Comprehensive Python Cheatsheet

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Main

if __name__ == '__main__':
    main()

List

<list> = <list>[from_inclusive : to_exclusive : step_size]
<list>.append(<el>)
<list>.extend(<collection>)
<list> += [<el>]
<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]))
flattened_list   = list(itertools.chain.from_iterable(<list>))
list_of_chars    = list(<str>)
product_of_elems = functools.reduce(lambda out, x: out * x, <collection>)
no_duplicates    = list(dict.fromkeys(<list>))

index = <list>.index(<el>)  # Returns first index of item. 
<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.
<list>.clear()              # Removes all items.   

Dictionary

<view> = <dict>.keys()
<view> = <dict>.values()
<view> = <dict>.items()

value  = <dict>.get(key, default)            # Returns default if key does not exist.
value  = <dict>.setdefault(key, default)     # Same, but also adds default to dict.
<dict> = collections.defaultdict(<type>)     # Creates a dictionary with default value of type.
<dict> = collections.defaultdict(lambda: 1)  # Creates a dictionary with default value 1.

<dict>.update(<dict>)                        # Or: dict_a = {**dict_a, **dict_b}.
<dict> = dict(<list>)                        # Initiates a dict from list of key-value pairs.
<dict> = dict(zip(keys, values))             # Initiates a dict from two lists.
<dict> = dict.fromkeys(keys [, value])       # Initiates a dict from list of keys.

value = <dict>.pop(key)                         # Removes item from dictionary.
{k: v for k, v in <dict>.items() if k in keys}  # Filters dictionary by keys.

Counter

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

Set

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

<set>.remove(<el>)   # Throws error.
<set>.discard(<el>)  # Doesn't throw error.

Frozenset

Is hashable and can be used as a key in dictionary.

<frozenset> = frozenset(<collection>)

Range

range(to_exclusive)
range(from_inclusive, to_exclusive)
range(from_inclusive, to_exclusive, step_size)
range(from_inclusive, to_exclusive, -step_size)

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

Enumerate

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

Named Tuple

>>> Point = collections.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')

Iterator

<iter> = iter(<collection>)
<iter> = iter(<function>, to_exclusive)

Skips first element:

next(<iter>)
for element in <iter>:
    ...

Reads input until it reaches an empty line:

for line in iter(input, ''):
    ...

Same, but prints a message every time:

from functools import partial
for line in iter(partial(input, 'Please enter value: '), ''):
    ...

Generator

Convenient way to implement the iterator protocol.

def step(start, step_size):
    while True:
        yield start
        start += step_size

>>> stepper = step(10, 2)
>>> next(stepper), next(stepper), next(stepper)
(10, 12, 14)

Type

<type> = type(<el>)  # <class 'int'> / <class 'str'> / ...

from numbers import Number, Integral, Real, Rational, Complex
<bool> = isinstance(<el>, Number)

<bool> = callable(<el>)

String

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

<list> = <str>.split()                       # Splits on any whitespace character.
<list> = <str>.split(sep=None, maxsplit=-1)  # Splits on 'sep' str at most 'maxsplit' times.
<str>  = <str>.join(<list>)                  # Joins elements using string as separator.

<str>  = <str>.replace(old_str, new_str)
<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>.index(<sub_str>)           # Returns first index of a substring.
<bool> = <str>.isnumeric()                # True if str contains only numeric characters.
<list> = textwrap.wrap(<str>, width)      # Nicely breaks string into lines.

Char

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

>>> ord('0'), ord('9')
(48, 57)
>>> ord('A'), ord('Z')
(65, 90)
>>> ord('a'), ord('z')
(97, 122)

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 iterator of all matches.

• Parameter 'flags=re.IGNORECASE' can be used with all functions.
• Parameter 'flags=re.DOTALL' makes dot also accept newline.
• Use r'\1' or '\\\\1' for backreference.
• Use '?' to make operators non-greedy.

Match Object

<str> = <Match>.group()   # Whole match.
<str> = <Match>.group(1)  # Part in first bracket.
<int> = <Match>.start()   # Start index of a match.
<int> = <Match>.end()     # Exclusive end index of a match.

Special Sequences

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>)

>>> Person = namedtuple('Person', 'name height')
>>> person = Person('Jean-Luc', 187)
>>> f'{person.height:10}'
'       187'
>>> '{p.height:10}'.format(p=person)
'       187'

General Options

{<el>:<10}       # '<el>      '
{<el>:>10}       # '      <el>'
{<el>:^10}       # '   <el>   '
{<el>:->10}      # '------<el>'
{<el>:>0}        # '<el>'

String Options

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

{'abcde'!r:<10}  # "'abcde'   "

{'abcde':.3}     # 'abc'
{'abcde':10.3}   # 'abc       '

Number Options

{1.23456:.3f}    # '1.235'
{1.23456:10.3f}  # '     1.235'

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

{65:c}           # 'A'
{3:08b}          # '00000011' -> Binary with leading zeros.
{3:0<8b}         # '11000000' -> Binary with trailing zeros.

