Codec.encode(input[, errors])
Encodes the object input and returns a tuple (output object, length consumed). For instance, text encoding converts a string object to a bytes object using a particular character set encoding (e.g., cp1252 or iso-8859-1). The errors argument defines the error handling to apply. It defaults to 'strict' handling. The method may not store state in the Codec instance. Use StreamWriter for codecs which have to keep state in order to make encoding efficient. The encoder must be able to handle zero length input and return an empty object of the output object type in this situation.


class codecs.CodecInfo(encode, decode, streamreader=None, streamwriter=None, incrementalencoder=None, incrementaldecoder=None, name=None)
Codec details when looking up the codec registry. The constructor arguments are stored in attributes of the same name:
name
The name of the encoding.

encode
decode
The stateless encoding and decoding functions. These must be functions or methods which have the same interface as the encode() and decode() methods of Codec instances (see Codec Interface). The functions or methods are expected to work in a stateless mode.

incrementalencoder
incrementaldecoder
Incremental encoder and decoder classes or factory functions. These have to provide the interface defined by the base classes IncrementalEncoder and IncrementalDecoder, respectively. Incremental codecs can maintain state.

streamwriter
streamreader
Stream writer and reader classes or factory functions. These have to provide the interface defined by the base classes StreamWriter and StreamReader, respectively. Stream codecs can maintain state.


encode
decode
The stateless encoding and decoding functions. These must be functions or methods which have the same interface as the encode() and decode() methods of Codec instances (see Codec Interface). The functions or methods are expected to work in a stateless mode.


incrementalencoder
incrementaldecoder
Incremental encoder and decoder classes or factory functions. These have to provide the interface defined by the base classes IncrementalEncoder and IncrementalDecoder, respectively. Incremental codecs can maintain state.


streamwriter
streamreader
Stream writer and reader classes or factory functions. These have to provide the interface defined by the base classes StreamWriter and StreamReader, respectively. Stream codecs can maintain state.


codecs.decode(obj, encoding='utf-8', errors='strict')
Decodes obj using the codec registered for encoding. Errors may be given to set the desired error handling scheme. The default error handler is 'strict' meaning that decoding errors raise ValueError (or a more codec specific subclass, such as UnicodeDecodeError). Refer to Codec Base Classes for more information on codec error handling.


codecs.encode(obj, encoding='utf-8', errors='strict')
Encodes obj using the codec registered for encoding. Errors may be given to set the desired error handling scheme. The default error handler is 'strict' meaning that encoding errors raise ValueError (or a more codec specific subclass, such as UnicodeEncodeError). Refer to Codec Base Classes for more information on codec error handling.


codecs.EncodedFile(file, data_encoding, file_encoding=None, errors='strict')
Return a StreamRecoder instance, a wrapped version of file which provides transparent transcoding. The original file is closed when the wrapped version is closed. Data written to the wrapped file is decoded according to the given data_encoding and then written to the original file as bytes using file_encoding. Bytes read from the original file are decoded according to file_encoding, and the result is encoded using data_encoding. If file_encoding is not given, it defaults to data_encoding. errors may be given to define the error handling. It defaults to 'strict', which causes ValueError to be raised in case an encoding error occurs.


codecs.getdecoder(encoding)
Look up the codec for the given encoding and return its decoder function. Raises a LookupError in case the encoding cannot be found.


codecs.getencoder(encoding)
Look up the codec for the given encoding and return its encoder function. Raises a LookupError in case the encoding cannot be found.


codecs.getincrementaldecoder(encoding)
Look up the codec for the given encoding and return its incremental decoder class or factory function. Raises a LookupError in case the encoding cannot be found or the codec doesn’t support an incremental decoder.


codecs.getincrementalencoder(encoding)
Look up the codec for the given encoding and return its incremental encoder class or factory function. Raises a LookupError in case the encoding cannot be found or the codec doesn’t support an incremental encoder.


codecs.getreader(encoding)
Look up the codec for the given encoding and return its StreamReader class or factory function. Raises a LookupError in case the encoding cannot be found.


codecs.getwriter(encoding)
Look up the codec for the given encoding and return its StreamWriter class or factory function. Raises a LookupError in case the encoding cannot be found.


codecs.ignore_errors(exception)
Implements the 'ignore' error handling: malformed data is ignored and encoding or decoding is continued without further notice.


class codecs.IncrementalDecoder(errors='strict')
Constructor for an IncrementalDecoder instance. All incremental decoders must provide this constructor interface. They are free to add additional keyword arguments, but only the ones defined here are used by the Python codec registry. The IncrementalDecoder may implement different error handling schemes by providing the errors keyword argument. See Error Handlers for possible values. The errors argument will be assigned to an attribute of the same name. Assigning to this attribute makes it possible to switch between different error handling strategies during the lifetime of the IncrementalDecoder object.
decode(object[, final])
Decodes object (taking the current state of the decoder into account) and returns the resulting decoded object. If this is the last call to decode() final must be true (the default is false). If final is true the decoder must decode the input completely and must flush all buffers. If this isn’t possible (e.g. because of incomplete byte sequences at the end of the input) it must initiate error handling just like in the stateless case (which might raise an exception).

reset()
Reset the decoder to the initial state.

getstate()
Return the current state of the decoder. This must be a tuple with two items, the first must be the buffer containing the still undecoded input. The second must be an integer and can be additional state info. (The implementation should make sure that 0 is the most common additional state info.) If this additional state info is 0 it must be possible to set the decoder to the state which has no input buffered and 0 as the additional state info, so that feeding the previously buffered input to the decoder returns it to the previous state without producing any output. (Additional state info that is more complicated than integers can be converted into an integer by marshaling/pickling the info and encoding the bytes of the resulting string into an integer.)

setstate(state)
Set the state of the decoder to state. state must be a decoder state returned by getstate().


decode(object[, final])
Decodes object (taking the current state of the decoder into account) and returns the resulting decoded object. If this is the last call to decode() final must be true (the default is false). If final is true the decoder must decode the input completely and must flush all buffers. If this isn’t possible (e.g. because of incomplete byte sequences at the end of the input) it must initiate error handling just like in the stateless case (which might raise an exception).


getstate()
Return the current state of the decoder. This must be a tuple with two items, the first must be the buffer containing the still undecoded input. The second must be an integer and can be additional state info. (The implementation should make sure that 0 is the most common additional state info.) If this additional state info is 0 it must be possible to set the decoder to the state which has no input buffered and 0 as the additional state info, so that feeding the previously buffered input to the decoder returns it to the previous state without producing any output. (Additional state info that is more complicated than integers can be converted into an integer by marshaling/pickling the info and encoding the bytes of the resulting string into an integer.)


reset()
Reset the decoder to the initial state.


