Data Structures

Werkzeug provides some subclasses of common Python objects to extend them with additional features. Some of them are used to make them immutable, others are used to change some semantics to better work with HTTP.

General Purpose

class werkzeug.datastructures.TypeConversionDict

Works like a regular dict but the get() method can perform type conversions. MultiDict and CombinedMultiDict are subclasses of this class and provide the same feature.

New in version 0.5.

get(key, default=None, type=None)

Return the default value if the requested data doesn’t exist. If type is provided and is a callable it should convert the value, return it or raise a ValueError if that is not possible. In this case the function will return the default as if the value was not found:

>>> d = TypeConversionDict(foo='42', bar='blub')
>>> d.get('foo', type=int)
42
>>> d.get('bar', -1, type=int)
-1

Parameters:	key – The key to be looked up. default – The default value to be returned if the key can’t be looked up. If not further specified None is returned. type – A callable that is used to cast the value in the MultiDict. If a ValueError is raised by this callable the default value is returned.

class werkzeug.datastructures.ImmutableTypeConversionDict

Works like a TypeConversionDict but does not support modifications.

New in version 0.5.

copy()

Return a shallow mutable copy of this object. Keep in mind that the standard library’s copy() function is a no-op for this class like for any other python immutable type (eg: tuple).

class werkzeug.datastructures.MultiDict(mapping=None)

A MultiDict is a dictionary subclass customized to deal with multiple values for the same key which is for example used by the parsing functions in the wrappers. This is necessary because some HTML form elements pass multiple values for the same key.

MultiDict implements all standard dictionary methods. Internally, it saves all values for a key as a list, but the standard dict access methods will only return the first value for a key. If you want to gain access to the other values, too, you have to use the list methods as explained below.

Basic Usage:

>>> d = MultiDict([('a', 'b'), ('a', 'c')])
>>> d
MultiDict([('a', 'b'), ('a', 'c')])
>>> d['a']
'b'
>>> d.getlist('a')
['b', 'c']
>>> 'a' in d
True

It behaves like a normal dict thus all dict functions will only return the first value when multiple values for one key are found.

From Werkzeug 0.3 onwards, the KeyError raised by this class is also a subclass of the BadRequest HTTP exception and will render a page for a 400 BAD REQUEST if caught in a catch-all for HTTP exceptions.

A MultiDict can be constructed from an iterable of (key, value) tuples, a dict, a MultiDict or from Werkzeug 0.2 onwards some keyword parameters.

Parameters:	mapping – the initial value for the MultiDict. Either a regular dict, an iterable of (key, value) tuples or None.

add(key, value)

Adds a new value for the key.

New in version 0.6.

Parameters:	key – the key for the value. value – the value to add.

clear() → None. Remove all items from D.

copy()

Return a shallow copy of this object.

deepcopy(memo=None)

Return a deep copy of this object.

fromkeys()

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, type=None)

Return the default value if the requested data doesn’t exist. If type is provided and is a callable it should convert the value, return it or raise a ValueError if that is not possible. In this case the function will return the default as if the value was not found:

>>> d = TypeConversionDict(foo='42', bar='blub')
>>> d.get('foo', type=int)
42
>>> d.get('bar', -1, type=int)
-1

Parameters:	key – The key to be looked up. default – The default value to be returned if the key can’t be looked up. If not further specified None is returned. type – A callable that is used to cast the value in the MultiDict. If a ValueError is raised by this callable the default value is returned.

getlist(key, type=None)

Return the list of items for a given key. If that key is not in the MultiDict, the return value will be an empty list. Just as get getlist accepts a type parameter. All items will be converted with the callable defined there.

Parameters:	key – The key to be looked up. type – A callable that is used to cast the value in the MultiDict. If a ValueError is raised by this callable the value will be removed from the list.
Returns:	a list of all the values for the key.

items(multi=False)

Return an iterator of (key, value) pairs.

Parameters:	multi – If set to True the iterator returned will have a pair for each value of each key. Otherwise it will only contain pairs for the first value of each key.

keys() → a set-like object providing a view on D's keys

lists()

Return a iterator of (key, values) pairs, where values is the list of all values associated with the key.

listvalues()

Return an iterator of all values associated with a key. Zipping keys() and this is the same as calling lists():

>>> d = MultiDict({"foo": [1, 2, 3]})
>>> zip(d.keys(), d.listvalues()) == d.lists()
True

pop(key, default=no value)

Pop the first item for a list on the dict. Afterwards the key is removed from the dict, so additional values are discarded:

>>> d = MultiDict({"foo": [1, 2, 3]})
>>> d.pop("foo")
1
>>> "foo" in d
False

Parameters:	key – the key to pop. default – if provided the value to return if the key was not in the dictionary.

popitem()

Pop an item from the dict.

popitemlist()

Pop a (key, list) tuple from the dict.

poplist(key)

Pop the list for a key from the dict. If the key is not in the dict an empty list is returned.

