Comprehensive Python Cheatsheet
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Contents
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: CSV
, JSON
, Pickle
, SQLite
, Bytes
, Struct
, Array
, MemoryView
, Deque
.
6. Advanced: Threading
, Introspection
, Metaprograming
, Operator
, Eval
, Coroutine
.
7. Libraries: Progress_Bar
, Plot
, Table
, Curses
, Logging
, Scraping
, Web
, Profile
,
NumPy
, Image
, Audio
.
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>)
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 dict 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 does not exist.
value = <dict>.setdefault(key, default=None) # Same, but also adds default to dict.
<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>.update(<dict>)
<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.
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 = ['red', 'blue', 'yellow', 'blue', 'red', 'blue']
>>> counter = Counter(colors)
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>
<set>.remove(<el>) # Raises KeyError.
<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>) # Calling `iter(<iter>)` returns unmodified iterator.
<iter> = iter(<function>, to_exclusive) # Sequence of return values until 'to_exclusive'.
<el> = next(<iter> [, default]) # Raises StopIteration or returns 'default' on end.
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 indefinitely.
<iter> = chain(<coll.>, <coll.> [, ...]) # 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)
<iter> = islice(<collection>, from_inclusive, to_exclusive, +step_size)
Generator
- Convenient way to implement the iterator protocol.
- Any function that contains a yield statement returns a generator object.
- 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 builtin 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 |
+--------------------+----------+----------+------+---------+--------+
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.
<str> = <str>.replace(old, new [, count]) # Replaces 'old' with 'new' at most 'count' times.
<bool> = <str>.isnumeric() # True if str contains only numeric characters.
<list> = textwrap.wrap(<str>, width) # Nicely breaks string into lines.
- Also:
'lstrip()'
,'rstrip()'
. - Also:
'lower()'
,'upper()'
,'capitalize()'
and'title()'
.
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 all occurrences as match objects.
- 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. - Use
'?'
to make an operator 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 letters 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
<int> = int(<float/str/bool>) # Or: math.floor(<float>)
<float> = float(<int/str/bool>)
<complex> = complex(real=0, imag=0) # Or: <real> + <real>j
<Fraction> = fractions.Fraction(numerator=0, denominator=1)
'int(<str>)'
and'float(<str>)'
raise 'ValueError' on malformed strings.
Basic Functions
<num> = pow(<num>, <num>) # Or: <num> ** <num>
<real> = abs(<num>)
<int> = round(<real>)
<real> = round(<real>, ±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('0b<bin>', 0) # Or: int('0x<hex>', 0)
<int> = int('<bin>', 2) # Or: int('<hex>', 16)
'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 system's timezone.
from datetime import date, time, datetime, timedelta
from dateutil.tz import UTC, tzlocal, gettz
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.
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>') # Timezone from 'Continent/City_Name' str.
<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.
<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'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"
- For abbreviated weekday and month use
'%a'
and'%b'
. - When parsing,
'%z'
also accepts'±HH:MM'
.
Arithmetics
<D/DT> = <D/DT> ± <TD>
<TD> = <TD> ± <TD>
<TD> = <TD> */ <real>
<float> = <TD> / <TD>
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)
<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, 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 a 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 metadata of function add() to function 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)
- Recursion depth is limited 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 be also used for str().
Str() use cases:
print(<el>)
print(f'{<el>}')
raise Exception(<el>)
logging.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
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)
- An object can be made sortable with
'order=True'
or immutable 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.
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 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
- 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)
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
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!
List of existing context managers:
with open('<path>', ...) as file: ...
with wave.open('<path>', ...) as wave_file: ...
with memoryview(<bytes/bytearray/array>) as view: ...
db = sqlite3.connect('<path>'); with db: db.execute('<insert_query>')
lock = threading.RLock(); with lock: ...
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
>>> a = MyIterable([1, 2, 3])
>>> iter(a)
<generator object MyIterable.__iter__ at 0x1026c18b8>
>>> 1 in a
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):
for el in self.a:
yield el
def __contains__(self, el):
return el in self.a
def __len__(self):
return len(self.a)
Sequence
- Only required methods are len() and getitem(), that 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):
for el in self.a:
yield el
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 it 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 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 useful ABCs that automatically generate missing methods are: MutableSequence, Set, MutableSet, Mapping and MutableMapping.
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()]
- If there are no numeric values before auto(), it returns 1.
- Otherwise it returns an increment of last numeric value.
<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)})
- 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_1>, <exception_2>, ...):
except (<exception_1>, <exception_2>, ...) as <name>:
- Also catches subclasses of the exception.