Float presentation types:

• 'f' - Fixed point: .<precision>f
• '%' - Percent: .<precision>%
• 'e' - Exponent

Integer presentation types:

• 'c' - character
• 'b' - binary
• 'x' - hex
• 'X' - HEX

Numbers

Basic Functions

<num>  = pow(<num>, <num>)  # Or: <num> ** <num>
<real> = abs(<num>)
<real> = round(<real> [, ndigits])

Constants

from math import e, pi

Trigonometry

from math import cos, acos, sin, asin, tan, atan, degrees, radians

Logarithm

from math import log, log10, log2
<float> = log(<real> [, base])  # Base e, if not specified.

Infinity, nan

from math import inf, nan, isinf, isnan

Or:

float('inf'), float('nan')

Random

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

Datetime

from datetime import datetime, strptime
now = datetime.now()
now.month                     # 3
now.strftime('%Y%m%d')        # '20180315'
now.strftime('%Y%m%d%H%M%S')  # '20180315002834'
<datetime> = strptime('2015-05-12 00:39', '%Y-%m-%d %H:%M')

Arguments

'*' is the splat operator, that takes a list as input, and expands it into actual positional arguments in the function call.

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)

Splat operator can also be used in function declarations:

def add(*a):
    return sum(a)

>>> add(1, 2, 3)
6

And in few other places:

>>> a = (1, 2, 3)
>>> [*a]
[1, 2, 3]

>>> head, *body, tail = [1, 2, 3, 4]
>>> body
[2, 3]

Inline

Lambda

lambda: <return_value>
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}
<dict> = {i: i*2 for i in range(10)}      # {0: 0, 1: 2, ..., 9: 18}
<iter> = (i+5 for i in range(10))         # (5, 6, ..., 14)

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)
<int>  = 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

<expression_if_true> if <condition> else <expression_if_false>

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

Namedtuple, Enum, Class

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

from enum import Enum
Direction = Enum('Direction', 'n e s w')
Cutlery   = Enum('Cutlery', {'fork': 1, 'knife': 2, 'spoon': 3})

# Warning: Objects will share the objects that are initialized in the dictionary!
Creature  = type('Creature', (), {'p': Point(0, 0), 'd': Direction.n})
creature  = Creature()

Closure

def get_multiplier(a):
    def out(b):
        return a * b
    return out

>>> multiply_by_3 = get_multiplier(3)
>>> multiply_by_3(10)
30

Or:

from functools import partial
<function> = partial(<function>, <argument_1> [, <argument_2>, ...])

>>> multiply_by_3 = partial(operator.mul, 3)
>>> multiply_by_3(10)
30

Nonlocal

If variable is assigned to anywhere in the scope, it is regarded as a local variable, unless it is declared as global or nonlocal.

def get_counter():
    a = 0
    def out():
        nonlocal a
        a += 1
        return a
    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 metadata of function add() to function out().
• Without it 'add.__name__' would return 'out'.

LRU Cache

Decorator that caches function's return values. All arguments must be hashable.

from functools import lru_cache

@lru_cache(maxsize=None)
def fib(n):
    return n if n < 2 else fib(n-1) + fib(n-2)

>>> [fib(n) for n in range(10)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
>>> fib.cache_info()
CacheInfo(hits=16, misses=10, maxsize=None, currsize=10)

Parametrized Decorator

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 = type(self).__name__
        return f'{class_name}({self.a!r})'
    def __str__(self):
        return str(self.a)

    @classmethod
    def get_class_name(cls):
        return cls.__name__

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

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.

class MyComparable:
    def __init__(self, a):
        self.a = a
    def __eq__(self, other):
        if isinstance(other, type(self)):
            return self.a == other.a
        return False 

Hashable

• Hashable object needs both hash() and eq() methods and it's 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 False 
    def __hash__(self):
        return hash(self.a)

Sequence

• Methods do not depend on each other, so they can be skipped if not needed.
• Any object with defined getitem() is considered iterable, even if it lacks iter().

class MySequence:
    def __init__(self, a):
        self.a = a
    def __len__(self):
        return len(self.a)
    def __getitem__(self, i):
        return self.a[i]
    def __iter__(self):
        for el in self.a:
            yield el

Callable

class Counter:
    def __init__(self):
        self.a = 0
    def __call__(self):
        self.a += 1
        return self.a

Copy

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

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()

    @classmethod
    def get_member_names(cls):
        return [a.name for a in cls.__members__.values()]

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

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)})

Exceptions

while True:
    try:
        x = int(input('Please enter a number: '))
    except ValueError:
        print('Oops!  That was no valid number.  Try again...')
    else:
        print('Thank you.')
        break

Raising exception:

raise ValueError('A very specific message!')

