Comprehensive Python Cheatsheet

#Contents

ToC = {
    '1. Collections': [List, Dictionary, Set, Tuple, Range, Enumerate, Iterator, Generator],
    '2. Types':       [Type, String, Regular_Exp, Format, Numbers, Combinatorics, Datetime],
    '3. Syntax':      [Args, Inline, Closure, Decorator, Class, Duck_Types, Enum, Exceptions],
    '4. System':      [Print, Input, Command_Line_Arguments, Open, Path, Command_Execution],
    '5. Data':        [JSON, Pickle, CSV, SQLite, Bytes, Struct, Array, MemoryView, Deque],
    '6. Advanced':    [Threading, Operator, Introspection, Metaprograming, Eval, Coroutine],
    '7. Libraries':   [Progress_Bar, Plot, Table, Curses, Logging, Scraping, Web, Profile,
                       NumPy, Image, Animation, Audio, Synthesizer]
}

#Main

if __name__ == '__main__':     # Runs main() if file wasn't imported.
    main()

#List

<list> = <list>[from_inclusive : to_exclusive : ±step_size]

<list>.append(<el>)            # Or: <list> += [<el>]
<list>.extend(<collection>)    # Or: <list> += <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, x: out * x, <collection>)
list_of_chars    = list(<str>)

<int> = <list>.count(<el>)     # Returns number of occurrences. Also works on strings.
index = <list>.index(<el>)     # Returns index of first occurrence or raises ValueError.
<list>.insert(index, <el>)     # Inserts item at index and moves the rest to the right.
<el> = <list>.pop([index])     # Removes and returns item at index or from the end.
<list>.remove(<el>)            # Removes first occurrence of item or raises ValueError.
<list>.clear()                 # Removes all items. Also works on dictionary and set.

#Dictionary

<view> = <dict>.keys()                          # Coll. of keys that reflects changes.
<view> = <dict>.values()                        # Coll. of values that reflects changes.
<view> = <dict>.items()                         # Coll. of key-value tuples.

value  = <dict>.get(key, default=None)          # Returns default if key is missing.
value  = <dict>.setdefault(key, default=None)   # Returns and writes default if key is missing.
<dict> = collections.defaultdict(<type>)        # Creates a dict with default value of type.
<dict> = collections.defaultdict(lambda: 1)     # Creates a dict with default value 1.

<dict> = dict(<collection>)                     # Creates a dict from coll. of key-value pairs.
<dict> = dict(zip(keys, values))                # Creates a dict from two collections.
<dict> = dict.fromkeys(keys [, value])          # Creates a dict from collection of keys.

<dict>.update(<dict>)                           # Adds items. Replaces ones with matching keys.
value = <dict>.pop(key)                         # Removes item or raises KeyError.
{k for k, v in <dict>.items() if v == value}    # Returns set of keys that point to the value.
{k: v for k, v in <dict>.items() if k in keys}  # Returns dictionary filtered by keys.

Counter

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

#Set

<set> = set()

<set>.add(<el>)                                 # Or: <set> |= {<el>}
<set>.update(<collection>)                      # Or: <set> |= <set>

<set>  = <set>.union(<coll.>)                   # Or: <set> | <set>
<set>  = <set>.intersection(<coll.>)            # Or: <set> & <set>
<set>  = <set>.difference(<coll.>)              # Or: <set> - <set>
<set>  = <set>.symmetric_difference(<coll.>)    # Or: <set> ^ <set>
<bool> = <set>.issubset(<coll.>)                # Or: <set> <= <set>
<bool> = <set>.issuperset(<coll.>)              # Or: <set> >= <set>

<el> = <set>.pop()                              # Raises KeyError if empty.
<set>.remove(<el>)                              # Raises KeyError if missing.
<set>.discard(<el>)                             # Doesn't raise an error.

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  # Or: Point._fields
('x', 'y')

#Range

<range> = range(to_exclusive)
<range> = range(from_inclusive, to_exclusive)
<range> = range(from_inclusive, to_exclusive, ±step_size)

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

#Enumerate

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

#Iterator

<iter> = iter(<collection>)                 # `iter(<iter>)` returns unmodified iterator.
<iter> = iter(<function>, to_exclusive)     # A sequence of return values until 'to_exclusive'.
<el>   = next(<iter> [, default])           # Raises StopIteration or returns 'default' on end.
<list> = list(<iter>)                       # Returns a list of iterator's remaining elements.

Itertools

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

<iter> = count(start=0, step=1)             # Returns incremented value endlessly.
<iter> = repeat(<el> [, times])             # Returns element endlessly or 'times' times.
<iter> = cycle(<collection>)                # Repeats the sequence endlessly.

<iter> = chain(<coll_1>, <coll_2> [, ...])  # Empties collections in order.
<iter> = chain.from_iterable(<collection>)  # Empties collections inside a collection in order.

<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>)                # Or: <el>.__class__
<bool> = isinstance(<el>, <type>)  # Or: issubclass(type(<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

ABC

An abstract base class introduces virtual subclasses, that don’t inherit from it but are still recognized by isinstance() and issubclass().