setstate(state)
Set the state of the decoder to state. state must be a decoder state returned by getstate().


class codecs.IncrementalEncoder(errors='strict')
Constructor for an IncrementalEncoder instance. All incremental encoders must provide this constructor interface. They are free to add additional keyword arguments, but only the ones defined here are used by the Python codec registry. The IncrementalEncoder may implement different error handling schemes by providing the errors keyword argument. See Error Handlers for possible values. The errors argument will be assigned to an attribute of the same name. Assigning to this attribute makes it possible to switch between different error handling strategies during the lifetime of the IncrementalEncoder object.
encode(object[, final])
Encodes object (taking the current state of the encoder into account) and returns the resulting encoded object. If this is the last call to encode() final must be true (the default is false).

reset()
Reset the encoder to the initial state. The output is discarded: call .encode(object, final=True), passing an empty byte or text string if necessary, to reset the encoder and to get the output.

getstate()
Return the current state of the encoder which must be an integer. The implementation should make sure that 0 is the most common state. (States that are more complicated than integers can be converted into an integer by marshaling/pickling the state and encoding the bytes of the resulting string into an integer.)

setstate(state)
Set the state of the encoder to state. state must be an encoder state returned by getstate().


encode(object[, final])
Encodes object (taking the current state of the encoder into account) and returns the resulting encoded object. If this is the last call to encode() final must be true (the default is false).


getstate()
Return the current state of the encoder which must be an integer. The implementation should make sure that 0 is the most common state. (States that are more complicated than integers can be converted into an integer by marshaling/pickling the state and encoding the bytes of the resulting string into an integer.)


reset()
Reset the encoder to the initial state. The output is discarded: call .encode(object, final=True), passing an empty byte or text string if necessary, to reset the encoder and to get the output.


setstate(state)
Set the state of the encoder to state. state must be an encoder state returned by getstate().


codecs.iterdecode(iterator, encoding, errors='strict', **kwargs)
Uses an incremental decoder to iteratively decode the input provided by iterator. This function is a generator. The errors argument (as well as any other keyword argument) is passed through to the incremental decoder. This function requires that the codec accept bytes objects to decode. Therefore it does not support text-to-text encoders such as rot_13, although rot_13 may be used equivalently with iterencode().


codecs.iterencode(iterator, encoding, errors='strict', **kwargs)
Uses an incremental encoder to iteratively encode the input provided by iterator. This function is a generator. The errors argument (as well as any other keyword argument) is passed through to the incremental encoder. This function requires that the codec accept text str objects to encode. Therefore it does not support bytes-to-bytes encoders such as base64_codec.


codecs.lookup(encoding)
Looks up the codec info in the Python codec registry and returns a CodecInfo object as defined below. Encodings are first looked up in the registry’s cache. If not found, the list of registered search functions is scanned. If no CodecInfo object is found, a LookupError is raised. Otherwise, the CodecInfo object is stored in the cache and returned to the caller.


codecs.lookup_error(name)
Return the error handler previously registered under the name name. Raises a LookupError in case the handler cannot be found.


codecs.namereplace_errors(exception)
Implements the 'namereplace' error handling (for encoding with text encodings only): the unencodable character is replaced by a \N{...} escape sequence.  New in version 3.5.


codecs.open(filename, mode='r', encoding=None, errors='strict', buffering=-1)
Open an encoded file using the given mode and return an instance of StreamReaderWriter, providing transparent encoding/decoding. The default file mode is 'r', meaning to open the file in read mode.  Note Underlying encoded files are always opened in binary mode. No automatic conversion of '\n' is done on reading and writing. The mode argument may be any binary mode acceptable to the built-in open() function; the 'b' is automatically added.  encoding specifies the encoding which is to be used for the file. Any encoding that encodes to and decodes from bytes is allowed, and the data types supported by the file methods depend on the codec used. errors may be given to define the error handling. It defaults to 'strict' which causes a ValueError to be raised in case an encoding error occurs. buffering has the same meaning as for the built-in open() function. It defaults to -1 which means that the default buffer size will be used.


codecs.register(search_function)
Register a codec search function. Search functions are expected to take one argument, being the encoding name in all lower case letters with hyphens and spaces converted to underscores, and return a CodecInfo object. In case a search function cannot find a given encoding, it should return None.  Changed in version 3.9: Hyphens and spaces are converted to underscore.   Note Search function registration is not currently reversible, which may cause problems in some cases, such as unit testing or module reloading.


codecs.register_error(name, error_handler)
Register the error handling function error_handler under the name name. The error_handler argument will be called during encoding and decoding in case of an error, when name is specified as the errors parameter. For encoding, error_handler will be called with a UnicodeEncodeError instance, which contains information about the location of the error. The error handler must either raise this or a different exception, or return a tuple with a replacement for the unencodable part of the input and a position where encoding should continue. The replacement may be either str or bytes. If the replacement is bytes, the encoder will simply copy them into the output buffer. If the replacement is a string, the encoder will encode the replacement. Encoding continues on original input at the specified position. Negative position values will be treated as being relative to the end of the input string. If the resulting position is out of bound an IndexError will be raised. Decoding and translating works similarly, except UnicodeDecodeError or UnicodeTranslateError will be passed to the handler and that the replacement from the error handler will be put into the output directly.


codecs.replace_errors(exception)
Implements the 'replace' error handling (for text encodings only): substitutes '?' for encoding errors (to be encoded by the codec), and '\ufffd' (the Unicode replacement character) for decoding errors.


class codecs.StreamReader(stream, errors='strict')
Constructor for a StreamReader instance. All stream readers must provide this constructor interface. They are free to add additional keyword arguments, but only the ones defined here are used by the Python codec registry. The stream argument must be a file-like object open for reading text or binary data, as appropriate for the specific codec. The StreamReader may implement different error handling schemes by providing the errors keyword argument. See Error Handlers for the standard error handlers the underlying stream codec may support. The errors argument will be assigned to an attribute of the same name. Assigning to this attribute makes it possible to switch between different error handling strategies during the lifetime of the StreamReader object. The set of allowed values for the errors argument can be extended with register_error().
read([size[, chars[, firstline]]])
Decodes data from the stream and returns the resulting object. The chars argument indicates the number of decoded code points or bytes to return. The read() method will never return more data than requested, but it might return less, if there is not enough available. The size argument indicates the approximate maximum number of encoded bytes or code points to read for decoding. The decoder can modify this setting as appropriate. The default value -1 indicates to read and decode as much as possible. This parameter is intended to prevent having to decode huge files in one step. The firstline flag indicates that it would be sufficient to only return the first line, if there are decoding errors on later lines. The method should use a greedy read strategy meaning that it should read as much data as is allowed within the definition of the encoding and the given size, e.g. if optional encoding endings or state markers are available on the stream, these should be read too.