Raising Exceptions
raise <exception>
raise <exception>()
raise <exception>(<el>)
raise <exception>(<el_1>, <el_2>, ...)
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!')
Re-raising caught exception:
except <exception>:
<code>
raise
Common Built-in Exceptions
BaseException
+-- SystemExit # Raised by the sys.exit() function.
+-- KeyboardInterrupt # Raised when the user hits the interrupt key.
+-- 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 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.
User-defined Exceptions
class MyError(Exception):
pass
class MyInputError(MyError):
pass
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(dir())
['__annotations__',
'__builtins__',
'__doc__', ...]
Input
- Reads a line from user input or pipe if present.
- Trailing newline gets stripped.
- Prompt string is printed to the standard output before reading input.
<str> = input(prompt=None)
Prints lines until EOF:
while True:
try:
print(input())
except EOFError:
break
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) # Argument
p.add_argument('<name>', type=<type>, nargs='+') # Arguments
args = p.parse_args()
value = args.<name>
- Use
'help=<str>'
for argument description. - Use
'type=FileType(<mode>)'
for files.
Open
Opens a file and returns a corresponding file object.
<file> = open('<path>', mode='r', encoding=None, newline=None)
'encoding=None'
means 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 lines are still broken by readline() 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
<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 lines or empty list.
<str/bytes> = next(<file>) # Returns a line using buffer. Do not mix.
<file>.write(<str/bytes>) # Writes a string or bytes object.
<file>.writelines(<coll.>) # 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 path, listdir
from glob import glob
<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() # Iterator of filenames located at path.
<iter> = <Path>.glob('<pattern>') # Filenames matching the wildcard pattern.
<str> = str(<Path>) # Returns path as a string.
<tup.> = <Path>.parts # Returns all components as strings.
<Path> = <Path>.resolve() # Returns absolute Path without symlinks.
<str> = <Path>.name # Final component.
<str> = <Path>.stem # Final component without extension.
<str> = <Path>.suffix # Final component's extension.
<Path> = <Path>.parent # Path without final component.
Command Execution
Files and Directories Commands
- Paths can be either strings or Path objects.
- All exceptions are either 'OSError' or its subclasses.
import os
os.chdir(<path>) # Changes the current working directory.
<str> = os.getcwd() # Returns current working directory.
os.remove(<path>) # Deletes the file.
os.rmdir(<path>) # Deletes empty directory.
shutil.rmtree(<path>) # Deletes an entire directory tree.
os.rename(from, to) # Renames the file or directory.
os.replace(from, to) # Same, but overwrites 'to' if it exists.
os.mkdir(<path>, mode=0o777) # Creates a directory.
<iter> = os.scandir(path='.') # Returns os.DirEntry objects located at path.
DirEntry:
<str> = <DirEntry>.name # Final component of the path.
<str> = <DirEntry>.path # Path with final component.
<bool> = <DirEntry>.is_file()
<bool> = <DirEntry>.is_dir()
<bool> = <DirEntry>.is_symlink()
<Path> = Path(<DirEntry>)
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
CSV
import csv
<reader> = csv.reader(<file>, dialect='excel', delimiter=',')
<list> = next(<reader>) # Returns a row as list of strings.
<writer> = csv.writer(<file>, dialect='excel', delimiter=',')
<writer>.writerow(<collection>)
<writer>.writerows(<coll_of_coll>)
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 csv.reader(file)
- If
'newline=""'
is not specified, then newlines embedded inside quoted fields will not be interpreted correctly.
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)
JSON
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
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
Server-less database engine that stores each database into separate file.
import sqlite3
db = sqlite3.connect('<path>') # Also ':memory:'.
...
db.close()
- New database will be created if path doesn't exist.
Read
cursor = db.execute('<query>')
if cursor:
<tuple> = cursor.fetchone() # First row.
<list> = cursor.fetchall() # Remaining rows.
- Returned values can be of type str, int, float, bytes or None.
Write
db.execute('<query>')
db.commit()
Placeholders
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.
- Passed values can be of type str, int, float, bytes, None, bool, datetime.date or datetime.datetme.
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>') # Connector doesn't have 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> = <str>.encode('utf-8') # Or: bytes(<str>, 'utf-8')
<bytes> = bytes(<coll_of_ints>) # Ints must be in range from 0 to 255.
<bytes> = <int>.to_bytes(<length>, byteorder='big|little', signed=False)
<bytes> = bytes.fromhex('<hex>')
Decode
<str> = <bytes>.decode('utf-8') # Or: str(<bytes>, 'utf-8')
<list> = list(<bytes>) # Returns ints in range from 0 to 255.
