Saravanan666/pypi-org huggingface dataset:
Package: marshmallow-mongoengine
marshmallow-mongoengineMongoengineintegration with themarshmallow(de)serialization library.See documentation athttp://marshmallow-mongoengine.rtfd.org/Declare your modelsimportmongoengineasmeclassAuthor(me.Document):id=me.IntField(primary_key=True,default=1)name=me.StringField()books=me.ListField(me.ReferenceField('Book'))def__repr__(self):return'<Author(name={self.name!r})>'.format(self=self)classBook(me.Document):title=me.StringField()Generate marshmallow schemasfrommarshmallow_mongoengineimportModelSchemaclassAuthorSchema(ModelSchema):classMeta:model=AuthorclassBookSchema(ModelSchema):classMeta:model=Bookauthor_schema=AuthorSchema()(De)serialize your dataauthor=Author(name='Chuck Paluhniuk').save()book=Book(title='Fight Club',author=author).save()dump_data=author_schema.dump(author).data# {'id': 1, 'name': 'Chuck Paluhniuk', 'books': ['5578726b7a58012298a5a7e2']}author_schema.load(dump_data).data# <Author(name='Chuck Paluhniuk')>Get it nowpip install -U marshmallow-mongoengineLicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: marshmallow-mongoengine-query-schema
Marshmallow Mongoengine Query SchemaMongoengine filters in your API with no effort!A marshmallow schema for use in url query which checks for possible mongoenginequery filters(as well asonlyandorder_byargs)
and converts their types based on requirements for filters (eg. ints for number comparition operators)
and fields themselves (eg. list inner type when using for examplelist__0)Supports all mongoengine filters exceptgeo queriesNote: This has nothing to do withmarshmallow-mongoengine, I just couldn't think of better nameUsage:frommarshmallow_mongoengine_query_schemaimportQuerySchemaclassSomeQuerySchema(QuerySchema):__query_fields__=SomeSchema().declared_fieldsclassMeta:# optional settingsquery_enable_filters=Falsequery_enable_only=Truequery_enable_order_by=Falsequery_allowed_infixes=['__']Usage in url:example.com/someendpoint?no_comments=5&name__startswith=b&list__size=2&list__0=somestringEvery type is converted automatically but there's couple of things to point out:When list type is needed, comma delimited list is used in url:?list=a,b,c,dmatchoperator and__raw__are expecting a dictionary(json):?__raw__={"name": "hello"}InstallationVery complicated black magic stuff:pip3installmarshmallow_mongoengine_query_schemaLicenseMIT
Package: marshmallow-muffin
marshmallowis an ORM/ODM/framework-agnostic library for converting complex datatypes, such as objects, to and from native Python datatypes.fromdatetimeimportdatefrommarshmallowimportSchema,fields,pprintclassArtistSchema(Schema):name=fields.Str()classAlbumSchema(Schema):title=fields.Str()release_date=fields.Date()artist=fields.Nested(ArtistSchema())bowie=dict(name='David Bowie')album=dict(artist=bowie,title='Hunky Dory',release_date=date(1971,12,17))schema=AlbumSchema()result=schema.dump(album)pprint(result,indent=2)# { 'artist': {'name': 'David Bowie'},#   'release_date': '1971-12-17',#   'title': 'Hunky Dory'}In short, marshmallow schemas can be used to:Validateinput data.Deserializeinput data to app-level objects.Serializeapp-level objects to primitive Python types. The serialized objects can then be rendered to standard formats such as JSON for use in an HTTP API.Get It Now$ pip install -U marshmallow --preDocumentationFull documentation is available athttps://marshmallow.readthedocs.io/.RequirementsPython >= 3.5marshmallow has no external dependencies outside of the Python standard library, althoughpython-dateutilis recommended for robust datetime deserialization.EcosystemA list of marshmallow-related libraries can be found at the GitHub wiki here:https://github.com/marshmallow-code/marshmallow/wiki/EcosystemCreditsContributorsThis project exists thanks to all the people who contribute.You’re highly encouraged to participate in marshmallow’s development.
Check out theContributing Guidelinesto see
how you can help.Thank you to all who have already contributed to marshmallow!BackersIf you find marshmallow useful, please consider supporting the team with
a donation. Your donation helps move marshmallow forward.Thank you to all our backers! [Become a backer]SponsorsSupport this project by becoming a sponsor (or ask your company to support this project by becoming a sponsor).