>>> from collections.abc import Sequence, Collection, Iterable
>>> isinstance([1, 2, 3], Iterable)
True

+------------------+------------+------------+------------+
|                  |  Sequence  | Collection |  Iterable  |
+------------------+------------+------------+------------+
| list, range, str |    yes     |    yes     |    yes     |
| dict, set        |            |    yes     |    yes     |
| iter             |            |            |    yes     |
+------------------+------------+------------+------------+

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

+--------------------+----------+----------+----------+----------+----------+
|                    | Integral | Rational |   Real   | Complex  |  Number  |
+--------------------+----------+----------+----------+----------+----------+
| int                |   yes    |   yes    |   yes    |   yes    |   yes    |
| fractions.Fraction |          |   yes    |   yes    |   yes    |   yes    |
| float              |          |          |   yes    |   yes    |   yes    |
| complex            |          |          |          |   yes    |   yes    |
| decimal.Decimal    |          |          |          |          |   yes    |
+--------------------+----------+----------+----------+----------+----------+

#String

<str>  = <str>.strip()                       # Strips all whitespace characters from both ends.
<str>  = <str>.strip('<chars>')              # Strips all passed characters from both ends.

<list> = <str>.split()                       # Splits on one or more whitespace characters.
<list> = <str>.split(sep=None, maxsplit=-1)  # Splits on 'sep' str at most 'maxsplit' times.
<list> = <str>.splitlines(keepends=False)    # Splits on line breaks. Keeps them if 'keepends'.
<str>  = <str>.join(<coll_of_strings>)       # Joins elements using string as separator.

<bool> = <sub_str> in <str>                  # Checks if string contains a substring.
<bool> = <str>.startswith(<sub_str>)         # Pass tuple of strings for multiple options.
<bool> = <str>.endswith(<sub_str>)           # Pass tuple of strings for multiple options.
<int>  = <str>.find(<sub_str>)               # Returns start index of first match or -1.
<int>  = <str>.index(<sub_str>)              # Same but raises ValueError if missing.

<str>  = <str>.replace(old, new [, count])   # Replaces 'old' with 'new' at most 'count' times.
<str>  = <str>.translate(<table>)            # Use `str.maketrans(<dict>)` to generate table.

<str>  = chr(<int>)                          # Converts int to unicode char.
<int>  = ord(<str>)                          # Converts unicode char to int.

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

Property Methods

+---------------+----------+----------+----------+----------+----------+
|               | [ !#$%…] | [a-zA-Z] |  [¼½¾…]  |  [²³¹…]  |  [0-9]   |
+---------------+----------+----------+----------+----------+----------+
| isprintable() |   yes    |   yes    |   yes    |   yes    |   yes    |
| isalnum()     |          |   yes    |   yes    |   yes    |   yes    |
| isnumeric()   |          |          |   yes    |   yes    |   yes    |
| isdigit()     |          |          |          |   yes    |   yes    |
| isdecimal()   |          |          |          |          |   yes    |
+---------------+----------+----------+----------+----------+----------+

Also: 'isspace()' checks for '[ \t\n\r…]'.

#Regex

import re
<str>   = re.sub(<regex>, new, text, count=0)  # Substitutes all occurrences.
<list>  = re.findall(<regex>, text)            # Returns all occurrences.
<list>  = re.split(<regex>, text, maxsplit=0)  # Use brackets in regex to keep the matches.
<Match> = re.search(<regex>, text)             # Searches for first occurrence of pattern.
<Match> = re.match(<regex>, text)              # Searches only at the beginning of the text.
<iter>  = re.finditer(<regex>, text)           # Returns all occurrences as match objects.

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 newline.
Use r'\1' or '\\1' for backreference.
Add '?' after an operator to make it non-greedy.

Match Object

<str>   = <Match>.group()                      # Whole match. Also group(0).
<str>   = <Match>.group(1)                     # Part in first bracket.
<tuple> = <Match>.groups()                     # All bracketed parts.
<int>   = <Match>.start()                      # Start index of a match.
<int>   = <Match>.end()                        # Exclusive end index of a match.

Special Sequences

By default digits, whitespaces and alphanumerics from all alphabets are matched, unless 'flags=re.ASCII' argument is used.
Use capital letter for negation.

'\d' == '[0-9]'                                # Digit
'\s' == '[ \t\n\r\f\v]'                        # Whitespace
'\w' == '[a-zA-Z0-9_]'                         # Alphanumeric

#Format

<str> = f'{<el_1>}, {<el_2>}'
<str> = '{}, {}'.format(<el_1>, <el_2>)

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>      '
{<el>:^10}                                     # '   <el>   '
{<el>:>10}                                     # '      <el>'

{<el>:.<10}                                    # '<el>......'
{<el>:<0}                                      # '<el>'

Strings

'!r' calls object's repr() method, instead of str(), to get a string.