Changed in version 0.5: If the key does no longer exist a list is returned instead of raising an error.

setdefault(key, default=None)

Returns the value for the key if it is in the dict, otherwise it returns default and sets that value for key.

Parameters:	key – The key to be looked up. default – The default value to be returned if the key is not in the dict. If not further specified it’s None.

setlist(key, new_list)

Remove the old values for a key and add new ones. Note that the list you pass the values in will be shallow-copied before it is inserted in the dictionary.

>>> d = MultiDict()
>>> d.setlist('foo', ['1', '2'])
>>> d['foo']
'1'
>>> d.getlist('foo')
['1', '2']

Parameters:	key – The key for which the values are set. new_list – An iterable with the new values for the key. Old values are removed first.

setlistdefault(key, default_list=None)

Like setdefault but sets multiple values. The list returned is not a copy, but the list that is actually used internally. This means that you can put new values into the dict by appending items to the list:

>>> d = MultiDict({"foo": 1})
>>> d.setlistdefault("foo").extend([2, 3])
>>> d.getlist("foo")
[1, 2, 3]

Parameters:	key – The key to be looked up. default_list – An iterable of default values. It is either copied (in case it was a list) or converted into a list before returned.
Returns:	a list

to_dict(flat=True)

Return the contents as regular dict. If flat is True the returned dict will only have the first item present, if flat is False all values will be returned as lists.

Parameters:	flat – If set to False the dict returned will have lists with all the values in it. Otherwise it will only contain the first value for each key.
Returns:	a dict

update(other_dict)

update() extends rather than replaces existing key lists:

>>> a = MultiDict({'x': 1})
>>> b = MultiDict({'x': 2, 'y': 3})
>>> a.update(b)
>>> a
MultiDict([('y', 3), ('x', 1), ('x', 2)])

If the value list for a key in other_dict is empty, no new values will be added to the dict and the key will not be created:

>>> x = {'empty_list': []}
>>> y = MultiDict()
>>> y.update(x)
>>> y
MultiDict([])

values()

Returns an iterator of the first value on every key’s value list.

class werkzeug.datastructures.OrderedMultiDict(mapping=None)

Works like a regular MultiDict but preserves the order of the fields. To convert the ordered multi dict into a list you can use the items() method and pass it multi=True.

In general an OrderedMultiDict is an order of magnitude slower than a MultiDict.

note

Due to a limitation in Python you cannot convert an ordered multi dict into a regular dict by using dict(multidict). Instead you have to use the to_dict() method, otherwise the internal bucket objects are exposed.

class werkzeug.datastructures.ImmutableMultiDict(mapping=None)

An immutable MultiDict.

New in version 0.5.

copy()

Return a shallow mutable copy of this object. Keep in mind that the standard library’s copy() function is a no-op for this class like for any other python immutable type (eg: tuple).

class werkzeug.datastructures.ImmutableOrderedMultiDict(mapping=None)

An immutable OrderedMultiDict.

New in version 0.6.

copy()

Return a shallow mutable copy of this object. Keep in mind that the standard library’s copy() function is a no-op for this class like for any other python immutable type (eg: tuple).

class werkzeug.datastructures.CombinedMultiDict(dicts=None)

A read only MultiDict that you can pass multiple MultiDict instances as sequence and it will combine the return values of all wrapped dicts:

>>> from werkzeug.datastructures import CombinedMultiDict, MultiDict
>>> post = MultiDict([('foo', 'bar')])
>>> get = MultiDict([('blub', 'blah')])
>>> combined = CombinedMultiDict([get, post])
>>> combined['foo']
'bar'
>>> combined['blub']
'blah'

This works for all read operations and will raise a TypeError for methods that usually change data which isn’t possible.

From Werkzeug 0.3 onwards, the KeyError raised by this class is also a subclass of the BadRequest HTTP exception and will render a page for a 400 BAD REQUEST if caught in a catch-all for HTTP exceptions.

class werkzeug.datastructures.ImmutableDict

An immutable dict.

