python cheatsheet
1.0.0
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1. Sammlungen: List , Dictionary , Set , Tuple , Range , Enumerate , Iterator , Generator .
2. Typen: Type , String , Regular_Exp , Format , Numbers , Combinatorics , Datetime .
3. Syntax: Args , Inline , Import , Decorator , Class , Duck_Types , Enum , Exception .
4. System: Exit , Print , Input , Command_Line_Arguments , Open , Path , OS_Commands .
5. Daten: JSON , Pickle , CSV , SQLite , Bytes , Struct , Array , Memory_View , Deque .
6. Fortgeschritten: Operator , Match_Stmt , Logging , Introspection , Threading , Coroutines .
7. Bibliotheken: Progress_Bar , Plot , Table , Console_App , GUI , Scraping , Web , Profile .
8. Multimedia: NumPy , Image , Animation , Audio , Synthesizer , Pygame , Pandas , Plotly .
if __name__ == '__main__' : # Skips next line if file was imported.
main () # Runs `def main(): ...` function. < list > = [ < el_1 > , < el_2 > , ...] # Creates new list. Also list(<collection>). < el > = < list > [ index ] # First index is 0. Last -1. Allows assignments.
< list > = < list > [ < slice > ] # Also <list>[from_inclusive : to_exclusive : ±step]. < list > . append ( < el > ) # Appends element to the end. Also <list> += [<el>].
< list > . extend ( < collection > ) # Appends elements to the end. Also <list> += <coll>. < list > . sort () # Sorts elements in ascending order.
< list > . reverse () # Reverses the list in-place.
< list > = sorted ( < collection > ) # Returns new list with sorted elements.
< iter > = reversed ( < list > ) # Returns reversed iterator of elements. < el > = max ( < collection > ) # Returns largest element. Also min(<el_1>, ...).
< num > = sum ( < collection > ) # Returns sum of elements. Also math.prod(<coll>). 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 > )) < int > = len ( < list > ) # Returns number of items. Also works on dict, set and string.
< int > = < list > . count ( < el > ) # Returns number of occurrences. Also `if <el> in <coll>: ...`.
< int > = < list > . index ( < el > ) # Returns index of the first occurrence or raises ValueError.
< el > = < list > . pop () # Removes and returns item from the end or at index if passed.
< list > . insert ( < int > , < el > ) # Inserts item at index and moves the rest to the right.
< list > . remove ( < el > ) # Removes first occurrence of the item or raises ValueError.
< list > . clear () # Removes all items. Also works on dictionary and set. < dict > = { key_1 : val_1 , key_2 : val_2 , ...} # Use `<dict>[key]` to get or set the value. < view > = < dict > . keys () # Collection of keys that reflects changes.
< view > = < dict > . values () # Collection of values that reflects changes.
< view > = < dict > . items () # Coll. of key-value tuples that reflects chgs. value = < dict > . get ( key , default = None ) # Returns default if key is missing.
value = < dict > . setdefault ( key , default = None ) # Returns and writes default if key is missing.
< dict > = collections . defaultdict ( < type > ) # Returns a dict with default value `<type>()`.
< dict > = collections . defaultdict ( lambda : 1 ) # Returns a dict with default value 1. < dict > = dict ( < collection > ) # Creates a dict from coll. of key-value pairs.
< dict > = dict ( zip ( keys , values )) # Creates a dict from two collections.
< dict > = dict . fromkeys ( keys [, value ]) # Creates a dict from collection of keys. < dict > . update ( < dict > ) # Adds items. Replaces ones with matching keys.
value = < dict > . pop ( key ) # Removes item or raises KeyError if missing.
{ k for k , v in < dict > . items () if v == value } # Returns set of keys that point to the value.
{ k : v for k , v in < dict > . items () if k in keys } # Filters the dictionary by keys. > >> from collections import Counter
> >> counter = Counter ([ 'blue' , 'blue' , 'blue' , 'red' , 'red' ])
> >> counter [ 'yellow' ] += 1
> >> print ( counter . most_common ())
[( 'blue' , 3 ), ( 'red' , 2 ), ( 'yellow' , 1 )] < set > = { < el_1 > , < el_2 > , ...} # Use `set()` for empty set. < set > . add ( < el > ) # Or: <set> |= {<el>}
< set > . update ( < collection > [, ...]) # Or: <set> |= <set> < set > = < set > . union ( < coll . > ) # Or: <set> | <set>
< set > = < set > . intersection ( < coll . > ) # Or: <set> & <set>
< set > = < set > . difference ( < coll . > ) # Or: <set> - <set>
< set > = < set > . symmetric_difference ( < coll . > ) # Or: <set> ^ <set>
< bool > = < set > . issubset ( < coll . > ) # Or: <set> <= <set>
< bool > = < set > . issuperset ( < coll . > ) # Or: <set> >= <set> < el > = < set > . pop () # Raises KeyError if empty.
< set > . remove ( < el > ) # Raises KeyError if missing.
< set > . discard ( < el > ) # Doesn't raise an error. < frozenset > = frozenset ( < collection > )Tupel ist eine unveränderliche und hashbare Liste.
< tuple > = () # Empty tuple.
< tuple > = ( < el > ,) # Or: <el>,
< tuple > = ( < el_1 > , < el_2 > [, ...]) # Or: <el_1>, <el_2> [, ...]Unterklasse des Tupels mit benannten Elementen.
> >> from collections import namedtuple
> >> Point = namedtuple ( 'Point' , 'x y' )
> >> p = Point ( 1 , y = 2 ); p
Point ( x = 1 , y = 2 )
> >> p [ 0 ]
1
> >> p . x
1
> >> getattr ( p , 'y' )
2 Unveränderliche und hashbare Folge von Ganzzahlen.
< range > = range ( stop ) # range(to_exclusive)
< range > = range ( start , stop ) # range(from_inclusive, to_exclusive)
< range > = range ( start , stop , ± step ) # range(from_inclusive, to_exclusive, ±step_size) > >> [ i for i in range ( 3 )]
[ 0 , 1 , 2 ] for i , el in enumerate ( < coll > , start = 0 ): # Returns next element and its index on each pass.
... < iter > = iter ( < collection > ) # `iter(<iter>)` returns unmodified iterator.
< iter > = iter ( < function > , to_exclusive ) # A sequence of return values until 'to_exclusive'.
< el > = next ( < iter > [, default ]) # Raises StopIteration or returns 'default' on end.
< list > = list ( < iter > ) # Returns a list of iterator's remaining elements. import itertools as it < iter > = it . count ( start = 0 , step = 1 ) # Returns updated value endlessly. Accepts floats.
< iter > = it . repeat ( < el > [, times ]) # Returns element endlessly or 'times' times.
< iter > = it . cycle ( < collection > ) # Repeats the sequence endlessly. < iter > = it . chain ( < coll > , < coll > [, ...]) # Empties collections in order (figuratively).
< iter > = it . chain . from_iterable ( < coll > ) # Empties collections inside a collection in order. < iter > = it . islice ( < coll > , to_exclusive ) # Only returns first 'to_exclusive' elements.
< iter > = it . islice ( < coll > , from_inc , …) # `to_exclusive, +step_size`. Indices can be None. def count ( start , step ):
while True :
yield start
start += step > >> counter = count ( 10 , 2 )
> >> next ( counter ), next ( counter ), next ( counter )
( 10 , 12 , 14 ) < 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' > ) from types import FunctionType , MethodType , LambdaType , GeneratorType , ModuleTypeJede abstrakte Basisklasse gibt eine Reihe virtueller Unterklassen an. Diese Klassen werden dann von isinstance() und issubclass() als Unterklassen des ABC erkannt, obwohl sie es eigentlich nicht sind. ABC kann auch manuell entscheiden, ob eine bestimmte Klasse ihre virtuelle Unterklasse ist oder nicht, normalerweise basierend auf den Methoden, die die Klasse implementiert hat. Beispielsweise sucht Iterable ABC nach der Methode iter(), während Collection ABC nach iter(), enthält() und len() sucht.
> >> from collections . abc import Iterable , Collection , Sequence
> >> isinstance ([ 1 , 2 , 3 ], Iterable )
True +------------------+------------+------------+------------+
| | Iterable | Collection | Sequence |
+------------------+------------+------------+------------+
| list, range, str | yes | yes | yes |
| dict, set | yes | yes | |
| iter | yes | | |
+------------------+------------+------------+------------+
> >> from numbers import Number , Complex , Real , Rational , Integral
> >> isinstance ( 123 , Number )
True +--------------------+----------+----------+----------+----------+----------+
| | Number | Complex | Real | Rational | Integral |
+--------------------+----------+----------+----------+----------+----------+
| int | yes | yes | yes | yes | yes |
| fractions.Fraction | yes | yes | yes | yes | |
| float | yes | yes | yes | | |
| complex | yes | yes | | | |
| decimal.Decimal | yes | | | | |
+--------------------+----------+----------+----------+----------+----------+
Unveränderliche Zeichenfolge.
< str > = < str > . strip () # Strips all whitespace characters from both ends.
< str > = < str > . strip ( '<chars>' ) # Strips passed characters. Also lstrip/rstrip(). < 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 ) # On [nrfvx1c-x1ex85u2028u2029] and rn.
< str > = < str > . join ( < coll_of_strings > ) # Joins elements using string as a separator. < bool > = < sub_str > in < str > # Checks if string contains the substring.
< bool > = < str > . startswith ( < sub_str > ) # Pass tuple of strings for multiple options.
< int > = < str > . find ( < sub_str > ) # Returns start index of the first match or -1.
< int > = < str > . index ( < sub_str > ) # Same, but raises ValueError if there's no match. < str > = < str > . lower () # Changes the case. Also upper/capitalize/title().
< str > = < str > . replace ( old , new [, count ]) # Replaces 'old' with 'new' at most 'count' times.
< str > = < str > . translate ( < table > ) # Use `str.maketrans(<dict>)` to generate table. < str > = chr ( < int > ) # Converts int to Unicode character.
< int > = ord ( < str > ) # Converts Unicode character to int.'unicodedata.normalize("NFC", <str>)' für Zeichenfolgen wie 'Motörhead' , bevor Sie sie mit anderen Zeichenfolgen vergleichen, da 'ö' als ein oder zwei Zeichen gespeichert werden kann.'NFC' wandelt solche Zeichen in ein einzelnes Zeichen um, während 'NFD' sie in zwei umwandelt. < bool > = < str > . isdecimal () # Checks for [0-9]. Also [०-९] and [٠-٩].
< bool > = < str > . isdigit () # Checks for [²³¹…] and isdecimal().
< bool > = < str > . isnumeric () # Checks for [¼½¾…], [零〇一…] and isdigit().
< bool > = < str > . isalnum () # Checks for [a-zA-Z…] and isnumeric().
< bool > = < str > . isprintable () # Checks for [ !#$%…] and isalnum().
< bool > = < str > . isspace () # Checks for [ tnrfvx1c-x1fx85xa0…]. Funktionen für den Abgleich regulärer Ausdrücke.
import re
< str > = re . sub ( r'<regex>' , new , text , count = 0 ) # Substitutes all occurrences with 'new'.
< list > = re . findall ( r'<regex>' , text ) # Returns all occurrences as strings.
< list > = re . split ( r'<regex>' , text , maxsplit = 0 ) # Add brackets around regex to keep matches.
< Match > = re . search ( r'<regex>' , text ) # First occurrence of the pattern or None.
< Match > = re . match ( r'<regex>' , text ) # Searches only at the beginning of the text.
< iter > = re . finditer ( r'<regex>' , text ) # Returns all occurrences as Match objects.'flags=re.IGNORECASE' kann mit allen Funktionen verwendet werden.'flags=re.MULTILINE' sorgt dafür, dass '^' und '$' mit dem Anfang/Ende jeder Zeile übereinstimmen.'flags=re.DOTALL' ergibt '.' Akzeptieren Sie auch das 'n' .'re.compile(<regex>)' gibt ein Pattern-Objekt mit den Methoden sub(), findall(), … zurück < str > = < Match > . group () # Returns the whole match. Also group(0).
< str > = < Match > . group ( 1 ) # Returns part inside the first brackets.
< tuple > = < Match > . groups () # Returns all bracketed parts.
< int > = < Match > . start () # Returns start index of the match.
< int > = < Match > . end () # Returns exclusive end index of the match. 'd' == '[0-9]' # Also [०-९…]. Matches a decimal character.
'w' == '[a-zA-Z0-9_]' # Also [ª²³…]. Matches an alphanumeric or _.
's' == '[ t n r f v ]' # Also [x1c-x1f…]. Matches a whitespace.'flags=re.ASCII' verwendet wird. Es beschränkt spezielle Sequenzübereinstimmungen auf die ersten 128 Unicode-Zeichen und verhindert außerdem, dass 's' 'x1c' , 'x1d' , 'x1e' und 'x1f' akzeptiert (nicht druckbare Zeichen, die Text in Dateien aufteilen). Tabellen, Zeilen bzw. Felder).<str> = f ' {<el_1>}, {<el_2>} ' # Curly brackets can also contain expressions.
<str> = ' {}, {} ' .format(<el_1>, <el_2>) # Or: '{0}, {a}'.format(<el_1>, a=<el_2>)
<str> = ' %s, %s ' % (<el_1>, <el_2>) # Redundant and inferior C-style formatting. > >> Person = collections . namedtuple ( 'Person' , 'name height' )
> >> person = Person ( 'Jean-Luc' , 187 )
> >> f' { person . name } is { person . height / 100 } meters tall.'
'Jean-Luc is 1.87 meters tall.'{ < el > : < 10 } # '<el> '
{ < el > : ^ 10 } # ' <el> '
{ < el > : > 10 } # ' <el>'
{ < el > :. < 10 } # '<el>......'
{ < el > : 0 } # '<el>''format(<el>, "<options>")' gerendert.f'{<el>:{<str/int>}[…]}' .'=' zum Ausdruck wird dieser der Ausgabe vorangestellt: f'{1+1=}' gibt '1+1=2' zurück.'!r' zum Ausdruck wird das Objekt durch Aufrufen seiner repr()-Methode in einen String konvertiert.{ 'abcde' : 10 } # 'abcde '
{ 'abcde' : 10.3 } # 'abc '
{ 'abcde' : .3 } # 'abc'
{ 'abcde' !r: 10 } # "'abcde' "{ 123456 : 10 } # ' 123456'
{ 123456 : 10 ,} # ' 123,456'
{ 123456 : 10_ } # ' 123_456'
{ 123456 : + 10 } # ' +123456'
{ 123456 : = + 10 } # '+ 123456'
{ 123456 : } # ' 123456'
{ - 123456 : } # '-123456'{ 1.23456 : 10.3 } # ' 1.23'
{ 1.23456 : 10.3 f } # ' 1.235'
{ 1.23456 : 10.3 e } # ' 1.235e+00'
{ 1.23456 : 10.3 % } # ' 123.456%' +--------------+----------------+----------------+----------------+----------------+
| | {<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%' |
+--------------+----------------+----------------+----------------+----------------+
+--------------+----------------+----------------+----------------+----------------+
| | {<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%' |
+--------------+----------------+----------------+----------------+----------------+
'{<float>:g}' ist '{<float>:.6}' mit entfernten Nullen, Exponent beginnt bei '1e+06' .'{6.5:.0f}' zu einer '6' und '{7.5:.0f}' zu einer '8' ..5 , .25 , …) dargestellt werden können.{ 90 : c } # 'Z'. Unicode character with value 90.
