Source code:
———
This module implements specialized container datatypes providing alternatives to Python’s general purpose built-in containers,
,
,
, and
.
factory function for creating tuple subclasses with named fields
list-like container with fast appends and pops on either end
dict-like class for creating a single view of multiple mappings
dict subclass for counting
objects
dict subclass that remembers the order entries were added
dict subclass that calls a factory function to supply missing values
wrapper around dictionary objects for easier dict subclassing
wrapper around list objects for easier list subclassing
wrapper around string objects for easier string subclassing
objects
Added in version 3.3.
A
class is provided for quickly linking a number of mappings so they can be treated as a single unit. It is often much faster than creating a new dictionary and running multiple
calls.
The class can be used to simulate nested scopes and is useful in templating.
classcollections.ChainMap(*maps)
A ChainMap groups multiple dicts or other mappings together to create a single, updateable view. If no maps are specified, a single empty dictionary is provided so that a new chain always has at least one mapping.
The underlying mappings are stored in a list. That list is public and can be accessed or updated using the maps attribute. There is no other state.
Lookups search the underlying mappings successively until a key is found. In contrast, writes, updates, and deletions only operate on the first mapping.
A ChainMap incorporates the underlying mappings by reference. So, if one of the underlying mappings gets updated, those changes will be reflected in ChainMap.
All of the usual dictionary methods are supported. In addition, there is a maps attribute, a method for creating new subcontexts, and a property for accessing all but the first mapping:
maps
A user updateable list of mappings. The list is ordered from first-searched to last-searched. It is the only stored state and can be modified to change which mappings are searched. The list should always contain at least one mapping.
new_child(m=None, **kwargs)
Returns a new ChainMap containing a new map followed by all of the maps in the current instance. If m is specified, it becomes the new map at the front of the list of mappings; if not specified, an empty dict is used, so that a call to d.new_child() is equivalent to: ChainMap({},*d.maps). If any keyword arguments are specified, they update passed map or new empty dict. This method is used for creating subcontexts that can be updated without altering values in any of the parent mappings.
Changed in version 3.4: The optional m parameter was added.
Changed in version 3.10: Keyword arguments support was added.
parents
Property returning a new ChainMap containing all of the maps in the current instance except the first one. This is useful for skipping the first map in the search. Use cases are similar to those for the
keyword used in
. The use cases also parallel those for the built-in
function. A reference to d.parents is equivalent to: ChainMap(*d.maps[1:]).
Note, the iteration order of a ChainMap is determined by scanning the mappings last to first:
>>> baseline={'music':'bach','art':'rembrandt'}>>> adjustments={'art':'van gogh','opera':'carmen'}>>> list(ChainMap(adjustments,baseline))['music', 'art', 'opera']This gives the same ordering as a series of
calls starting with the last mapping:
>>> combined=baseline.copy()>>> combined.update(adjustments)>>> list(combined)['music', 'art', 'opera']Changed in version 3.9: Added support for | and |= operators, specified in
.
See also
The
in the Enthought
has options to support writing to any mapping in the chain.
Django’s
for templating is a read-only chain of mappings. It also features pushing and popping of contexts similar to the
method and the
property.
The
has options to control whether writes and other mutations apply only to the first mapping or to any mapping in the chain.
A
greatly simplified read-only version of Chainmap
.
Examples and Recipes
This section shows various approaches to working with chained maps.