Your logo will show up here with a link to your website. [Become a sponsor]Professional SupportProfessionally-supported marshmallow is now available through theTidelift Subscription.Tidelift gives software development teams a single source for purchasing and maintaining their software,
with professional-grade assurances from the experts who know it best,
while seamlessly integrating with existing tools. [Get professional support]Security Contact InformationTo report a security vulnerability, please use theTidelift security contact.
Tidelift will coordinate the fix and disclosure.Project LinksDocs:https://marshmallow.readthedocs.io/Changelog:https://marshmallow.readthedocs.io/en/latest/changelog.htmlPyPI:https://pypi.python.org/pypi/marshmallowIssues:https://github.com/marshmallow-code/marshmallow/issuesDonate:https://opencollective.com/marshmallowLicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: marshmallow-muffin-sqlalchemy
Homepage:https://marshmallow-sqlalchemy.readthedocs.io/SQLAlchemyintegration with themarshmallow(de)serialization library.Declare your modelsimportsqlalchemyassafromsqlalchemy.ext.declarativeimportdeclarative_basefromsqlalchemy.ormimportscoped_session,sessionmaker,relationship,backrefengine=sa.create_engine("sqlite:///:memory:")session=scoped_session(sessionmaker(bind=engine))Base=declarative_base()classAuthor(Base):__tablename__="authors"id=sa.Column(sa.Integer,primary_key=True)name=sa.Column(sa.String)def__repr__(self):return"<Author(name={self.name!r})>".format(self=self)classBook(Base):__tablename__="books"id=sa.Column(sa.Integer,primary_key=True)title=sa.Column(sa.String)author_id=sa.Column(sa.Integer,sa.ForeignKey("authors.id"))author=relationship("Author",backref=backref("books"))Base.metadata.create_all(engine)Generate marshmallow schemasfrommarshmallow_sqlalchemyimportModelSchemaclassAuthorSchema(ModelSchema):classMeta:model=AuthorclassBookSchema(ModelSchema):classMeta:model=Book# optionally attach a Session# to use for deserializationsqla_session=sessionauthor_schema=AuthorSchema()(De)serialize your dataauthor=Author(name="Chuck Paluhniuk")author_schema=AuthorSchema()book=Book(title="Fight Club",author=author)session.add(author)session.add(book)session.commit()dump_data=author_schema.dump(author).data# {'books': [123], 'id': 321, 'name': 'Chuck Paluhniuk'}author_schema.load(dump_data,session=session).data# <Author(name='Chuck Paluhniuk')>Get it nowpip install -U marshmallow-sqlalchemyDocumentationDocumentation is available athttps://marshmallow-sqlalchemy.readthedocs.io/.Project LinksDocs:https://marshmallow-sqlalchemy.readthedocs.io/Changelog:https://marshmallow-sqlalchemy.readthedocs.io/en/latest/changelog.htmlPyPI:https://pypi.python.org/pypi/marshmallow-sqlalchemyIssues:https://github.com/marshmallow-code/marshmallow-sqlalchemy/issuesLicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: marshmallow-numerical
marshmallow-numericalmarshmallow-numericalis a library that helps you generatemarshmallowSchemas for Pandas and Numpy
data structures.UsageLet's start by creating an example dataframe for which we want to create aSchema. This dataframe has four columns: two of them are of string type, one
is a float, and the last one is an integer.importpandasaspdimportnumpyasnpfrommarshmallow_numericalimportSplitDataFrameSchemaanimal_df=pd.DataFrame([("falcon","bird",389.0,2),("parrot","bird",24.0,2),("lion","mammal",80.5,4),("monkey","mammal",np.nan,4),],columns=["name","class","max_speed","num_legs"],)You can then create a marshmallow schema that will validate and load dataframes
that follow the same structure as the one above and that have been serialized