New in version 0.5.

copy()

Return a shallow mutable copy of this object. Keep in mind that the standard library’s copy() function is a no-op for this class like for any other python immutable type (eg: tuple).

class werkzeug.datastructures.ImmutableList

An immutable list.

New in version 0.5.

Private:

class werkzeug.datastructures.FileMultiDict(mapping=None)

A special MultiDict that has convenience methods to add files to it. This is used for EnvironBuilder and generally useful for unittesting.

New in version 0.5.

add_file(name, file, filename=None, content_type=None)

Adds a new file to the dict. file can be a file name or a file-like or a FileStorage object.

Parameters:	name – the name of the field. file – a filename or file-like object filename – an optional filename content_type – an optional content type

HTTP Related

class werkzeug.datastructures.Headers([defaults])

An object that stores some headers. It has a dict-like interface but is ordered and can store the same keys multiple times.

This data structure is useful if you want a nicer way to handle WSGI headers which are stored as tuples in a list.

From Werkzeug 0.3 onwards, the KeyError raised by this class is also a subclass of the BadRequest HTTP exception and will render a page for a 400 BAD REQUEST if caught in a catch-all for HTTP exceptions.

Headers is mostly compatible with the Python wsgiref.headers.Headers class, with the exception of __getitem__. wsgiref will return None for headers['missing'], whereas Headers will raise a KeyError.

To create a new Headers object pass it a list or dict of headers which are used as default values. This does not reuse the list passed to the constructor for internal usage.

Parameters:	defaults – The list of default values for the Headers.

Changed in version 0.9: This data structure now stores unicode values similar to how the multi dicts do it. The main difference is that bytes can be set as well which will automatically be latin1 decoded.

Changed in version 0.9: The linked() function was removed without replacement as it was an API that does not support the changes to the encoding model.

add(_key, _value, **kw)

Add a new header tuple to the list.

Keyword arguments can specify additional parameters for the header value, with underscores converted to dashes:

>>> d = Headers()
>>> d.add('Content-Type', 'text/plain')
>>> d.add('Content-Disposition', 'attachment', filename='foo.png')

The keyword argument dumping uses dump_options_header() behind the scenes.

New in version 0.4.1: keyword arguments were added for wsgiref compatibility.

add_header(_key, _value, **_kw)

Add a new header tuple to the list.

An alias for add() for compatibility with the wsgiref add_header() method.

clear()

Clears all headers.

extend(iterable)

Extend the headers with a dict or an iterable yielding keys and values.

get(key, default=None, type=None, as_bytes=False)

Return the default value if the requested data doesn’t exist. If type is provided and is a callable it should convert the value, return it or raise a ValueError if that is not possible. In this case the function will return the default as if the value was not found:

>>> d = Headers([('Content-Length', '42')])
>>> d.get('Content-Length', type=int)
42

If a headers object is bound you must not add unicode strings because no encoding takes place.

New in version 0.9: Added support for as_bytes.

Parameters:	key – The key to be looked up. default – The default value to be returned if the key can’t be looked up. If not further specified None is returned. type – A callable that is used to cast the value in the Headers. If a ValueError is raised by this callable the default value is returned. as_bytes – return bytes instead of unicode strings.

get_all(name)

Return a list of all the values for the named field.

This method is compatible with the wsgiref get_all() method.

getlist(key, type=None, as_bytes=False)

Return the list of items for a given key. If that key is not in the Headers, the return value will be an empty list. Just as get() getlist() accepts a type parameter. All items will be converted with the callable defined there.

New in version 0.9: Added support for as_bytes.

Parameters:	key – The key to be looked up. type – A callable that is used to cast the value in the Headers. If a ValueError is raised by this callable the value will be removed from the list. as_bytes – return bytes instead of unicode strings.
Returns:	a list of all the values for the key.

has_key(key)

Check if a key is present.

pop(key=None, default=no value)

Removes and returns a key or index.

Parameters:	key – The key to be popped. If this is an integer the item at that position is removed, if it’s a string the value for that key is. If the key is omitted or None the last item is removed.
Returns:	an item.

popitem()

Removes a key or index and returns a (key, value) item.

remove(key)

Remove a key.

Parameters:	key – The key to be removed.

set(_key, _value, **kw)

Remove all header tuples for key and add a new one. The newly added key either appears at the end of the list if there was no entry or replaces the first one.