readline([size[, keepends]])
Read one line from the input stream and return the decoded data. size, if given, is passed as size argument to the stream’s read() method. If keepends is false line-endings will be stripped from the lines returned.

readlines([sizehint[, keepends]])
Read all lines available on the input stream and return them as a list of lines. Line-endings are implemented using the codec’s decode() method and are included in the list entries if keepends is true. sizehint, if given, is passed as the size argument to the stream’s read() method.

reset()
Resets the codec buffers used for keeping internal state. Note that no stream repositioning should take place. This method is primarily intended to be able to recover from decoding errors.


read([size[, chars[, firstline]]])
Decodes data from the stream and returns the resulting object. The chars argument indicates the number of decoded code points or bytes to return. The read() method will never return more data than requested, but it might return less, if there is not enough available. The size argument indicates the approximate maximum number of encoded bytes or code points to read for decoding. The decoder can modify this setting as appropriate. The default value -1 indicates to read and decode as much as possible. This parameter is intended to prevent having to decode huge files in one step. The firstline flag indicates that it would be sufficient to only return the first line, if there are decoding errors on later lines. The method should use a greedy read strategy meaning that it should read as much data as is allowed within the definition of the encoding and the given size, e.g. if optional encoding endings or state markers are available on the stream, these should be read too.


readline([size[, keepends]])
Read one line from the input stream and return the decoded data. size, if given, is passed as size argument to the stream’s read() method. If keepends is false line-endings will be stripped from the lines returned.


readlines([sizehint[, keepends]])
Read all lines available on the input stream and return them as a list of lines. Line-endings are implemented using the codec’s decode() method and are included in the list entries if keepends is true. sizehint, if given, is passed as the size argument to the stream’s read() method.


reset()
Resets the codec buffers used for keeping internal state. Note that no stream repositioning should take place. This method is primarily intended to be able to recover from decoding errors.


class codecs.StreamReaderWriter(stream, Reader, Writer, errors='strict')
Creates a StreamReaderWriter instance. stream must be a file-like object. Reader and Writer must be factory functions or classes providing the StreamReader and StreamWriter interface resp. Error handling is done in the same way as defined for the stream readers and writers.


class codecs.StreamRecoder(stream, encode, decode, Reader, Writer, errors='strict')
Creates a StreamRecoder instance which implements a two-way conversion: encode and decode work on the frontend — the data visible to code calling read() and write(), while Reader and Writer work on the backend — the data in stream. You can use these objects to do transparent transcodings, e.g., from Latin-1 to UTF-8 and back. The stream argument must be a file-like object. The encode and decode arguments must adhere to the Codec interface. Reader and Writer must be factory functions or classes providing objects of the StreamReader and StreamWriter interface respectively. Error handling is done in the same way as defined for the stream readers and writers.


class codecs.StreamWriter(stream, errors='strict')
Constructor for a StreamWriter instance. All stream writers must provide this constructor interface. They are free to add additional keyword arguments, but only the ones defined here are used by the Python codec registry. The stream argument must be a file-like object open for writing text or binary data, as appropriate for the specific codec. The StreamWriter may implement different error handling schemes by providing the errors keyword argument. See Error Handlers for the standard error handlers the underlying stream codec may support. The errors argument will be assigned to an attribute of the same name. Assigning to this attribute makes it possible to switch between different error handling strategies during the lifetime of the StreamWriter object.
write(object)
Writes the object’s contents encoded to the stream.

writelines(list)
Writes the concatenated list of strings to the stream (possibly by reusing the write() method). The standard bytes-to-bytes codecs do not support this method.

reset()
Resets the codec buffers used for keeping internal state. Calling this method should ensure that the data on the output is put into a clean state that allows appending of new fresh data without having to rescan the whole stream to recover state.


reset()
Resets the codec buffers used for keeping internal state. Calling this method should ensure that the data on the output is put into a clean state that allows appending of new fresh data without having to rescan the whole stream to recover state.


write(object)
Writes the object’s contents encoded to the stream.


writelines(list)
Writes the concatenated list of strings to the stream (possibly by reusing the write() method). The standard bytes-to-bytes codecs do not support this method.


codecs.strict_errors(exception)
Implements the 'strict' error handling: each encoding or decoding error raises a UnicodeError.


codecs.xmlcharrefreplace_errors(exception)
Implements the 'xmlcharrefreplace' error handling (for encoding with text encodings only): the unencodable character is replaced by an appropriate XML character reference.


codeop — Compile Python code Source code: Lib/codeop.py The codeop module provides utilities upon which the Python read-eval-print loop can be emulated, as is done in the code module. As a result, you probably don’t want to use the module directly; if you want to include such a loop in your program you probably want to use the code module instead. There are two parts to this job:  Being able to tell if a line of input completes a Python statement: in short, telling whether to print ‘>>>’ or ‘...’ next. Remembering which future statements the user has entered, so subsequent input can be compiled with these in effect.  The codeop module provides a way of doing each of these things, and a way of doing them both. To do just the former:
codeop.compile_command(source, filename="<input>", symbol="single")
Tries to compile source, which should be a string of Python code and return a code object if source is valid Python code. In that case, the filename attribute of the code object will be filename, which defaults to '<input>'. Returns None if source is not valid Python code, but is a prefix of valid Python code. If there is a problem with source, an exception will be raised. SyntaxError is raised if there is invalid Python syntax, and OverflowError or ValueError if there is an invalid literal. The symbol argument determines whether source is compiled as a statement ('single', the default), as a sequence of statements ('exec') or as an expression ('eval'). Any other value will cause ValueError to be raised.  Note It is possible (but not likely) that the parser stops parsing with a successful outcome before reaching the end of the source; in this case, trailing symbols may be ignored instead of causing an error. For example, a backslash followed by two newlines may be followed by arbitrary garbage. This will be fixed once the API for the parser is better.

class codeop.Compile
Instances of this class have __call__() methods identical in signature to the built-in function compile(), but with the difference that if the instance compiles program text containing a __future__ statement, the instance ‘remembers’ and compiles all subsequent program texts with the statement in force.

class codeop.CommandCompiler
Instances of this class have __call__() methods identical in signature to compile_command(); the difference is that if the instance compiles program text containing a __future__ statement, the instance ‘remembers’ and compiles all subsequent program texts with the statement in force.


class codeop.CommandCompiler
Instances of this class have __call__() methods identical in signature to compile_command(); the difference is that if the instance compiles program text containing a __future__ statement, the instance ‘remembers’ and compiles all subsequent program texts with the statement in force.


class codeop.Compile
Instances of this class have __call__() methods identical in signature to the built-in function compile(), but with the difference that if the instance compiles program text containing a __future__ statement, the instance ‘remembers’ and compiles all subsequent program texts with the statement in force.