withDataFrame.to_jsonwith theorient=splitformat:classAnimalSchema(SplitDataFrameSchema):"""Automatically generated schema for animal dataframe"""dtypes=animal_df.dtypesWhen passing a valid payload for a new animal, this schema will validate it and
build a dataframe:animal_schema=AnimalSchema()new_animal={"data":[("leopard","mammal",58.0,4),("ant","insect",0.288,6)],"columns":["name","class","max_speed","num_legs"],"index":[0,1],}new_animal_df=animal_schema.load(new_animal)print(type(new_animal_df))# <class 'pandas.core.frame.DataFrame'>print(new_animal_df)#       name   class  max_speed  num_legs# 0  leopard  mammal     58.000         4# 1      ant  insect      0.288         6However, if we pass a payload that doesn't conform to the schema, it will raise
a marshmallowValidationErrorexception with informative message about errors:invalid_animal={"data":[("leopard","mammal",58.0,"four")],# num_legs is not an int"columns":["name","class","num_legs"],# missing  max_speed column"index":[0],}animal_schema.load(invalid_animal)# Raises:# marshmallow.exceptions.ValidationError: {'columns': ["Must be equal to ['name', 'class', 'max_speed', 'num_legs']."], 'data': {0: {3: ['Not a valid integer.']}}}marshmallow_numericalcan also generate Schemas for theorient=recordsformat by following the above steps but usingmarshmallow_numerical.RecordsDataFrameSchemaas the superclass forAnimalSchema.Installationmarshmallow-numerical requires Python >= 3.6 and marshmallow >= 3.0. You can
install it with pip:pip install marshmallow-numericalContributingContributions are welcome!You can report a problem or feature request in theissue
tracker. If you feel that
you can fix it or implement it, please submit a pull request referencing the
issues it solves.Unit tests written using thepytestframework are in thetestsdirectory, and are run usingtoxon Python 3.6 and 3.7. You can run
the tests by installing tox:pip install toxand running the linters and tests for all Python versions by runningtox, or
for a specific Python version by running:tox -e py36We format the code withblack, and you can
format your checkout of the code before commiting it by running:tox -e black -- .
Package: marshmallow-objects
marshmallow-objectsMarshmallow Objects and ModelsSerializing/Deserializing Python objects usingMarshmallowlibrary.importmarshmallow_objectsasmarshmallowclassArtist(marshmallow.Model):name=marshmallow.fields.Str()classAlbum(marshmallow.Model):title=marshmallow.fields.Str()release_date=marshmallow.fields.Date()artist=marshmallow.NestedModel(Artist)bowie_raw=dict(name='David Bowie')album_raw=dict(artist=bowie_raw,title='Hunky Dory',release_date='1971-12-17')album=Album(**album_raw)print(album.title)print(album.release_date)print(album.artist.name)# Hunky Dory# 1971-12-17# David BowieGet It Now$pipinstall-Umarshmallow-objectsProject LinksMarshmallowPyPiCookbookLicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: marshmallow-oneofschema
An extension to marshmallow to support schema (de)multiplexing.marshmallow is a fantastic library for serialization and deserialization of data.
For more on that project see itsGitHubpage or itsDocumentation.This library adds a special kind of schema that actually multiplexes other schemas
based on object type. When serializing values, it uses get_obj_type() method
to get object type name. Then it usestype_schemasname-to-Schema mapping
to get schema for that particular object type, serializes object using that
schema and adds an extra field with name of object type. Deserialization is reverse.Installing$ pip install marshmallow-oneofschemaExampleThe code below demonstrates how to set up a polymorphic schema. For the full context check out the tests.
