

Write Float Samples to WAV File def write_to_wav_file (filename, float_samples, nchannels= 1 , sampwidth= 2 , framerate= 44100 ) : def get_bytes (a_float) : a_float = max( -1 , min( 1 - 2e-16 , a_float)) a_float += sampwidth == 1 a_float *= pow( 2 , sampwidth * 8 - 1 ) return int(a_float).to_bytes(sampwidth, 'little' , signed=sampwidth!= 1 ) with wave.open(filename, 'wb' ) as file: file.setnchannels(nchannels) file.setsampwidth(sampwidth) file.setframerate(framerate) file.writeframes( b'' .join(get_bytes(f) for f in float_samples))

Examples Saves a sine wave to a mono WAV file: from math import pi, sin samples_f = (sin(i * 2 * pi * 440 / 44100 ) for i in range( 100000 )) write_to_wav_file( 'test.wav' , samples_f)

Adds noise to a mono WAV file: from random import random add_noise = lambda value: value + (random() - 0.5 ) * 0.03 samples_f = (add_noise(f) for f in read_wav_file( 'test.wav' )) write_to_wav_file( 'test.wav' , samples_f)

Plays a WAV file: from simpleaudio import play_buffer with wave.open( 'test.wav' , 'rb' ) as file: p = file.getparams() frames = file.readframes( -1 ) play_buffer(frames, p.nchannels, p.sampwidth, p.framerate)

Text to Speech import pyttsx3 engine = pyttsx3.init() engine.say( 'Sally sells seashells by the seashore.' ) engine.runAndWait()

#Synthesizer Plays Popcorn by Gershon Kingsley: import simpleaudio, math, struct from itertools import chain, repeat F = 44100 P1 = '71♪,69,,71♪,66,,62♪,66,,59♪,,,' P2 = '71♪,73,,74♪,73,,74,,71,,73♪,71,,73,,69,,71♪,69,,71,,67,,71♪,,,' get_pause = lambda seconds: repeat( 0 , int(seconds * F)) sin_f = lambda i, hz: math.sin(i * 2 * math.pi * hz / F) get_wave = lambda hz, seconds: (sin_f(i, hz) for i in range(int(seconds * F))) get_hz = lambda key: 8.176 * 2 ** (int(key) / 12 ) parse_note = lambda note: (get_hz(note[: 2 ]), 0.25 if '♪' in note else 0.125 ) get_samples = lambda note: get_wave(*parse_note(note)) if note else get_pause( 0.125 ) samples_f = chain.from_iterable(get_samples(n) for n in f' {P1} {P1} {P2} ' .split( ',' )) samples_b = b'' .join(struct.pack( '<h' , int(f * 30000 )) for f in samples_f) simpleaudio.play_buffer(samples_b, 1 , 2 , F)

#Pygame Basic Example import pygame as pg pg.init() screen = pg.display.set_mode(( 500 , 500 )) rect = pg.Rect( 240 , 240 , 20 , 20 ) while all(event.type != pg.QUIT for event in pg.event.get()): deltas = {pg.K_UP: ( 0 , -3 ), pg.K_RIGHT: ( 3 , 0 ), pg.K_DOWN: ( 0 , 3 ), pg.K_LEFT: ( -3 , 0 )} for delta in (deltas.get(i) for i, on in enumerate(pg.key.get_pressed()) if on): rect = rect.move(delta) if delta else rect screen.fill(( 0 , 0 , 0 )) pg.draw.rect(screen, ( 255 , 255 , 255 ), rect) pg.display.flip()

Rectangle Object for storing rectangular coordinates. <Rect> = pg.Rect(x, y, width, height) <int> = <Rect>.x/y/centerx/centery/… <tup.> = <Rect>.topleft/center/… <Rect> = <Rect>.move((x, y))

<bool> = <Rect>.collidepoint((x, y)) <bool> = <Rect>.colliderect(<Rect>) <int> = <Rect>.collidelist(<list_of_Rect>) <list> = <Rect>.collidelistall(<list_of_Rect>)