codeop.compile_command(source, filename="<input>", symbol="single")
Tries to compile source, which should be a string of Python code and return a code object if source is valid Python code. In that case, the filename attribute of the code object will be filename, which defaults to '<input>'. Returns None if source is not valid Python code, but is a prefix of valid Python code. If there is a problem with source, an exception will be raised. SyntaxError is raised if there is invalid Python syntax, and OverflowError or ValueError if there is an invalid literal. The symbol argument determines whether source is compiled as a statement ('single', the default), as a sequence of statements ('exec') or as an expression ('eval'). Any other value will cause ValueError to be raised.  Note It is possible (but not likely) that the parser stops parsing with a successful outcome before reaching the end of the source; in this case, trailing symbols may be ignored instead of causing an error. For example, a backslash followed by two newlines may be followed by arbitrary garbage. This will be fixed once the API for the parser is better.


collections — Container datatypes Source code: Lib/collections/__init__.py This module implements specialized container datatypes providing alternatives to Python’s general purpose built-in containers, dict, list, set, and tuple.
namedtuple() factory function for creating tuple subclasses with named fields
deque list-like container with fast appends and pops on either end
ChainMap dict-like class for creating a single view of multiple mappings
Counter dict subclass for counting hashable objects
OrderedDict dict subclass that remembers the order entries were added
defaultdict dict subclass that calls a factory function to supply missing values
UserDict wrapper around dictionary objects for easier dict subclassing
UserList wrapper around list objects for easier list subclassing
UserString wrapper around string objects for easier string subclassing    Deprecated since version 3.3, will be removed in version 3.10: Moved Collections Abstract Base Classes to the collections.abc module. For backwards compatibility, they continue to be visible in this module through Python 3.9.  ChainMap objects  New in version 3.3.  A ChainMap 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 update() calls. The class can be used to simulate nested scopes and is useful in templating.
class collections.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)
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). 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.

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 nonlocal keyword used in nested scopes. The use cases also parallel those for the built-in super() 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 dict.update() 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 PEP 584.
  See also  The MultiContext class in the Enthought CodeTools package has options to support writing to any mapping in the chain. Django’s Context class for templating is a read-only chain of mappings. It also features pushing and popping of contexts similar to the new_child() method and the parents property. The Nested Contexts recipe 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.
ChainMap Examples and Recipes This section shows various approaches to working with chained maps. Example of simulating Python’s internal lookup chain: import builtins
pylookup = 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: import os, argparse

defaults = {'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: v for k, v in vars(namespace).items() if v is not None}

combined = ChainMap(command_line_args, os.environ, defaults)
print(combined['color'])
print(combined['user'])
 Example patterns for using the ChainMap class to simulate nested contexts: c = ChainMap()        # Create root context
d = c.new_child()     # Create nested child context
e = c.new_child()     # Child of c, independent from d
e.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 nonlocals

d['x'] = 1            # Set value in current context
d['x']                # Get first key in the chain of contexts
del d['x']            # Delete from current context
list(d)               # All nested values
k in d                # Check all nested values
len(d)                # Number of nested values
d.items()             # All nested items
dict(d)               # Flatten into a regular dictionary
 The ChainMap 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: class DeepChainMap(ChainMap):
    'Variant of ChainMap that allows direct updates to inner scopes'

    def __setitem__(self, key, value):
        for mapping in self.maps:
            if key in mapping:
                mapping[key] = value
                return
        self.maps[0][key] = value

    def __delitem__(self, key):
        for mapping in self.maps:
            if key in mapping:
                del mapping[key]
                return
        raise KeyError(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
>>> del d['elephant']            # remove an existing key one level down
>>> d                            # display result
DeepChainMap({'zebra': 'black', 'snake': 'red'}, {}, {'lion': 'orange'})
 Counter objects A counter tool is provided to support convenient and rapid tallies. For example: >>> # Tally occurrences of words in a list
>>> cnt = Counter()
>>> for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:
...     cnt[word] += 1
>>> cnt
Counter({'blue': 3, 'red': 2, 'green': 1})

>>> # Find the ten most common words in Hamlet
>>> import re
>>> 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)]

class collections.Counter([iterable-or-mapping])
A Counter is a dict subclass for counting hashable 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 args
 Counter objects have a dictionary interface except that they return a zero count for missing items instead of raising a KeyError: >>> c = Counter(['eggs', 'ham'])
>>> c['bacon']                              # count of a missing element is zero
0
 Setting 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
>>> del c['sausage']                        # del actually removes the entry
  New in version 3.1.   Changed in version 3.7: As a dict 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 three 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])
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([iterable-or-mapping])
Elements are subtracted from an iterable or from another mapping (or counter). Like dict.update() 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)
>>> c
Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})
  New in version 3.2.
 The usual dictionary methods are available for Counter objects except for two which work differently for counters.
fromkeys(iterable)
This class method is not implemented for Counter objects.

update([iterable-or-mapping])
Elements are counted from an iterable or added-in from another mapping (or counter). Like dict.update() 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.

 Common patterns for working with Counter objects: sum(c.values())                 # total of all counts
c.clear()                       # reset all counts
list(c)                         # list unique elements
set(c)                          # convert to a set
dict(c)                         # convert to a regular dictionary
c.items()                       # convert to a list of (elem, cnt) pairs
Counter(dict(list_of_pairs))    # convert from a list of (elem, cnt) pairs
c.most_common()[:-n-1:-1]       # n least common elements
+c                              # remove zero and negative counts
 Several mathematical operations are provided for combining Counter 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. Each operation can accept inputs with signed counts, but the output will exclude results with counts of zero or less. >>> 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})
 Unary addition and subtraction are shortcuts for adding an empty counter or subtracting from an empty counter. >>> c = Counter(a=2, b=-4)
>>> +c
Counter({'a': 2})
>>> -c
Counter({'b': 4})
  New in version 3.3: Added support for unary plus, unary minus, and in-place multiset operations.   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 Counter 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 most_common() 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 update() and subtract() 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 elements() method requires integer counts. It ignores zero and negative counts.    See also
Bag class in Smalltalk. Wikipedia entry for Multisets.
C++ multisets 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
    deque objects
class collections.deque([iterable[, maxlen]])
Returns a new deque object initialized left-to-right (using append()) 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 list 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. Deque objects support the following methods:
append(x)
Add x to the right side of the deque.

appendleft(x)
Add x 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.  New in version 3.5.

count(x)
Count the number of deque elements equal to x.  New 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(x[, start[, stop]])
Return the position of x in the deque (at or after index start and before index stop). Returns the first match or raises ValueError if not found.  New in version 3.5.