Keyword arguments can specify additional parameters for the header value, with underscores converted to dashes. See add() for more information.

Changed in version 0.6.1: set() now accepts the same arguments as add().

Parameters:	key – The key to be inserted. value – The value to be inserted.

setdefault(key, default)

Returns the value for the key if it is in the dict, otherwise it returns default and sets that value for key.

Parameters:	key – The key to be looked up. default – The default value to be returned if the key is not in the dict. If not further specified it’s None.

to_list(charset='iso-8859-1')

Convert the headers into a list suitable for WSGI.

Deprecated since version 0.9.

to_wsgi_list()

Convert the headers into a list suitable for WSGI.

The values are byte strings in Python 2 converted to latin1 and unicode strings in Python 3 for the WSGI server to encode.

Returns:	list

class werkzeug.datastructures.EnvironHeaders(environ)

Read only version of the headers from a WSGI environment. This provides the same interface as Headers and is constructed from a WSGI environment.

From Werkzeug 0.3 onwards, the KeyError raised by this class is also a subclass of the BadRequest HTTP exception and will render a page for a 400 BAD REQUEST if caught in a catch-all for HTTP exceptions.

class werkzeug.datastructures.HeaderSet(headers=None, on_update=None)

Similar to the ETags class this implements a set-like structure. Unlike ETags this is case insensitive and used for vary, allow, and content-language headers.

If not constructed using the parse_set_header() function the instantiation works like this:

>>> hs = HeaderSet(['foo', 'bar', 'baz'])
>>> hs
HeaderSet(['foo', 'bar', 'baz'])

add(header)

Add a new header to the set.

as_set(preserve_casing=False)

Return the set as real python set type. When calling this, all the items are converted to lowercase and the ordering is lost.

Parameters:	preserve_casing – if set to True the items in the set returned will have the original case like in the HeaderSet, otherwise they will be lowercase.

clear()

Clear the set.

discard(header)

Like remove() but ignores errors.

Parameters:	header – the header to be discarded.

find(header)

Return the index of the header in the set or return -1 if not found.

Parameters:	header – the header to be looked up.

index(header)

Return the index of the header in the set or raise an IndexError.

Parameters:	header – the header to be looked up.

remove(header)

Remove a header from the set. This raises an KeyError if the header is not in the set.

Changed in version 0.5: In older versions a IndexError was raised instead of a KeyError if the object was missing.

Parameters:	header – the header to be removed.

to_header()

Convert the header set into an HTTP header string.

update(iterable)

Add all the headers from the iterable to the set.

Parameters:	iterable – updates the set with the items from the iterable.

class werkzeug.datastructures.Accept(values=())

An Accept object is just a list subclass for lists of (value, quality) tuples. It is automatically sorted by specificity and quality.

All Accept objects work similar to a list but provide extra functionality for working with the data. Containment checks are normalized to the rules of that header:

>>> a = CharsetAccept([('ISO-8859-1', 1), ('utf-8', 0.7)])
>>> a.best
'ISO-8859-1'
>>> 'iso-8859-1' in a
True
>>> 'UTF8' in a
True
>>> 'utf7' in a
False

To get the quality for an item you can use normal item lookup:

>>> print a['utf-8']
0.7
>>> a['utf7']
0

Changed in version 0.5: Accept objects are forced immutable now.

best

The best match as value.

best_match(matches, default=None)

Returns the best match from a list of possible matches based on the specificity and quality of the client. If two items have the same quality and specificity, the one is returned that comes first.

Parameters:	matches – a list of matches to check for default – the value that is returned if none match

find(key)

Get the position of an entry or return -1.

Parameters:	key – The key to be looked up.

index(key)

Get the position of an entry or raise ValueError.

Parameters:	key – The key to be looked up.

Changed in version 0.5: This used to raise IndexError, which was inconsistent with the list API.

quality(key)

Returns the quality of the key.

New in version 0.6: In previous versions you had to use the item-lookup syntax (eg: obj[key] instead of obj.quality(key))

to_header()

Convert the header set into an HTTP header string.

values()

Iterate over all values.

class werkzeug.datastructures.MIMEAccept(values=())

Like Accept but with special methods and behavior for mimetypes.

accept_html

True if this object accepts HTML.

accept_json

True if this object accepts JSON.

accept_xhtml

True if this object accepts XHTML.

class werkzeug.datastructures.CharsetAccept(values=())

Like Accept but with normalization for charsets.

class werkzeug.datastructures.LanguageAccept(values=())

Like Accept but with normalization for languages.

class werkzeug.datastructures.RequestCacheControl(values=(), on_update=None)

A cache control for requests. This is immutable and gives access to all the request-relevant cache control headers.