Finally

>>> try:
...     raise KeyboardInterrupt
... finally:
...     print('Goodbye, world!')
Goodbye, world!
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
KeyboardInterrupt

System

Command Line Arguments

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

Print Function

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

• Use 'file=sys.stderr' for errors.

Pretty print:

>>> from pprint import pprint
>>> pprint(dir())
['__annotations__',
 '__builtins__',
 '__doc__', ...]

Input Function

• Reads a line from user input or pipe if present.
• The trailing newline gets stripped.
• The prompt string is printed to standard output before reading input.

<str> = input(prompt=None)

Prints lines until EOF:

while True:
    try:
        print(input())
    except EOFError:
        break

Open Function

Opens file and returns a corresponding file object.

<file> = open(<path>, mode='r', encoding=None)

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 beginning
• 'a+' - read and write from end
• 'b' - binary mode
• 't' - text mode (default)

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 path, listdir
<bool> = path.exists(<path>)
<bool> = path.isfile(<path>)
<bool> = path.isdir(<path>)
<list> = listdir(<path>)

>>> from glob import glob
>>> glob('../*.gif')
['1.gif', 'card.gif']

Command Execution

import os
<str> = os.popen(<command>).read()

Or:

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

Recursion Limit

>>> import sys
>>> sys.getrecursionlimit()
1000
>>> sys.setrecursionlimit(5000)

JSON

import json
<str>    = json.dumps(<object>, ensure_ascii=True, indent=None)
<object> = json.loads(<str>)

To preserve order:

from collections import OrderedDict
<object> = json.loads(<str>, object_pairs_hook=OrderedDict)

Read File

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

Write to 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

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)

SQLite

import sqlite3
db = sqlite3.connect(<filename>)
...
db.close()

Read

cursor = db.execute(<query>)
if cursor:
    <tuple> = cursor.fetchone()  # First row.
    <list>  = cursor.fetchall()  # Remaining rows.

Write

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

Bytes

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

<bytes> = b'<str>'
<int>   = <bytes>[<index>]
<bytes> = <bytes>[<slice>]
<bytes> = b''.join(<coll_of_bytes>)

Encode

<bytes> = <str>.encode(encoding='utf-8')
<bytes> = <int>.to_bytes(length, byteorder='big|little', signed=False)
<bytes> = bytes.fromhex(<hex>)

Decode

<str>   = <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 Python values and a C struct, represented as a Python bytes object.
• Machine’s native type sizes and byte order are used by default.

from struct import pack, unpack, calcsize
<bytes> = pack('<format>', <value_1> [, <value_2>, ...])
<tuple> = 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)
>>> calcsize('>hhl')
8

Format

For standard sizes start format string with:

• '=' - native byte order
• '<' - little-endian
• '>' - big-endian

Use capital letter for unsigned type. Standard size in brackets:

• 'x' - pad byte
• 'c' - char (1)
• 'h' - short (2)
• 'i' - int (4)
• 'l' - long (4)
• 'q' - long long (8)
• 'f' - float (4)
• 'd' - double (8)

Array

List that can only hold elements of predefined type. Available types are listed above.

from array import array
<array> = array(<typecode> [, <collection>])

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>)  # Collection gets reversed.
<el> = <deque>.popleft()
<deque>.rotate(n=1)               # Rotates elements to the right.

Threading

from threading import Thread, RLock

Thread

thread = Thread(target=<function>, args=(<first_arg>, ))
thread.start()
...
thread.join()

Lock

lock = RLock()
lock.acquire()
...
lock.release()

Hashlib

>>> import hashlib
>>> hashlib.md5(<str>.encode()).hexdigest()
'33d0eba106da4d3ebca17fcd3f4c3d77'

Itertools

• Every function returns an iterator and can accept any collection and/or iterator.
• If you want to print the iterator, you need to pass it to the list() function!

from itertools import *

Combinatoric iterators

>>> 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')]

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

>>> 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)]

Infinite iterators

>>> i = count(5, 2)
>>> next(i), next(i), next(i)
(5, 7, 9)