insert(i, x)
Insert x into the deque at position i. If the insertion would cause a bounded deque to grow beyond maxlen, an IndexError is raised.  New in version 3.5.

pop()
Remove and return an element from the right side of the deque. If no elements are present, raises an IndexError.

popleft()
Remove and return an element from the left side of the deque. If no elements are present, raises an IndexError.

remove(value)
Remove the first occurrence of value. If not found, raises a ValueError.

reverse()
Reverse the elements of the deque in-place and then return None.  New 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.  New 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 in 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: >>> from collections import deque
>>> d = deque('ghi')                 # make a new deque with three items
>>> for elem in d:                   # iterate over the deque's elements
...     print(elem.upper())
G
H
I

>>> 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 deque
deque(['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' in d                         # search the deque
True
>>> d.extend('jkl')                  # add multiple elements at once
>>> d
deque(['g', 'h', 'i', 'j', 'k', 'l'])
>>> d.rotate(1)                      # right rotation
>>> d
deque(['l', 'g', 'h', 'i', 'j', 'k'])
>>> d.rotate(-1)                     # left rotation
>>> d
deque(['g', 'h', 'i', 'j', 'k', 'l'])

>>> deque(reversed(d))               # make a new deque in reverse order
deque(['l', 'k', 'j', 'i', 'h', 'g'])
>>> d.clear()                        # empty the deque
>>> d.pop()                          # cannot pop from an empty deque
Traceback (most recent call last):
    File "<pyshell#6>", line 1, in -toplevel-
        d.pop()
IndexError: pop from an empty deque

>>> d.extendleft('abc')              # extendleft() reverses the input order
>>> d
deque(['c', 'b', 'a'])

deque Recipes This section shows various approaches to working with deques. Bounded length deques provide functionality similar to the tail filter in Unix: def tail(filename, n=10):
    'Return the last n lines of a file'
    with open(filename) as f:
        return deque(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: def moving_average(iterable, n=3):
    # moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0
    # http://en.wikipedia.org/wiki/Moving_average
    it = iter(iterable)
    d = deque(itertools.islice(it, n-1))
    d.appendleft(0)
    s = sum(d)
    for elem in it:
        s += elem - d.popleft()
        d.append(elem)
        yield s / n
 A round-robin scheduler can be implemented with input iterators stored in a deque. Values are yielded from the active iterator in position zero. If that iterator is exhausted, it can be removed with popleft(); otherwise, it can be cycled back to the end with the rotate() method: def roundrobin(*iterables):
    "roundrobin('ABC', 'D', 'EF') --> A D E B F C"
    iterators = deque(map(iter, iterables))
    while iterators:
        try:
            while True:
                yield next(iterators[0])
                iterators.rotate(-1)
        except StopIteration:
            # Remove an exhausted iterator.
            iterators.popleft()
 The rotate() method provides a way to implement deque slicing and deletion. For example, a pure Python implementation of del d[n] relies on the rotate() method to position elements to be popped: def delete_nth(d, n):
    d.rotate(-n)
    d.popleft()
    d.rotate(n)
 To implement deque slicing, use a similar approach applying rotate() to bring a target element to the left side of the deque. Remove old entries with popleft(), add new entries with extend(), 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. defaultdict objects
class collections.defaultdict([default_factory[, ...]])
Returns a new dictionary-like object. defaultdict is a subclass of the built-in dict 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 default_factory attribute; it defaults to None. All remaining arguments are treated the same as if they were passed to the dict constructor, including keyword arguments. defaultdict objects support the following method in addition to the standard dict operations:
__missing__(key)
If the default_factory attribute is None, this raises a KeyError exception with the key as argument. If default_factory 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 default_factory raises an exception this exception is propagated unchanged. This method is called by the __getitem__() method of the dict 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 __getitem__(). This means that get() will, like normal dictionaries, return None as a default rather than using default_factory.
 defaultdict objects support the following instance variable:
default_factory
This attribute is used by the __missing__() 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 PEP 584.

defaultdict Examples Using list as the default_factory, 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)
>>> for k, v in s:
...     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 default_factory function which returns an empty list. The list.append() 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 dict.setdefault(): >>> d = {}
>>> for k, v in s:
...     d.setdefault(k, []).append(v)
...
>>> sorted(d.items())
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
 Setting the default_factory to int makes the defaultdict useful for counting (like a bag or multiset in other languages): >>> s = 'mississippi'
>>> d = defaultdict(int)
>>> for k in s:
...     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 default_factory function calls int() to supply a default count of zero. The increment operation then builds up the count for each letter. The function int() 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): >>> def constant_factory(value):
...     return lambda: 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 default_factory to set makes the defaultdict useful for building a dictionary of sets: >>> s = [('red', 1), ('blue', 2), ('red', 3), ('blue', 4), ('red', 1), ('blue', 4)]
>>> d = defaultdict(set)
>>> for k, v in s:
...     d[k].add(v)
...
>>> sorted(d.items())
[('blue', {2, 4}), ('red', {1, 3})]
 namedtuple() 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 __repr__() 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 'x y' 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 keyword 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 iterable 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 __module__ 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: The verbose and rename parameters became keyword-only arguments.   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 _field_defaults 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 name
33
>>> p                       # readable __repr__ with a name=value style
Point(x=11, y=22)
 Named tuples are especially useful for assigning field names to result tuples returned by the csv or sqlite3 modules: EmployeeRecord = namedtuple('EmployeeRecord', 'name, age, title, department, paygrade')

import csv
for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "rb"))):
    print(emp.name, emp.title)

import sqlite3
conn = sqlite3.connect('/companydata')
cursor = conn.cursor()
cursor.execute('SELECT name, age, title, department, paygrade FROM employees')
for emp in map(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.
classmethod somenamedtuple._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 dict 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 OrderedDict instead of a regular dict.   Changed in version 3.8: Returns a regular dict instead of an OrderedDict. 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)

>>> for partnum, record in inventory.items():
...     inventory[partnum] = record._replace(price=newprices[partnum], timestamp=time.now())

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 getattr() function: >>> getattr(p, 'x')
11
 To convert a dictionary to a named tuple, use the double-star-operator (as described in Unpacking Argument Lists): >>> 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: >>> class Point(namedtuple('Point', ['x', 'y'])):
...     __slots__ = ()
...     @property
...     def hypot(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)