{ 90 : b } # '1011010'. Number 90 in binary.
{ 90 : X } # '5A'. Number 90 in uppercase hexadecimal. < int > = int ( < float / str / bool > ) # Or: math.trunc(<float>)
< float > = float ( < int / str / bool > ) # Or: <int/float>e±<int>
< complex > = complex ( real = 0 , imag = 0 ) # Or: <int/float> ± <int/float>j
< Fraction > = fractions . Fraction ( 0 , 1 ) # Or: Fraction(numerator=0, denominator=1)
< Decimal > = decimal . Decimal ( < str / int > ) # Or: Decimal((sign, digits, exponent))'int(<str>)' und 'float(<str>)' lösen ValueError bei fehlerhaften Zeichenfolgen aus.'1.1 + 2.2 != 3.3' .'math.isclose(<float>, <float>)' .'decimal.getcontext().prec = <int>' . < num > = pow ( < num > , < num > ) # Or: <number> ** <number>
< num > = abs ( < num > ) # <float> = abs(<complex>)
< num > = round ( < num > [, ± ndigits ]) # `round(126, -1) == 130` from math import e , pi , inf , nan , isinf , isnan # `<el> == nan` is always False.
from math import sin , cos , tan , asin , acos , atan # Also: degrees, radians.
from math import log , log10 , log2 # Log can accept base as second arg. from statistics import mean , median , variance # Also: stdev, quantiles, groupby. from random import random , randint , choice # Also: shuffle, gauss, triangular, seed.
< float > = random () # A float inside [0, 1).
< int > = randint ( from_inc , to_inc ) # An int inside [from_inc, to_inc].
< el > = choice ( < sequence > ) # Keeps the sequence intact. < int > = ± 0 b < bin > # Or: ±0x<hex>
< int > = int ( '±<bin>' , 2 ) # Or: int('±<hex>', 16)
< int > = int ( '±0b<bin>' , 0 ) # Or: int('±0x<hex>', 0)
< str > = bin ( < int > ) # Returns '[-]0b<bin>'. Also hex(). < int > = < int > & < int > # And (0b1100 & 0b1010 == 0b1000).
< int > = < int > | < int > # Or (0b1100 | 0b1010 == 0b1110).
< int > = < int > ^ < int > # Xor (0b1100 ^ 0b1010 == 0b0110).
< int > = < int > < < n_bits # Left shift. Use >> for right.
< int > = ~ < int > # Not. Also -<int> - 1. import itertools as it > >> list ( it . 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 )] > >> list ( it . product ( 'abc' , 'abc' )) # a b c
[( 'a' , 'a' ), ( 'a' , 'b' ), ( 'a' , 'c' ), # a x x x
( 'b' , 'a' ), ( 'b' , 'b' ), ( 'b' , 'c' ), # b x x x
( 'c' , 'a' ), ( 'c' , 'b' ), ( 'c' , 'c' )] # c x x x > >> list ( it . combinations ( 'abc' , 2 )) # a b c
[( 'a' , 'b' ), ( 'a' , 'c' ), # a . x x
( 'b' , 'c' )] # b . . x > >> list ( it . combinations_with_replacement ( 'abc' , 2 )) # a b c
[( 'a' , 'a' ), ( 'a' , 'b' ), ( 'a' , 'c' ), # a x x x
( 'b' , 'b' ), ( 'b' , 'c' ), # b . x x
( 'c' , 'c' )] # c . . x > >> list ( it . permutations ( 'abc' , 2 )) # a b c
[( 'a' , 'b' ), ( 'a' , 'c' ), # a . x x
( 'b' , 'a' ), ( 'b' , 'c' ), # b x . x
( 'c' , 'a' ), ( 'c' , 'b' )] # c x x . Stellt die Klassen „date“, „time“, „datetime“ und „timedelta“ bereit. Alle sind unveränderlich und hashbar.
# $ pip3 install python-dateutil
from datetime import date , time , datetime , timedelta , timezone
import zoneinfo , dateutil . tz < D > = date ( year , month , day ) # Only accepts valid dates from 1 to 9999 AD.
< T > = time ( hour = 0 , minute = 0 , second = 0 ) # Also: `microsecond=0, tzinfo=None, fold=0`.
< DT > = datetime ( year , month , day , hour = 0 ) # Also: `minute=0, second=0, microsecond=0, …`.
< TD > = timedelta ( weeks = 0 , days = 0 , hours = 0 ) # Also: `minutes=0, seconds=0, microseconds=0`.'fold=1' ist der zweite Durchgang gemeint, wenn die Zeit um eine Stunde zurückspringt.'[±D, ]H:MM:SS[.…]' und total_seconds() einen Float aller Sekunden zurück.'<D/DT>.weekday()' um den Wochentag als Ganzzahl abzurufen, wobei Montag 0 ist. < D / DTn > = D / DT . today () # Current local date or naive DT. Also DT.now().
< DTa > = DT . now ( < tzinfo > ) # Aware DT from current time in passed timezone.'<DTn>.time()' , '<DTa>.time()' oder '<DTa>.timetz()' . < tzinfo > = timezone . utc # London without daylight saving time (DST).
< tzinfo > = timezone ( < timedelta > ) # Timezone with fixed offset from UTC.
< tzinfo > = dateutil . tz . tzlocal () # Local timezone with dynamic offset from UTC.
< tzinfo > = zoneinfo . ZoneInfo ( '<iana_key>' ) # 'Continent/City_Name' zone with dynamic offset.
< DTa > = < DT > . astimezone ([ < tzinfo > ]) # Converts DT to the passed or local fixed zone.
< Ta / DTa > = < T / DT > . replace ( tzinfo = < tzinfo > ) # Changes object's timezone without conversion.'> pip3 install tzdata' aus. < D / T / DT > = D / T / DT . fromisoformat ( < str > ) # Object from ISO string. Raises ValueError.
< DT > = DT . strptime ( < str > , '<format>' ) # Datetime from str, according to format.
< D / DTn > = D / DT . fromordinal ( < int > ) # D/DT from days since the Gregorian NYE 1.
< DTn > = DT . fromtimestamp ( < float > ) # Local naive DT from seconds since the Epoch.
< DTa > = DT . fromtimestamp ( < float > , < tz > ) # Aware datetime from seconds since the Epoch.'YYYY-MM-DD' , 'HH:MM:SS.mmmuuu[±HH:MM]' oder beide, getrennt durch ein beliebiges Zeichen. Alle Teile nach den Stunden sind optional.'1970-01-01 00:00 UTC' , '1970-01-01 01:00 CET' , ... < str > = < D / T / DT > . isoformat ( sep = 'T' ) # Also `timespec='auto/hours/minutes/seconds/…'`.
< str > = < D / T / DT > . strftime ( '<format>' ) # Custom string representation of the object.
< int > = < D / DT > . toordinal () # Days since Gregorian NYE 1, ignoring time and tz.
< float > = < DTn > . timestamp () # Seconds since the Epoch, from local naive DT.
< float > = < DTa > . timestamp () # Seconds since the Epoch, from aware datetime. > >> dt = datetime . strptime ( '2025-08-14 23:39:00.00 +0200' , '%Y-%m-%d %H:%M:%S.%f %z' )
> >> dt . strftime ( "%dth of %B '%y (%a), %I:%M %p %Z" )
"14th of August '25 (Thu), 11:39 PM UTC+02:00"'%z' akzeptiert '±HH[:]MM' und gibt '±HHMM' oder eine leere Zeichenfolge zurück, wenn datetime naiv ist.'%Z' akzeptiert 'UTC/GMT' und den Code der lokalen Zeitzone und gibt den Namen der Zeitzone zurück, 'UTC[±HH:MM]' , wenn die Zeitzone namenlos ist, oder eine leere Zeichenfolge, wenn datetime naiv ist. < bool > = < D / T / DTn > > < D / T / DTn > # Ignores time jumps (fold attribute). Also ==.
< bool > = < DTa > > < DTa > # Ignores jumps if they share tz object. Broken ==.
< TD > = < D / DTn > - < D / DTn > # Ignores jumps. Convert to UTC for actual delta.
< TD > = < DTa > - < DTa > # Ignores jumps if they share tzinfo object.
< D / DT > = < D / DT > ± < TD > # Returned datetime can fall into missing hour.
< TD > = < TD > * < float > # Also: <TD> = abs(<TD>) and <TD> = <TD> ±% <TD>.
< float > = < TD > / < TD > # E.g. how many hours are in TD. Also //, divmod(). func ( < positional_args > ) # func(0, 0)
func ( < keyword_args > ) # func(x=0, y=0)
func ( < positional_args > , < keyword_args > ) # func(0, y=0) def func ( < nondefault_args > ): ... # def func(x, y): ...
def func ( < default_args > ): ... # def func(x=0, y=0): ...
def func ( < nondefault_args > , < default_args > ): ... # def func(x, y=0): ...Splat erweitert eine Sammlung in Positionsargumente, während splatty-splat ein Wörterbuch in Schlüsselwortargumente erweitert.
args = ( 1 , 2 )
kwargs = { 'x' : 3 , 'y' : 4 , 'z' : 5 }
func ( * args , ** kwargs ) func ( 1 , 2 , x = 3 , y = 4 , z = 5 )Splat kombiniert null oder mehr Positionsargumente zu einem Tupel, während splatty-splat null oder mehr Schlüsselwortargumente zu einem Wörterbuch kombiniert.
def add ( * a ):
return sum ( a ) > >> add ( 1 , 2 , 3 )
6 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 ( ** 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 ( * 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 , 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) < list > = [ * < coll . > [, ...]] # Or: list(<collection>) [+ ...]
< tuple > = ( * < coll . > , [...]) # Or: tuple(<collection>) [+ ...]
< set > = { * < coll . > [, ...]} # Or: set(<collection>) [| ...]
< dict > = { ** < dict > [, ...]} # Or: <dict> | ... head , * body , tail = < coll . > # Head or tail can be omitted. < func > = lambda : < return_value > # A single statement function.
< func > = lambda < arg_1 > , < arg_2 > : < return_value > # Also allows default arguments. < list > = [ i + 1 for i in range ( 10 )] # Or: [1, 2, ..., 10]
< iter > = ( i for i in range ( 10 ) if i > 5 ) # Or: iter([6, 7, 8, 9])
< set > = { i + 5 for i in range ( 10 )} # Or: {5, 6, ..., 14}
< dict > = { i : i * 2 for i in range ( 10 )} # Or: {0: 0, 1: 2, ..., 9: 18} > >> [ l + r for l in 'abc' for r in 'abc' ] # Inner loop is on the right side.
[ 'aa' , 'ab' , 'ac' , ..., 'cc' ] from functools import reduce < iter > = map ( lambda x : x + 1 , range ( 10 )) # Or: iter([1, 2, ..., 10])
< iter > = filter ( lambda x : x > 5 , range ( 10 )) # Or: iter([6, 7, 8, 9])
< obj > = reduce ( lambda out , x : out + x , range ( 10 )) # Or: 45 < bool > = any ( < collection > ) # Is `bool(<el>)` True for any el?
< bool > = all ( < collection > ) # True for all? Also True if empty. < obj > = < exp > if < condition > else < exp > # Only one expression is evaluated. > >> [ a if a else 'zero' for a in ( 0 , 1 , 2 , 3 )] # `any([0, '', [], None]) == False`
[ 'zero' , 1 , 2 , 3 ] from collections import namedtuple
Point = namedtuple ( 'Point' , 'x y' ) # Creates a tuple's subclass.
point = Point ( 0 , 0 ) # Returns its instance. from enum import Enum
Direction = Enum ( 'Direction' , 'N E S W' ) # Creates an enum.
direction = Direction . N # Returns its member. from dataclasses import make_dataclass
Player = make_dataclass ( 'Player' , [ 'loc' , 'dir' ]) # Creates a class.
player = Player ( point , direction ) # Returns its instance. Mechanismus, der Code in einer Datei für eine andere Datei verfügbar macht.
import < module > # Imports a built-in or '<module>.py'.
import < package > # Imports a built-in or '<package>/__init__.py'.
import < package > . < module > # Imports a built-in or '<package>/<module>.py'.'import <package>' erhalten Sie nicht automatisch Zugriff auf die Module des Pakets, es sei denn, sie werden explizit in das Init-Skript importiert.'from .[…][<pkg/module>[.…]] import <obj>' . Wir haben/erhalten einen Abschluss in Python, wenn eine verschachtelte Funktion auf einen Wert ihrer einschließenden Funktion verweist und die einschließende Funktion dann die verschachtelte Funktion zurückgibt.
def get_multiplier ( a ):
def out ( b ):
return a * b
return out > >> multiply_by_3 = get_multiplier ( 3 )
> >> multiply_by_3 ( 10 )
30 from functools import partial
< function > = partial ( < function > [, < arg_1 > [, ...]]) > >> def multiply ( a , b ):
... return a * b
> >> multiply_by_3 = partial ( multiply , 3 )
> >> multiply_by_3 ( 10 )
30'defaultdict(<func>)' , 'iter(<func>, to_exc)' und dataclass's 'field(default_factory=<func>)' .Wenn eine Variable irgendwo im Bereich zugewiesen wird, wird sie als lokale Variable betrachtet, es sei denn, sie ist als „global“ oder „nichtlokal“ deklariert.
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_name
def function_that_gets_passed_to_decorator ():
...Dekorator, der bei jedem Aufruf der Funktion den Namen der Funktion ausgibt.
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'add.__name__' 'out' zurückgeben.Dekorator, der die Rückgabewerte der Funktion zwischenspeichert. Alle Argumente der Funktion müssen hashbar sein.
from functools import cache
@ cache
def fib ( n ):
return n if n < 2 else fib ( n - 2 ) + fib ( n - 1 )'fib.cache_clear()' aus oder verwenden Sie stattdessen den Dekorator '@lru_cache(maxsize=<int>)' .'sys.setrecursionlimit(<int>)' aus.Ein Dekorator, der Argumente akzeptiert und einen normalen Dekorator zurückgibt, der eine Funktion akzeptiert.
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'@debug' zum Dekorieren der Funktion „add()“ würde hier nicht funktionieren, da Debug dann die Funktion „add()“ als Argument „print_result“ erhalten würde. Dekorateure können jedoch manuell prüfen, ob das empfangene Argument eine Funktion ist, und entsprechend handeln. Eine Vorlage zum Erstellen benutzerdefinierter Objekte.
class MyClass :
def __init__ ( self , a ):
self . a = a
def __str__ ( self ):
return str ( self . a )
def __repr__ ( self ):
class_name = self . __class__ . __name__
return f' { class_name } ( { self . a !r } )'
@ classmethod
def get_class_name ( cls ):
return cls . __name__ > >> obj = MyClass ( 1 )
> >> obj . a , str ( obj ), repr ( obj )
( 1 , '1' , 'MyClass(1)' )'@staticmethod' dekorierte Methoden erhalten weder „self“ noch „cls“ als erstes Argument. print ( < obj > )
f' { < obj > } '
logging . warning ( < obj > )
csv . writer ( < file > ). writerow ([ < obj > ])
raise Exception ( < obj > ) print / str / repr ([ < obj > ])
print / str / repr ({ < obj > : < obj > })
f' { < obj > !r } '
Z = dataclasses . make_dataclass ( 'Z' , [ 'a' ]); print / str / repr ( Z ( < obj > ))
> >> < obj > class Person :
def __init__ ( self , name ):
self . name = name
class Employee ( Person ):
def __init__ ( self , name , staff_num ):
super (). __init__ ( name )
self . staff_num = staff_num class A : pass
class B : pass
class C ( A , B ): passMRO bestimmt die Reihenfolge, in der übergeordnete Klassen bei der Suche nach einer Methode oder einem Attribut durchlaufen werden:
> >> C . mro ()
[ < class 'C' > , < class 'A' > , < class 'B' > , < class 'object' > ]'def f() -> <type>:' ). from collections import abc
< name > : < type > [ | ...] [ = < obj > ] # `|` since 3.10.