Example of simulating Python’s internal lookup chain:
importbuiltinspylookup=ChainMap(locals(),globals(),vars(builtins))Example of letting user specified command-line arguments take precedence over environment variables which in turn take precedence over default values:
importos,argparsedefaults={'color':'red','user':'guest'}parser=argparse.ArgumentParser()parser.add_argument('-u','--user')parser.add_argument('-c','--color')namespace=parser.parse_args()command_line_args={k:vfork,vinvars(namespace).items()ifvisnotNone}combined=ChainMap(command_line_args,os.environ,defaults)print(combined['color'])print(combined['user'])Example patterns for using the
class to simulate nested contexts:
c=ChainMap()# Create root contextd=c.new_child()# Create nested child contexte=c.new_child()# Child of c, independent from de.maps[0]# Current context dictionary -- like Python's locals()e.maps[-1]# Root context -- like Python's globals()e.parents# Enclosing context chain -- like Python's nonlocalsd['x']=1# Set value in current contextd['x']# Get first key in the chain of contextsdeld['x']# Delete from current contextlist(d)# All nested valueskind# Check all nested valueslen(d)# Number of nested valuesd.items()# All nested itemsdict(d)# Flatten into a regular dictionaryThe
class only makes updates (writes and deletions) to the first mapping in the chain while lookups will search the full chain. However, if deep writes and deletions are desired, it is easy to make a subclass that updates keys found deeper in the chain:
classDeepChainMap(ChainMap):'Variant of ChainMap that allows direct updates to inner scopes'def__setitem__(self,key,value):formappinginself.maps:ifkeyinmapping:mapping[key]=valuereturnself.maps[0][key]=valuedef__delitem__(self,key):formappinginself.maps:ifkeyinmapping:delmapping[key]returnraiseKeyError(key)>>>d=DeepChainMap({'zebra':'black'},{'elephant':'blue'},{'lion':'yellow'})>>>d['lion']='orange'# update an existing key two levels down>>>d['snake']='red'# new keys get added to the topmost dict>>>deld['elephant']# remove an existing key one level down>>>d# display resultDeepChainMap({'zebra':'black','snake':'red'},{},{'lion':'orange'})
objects
A counter tool is provided to support convenient and rapid tallies. For example:
>>> # Tally occurrences of words in a list>>> cnt=Counter()>>> forwordin['red','blue','red','green','blue','blue']:... cnt[word]+=1...>>> cntCounter({'blue': 3, 'red': 2, 'green': 1})>>> # Find the ten most common words in Hamlet>>> importre>>> words=re.findall(r'\w+',open('hamlet.txt').read().lower())>>> Counter(words).most_common(10)[('the', 1143), ('and', 966), ('to', 762), ('of', 669), ('i', 631), ('you', 554), ('a', 546), ('my', 514), ('hamlet', 471), ('in', 451)]classcollections.Counter(**kwargs)
classcollections.Counter(iterable, /, **kwargs)classcollections.Counter(mapping, /, **kwargs)A Counter is a
subclass for counting
objects. It is a collection where elements are stored as dictionary keys and their counts are stored as dictionary values. Counts are allowed to be any integer value including zero or negative counts. The Counter class is similar to bags or multisets in other languages.
Elements are counted from an iterable or initialized from another mapping (or counter):
>>> c=Counter()# a new, empty counter>>> c=Counter('gallahad')# a new counter from an iterable>>> c=Counter({'red':4,'blue':2})# a new counter from a mapping>>> c=Counter(cats=4,dogs=8)# a new counter from keyword argsCounter objects have a dictionary interface except that they return a zero count for missing items instead of raising a
:
>>> c=Counter(['eggs','ham'])>>> c['bacon']# count of a missing element is zero0Setting a count to zero does not remove an element from a counter. Use del to remove it entirely:
>>> c['sausage']=0# counter entry with a zero count>>> delc['sausage']# del actually removes the entryCounters maintain insertion order internally but display from most common to least common when possible:
>>> c=Counter(a=1,b=2,c=3)>>> c# display most common to leastCounter({'c': 3, 'b': 2, 'a': 1})>>> list(c.items())# original insertion order[('a', 1), ('b', 2), ('c', 3)]Added in version 3.1.
Changed in version 3.7: As a
subclass, Counter inherited the capability to remember insertion order. Math operations on Counter objects also preserve order. Results are ordered according to when an element is first encountered in the left operand and then by the order encountered in the right operand.
Counter objects support additional methods beyond those available for all dictionaries:
elements()
Return an iterator over elements repeating each as many times as its count. Elements are returned in the order first encountered. If an element’s count is less than one, elements() will ignore it.
>>> c=Counter(a=4,b=2,c=0,d=-2)>>> sorted(c.elements())['a', 'a', 'a', 'a', 'b', 'b']most_common(n=None)
Return a list of the n most common elements and their counts from the most common to the least. If n is omitted or None, most_common() returns all elements in the counter. Elements with equal counts are ordered in the order first encountered:
>>> Counter('abracadabra').most_common(3)[('a', 5), ('b', 2), ('r', 2)]subtract(**kwargs)
subtract(iterable, /, **kwargs)subtract(mapping, /, **kwargs)Elements are subtracted from an iterable or from another mapping (or counter). Like
but subtracts counts instead of replacing them. Both inputs and outputs may be zero or negative.
>>> c=Counter(a=4,b=2,c=0,d=-2)>>> d=Counter(a=1,b=2,c=3,d=4)>>> c.subtract(d)>>> cCounter({'a': 3, 'b': 0, 'c': -3, 'd': -6})Added in version 3.2.
total()
Compute the sum of the counts.
>>> c=Counter(a=10,b=5,c=0)>>> c.total()15Added in version 3.10.