{'abcde'!r:<10}                                # "'abcde'   "
{'abcde':.3}                                   # 'abc'
{'abcde':10.3}                                 # 'abc       '

Numbers

{ 123456:10,}                                  # '   123,456'
{ 123456:10_}                                  # '   123_456'
{ 123456:+10}                                  # '   +123456'
{-123456:=10}                                  # '-   123456'
{ 123456: }                                    # ' 123456'
{-123456: }                                    # '-123456'

Floats

{1.23456:10.3}                                 # '      1.23'
{1.23456:10.3f}                                # '     1.235'
{1.23456:10.3e}                                # ' 1.235e+00'
{1.23456:10.3%}                                # '  123.456%'

Comparison of float 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}                                # 'Z'
{90:X}                                # '5A'
{90:b}                                # '1011010'

#Numbers

Types

<int>      = int(<float/str/bool>)    # Or: math.floor(<float>)
<float>    = float(<int/str/bool>)    # Or: <real>±e<int>
<complex>  = complex(real=0, imag=0)  # Or: <real> + <real>j
<Fraction> = fractions.Fraction(numerator=0, denominator=1)
<Decimal>  = decimal.Decimal(<str/int/float>)

'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>)             # Or: <num> ** <num>
<num> = abs(<num>)                    # <float> = abs(<complex>)
<num> = round(<num> [, ±ndigits])     # `round(126, -1) == 130`

Math

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

Statistics

from statistics import mean, median, variance, 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>     = 0b<bin>                   # Or: 0x<hex>
<int>     = int('<bin>', 2)           # Or: int('<hex>', 16)
<int>     = int('0b<bin>', 0)         # Or: int('0x<hex>', 0)
'0b<bin>' = bin(<int>)                # Or: '0x<hex>' = hex(<int>)

Bitwise Operators

<int>     = <int> & <int>             # And
<int>     = <int> | <int>             # Or
<int>     = <int> ^ <int>             # Xor (0 if both bits equal)
<int>     = <int> << n_bits           # Shift left
<int>     = <int> >> n_bits           # Shift right
<int>     = ~<int>                    # Compliment (flips bits)

#Combinatorics

Every function returns an iterator.
If you want to print the iterator, you need to pass it to the list() function!

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')]

#Datetime

Module 'datetime' provides 'date' <D>, 'time' <T>, 'datetime' <DT> and 'timedelta' <TD> classes. All are immutable and hashable.
Time and datetime 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 second pass in case of time jumping back for one hour.
'<DTa> = resolve_imaginary(<DTa>)' fixes DTs that fall into missing hour.

Now

<D/DTn>  = D/DT.today()                     # Current local date or naive datetime.
<DTn>    = DT.utcnow()                      # Naive datetime from current UTC time.
<DTa>    = DT.now(<tzinfo>)                 # Aware datetime from current tz time.

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

Timezone

<tzinfo> = UTC                              # UTC timezone. London without DST.
<tzinfo> = tzlocal()                        # Local timezone. Also gettz().
<tzinfo> = gettz('<Cont.>/<City>')          # 'Continent/City_Name' timezone or None.

<DTa>    = <DT>.astimezone(<tzinfo>)        # Datetime, converted to passed timezone.
<Ta/DTa> = <T/DT>.replace(tzinfo=<tzinfo>)  # Unconverted object with new timezone.

Encode

<D/T/DT> = D/T/DT.fromisoformat('<iso>')    # Object from ISO string. Raises ValueError.
<DT>     = DT.strptime(<str>, '<format>')   # Datetime from str, according to format.
<D/DTn>  = D/DT.fromordinal(<int>)          # D/DTn from days since Christ, at midnight.
<DTn>    = DT.fromtimestamp(<real>)         # Local time DTn from seconds since Epoch.
<DTa>    = DT.fromtimestamp(<real>, <tz.>)  # Aware datetime from seconds since Epoch.

ISO strings come in following forms: 'YYYY-MM-DD', 'HH:MM:SS.ffffff[±<offset>]', or both separated by a space or a 'T'. Offset is formatted as: 'HH:MM'.
On Unix systems Epoch is '1970-01-01 00:00 UTC', '1970-01-01 01:00 CET', …

Decode

<str>    = <D/T/DT>.isoformat()             # ISO string representation.
<str>    = <D/T/DT>.strftime('<format>')    # Custom string representation.
<int>    = <D/DT>.toordinal()               # Days since Christ, ignoring time and tz.
<float>  = <DTn>.timestamp()                # Seconds since Epoch from DTn in local time.
<float>  = <DTa>.timestamp()                # Seconds since Epoch from DTa.

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>                    # Result can fall into missing hour.
<TD>     = <D/DT> - <D/DT>                  # Returns the difference, ignoring time jumps.
<TD>     = <DT_UTC> - <DT_UTC>              # Convert DTs to UTC to get the actual delta.