>>> for p in Point(3, 4), Point(14, 5/7):
...     print(p)
Point: x= 3.000  y= 4.000  hypot= 5.000
Point: x=14.000  y= 0.714  hypot=14.018
 The 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 _fields 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 typing.NamedTuple for a way to add type hints for named tuples. It also provides an elegant notation using the class keyword: class Component(NamedTuple):
    part_number: int
    weight: float
    description: Optional[str] = None
  See types.SimpleNamespace() for a mutable namespace based on an underlying dictionary instead of a tuple. The dataclasses module provides a decorator and functions for automatically adding generated special methods to user-defined classes.   OrderedDict 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 dict class gained the ability to remember insertion order (this new behavior became guaranteed in Python 3.7). Some differences from dict still remain:  The regular dict was designed to be very good at mapping operations. Tracking insertion order was secondary. The OrderedDict was designed to be good at reordering operations. Space efficiency, iteration speed, and the performance of update operations were secondary. Algorithmically, OrderedDict can handle frequent reordering operations better than dict. This makes it suitable for tracking recent accesses (for example in an LRU cache). The equality operation for OrderedDict checks for matching order. The popitem() method of OrderedDict has a different signature. It accepts an optional argument to specify which item is popped.
OrderedDict has a move_to_end() method to efficiently reposition an element to an endpoint. Until Python 3.8, dict lacked a __reversed__() method.
class collections.OrderedDict([items])
Return an instance of a dict subclass that has methods specialized for rearranging dictionary order.  New 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 KeyError if the key does not exist: >>> d = OrderedDict.fromkeys('abcde')
>>> d.move_to_end('b')
>>> ''.join(d.keys())
'acdeb'
>>> d.move_to_end('b', last=False)
>>> ''.join(d.keys())
'bacde'
  New in version 3.2.

 In addition to the usual mapping methods, ordered dictionaries also support reverse iteration using reversed(). Equality tests between OrderedDict objects are order-sensitive and are implemented as list(od1.items())==list(od2.items()). Equality tests between OrderedDict objects and other Mapping 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 views of OrderedDict now support reverse iteration using reversed().   Changed in version 3.6: With the acceptance of PEP 468, order is retained for keyword arguments passed to the OrderedDict constructor and its update() method.   Changed in version 3.9: Added merge (|) and update (|=) operators, specified in PEP 584.
OrderedDict 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: class LastUpdatedOrderedDict(OrderedDict):
    'Store items in the order the keys were last added'

    def __setitem__(self, key, value):
        super().__setitem__(key, value)
        self.move_to_end(key)
 An OrderedDict would also be useful for implementing variants of functools.lru_cache(): class LRU(OrderedDict):
    'Limit size, evicting the least recently looked-up key when full'

    def __init__(self, maxsize=128, /, *args, **kwds):
        self.maxsize = maxsize
        super().__init__(*args, **kwds)

    def __getitem__(self, key):
        value = super().__getitem__(key)
        self.move_to_end(key)
        return value

    def __setitem__(self, key, value):
        if key in self:
            self.move_to_end(key)
        super().__setitem__(key, value)
        if len(self) > self.maxsize:
            oldest = next(iter(self))
            del self[oldest]
 UserDict objects The class, UserDict acts as a wrapper around dictionary objects. The need for this class has been partially supplanted by the ability to subclass directly from dict; however, this class can be easier to work with because the underlying dictionary is accessible as an attribute.
class collections.UserDict([initialdata])
Class that simulates a dictionary. The instance’s contents are kept in a regular dictionary, which is accessible via the data attribute of UserDict instances. If initialdata is provided, data is initialized with its contents; note that a reference to initialdata will not be kept, allowing it be used for other purposes. 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.

 UserList 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 list; however, this class can be easier to work with because the underlying list is accessible as an attribute.
class collections.UserList([list])
Class that simulates a list. The instance’s contents are kept in a regular list, which is accessible via the data 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 list object used to store the contents of the UserList class.

 Subclassing requirements: Subclasses of UserList 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. UserString objects The class, UserString acts as a wrapper around string objects. The need for this class has been partially supplanted by the ability to subclass directly from str; however, this class can be easier to work with because the underlying string is accessible as an attribute.
class collections.UserString(seq)
Class that simulates a string object. The instance’s content is kept in a regular string object, which is accessible via the data 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 str() function. In addition to supporting the methods and operations of strings, UserString instances provide the following attribute:
data
A real str 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.


collections.abc — Abstract Base Classes for Containers  New in version 3.3: Formerly, this module was part of the collections module.  Source code: Lib/_collections_abc.py This module provides abstract base classes that can be used to test whether a class provides a particular interface; for example, whether it is hashable or whether it is a mapping. Collections Abstract Base Classes The collections module offers the following ABCs:
ABC Inherits from Abstract Methods Mixin Methods
Container  __contains__
Hashable  __hash__
Iterable  __iter__
Iterator Iterable __next__ __iter__
Reversible Iterable __reversed__
Generator Iterator send, throw close, __iter__, __next__
Sized  __len__
Callable  __call__
Collection Sized, Iterable, Container __contains__, __iter__, __len__
Sequence Reversible, Collection __getitem__, __len__ __contains__, __iter__, __reversed__, index, and count
MutableSequence Sequence __getitem__, __setitem__, __delitem__, __len__, insert Inherited Sequence methods and append, reverse, extend, pop, remove, and __iadd__
ByteString Sequence __getitem__, __len__ Inherited Sequence methods
Set Collection __contains__, __iter__, __len__ __le__, __lt__, __eq__, __ne__, __gt__, __ge__, __and__, __or__, __sub__, __xor__, and isdisjoint
MutableSet Set __contains__, __iter__, __len__, add, discard Inherited Set methods and clear, pop, remove, __ior__, __iand__, __ixor__, and __isub__
Mapping Collection __getitem__, __iter__, __len__ __contains__, keys, items, values, get, __eq__, and __ne__
MutableMapping Mapping __getitem__, __setitem__, __delitem__, __iter__, __len__ Inherited Mapping methods and pop, popitem, clear, update, and setdefault
MappingView Sized  __len__
ItemsView MappingView, Set  __contains__, __iter__
KeysView MappingView, Set  __contains__, __iter__
ValuesView MappingView, Collection  __contains__, __iter__
Awaitable  __await__
Coroutine Awaitable send, throw close
AsyncIterable  __aiter__
AsyncIterator AsyncIterable __anext__ __aiter__
AsyncGenerator AsyncIterator asend, athrow aclose, __aiter__, __anext__
class collections.abc.Container
ABC for classes that provide the __contains__() method.

class collections.abc.Hashable
ABC for classes that provide the __hash__() method.

class collections.abc.Sized
ABC for classes that provide the __len__() method.

class collections.abc.Callable
ABC for classes that provide the __call__() method.

class collections.abc.Iterable
ABC for classes that provide the __iter__() method. Checking isinstance(obj, Iterable) detects classes that are registered as Iterable or that have an __iter__() method, but it does not detect classes that iterate with the __getitem__() method. The only reliable way to determine whether an object is iterable is to call iter(obj).

class collections.abc.Collection
ABC for sized iterable container classes.  New in version 3.6.