The usual dictionary methods are available for Counter objects except for these two which work differently for counters:
fromkeys(iterable)
This class method is not implemented for Counter objects.
update(**kwargs)
update(iterable, /, **kwargs)update(mapping, /, **kwargs)Elements are counted from an iterable or added-in from another mapping (or counter). Like
but adds counts instead of replacing them. Also, the iterable is expected to be a sequence of elements, not a sequence of (key,value) pairs.
Counters support rich comparison operators for equality, subset, and superset relationships: ==, !=, <, <=, >, >=. All of those tests treat missing elements as having zero counts so that Counter(a=1)==Counter(a=1,b=0) returns true.
Changed in version 3.10: Rich comparison operations were added.
Changed in version 3.10: In equality tests, missing elements are treated as having zero counts. Formerly, Counter(a=3) and Counter(a=3,b=0) were considered distinct.
Common patterns for working with
objects:
c.total()# total of all countsc.clear()# reset all countslist(c)# list unique elementsset(c)# convert to a setdict(c)# convert to a regular dictionaryc.items()# access the (elem, cnt) pairsCounter(dict(list_of_pairs))# convert from a list of (elem, cnt) pairsc.most_common()[:-n-1:-1]# n least common elements+c# remove zero and negative countsSeveral mathematical operations are provided for combining
objects to produce multisets (counters that have counts greater than zero). Addition and subtraction combine counters by adding or subtracting the counts of corresponding elements. Intersection and union return the minimum and maximum of corresponding counts. Symmetric difference returns the difference between the maximum and minimum of the corresponding counts. Equality and inclusion compare corresponding counts. Each operation can accept inputs with signed counts, but the output will exclude results with counts of zero or below.
>>> c=Counter(a=3,b=1)>>> d=Counter(a=1,b=2)>>> c+d# add two counters together: c[x] + d[x]Counter({'a': 4, 'b': 3})>>> c-d# subtract (keeping only positive counts)Counter({'a': 2})>>> c&d# intersection: min(c[x], d[x])Counter({'a': 1, 'b': 1})>>> c|d# union: max(c[x], d[x])Counter({'a': 3, 'b': 2})>>> c^d# max(c[x], d[x]) - min(c[x], d[x])Counter({'a': 2, 'b': 1})>>> c==d# equality: c[x] == d[x]False>>> c<=d# inclusion: c[x] <= d[x]FalseUnary addition and subtraction are shortcuts for adding an empty counter or subtracting from an empty counter.
>>> c=Counter(a=2,b=-4)>>> +cCounter({'a': 2})>>> -cCounter({'b': 4})Added in version 3.3: Added support for unary plus, unary minus, and in-place multiset operations.
Added in version 3.15: Added support for the symmetric difference multiset operation, c^d.
Note
Counters were primarily designed to work with positive integers to represent running counts; however, care was taken to not unnecessarily preclude use cases needing other types or negative values. To help with those use cases, this section documents the minimum range and type restrictions.
The
class itself is a dictionary subclass with no restrictions on its keys and values. The values are intended to be numbers representing counts, but you could store anything in the value field.
The
method requires only that the values be orderable.
For in-place operations such as c[key]+=1, the value type need only support addition and subtraction. So fractions, floats, and decimals would work and negative values are supported. The same is also true for
and
which allow negative and zero values for both inputs and outputs.
The multiset methods are designed only for use cases with positive values. The inputs may be negative or zero, but only outputs with positive values are created. There are no type restrictions, but the value type needs to support addition, subtraction, and comparison.
The
method requires integer counts. It ignores zero and negative counts.
See also
in Smalltalk.
Wikipedia entry for
.
tutorial with examples.
For mathematical operations on multisets and their use cases, see Knuth, Donald. The Art of Computer Programming Volume II, Section 4.6.3, Exercise 19.
To enumerate all distinct multisets of a given size over a given set of elements, see
itertools.combinations_with_replacement()
:
map(Counter,combinations_with_replacement('ABC',2))# --> AA AB AC BB BC CC
objects
classcollections.deque([iterable[, maxlen]])
Returns a new deque object initialized left-to-right (using
) with data from iterable. If iterable is not specified, the new deque is empty.
Deques are a generalization of stacks and queues (the name is pronounced “deck” and is short for “double-ended queue”). Deques support thread-safe, memory efficient appends and pops from either side of the deque with approximately the same O(1) performance in either direction.
Though
objects support similar operations, they are optimized for fast fixed-length operations and incur O(n) memory movement costs for pop(0) and insert(0,v) operations which change both the size and position of the underlying data representation.
If maxlen is not specified or is None, deques may grow to an arbitrary length. Otherwise, the deque is bounded to the specified maximum length. Once a bounded length deque is full, when new items are added, a corresponding number of items are discarded from the opposite end. Bounded length deques provide functionality similar to the tail filter in Unix. They are also useful for tracking transactions and other pools of data where only the most recent activity is of interest.