Once setup the schema should act like any other schema. If it does not then please file an Issue.importmarshmallowimportmarshmallow.fieldsfrommarshmallow_oneofschemaimportOneOfSchemaclassFoo:def__init__(self,foo):self.foo=fooclassBar:def__init__(self,bar):self.bar=barclassFooSchema(marshmallow.Schema):foo=marshmallow.fields.String(required=True)@marshmallow.post_loaddefmake_foo(self,data,**kwargs):returnFoo(**data)classBarSchema(marshmallow.Schema):bar=marshmallow.fields.Integer(required=True)@marshmallow.post_loaddefmake_bar(self,data,**kwargs):returnBar(**data)classMyUberSchema(OneOfSchema):type_schemas={"foo":FooSchema,"bar":BarSchema}defget_obj_type(self,obj):ifisinstance(obj,Foo):return"foo"elifisinstance(obj,Bar):return"bar"else:raiseException("Unknown object type:{}".format(obj.__class__.__name__))MyUberSchema().dump([Foo(foo="hello"),Bar(bar=123)],many=True)# => [{'type': 'foo', 'foo': 'hello'}, {'type': 'bar', 'bar': 123}]MyUberSchema().load([{"type":"foo","foo":"hello"},{"type":"bar","bar":123}],many=True)# => [Foo('hello'), Bar(123)]By default get_obj_type() returns obj.__class__.__name__, so you can just reuse that
to save some typing:classMyUberSchema(OneOfSchema):type_schemas={"Foo":FooSchema,"Bar":BarSchema}You can customize type field withtype_fieldclass property:classMyUberSchema(OneOfSchema):type_field="object_type"type_schemas={"Foo":FooSchema,"Bar":BarSchema}MyUberSchema().dump([Foo(foo="hello"),Bar(bar=123)],many=True)# => [{'object_type': 'Foo', 'foo': 'hello'}, {'object_type': 'Bar', 'bar': 123}]You can use resulting schema everywhere marshmallow.Schema can be used, e.g.importmarshmallowasmimportmarshmallow.fieldsasfclassMyOtherSchema(m.Schema):items=f.List(f.Nested(MyUberSchema))LicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: marshmallow-pageinfo
marshmallow_pageinfoPage info marshmallow schema for apiInstallationpip install -U marshmallow_pageinfoExamplefrommarshmallow_pageinfoimportPAGE_INFO_SCHEMA@controller.route('/',methods=['GET'])defget_todo():page_info=PAGE_INFO_SCHEMA.load(request.args)pagination=todo_q.paginate(page_info['page'],page_info['per_page'])return{'todos':TODO_SCHEMA.dump(pagination.items),'pageInfo':PAGE_INFO_SCHEMA.dump(pagination),}
Package: marshmallow-peewee
Marshmallow-PeeweeMarshmallow-Peewee --Peewee ORMintegration with theMarshmallow(de)serialization library.Requirementspython >= 3.7Installationmarshmallow-peeweeshould be installed using pip:$pipinstallmarshmallow-peeweeQuickstartimportpeeweeaspwclassRole(pw.Model):name=pw.CharField(255,default='user')classUser(pw.Model):created=pw.DateTimeField(default=dt.datetime.now())name=pw.CharField(255)title=pw.CharField(127,null=True)active=pw.BooleanField(default=True)rating=pw.IntegerField(default=0)role=pw.ForeignKeyField(Role)frommarshmallow_peeweeimportModelSchemaclassUserSchema(ModelSchema):classMeta:model=Userrole=Role.create()user=User.create(name='Mike',role=role)result=UserSchema().dump(user)print(result)# {'active': True,#  'created': '2016-03-29T15:27:18.600034+00:00',#  'id': 1,#  'name': 'Mike',#  'rating': 0,#  'role': 1,#  'title': None}result=UserSchema().load(result)assertisinstance(result,User)assertresult.name=='Mike'frommarshmallow_peeweeimportRelatedclassUserSchema(ModelSchema):role=Related()classMeta:model=Userresult=UserSchema().dump(user)print(result)# {'active': True,#  'created': '2016-03-29T15:30:32.767483+00:00',#  'id': 1,#  'name': 'Mike',#  'rating': 0,#  'role': {'id': 5, 'name': 'user'},#  'title': None}result=UserSchema().load(result)assertisinstance(result,User)assertisinstance(result.role,Role)UsageimportpeeweeaspwclassRole(pw.Model):name=pw.CharField(255,default='user')classUser(pw.Model):created=pw.DateTimeField(default=dt.datetime.now())name=pw.CharField(255)title=pw.CharField(127,null=True)active=pw.BooleanField(default=True)rating=pw.IntegerField(default=0)role=pw.ForeignKeyField(Role)frommarshmallow_peeweeimportModelSchemaclassUserSchema(ModelSchema):classMeta:# model: Bind peewee.Model to the Schemamodel=User# model_converter: Use custom model_converter# model_converter = marshmallow_peewee.DefaultConverter# dump_only_pk: Primary key is dump only# dump_only_pk = True# string_keys: Convert keys to strings# string_keys = True# id_keys: serialize (and deserialize) ForeignKey fields with _id suffix# id_keys = FalseYou may set global options formarshmallow-peewee:frommarshmallow_peeweeimportsetupsetup(id_keys=True,string_keys=False)# Set options for all schemasclassUserSchema(ModelSchema):# ...Customize fields convertion:frommarshmallow_peeweeimportDefaultConverter# Customize global# Serialize boolean as stringDefaultConverter.register(peewee.BooleanField,marshmallow.fields.String)# Alternative method@DefaultConverter.register(peewee.BooleanField)defbuild_field(field:peewee.Field,opts,**field_params):returnmarshmallow.fields.String(**params)# Customize only for a schemeclassCustomConverter(DefaultConverter):passCustomConverter.register(...)classCustomSchema(ModelSchema):# may be inheritedclassMeta:model_converter=CustomConverterBug trackerIf you have any suggestions, bug reports or annoyances please report them to
the issue tracker athttps://github.com/klen/marshmallow-peewee/issuesContributingDevelopment of the project happens at:https://github.com/klen/marshmallow-peeweeLicenseLicensed under aMIT License
Package: marshmallow-polyfield
This branch supports Marshmallow 3.0 and above. For 2.0 support seeThe 2.0 branchAn unofficial extension to Marshmallow to allow for polymorphic fields.Marshmallow is a fantastic library for serialization and deserialization of data.