To get a header of the RequestCacheControl object again you can convert the object into a string or call the to_header() method. If you plan to subclass it and add your own items have a look at the sourcecode for that class.

New in version 0.5: In previous versions a CacheControl class existed that was used both for request and response.

no_cache

accessor for ‘no-cache’

no_store

accessor for ‘no-store’

max_age

accessor for ‘max-age’

no_transform

accessor for ‘no-transform’

max_stale

accessor for ‘max-stale’

min_fresh

accessor for ‘min-fresh’

no_transform

accessor for ‘no-transform’

only_if_cached

accessor for ‘only-if-cached’

class werkzeug.datastructures.ResponseCacheControl(values=(), on_update=None)

A cache control for responses. Unlike RequestCacheControl this is mutable and gives access to response-relevant cache control headers.

To get a header of the ResponseCacheControl object again you can convert the object into a string or call the to_header() method. If you plan to subclass it and add your own items have a look at the sourcecode for that class.

New in version 0.5: In previous versions a CacheControl class existed that was used both for request and response.

no_cache

accessor for ‘no-cache’

no_store

accessor for ‘no-store’

max_age

accessor for ‘max-age’

no_transform

accessor for ‘no-transform’

must_revalidate

accessor for ‘must-revalidate’

private

accessor for ‘private’

proxy_revalidate

accessor for ‘proxy-revalidate’

public

accessor for ‘public’

s_maxage

accessor for ‘s-maxage’

class werkzeug.datastructures.ETags(strong_etags=None, weak_etags=None, star_tag=False)

A set that can be used to check if one etag is present in a collection of etags.

as_set(include_weak=False)

Convert the ETags object into a python set. Per default all the weak etags are not part of this set.

contains(etag)

Check if an etag is part of the set ignoring weak tags. It is also possible to use the in operator.

contains_raw(etag)

When passed a quoted tag it will check if this tag is part of the set. If the tag is weak it is checked against weak and strong tags, otherwise strong only.

contains_weak(etag)

Check if an etag is part of the set including weak and strong tags.

is_strong(etag)

Check if an etag is strong.

is_weak(etag)

Check if an etag is weak.

to_header()

Convert the etags set into a HTTP header string.

class werkzeug.datastructures.Authorization(auth_type, data=None)

Represents an Authorization header sent by the client. You should not create this kind of object yourself but use it when it’s returned by the parse_authorization_header function.

This object is a dict subclass and can be altered by setting dict items but it should be considered immutable as it’s returned by the client and not meant for modifications.

Changed in version 0.5: This object became immutable.

cnonce

If the server sent a qop-header in the WWW-Authenticate header, the client has to provide this value for HTTP digest auth. See the RFC for more details.

nc

The nonce count value transmitted by clients if a qop-header is also transmitted. HTTP digest auth only.

nonce

The nonce the server sent for digest auth, sent back by the client. A nonce should be unique for every 401 response for HTTP digest auth.

opaque

The opaque header from the server returned unchanged by the client. It is recommended that this string be base64 or hexadecimal data. Digest auth only.

password

When the authentication type is basic this is the password transmitted by the client, else None.

qop

Indicates what “quality of protection” the client has applied to the message for HTTP digest auth. Note that this is a single token, not a quoted list of alternatives as in WWW-Authenticate.

realm

This is the server realm sent back for HTTP digest auth.

response

A string of 32 hex digits computed as defined in RFC 2617, which proves that the user knows a password. Digest auth only.

uri

The URI from Request-URI of the Request-Line; duplicated because proxies are allowed to change the Request-Line in transit. HTTP digest auth only.

username

The username transmitted. This is set for both basic and digest auth all the time.

class werkzeug.datastructures.WWWAuthenticate(auth_type=None, values=None, on_update=None)

Provides simple access to WWW-Authenticate headers.

algorithm

A string indicating a pair of algorithms used to produce the digest and a checksum. If this is not present it is assumed to be “MD5”. If the algorithm is not understood, the challenge should be ignored (and a different one used, if there is more than one).

static auth_property(name, doc=None)

A static helper function for subclasses to add extra authentication system properties onto a class:

class FooAuthenticate(WWWAuthenticate):
    special_realm = auth_property('special_realm')