>>> a = cycle('abc')
>>> [next(a) for _ in range(10)]
['a', 'b', 'c', 'a', 'b', 'c', 'a', 'b', 'c', 'a']

>>> repeat(10, 3)
[10, 10, 10]

Iterators

>>> chain([1, 2], range(3, 5))
[1, 2, 3, 4]

>>> compress('abc', [True, 0, 1])
['a', 'c']

>>> # islice(<collection>, from_inclusive, to_exclusive) 
>>> islice([1, 2, 3], 1, None)
[2, 3]

>>> people = [{'id': 1, 'name': 'Bob'}, 
              {'id': 2, 'name': 'Bob'}, 
              {'id': 3, 'name': 'Peter'}]
>>> groups = groupby(people, key=lambda a: a['name'])
>>> {name: list(group) for name, group in groups}
{'Bob':   [{'id': 1, 'name': 'Bob'}, 
           {'id': 2, 'name': 'Bob'}], 
 'Peter': [{'id': 3, 'name': 'Peter'}]}

Introspection and Metaprograming

Inspecting code at runtime and code that generates code. You can:

• Look at the attributes
• Set new attributes
• Create functions dynamically
• Traverse the parent classes
• Change values in the class

Variables

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

Attributes

class Z:
    def __init__(self):
        self.a = 'abcde'
        self.b = 12345

>>> z = Z()

>>> vars(z)
{'a': 'abcde', 'b': 12345}

>>> getattr(z, 'a')
'abcde'

>>> hasattr(z, 'c')
False

>>> setattr(z, 'c', 10)

Parameters

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

Type

Type is the root class. If only passed the object it returns it's type. Otherwise it creates a new class (and not the instance!).

type(<class_name>, <parents_tuple>, <attributes_dict>)

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

Meta Class

Class that creates class.

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)

Metaclass Attribute

When class is created it checks if it has metaclass defined. If not, it recursively checks if any of his parents has it defined and eventually comes to type.

class MyClass(metaclass=MyMetaClass):
    def __init__(self):
        self.b = 12345

Operator

from operator import add, sub, mul, truediv, floordiv, mod, pow, neg, abs, \
                     eq, ne, lt, le, gt, ge, \
                     not_, and_, or_, \
                     itemgetter, attrgetter, methodcaller

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

Eval

Basic

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

Using Abstract Syntax Trees

import ast
from ast import Num, BinOp, UnaryOp
import operator as op

legal_operators = {ast.Add:    op.add, 
                   ast.Sub:    op.sub, 
                   ast.Mult:   op.mul,
                   ast.Div:    op.truediv, 
                   ast.Pow:    op.pow, 
                   ast.BitXor: op.xor,
                   ast.USub:   op.neg}

def evaluate(expression):
    root = ast.parse(expression, mode='eval')
    return eval_node(root.body)

def eval_node(node):
    node_type = type(node)
    if node_type == Num:
        return node.n
    if node_type not in [BinOp, UnaryOp]:
        raise TypeError(node)
    operator_type = type(node.op)
    if operator_type not in legal_operators:
        raise TypeError(f'Illegal operator {node.op}')
    operator = legal_operators[operator_type]
    if node_type == BinOp:
        left, right = eval_node(node.left), eval_node(node.right)
        return operator(left, right)
    elif node_type == UnaryOp:
        operand = eval_node(node.operand)
        return operator(operand)

>>> evaluate('2 ^ 6')
4
>>> evaluate('2 ** 6')
64
>>> evaluate('1 + 2 * 3 ** (4 ^ 5) / (6 + -7)')
-5.0

Coroutine

• Similar to Generator, but Generator pulls data through the pipe with iteration, while Coroutine pushes data into the pipeline with send().
• Coroutines provide more powerful data routing possibilities than iterators.
• If you built a collection of simple data processing components, you can glue them together into complex arrangements of pipes, branches, merging, etc.

Helper Decorator

• All coroutines must be "primed" by first calling next().
• Remembering to call next() is easy to forget.
• Solved by wrapping coroutines with a 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:
        item = (yield)
        target.send(item + 100)

@coroutine
def printer():
    while True:
        item = (yield)
        print(item)

reader(adder(printer()))  # 100, 101, ..., 109

Libraries

Argparse

from argparse import ArgumentParser
parser = ArgumentParser(description='calculate X to the power of Y')
group = parser.add_mutually_exclusive_group()
group.add_argument('-v', '--verbose', action='store_true')
group.add_argument('-q', '--quiet', action='store_true')
parser.add_argument('x', type=int, help='the base')
parser.add_argument('y', type=int, help='the exponent')
args = parser.parse_args()
answer = args.x ** args.y

if args.quiet:
    print(answer)
elif args.verbose:
    print(f'{args.x} to the power {args.y} equals {answer}')
else:
    print(f'{args.x}^{args.y} == {answer}')