Surface Object for representing images. <Surf> = pg.display.set_mode((width, height)) <Surf> = pg.Surface((width, height)) <Surf> = pg.image.load( '<path>' ) <Surf> = <Surf>.subsurface(<Rect>)

<Surf>.fill(color) <Surf>.set_at((x, y), color) <Surf>.blit(<Surface>, (x, y))

<Surf> = pg.transform.flip(<Surf>, xbool, ybool) <Surf> = pg.transform.rotate(<Surf>, degrees) <Surf> = pg.transform.scale(<Surf>, (width, height))

pg.draw.line(<Surf>, color, (x1, y1), (x2, y2), width) pg.draw.arc(<Surf>, color, <Rect>, from_radians, to_radians) pg.draw.rect(<Surf>, color, <Rect>) pg.draw.polygon(<Surf>, color, points) pg.draw.ellipse(<Surf>, color, <Rect>)

Font <Font> = pg.font.SysFont( '<name>' , size, bold= False , italic= False ) <Font> = pg.font.Font( '<path>' , size) <Surf> = <Font>.render(text, antialias, color [, background])

Sound <Sound> = pg.mixer.Sound( '<path>' ) <Sound>.play()

Basic Mario Brothers Example import collections, dataclasses, enum, io, pygame, urllib.request, itertools as it from random import randint P = collections.namedtuple( 'P' , 'x y' ) D = enum.Enum( 'D' , 'n e s w' ) SIZE, MAX_SPEED = 50 , P( 5 , 10 ) def main () : def get_screen () : pygame.init() return pygame.display.set_mode( 2 * [SIZE* 16 ]) def get_images () : url = 'https://gto76.github.io/python-cheatsheet/web/mario_bros.png' img = pygame.image.load(io.BytesIO(urllib.request.urlopen(url).read())) return [img.subsurface(get_rect(x, 0 )) for x in range(img.get_width() // 16 )] def get_mario () : Mario = dataclasses.make_dataclass( 'Mario' , 'rect spd facing_left frame_cycle' .split()) return Mario(get_rect( 1 , 1 ), P( 0 , 0 ), False , it.cycle(range( 3 ))) def get_tiles () : positions = [p for p in it.product(range(SIZE), repeat= 2 ) if {*p} & { 0 , SIZE -1 }] + \ [(randint( 1 , SIZE -2 ), randint( 2 , SIZE -2 )) for _ in range(SIZE** 2 // 10 )] return [get_rect(*p) for p in positions] def get_rect (x, y) : return pygame.Rect(x* 16 , y* 16 , 16 , 16 ) run(get_screen(), get_images(), get_mario(), get_tiles()) def run (screen, images, mario, tiles) : clock = pygame.time.Clock() while all(event.type != pygame.QUIT for event in pygame.event.get()): keys = {pygame.K_UP: D.n, pygame.K_RIGHT: D.e, pygame.K_DOWN: D.s, pygame.K_LEFT: D.w} pressed = {keys.get(i) for i, on in enumerate(pygame.key.get_pressed()) if on} update_speed(mario, tiles, pressed) update_position(mario, tiles) draw(screen, images, mario, tiles, pressed) clock.tick( 28 ) def update_speed (mario, tiles, pressed) : x, y = mario.spd x += 2 * ((D.e in pressed) - (D.w in pressed)) x -= x // abs(x) if x else 0 y += 1 if D.s not in get_boundaries(mario.rect, tiles) else (D.n in pressed) * -10 mario.spd = P(*[max(-limit, min(limit, s)) for limit, s in zip(MAX_SPEED, P(x, y))]) def update_position (mario, tiles) : new_p = mario.rect.topleft larger_speed = max(abs(s) for s in mario.spd) for _ in range(larger_speed): mario.spd = stop_on_collision(mario.spd, get_boundaries(mario.rect, tiles)) new_p = P(*[a + s/larger_speed for a, s in zip(new_p, mario.spd)]) mario.rect.topleft = new_p def get_boundaries (rect, tiles) : deltas = {D.n: P( 0 , -1 ), D.e: P( 1 , 0 ), D.s: P( 0 , 1 ), D.w: P( -1 , 0 )} return {d for d, delta in deltas.items() if rect.move(delta).collidelist(tiles) != -1 } def stop_on_collision (spd, bounds) : return P(x= 0 if (D.w in bounds and spd.x < 0 ) or (D.e in bounds and spd.x > 0 ) else spd.x, y= 0 if (D.n in bounds and spd.y < 0 ) or (D.s in bounds and spd.y > 0 ) else spd.y) def draw (screen, images, mario, tiles, pressed) : def get_frame_index () : if D.s not in get_boundaries(mario.rect, tiles): return 4 return next(mario.frame_cycle) if {D.w, D.e} & pressed else 6 screen.fill(( 85 , 168 , 255 )) mario.facing_left = (D.w in pressed) if {D.w, D.e} & pressed else mario.facing_left screen.blit(images[get_frame_index() + mario.facing_left * 9 ], mario.rect) for rect in tiles: screen.blit(images[ 18 if {*rect.topleft} & { 0 , (SIZE -1 )* 16 } else 19 ], rect) pygame.display.flip() if __name__ == '__main__' : main()