< name > : list / set / abc . Iterable / abc . Sequence [ < type > ] [ = < obj > ] # Since 3.9.
< name > : dict / tuple [ < type > , ...] [ = < obj > ] # Since 3.9.Dekorator, der Klassenvariablen verwendet, um die speziellen Methoden init(), repr() und eq() zu generieren.
from dataclasses import dataclass , field , make_dataclass
@ dataclass ( order = False , frozen = False )
class < class_name > :
< attr_name > : < type >
< attr_name > : < type > = < default_value >
< attr_name > : list / dict / set = field ( default_factory = list / dict / set )'order=True' sortierbar und mit 'frozen=True' unveränderlich gemacht werden.'<attr_name>: list = []' eine Liste erstellen würde, die von allen Instanzen gemeinsam genutzt wird. Sein Argument „default_factory“ kann beliebig aufrufbar sein.'typing.Any' . Point = make_dataclass ( 'Point' , [ 'x' , 'y' ])
Point = make_dataclass ( 'Point' , [( 'x' , float ), ( 'y' , float )])
Point = make_dataclass ( 'Point' , [( 'x' , float , 0 ), ( 'y' , float , 0 )])Pythonische Methode zur Implementierung von Gettern und Settern.
class Person :
@ property
def name ( self ):
return ' ' . join ( self . _name )
@ name . setter
def name ( self , value ):
self . _name = value . split () > >> person = Person ()
> >> person . name = ' t Guido van Rossum n '
> >> person . name
'Guido van Rossum'Ein Mechanismus, der Objekte auf in „Slots“ aufgeführte Attribute beschränkt, reduziert ihren Speicherbedarf.
class MyClassWithSlots :
__slots__ = [ 'a' ]
def __init__ ( self ):
self . a = 1 from copy import copy , deepcopy
< object > = copy / deepcopy ( < object > )Ein Duck-Typ ist ein impliziter Typ, der eine Reihe spezieller Methoden vorschreibt. Jedes Objekt, für das diese Methoden definiert sind, wird als Mitglied dieses Duck-Typs betrachtet.
'id(self) == id(other)' zurück, was dasselbe ist wie 'self is other' . 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'id(self)' zurückgibt, nicht ausreicht. class MyHashable :
def __init__ ( self , a ):
self . _a = 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 )'key=locale.strxfrm' an sorted(), nachdem Sie 'locale.setlocale(locale.LC_COLLATE, "en_US.UTF-8")' ausgeführt haben. 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 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(<obj>)' oder 'isinstance(<obj>, collections.abc.Callable)' um zu prüfen, ob das Objekt aufrufbar ist.'<function>' als Argument verwendet, bedeutet es '<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 ) class MyOpen :
def __init__ ( self , filename ):
self . filename = filename
def __enter__ ( self ):
self . file = open ( self . filename )
return self . file
def __exit__ ( self , exc_type , exception , traceback ):
self . file . close () > >> with open ( 'test.txt' , 'w' ) as file :
... file . write ( 'Hello World!' )
> >> with MyOpen ( 'test.txt' ) as file :
... print ( file . read ())
Hello World ! class MyIterable :
def __init__ ( self , a ):
self . a = a
def __iter__ ( self ):
return iter ( self . a )
def __contains__ ( self , el ):
return el in self . a > >> obj = MyIterable ([ 1 , 2 , 3 ])
> >> [ el for el in obj ]
[ 1 , 2 , 3 ]
> >> 1 in obj
True'<iterable>' wenn es '<collection>' verwendet. class MyCollection :
def __init__ ( self , a ):
self . a = a
def __iter__ ( self ):
return iter ( self . a )
def __contains__ ( self , el ):
return el in self . a
def __len__ ( self ):
return len ( self . a ) class MySequence :
def __init__ ( self , a ):
self . a = a
def __iter__ ( self ):
return iter ( self . a )
def __contains__ ( self , el ):
return el in self . a
def __len__ ( self ):
return len ( self . a )
def __getitem__ ( self , i ):
return self . a [ i ]
def __reversed__ ( self ):
return reversed ( self . a )'abc.Iterable' und 'abc.Collection' handelt es sich nicht um einen Duck-Typ. Aus diesem Grund würde 'issubclass(MySequence, abc.Sequence)' False zurückgeben, selbst wenn in MySequence alle Methoden definiert wären. Es erkennt jedoch Liste, Tupel, Bereich, Str, Bytes, Bytearray, Array, Memoryview und Deque, da sie als virtuelle Unterklassen von Sequence registriert sind. from collections import abc
class MyAbcSequence ( abc . Sequence ):
def __init__ ( self , a ):
self . a = a
def __len__ ( self ):
return len ( self . a )
def __getitem__ ( self , i ):
return self . a [ i ] +------------+------------+------------+------------+--------------+
| | 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 |
+------------+------------+------------+------------+--------------+
'isinstance(<obj>, abc.Iterable)' „True“ zurückgibt. Allerdings funktioniert jedes Objekt mit getitem() mit jedem Code, der ein Iterable erwartet.'<abc>.__abstractmethods__' um Namen der erforderlichen Methoden abzurufen. Klasse benannter Konstanten, die als Mitglieder bezeichnet werden.
from enum import Enum , auto class < enum_name > ( Enum ):
< member_name > = auto () # Increment of the last numeric value or 1.
< member_name > = < value > # Values don't have to be hashable.
< member_name > = < el_1 > , < el_2 > # Values can be collections (this is a tuple). < member > = < enum > . < member_name > # Returns a member. Raises AttributeError.
< member > = < enum > [ '<member_name>' ] # Returns a member. Raises KeyError.
< member > = < enum > ( < value > ) # Returns a member. Raises ValueError.
< str > = < member > . name # Returns member's name.
< obj > = < member > . value # Returns member's value. < list > = list ( < enum > ) # Returns enum's members.
< list > = [ a . name for a in < enum > ] # Returns enum's member names.
< list > = [ a . value for a in < enum > ] # Returns enum's member values. < enum > = type ( < member > ) # Returns member's enum.
< iter > = itertools . cycle ( < enum > ) # Returns endless iterator of members.
< member > = random . choice ( list ( < enum > )) # Returns a random member. Cutlery = Enum ( 'Cutlery' , 'FORK KNIFE SPOON' )
Cutlery = Enum ( 'Cutlery' , [ 'FORK' , 'KNIFE' , 'SPOON' ])
Cutlery = Enum ( 'Cutlery' , { 'FORK' : 1 , 'KNIFE' : 2 , 'SPOON' : 3 }) from functools import partial
LogicOp = Enum ( 'LogicOp' , { 'AND' : partial ( lambda l , r : l and r ),
'OR' : partial ( lambda l , r : l or r )}) try :
< code >
except < exception > :
< code > try :
< code_1 >
except < exception_a > :
< code_2_a >
except < exception_b > :
< code_2_b >
else :
< code_2_c >
finally :
< code_3 >'else' -Block wird nur ausgeführt, wenn im 'try' -Block keine Ausnahmen aufgetreten sind.'finally' -Block wird immer ausgeführt (es sei denn, es wird ein Signal empfangen).'signal.signal(signal_number, <func>)' . except < exception > : ...
except < exception > as < name > : ...
except ( < exception > , [...]): ...
except ( < exception > , [...]) as < name > : ...'traceback.print_exc()' um die vollständige Fehlermeldung an stderr zu drucken.'print(<name>)' um nur die Ursache der Ausnahme (ihre Argumente) auszugeben.'logging.exception(<str>)' , um die übergebene Nachricht zu protokollieren, gefolgt von der vollständigen Fehlermeldung der abgefangenen Ausnahme. Einzelheiten finden Sie unter Protokollierung.'sys.exc_info()' um den Ausnahmetyp, das Objekt und die Rückverfolgung der abgefangenen Ausnahme abzurufen. raise < exception >
raise < exception > ()
raise < exception > ( < obj > [, ...]) except < exception > [ as < name > ]:
...
raise arguments = < name > . args
exc_type = < name > . __class__
filename = < name > . __traceback__ . tb_frame . f_code . co_filename
func_name = < name > . __traceback__ . tb_frame . f_code . co_name
line = linecache . getline ( filename , < name > . __traceback__ . tb_lineno )
trace_str = '' . join ( traceback . format_tb ( < name > . __traceback__ ))
error_msg = '' . join ( traceback . format_exception ( type ( < name > ), < name > , < name > . __traceback__ )) BaseException
+-- SystemExit # Raised by the sys.exit() function.
+-- KeyboardInterrupt # Raised when the user hits the interrupt key (ctrl-c).
+-- Exception # User-defined exceptions should be derived from this class.
+-- ArithmeticError # Base class for arithmetic errors such as ZeroDivisionError.
+-- AssertionError # Raised by `assert <exp>` if expression returns false value.
+-- AttributeError # Raised when object doesn't have requested attribute/method.
+-- EOFError # Raised by input() when it hits an end-of-file condition.
+-- LookupError # Base class for errors when a collection can't find an item.
| +-- IndexError # Raised when a sequence index is out of range.
| +-- KeyError # Raised when a dictionary key or set element is missing.
+-- MemoryError # Out of memory. May be too late to start deleting variables.
+-- NameError # Raised when nonexistent name (variable/func/class) is used.
| +-- UnboundLocalError # Raised when local name is used before it's being defined.
+-- OSError # Errors such as FileExistsError/TimeoutError (see #Open).
| +-- ConnectionError # Errors such as BrokenPipeError/ConnectionAbortedError.
+-- RuntimeError # Raised by errors that don't fall into other categories.
| +-- NotImplementedEr… # Can be raised by abstract methods or by unfinished code.
| +-- RecursionError # Raised when the maximum recursion depth is exceeded.
+-- StopIteration # Raised when an empty iterator is passed to next().
+-- TypeError # When an argument of the wrong type is passed to function.
+-- ValueError # When argument has the right type but inappropriate value.
+-----------+------------+------------+------------+
| | List | Set | Dict |
+-----------+------------+------------+------------+
| getitem() | IndexError | | KeyError |
| pop() | IndexError | KeyError | KeyError |
| remove() | ValueError | KeyError | |
| index() | ValueError | | |
+-----------+------------+------------+------------+
raise TypeError ( 'Argument is of the wrong type!' )
raise ValueError ( 'Argument has the right type but an inappropriate value!' )
raise RuntimeError ( 'I am too lazy to define my own exception!' ) class MyError ( Exception ): pass
class MyInputError ( MyError ): pass Beendet den Interpreter durch Auslösen einer SystemExit-Ausnahme.
import sys
sys . exit () # Exits with exit code 0 (success).
sys . exit ( < int > ) # Exits with the passed exit code.
sys . exit ( < obj > ) # Prints to stderr and exits with 1. print ( < el_1 > , ..., sep = ' ' , end = ' n ' , file = sys . stdout , flush = False )'file=sys.stderr' für Meldungen zu Fehlern.'flush=True' verwendet wird oder das Programm beendet wird. from pprint import pprint
pprint ( < collection > , width = 80 , depth = None , compact = False , sort_dicts = True ) < str > = input ( prompt = None ) import sys
scripts_path = sys . argv [ 0 ]
arguments = sys . argv [ 1 :] from argparse import ArgumentParser , FileType
p = ArgumentParser ( description = < str > ) # Returns a parser.
p . add_argument ( '-<short_name>' , '--<name>' , action = 'store_true' ) # Flag (defaults to False).
p . add_argument ( '-<short_name>' , '--<name>' , type = < type > ) # Option (defaults to None).
p . add_argument ( '<name>' , type = < type > , nargs = 1 ) # Mandatory first argument.
p . add_argument ( '<name>' , type = < type > , nargs = '+' ) # Mandatory remaining args.
p . add_argument ( '<name>' , type = < type > , nargs = '?/*' ) # Optional argument/s.
args = p . parse_args () # Exits on parsing error.
< obj > = args . < name > # Returns `<type>(<arg>)`.'help=<str>' um die Argumentbeschreibung festzulegen, die in der Hilfemeldung angezeigt wird.'default=<obj>' um den Standardwert der Option oder des optionalen Arguments festzulegen.'type=FileType(<mode>)' für Dateien. Akzeptiert „Encoding“, aber „Newline“ ist None. Öffnet die Datei und gibt ein entsprechendes Dateiobjekt zurück.
< file > = open ( < path > , mode = 'r' , encoding = None , newline = None )'encoding=None' bedeutet, dass die Standardkodierung verwendet wird, die plattformabhängig ist. Die beste Vorgehensweise besteht darin, wann immer möglich 'encoding="utf-8"' zu verwenden.'newline=None' bedeutet, dass alle unterschiedlichen Zeilenendekombinationen beim Lesen in „n“ konvertiert werden, während beim Schreiben alle „n“-Zeichen in das standardmäßige Zeilentrennzeichen des Systems konvertiert werden.'newline=""' bedeutet, dass keine Konvertierungen stattfinden, die Eingabe jedoch bei jedem 'n', 'r' und 'rn' durch readline() und readlines() in Blöcke aufgeteilt wird.'r' – Lesen. Standardmäßig verwendet.'w' – Schreiben. Löscht vorhandene Inhalte.'x' – Schreiben oder fehlschlagen, wenn die Datei bereits vorhanden ist.'a' – Anhängen. Erstellt eine neue Datei, wenn diese nicht vorhanden ist.'w+' – Lesen und schreiben. Löscht vorhandene Inhalte.'r+' – Von Anfang an lesen und schreiben.'a+' – Vom Ende an lesen und schreiben.'b' – Binärmodus ( 'rb' , 'wb' , 'xb' , …).'FileNotFoundError' kann beim Lesen mit 'r' oder 'r+' ausgelöst werden.'x' kann 'FileExistsError' ausgelöst werden.'IsADirectoryError' und 'PermissionError' können von jedem ausgelöst werden.'OSError' ist die übergeordnete Klasse aller aufgelisteten Ausnahmen. < 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 , origin ) # Origin: 0 start, 1 current position, 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/bytes on EOF.
< list > = < file > . readlines () # Returns a list of remaining lines.
< str / bytes > = next ( < file > ) # Returns a line using buffer. Do not mix. < file > . write ( < str / bytes > ) # Writes a string or bytes object.
< file > . writelines ( < collection > ) # Writes a coll. of strings or bytes objects.
< file > . flush () # Flushes write buffer. Runs every 4096/8192 B.
< file > . close () # Closes the file after flushing write buffer. def read_file ( filename ):
with open ( filename , encoding = 'utf-8' ) as file :
return file . readlines () def write_to_file ( filename , text ):
with open ( filename , 'w' , encoding = 'utf-8' ) as file :
file . write ( text ) import os , glob
from pathlib import Path < str > = os . getcwd () # Returns working dir. Starts as shell's $PWD.