<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 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, iter_unpack, calcsize
<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)
>>> calcsize('>hhl')
8
Format
For standard sizes start format string with:
'='
- native byte order'<'
- little-endian'>'
- big-endian
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 predefined type. Available types and their sizes in bytes are listed above.
from array import array
<array> = array('<typecode>' [, <collection>])
Memory View
Used for accessing the internal data of an object that supports the buffer protocol.
<memoryview> = memoryview(<bytes> / <bytearray> / <array>)
<memoryview>.release()
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>)
<el> = <deque>.popleft()
<deque>.extendleft(<collection>) # Collection gets reversed.
<deque>.rotate(n=1) # Rotates elements to the right.
>>> a = deque([1, 2, 3], maxlen=3)
>>> a.append(4)
[2, 3, 4]
>>> a.appendleft(5)
[5, 2, 3]
>>> a.insert(1, 6)
IndexError: deque already at its maximum size
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()
Or:
lock = RLock()
with lock:
...
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 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)
- 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 class of returned instance (
'MyMetaClass'
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)
), in which case init() is not called.
Metaclass Attribute
Right before a 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):
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 | |
+---------+-------------+
Operator
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>)
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
- 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 build 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:
value = (yield)
target.send(value + 100)
@coroutine
def printer():
while True:
value = (yield)
print(value)
reader(adder(printer())) # 100, 101, ..., 109
Libraries
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)
Plot
# $ pip3 install matplotlib
from matplotlib import pyplot
pyplot.plot(<data_1> [, <data_2>, ...]) # Or: hist(<data>).
pyplot.savefig(<filename>)
pyplot.show()
pyplot.clf() # Clears figure.
Table
Prints a CSV file as an ASCII table:
# $ pip3 install tabulate
from tabulate import tabulate
import csv
with open(<filename>, encoding='utf-8', newline='') as file:
lines = csv.reader(file)
headers = [header.title() for header in next(lines)]
table = tabulate(lines, headers)
print(table)
Curses
from curses import wrapper, ascii
def main():
wrapper(draw)
def draw(screen):
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', 'y x')
height, width = screen.getmaxyx()
return P(height-1, width-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
Error 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 are 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 and prints Python's URL and version number from Wikipedia:
# $ pip3 install requests beautifulsoup4
import requests
from bs4 import BeautifulSoup
url = 'https://en.wikipedia.org/wiki/Python_(programming_language)'
page = requests.get(url)
doc = BeautifulSoup(page.text, '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]
print(link, ver)
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'
>>> data = {'team': 'arsenal f.c.'}
>>> response = requests.post(url, data=data)
>>> response.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')
0.34986
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 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
[0.1, 0.6, 0.8] => [1, 2, 1]
):
For each point returns index of its nearest point (>>> 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
Creates a PNG image of a rainbow gradient:
width = 100
height = 100
size = width * height
pixels = [255 * i/size for i in range(size)]
img = Image.new('HSV', (width, height))
img.putdata([(int(a), 255, 255) for a in pixels])
img.convert(mode='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(mode='HSV')
img.putdata([(add_noise(h), s, v) for h, s, v in img.getdata()])
img.convert(mode='RGB').save('test.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
import wave
from struct import pack, iter_unpack
Read Frames from WAV File
def read_wav_file(filename):
with wave.open(filename, 'rb') as wf:
frames = wf.readframes(wf.getnframes())
return [a[0] for a in iter_unpack('<h', frames)]
Write Frames to WAV File
def write_to_wav_file(filename, frames_int, mono=True):
frames_short = (pack('<h', a) for a in frames_int)
with wave.open(filename, 'wb') as wf:
wf.setnchannels(1 if mono else 2)
wf.setsampwidth(2)
wf.setframerate(44100)
wf.writeframes(b''.join(frames_short))
Examples
Saves a sine wave to a mono WAV file:
from math import pi, sin
frames_f = (sin(i * 2 * pi * 440 / 44100) for i in range(100000))
frames_i = (int(a * 30000) for a in frames_f)
write_to_wav_file('test.wav', frames_i)
Adds noise to a mono WAV file:
from random import randint
add_noise = lambda value: max(-32768, min(32767, value + randint(-500, 500)))
frames_i = (add_noise(a) for a in read_wav_file('test.wav'))
write_to_wav_file('test.wav', frames_i)
Synthesizer
# $ 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_frames = lambda note: get_wave(*parse_note(note)) if note else get_pause(0.125)
frames_f = chain.from_iterable(get_frames(n) for n in f'{P1}{P1}{P2}'.split(','))
frames_b = b''.join(struct.pack('<h', int(f * 30000)) for f in frames_f)
simpleaudio.play_buffer(frames_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
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()