Package: dictfier
dictfierdictfieris a library to convert/serialize Python class instances(Objects) bothflatandnestedinto a dictionary data structure. It's very useful in converting Python Objects into JSON format especially for nested objects, because they can't be handled well by json libraryPrerequisitespython version >= 2.7InstallingpipinstalldictfierGetting StartedConverting a flat object into a dictimportdictfierclassStudent(object):def__init__(self,name,age):self.name=nameself.age=agestudent=Student("Danish",24)query=["name","age"]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age':24}Converting nested object into a dictimportdictfierclassCourse(object):def__init__(self,code,name):self.code=codeself.name=nameclassStudent(object):def__init__(self,name,age,course):self.name=nameself.age=ageself.course=coursecourse=Course("CS201","Data Structures")student=Student("Danish",24,course)query=["name","age",{"course":["code","name",]}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age':24,'course':{'code':'CS201','name':'Data Structures'}}Converting object nested with iterable object into a dictimportdictfierclassCourse(object):def__init__(self,code,name):self.code=codeself.name=nameclassStudent(object):def__init__(self,name,age,courses):self.name=nameself.age=ageself.courses=coursescourse1=Course("CS201","Data Structures")course2=Course("CS205","Computer Networks")student=Student("Danish",24,[course1,course2])query=["name","age",{"courses":[["code","name",]]}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age':24,'courses':[{'code':'CS201','name':'Data Structures'},{'code':'CS205','name':'Computer Networks'}]}What about instance methods or callable object fields?Well we've got good news for that,dictfiercan use callables which return values as fields, It's very simple, you just have to pass "call=True" as a keyword argument to objfield API and add your callable field to a query. E.g.importdictfierclassStudent(object):def__init__(self,name,age):self.name=nameself.age=agedefage_in_days(self):returnself.age*365student=Student("Danish",24)query=["name",{"age_in_days":dictfier.objfield("age_in_days",call=True)}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age_in_days':8760}You can also add your custom field by usingnewfieldAPI. E.g.importdictfierclassStudent(object):def__init__(self,name,age):self.name=nameself.age=agestudent=Student("Danish",24)query=["name","age",{"school":dictfier.newfield("St Patrick")}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age':24,'school':'St Patrick'}What if we want to use object field on a custom field to do some computations?.Well there is a way to do that too,dictfierAPI providesuseobjhook which is used to hook or pull the object on a current query node. To use the current object, just define a fuction which accept single argument(which is an object) and perform your computations on such function and then return a result, calluseobjand pass that defined fuction to it.Let's say we want to calculate age of a student in terms of months from a student object with age field in terms of years. Here is how we would do this by usinguseobjhook.importdictfierclassStudent(object):def__init__(self,name,age):self.name=nameself.age=agestudent=Student("Danish",24)defage_in_months(obj):# Do the computation here then return the resultreturnobj.age*12query=["name",# This is a custom field which is computed by using age field from a student object# Note how age_in_months function is passed to useobj hook(This is very important for API to work){"age_in_months":dictfier.useobj(age_in_months)}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age_in_months':288}What if we want to use object field on a custom field(Rename obj field)?This can be accomplished in two ways, As you might have guessed, one way to do it is to useuseobjhook by passing a function which return the value of a field which you want to use, another simple way is to useobjfieldhook. Just likeuseobjhook,objfieldhook is used to hook or pull object field on a current query node. To use the current object field, just callobjfieldand pass a field name which you want to use or replace.Let's say we want to renameagefield toage_in_yearsin our results. Here is how we would do this by usingobjfieldhook.importdictfierclassStudent(object):def__init__(self,name,age):self.name=nameself.age=agestudent=Student("Danish",24)query=["name",{"age_in_years":dictfier.objfield("age")}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age_in_years':24}And if you want to useuseobjhook then this is how you would do it.importdictfierclassStudent(object):def__init__(self,name,age):self.name=nameself.age=agestudent=Student("Danish",24)query=["name",{"age_in_years":dictfier.useobj(lambdaobj:obj.age)}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age_in_years':24}Infactobjfieldhook is implemented by usinguseobj, so both methods are the same interms of performance, but I think you would agree with me that in this caseobjfieldis more readable thanuseobj.You can also query an object returned byuseobjhook, This can be done by passing a query as a second argument touseobjor use 'query=your_query' as a kwarg. E.g.importjsonimportdictfierclassCourse(object):def__init__(self,code,name):self.code=codeself.name=nameclassStudent(object):def__init__(self,name,age,course):self.name=nameself.age=ageself.course=coursecourse=Course("CS201","Data Structures")student=Student("Danish",24,course)query=["name","age",{"course":dictfier.useobj(lambdaobj:obj.course,["name","code"]# This