< str > = os . path . join ( < path > , ...) # Joins two or more pathname components.
< str > = os . path . realpath ( < path > ) # Resolves symlinks and calls path.abspath(). < str > = os . path . basename ( < path > ) # Returns final component of the path.
< str > = os . path . dirname ( < path > ) # Returns path without the final component.
< tup . > = os . path . splitext ( < path > ) # Splits on last period of the final component. < list > = os . listdir ( path = '.' ) # Returns filenames located at the path.
< list > = glob . glob ( '<pattern>' ) # Returns paths matching the wildcard pattern. < bool > = os . path . exists ( < path > ) # Or: <Path>.exists()
< bool > = os . path . isfile ( < path > ) # Or: <DirEntry/Path>.is_file()
< bool > = os . path . isdir ( < path > ) # Or: <DirEntry/Path>.is_dir() < stat > = os . stat ( < path > ) # Or: <DirEntry/Path>.stat()
< num > = < stat > . st_mtime / st_size / … # Modification time, size in bytes, etc.Im Gegensatz zu listdir() gibt scandir() DirEntry-Objekte zurück, die isfile-, isdir- und unter Windows auch stat-Informationen zwischenspeichern, wodurch die Leistung des Codes, der dies erfordert, erheblich gesteigert wird.
< iter > = os . scandir ( path = '.' ) # Returns DirEntry objects located at the path.
< str > = < DirEntry > . path # Returns the whole path as a string.
< str > = < DirEntry > . name # Returns final component as a string.
< file > = open ( < DirEntry > ) # Opens the file and returns a file object. < Path > = Path ( < path > [, ...]) # Accepts strings, Paths, and DirEntry objects.
< Path > = < path > / < path > [ / ...] # First or second path must be a Path object.
< Path > = < Path > . resolve () # Returns absolute path with resolved symlinks. < Path > = Path () # Returns relative CWD. Also Path('.').
< Path > = Path . cwd () # Returns absolute CWD. Also Path().resolve().
< Path > = Path . home () # Returns user's home directory (absolute).
< Path > = Path ( __file__ ). resolve () # Returns script's path if CWD wasn't changed. < Path > = < Path > . parent # Returns Path without the final component.
< str > = < Path > . name # Returns final component as a string.
< str > = < Path > . stem # Returns final component without extension.
< str > = < Path > . suffix # Returns final component's extension.
< tup . > = < Path > . parts # Returns all components as strings. < iter > = < Path > . iterdir () # Returns directory contents as Path objects.
< iter > = < Path > . glob ( '<pattern>' ) # Returns Paths matching the wildcard pattern. < str > = str ( < Path > ) # Returns path as a string.
< file > = open ( < Path > ) # Also <Path>.read/write_text/bytes(<args>). import os , shutil , subprocess os . chdir ( < path > ) # Changes the current working directory.
os . mkdir ( < path > , mode = 0o777 ) # Creates a directory. Permissions are in octal.
os . makedirs ( < path > , mode = 0o777 ) # Creates all path's dirs. Also `exist_ok=False`. shutil . copy ( from , to ) # Copies the file. 'to' can exist or be a dir.
shutil . copy2 ( from , to ) # Also copies creation and modification time.
shutil . copytree ( from , to ) # Copies the directory. 'to' must not exist. os . rename ( from , to ) # Renames/moves the file or directory.
os . replace ( from , to ) # Same, but overwrites file 'to' even on Windows.
shutil . move ( from , to ) # Rename() that moves into 'to' if it's a dir. os . remove ( < path > ) # Deletes the file.
os . rmdir ( < path > ) # Deletes the empty directory.
shutil . rmtree ( < path > ) # Deletes the directory. < pipe > = os . popen ( '<commands>' ) # Executes commands in sh/cmd. Returns combined stdout.
< str > = < pipe > . read ( size = - 1 ) # Reads 'size' chars or until EOF. Also readline/s().
< int > = < pipe > . close () # Returns None if last command exited with returncode 0. > >> subprocess . run ( 'bc' , input = '1 + 1 n ' , capture_output = True , text = True )
CompletedProcess ( args = 'bc' , returncode = 0 , stdout = '2 n ' , stderr = '' ) > >> from shlex import split
> >> os . popen ( 'echo 1 + 1 > test.in' )
> >> subprocess . run ( split ( 'bc -s' ), stdin = open ( 'test.in' ), stdout = open ( 'test.out' , 'w' ))
CompletedProcess ( args = [ 'bc' , '-s' ], returncode = 0 )
> >> open ( 'test.out' ). read ()
'2 n ' Textdateiformat zum Speichern von Sammlungen von Zeichenfolgen und Zahlen.
import json
< str > = json . dumps ( < list / dict > ) # Converts collection to JSON string.
< coll > = json . loads ( < str > ) # Converts JSON string to collection. def read_json_file ( filename ):
with open ( filename , encoding = 'utf-8' ) as file :
return json . load ( file ) def write_to_json_file ( filename , list_or_dict ):
with open ( filename , 'w' , encoding = 'utf-8' ) as file :
json . dump ( list_or_dict , file , ensure_ascii = False , indent = 2 )Binäres Dateiformat zum Speichern von Python-Objekten.
import pickle
< bytes > = pickle . dumps ( < object > ) # Converts object to bytes object.
< object > = pickle . loads ( < bytes > ) # Converts bytes object to object. def read_pickle_file ( filename ):
with open ( filename , 'rb' ) as file :
return pickle . load ( file ) def write_to_pickle_file ( filename , an_object ):
with open ( filename , 'wb' ) as file :
pickle . dump ( an_object , file )Textdateiformat zum Speichern von Tabellenkalkulationen.
import csv < reader > = csv . reader ( < file > ) # Also: `dialect='excel', delimiter=','`.
< list > = next ( < reader > ) # Returns next row as a list of strings.
< list > = list ( < reader > ) # Returns a list of remaining rows.'newline=""' geöffnet werden, sonst werden in Feldern in Anführungszeichen eingebettete Zeilenumbrüche nicht korrekt interpretiert! < writer > = csv . writer ( < file > ) # Also: `dialect='excel', delimiter=','`.
< writer > . writerow ( < collection > ) # Encodes objects using `str(<el>)`.
< writer > . writerows ( < coll_of_coll > ) # Appends multiple rows.'newline=""' geöffnet werden, sonst wird auf Plattformen, die „rn“-Zeilenenden verwenden, vor jedem „n“ ein „r“ eingefügt!'mode="a"' zum Anhängen oder 'mode="w"' zum Überschreiben.'dialect' – Master-Parameter, der die Standardwerte festlegt. String oder ein „csv.Dialect“-Objekt.'delimiter' – Eine Zeichenfolge aus einem Zeichen, die zum Trennen von Feldern verwendet wird.'lineterminator' – Wie Writer Zeilen beendet. Der Reader ist fest codiert auf „n“, „r“, „rn“.'quotechar' – Zeichen für Anführungszeichen in Feldern, die Sonderzeichen enthalten.'escapechar' – Zeichen zum Maskieren von Anführungszeichen.'doublequote' – Ob Anführungszeichen in Feldern verdoppelt oder maskiert werden.'quoting' - 0: Nach Bedarf, 1: Alle, 2: Alle außer Zahlen, die als Gleitkommazahlen gelesen werden, 3: Keine.'skipinitialspace' – ist ein Leerzeichen am Anfang des Feldes, das vom Leser entfernt wird. +------------------+--------------+--------------+--------------+
| | excel | excel-tab | unix |
+------------------+--------------+--------------+--------------+
| delimiter | ',' | 't' | ',' |
| lineterminator | 'rn' | 'rn' | 'n' |
| quotechar | '"' | '"' | '"' |
| escapechar | None | None | None |
| doublequote | True | True | True |
| quoting | 0 | 0 | 1 |
| skipinitialspace | False | False | False |
+------------------+--------------+--------------+--------------+
def read_csv_file ( filename , ** csv_params ):
with open ( filename , encoding = 'utf-8' , newline = '' ) as file :
return list ( csv . reader ( file , ** csv_params )) def write_to_csv_file ( filename , rows , mode = 'w' , ** csv_params ):
with open ( filename , mode , encoding = 'utf-8' , newline = '' ) as file :
writer = csv . writer ( file , ** csv_params )
writer . writerows ( rows )Eine serverlose Datenbank-Engine, die jede Datenbank in einer eigenen Datei speichert.
import sqlite3
< conn > = sqlite3 . connect ( < path > ) # Opens existing or new file. Also ':memory:'.
< conn > . close () # Closes connection. Discards uncommitted data. < cursor > = < conn > . execute ( '<query>' ) # Can raise a subclass of sqlite3.Error.
< tuple > = < cursor > . fetchone () # Returns next row. Also next(<cursor>).
< list > = < cursor > . fetchall () # Returns remaining rows. Also list(<cursor>). < conn > . execute ( '<query>' ) # Can raise a subclass of sqlite3.Error.
< conn > . commit () # Saves all changes since the last commit.
< conn > . rollback () # Discards all changes since the last commit. with < conn > : # Exits the block with commit() or rollback(),
< conn > . execute ( '<query>' ) # depending on whether any exception occurred. < conn > . execute ( '<query>' , < list / tuple > ) # Replaces '?'s in query with values.
< conn > . execute ( '<query>' , < dict / namedtuple > ) # Replaces ':<key>'s with values.
< conn > . executemany ( '<query>' , < coll_of_coll > ) # Runs execute() multiple times. Werte werden in diesem Beispiel nicht wirklich gespeichert, da 'conn.commit()' weggelassen wird!
> >> conn = sqlite3 . connect ( 'test.db' )
> >> conn . execute ( 'CREATE TABLE person (person_id INTEGER PRIMARY KEY, name, height)' )
> >> conn . execute ( 'INSERT INTO person VALUES (NULL, ?, ?)' , ( 'Jean-Luc' , 187 )). lastrowid
1
> >> conn . execute ( 'SELECT * FROM person' ). fetchall ()
[( 1 , 'Jean-Luc' , 187 )]Bibliothek zur Interaktion mit verschiedenen DB-Systemen über SQL, Methodenverkettung oder ORM.
# $ pip3 install sqlalchemy
from sqlalchemy import create_engine , text
< engine > = create_engine ( '<url>' ) # Url: 'dialect://user:password@host/dbname'.
< conn > = < engine > . connect () # Creates a connection. Also <conn>.close().
< cursor > = < conn > . execute ( text ( '<query>' ), …) # `<dict>`. Replaces ':<key>'s with values.
with < conn > . begin (): ... # Exits the block with commit or rollback. +-----------------+--------------+----------------------------------+
| Dialect | pip3 install | Dependencies |
+-----------------+--------------+----------------------------------+
| mysql | mysqlclient | www.pypi.org/project/mysqlclient |
| postgresql | psycopg2 | www.pypi.org/project/psycopg2 |
| mssql | pyodbc | www.pypi.org/project/pyodbc |
| oracle+oracledb | oracledb | www.pypi.org/project/oracledb |
+-----------------+--------------+----------------------------------+
Ein Byteobjekt ist eine unveränderliche Folge einzelner Bytes. Die veränderliche Version heißt Bytearray.
< bytes > = b'<str>' # Only accepts ASCII characters and x00-xff.
< int > = < bytes > [ index ] # Returns an 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 as a separator. < bytes > = bytes ( < coll_of_ints > ) # Ints must be in range from 0 to 255.
< bytes > = bytes ( < str > , 'utf-8' ) # Encodes the string. Also <str>.encode().
< bytes > = bytes . fromhex ( '<hex>' ) # Hex pairs can be separated by whitespaces.
< bytes > = < int > . to_bytes ( n_bytes , …) # `byteorder='big/little', signed=False`. < list > = list ( < bytes > ) # Returns ints in range from 0 to 255.
< str > = str ( < bytes > , 'utf-8' ) # Returns a string. Also <bytes>.decode().
< str > = < bytes > . hex () # Returns hex pairs. Accepts `sep=<str>`.
< int > = int . from_bytes ( < bytes > , …) # `byteorder='big/little', signed=False`. def read_bytes ( filename ):
with open ( filename , 'rb' ) as file :
return file . read () def write_bytes ( filename , bytes_obj ):
with open ( filename , 'wb' ) as file :
file . write ( bytes_obj ) from struct import pack , unpack
< bytes > = pack ( '<format>' , < el_1 > [, ...]) # Packs objects according to format string.
< tuple > = unpack ( '<format>' , < bytes > ) # Use iter_unpack() to get iterator of tuples. > >> 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 )'=' – Bytereihenfolge des Systems (normalerweise Little-Endian).'<' – Little-Endian (dh das niedrigstwertige Byte zuerst).'>' – Big-Endian (auch '!' ). 'c' – Ein Byte-Objekt mit einem einzelnen Element. Für Füllbytes verwenden Sie 'x' .'<n>s' – Ein Byte-Objekt mit n Elementen (nicht von der Byte-Reihenfolge beeinflusst). 'b' - char (1/1)'h' – kurz (2/2)'i' - int (2/4)'l' - lang (4/4)'q' - lang lang (8/8) 'f' - Float (4/4)'d' - doppelt (8/8) Liste, die nur Zahlen eines vordefinierten Typs enthalten kann. Verfügbare Typen und ihre Mindestgrößen in Bytes sind oben aufgeführt. Typgrößen und Bytereihenfolge werden immer vom System bestimmt, die Bytes jedes Elements können jedoch mit der Methode byteswap() umgekehrt werden.
from array import array < array > = array ( '<typecode>' , < coll_of_nums > ) # Creates array from collection of numbers.
< array > = array ( '<typecode>' , < bytes > ) # Writes passed bytes to array's memory.
< array > = array ( '<typecode>' , < array > ) # Treats passed array as a sequence of numbers.
< array > . fromfile ( < file > , n_items ) # Appends file's contents to array's memory. < bytes > = bytes ( < array > ) # Returns a copy of array's memory.
< file > . write ( < array > ) # Writes array's memory to the binary file. Ein Sequenzobjekt, das auf den Speicher eines anderen byteähnlichen Objekts verweist. Jedes Element kann je nach Format auf ein einzelnes oder mehrere aufeinanderfolgende Bytes verweisen. Reihenfolge und Anzahl der Elemente können durch Slicing geändert werden.
< mview > = memoryview ( < bytes / bytearray / array > ) # Immutable if bytes is passed, else mutable.
< obj > = < mview > [ index ] # Returns int or float. Bytes if format is 'c'.
< mview > = < mview > [ < slice > ] # Returns memoryview with rearranged elements.
< mview > = < mview > . cast ( '<typecode>' ) # Only works between B/b/c and other types.
< mview > . release () # Releases memory buffer of the base object. < bytes > = bytes ( < mview > ) # Returns a new bytes object. Also bytearray().
< bytes > = < bytes > . join ( < coll_of_mviews > ) # Joins memoryviews using bytes as a separator.
< array > = array ( '<typecode>' , < mview > ) # Treats memoryview as a sequence of numbers.
< file > . write ( < mview > ) # Writes `bytes(<mview>)` to the binary file. < list > = list ( < mview > ) # Returns a list of ints, floats, or bytes.
< str > = str ( < mview > , 'utf-8' ) # Treats memoryview as a bytes object.