Deques are
over the type of their contents.
Deque objects support the following methods:
append(item, /)
Add item to the right side of the deque.
appendleft(item, /)
Add item to the left side of the deque.
clear()
Remove all elements from the deque leaving it with length 0.
copy()
Create a shallow copy of the deque.
Added in version 3.5.
count(value, /)
Count the number of deque elements equal to value.
Added in version 3.2.
extend(iterable, /)
Extend the right side of the deque by appending elements from the iterable argument.
extendleft(iterable, /)
Extend the left side of the deque by appending elements from iterable. Note, the series of left appends results in reversing the order of elements in the iterable argument.
index(value[, start[, stop]])
Return the position of value in the deque (at or after index start and before index stop). Returns the first match or raises
if not found.
Added in version 3.5.
insert(index, value, /)
Insert value into the deque at position index.
If the insertion would cause a bounded deque to grow beyond maxlen, an
is raised.
Added in version 3.5.
pop()
Remove and return an element from the right side of the deque. If no elements are present, raises an
.
popleft()
Remove and return an element from the left side of the deque. If no elements are present, raises an
.
remove(value, /)
Remove the first occurrence of value. If not found, raises a
.
reverse()
Reverse the elements of the deque in-place and then return None.
Added in version 3.2.
rotate(n=1, /)
Rotate the deque n steps to the right. If n is negative, rotate to the left.
When the deque is not empty, rotating one step to the right is equivalent to d.appendleft(d.pop()), and rotating one step to the left is equivalent to d.append(d.popleft()).
Deque objects also provide one read-only attribute:
maxlen
Maximum size of a deque or None if unbounded.
Added in version 3.1.
In addition to the above, deques support iteration, pickling, len(d), reversed(d), copy.copy(d), copy.deepcopy(d), membership testing with the
operator, and subscript references such as d[0] to access the first element. Indexed access is O(1) at both ends but slows to O(n) in the middle. For fast random access, use lists instead.
Starting in version 3.5, deques support __add__(), __mul__(), and __imul__().
Example:
>>> fromcollectionsimportdeque>>> d=deque('ghi')# make a new deque with three items>>> forelemind:# iterate over the deque's elements... print(elem.upper())GHI>>> d.append('j')# add a new entry to the right side>>> d.appendleft('f')# add a new entry to the left side>>> d# show the representation of the dequedeque(['f', 'g', 'h', 'i', 'j'])>>> d.pop()# return and remove the rightmost item'j'>>> d.popleft()# return and remove the leftmost item'f'>>> list(d)# list the contents of the deque['g', 'h', 'i']>>> d[0]# peek at leftmost item'g'>>> d[-1]# peek at rightmost item'i'>>> list(reversed(d))# list the contents of a deque in reverse['i', 'h', 'g']>>> 'h'ind# search the dequeTrue>>> d.extend('jkl')# add multiple elements at once>>> ddeque(['g', 'h', 'i', 'j', 'k', 'l'])>>> d.rotate(1)# right rotation>>> ddeque(['l', 'g', 'h', 'i', 'j', 'k'])>>> d.rotate(-1)# left rotation>>> ddeque(['g', 'h', 'i', 'j', 'k', 'l'])>>> deque(reversed(d))# make a new deque in reverse orderdeque(['l', 'k', 'j', 'i', 'h', 'g'])>>> d.clear()# empty the deque>>> d.pop()# cannot pop from an empty dequeTraceback (most recent call last):File"<pyshell#6>",line1,in-toplevel-d.pop()IndexError: pop from an empty deque>>> d.extendleft('abc')# extendleft() reverses the input order>>> ddeque(['c', 'b', 'a'])
Recipes
This section shows various approaches to working with deques.
Bounded length deques provide functionality similar to the tail filter in Unix:
deftail(filename,n=10):'Return the last n lines of a file'withopen(filename)asf:returndeque(f,n)Another approach to using deques is to maintain a sequence of recently added elements by appending to the right and popping to the left:
defmoving_average(iterable,n=3):# moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0# https://en.wikipedia.org/wiki/Moving_averageit=iter(iterable)d=deque(itertools.islice(it,n-1))d.appendleft(0)s=sum(d)foreleminit:s+=elem-d.popleft()d.append(elem)yields/nA
can be implemented with input iterators stored in a
. Values are yielded from the active iterator in position zero. If that iterator is exhausted, it can be removed with
; otherwise, it can be cycled back to the end with the
method:
defroundrobin(*iterables):"roundrobin('ABC', 'D', 'EF') --> A D E B F C"iterators=deque(map(iter,iterables))whileiterators:try:whileTrue:yieldnext(iterators[0])iterators.rotate(-1)exceptStopIteration:# Remove an exhausted iterator.iterators.popleft()The
method provides a way to implement
slicing and deletion. For example, a pure Python implementation of deld[n] relies on the rotate() method to position elements to be popped:
defdelete_nth(d,n):d.rotate(-n)d.popleft()d.rotate(n)To implement
slicing, use a similar approach applying
to bring a target element to the left side of the deque. Remove old entries with
, add new entries with
, and then reverse the rotation. With minor variations on that approach, it is easy to implement Forth style stack manipulations such as dup, drop, swap, over, pick, rot, and roll.