#Arguments

Inside Function Call

<function>(<positional_args>)                  # f(0, 0)
<function>(<keyword_args>)                     # f(x=0, y=0)
<function>(<positional_args>, <keyword_args>)  # f(0, y=0)

Inside Function Definition

def f(<nondefault_args>):                      # def f(x, y):
def f(<default_args>):                         # def f(x=0, y=0):
def f(<nondefault_args>, <default_args>):      # def f(x, y=0):

#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):                # f(x=1, y=2, z=3) | f(1, y=2, z=3) | f(1, 2, z=3) | f(1, 2, 3)
def f(*, x, y, z):             # f(x=1, y=2, z=3)
def f(x, *, y, z):             # f(x=1, y=2, z=3) | f(1, y=2, z=3)
def f(x, y, *, z):             # f(x=1, y=2, z=3) | f(1, y=2, z=3) | f(1, 2, z=3)

def f(*args):                  # f(1, 2, 3)
def f(x, *args):               # f(1, 2, 3)
def f(*args, z):               # f(1, 2, z=3)
def f(x, *args, z):            # f(1, 2, z=3)

def f(**kwargs):               # f(x=1, y=2, z=3)
def f(x, **kwargs):            # f(x=1, y=2, z=3) | f(1, y=2, z=3)
def f(*, x, **kwargs):         # f(x=1, y=2, z=3)

def f(*args, **kwargs):        # f(x=1, y=2, z=3) | f(1, y=2, z=3) | f(1, 2, z=3) | f(1, 2, 3)
def f(x, *args, **kwargs):     # f(x=1, y=2, z=3) | f(1, y=2, z=3) | f(1, 2, z=3) | f(1, 2, 3)
def f(*args, y, **kwargs):     # f(x=1, y=2, z=3) | f(1, y=2, z=3)
def f(x, *args, z, **kwargs):  # f(x=1, y=2, z=3) | f(1, y=2, z=3) | f(1, 2, z=3)

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>

Comprehension

<list> = [i+1 for i in range(10)]                   # [1, 2, ..., 10]
<set>  = {i for i in range(10) if i > 5}            # {6, 7, 8, 9}
<iter> = (i+5 for i in range(10))                   # (5, 6, ..., 14)
<dict> = {i: i*2 for i in range(10)}                # {0: 0, 1: 2, ..., 9: 18}

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))            # (1, 2, ..., 10)
<iter> = filter(lambda x: x > 5, range(10))         # (6, 7, 8, 9)
<obj>  = reduce(lambda out, x: out + x, range(10))  # 45

Any, All

<bool> = any(<collection>)                          # False if empty.
<bool> = all(el[1] for el in <collection>)          # True if empty.

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>)'.

Nonlocal

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.

@decorator_name
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):
    @wraps(func)
    def out(*args, **kwargs):
        print(func.__name__)
        return func(*args, **kwargs)
    return out

@debug
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

@lru_cache(maxsize=None)
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):
        @wraps(func)
        def out(*args, **kwargs):
            result = func(*args, **kwargs)
            print(func.__name__, result if print_result else '')
            return result
        return out
    return decorator

@debug(print_result=True)
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)

    @classmethod
    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:
    @property
    def a(self):
        return self._a

    @a.setter
    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

@dataclass(order=False, frozen=False)
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 = copy.deepcopy(a)
    @property
    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

@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 defines 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>' for 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 return an object.
Exit() should release the resources.

class MyOpen():
    def __init__(self, filename):
        self.filename = filename
    def __enter__(self):
        self.file = open(self.filename)
        return self.file
    def __exit__(self, *args):
        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):
        for el in self.a:
            yield el

>>> z = MyIterable([1, 2, 3])
>>> iter(z)
<generator object MyIterable.__iter__>
>>> 1 in z
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)

Collections.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, collections.abc.Sequence)' would return False even if MySequence had all the methods defined.

class MyAbcSequence(collections.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()     |    REQ     |    REQ     |    yes     |     yes      |
| contains() |    yes     |    yes     |    yes     |     yes      |
| len()      |            |    REQ     |    REQ     |     REQ      |
| getitem()  |            |            |    REQ     |     REQ      |
| reversed() |            |            |    yes     |     yes      |
| index()    |            |            |            |     yes      |
| count()    |            |            |            |     yes      |
+------------+------------+------------+------------+--------------+

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>                 # Returns a member.
<member> = <enum>['<member_name>']              # Returns a member or raises KeyError.
<member> = <enum>(<value>)                      # Returns a member or raises ValueError.
name     = <member>.name
value    = <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})

Functions can not 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 'and_' and 'or_' functions from 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>

Catching Exceptions

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

Also catches subclasses of the exception.

Raising Exceptions

raise <exception>
raise <exception>()
raise <exception>(<el>)
raise <exception>(<el>, ...)

Re-raising caught exception:

except <exception>:
    <code>
    raise

Useful built-in exceptions:

raise ValueError('Argument is of right type but inappropriate value!')
raise TypeError('Argument is of wrong type!')
raise RuntimeError('None of above!')