< str > = < mview > . hex () # Returns hex pairs. Accepts `sep=<str>`. Liste mit effizienten Anhängen und Pops von beiden Seiten. Ausgesprochen „Deck“.
from collections import deque < deque > = deque ( < collection > ) # Use `maxlen=<int>` to set size limit.
< deque > . appendleft ( < el > ) # Opposite element is dropped if full.
< deque > . extendleft ( < collection > ) # Passed collection gets reversed.
< deque > . rotate ( n = 1 ) # Last element becomes first.
< el > = < deque > . popleft () # Raises IndexError if deque is empty. Funktionsmodul, das die Funktionalität von Operatoren bereitstellt. Funktionen werden nach Operatorpriorität geordnet und gruppiert, von der geringsten zur höchsten Bindung. Logische und arithmetische Operatoren in den Zeilen 1, 3 und 5 sind auch innerhalb einer Gruppe nach Priorität geordnet.
import operator as op < bool > = op . not_ ( < obj > ) # or, and, not (or/and missing)
< bool > = op . eq / ne / lt / ge / is_ / is_not / contains ( < obj > , < obj > ) # ==, !=, <, >=, is, is not, in
< obj > = op . or_ / xor / and_ ( < int / set > , < int / set > ) # |, ^, &
< int > = op . lshift / rshift ( < int > , < int > ) # <<, >>
< obj > = op . add / sub / mul / truediv / floordiv / mod ( < obj > , < obj > ) # +, -, *, /, //, %
< num > = op . neg / invert ( < num > ) # -, ~
< num > = op . pow ( < num > , < num > ) # **
< func > = op . itemgetter / attrgetter / methodcaller ( < obj > [, ...]) # [index/key], .name, .name([…]) elementwise_sum = map ( op . add , list_a , list_b )
sorted_by_second = sorted ( < coll > , key = op . itemgetter ( 1 ))
sorted_by_both = sorted ( < coll > , key = op . itemgetter ( 1 , 0 ))
first_element = op . methodcaller ( 'pop' , 0 )( < list > )'x < y < z' wird in '(x < y) and (y < z) “ umgewandelt. Führt den ersten Block mit passendem Muster aus. Hinzugefügt in Python 3.10.
match < object / expression > :
case < pattern > [ if < condition > ]:
< code >
... < value_pattern > = 1 / 'abc' / True / None / math . pi # Matches the literal or a dotted name.
< class_pattern > = < type > () # Matches any object of that type (or ABC).
< wildcard_patt > = _ # Matches any object. Useful in last case.
< capture_patt > = < name > # Matches any object and binds it to name.
< as_pattern > = < pattern > as < name > # Binds match to name. Also <type>(<name>).
< or_pattern > = < pattern > | < pattern > [ | ...] # Matches any of the patterns.
< sequence_patt > = [ < pattern > , ...] # Matches sequence with matching items.
< mapping_patt > = { < value_pattern > : < patt > , ...} # Matches dictionary with matching items.
< class_pattern > = < type > ( < attr_name >= < patt > , ...) # Matches object with matching attributes.'*<name>' und '**<name>' in Sequenz-/Zuordnungsmustern, um verbleibende Elemente zu binden.'|' > 'as' > ',' ). > >> from pathlib import Path
> >> match Path ( '/home/gto/python-cheatsheet/README.md' ):
... case Path (
... parts = [ '/' , 'home' , user , * _ ]
... ) as p if p . name . lower (). startswith ( 'readme' ) and p . is_file ():
... print ( f' { p . name } is a readme file that belongs to user { user } .' )
'README.md is a readme file that belongs to user gto.' import logging as log log . basicConfig ( filename = < path > , level = 'DEBUG' ) # Configures the root logger (see Setup).
log . debug / info / warning / error / critical ( < str > ) # Sends message to the root logger.
< Logger > = log . getLogger ( __name__ ) # Returns logger named after the module.
< Logger > . < level > ( < str > ) # Sends message to the logger.
< Logger > . exception ( < str > ) # Error() that appends caught exception. log . basicConfig (
filename = None , # Logs to stderr or appends to file.
format = '%(levelname)s:%(name)s:%(message)s' , # Add '%(asctime)s' for local datetime.
level = log . WARNING , # Drops messages with lower priority.
handlers = [ log . StreamHandler ( sys . stderr )] # Uses FileHandler if filename is set.
) < Formatter > = log . Formatter ( '<format>' ) # Creates a Formatter.
< Handler > = log . FileHandler ( < path > , mode = 'a' ) # Creates a Handler. Also `encoding=None`.
< Handler > . setFormatter ( < Formatter > ) # Adds Formatter to the Handler.
< Handler > . setLevel ( < int / str > ) # Processes all messages by default.
< Logger > . addHandler ( < Handler > ) # Adds Handler to the Logger.
< Logger > . setLevel ( < int / str > ) # What is sent to its/ancestors' handlers.
< Logger > . propagate = < bool > # Cuts off ancestors' handlers if False.'<parent>.<name>' angegeben werden.'filter(<LogRecord>)' (oder die Methode selbst) kann über addFilter() zu Loggern und Handlern hinzugefügt werden. Die Nachricht wird verworfen, wenn filter() einen falschen Wert zurückgibt. > >> logger = log . getLogger ( 'my_module' )
> >> handler = log . FileHandler ( 'test.log' , encoding = 'utf-8' )
> >> handler . setFormatter ( log . Formatter ( '%(asctime)s %(levelname)s:%(name)s:%(message)s' ))
> >> logger . addHandler ( handler )
> >> logger . setLevel ( 'DEBUG' )
> >> log . basicConfig ()
> >> log . root . handlers [ 0 ]. setLevel ( 'WARNING' )
> >> logger . critical ( 'Running out of disk space.' )
CRITICAL : my_module : Running out of disk space .
> >> print ( open ( 'test.log' ). read ())
2023 - 02 - 07 23 : 21 : 01 , 430 CRITICAL : my_module : Running out of disk space . < list > = dir () # List of local names (variables, funcs, classes, modules).
< dict > = vars () # Dict of local names and their objects. Also locals().
< dict > = globals () # Dict of global names and their objects, e.g. __builtin__. < list > = dir ( < obj > ) # Returns names of object's attributes (including methods).
< dict > = vars ( < obj > ) # Returns dict of writable attributes. Also <obj>.__dict__.
< bool > = hasattr ( < obj > , '<name>' ) # Checks if object possesses attribute with passed name.
value = getattr ( < obj > , '<name>' ) # Returns object's attribute or raises AttributeError.
setattr ( < obj > , '<name>' , value ) # Sets attribute. Only works on objects with __dict__ attr.
delattr ( < obj > , '<name>' ) # Deletes attribute from __dict__. Also `del <obj>.<name>`. < Sig > = inspect . signature ( < func > ) # Returns a Signature object of the passed function.
< dict > = < Sig > . parameters # Returns dict of Parameters. Also <Sig>.return_annotation.
< memb > = < Param > . kind # Returns ParameterKind member (Parameter.KEYWORD_ONLY, …).
< type > = < Param > . annotation # Returns Parameter.empty if missing. Also <Param>.default. Der CPython-Interpreter kann jeweils nur einen einzelnen Thread ausführen. Die Verwendung mehrerer Threads führt nicht zu einer schnelleren Ausführung, es sei denn, mindestens einer der Threads enthält einen E/A-Vorgang.
from threading import Thread , Lock , RLock , Semaphore , Event , Barrier
from concurrent . futures import ThreadPoolExecutor , as_completed < Thread > = Thread ( target = < function > ) # Use `args=<collection>` to set the arguments.
< Thread > . start () # Starts the thread. Also <Thread>.is_alive().
< Thread > . join () # Waits for the thread to finish.'kwargs=<dict>' um Schlüsselwortargumente an die Funktion zu übergeben.'daemon=True' , sonst kann das Programm nicht beendet werden, während der Thread aktiv ist. < lock > = Lock / RLock () # RLock can only be released by acquirer.
< lock > . acquire () # Waits for the lock to be available.
< lock > . release () # Makes the lock available again. with < lock > : # Enters the block by calling acquire() and
... # exits it with release(), even on error. < Semaphore > = Semaphore ( value = 1 ) # Lock that can be acquired by 'value' threads.
< Event > = Event () # Method wait() blocks until set() is called.
< Barrier > = Barrier ( n_times ) # Wait() blocks until it's called n times. < Queue > = queue . Queue ( maxsize = 0 ) # A thread-safe first-in-first-out queue.
< Queue > . put ( < el > ) # Blocks until queue stops being full.
< Queue > . put_nowait ( < el > ) # Raises queue.Full exception if full.
< el > = < Queue > . get () # Blocks until queue stops being empty.
< el > = < Queue > . get_nowait () # Raises queue.Empty exception if empty. < Exec > = ThreadPoolExecutor ( max_workers = None ) # Or: `with ThreadPoolExecutor() as <name>: ...`
< iter > = < Exec > . map ( < func > , < args_1 > , ...) # Multithreaded and non-lazy map(). Keeps order.
< Futr > = < Exec > . submit ( < func > , < arg_1 > , ...) # Creates a thread and returns its Future obj.
< Exec > . shutdown () # Blocks until all threads finish executing. < bool > = < Future > . done () # Checks if the thread has finished executing.
< obj > = < Future > . result ( timeout = None ) # Waits for thread to finish and returns result.
< bool > = < Future > . cancel () # Cancels or returns False if running/finished.
< iter > = as_completed ( < coll_of_Futures > ) # `next(<iter>)` returns next completed Future.'if __name__ == "__main__": ...' . 'async' und ihr Aufruf mit 'await' .'asyncio.run(<coroutine>)' um die erste/Haupt-Coroutine zu starten. import asyncio as aio < coro > = < async_function > ( < args > ) # Creates a coroutine by calling async def function.
< obj > = await < coroutine > # Starts the coroutine and returns its result.
< task > = aio . create_task ( < coroutine > ) # Schedules the coroutine for execution.
< obj > = await < task > # Returns coroutine's result. Also <task>.cancel(). < coro > = aio . gather ( < coro / task > , ...) # Schedules coros. Returns list of results on await.
< coro > = aio . wait ( < tasks > , …) # `aio.ALL/FIRST_COMPLETED`. Returns (done, pending).
< iter > = aio . as_completed ( < coros / tasks > ) # Iterator of coros. All return next result on await. import asyncio , collections , curses , curses . textpad , enum , random
P = collections . namedtuple ( 'P' , 'x y' ) # Position
D = enum . Enum ( 'D' , 'n e s w' ) # Direction
W , H = 15 , 7 # Width, Height
def main ( screen ):
curses . curs_set ( 0 ) # Makes cursor invisible.
screen . nodelay ( True ) # Makes getch() non-blocking.
asyncio . run ( main_coroutine ( screen )) # Starts running asyncio code.
async def main_coroutine ( screen ):
moves = asyncio . Queue ()
state = { '*' : P ( 0 , 0 )} | { id_ : P ( W // 2 , H // 2 ) for id_ in range ( 10 )}
ai = [ random_controller ( id_ , moves ) for id_ in range ( 10 )]
mvc = [ human_controller ( screen , moves ), model ( moves , state ), view ( state , screen )]
tasks = [ asyncio . create_task ( coro ) for coro in ai + mvc ]
await asyncio . wait ( tasks , return_when = asyncio . FIRST_COMPLETED )
async def random_controller ( id_ , moves ):
while True :
d = random . choice ( list ( D ))
moves . put_nowait (( id_ , d ))
await asyncio . sleep ( random . triangular ( 0.01 , 0.65 ))
async def human_controller ( screen , moves ):
while True :
key_mappings = { 258 : D . s , 259 : D . n , 260 : D . w , 261 : D . e }
if d := key_mappings . get ( screen . getch ()):
moves . put_nowait (( '*' , d ))
await asyncio . sleep ( 0.005 )
async def model ( moves , state ):
while state [ '*' ] not in ( state [ id_ ] for id_ in range ( 10 )):
id_ , d = await moves . get ()
deltas = { D . n : P ( 0 , - 1 ), D . e : P ( 1 , 0 ), D . s : P ( 0 , 1 ), D . w : P ( - 1 , 0 )}
state [ id_ ] = P (( state [ id_ ]. x + deltas [ d ]. x ) % W , ( state [ id_ ]. y + deltas [ d ]. y ) % H )
async def view ( state , screen ):
offset = P ( curses . COLS // 2 - W // 2 , curses . LINES // 2 - H // 2 )
while True :
screen . erase ()
curses . textpad . rectangle ( screen , offset . y - 1 , offset . x - 1 , offset . y + H , offset . x + W )
for id_ , p in state . items ():
screen . addstr ( offset . y + ( p . y - state [ '*' ]. y + H // 2 ) % H ,
offset . x + ( p . x - state [ '*' ]. x + W // 2 ) % W , str ( id_ ))
screen . refresh ()
await asyncio . sleep ( 0.005 )
if __name__ == '__main__' :
curses . wrapper ( main ) # $ pip3 install tqdm
> >> import tqdm , time
> >> for el in tqdm . tqdm ([ 1 , 2 , 3 ], desc = 'Processing' ):
... time . sleep ( 1 )
Processing : 100 % | ████████████████████ | 3 / 3 [ 00 : 03 < 00 : 00 , 1.00 s / it ] # $ pip3 install matplotlib
import matplotlib . pyplot as plt
plt . plot / bar / scatter ( x_data , y_data [, label = < str > ]) # Also plt.plot(y_data).
plt . legend () # Adds a legend.
plt . title / xlabel / ylabel ( < str > ) # Adds a title or label.
plt . savefig ( < path > ) # Saves the plot.
plt . show () # Displays the plot.
plt . clf () # Clears the plot. # $ pip3 install tabulate
import csv , tabulate
with open ( 'test.csv' , encoding = 'utf-8' , newline = '' ) as file :
rows = list ( csv . reader ( file ))
print ( tabulate . tabulate ( rows , headers = 'firstrow' )) # $ pip3 install windows-curses
import curses , os
from curses import A_REVERSE , KEY_DOWN , KEY_UP , KEY_LEFT , KEY_RIGHT , KEY_ENTER
def main ( screen ):
ch , first , selected , paths = 0 , 0 , 0 , os . listdir ()
while ch != ord ( 'q' ):
height , width = screen . getmaxyx ()
screen . erase ()
for y , filename in enumerate ( paths [ first : first + height ]):
color = A_REVERSE if filename == paths [ selected ] else 0
screen . addnstr ( y , 0 , filename , width - 1 , color )
ch = screen . getch ()
selected += ( ch == KEY_DOWN ) - ( ch == KEY_UP )
selected = max ( 0 , min ( len ( paths ) - 1 , selected ))
first += ( selected >= first + height ) - ( selected < first )
if ch in [ KEY_LEFT , KEY_RIGHT , KEY_ENTER , ord ( ' n ' ), ord ( ' r ' )]:
new_dir = '..' if ch == KEY_LEFT else paths [ selected ]
if os . path . isdir ( new_dir ):
os . chdir ( new_dir )
first , selected , paths = 0 , 0 , os . listdir ()
if __name__ == '__main__' :
curses . wrapper ( main ) # $ pip3 install PySimpleGUI
import PySimpleGUI as sg
text_box = sg . Input ( default_text = '100' , enable_events = True , key = '-QUANTITY-' )
dropdown = sg . InputCombo ([ 'g' , 'kg' , 't' ], 'kg' , readonly = True , enable_events = True , k = '-UNIT-' )
label = sg . Text ( '100 kg is 220.462 lbs.' , key = '-OUTPUT-' )
button = sg . Button ( 'Close' )
window = sg . Window ( 'Weight Converter' , [[ text_box , dropdown ], [ label ], [ button ]])
while True :
event , values = window . read ()
if event in [ sg . WIN_CLOSED , 'Close' ]:
break
try :
quantity = float ( values [ '-QUANTITY-' ])
except ValueError :
continue
unit = values [ '-UNIT-' ]
factors = { 'g' : 0.001 , 'kg' : 1 , 't' : 1000 }
lbs = quantity * factors [ unit ] / 0.45359237
window [ '-OUTPUT-' ]. update ( value = f' { quantity } { unit } is { lbs :g } lbs.' )
window . close () # $ pip3 install requests beautifulsoup4
import requests , bs4 , os
response = requests . get ( 'https://en.wikipedia.org/wiki/Python_(programming_language)' )
document = bs4 . BeautifulSoup ( response . text , 'html.parser' )
table = document . find ( 'table' , class_ = 'infobox vevent' )
python_url = table . find ( 'th' , text = 'Website' ). next_sibling . a [ 'href' ]
logo_url = table . find ( 'img' )[ 'src' ]
logo = requests . get ( f'https: { logo_url } ' ). content
filename = os . path . basename ( logo_url )
with open ( filename , 'wb' ) as file :
file . write ( logo )
print ( f' { python_url } , file:// { os . path . abspath ( filename ) } ' )Bibliothek zum Scrapen von Websites mit dynamischen Inhalten.