For more information have a look at the sourcecode to see how the regular properties (realm etc.) are implemented.

domain

A list of URIs that define the protection space. If a URI is an absolute path, it is relative to the canonical root URL of the server being accessed.

nonce

A server-specified data string which should be uniquely generated each time a 401 response is made. It is recommended that this string be base64 or hexadecimal data.

opaque

A string of data, specified by the server, which should be returned by the client unchanged in the Authorization header of subsequent requests with URIs in the same protection space. It is recommended that this string be base64 or hexadecimal data.

qop

A set of quality-of-privacy directives such as auth and auth-int.

realm

A string to be displayed to users so they know which username and password to use. This string should contain at least the name of the host performing the authentication and might additionally indicate the collection of users who might have access.

set_basic(realm='authentication required')

Clear the auth info and enable basic auth.

set_digest(realm, nonce, qop=('auth', ), opaque=None, algorithm=None, stale=False)

Clear the auth info and enable digest auth.

stale

A flag, indicating that the previous request from the client was rejected because the nonce value was stale.

to_header()

Convert the stored values into a WWW-Authenticate header.

type

The type of the auth mechanism. HTTP currently specifies Basic and Digest.

class werkzeug.datastructures.IfRange(etag=None, date=None)

Very simple object that represents the If-Range header in parsed form. It will either have neither a etag or date or one of either but never both.

New in version 0.7.

date = None

The date in parsed format or None.

etag = None

The etag parsed and unquoted. Ranges always operate on strong etags so the weakness information is not necessary.

to_header()

Converts the object back into an HTTP header.

class werkzeug.datastructures.Range(units, ranges)

Represents a Range header. All methods only support only bytes as the unit. Stores a list of ranges if given, but the methods only work if only one range is provided.

Raises:	ValueError – If the ranges provided are invalid.

Changed in version 0.15: The ranges passed in are validated.

New in version 0.7.

make_content_range(length)

Creates a ContentRange object from the current range and given content length.

range_for_length(length)

If the range is for bytes, the length is not None and there is exactly one range and it is satisfiable it returns a (start, stop) tuple, otherwise None.

ranges = None

A list of (begin, end) tuples for the range header provided. The ranges are non-inclusive.

to_content_range_header(length)

Converts the object into Content-Range HTTP header, based on given length

to_header()

Converts the object back into an HTTP header.

units = None

The units of this range. Usually “bytes”.

class werkzeug.datastructures.ContentRange(units, start, stop, length=None, on_update=None)

Represents the content range header.

New in version 0.7.

length

The length of the range or None.

set(start, stop, length=None, units='bytes')

Simple method to update the ranges.

start

The start point of the range or None.

stop

The stop point of the range (non-inclusive) or None. Can only be None if also start is None.

units

The units to use, usually “bytes”

unset()

Sets the units to None which indicates that the header should no longer be used.

Others

class werkzeug.datastructures.FileStorage(stream=None, filename=None, name=None, content_type=None, content_length=None, headers=None)

The FileStorage class is a thin wrapper over incoming files. It is used by the request object to represent uploaded files. All the attributes of the wrapper stream are proxied by the file storage so it’s possible to do storage.read() instead of the long form storage.stream.read().

stream

The input stream for the uploaded file. This usually points to an open temporary file.

filename

The filename of the file on the client.

name

The name of the form field.

headers

The multipart headers as Headers object. This usually contains irrelevant information but in combination with custom multipart requests the raw headers might be interesting.

New in version 0.6.

close()

Close the underlying file if possible.

content_length

The content-length sent in the header. Usually not available

content_type

The content-type sent in the header. Usually not available

mimetype

Like content_type, but without parameters (eg, without charset, type etc.) and always lowercase. For example if the content type is text/HTML; charset=utf-8 the mimetype would be 'text/html'.

New in version 0.7.

mimetype_params

The mimetype parameters as dict. For example if the content type is text/html; charset=utf-8 the params would be {'charset': 'utf-8'}.

New in version 0.7.

save(dst, buffer_size=16384)

Save the file to a destination path or file object. If the destination is a file object you have to close it yourself after the call. The buffer size is the number of bytes held in memory during the copy process. It defaults to 16KB.

For secure file saving also have a look at secure_filename().

Parameters:	dst – a filename or open file object the uploaded file is saved to. buffer_size – the size of the buffer. This works the same as the length parameter of shutil.copyfileobj().