Common Built-in Exceptions

BaseException
 +-- SystemExit                   # Raised by the sys.exit() function.
 +-- KeyboardInterrupt            # Raised when the user hits the interrupt key (ctrl-c).
 +-- Exception                    # User-defined exceptions should be derived from this class.
      +-- StopIteration           # Raised by next() when run on an empty iterator.
      +-- ArithmeticError         # Base class for arithmetic errors.
      |    +-- ZeroDivisionError  # Raised when dividing by zero.
      +-- AttributeError          # Raised when an attribute is missing.
      +-- EOFError                # Raised by input() when it hits end-of-file condition.
      +-- LookupError             # Raised when a look-up on a sequence or dict fails.
      |    +-- IndexError         # Raised when a sequence index is out of range.
      |    +-- KeyError           # Raised when a dictionary key is not found.
      +-- NameError               # Raised when a variable name is not found.
      +-- OSError                 # Failures such as “file not found” or “disk full”.
      |    +-- FileNotFoundError  # When a file or directory is requested but doesn't exist.
      +-- RuntimeError            # Raised by errors that don't fall in other categories.
      |    +-- RecursionError     # Raised when the the maximum recursion depth is exceeded.
      +-- TypeError               # Raised when an argument is of wrong type.
      +-- ValueError              # When an argument is of right type but inappropriate value.
           +-- UnicodeError       # Raised when encoding/decoding strings from/to bytes fails.

Collections and their exceptions:

+-----------+------------+------------+------------+
|           |    list    |    dict    |    set     |
+-----------+------------+------------+------------+
| getitem() | IndexError |  KeyError  |            |
| pop()     | IndexError |  KeyError  |  KeyError  |
| remove()  | ValueError |            |  KeyError  |
| index()   | ValueError |            |            |
+-----------+------------+------------+------------+

User-defined Exceptions

class MyError(Exception):
    pass

class MyInputError(MyError):
    pass

#Print

print(<el_1>, ..., sep=' ', end='\n', file=sys.stdout, flush=False)

Use 'file=sys.stderr' for errors.
Use 'flush=True' to forcibly flush the stream.

Pretty Print

from pprint import pprint
pprint(<collection>, width=80, depth=None)

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 or input stream gets exhausted.

#Command Line Arguments

import sys
script_name = sys.argv[0]
arguments   = sys.argv[1:]

Argparse

from argparse import ArgumentParser, FileType
p = ArgumentParser(description=<str>)
p.add_argument('-<short_name>', '--<name>', action='store_true')  # Flag
p.add_argument('-<short_name>', '--<name>', type=<type>)          # Option
p.add_argument('<name>', type=<type>, nargs=1)                    # First argument
p.add_argument('<name>', type=<type>, nargs='+')                  # Remaining arguments
p.add_argument('<name>', type=<type>, nargs='*')                  # Optional arguments
args  = p.parse_args()                                            # Exits on error.
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 '\n' on read, while on write all '\n' 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 '\n', '\r' or '\r\n'.

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 risen when reading with 'r' or 'r+'.
'FileExistsError' can be risen when writing with 'x'.
'IsADirectoryError' and 'PermissionError' can be risen by any.
'OSError' is the parent class of all listed exceptions.

File Object

<file>.seek(0)                      # Moves to the start of the file.
<file>.seek(offset)                 # Moves 'offset' chars/bytes from the start.
<file>.seek(0, 2)                   # Moves to the end of the file.
<bin_file>.seek(±offset, <anchor>)  # Anchor: 0 start, 1 current pos., 2 end.

<str/bytes> = <file>.read(size=-1)  # Reads 'size' chars/bytes or until EOF.
<str/bytes> = <file>.readline()     # Returns a line or empty string on EOF.
<list>      = <file>.readlines()    # Returns a list of remaining lines.
<str/bytes> = next(<file>)          # Returns a line using buffer. Do not mix.

<file>.write(<str/bytes>)           # Writes a string or bytes object.
<file>.writelines(<collection>)     # Writes a coll. of strings or bytes objects.
<file>.flush()                      # Flushes write buffer.

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()                   # Returns the current working directory.

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

<list> = listdir('<path>')          # List of filenames located at path.
<list> = glob('<pattern>')          # Filenames matching the wildcard pattern.

Pathlib

from pathlib import Path

cwd    = Path()
<Path> = Path('<path>' [, '<path>', <Path>, ...])
<Path> = <Path> / '<dir>' / '<file>'

<bool> = <Path>.exists()
<bool> = <Path>.is_file()
<bool> = <Path>.is_dir()

<iter> = <Path>.iterdir()           # Returns dir contents as Path objects.
<iter> = <Path>.glob('<pattern>')   # Returns Paths matching the wildcard pattern.

<str>  = str(<Path>)                # Path as a string.
<str>  = <Path>.name                # Final component.
<str>  = <Path>.stem                # Final component without extension.
<str>  = <Path>.suffix              # Final component's extension.
<tup.> = <Path>.parts               # All components as strings.

<Path> = <Path>.resolve()           # Returns absolute path without symlinks.
<Path> = <Path>.parent              # Returns path without final component.
<file> = open(<Path>)               # Opens the file and returns a file object.