objects
classcollections.defaultdict(default_factory=None, /, **kwargs)
classcollections.defaultdict(default_factory, mapping, /, **kwargs)classcollections.defaultdict(default_factory, iterable, /, **kwargs)Return a new dictionary-like object. defaultdict is a subclass of the built-in
class. It overrides one method and adds one writable instance variable. The remaining functionality is the same as for the dict class and is not documented here.
The first argument provides the initial value for the
attribute; it defaults to None. All remaining arguments are treated the same as if they were passed to the
constructor, including keyword arguments.
defaultdicts are
over two types, signifying (respectively) the types of the dictionary’s keys and values.
defaultdict objects support the following method in addition to the standard
operations:
__missing__(key, /)
If the
attribute is None, this raises a
exception with the key as argument.
If
is not None, it is called without arguments to provide a default value for the given key, this value is inserted in the dictionary for the key, and returned.
If calling
raises an exception this exception is propagated unchanged.
This method is called by the
method of the
class when the requested key is not found; whatever it returns or raises is then returned or raised by __getitem__().
Note that __missing__() is not called for any operations besides
. This means that
will, like normal dictionaries, return None as a default rather than using
.
defaultdict objects support the following instance variable:
default_factory
This attribute is used by the
method; it is initialized from the first argument to the constructor, if present, or to None, if absent.
Changed in version 3.9: Added merge (|) and update (|=) operators, specified in
.
Examples
Using
as the
, it is easy to group a sequence of key-value pairs into a dictionary of lists:
>>> s=[('yellow',1),('blue',2),('yellow',3),('blue',4),('red',1)]>>> d=defaultdict(list)>>> fork,vins:... d[k].append(v)...>>> sorted(d.items())[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]When each key is encountered for the first time, it is not already in the mapping; so an entry is automatically created using the
function which returns an empty
. The
operation then attaches the value to the new list. When keys are encountered again, the look-up proceeds normally (returning the list for that key) and the list.append() operation adds another value to the list. This technique is simpler and faster than an equivalent technique using
:
>>> d={}>>> fork,vins:... d.setdefault(k,[]).append(v)...>>> sorted(d.items())[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]Setting the
to
makes the
useful for counting (like a bag or multiset in other languages):
>>> s='mississippi'>>> d=defaultdict(int)>>> forkins:... d[k]+=1...>>> sorted(d.items())[('i', 4), ('m', 1), ('p', 2), ('s', 4)]When a letter is first encountered, it is missing from the mapping, so the
function calls
to supply a default count of zero. The increment operation then builds up the count for each letter.
The function
which always returns zero is just a special case of constant functions. A faster and more flexible way to create constant functions is to use a lambda function which can supply any constant value (not just zero):
>>> defconstant_factory(value):... returnlambda:value...>>> d=defaultdict(constant_factory('<missing>'))>>> d.update(name='John',action='ran')>>> '%(name)s%(action)s to %(object)s'%d'John ran to <missing>'Setting the
to
makes the
useful for building a dictionary of sets:
>>> s=[('red',1),('blue',2),('red',3),('blue',4),('red',1),('blue',4)]>>> d=defaultdict(set)>>> fork,vins:... d[k].add(v)...>>> sorted(d.items())[('blue', {2, 4}), ('red', {1, 3})]
Factory Function for Tuples with Named Fields
Named tuples assign meaning to each position in a tuple and allow for more readable, self-documenting code. They can be used wherever regular tuples are used, and they add the ability to access fields by name instead of position index.
collections.namedtuple(typename, field_names, *, rename=False, defaults=None, module=None)
Returns a new tuple subclass named typename. The new subclass is used to create tuple-like objects that have fields accessible by attribute lookup as well as being indexable and iterable. Instances of the subclass also have a helpful docstring (with typename and field_names) and a helpful
method which lists the tuple contents in a name=value format.
The field_names are a sequence of strings such as ['x','y']. Alternatively, field_names can be a single string with each fieldname separated by whitespace and/or commas, for example 'xy' or 'x,y'.