# $ pip3 install selenium
from selenium import webdriver
< WebDrv > = webdriver . Chrome / Firefox / Safari / Edge () # Opens a browser. Also <WebDrv>.quit().
< WebDrv > . get ( '<url>' ) # Also <WebDrv>.implicitly_wait(seconds).
< El > = < WebDrv / El > . find_element ( 'css selector' , …) # '<tag>#<id>.<class>[<attr>="<val>"]…'.
< list > = < WebDrv / El > . find_elements ( 'xpath' , …) # '//<tag>[@<attr>="<val>"]…'. See XPath.
< str > = < El > . get_attribute ( < str > ) # Property if exists. Also <El>.text.
< El > . click / clear () # Also <El>.send_keys(<str>). '$x("<xpath>")' : < xpath > = // < element > [ / or // < element > ] # /<child>, //<descendant>, /../<sibling>
< xpath > = // < element > / following :: < element > # Next element. Also preceding/parent/…
< element > = < tag > < conditions > < index > # `<tag> = */a/…`, `<index> = [1/2/…]`.
< condition > = [ < sub_cond > [ and / or < sub_cond > ]] # For negation use `not(<sub_cond>)`.
< sub_cond > = @ < attr > [ = "<val>" ] # `text()=`, `.=` match (complete) text.
< sub_cond > = contains (@ < attr > , "<val>" ) # Is <val> a substring of attr's value?
< sub_cond > = [ // ] < element > # Has matching child? Descendant if //. Flask ist ein Micro-Web-Framework/Server. Wenn Sie nur eine HTML-Datei in einem Webbrowser öffnen möchten, verwenden Sie stattdessen 'webbrowser.open(<path>)' .
# $ pip3 install flask
import flask as fl app = fl . Flask ( __name__ ) # Returns the app object. Put at the top.
app . run ( host = None , port = None , debug = None ) # Or: $ flask --app FILE run [--ARG[=VAL]]…'http://localhost:5000' . Verwenden Sie 'host="0.0.0.0"' für die externe Ausführung. @ app . route ( '/img/<path:filename>' )
def serve_file ( filename ):
return fl . send_from_directory ( 'dirname/' , filename ) @ app . route ( '/<sport>' )
def serve_html ( sport ):
return fl . render_template_string ( '<h1>{{title}}</h1>' , title = sport )'fl.render_template(filename, <kwargs>)' rendert eine Datei, die sich im Verzeichnis „templates“ befindet.'fl.abort(<int>)' gibt einen Fehlercode zurück und 'return fl.redirect(<url>)' leitet weiter.'fl.request.args[<str>]' gibt Parameter aus der Abfragezeichenfolge zurück (URL rechts von „?“).'fl.session[<str>] = <obj>' speichert Sitzungsdaten. Es erfordert, dass beim Start ein geheimer Schlüssel mit 'app.secret_key = <str>' festgelegt wird. @ app . post ( '/<sport>/odds' )
def serve_json ( sport ):
team = fl . request . form [ 'team' ]
return { 'team' : team , 'odds' : [ 2.09 , 3.74 , 3.68 ]} # $ pip3 install requests
> >> import threading , requests
> >> threading . Thread ( target = app . run , daemon = True ). start ()
> >> url = 'http://localhost:5000/football/odds'
> >> response = requests . post ( url , data = { 'team' : 'arsenal f.c.' })
> >> response . json ()
{ 'team' : 'arsenal f.c.' , 'odds' : [ 2.09 , 3.74 , 3.68 ]} from time import perf_counter
start_time = perf_counter ()
...
duration_in_seconds = perf_counter () - start_time > >> from timeit import timeit
> >> timeit ( 'list(range(10000))' , number = 1000 , globals = globals (), setup = 'pass' )
0.19373 $ pip3 install line_profiler
$ echo '@profile
def main():
a = list(range(10000))
b = set(range(10000))
main()' > test.py
$ kernprof -lv test.py
Line # Hits Time Per Hit % Time Line Contents
==============================================================
1 @profile
2 def main():
3 1 253.4 253.4 32.2 a = list(range(10000))
4 1 534.1 534.1 67.8 b = set(range(10000))
$ apt/brew install graphviz && pip3 install gprof2dot snakeviz # Or download installer.
$ tail --lines=+2 test.py > test.py # Removes first line.
$ python3 -m cProfile -o test.prof test.py # Runs built-in profiler.
$ gprof2dot --format=pstats test.prof | dot -T png -o test.png # Generates call graph.
$ xdg-open/open test.png # Displays call graph.
$ snakeviz test.prof # Displays flame graph. +--------------+------------+-------------------------------+-------+------+
| pip3 install | Target | How to run | Lines | Live |
+--------------+------------+-------------------------------+-------+------+
| pyinstrument | CPU | pyinstrument test.py | No | No |
| py-spy | CPU | py-spy top -- python3 test.py | No | Yes |
| scalene | CPU+Memory | scalene test.py | Yes | No |
| memray | Memory | memray run --live test.py | Yes | Yes |
+--------------+------------+-------------------------------+-------+------+
Minisprache zur Array-Manipulation. Es kann bis zu hundertmal schneller ausgeführt werden als der entsprechende Python-Code. Eine noch schnellere Alternative, die auf einer GPU läuft, heißt CuPy.
# $ pip3 install numpy
import numpy as np < array > = np . array ( < list / list_of_lists / … > ) # Returns a 1d/2d/… NumPy array.
< array > = np . zeros / ones / empty ( < shape > ) # Also np.full(<shape>, <el>).
< array > = np . arange ( from_inc , to_exc , ± step ) # Also np.linspace(start, stop, len).
< array > = np . random . randint ( from_inc , to_exc , < shape > ) # Also np.random.random(<shape>). < view > = < array > . reshape ( < shape > ) # Also `<array>.shape = <shape>`.
< array > = < array > . flatten () # Also `<view> = <array>.ravel()`.
< view > = < array > . transpose () # Or: <array>.T < array > = np . copy / abs / sqrt / log / int64 ( < array > ) # Returns new array of the same shape.
< array > = < array > . sum / max / mean / argmax / all ( axis ) # Aggregates specified dimension.
< array > = np . apply_along_axis ( < func > , axis , < array > ) # Func can return a scalar or array. < array > = np . concatenate ( < list_of_arrays > , axis = 0 ) # Links arrays along first axis (rows).
< array > = np . vstack / column_stack ( < list_of_arrays > ) # Treats 1d arrays as rows or columns.
< array > = np . tile / repeat ( < array > , < int / list > [, axis ]) # Tiles array or repeats its elements.<el> = <2d>[row_index, col_index] # Or: <3d>[<int>, <int>, <int>]
<1d_view> = <2d>[row_index] # Or: <3d>[<int>, <int>, <slice>]
<1d_view> = <2d>[:, col_index] # Or: <3d>[<int>, <slice>, <int>]
<2d_view> = <2d>[from:to_row_i, from:to_col_i] # Or: <3d>[<int>, <slice>, <slice>] <1d_array> = <2d>[row_indices, col_indices] # Or: <3d>[<int/1d>, <1d>, <1d>]
<2d_array> = <2d>[row_indices] # Or: <3d>[<int/1d>, <1d>, <slice>]
<2d_array> = <2d>[:, col_indices] # Or: <3d>[<int/1d>, <slice>, <1d>]
<2d_array> = <2d>[np.ix_(row_indices, col_indices)] # Or: <3d>[<int/1d/2d>, <2d>, <2d>] <2d_bools> = <2d> > <el/1d/2d> # 1d object must have size of a row.
<1/2d_arr> = <2d>[<2d/1d_bools>] # 1d_bools must have size of a column.':' gibt einen Ausschnitt aller Dimensionsindizes zurück. Ausgelassene Dimensionen werden standardmäßig auf ':' gesetzt.'obj[i, j]' in 'obj[(i, j)]' konvertiert!'ix_([1, 2], [3, 4])' gibt '[[1], [2]]' und '[[3, 4]]' zurück. Aufgrund der Senderegeln ist dies dasselbe wie die Verwendung '[[1, 1], [2, 2]]' und '[[3, 4], [3, 4]]' .Eine Reihe von Regeln, nach denen NumPy-Funktionen auf Arrays unterschiedlicher Form arbeiten.
left = [ 0.1 , 0.6 , 0.8 ] # Shape: (3,)
right = [[ 0.1 ], [ 0.6 ], [ 0.8 ]] # Shape: (3, 1) left = [[ 0.1 , 0.6 , 0.8 ]] # Shape: (1, 3) <- !
right = [[ 0.1 ], [ 0.6 ], [ 0.8 ]] # Shape: (3, 1) left = [[ 0.1 , 0.6 , 0.8 ], # Shape: (3, 3) <- !
[ 0.1 , 0.6 , 0.8 ],
[ 0.1 , 0.6 , 0.8 ]]
right = [[ 0.1 , 0.1 , 0.1 ], # Shape: (3, 3) <- !
[ 0.6 , 0.6 , 0.6 ],
[ 0.8 , 0.8 , 0.8 ]][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 = points - wrapped_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. ]]
> >> distances [ range ( 3 ), range ( 3 )] = np . inf
[[ inf , 0.5 , 0.7 ],
[ 0.5 , inf , 0.2 ],
[ 0.7 , 0.2 , inf ]]
> >> distances . argmin ( 1 )
[ 1 , 2 , 1 ] # $ pip3 install pillow
from PIL import Image < Image > = Image . new ( '<mode>' , ( width , height )) # Creates new image. Also `color=<int/tuple>`.
< Image > = Image . open ( < path > ) # Identifies format based on file's contents.
< Image > = < Image > . convert ( '<mode>' ) # Converts image to the new mode (see Modes).
< Image > . save ( < path > ) # Selects format based on extension (PNG/JPG…).
< Image > . show () # Displays image in default preview app. < int / tup > = < Image > . getpixel (( x , y )) # Returns pixel's value (its color).
< ImgCore > = < Image > . getdata () # Returns a flattened view of pixel values.
< Image > . putpixel (( x , y ), < int / tuple > ) # Updates pixel's value. Clips passed int/s.
< Image > . putdata ( < list / ImgCore > ) # Updates pixels with a copy of the sequence.
< Image > . paste ( < Image > , ( x , y )) # Draws passed image at the specified location. < Image > = < Image > . filter ( < Filter > ) # Use ImageFilter.<name>(<args>) for Filter.
< Image > = < Enhance > . enhance ( < float > ) # Use ImageEnhance.<name>(<Image>) for Enhance. < array > = np . array ( < Image > ) # Creates a 2d/3d NumPy array from the image.
< Image > = Image . fromarray ( np . uint8 ( < array > )) # Use <array>.clip(0, 255) to clip the values.'L' – Helligkeit (Graustufenbild). Jedes Pixel ist ein Ganzzahlwert zwischen 0 und 255.'RGB' – Rot, Grün, Blau (Echtfarbenbild). Jedes Pixel ist ein Tupel aus drei Ganzzahlen.'RGBA' – RGB mit Alpha. Ein niedriger Alpha-Wert (z. B. her int) macht Pixel transparenter.'HSV' – Farbton, Sättigung, Wert. Drei Ganzzahlen, die die Farbe im HSV-Farbraum darstellen. WIDTH , HEIGHT = 100 , 100
n_pixels = WIDTH * HEIGHT
hues = ( 255 * i / n_pixels for i in range ( n_pixels ))
img = Image . new ( 'HSV' , ( WIDTH , HEIGHT ))
img . putdata ([( int ( h ), 255 , 255 ) for h in hues ])
img . convert ( 'RGB' ). save ( 'test.png' ) from random import randint
add_noise = lambda value : max ( 0 , min ( 255 , value + randint ( - 20 , 20 )))
img = Image . open ( 'test.png' ). convert ( 'HSV' )
img . putdata ([( add_noise ( h ), s , v ) for h , s , v in img . getdata ()])
img . show () from PIL import ImageDraw
< Draw > = ImageDraw . Draw ( < Image > ) # Object for adding 2D graphics to the image.
< Draw > . point (( x , y )) # Draws a point. Truncates floats into ints.
< Draw > . line (( x1 , y1 , x2 , y2 [, ...])) # To get anti-aliasing use Image's resize().
< Draw > . arc (( x1 , y1 , x2 , y2 ), deg1 , deg2 ) # Draws in clockwise dir. Also pieslice().
< Draw > . rectangle (( x1 , y1 , x2 , y2 )) # Also rounded_rectangle(), regular_polygon().
< Draw > . polygon (( x1 , y1 , x2 , y2 , ...)) # Last point gets connected to the first.
< Draw > . ellipse (( x1 , y1 , x2 , y2 )) # To rotate use Image's rotate() and paste().
< Draw > . text (( x , y ), < str > , font = < Font > ) # `<Font> = ImageFont.truetype(<path>, size)`.'fill=<color>' um die Primärfarbe festzulegen.'width=<int>' legen Sie die Breite von Linien oder Konturen fest.'outline=<color>' legen Sie die Farbe der Konturen fest.'#rrggbb[aa]' -String oder ein Farbname sein. # $ pip3 install imageio
from PIL import Image , ImageDraw
import imageio
WIDTH , HEIGHT , R = 126 , 126 , 10
frames = []
for velocity in range ( 1 , 16 ):
y = sum ( range ( velocity ))
frame = Image . new ( 'L' , ( WIDTH , HEIGHT ))
draw = ImageDraw . Draw ( frame )
draw . ellipse (( WIDTH / 2 - R , y , WIDTH / 2 + R , y + R * 2 ), fill = 'white' )
frames . append ( frame )
frames += reversed ( frames [ 1 : - 1 ])
imageio . mimsave ( 'test.gif' , frames , duration = 0.03 ) import wave < Wave > = wave . open ( '<path>' ) # Opens the WAV file for reading.
< int > = < Wave > . getframerate () # Returns number of frames per second.
< int > = < Wave > . getnchannels () # Returns number of samples per frame.