#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>)                    # Changes current working directory.
os.mkdir(<path>, mode=0o777)        # Creates a directory. Mode is in octal.

shutil.copy(from, to)               # Copies the file.
shutil.copytree(from, to)           # Copies the entire directory tree.

os.rename(from, to)                 # Renames the file or directory.
os.replace(from, to)                # Same, but overwrites 'to' if it exists.

os.remove(<path>)                   # Deletes the file.
os.rmdir(<path>)                    # Deletes empty directory.
shutil.rmtree(<path>)               # Deletes the entire directory tree.

<iter> = os.scandir(path='.')       # Returns os.DirEntry objects located at path.

DirEntry:

<bool> = <DirEntry>.is_file()
<bool> = <DirEntry>.is_dir()

<str>  = <DirEntry>.path            # Path as a string.
<str>  = <DirEntry>.name            # Final component.

<Path> = Path(<DirEntry>)           # Path object.
<file> = open(<DirEntry>)           # File object.

Shell Commands

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

Using subprocess:

>>> import subprocess, shlex
>>> a = subprocess.run(shlex.split('ls -a'), stdout=subprocess.PIPE)
>>> a.stdout
b'.\n..\nfile1.txt\nfile2.txt\n'
>>> a.returncode
0

#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>, dialect='excel', delimiter=',')
<list>   = next(<reader>)           # Returns next row as a list of strings.
<list>   = list(<reader>)           # Returns list of remaining rows.

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

Write

<writer> = csv.writer(<file>, dialect='excel', delimiter=',')
<writer>.writerow(<collection>)     # Encodes objects using `str(<el>)`.
<writer>.writerows(<coll_of_coll>)  # Appends multiple rows.

File must be opened with 'newline=""' argument, or an extra '\r' will be added on platforms that use '\r\n' linendings!

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' - How does writer terminate lines.
'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_dialect |
+------------------+--------------+--------------+--------------+
| delimiter        |        ','   |       '\t'   |       ','    |
| quotechar        |        '"'   |        '"'   |       '"'    |
| doublequote      |       True   |       True   |      True    |
| skipinitialspace |      False   |      False   |     False    |
| lineterminator   |     '\r\n'   |     '\r\n'   |      '\n'    |
| 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 separate file.

Connect

Opens a connection to the database file. Creates a new file if path doesn't exist.

import sqlite3
db = sqlite3.connect('<path>')                  # Also ':memory:'.
...
db.close()

Read

Returned values can be of type str, int, float, bytes or None.

<cursor> = db.execute('<query>')                # Can raise sqlite3.OperationalError.
<tuple>  = <cursor>.fetchone()                  # Returns next row. Also next(<cursor>).
<list>   = <cursor>.fetchall()                  # Returns remaining rows.

Write

db.execute('<query>')
db.commit()

Or:

with db:
    db.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.

db.execute('<query>', <list/tuple>)             # Replaces '?'s in query with values.
db.execute('<query>', <dict/namedtuple>)        # Replaces ':<key>'s with values.
db.executemany('<query>', <coll_of_above>)      # Runs execute() many times.

Example

In this example values are not actually saved because 'db.commit()' is omitted!

>>> db = sqlite3.connect('test.db')
>>> db.execute('create table t (a, b, c)')
>>> db.execute('insert into t values (1, 2, 3)')
>>> db.execute('select * from t').fetchall()
[(1, 2, 3)]

MySQL

Has a very similar interface, with differences listed below.

# $ pip3 install mysql-connector
from mysql import connector
db = connector.connect(host=<str>, user=<str>, password=<str>, database=<str>)
<cursor> = db.cursor()
<cursor>.execute('<query>')                     # Only cursor has execute method.
<cursor>.execute('<query>', <list/tuple>)       # Replaces '%s's in query with values.
<cursor>.execute('<query>', <dict/namedtuple>)  # Replaces '%(<key>)s's with values.

#Bytes

Bytes object is an immutable sequence of single bytes. Mutable version is called bytearray.

<bytes> = b'<str>'                       # Only accepts ASCII characters and \x00 - \xff.
<int>   = <bytes>[<index>]               # Returns int in range from 0 to 255.
<bytes> = <bytes>[<slice>]               # Returns bytes even if it has only one element.
<bytes> = <bytes>.join(<coll_of_bytes>)  # Joins elements using bytes object as separator.

Encode

<bytes> = bytes(<coll_of_ints>)          # Ints must be in range from 0 to 255.
<bytes> = bytes(<str>, 'utf-8')          # Or: <str>.encode('utf-8')
<bytes> = <int>.to_bytes(n_bytes, byteorder='big|little', signed=False)
<bytes> = bytes.fromhex('<hex>')

Decode

<list>  = list(<bytes>)                  # Returns ints in range from 0 to 255.
<str>   = str(<bytes>, 'utf-8')          # Or: <bytes>.decode('utf-8')
<int>   = int.from_bytes(<bytes>, byteorder='big|little', signed=False)
'<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 sizes start format string with:

'=' - native byte order
'<' - little-endian
'>' - big-endian (also '!')