#Pandas import pandas as pd from pandas import Series, DataFrame

Series Ordered dictionary with a name. Series([ 1 , 2 ], index=[ 'x' , 'y' ], name= 'a' ) x 1 y 2 Name: a, dtype: int64

<Sr> = Series(<list>) <Sr> = Series(<dict>) <Sr> = Series(<dict/Series>, index=<list>)

<el> = <Sr>.loc[key] <Sr> = <Sr>.loc[keys] <Sr> = <Sr>.loc[from_key : to_key_inclusive]

<el> = <Sr>[key/index] <Sr> = <Sr>[keys/indexes] <Sr> = <Sr>[bools]

<Sr> = <Sr> ><== <el/Sr> <Sr> = <Sr> +-*/ <el/Sr>

<Sr> = <Sr>.append(<Sr>) <Sr> = <Sr>.combine_first(<Sr>) <Sr>.update(<Sr>)

Aggregate, Transform, Map: <el> = <Sr>.sum/max/mean/idxmax/all() <Sr> = <Sr>.rank/diff/cumsum/ffill/interpl() <Sr> = <Sr>.fillna(<el>)

The way 'aggregate()' and 'transform()' find out whether the passed function accepts an element or the whole Series is by passing it a single value at first and if it raises an error, then they pass it the whole Series.

sr = Series([ 1 , 2 ], index=[ 'x' , 'y' ]) x 1 y 2

┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓ ┃ │ 'sum' │ [ 'sum' ] │ { 's' : 'sum' } ┃ ┠─────────────┼─────────────┼─────────────┼───────────────┨ ┃ sr.apply(…) │ 3 │ sum 3 │ s 3 ┃ ┃ sr.agg(…) │ │ │ ┃ ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛

┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓ ┃ │ 'rank' │ [ 'rank' ] │ { 'r' : 'rank' } ┃ ┠─────────────┼─────────────┼─────────────┼───────────────┨ ┃ sr.apply(…) │ │ rank │ ┃ ┃ sr.agg(…) │ x 1 │ x 1 │ r x 1 ┃ ┃ sr.trans(…) │ y 2 │ y 2 │ y 2 ┃ ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛

Last result has a hierarchical index. Use '<Sr>[key_1, key_2]' to get its values.

DataFrame Table with labeled rows and columns. DataFrame([[ 1 , 2 ], [ 3 , 4 ]], index=[ 'a' , 'b' ], columns=[ 'x' , 'y' ]) x y a 1 2 b 3 4

<DF> = DataFrame(<list_of_rows>) <DF> = DataFrame(<dict_of_columns>)

<el> = <DF>.loc[row_key, column_key] <Sr/DF> = <DF>.loc[row_key/s] <Sr/DF> = <DF>.loc[:, column_key/s] <DF> = <DF>.loc[row_bools, column_bools]