For more on that project see itsGitHubpage or itsDocumentation.This project adds a custom field designed for polymorphic types. This allows you to define a schema that says “This field accepts anything of type X”The secret to this field is that you need to define two functions. One to be used when serializing, and another for deserializing. These functions
take in the raw value and return the schema to use.This field should support the same properties as other Marshmallow fields. I have worked withrequiredallow_noneandmany.Last version support v2 is tagged FINAL_V2_VERSIONInstalling$ pip install marshmallow-polyfieldImportingHere is how to import the necessary field classfrom marshmallow_polyfield import PolyFieldExampleThe code below demonstrates how to setup a schema with a PolyField. For the full context check out the tests.
Once setup the schema should act like any other schema. If it does not then please file an Issue.defshape_schema_serialization_disambiguation(base_object,parent_obj):class_to_schema={Rectangle.__name__:RectangleSchema,Triangle.__name__:TriangleSchema}try:returnclass_to_schema[base_object.__class__.__name__]()exceptKeyError:passraiseTypeError("Could not detect type. ""Did not have a base or a length. ""Are you sure this is a shape?")defshape_schema_deserialization_disambiguation(object_dict,parent_object_dict):ifobject_dict.get("base"):returnTriangleSchema()elifobject_dict.get("length"):returnRectangleSchema()raiseTypeError("Could not detect type. ""Did not have a base or a length. ""Are you sure this is a shape?")classContrivedShapeClass(object):def__init__(self,main,others):self.main=mainself.others=othersdef__eq__(self,other):returnself.__dict__==other.__dict__classContrivedShapeClassSchema(Schema):main=PolyField(serialization_schema_selector=shape_schema_serialization_disambiguation,deserialization_schema_selector=shape_schema_deserialization_disambiguation,required=True)others=PolyField(serialization_schema_selector=shape_schema_serialization_disambiguation,deserialization_schema_selector=shape_schema_deserialization_disambiguation,allow_none=True,many=True)@post_loaddefmake_object(self,data):returnTestPolyField.ContrivedShapeClass(data.get('main'),data.get('others'))
Package: marshmallow-pynamodb
PynamoDBintegration with themarshmallow(de)serialization library.InstallationFrom PyPi:$ pip install marshmallow-pynamodbFrom GitHub:$ pip install git+https://github.com/mathewmarcus/marshmallow-pynamodb#egg=marshmallow_pynamodbDeclare your modelsfrompynamodb.modelsimportModelfrompynamodb.attributesimportUnicodeAttributeclassUser(Model):classMeta:table_name="user"email=UnicodeAttribute(null=True)first_name=UnicodeAttribute(range_key=True)last_name=UnicodeAttribute(hash_key=True)Generate marshmallow schemasfrommarshmallow_pynamodbimportModelSchemaclassUserSchema(ModelSchema):classMeta:model=Useruser_schema=UserSchema()(De)serialize your datauser=User(last_name="Smith",first_name="John")user_schema.dump(user).data# {u'first_name': u'John', u'last_name': u'Smith', u'email': None}user_schema.load({"last_name":"Smith","first_name":"John"}).data# user<Smith>Nested models? No