class collections.abc.Iterator
ABC for classes that provide the __iter__() and __next__() methods. See also the definition of iterator.

class collections.abc.Reversible
ABC for iterable classes that also provide the __reversed__() method.  New in version 3.6.

class collections.abc.Generator
ABC for generator classes that implement the protocol defined in PEP 342 that extends iterators with the send(), throw() and close() methods. See also the definition of generator.  New in version 3.5.

class collections.abc.Sequence
class collections.abc.MutableSequence
class collections.abc.ByteString
ABCs for read-only and mutable sequences. Implementation note: Some of the mixin methods, such as __iter__(), __reversed__() and index(), make repeated calls to the underlying __getitem__() method. Consequently, if __getitem__() is implemented with constant access speed, the mixin methods will have linear performance; however, if the underlying method is linear (as it would be with a linked list), the mixins will have quadratic performance and will likely need to be overridden.  Changed in version 3.5: The index() method added support for stop and start arguments.

class collections.abc.Set
class collections.abc.MutableSet
ABCs for read-only and mutable sets.

class collections.abc.Mapping
class collections.abc.MutableMapping
ABCs for read-only and mutable mappings.

class collections.abc.MappingView
class collections.abc.ItemsView
class collections.abc.KeysView
class collections.abc.ValuesView
ABCs for mapping, items, keys, and values views.

class collections.abc.Awaitable
ABC for awaitable objects, which can be used in await expressions. Custom implementations must provide the __await__() method. Coroutine objects and instances of the Coroutine ABC are all instances of this ABC.  Note In CPython, generator-based coroutines (generators decorated with types.coroutine() or asyncio.coroutine()) are awaitables, even though they do not have an __await__() method. Using isinstance(gencoro, Awaitable) for them will return False. Use inspect.isawaitable() to detect them.   New in version 3.5.

class collections.abc.Coroutine
ABC for coroutine compatible classes. These implement the following methods, defined in Coroutine Objects: send(), throw(), and close(). Custom implementations must also implement __await__(). All Coroutine instances are also instances of Awaitable. See also the definition of coroutine.  Note In CPython, generator-based coroutines (generators decorated with types.coroutine() or asyncio.coroutine()) are awaitables, even though they do not have an __await__() method. Using isinstance(gencoro, Coroutine) for them will return False. Use inspect.isawaitable() to detect them.   New in version 3.5.

class collections.abc.AsyncIterable
ABC for classes that provide __aiter__ method. See also the definition of asynchronous iterable.  New in version 3.5.

class collections.abc.AsyncIterator
ABC for classes that provide __aiter__ and __anext__ methods. See also the definition of asynchronous iterator.  New in version 3.5.

class collections.abc.AsyncGenerator
ABC for asynchronous generator classes that implement the protocol defined in PEP 525 and PEP 492.  New in version 3.6.
 These ABCs allow us to ask classes or instances if they provide particular functionality, for example: size = None
if isinstance(myvar, collections.abc.Sized):
    size = len(myvar)
 Several of the ABCs are also useful as mixins that make it easier to develop classes supporting container APIs. For example, to write a class supporting the full Set API, it is only necessary to supply the three underlying abstract methods: __contains__(), __iter__(), and __len__(). The ABC supplies the remaining methods such as __and__() and isdisjoint(): class ListBasedSet(collections.abc.Set):
    ''' Alternate set implementation favoring space over speed
        and not requiring the set elements to be hashable. '''
    def __init__(self, iterable):
        self.elements = lst = []
        for value in iterable:
            if value not in lst:
                lst.append(value)

    def __iter__(self):
        return iter(self.elements)

    def __contains__(self, value):
        return value in self.elements

    def __len__(self):
        return len(self.elements)

s1 = ListBasedSet('abcdef')
s2 = ListBasedSet('defghi')
overlap = s1 & s2            # The __and__() method is supported automatically
 Notes on using Set and MutableSet as a mixin:  Since some set operations create new sets, the default mixin methods need a way to create new instances from an iterable. The class constructor is assumed to have a signature in the form ClassName(iterable). That assumption is factored-out to an internal classmethod called _from_iterable() which calls cls(iterable) to produce a new set. If the Set mixin is being used in a class with a different constructor signature, you will need to override _from_iterable() with a classmethod or regular method that can construct new instances from an iterable argument. To override the comparisons (presumably for speed, as the semantics are fixed), redefine __le__() and __ge__(), then the other operations will automatically follow suit. The Set mixin provides a _hash() method to compute a hash value for the set; however, __hash__() is not defined because not all sets are hashable or immutable. To add set hashability using mixins, inherit from both Set() and Hashable(), then define __hash__ = Set._hash.   See also
OrderedSet recipe for an example built on MutableSet. For more about ABCs, see the abc module and PEP 3119.


class collections.abc.AsyncGenerator
ABC for asynchronous generator classes that implement the protocol defined in PEP 525 and PEP 492.  New in version 3.6.


class collections.abc.AsyncIterable
ABC for classes that provide __aiter__ method. See also the definition of asynchronous iterable.  New in version 3.5.


class collections.abc.AsyncIterator
ABC for classes that provide __aiter__ and __anext__ methods. See also the definition of asynchronous iterator.  New in version 3.5.


class collections.abc.Awaitable
ABC for awaitable objects, which can be used in await expressions. Custom implementations must provide the __await__() method. Coroutine objects and instances of the Coroutine ABC are all instances of this ABC.  Note In CPython, generator-based coroutines (generators decorated with types.coroutine() or asyncio.coroutine()) are awaitables, even though they do not have an __await__() method. Using isinstance(gencoro, Awaitable) for them will return False. Use inspect.isawaitable() to detect them.   New in version 3.5.


class collections.abc.Sequence
class collections.abc.MutableSequence
class collections.abc.ByteString
ABCs for read-only and mutable sequences. Implementation note: Some of the mixin methods, such as __iter__(), __reversed__() and index(), make repeated calls to the underlying __getitem__() method. Consequently, if __getitem__() is implemented with constant access speed, the mixin methods will have linear performance; however, if the underlying method is linear (as it would be with a linked list), the mixins will have quadratic performance and will likely need to be overridden.  Changed in version 3.5: The index() method added support for stop and start arguments.


class collections.abc.Callable
ABC for classes that provide the __call__() method.


class collections.abc.Collection
ABC for sized iterable container classes.  New in version 3.6.


class collections.abc.Container
ABC for classes that provide the __contains__() method.


class collections.abc.Coroutine
ABC for coroutine compatible classes. These implement the following methods, defined in Coroutine Objects: send(), throw(), and close(). Custom implementations must also implement __await__(). All Coroutine instances are also instances of Awaitable. See also the definition of coroutine.  Note In CPython, generator-based coroutines (generators decorated with types.coroutine() or asyncio.coroutine()) are awaitables, even though they do not have an __await__() method. Using isinstance(gencoro, Coroutine) for them will return False. Use inspect.isawaitable() to detect them.   New in version 3.5.