<Sr/DF> = <DF>[column_key/s] <DF> = <DF>[row_bools] <DF> = <DF>[<DF_of_bools>]

<DF> = <DF> ><== <el/Sr/DF> <DF> = <DF> +-*/ <el/Sr/DF>

<DF> = <DF>.set_index(column_key) <DF> = <DF>.reset_index() <DF> = <DF>.filter( '<regex>' , axis= 1 ) <DF> = <DF>.melt(id_vars=column_key/s)

Merge, Join, Concat: l = DataFrame([[ 1 , 2 ], [ 3 , 4 ]], index=[ 'a' , 'b' ], columns=[ 'x' , 'y' ]) x y a 1 2 b 3 4 r = DataFrame([[ 4 , 5 ], [ 6 , 7 ]], index=[ 'b' , 'c' ], columns=[ 'y' , 'z' ]) y z b 4 5 c 6 7

┏━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ │ 'outer' │ 'inner' │ 'left' │ description ┃ ┠────────────────────────┼───────────────┼────────────┼────────────┼──────────────────────────┨ ┃ l.merge(r, on= 'y' , │ x y z │ x y z │ x y z │ Joins/merges on column. ┃ ┃ how=…) │ 0 1 2 . │ 3 4 5 │ 1 2 . │ Also accepts left_on and ┃ ┃ │ 1 3 4 5 │ │ 3 4 5 │ right_on parameters. ┃ ┃ │ 2 . 6 7 │ │ │ Uses 'inner' by default. ┃ ┠────────────────────────┼───────────────┼────────────┼────────────┼──────────────────────────┨ ┃ l.join(r, lsuffix= 'l' , │ x yl yr z │ │ x yl yr z │ Joins/merges on row keys.┃ ┃ rsuffix= 'r' , │ a 1 2 . . │ x yl yr z │ 1 2 . . │ Uses 'left' by default. ┃ ┃ how=…) │ b 3 4 4 5 │ 3 4 4 5 │ 3 4 4 5 │ ┃ ┃ │ c . . 6 7 │ │ │ ┃ ┠────────────────────────┼───────────────┼────────────┼────────────┼──────────────────────────┨ ┃ pd.concat([l, r], │ x y z │ y │ │ Adds rows at the bottom. ┃ ┃ axis= 0 , │ a 1 2 . │ 2 │ │ Uses 'outer' by default. ┃ ┃ join=…) │ b 3 4 . │ 4 │ │ By default works the ┃ ┃ │ b . 4 5 │ 4 │ │ same as `l.append(r)`. ┃ ┃ │ c . 6 7 │ 6 │ │ ┃ ┠────────────────────────┼───────────────┼────────────┼────────────┼──────────────────────────┨ ┃ pd.concat([l, r], │ x y y z │ │ │ Adds columns at the ┃ ┃ axis= 1 , │ a 1 2 . . │ x y y z │ │ right end. ┃ ┃ join=…) │ b 3 4 4 5 │ 3 4 4 5 │ │ Uses 'outer' by default. ┃ ┃ │ c . . 6 7 │ │ │ ┃ ┠────────────────────────┼───────────────┼────────────┼────────────┼──────────────────────────┨ ┃ l.combine_first(r) │ x y z │ │ │ Adds missing rows and ┃ ┃ │ a 1 2 . │ │ │ columns. ┃ ┃ │ b 3 4 5 │ │ │ ┃ ┃ │ c . 6 7 │ │ │ ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━┛

Aggregate, Transform, Map: <Sr> = <DF>.sum/max/mean/idxmax/all() <DF> = <DF>.rank/diff/cumsum/ffill/interpl() <DF> = <DF>.fillna(<el>)

All operations operate on columns by default. Use 'axis=1' parameter to process the rows instead.

df = DataFrame([[ 1 , 2 ], [ 3 , 4 ]], index=[ 'a' , 'b' ], columns=[ 'x' , 'y' ]) x y a 1 2 b 3 4

┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓ ┃ │ 'sum' │ [ 'sum' ] │ { 'x' : 'sum' } ┃ ┠─────────────┼─────────────┼─────────────┼───────────────┨ ┃ df.apply(…) │ │ x y │ ┃ ┃ df.agg(…) │ x 4 │ sum 4 6 │ x 4 ┃ ┃ │ y 6 │ │ ┃ ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛

┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓ ┃ │ 'rank' │ [ 'rank' ] │ { 'x' : 'rank' } ┃ ┠─────────────┼─────────────┼─────────────┼───────────────┨ ┃ df.apply(…) │ x y │ x y │ x ┃ ┃ df.agg(…) │ a 1 1 │ rank rank │ a 1 ┃ ┃ df.trans(…) │ b 2 2 │ a 1 1 │ b 2 ┃ ┃ │ │ b 2 2 │ ┃ ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛

Use '<DF>[col_key_1, col_key_2][row_key]' to get the fifth result's values.

Encode, Decode: <DF> = pd.read_json/html( '<str/path/url>' ) <DF> = pd.read_csv/pickle/excel( '<path/url>' ) <DF> = pd.read_sql( '<query>' , <connection>) <DF> = pd.read_clipboard()

<dict> = <DF>.to_dict([ 'd/l/s/sp/r/i' ]) <str> = <DF>.to_json/html/csv/markdown/latex([<path>]) <DF>.to_pickle/excel(<path>) <DF>.to_sql( '<table_name>' , <connection>)

GroupBy Object that groups together rows of a dataframe based on the value of the passed column. df = DataFrame([[ 1 , 2 , 3 ], [ 4 , 5 , 6 ], [ 7 , 8 , 6 ]], index=list( 'abc' ), columns=list( 'xyz' )) df.groupby( 'z' ).get_group( 3 ) x y a 1 2 df.groupby( 'z' ).get_group( 6 ) x y b 4 5 c 7 8

<GB> = <DF>.groupby(column_key/s) <DF> = <GB>.get_group(group_key)

Aggregate, Transform, Map: <DF> = <GB>.sum/max/mean/idxmax/all() <DF> = <GB>.rank/diff/cumsum/ffill() <DF> = <GB>.fillna(<el>)

gb = df.groupby( 'z' ) x y z 3 : a 1 2 3 6 : b 4 5 6 c 7 8 6

┏━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┓ ┃ │ 'sum' │ 'rank' │ [ 'rank' ] │ { 'x' : 'rank' } ┃ ┠─────────────┼─────────────┼─────────────┼─────────────┼───────────────┨ ┃ gb.agg(…) │ x y │ x y │ x y │ x ┃ ┃ │ z │ a 1 1 │ rank rank │ a 1 ┃ ┃ │ 3 1 2 │ b 1 1 │ a 1 1 │ b 1 ┃ ┃ │ 6 11 13 │ c 2 2 │ b 1 1 │ c 2 ┃ ┃ │ │ │ c 2 2 │ ┃ ┠─────────────┼─────────────┼─────────────┼─────────────┼───────────────┨ ┃ gb.trans(…) │ x y │ x y │ │ ┃ ┃ │ a 1 2 │ a 1 1 │ │ ┃ ┃ │ b 11 13 │ b 1 1 │ │ ┃ ┃ │ c 11 13 │ c 1 1 │ │ ┃ ┗━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┛

Rolling Object for rolling window calculations. <R_Sr/R_DF/R_GB> = <Sr/DF/GB>.rolling(window_size) <R_Sr/R_DF> = <R_DF/R_GB>[column_key/s] <Sr/DF/DF> = <R_Sr/R_DF/R_GB>.sum/max/mean()