problemfrommarshmallow_pynamodb.schemaimportModelSchemafrompynamodb.modelsimportModelfrompynamodb.attributesimportListAttribute,MapAttribute,NumberAttribute,UnicodeAttributeclassLocation(MapAttribute):latitude=NumberAttribute()longitude=NumberAttribute()name=UnicodeAttribute()classPerson(MapAttribute):firstName=UnicodeAttribute()lastName=UnicodeAttribute()age=NumberAttribute()classOfficeEmployeeMap(MapAttribute):office_employee_id=NumberAttribute()person=Person()office_location=Location()classOffice(Model):classMeta:table_name='OfficeModel'office_id=NumberAttribute(hash_key=True)address=Location()employees=ListAttribute(of=OfficeEmployeeMap)classOfficeSchema(ModelSchema):classMeta:model=OfficeOfficeSchema().load({'office_id':789,'address':{'latitude':6.98454,'longitude':172.38832,'name':'some_location'},'employees':[{'office_employee_id':123,'person':{'firstName':'John','lastName':'Smith','age':45},'office_location':{'latitude':-24.0853,'longitude':144.87660,'name':'other_location'}},{'office_employee_id':456,'person':{'firstName':'Jane','lastName':'Doe','age':33},'office_location':{'latitude':-20.57989,'longitude':92.30463,'name':'yal'}}]}).data# Office<789>LicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: marshmallow-sa-core
marshmallow-sa-coreSQLAlchemy-core integration with marshmallow (de)serialization library.Declare tabletable.json:{"name":"market","fields":[{"name":"id","type":"int","constraints":{"required":true}},{"name":"name","type":"str","constraints":{"required":true}},{"name":"type","type":"str"},{"name":"city","type":"str"},{"name":"tel","type":"str"},{"name":"address","type":"str","constraints":{"unique":true}},{"name":"lng","type":"float","constraints":{"minimum":-180,"maximum":180}},{"name":"lat","type":"float","constraints":{"minimum":-90,"maximum":90}},{"name":"create_time","type":"datetime"},{"name":"update_time","type":"datetime"}],"primaryKey":["id"]}Generate(Deserialize) SQLAlchemy Table>>>importjson>>>withopen('table.json','r')asfp:table_definition=json.load(fp)>>>frommarshmallow_sa_coreimportJSONTableSchema>>>schema=JSONTableSchema()>>>table=schema.load(table_definition)>>>tableTable('market',MetaData(),Column('id',Integer(),table=<market>,primary_key=True,nullable=False),Column('name',String(),table=<market>,nullable=False),...check DDL of create table:>>>fromsqlalchemyimportcreate_engine>>>fromsqlalchemy.schemaimportCreateTable>>>db=create_engine('sqlite:///:memory:')>>>print(CreateTable(table).compile(db))CREATETABLEmarket(idINTEGERNOTNULL,nameVARCHARNOTNULL,typeVARCHAR,cityVARCHAR,telVARCHAR,addressVARCHAR,lngFLOATCHECK("lng"<=180.0)CHECK("lng">=-180.0),latFLOATCHECK("lat"<=90.0)CHECK("lat">=-90.0),create_timeDATETIME,update_timeDATETIME,PRIMARYKEY(id),UNIQUE(address))Serialize SQLAlchemy Table object>>>fromsqlalchemyimportTable,MetaData,Column,Integer,String,DateTime>>>table=Table('employee',MetaData(),Column('id',Integer,primary_key=True),Column('LastName',String,nullable=False),Column('FirstName',String,nullable=False),Column('Title',String),Column('BirthDate',DateTime),Column('HireDate',DateTime),)>>>frommarshmallow_sa_coreimportJSONTableSchema>>>frompprintimportpp>>>pp(JSONTableSchema().dump(table)){'schema':None,'fields':[{'type':'int','constraints':{'required':True},'name':'id','__version__':'0.0.4'},{'type':'str','constraints':{'required':True},'name':'LastName','__version__':'0.0.4'},{'type':'str','constraints':{'required':True},'name':'FirstName','__version__':'0.0.4'},{'type':'str','name':'Title','__version__':'0.0.4'},{'type':'datetime','name':'BirthDate','__version__':'0.0.4'},{'type':'datetime','name':'HireDate','__version__':'0.0.4'}],'name':'employee','primaryKey':['id'],'__version__':'0.0.4'}Get it now$pipinstallmarshmallow-sa-coreLicenseMIT licensed. See the bundledLICENSEfile for more details.