class collections.abc.Generator
ABC for generator classes that implement the protocol defined in PEP 342 that extends iterators with the send(), throw() and close() methods. See also the definition of generator.  New in version 3.5.


class collections.abc.Hashable
ABC for classes that provide the __hash__() method.


class collections.abc.MappingView
class collections.abc.ItemsView
class collections.abc.KeysView
class collections.abc.ValuesView
ABCs for mapping, items, keys, and values views.


class collections.abc.Iterable
ABC for classes that provide the __iter__() method. Checking isinstance(obj, Iterable) detects classes that are registered as Iterable or that have an __iter__() method, but it does not detect classes that iterate with the __getitem__() method. The only reliable way to determine whether an object is iterable is to call iter(obj).


class collections.abc.Iterator
ABC for classes that provide the __iter__() and __next__() methods. See also the definition of iterator.


class collections.abc.Mapping
class collections.abc.MutableMapping
ABCs for read-only and mutable mappings.


class collections.abc.Set
class collections.abc.MutableSet
ABCs for read-only and mutable sets.


class collections.abc.Reversible
ABC for iterable classes that also provide the __reversed__() method.  New in version 3.6.


class collections.abc.Sized
ABC for classes that provide the __len__() method.


class collections.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)
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). 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.

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 nonlocal keyword used in nested scopes. The use cases also parallel those for the built-in super() 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 dict.update() 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 PEP 584.


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)
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). 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.


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 nonlocal keyword used in nested scopes. The use cases also parallel those for the built-in super() function. A reference to d.parents is equivalent to: ChainMap(*d.maps[1:]).


class collections.Counter([iterable-or-mapping])
A Counter is a dict subclass for counting hashable 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 args
 Counter objects have a dictionary interface except that they return a zero count for missing items instead of raising a KeyError: >>> c = Counter(['eggs', 'ham'])
>>> c['bacon']                              # count of a missing element is zero
0
 Setting 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
>>> del c['sausage']                        # del actually removes the entry
  New in version 3.1.   Changed in version 3.7: As a dict 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 three 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])
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([iterable-or-mapping])
Elements are subtracted from an iterable or from another mapping (or counter). Like dict.update() 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)
>>> c
Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})
  New in version 3.2.
 The usual dictionary methods are available for Counter objects except for two which work differently for counters.
fromkeys(iterable)
This class method is not implemented for Counter objects.

update([iterable-or-mapping])
Elements are counted from an iterable or added-in from another mapping (or counter). Like dict.update() 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.


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']


fromkeys(iterable)
This class method is not implemented for Counter objects.


most_common([n])
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([iterable-or-mapping])
Elements are subtracted from an iterable or from another mapping (or counter). Like dict.update() 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)
>>> c
Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})
  New in version 3.2.


update([iterable-or-mapping])
Elements are counted from an iterable or added-in from another mapping (or counter). Like dict.update() 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.


class collections.defaultdict([default_factory[, ...]])
Returns a new dictionary-like object. defaultdict is a subclass of the built-in dict 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 default_factory attribute; it defaults to None. All remaining arguments are treated the same as if they were passed to the dict constructor, including keyword arguments. defaultdict objects support the following method in addition to the standard dict operations:
__missing__(key)
If the default_factory attribute is None, this raises a KeyError exception with the key as argument. If default_factory 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 default_factory raises an exception this exception is propagated unchanged. This method is called by the __getitem__() method of the dict 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 __getitem__(). This means that get() will, like normal dictionaries, return None as a default rather than using default_factory.
 defaultdict objects support the following instance variable:
default_factory
This attribute is used by the __missing__() 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 PEP 584.


default_factory
This attribute is used by the __missing__() method; it is initialized from the first argument to the constructor, if present, or to None, if absent.


__missing__(key)
If the default_factory attribute is None, this raises a KeyError exception with the key as argument. If default_factory 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 default_factory raises an exception this exception is propagated unchanged. This method is called by the __getitem__() method of the dict 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 __getitem__(). This means that get() will, like normal dictionaries, return None as a default rather than using default_factory.


class collections.deque([iterable[, maxlen]])
Returns a new deque object initialized left-to-right (using append()) 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 list 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. Deque objects support the following methods:
append(x)
Add x to the right side of the deque.

appendleft(x)
Add x 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.  New in version 3.5.

count(x)
Count the number of deque elements equal to x.  New 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(x[, start[, stop]])
Return the position of x in the deque (at or after index start and before index stop). Returns the first match or raises ValueError if not found.  New in version 3.5.

insert(i, x)
Insert x into the deque at position i. If the insertion would cause a bounded deque to grow beyond maxlen, an IndexError is raised.  New in version 3.5.

pop()
Remove and return an element from the right side of the deque. If no elements are present, raises an IndexError.

popleft()
Remove and return an element from the left side of the deque. If no elements are present, raises an IndexError.

remove(value)
Remove the first occurrence of value. If not found, raises a ValueError.

reverse()
Reverse the elements of the deque in-place and then return None.  New 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.  New in version 3.1.


append(x)
Add x to the right side of the deque.


appendleft(x)
Add x 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.  New in version 3.5.


count(x)
Count the number of deque elements equal to x.  New 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(x[, start[, stop]])
Return the position of x in the deque (at or after index start and before index stop). Returns the first match or raises ValueError if not found.  New in version 3.5.


insert(i, x)
Insert x into the deque at position i. If the insertion would cause a bounded deque to grow beyond maxlen, an IndexError is raised.  New in version 3.5.


maxlen
Maximum size of a deque or None if unbounded.  New in version 3.1.


pop()
Remove and return an element from the right side of the deque. If no elements are present, raises an IndexError.


popleft()
Remove and return an element from the left side of the deque. If no elements are present, raises an IndexError.


remove(value)
Remove the first occurrence of value. If not found, raises a ValueError.


reverse()
Reverse the elements of the deque in-place and then return None.  New 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()).


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 __repr__() 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 'x y' 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 keyword 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 iterable 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 __module__ 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: The verbose and rename parameters became keyword-only arguments.   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 _field_defaults attribute.


class collections.OrderedDict([items])
Return an instance of a dict subclass that has methods specialized for rearranging dictionary order.  New 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 KeyError if the key does not exist: >>> d = OrderedDict.fromkeys('abcde')
>>> d.move_to_end('b')
>>> ''.join(d.keys())
'acdeb'
>>> d.move_to_end('b', last=False)
>>> ''.join(d.keys())
'bacde'
  New in version 3.2.