Integer types. Use 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 from coll. of numbers.
<array> = array('<typecode>', <bytes>)         # Array from bytes object.
<bytes> = bytes(<array>)                       # Or: <array>.tobytes()

#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>)  # Immutable if bytes, else mutable.
<real>  = <mview>[<index>]                     # Returns an int or a float.
<mview> = <mview>[<slice>]                     # Mview with rearranged elements.
<mview> = <mview>.cast('<typecode>')           # Casts memoryview to the new format.
<mview>.release()                              # Releases the object's memory buffer.

Decode

<bin_file>.write(<mview>)                      # Appends mview to the binary file.
<bytes> = bytes(<mview>)                       # Creates a new bytes object.
<bytes> = <bytes>.join(<coll_of_mviews>)       # Joins mviews using bytes object as sep.
<list>  = list(<mview>)                        # Returns list of ints or floats.
<str>   = str(<mview>, 'utf-8')
<int>   = int.from_bytes(<mview>, byteorder='big|little', signed=False)
'<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>)                       # Opposite element is dropped if full.
<el> = <deque>.popleft()                       # Raises IndexError if empty.
<deque>.extendleft(<collection>)               # Collection gets reversed.
<deque>.rotate(n=1)                            # Rotates elements to the right.

#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 there is an I/O operation in the thread.

from threading import Thread, RLock

Thread

thread = Thread(target=<function>, args=(<first_arg>, ))
thread.start()
...
<bool> = thread.is_alive()           # Checks if thread has finished executing.
thread.join()                        # Waits for thread to finish.

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()                       # Waits for lock to be available.
...
lock.release()

Or:

lock = RLock()
with lock:
    ...

Thread Pool Executor

from concurrent.futures import ThreadPoolExecutor
with ThreadPoolExecutor(max_workers=None) as executor:
    <iter>   = executor.map(lambda x: x + 1, range(3))         # (1, 2, 3)
    <iter>   = executor.map(lambda x, y: x + y, 'abc', '123')  # ('a1', 'b2', 'c3')
    <Future> = executor.submit(<function> [, <arg_1>, ...])

Future:

<bool> = <Future>.done()             # Checks if thread has finished executing.
<obj>  = <Future>.result()           # Waits for thread to finish and returns result.

Queue

A thread-safe FIFO queue. For LIFO queue use LifoQueue.

from queue import Queue
<Queue> = Queue(maxsize=0)

<Queue>.put(<el>)                    # Blocks until queue stops being full.
<Queue>.put_nowait(<el>)             # Raises queue.Full exception if full.
<el> = <Queue>.get()                 # Blocks until queue stops being empty.
<el> = <Queue>.get_nowait()          # Raises _queue.Empty exception if empty.

#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
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()      # Names of variables in current scope.
<dict> = locals()   # Dict of local variables. Also vars().
<dict> = globals()  # Dict of global variables.

Attributes

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

Parameters

from inspect import signature
<sig>        = signature(<function>)
no_of_params = len(<sig>.parameters)
param_names  = list(<sig>.parameters.keys())

#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

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 a '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  # MyClass is an instance of MyMetaClass.
type(MyMetaClass) == type         # MyMetaClass is an instance of type.

+-------------+-------------+
|   Classes   | Metaclasses |
+-------------+-------------|
|   MyClass --> MyMetaClass |
|             |     v       |
|    object -----> type <+  |
|             |    ^ +---+  |
|     str ---------+        |
+-------------+-------------+

Inheritance Diagram

MyClass.__base__     == object    # MyClass is a subclass of object.
MyMetaClass.__base__ == type      # MyMetaClass is a subclass of type.

+-------------+-------------+
|   Classes   | Metaclasses |
+-------------+-------------|
|   MyClass   | MyMetaClass |
|      v      |     v       |
|    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

#Coroutine

Any function that contains a '(yield)' expression returns a coroutine.
Coroutines are similar to iterators, but data needs to be pulled out of an iterator by calling 'next(<iter>)', while we push data into the coroutine by calling '<coroutine>.send(<el>)'.
Coroutines provide more powerful data routing possibilities than iterators.

Helper Decorator

All coroutines must first be "primed" by calling 'next(<coroutine>)'.
Remembering to call next() is easy to forget.
Solved by wrapping coroutine functions with the following decorator:

def coroutine(func):
    def out(*args, **kwargs):
        cr = func(*args, **kwargs)
        next(cr)
        return cr
    return out

Pipeline Example

def reader(target):
    for i in range(10):
        target.send(i)
    target.close()

@coroutine
def adder(target):
    while True:
        value = (yield)
        target.send(value + 100)

@coroutine
def printer():
    while True:
        value = (yield)
        print(value, end=' ')

>>> reader(adder(printer()))
100 101 102 103 104 105 106 107 108 109

Libraries

#Progress Bar

# $ pip3 install tqdm
from tqdm import tqdm
from time import sleep
for el in tqdm([1, 2, 3]):
    sleep(0.2)

#Plot

# $ pip3 install matplotlib
from matplotlib import pyplot
pyplot.plot(<y_data> [, label=<str>])
pyplot.plot(<x_data>, <y_data>)
pyplot.legend()                                # Adds a legend.
pyplot.savefig(<filename>)                     # Saves the figure.
pyplot.show()                                  # Displays the figure.
pyplot.clf()                                   # Clears the figure.