is a query)}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age':24,'course':{'name':'Data Structures','code':'CS201'}}For iterable objects, here is how you would do it.importjsonimportdictfierclassCourse(object):def__init__(self,code,name):self.code=codeself.name=nameclassStudent(object):def__init__(self,name,age,courses):self.name=nameself.age=ageself.courses=coursescourse1=Course("CS201","Data Structures")course2=Course("CS205","Computer Networks")student=Student("Danish",24,[course1,course2])query=["name","age",{"courses":dictfier.useobj(lambdaobj:obj.courses,[["name","code"]]# This is a query)}]std_info=dictfier.dictfy(student,query)print(std_info)# Output{'name':'Danish','age':24,'courses':[{'name':'Data Structures','code':'CS201'},{'name':'Computer Networks','code':'CS205'}]}How dictfier works?dictfierworks by converting given Object into a corresponding dictrecursively(Hence works on nested objects)by using aQuery. So what's important here is to know how to structure right queries to extract right data from the object.What's a Query anyway?A Query is basically a template which tells dictfier what to extract from an object. It is defined as a list or tuple of Object's fields to be extracted.Sample conversions.When a flat student object is queried using a query belowquery=["name","age",]dictfierwill convert it into{"name":student.name,"age":student.age,}For nested queries it goes likequery=["name","age",{"course":["code","name",]}]Corresponding dict{"name":student.name,"age":student.age,"course":{"code":student.course.code,"name":student.course.name,}}For iterable objects it goes likequery=["name","age",{"course":[["code","name",]]}]Putting a list or tuple inside a list or tuple of object fields is a way to declare that the Object is iterable. In this case[["code","name",]]Corresponding dict{"name":student.name,"age":student.age,"courses":[{"code":course.code,"name":course.name,}forcourseinstudent.courses]}Notice the list or tuple on "courses" unlike in other fields like "name" and "age", it makes "courses" iterable, This is the reason for having nested list or tuple on "courses" query.It's pretty simple right?What if I want to customize how dictfier works?You might encounter a case where you have to change how dictfier works to get the result which you want, don't worry we have got your back.dictfieris highly configurable, it allows you to configure how each type of object is converted into a dictionary data structure.dictfierconfiguration is divided into three parts which areFlat objects config(pass flat_obj=function kwarg to dictfy)Nested flat objects config(pass nested_flat_obj=function kwarg to dictfy)Nested iterable objects config(pass nested_iter_obj=function kwarg to dictfy)In all cases above, function assigned to flat_obj, nested_flat_obj or nested_iter_obj accepts three positional arguments which are field value(object) and parent object and field name. Now consider an example of a simple ORM with two relationsManyandOnewhich are used to show how objects are related.# Customize how dictfier obtains flat obj,# nested flat obj and nested iterable objimportdictfierclassMany(object):def__init__(self,data):self.data=dataclassOne(object):def__init__(self,data):self.data=dataclassBook(object):def__init__(self,pk,title,publish_date):self.pk=pkself.title=titleself.publish_date=publish_dateclassMentor(object):def__init__(self,pk,name,profession):self.pk=pkself.name=nameself.profession=professionclassCourse(object):def__init__(self,pk,code,name,books):self.pk=pkself.code=codeself.name=nameself.books=Many(books)classStudent(object):def__init__(self,pk,name,age,mentor,courses):self.pk=pkself.name=nameself.age=ageself.mentor=One(mentor)self.courses=Many(courses)book1=Book(1,"Advanced Data Structures","2018")book2=Book(2,"Basic Data Structures","2010")book3=Book(1,"Computer Networks","2011")course1=Course(1,"CS201","Data Structures",[book1,book2])course2=Course(2,"CS220","Computer Networks",[book3])mentor=Mentor(1,"Van Donald","Software Eng")student=Student(1,"Danish",24,mentor,[course1,course2])query=["name","age",{"mentor":["name","profession"],"courses":[["name","code",{"books":[["title","publish_date"]]}]]}]result=dictfier.dictfy(student,query,flat_obj=lambdaobj,parent:obj,nested_iter_obj=lambdaobj,parent:obj.data,nested_flat_obj=lambdaobj,parent:obj.data)print(result)# Output{'name':'Danish','age':24,'mentor':{'name':'Van Donald','profession':'Software Eng'},'courses':[{'name':'Data Structures','code':'CS201','books':[{'title':'Advanced Data Structures','publish_date':'2018'},{'title':'Basic Data Structures','publish_date':'2010'}]},{'name':'Computer Networks','code':'CS220','books':[{'title':'Computer Networks','publish_date':'2011'}]}]}From an example above, if you want to return primary key(pk) for nested flat or nested iterable object(which is very common in API design and serializing models) you can do it as follows.query=["name","age","mentor","courses"]defget_pk(obj,parent,field_name):ifisinstance(obj,One):returnobj.data.pkelifisinstance(obj,Many):return[rec.pkforrecinobj.data]else:returnobjresult=dictfier.dictfy(student,query,flat_obj=get_pk,nested_iter_obj=lambdaobj,parent:obj.data,nested_flat_obj=lambdaobj,parent:obj.data)print(result)# Output{'name':'Danish','age':24,'mentor':1,'courses':[1,2]}ContributingI welcome all contributions. Please readCONTRIBUTING.mdfirst. You can submit any ideas aspull requestsor asGitHub issues. If you'd like to improve code, check out theCode Style Guideand have a good time!.