< int > = < Wave > . getsampwidth () # Returns number of bytes per sample.
< tuple > = < Wave > . getparams () # Returns namedtuple of all parameters.
< bytes > = < Wave > . readframes ( nframes ) # Returns next n frames (-1 returns all). < Wave > = wave . open ( '<path>' , 'wb' ) # Creates/truncates a file for writing.
< Wave > . setframerate ( < int > ) # Pass 44100 for CD, 48000 for video.
< Wave > . setnchannels ( < int > ) # Pass 1 for mono, 2 for stereo.
< Wave > . setsampwidth ( < int > ) # Pass 2 for CD, 3 for hi-res sound.
< Wave > . setparams ( < tuple > ) # Tuple must contain all parameters.
< Wave > . writeframes ( < bytes > ) # Appends frames to the file. +-----------+-----------+------+-----------+
| sampwidth | min | zero | max |
+-----------+-----------+------+-----------+
| 1 | 0 | 128 | 255 |
| 2 | -32768 | 0 | 32767 |
| 3 | -8388608 | 0 | 8388607 |
+-----------+-----------+------+-----------+
def read_wav_file ( filename ):
def get_int ( bytes_obj ):
an_int = int . from_bytes ( bytes_obj , 'little' , signed = ( p . sampwidth != 1 ))
return an_int - 128 * ( p . sampwidth == 1 )
with wave . open ( filename ) as file :
p = file . getparams ()
frames = file . readframes ( - 1 )
bytes_samples = ( frames [ i : i + p . sampwidth ] for i in range ( 0 , len ( frames ), p . sampwidth ))
return [ get_int ( b ) / pow ( 2 , ( p . sampwidth * 8 ) - 1 ) for b in bytes_samples ], p def write_to_wav_file ( filename , samples_f , p = None , nchannels = 1 , sampwidth = 2 , framerate = 44100 ):
def get_bytes ( a_float ):
a_float = max ( - 1 , min ( 1 - 2e-16 , a_float ))
a_float += p . sampwidth == 1
a_float *= pow ( 2 , ( p . sampwidth * 8 ) - 1 )
return int ( a_float ). to_bytes ( p . sampwidth , 'little' , signed = ( p . sampwidth != 1 ))
if p is None :
p = wave . _wave_params ( nchannels , sampwidth , framerate , 0 , 'NONE' , 'not compressed' )
with wave . open ( filename , 'wb' ) as file :
file . setparams ( p )
file . writeframes ( b'' . join ( get_bytes ( f ) for f in samples_f )) from math import pi , sin
samples_f = ( sin ( i * 2 * pi * 440 / 44100 ) for i in range ( 100_000 ))
write_to_wav_file ( 'test.wav' , samples_f ) from random import uniform
samples_f , params = read_wav_file ( 'test.wav' )
samples_f = ( f + uniform ( - 0.05 , 0.05 ) for f in samples_f )
write_to_wav_file ( 'test.wav' , samples_f , params ) # $ pip3 install simpleaudio
from simpleaudio import play_buffer
with wave . open ( 'test.wav' ) as file :
p = file . getparams ()
frames = file . readframes ( - 1 )
play_buffer ( frames , p . nchannels , p . sampwidth , p . framerate ). wait_done () # $ pip3 install pyttsx3
import pyttsx3
engine = pyttsx3 . init ()
engine . say ( 'Sally sells seashells by the seashore.' )
engine . runAndWait () # $ pip3 install simpleaudio
import array , itertools as it , math , simpleaudio
F = 44100
P1 = '71♩,69♪,,71♩,66♪,,62♩,66♪,,59♩,,,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 : it . 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 note : 440 * 2 ** (( int ( note [: 2 ]) - 69 ) / 12 )
get_sec = lambda note : 1 / 4 if '♩' in note else 1 / 8
get_samples = lambda note : get_wave ( get_hz ( note ), get_sec ( note )) if note else get_pause ( 1 / 8 )
samples_f = it . chain . from_iterable ( get_samples ( n ) for n in ( P1 + P2 ). split ( ',' ))
samples_i = array . array ( 'h' , ( int ( f * 30000 ) for f in samples_f ))
simpleaudio . play_buffer ( samples_i , 1 , 2 , F ). wait_done () # $ pip3 install pygame
import pygame as pg
pg . init ()
screen = pg . display . set_mode (( 500 , 500 ))
rect = pg . Rect ( 240 , 240 , 20 , 20 )
while not pg . event . get ( pg . QUIT ):
deltas = { pg . K_UP : ( 0 , - 20 ), pg . K_RIGHT : ( 20 , 0 ), pg . K_DOWN : ( 0 , 20 ), pg . K_LEFT : ( - 20 , 0 )}
for event in pg . event . get ( pg . KEYDOWN ):
dx , dy = deltas . get ( event . key , ( 0 , 0 ))
rect = rect . move (( dx , dy ))
screen . fill ( pg . Color ( 'black' ))
pg . draw . rect ( screen , pg . Color ( 'white' ), rect )
pg . display . flip ()Objekt zum Speichern rechtwinkliger Koordinaten.
< Rect > = pg . Rect ( x , y , width , height ) # Creates Rect object. Truncates passed floats.
< int > = < Rect > . x / y / centerx / centery / … # Top, right, bottom, left. Allows assignments.
< tup . > = < Rect > . topleft / center / … # Topright, bottomright, bottomleft. Same.
< Rect > = < Rect > . move (( delta_x , delta_y )) # Use move_ip() to move in-place. < bool > = < Rect > . collidepoint (( x , y )) # Checks if rectangle contains the point.
< bool > = < Rect > . colliderect ( < Rect > ) # Checks if the two rectangles overlap.
< int > = < Rect > . collidelist ( < list_of_Rect > ) # Returns index of first colliding Rect or -1.
< list > = < Rect > . collidelistall ( < list_of_Rect > ) # Returns indices of all colliding rectangles.Objekt zur Darstellung von Bildern.
< Surf > = pg . display . set_mode (( width , height )) # Opens new window and returns its surface.
< Surf > = pg . Surface (( width , height )) # New RGB surface. RGBA if `flags=pg.SRCALPHA`.
< Surf > = pg . image . load ( < path / file > ) # Loads the image. Format depends on source.
< Surf > = pg . surfarray . make_surface ( < np_array > ) # Also `<np_arr> = surfarray.pixels3d(<Surf>)`.
< Surf > = < Surf > . subsurface ( < Rect > ) # Creates a new surface from the cutout. < Surf > . fill ( color ) # Tuple, Color('#rrggbb[aa]') or Color(<name>).
< Surf > . set_at (( x , y ), color ) # Updates pixel. Also <Surf>.get_at((x, y)).
< Surf > . blit ( < Surf > , ( x , y )) # Draws passed surface at specified location. from pygame . transform import scale , ...
< Surf > = scale ( < Surf > , ( width , height )) # Returns scaled surface.
< Surf > = rotate ( < Surf > , anticlock_degrees ) # Returns rotated and scaled surface.
< Surf > = flip ( < Surf > , x_bool , y_bool ) # Returns flipped surface. from pygame . draw import line , ...
line ( < Surf > , color , ( x1 , y1 ), ( x2 , y2 ), width ) # Draws a line to the surface.
arc ( < Surf > , color , < Rect > , from_rad , to_rad ) # Also ellipse(<Surf>, color, <Rect>, width=0).
rect ( < Surf > , color , < Rect > , width = 0 ) # Also polygon(<Surf>, color, points, width=0). < Font > = pg . font . Font ( < path / file > , size ) # Loads TTF file. Pass None for default font.
< Surf > = < Font > . render ( text , antialias , color ) # Background color can be specified at the end. < Sound > = pg . mixer . Sound ( < path / file / bytes > ) # WAV file or bytes/array of signed shorts.
< Sound > . play / stop () # Also set_volume(<float>), fadeout(msec). import collections , dataclasses , enum , io , itertools as it , pygame as pg , urllib . request
from random import randint
P = collections . namedtuple ( 'P' , 'x y' ) # Position
D = enum . Enum ( 'D' , 'n e s w' ) # Direction
W , H , MAX_S = 50 , 50 , P ( 5 , 10 ) # Width, Height, Max speed
def main ():
def get_screen ():
pg . init ()
return pg . display . set_mode (( W * 16 , H * 16 ))
def get_images ():
url = 'https://gto76.github.io/python-cheatsheet/web/mario_bros.png'
img = pg . image . load ( io . BytesIO ( urllib . request . urlopen ( url ). read ()))
return [ img . subsurface ( get_rect ( x , 0 )) for x in range ( img . get_width () // 16 )]
def get_mario ():
Mario = dataclasses . make_dataclass ( 'Mario' , 'rect spd facing_left frame_cycle' . split ())
return Mario ( get_rect ( 1 , 1 ), P ( 0 , 0 ), False , it . cycle ( range ( 3 )))
def get_tiles ():
border = [( x , y ) for x in range ( W ) for y in range ( H ) if x in [ 0 , W - 1 ] or y in [ 0 , H - 1 ]]
platforms = [( randint ( 1 , W - 2 ), randint ( 2 , H - 2 )) for _ in range ( W * H // 10 )]
return [ get_rect ( x , y ) for x , y in border + platforms ]
def get_rect ( x , y ):
return pg . Rect ( x * 16 , y * 16 , 16 , 16 )
run ( get_screen (), get_images (), get_mario (), get_tiles ())
def run ( screen , images , mario , tiles ):
clock = pg . time . Clock ()
pressed = set ()
while not pg . event . get ( pg . QUIT ) and clock . tick ( 28 ):
keys = { pg . K_UP : D . n , pg . K_RIGHT : D . e , pg . K_DOWN : D . s , pg . K_LEFT : D . w }
pressed |= { keys . get ( e . key ) for e in pg . event . get ( pg . KEYDOWN )}
pressed -= { keys . get ( e . key ) for e in pg . event . get ( pg . KEYUP )}
update_speed ( mario , tiles , pressed )
update_position ( mario , tiles )
draw ( screen , images , mario , tiles )
def update_speed ( mario , tiles , pressed ):
x , y = mario . spd
x += 2 * (( D . e in pressed ) - ( D . w in pressed ))
x += ( x < 0 ) - ( x > 0 )
y += 1 if D . s not in get_boundaries ( mario . rect , tiles ) else ( D . n in pressed ) * - 10
mario . spd = P ( x = max ( - MAX_S . x , min ( MAX_S . x , x )), y = max ( - MAX_S . y , min ( MAX_S . y , y )))
def update_position ( mario , tiles ):
x , y = mario . rect . topleft
n_steps = max ( abs ( s ) for s in mario . spd )
for _ in range ( n_steps ):
mario . spd = stop_on_collision ( mario . spd , get_boundaries ( mario . rect , tiles ))
x , y = x + ( mario . spd . x / n_steps ), y + ( mario . spd . y / n_steps )
mario . rect . topleft = x , y
def get_boundaries ( rect , tiles ):
deltas = { D . n : P ( 0 , - 1 ), D . e : P ( 1 , 0 ), D . s : P ( 0 , 1 ), D . w : P ( - 1 , 0 )}
return { d for d , delta in deltas . items () if rect . move ( delta ). collidelist ( tiles ) != - 1 }
def stop_on_collision ( spd , bounds ):
return P ( x = 0 if ( D . w in bounds and spd . x < 0 ) or ( D . e in bounds and spd . x > 0 ) else spd . x ,
y = 0 if ( D . n in bounds and spd . y < 0 ) or ( D . s in bounds and spd . y > 0 ) else spd . y )
def draw ( screen , images , mario , tiles ):
screen . fill (( 85 , 168 , 255 ))
mario . facing_left = mario . spd . x < 0 if mario . spd . x else mario . facing_left
is_airborne = D . s not in get_boundaries ( mario . rect , tiles )
image_index = 4 if is_airborne else ( next ( mario . frame_cycle ) if mario . spd . x else 6 )
screen . blit ( images [ image_index + ( mario . facing_left * 9 )], mario . rect )
for t in tiles :
is_border = t . x in [ 0 , ( W - 1 ) * 16 ] or t . y in [ 0 , ( H - 1 ) * 16 ]
screen . blit ( images [ 18 if is_border else 19 ], t )
pg . display . flip ()
if __name__ == '__main__' :
main ()Datenanalysebibliothek. Beispiele finden Sie unter Plotly.
# $ pip3 install pandas matplotlib
import pandas as pd , matplotlib . pyplot as pltBestelltes Wörterbuch mit einem Namen.
> >> s = pd . Series ([ 1 , 2 ], index = [ 'x' , 'y' ], name = 'a' ); s
x 1
y 2
Name : a , dtype : int64 < S > = pd . Series ( < list > ) # Uses list's indices for 'index'.
< S > = pd . Series ( < dict > ) # Uses dictionary's keys for 'index'. < el > = < S > . loc [ key ] # Or: <S>.iloc[i]
< S > = < S > . loc [ coll_of_keys ] # Or: <S>.iloc[coll_of_i]
< S > = < S > . loc [ from_key : to_key_inc ] # Or: <S>.iloc[from_i : to_i_exc] < el > = < S > [ key / i ] # Or: <S>.<key>
< S > = < S > [ coll_of_keys / coll_of_i ] # Or: <S>[key/i : key/i]
< S > = < S > [ bools ] # Or: <S>.loc/iloc[bools] < S > = < S > > < el / S > # Returns S of bools. Pairs items by keys.
< S > = < S > + < el / S > # Items with non-matching keys get value NaN. < S > = pd . concat ( < coll_of_S > ) # Concats multiple series into one long Series.
< S > = < S > . combine_first ( < S > ) # Adds items that are not yet present.
< S > . update ( < S > ) # Updates items that are already present. < S > . plot . line / area / bar / pie / hist () # Generates a plot. `plt.show()` displays it.'obj[x, y]' in 'obj[(x, y)]' konvertiert wird!'np.int64' . Die Serie wird in 'float64' konvertiert, wenn wir einem Element np.nan zuweisen. Verwenden Sie '<S>.astype(<str/type>)' um konvertierte Serien zu erhalten.'pd.Series([100], dtype="int8") + 100' ausführen. < el > = < S > . sum / max / mean / idxmax / all () # Or: <S>.agg(lambda <S>: <el>)
< S > = < S > . rank / diff / cumsum / ffill / interpol …() # Or: <S>.agg/transform(lambda <S>: <S>)
< S > = < S > . isna / fillna / isin ([ < el / coll > ]) # Or: <S>.agg/transform/map(lambda <el>: <el>) +--------------+-------------+-------------+---------------+
| | 'sum' | ['sum'] | {'s': 'sum'} |
+--------------+-------------+-------------+---------------+
| s.apply(…) | 3 | sum 3 | s 3 |
| s.agg(…) | | | |
+--------------+-------------+-------------+---------------+
+--------------+-------------+-------------+---------------+
| | 'rank' | ['rank'] | {'r': 'rank'} |
+--------------+-------------+-------------+---------------+
| s.apply(…) | | rank | |
| s.agg(…) | x 1.0 | x 1.0 | r x 1.0 |
| | y 2.0 | y 2.0 | y 2.0 |
+--------------+-------------+-------------+---------------+
'inplace=True' .'<S>[key_1, key_2]' um seine Werte abzurufen.Tabelle mit beschrifteten Zeilen und Spalten.