Any valid Python identifier may be used for a fieldname except for names starting with an underscore. Valid identifiers consist of letters, digits, and underscores but do not start with a digit or underscore and cannot be a
such as class, for, return, global, pass, or raise.
If rename is true, invalid fieldnames are automatically replaced with positional names. For example, ['abc','def','ghi','abc'] is converted to ['abc','_1','ghi','_3'], eliminating the keyword def and the duplicate fieldname abc.
defaults can be None or an
of default values. Since fields with a default value must come after any fields without a default, the defaults are applied to the rightmost parameters. For example, if the fieldnames are ['x','y','z'] and the defaults are (1,2), then x will be a required argument, y will default to 1, and z will default to 2.
If module is defined, the
attribute of the named tuple is set to that value.
Named tuple instances do not have per-instance dictionaries, so they are lightweight and require no more memory than regular tuples.
To support pickling, the named tuple class should be assigned to a variable that matches typename.
Changed in version 3.1: Added support for rename.
Changed in version 3.6: Added the module parameter.
Changed in version 3.7: Removed the verbose parameter and the _source attribute.
Changed in version 3.7: Added the defaults parameter and the
attribute.
>>> # Basic example>>> Point=namedtuple('Point',['x','y'])>>> p=Point(11,y=22)# instantiate with positional or keyword arguments>>> p[0]+p[1]# indexable like the plain tuple (11, 22)33>>> x,y=p# unpack like a regular tuple>>> x,y(11, 22)>>> p.x+p.y# fields also accessible by name33>>> p# readable __repr__ with a name=value stylePoint(x=11, y=22)Named tuples are especially useful for assigning field names to result tuples returned by the
or
modules:
EmployeeRecord=namedtuple('EmployeeRecord','name, age, title, department, paygrade')importcsvforempinmap(EmployeeRecord._make,csv.reader(open("employees.csv","rb"))):print(emp.name,emp.title)importsqlite3conn=sqlite3.connect('/companydata')cursor=conn.cursor()cursor.execute('SELECT name, age, title, department, paygrade FROM employees')forempinmap(EmployeeRecord._make,cursor.fetchall()):print(emp.name,emp.title)In addition to the methods inherited from tuples, named tuples support three additional methods and two attributes. To prevent conflicts with field names, the method and attribute names start with an underscore.
classmethodsomenamedtuple._make(iterable, /)
Class method that makes a new instance from an existing sequence or iterable.
>>> t=[11,22]>>> Point._make(t)Point(x=11, y=22)somenamedtuple._asdict()
Return a new
which maps field names to their corresponding values:
>>> p=Point(x=11,y=22)>>> p._asdict(){'x': 11, 'y': 22}Changed in version 3.1: Returns an
instead of a regular
.
Changed in version 3.8: Returns a regular
instead of an
. As of Python 3.7, regular dicts are guaranteed to be ordered. If the extra features of OrderedDict are required, the suggested remediation is to cast the result to the desired type: OrderedDict(nt._asdict()).
somenamedtuple._replace(**kwargs)
Return a new instance of the named tuple replacing specified fields with new values:
>>> p=Point(x=11,y=22)>>> p._replace(x=33)Point(x=33, y=22)>>> forpartnum,recordininventory.items():... inventory[partnum]=record._replace(price=newprices[partnum],timestamp=time.now())Named tuples are also supported by generic function
.
Changed in version 3.13: Raise
instead of
for invalid keyword arguments.
somenamedtuple._fields
Tuple of strings listing the field names. Useful for introspection and for creating new named tuple types from existing named tuples.
>>> p._fields# view the field names('x', 'y')>>> Color=namedtuple('Color','red green blue')>>> Pixel=namedtuple('Pixel',Point._fields+Color._fields)>>> Pixel(11,22,128,255,0)Pixel(x=11, y=22, red=128, green=255, blue=0)somenamedtuple._field_defaults
Dictionary mapping field names to default values.
>>> Account=namedtuple('Account',['type','balance'],defaults=[0])>>> Account._field_defaults{'balance': 0}>>> Account('premium')Account(type='premium', balance=0)To retrieve a field whose name is stored in a string, use the
function:
>>> getattr(p,'x')11To convert a dictionary to a named tuple, use the double-star-operator (as described in
):
>>> d={'x':11,'y':22}>>> Point(**d)Point(x=11, y=22)Since a named tuple is a regular Python class, it is easy to add or change functionality with a subclass. Here is how to add a calculated field and a fixed-width print format:
>>> classPoint(namedtuple('Point',['x','y'])):... __slots__=()... @property... defhypot(self):... return(self.x**2+self.y**2)**0.5... def__str__(self):... return'Point: x=%6.3f y=%6.3f hypot=%6.3f'%(self.x,self.y,self.hypot)>>> forpinPoint(3,4),Point(14,5/7):... print(p)Point: x= 3.000 y= 4.000 hypot= 5.000Point: x=14.000 y= 0.714 hypot=14.018The subclass shown above sets __slots__ to an empty tuple. This helps keep memory requirements low by preventing the creation of instance dictionaries.