#Table

Prints a CSV file as an ASCII table:

# $ pip3 install tabulate
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 an 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)                                # Makes cursor invisible.
    screen.nodelay(True)                       # Makes getch() non-blocking.
    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

# $ pip3 install loguru
from loguru import logger

logger.add('debug_{time}.log', colorize=True)  # Connects a log file.
logger.add('error_{time}.log', level='ERROR')  # Another file for errors or higher.
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:

# $ pip3 install requests beautifulsoup4
import requests
from bs4 import BeautifulSoup
url   = 'https://en.wikipedia.org/wiki/Python_(programming_language)'
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)

#Web

# $ pip3 install bottle
from bottle import run, route, static_file, template, post, request, response
import json

Run

run(host='localhost', port=8080)        # Runs locally.
run(host='0.0.0.0', port=80)            # Runs globally.

Static Request

@route('/img/<image>')
def send_image(image):
    return static_file(image, 'img_dir/', mimetype='image/png')

Dynamic Request

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

REST Request

@post('/odds/<sport>')
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:

# $ pip3 install requests
>>> 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()                     # Seconds since the Epoch.
...
duration = time() - start_time

High performance:

from time import perf_counter
start_time = perf_counter()             # Seconds since restart.
...
duration = perf_counter() - start_time

Timing a Snippet

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

Profiling by Line

# $ pip3 install line_profiler memory_profiler
@profile
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:

# $ pip3 install pycallgraph
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. Can run up to one hundred times faster than equivalent Python code.

# $ pip3 install numpy
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 an index of dimension that gets collapsed. Leftmost dimension has index 0.

Indexing

<el>       = <2d_array>[0, 0]        # First element.
<1d_view>  = <2d_array>[0]           # First row.
<1d_view>  = <2d_array>[:, 0]        # First column. Also [..., 0].
<3d_view>  = <2d_array>[None, :, :]  # Expanded by dimension of size 1.

<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]]  # Shape: (3, 1)
right = [ 0.1 ,  0.6 ,  0.8 ]  # Shape: (3)

1. If array shapes differ in length, left-pad the shorter shape with ones:

left  = [[0.1], [0.6], [0.8]]  # Shape: (3, 1)
right = [[0.1 ,  0.6 ,  0.8]]  # Shape: (1, 3) <- !

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]]  # Shape: (3, 3) <- !
right = [[0.1, 0.6, 0.8], [0.1, 0.6, 0.8], [0.1, 0.6, 0.8]]  # Shape: (3, 3) <- !

3. If neither non-matching dimension has size 1, rise 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

# $ pip3 install pillow
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))          # Returns a pixel.
<Image>.putpixel((x, y), <tuple/int>)           # Writes a pixel to image.
<ImagingCore> = <Image>.getdata()               # Returns a sequence of pixels.
<Image>.putdata(<list/ImagingCore>)             # Writes a sequence of pixels.
<Image>.paste(<Image>, (x, y))                  # Writes an image to image.

<2d_array> = np.array(<Image>)                  # NumPy array from greyscale image.
<3d_array> = np.array(<Image>)                  # NumPy array from color image.
<Image>    = Image.fromarray(<array>)           # Image from NumPy 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:

# $ pip3 install pillow imageio
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+2*R), 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()       # Number of frames per second.
nchannels    = <Wave_read>.getnchannels()       # Number of samples per frame.
sampwidth    = <Wave_read>.getsampwidth()       # Sample size in bytes.
nframes      = <Wave_read>.getnframes()         # Number of frames.
<bytes>      = <Wave_read>.readframes(nframes)  # Returns next 'nframes' frames.

<Wave_write> = wave.open('<path>', 'wb')
<Wave_write>.setframerate(<int>)                # 44100 for CD, 48000 for video.
<Wave_write>.setnchannels(<int>)                # 1 for mono, 2 for stereo.
<Wave_write>.setsampwidth(<int>)                # 2 for CD quality sound.
<Wave_write>.writeframes(<bytes>)               # Appends frames to file.

Bytes object contains a sequence of frames, each consisting of one or more samples.
In stereo signal 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(file.getnframes())
    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)

#Synthesizer

Plays Popcorn by Gershon Kingsley:

# $ pip3 install simpleaudio
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)

#Basic Script Template

#!/usr/bin/env python3
#
# Usage: .py
#

from collections import namedtuple
from dataclasses import make_dataclass
from enum import Enum
from sys import argv
import re


def main():
    pass


###
##  UTIL
#

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


if __name__ == '__main__':
    main()