> >> df = pd . DataFrame ([[ 1 , 2 ], [ 3 , 4 ]], index = [ 'a' , 'b' ], columns = [ 'x' , 'y' ]); df
x y
a 1 2
b 3 4 < DF > = pd . DataFrame ( < list_of_rows > ) # Rows can be either lists, dicts or series.
< DF > = pd . DataFrame ( < dict_of_columns > ) # Columns can be either lists, dicts or series. < el > = < DF > . loc [ row_key , col_key ] # Or: <DF>.iloc[row_i, col_i]
< S / DF > = < DF > . loc [ row_key / s ] # Or: <DF>.iloc[row_i/s]
< S / DF > = < DF > . loc [:, col_key / s ] # Or: <DF>.iloc[:, col_i/s]
< DF > = < DF > . loc [ row_bools , col_bools ] # Or: <DF>.iloc[row_bools, col_bools] < S / DF > = < DF > [ col_key / s ] # Or: <DF>.<col_key>
< DF > = < DF > [ < S_of_bools > ] # Filters rows. For example `df[df.x > 1]`.
< DF > = < DF > [ < DF_of_bools > ] # Assigns NaN to items that are False in bools. < DF > = < DF > > < el / S / DF > # Returns DF of bools. S is treated as a row.
< DF > = < DF > + < el / S / DF > # Items with non-matching keys get value NaN. < DF > = < DF > . set_index ( col_key ) # Replaces row keys with column's values.
< DF > = < DF > . reset_index ( drop = False ) # Drops or moves row keys to column named index.
< DF > = < DF > . sort_index ( ascending = True ) # Sorts rows by row keys. Use `axis=1` for cols.
< DF > = < DF > . sort_values ( col_key / s ) # Sorts rows by passed column/s. Also `axis=1`. < DF > = < DF > . head / tail / sample ( < int > ) # Returns first, last, or random n rows.
< DF > = < DF > . describe () # Describes columns. Also info(), corr(), shape.
< DF > = < DF > . query ( '<query>' ) # Filters rows. For example `df.query('x > 1')`. < DF > . plot . line / area / bar / scatter ( x = col_key , …) # `y=col_key/s`. Also hist/box(by=col_key).
plt . show () # Displays the plot. Also plt.savefig(<path>). > >> df_2 = pd . DataFrame ([[ 4 , 5 ], [ 6 , 7 ]], index = [ 'b' , 'c' ], columns = [ 'y' , 'z' ]); df_2
y z
b 4 5
c 6 7 +-----------------------+---------------+------------+------------+---------------------------+
| | 'outer' | 'inner' | 'left' | Description |
+-----------------------+---------------+------------+------------+---------------------------+
| df.merge(df_2, | x y z | x y z | x y z | Merges on column if 'on' |
| on='y', | 0 1 2 . | 3 4 5 | 1 2 . | or 'left_on/right_on' are |
| how=…) | 1 3 4 5 | | 3 4 5 | set, else on shared cols. |
| | 2 . 6 7 | | | Uses 'inner' by default. |
+-----------------------+---------------+------------+------------+---------------------------+
| df.join(df_2, | x yl yr z | | x yl yr z | Merges on row keys. |
| lsuffix='l', | a 1 2 . . | x yl yr z | 1 2 . . | Uses 'left' by default. |
| rsuffix='r', | b 3 4 4 5 | 3 4 4 5 | 3 4 4 5 | If Series is passed, it |
| how=…) | c . . 6 7 | | | is treated as a column. |
+-----------------------+---------------+------------+------------+---------------------------+
| pd.concat([df, df_2], | x y z | y | | Adds rows at the bottom. |
| axis=0, | a 1 2 . | 2 | | Uses 'outer' by default. |
| join=…) | b 3 4 . | 4 | | A Series is treated as a |
| | b . 4 5 | 4 | | column. To add a row use |
| | c . 6 7 | 6 | | pd.concat([df, DF([s])]). |
+-----------------------+---------------+------------+------------+---------------------------+
| pd.concat([df, df_2], | x y y z | | | Adds columns at the |
| axis=1, | a 1 2 . . | x y y z | | right end. Uses 'outer' |
| join=…) | b 3 4 4 5 | 3 4 4 5 | | by default. A Series is |
| | c . . 6 7 | | | treated as a column. |
+-----------------------+---------------+------------+------------+---------------------------+
< S > = < DF > . sum / max / mean / idxmax / all () # Or: <DF>.apply/agg(lambda <S>: <el>)
< DF > = < DF > . rank / diff / cumsum / ffill / interpo …() # Or: <DF>.apply/agg/transform(lambda <S>: <S>)
< DF > = < DF > . isna / fillna / isin ([ < el / coll > ]) # Or: <DF>.applymap(lambda <el>: <el>) +-----------------+---------------+---------------+---------------+
| | 'sum' | ['sum'] | {'x': 'sum'} |
+-----------------+---------------+---------------+---------------+
| df.apply(…) | x 4 | x y | x 4 |
| df.agg(…) | y 6 | sum 4 6 | |
+-----------------+---------------+---------------+---------------+
+-----------------+---------------+---------------+---------------+
| | 'rank' | ['rank'] | {'x': 'rank'} |
+-----------------+---------------+---------------+---------------+
| df.apply(…) | | x y | |
| df.agg(…) | x y | rank rank | x |
| df.transform(…) | a 1.0 1.0 | a 1.0 1.0 | a 1.0 |
| | b 2.0 2.0 | b 2.0 2.0 | b 2.0 |
+-----------------+---------------+---------------+---------------+
'axis=1' um die Zeilen zu verarbeiten.'<DF>.loc[row_key, (col_key_1, col_key_2)]' . < DF > = < DF > . xs ( row_key , level = < int > ) # Rows with key on passed level of multi-index.
< DF > = < DF > . xs ( row_keys , level = < ints > ) # Rows that have first key on first level, etc.
< DF > = < DF > . set_index ( col_keys ) # Combines multiple columns into a multi-index.
< S / DF > = < DF > . stack / unstack ( level = - 1 ) # Combines col keys with row keys or vice versa.
< DF > = < DF > . pivot_table ( index = col_key / s ) # `columns=key/s, values=key/s, aggfunc='mean'`. < DF > = pd . read_json / html ( '<str/path/url>' ) # Run `$ pip3 install beautifulsoup4 lxml`.
< DF > = pd . read_csv ( '<path/url>' ) # `header/index_col/dtype/usecols/…=<obj>`.
< DF > = pd . read_pickle / excel ( '<path/url>' ) # Use `sheet_name=None` to get all Excel sheets.
< DF > = pd . read_sql ( '<table/query>' , < conn . > ) # SQLite3/SQLAlchemy connection (see #SQLite). < dict > = < DF > . to_dict ( 'd/l/s/…' ) # Returns columns as dicts, lists or series.
< str > = < DF > . to_json / html / csv / latex () # Saves output to a file if path is passed.
< DF > . to_pickle / excel ( < path > ) # Run `$ pip3 install "pandas[excel]" odfpy`.
< DF > . to_sql ( '<table_name>' , < connection > ) # Also `if_exists='fail/replace/append'`.'<S> = pd.to_datetime(<S>, errors="coerce")' , das Pd.nat verwendet.'<S>.dt.year/date/…' .Objekt, das die Zeilen eines Datenrahmens zusammenfasst, basierend auf dem Wert der bestandenen Spalte.
< GB > = < DF > . groupby ( col_key / s ) # Splits DF into groups based on passed column.
< DF > = < GB > . apply ( < func > ) # Maps each group. Func can return DF, S or el.
< DF > = < GB > . filter ( < func > ) # Drops a group if function returns False.
< DF > = < GB > . get_group ( < el > ) # Selects a group by grouping column's value.
< S > = < GB > . size () # S of group sizes. Same keys as get_group().
< GB > = < GB > [ col_key ] # Single column GB. All operations return S. < DF > = < GB > . sum / max / mean / idxmax / all () # Or: <GB>.agg(lambda <S>: <el>)
< DF > = < GB > . rank / diff / cumsum / ffill () # Or: <GB>.transform(lambda <S>: <S>)
< DF > = < GB > . fillna ( < el > ) # Or: <GB>.transform(lambda <S>: <S>) 'z' auf reset_index () erstellt: > >> df = pd . DataFrame ([[ 1 , 2 , 3 ], [ 4 , 5 , 6 ], [ 7 , 8 , 6 ]], list ( 'abc' ), list ( 'xyz' ))
> >> gb = df . groupby ( 'z' ); gb . apply ( print )
x y z
a 1 2 3
x y z
b 4 5 6
c 7 8 6
>> > gb . sum ()
x y
z
3 1 2
6 11 13Objekt für Rollfensterberechnungen.
< RS / RDF / RGB > = < S / DF / GB > . rolling ( win_size ) # Also: `min_periods=None, center=False`.
< RS / RDF / RGB > = < RDF / RGB > [ col_key / s ] # Or: <RDF/RGB>.col_key
< S / DF > = < R > . mean / sum / max () # Or: <R>.apply/agg(<agg_func/str>) # $ pip3 install plotly kaleido pandas
import plotly . express as px , pandas as pd < Fig > = px . line ( < DF > , x = col_key , y = col_key ) # Or: px.line(x=<list>, y=<list>)
< Fig > . update_layout ( margin = dict ( t = 0 , r = 0 , b = 0 , l = 0 )) # Also `paper_bgcolor='rgb(0, 0, 0)'`.
< Fig > . write_html / json / image ( '<path>' ) # <Fig>.show() displays the plot. < Fig > = px . area / bar / box ( < DF > , x = col_key , y = col_key ) # Also `color=col_key`.
< Fig > = px . scatter ( < DF > , x = col_key , y = col_key ) # Also `color/size/symbol=col_key`.
< Fig > = px . scatter_3d ( < DF > , x = col_key , y = col_key , …) # `z=col_key`. Also color/size/symbol.
< Fig > = px . histogram ( < DF > , x = col_key [, nbins = < int > ]) # Number of bins depends on DF size. 
covid = pd . read_csv ( 'https://raw.githubusercontent.com/owid/covid-19-data/8dde8ca49b'
'6e648c17dd420b2726ca0779402651/public/data/owid-covid-data.csv' ,
usecols = [ 'iso_code' , 'date' , 'total_deaths' , 'population' ])
continents = pd . read_csv ( 'https://gto76.github.io/python-cheatsheet/web/continents.csv' ,
usecols = [ 'Three_Letter_Country_Code' , 'Continent_Name' ])
df = pd . merge ( covid , continents , left_on = 'iso_code' , right_on = 'Three_Letter_Country_Code' )
df = df . groupby ([ 'Continent_Name' , 'date' ]). sum (). reset_index ()
df [ 'Total Deaths per Million' ] = df . total_deaths * 1e6 / df . population
df = df [ df . date > '2020-03-14' ]
df = df . rename ({ 'date' : 'Date' , 'Continent_Name' : 'Continent' }, axis = 'columns' )
px . line ( df , x = 'Date' , y = 'Total Deaths per Million' , color = 'Continent' ). show ()
import pandas as pd , plotly . graph_objects as go
def main ():
covid , bitcoin , gold , dow = scrape_data ()
df = wrangle_data ( covid , bitcoin , gold , dow )
display_data ( df )
def scrape_data ():
def get_covid_cases ():
url = 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
df = pd . read_csv ( url , usecols = [ 'location' , 'date' , 'total_cases' ])
df = df [ df . location == 'World' ]
return df . set_index ( 'date' ). total_cases
def get_ticker ( symbol ):
url = ( f'https://query1.finance.yahoo.com/v7/finance/download/ { symbol } ?'
'period1=1579651200&period2=9999999999&interval=1d&events=history' )
df = pd . read_csv ( url , usecols = [ 'Date' , 'Close' ])
return df . set_index ( 'Date' ). Close
out = get_covid_cases (), get_ticker ( 'BTC-USD' ), get_ticker ( 'GC=F' ), get_ticker ( '^DJI' )
names = [ 'Total Cases' , 'Bitcoin' , 'Gold' , 'Dow Jones' ]
return map ( pd . Series . rename , out , names )
def wrangle_data ( covid , bitcoin , gold , dow ):
df = pd . concat ([ bitcoin , gold , dow ], axis = 1 ) # Creates table by joining columns on dates.
df = df . sort_index (). interpolate () # Sorts rows by date and interpolates NaN-s.
df = df . loc [ '2020-02-23' :] # Discards rows before '2020-02-23'.
df = ( df / df . iloc [ 0 ]) * 100 # Calculates percentages relative to day 1.
df = df . join ( covid ) # Adds column with covid cases.
return df . sort_values ( df . index [ - 1 ], axis = 1 ) # Sorts columns by last day's value.
def display_data ( df ):
figure = go . Figure ()
for col_name in reversed ( df . columns ):
yaxis = 'y1' if col_name == 'Total Cases' else 'y2'
trace = go . Scatter ( x = df . index , y = df [ col_name ], name = col_name , yaxis = yaxis )
figure . add_trace ( trace )
figure . update_layout (
yaxis1 = dict ( title = 'Total Cases' , rangemode = 'tozero' ),
yaxis2 = dict ( title = '%' , rangemode = 'tozero' , overlaying = 'y' , side = 'right' ),
legend = dict ( x = 1.08 ),
width = 944 ,
height = 423
)
figure . show ()
if __name__ == '__main__' :
main ()Bibliothek, die den Python-ähnlichen Code in C zusammenstellt, in C.
# $ pip3 install cython
import pyximport ; pyximport . install () # Module that runs imported Cython scripts.
import < cython_script > # Script needs a '.pyx' extension.
< cython_script > . main () # Main() isn't automatically executed. 'cdef' Definitionen sind optional, tragen jedoch zur Beschleunigung bei.'*' und '&' , Strukturen, Gewerkschaften und Enums. cdef < ctype / type > < var_name > [ = < obj > ]
cdef < ctype > [ n_elements ] < var_name > [ = < coll_of_nums > ]
cdef < ctype / type / void > < func_name > ( < ctype / type > < arg_name > ): ... cdef class < class_name > :
cdef public < ctype / type > < attr_name >
def __init__ ( self , < ctype / type > < arg_name > ):
self . < attr_name > = < arg_name >System zur Installation von Bibliotheken direkt in das Projekt des Projekts.
$ python3 -m venv NAME # Creates virtual environment in current directory.
$ source NAME/bin/activate # Activates env. On Windows run `NAMEScriptsactivate`.
$ pip3 install LIBRARY # Installs the library into active environment.
$ python3 FILE # Runs the script in active environment. Also `./FILE`.
$ deactivate # Deactivates the active virtual environment. Führen Sie das Skript mit '$ python3 FILE' oder '$ chmod u+x FILE; ./FILE' . Um den Debugger automatisch zu starten, wenn eine ungewöhnliche Ausnahme auftritt, laufen Sie '$ python3 -m pdb -cc FILE' .
#!/usr/bin/env python3
#
# Usage: .py
#
from sys import argv , exit
from collections import defaultdict , namedtuple
from dataclasses import make_dataclass
from enum import Enum
import functools as ft , itertools as it , operator as op , re
def main ():
pass
###
## UTIL
#
def read_file ( filename ):
with open ( filename , encoding = 'utf-8' ) as file :
return file . readlines ()
if __name__ == '__main__' :
main ()'#<title>' auf der Webseite beschränkt die Suche auf die Titel.'?' Um einen Link zu seinem Abschnitt zu erhalten.