Subclassing is not useful for adding new, stored fields. Instead, simply create a new named tuple type from the
attribute:
>>> Point3D=namedtuple('Point3D',Point._fields+('z',))Docstrings can be customized by making direct assignments to the __doc__ fields:
>>> Book=namedtuple('Book',['id','title','authors'])>>> Book.__doc__+=': Hardcover book in active collection'>>> Book.id.__doc__='13-digit ISBN'>>> Book.title.__doc__='Title of first printing'>>> Book.authors.__doc__='List of authors sorted by last name'Changed in version 3.5: Property docstrings became writeable.
See also
See
for a way to add type hints for named tuples. It also provides an elegant notation using the
keyword:
classComponent(NamedTuple):part_number:intweight:floatdescription:Optional[str]=None
See
for a mutable namespace based on an underlying dictionary instead of a tuple.
The
module provides a decorator and functions for automatically adding generated special methods to user-defined classes.
objects
Ordered dictionaries are just like regular dictionaries but have some extra capabilities relating to ordering operations. They have become less important now that the built-in
class gained the ability to remember insertion order (this new behavior became guaranteed in Python 3.7).
Some differences from
still remain:
The regular
was designed to be very good at mapping operations. Tracking insertion order was secondary.
The
was designed to be good at reordering operations. Space efficiency, iteration speed, and the performance of update operations were secondary.
The
algorithm can handle frequent reordering operations better than
. As shown in the recipes below, this makes it suitable for implementing various kinds of LRU caches.
The equality operation for
checks for matching order.
A regular
can emulate the order sensitive equality test with p==qandall(k1==k2fork1,k2inzip(p,q)).
The
method of
has a different signature. It accepts an optional argument to specify which item is popped.
A regular
can emulate OrderedDict’s od.popitem(last=True) with d.popitem() which is guaranteed to pop the rightmost (last) item.
A regular
can emulate OrderedDict’s od.popitem(last=False) with (k:=next(iter(d)),d.pop(k)) which will return and remove the leftmost (first) item if it exists.
has a
method to efficiently reposition an element to an endpoint.
A regular
can emulate OrderedDict’s od.move_to_end(k,last=True) with d[k]=d.pop(k) which will move the key and its associated value to the rightmost (last) position.
A regular
does not have an efficient equivalent for OrderedDict’s od.move_to_end(k,last=False) which moves the key and its associated value to the leftmost (first) position.
Until Python 3.8,
lacked a
method.
classcollections.OrderedDict(**kwargs)
classcollections.OrderedDict(mapping, /, **kwargs)classcollections.OrderedDict(iterable, /, **kwargs)Return an instance of a
subclass that has methods specialized for rearranging dictionary order.
Added in version 3.1.
popitem(last=True)
The popitem() method for ordered dictionaries returns and removes a (key, value) pair. The pairs are returned in LIFO order if last is true or FIFO order if false.
move_to_end(key, last=True)
Move an existing key to either end of an ordered dictionary. The item is moved to the right end if last is true (the default) or to the beginning if last is false. Raises
if the key does not exist:
>>> d=OrderedDict.fromkeys('abcde')>>> d.move_to_end('b')>>> ''.join(d)'acdeb'>>> d.move_to_end('b',last=False)>>> ''.join(d)'bacde'Added in version 3.2.
In addition to the usual mapping methods, ordered dictionaries also support reverse iteration using
.
Equality tests between
objects are order-sensitive and are roughly equivalent to list(od1.items())==list(od2.items()).
Equality tests between
objects and other
objects are order-insensitive like regular dictionaries. This allows OrderedDict objects to be substituted anywhere a regular dictionary is used.
Changed in version 3.5: The items, keys, and values
of
now support reverse iteration using
.
Changed in version 3.6: With the acceptance of
, order is retained for keyword arguments passed to the
constructor and its
method.
Changed in version 3.9: Added merge (|) and update (|=) operators, specified in
.