Usage

$ python3 prog.py --help
usage: test2.py [-h] [-v | -q] x y

calculate X to the power of Y

positional arguments:
  x              the base
  y              the exponent

optional arguments:
  -h, --help     show this help message and exit
  -v, --verbose
  -q, --quiet

Progress Bar

# $ pip3 install tqdm
from tqdm import tqdm
from time import sleep
for i in tqdm([1, 2, 3]):
    sleep(0.2)
for i in tqdm(range(100)):
    sleep(0.02)

Table

Prints CSV file as ASCII table:

# $ pip3 install tabulate
from csv import reader
from tabulate import tabulate
with open(<filename>, encoding='utf-8', newline='') as csv_file:
    reader = reader(csv_file, delimiter=';')
    headers = [a.title() for a in next(reader)]
    print(tabulate(reader, headers))

Curses

# $ pip3 install curses
from curses import wrapper

def main():
    wrapper(draw)

def draw(screen):
    screen.clear()
    screen.addstr(0, 0, 'Press ESC to quit.')
    while screen.getch() != 27:
        pass

def get_border(screen):
    from collections import namedtuple
    P = namedtuple('P', 'x y')
    height, width = screen.getmaxyx()
    return P(width - 1, height - 1)

Plot

# $ pip3 install matplotlib
from matplotlib import pyplot
pyplot.plot(<data_1> [, <data_2>, ...])
pyplot.savefig(<filename>, transparent=True)
pyplot.show()

Image

Creates PNG image of greyscale gradient:

# $ pip3 install pillow
from PIL import Image
width, height = 100, 100
img = Image.new('L', (width, height), 'white')
img.putdata([255 * a/(width*height) for a in range(width*height)])
img.save('out.png')

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.

Audio

Saves a list of floats with values between 0 and 1 to a WAV file:

import wave, struct
samples = [struct.pack('h', int((a-0.5)*60000)) for a in <list>]
wf = wave.open(<filename>, 'wb')
wf.setnchannels(1)
wf.setsampwidth(4)
wf.setframerate(44100)
wf.writeframes(b''.join(samples))
wf.close()

Url

from urllib.parse import quote, quote_plus, unquote, unquote_plus

Encode

>>> quote("Can't be in URL!")
'Can%27t%20be%20in%20URL%21'
>>> quote_plus("Can't be in URL!")
'Can%27t+be+in+URL%21'

Decode

>>> unquote('Can%27t+be+in+URL%21')
"Can't+be+in+URL!"
>>> unquote_plus('Can%27t+be+in+URL%21')
"Can't be in URL!"

Scraping

# $ pip3 install requests beautifulsoup4
>>> import requests
>>> from bs4 import BeautifulSoup
>>> url      = 'https://en.wikipedia.org/wiki/Python_(programming_language)'
>>> page     = requests.get(url)
>>> document = BeautifulSoup(page.text, 'html.parser')
>>> table    = document.find('table', class_='infobox vevent')
>>> rows     = table.find_all('tr')
>>> link     = rows[11].find('a')['href']
'https://www.python.org/'
>>> latest_v = rows[6].find('div').text.split()[0]
'3.7.2'

Web

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

Run

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

Static Request

@route('/img/<image>')
def send_image(image):
    return static_file(image, 'images/', 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'
>>> r = requests.post(url, data={'team': 'arsenal f.c.'})
>>> r.json()
['arsenal f.c.', 2.44, 3.29]

Profile

Basic

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

High Performance

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

Timing a Snippet

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

Line Profiler

# $ pip3 install line_profiler
@profile
def main():
    a = [*range(10000)]
    b = {*range(10000)}
main()

Usage:

$ 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)}

Call Graph

Generates a PNG image of call graph with highlighted bottlenecks:

# $ pip3 install pycallgraph
from pycallgraph import output, PyCallGraph
from datetime import datetime
graph = output.GraphvizOutput()
time_str = datetime.now().strftime('%Y%m%d%H%M%S')
graph.output_file = f'profile-{time_str}.png'
with PyCallGraph(output=graph):
    <code_to_be_profiled>

NumPy

Array manipulation mini language. Can run up to 100 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.

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, left-pad the smaller 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]

Basic Script Template

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

from collections import namedtuple
from enum import Enum
import re
import sys


def main():
    pass


###
##  UTIL
#

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


if __name__ == '__main__':
    main()