#Plotly Covid Deaths by Continent import pandas as pd import plotly.express covid = pd.read_csv( 'https://covid.ourworldindata.org/data/owid-covid-data.csv' , usecols=[ 'iso_code' , 'date' , 'total_deaths' , 'population' ]) continents = pd.read_csv( 'https://datahub.io/JohnSnowLabs/country-and-continent-codes-' + \ 'list/r/country-and-continent-codes-list-csv.csv' , usecols=[ 'Three_Letter_Country_Code' , 'Continent_Name' ]) df = pd.merge(covid, continents, left_on= 'iso_code' , right_on= 'Three_Letter_Country_Code' ) df = df.groupby([ 'Continent_Name' , 'date' ]).sum().reset_index() df[ 'Total Deaths per Million' ] = df.total_deaths * 1e6 / df.population df = df[( '2020-03-14' < df.date) & (df.date < '2020-06-25' )] df = df.rename({ 'date' : 'Date' , 'Continent_Name' : 'Continent' }, axis= 'columns' ) plotly.express.line(df, x= 'Date' , y= 'Total Deaths per Million' , color= 'Continent' ).show()

Confirmed Covid Cases, Dow Jones, Gold, and Bitcoin Price import pandas as pd import plotly.graph_objects as go import datetime def main () : data = scrape_data() df = wrangle_data(*data) display_data(df) def scrape_data () : def scrape_yahoo (id_) : BASE_URL = 'https://query1.finance.yahoo.com/v7/finance/download/' now = int(datetime.datetime.now().timestamp()) url = f' {BASE_URL} {id_} ?period1=1579651200&period2= {now} &interval=1d&events=history' return pd.read_csv(url, usecols=[ 'Date' , 'Close' ]).set_index( 'Date' ).Close covid = pd.read_csv( 'https://covid.ourworldindata.org/data/owid-covid-data.csv' , usecols=[ 'date' , 'total_cases' ]) covid = covid.groupby( 'date' ).sum() dow, gold, bitcoin = [scrape_yahoo(id_) for id_ in ( '^DJI' , 'GC=F' , 'BTC-USD' )] dow.name, gold.name, bitcoin.name = 'Dow Jones' , 'Gold' , 'Bitcoin' return covid, dow, gold, bitcoin def wrangle_data (covid, dow, gold, bitcoin) : df = pd.concat([dow, gold, bitcoin], axis= 1 ) df = df.sort_index().interpolate() df = df.rolling( 10 , min_periods= 1 , center= True ).mean() df = df.loc[ '2020-02-23' :].iloc[: -2 ] df = df / df.iloc[ 0 ] * 100 return pd.concat([covid, df], axis= 1 , join= 'inner' ) def display_data (df) : def get_trace (col_name) : return go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis= 'y2' ) traces = [get_trace(col_name) for col_name in df.columns[ 1 :]] traces.append(go.Scatter(x=df.index, y=df.total_cases, name= 'Total Cases' , yaxis= 'y1' )) figure = go.Figure() figure.add_traces(traces) figure.update_layout( yaxis1=dict(title= 'Total Cases' , rangemode= 'tozero' ), yaxis2=dict(title= '%' , rangemode= 'tozero' , overlaying= 'y' , side= 'right' ), legend=dict(x= 1.1 ) ).show() if __name__ == '__main__' : main()

#Cython Library that compiles Python code into C. import pyximport; pyximport.install() import <cython_script> <cython_script>.main()

Definitions All 'cdef' definitions are optional, but they contribute to the speed-up.

Script needs to be saved with a 'pyx' extension. cdef <type> <var_name> = <el> cdef <type>[n_elements] <var_name> = [<el_1>, <el_2>, ...] cdef <type/void> <func_name>(<type> <arg_name_1>, ...):

cdef class < class_name >: cdef public <type> <attr_name> def __init__ (self, <type> <arg_name>) : self.<attr_name> = <arg_name>

cdef enum <enum_name>: <member_name_1>, <member_name_2>, ...

#Appendix PyInstaller $ pip3 install pyinstaller $ pyinstaller script.py $ pyinstaller script.py --onefile $ pyinstaller script.py --windowed $ pyinstaller script.py --add-data '<path>:.'

File paths need to be updated to 'os.path.join(sys._MEIPASS, <path>)' .

Basic Script Template from collections import namedtuple from dataclasses import make_dataclass from enum import Enum from sys import argv, exit import re def main () : pass def read_file (filename) : with open(filename, encoding= 'utf-8' ) as file: return file.readlines() if __name__ == '__main__' : main()