Examples and Recipes
It is straightforward to create an ordered dictionary variant that remembers the order the keys were last inserted. If a new entry overwrites an existing entry, the original insertion position is changed and moved to the end:
classLastUpdatedOrderedDict(OrderedDict):'Store items in the order that the keys were last updated.'def__setitem__(self,key,value):super().__setitem__(key,value)self.move_to_end(key)An
would also be useful for implementing variants of
:
fromcollectionsimportOrderedDictfromtimeimportmonotonicclassTimeBoundedLRU:"LRU Cache that invalidates and refreshes old entries."def__init__(self,func,maxsize=128,maxage=30):self.cache=OrderedDict()# { args : (timestamp, result)}self.func=funcself.maxsize=maxsizeself.maxage=maxagedef__call__(self,*args):ifargsinself.cache:self.cache.move_to_end(args)timestamp,result=self.cache[args]ifmonotonic()-timestamp<=self.maxage:returnresultresult=self.func(*args)self.cache[args]=monotonic(),resultiflen(self.cache)>self.maxsize:self.cache.popitem(last=False)returnresultclassMultiHitLRUCache:""" LRU cache that defers caching a result until it has been requested multiple times. To avoid flushing the LRU cache with one-time requests, we don't cache until a request has been made more than once. """def__init__(self,func,maxsize=128,maxrequests=4096,cache_after=1):self.requests=OrderedDict()# { uncached_key : request_count }self.cache=OrderedDict()# { cached_key : function_result }self.func=funcself.maxrequests=maxrequests# max number of uncached requestsself.maxsize=maxsize# max number of stored return valuesself.cache_after=cache_afterdef__call__(self,*args):ifargsinself.cache:self.cache.move_to_end(args)returnself.cache[args]result=self.func(*args)self.requests[args]=self.requests.get(args,0)+1ifself.requests[args]<=self.cache_after:self.requests.move_to_end(args)iflen(self.requests)>self.maxrequests:self.requests.popitem(last=False)else:self.requests.pop(args,None)self.cache[args]=resultiflen(self.cache)>self.maxsize:self.cache.popitem(last=False)returnresult
objects
The class,
acts as a wrapper around dictionary objects. The need for this class has been partially supplanted by the ability to subclass directly from
; however, this class can be easier to work with because the underlying dictionary is accessible as an attribute.
classcollections.UserDict(**kwargs)
classcollections.UserDict(mapping, /, **kwargs)classcollections.UserDict(iterable, /, **kwargs)Class that simulates a dictionary. The instance’s contents are kept in a regular dictionary, which is accessible via the
attribute of UserDict instances. If arguments are provided, they are used to initialize data, like a regular dictionary.
In addition to supporting the methods and operations of mappings, UserDict instances provide the following attribute:
data
A real dictionary used to store the contents of the UserDict class.
UserDict instances also override the following method:
popitem()
Remove and return a (key,value) pair from the wrapped dictionary. Pairs are returned in the same order as data.popitem(). (For the default
, this order is LIFO.) If the dictionary is empty, raises a
.
objects
This class acts as a wrapper around list objects. It is a useful base class for your own list-like classes which can inherit from them and override existing methods or add new ones. In this way, one can add new behaviors to lists.
The need for this class has been partially supplanted by the ability to subclass directly from
; however, this class can be easier to work with because the underlying list is accessible as an attribute.
classcollections.UserList([list])
Class that simulates a list. The instance’s contents are kept in a regular list, which is accessible via the
attribute of UserList instances. The instance’s contents are initially set to a copy of list, defaulting to the empty list []. list can be any iterable, for example a real Python list or a UserList object.
In addition to supporting the methods and operations of mutable sequences, UserList instances provide the following attribute:
data
A real
object used to store the contents of the UserList class.
Subclassing requirements: Subclasses of
are expected to offer a constructor which can be called with either no arguments or one argument. List operations which return a new sequence attempt to create an instance of the actual implementation class. To do so, it assumes that the constructor can be called with a single parameter, which is a sequence object used as a data source.
If a derived class does not wish to comply with this requirement, all of the special methods supported by this class will need to be overridden; please consult the sources for information about the methods which need to be provided in that case.
objects
The class,
acts as a wrapper around string objects. The need for this class has been partially supplanted by the ability to subclass directly from
; however, this class can be easier to work with because the underlying string is accessible as an attribute.
classcollections.UserString(seq)
Class that simulates a string object. The instance’s content is kept in a regular string object, which is accessible via the
attribute of UserString instances. The instance’s contents are initially set to a copy of seq. The seq argument can be any object which can be converted into a string using the built-in
function.
In addition to supporting the methods and operations of strings, UserString instances provide the following attribute:
data
A real
object used to store the contents of the UserString class.
Changed in version 3.5: New methods __getnewargs__, __rmod__, casefold, format_map, isprintable, and maketrans.