dictfier is a library to convert/serialize Python class instances(Objects) both flat and nested into 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 library
python version >= 2.7
pip install dictfierimport dictfier
class Student(object):
def __init__(self, name, age):
self.name = name
self.age = age
student = Student("Danish", 24)
query = [
"name",
"age"
]
std_info = dictfier.dictfy(student, query)
print(std_info)# Output
{'name': 'Danish', 'age': 24}import dictfier
class Course(object):
def __init__(self, code, name):
self.code = code
self.name = name
class Student(object):
def __init__(self, name, age, course):
self.name = name
self.age = age
self.course = course
course = 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'}
}import dictfier
class Course(object):
def __init__(self, code, name):
self.code = code
self.name = name
class Student(object):
def __init__(self, name, age, courses):
self.name = name
self.age = age
self.courses = courses
course1 = 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'}
]
}Well we've got good news for that, dictfier can 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.
import dictfier
class Student(object):
def __init__(self, name, age):
self.name = name
self.age = age
def age_in_days(self):
return self.age * 365
student = 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 using newfield API. E.g.
import dictfier
class Student(object):
def __init__(self, name, age):
self.name = name
self.age = age
student = 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'}Well there is a way to do that too, dictfier API provides useobj hook 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, call useobj and 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 using useobj hook.
import dictfier
class Student(object):
def __init__(self, name, age):
self.name = name
self.age = age
student = Student("Danish", 24)
def age_in_months(obj):
# Do the computation here then return the result
return obj.age * 12
query = [
"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}This can be accomplished in two ways, As you might have guessed, one way to do it is to use useobj hook by passing a function which return the value of a field which you want to use, another simple way is to use objfield hook. Just like useobj hook, objfield hook is used to hook or pull object field on a current query node. To use the current object field, just call objfield and pass a field name which you want to use or replace.
Let's say we want to rename age field to age_in_years in our results. Here is how we would do this by using objfield hook.
import dictfier
class Student(object):
def __init__(self, name, age):
self.name = name
self.age = age
student = 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 use useobj hook then this is how you would do it.
import dictfier
class Student(object):
def __init__(self, name, age):
self.name = name
self.age = age
student = Student("Danish", 24)
query = [
"name",
{"age_in_years": dictfier.useobj(lambda obj: obj.age)}
]
std_info = dictfier.dictfy(student, query)
print(std_info)# Output
{'name': 'Danish', 'age_in_years': 24}Infact objfield hook is implemented by using useobj, so both methods are the same interms of performance, but I think you would agree with me that in this case objfield is more readable than useobj.
You can also query an object returned by useobj hook, This can be done by passing a query as a second argument to useobj or use 'query=your_query' as a kwarg. E.g.
import json
import dictfier
class Course(object):
def __init__(self, code, name):
self.code = code
self.name = name
class Student(object):
def __init__(self, name, age, course):
self.name = name
self.age = age
self.course = course
course = Course("CS201", "Data Structures")
student = Student("Danish", 24, course)
query = [
"name",
"age",
{
"course": dictfier.useobj(
lambda obj: 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'
}
}import json
import dictfier
class Course(object):
def __init__(self, code, name):
self.code = code
self.name = name
class Student(object):
def __init__(self, name, age, courses):
self.name = name
self.age = age
self.courses = courses
course1 = Course("CS201", "Data Structures")
course2 = Course("CS205", "Computer Networks")
student = Student("Danish", 24, [course1, course2])
query = [
"name",
"age",
{
"courses": dictfier.useobj(
lambda obj: 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'}
]
}dictfier works by converting given Object into a corresponding dict recursively(Hence works on nested objects) by using a Query. So what's important here is to know how to structure right queries to extract right data from the object.
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.
When a flat student object is queried using a query below
query = [
"name",
"age",
]dictfier will convert it into
{
"name": student.name,
"age": student.age,
} For nested queries it goes like
query = [
"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 like
query = [
"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,
}
for course in student.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?
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. dictfier is highly configurable, it allows you to configure how each type of object is converted into a dictionary data structure. dictfier configuration is divided into three parts which are
-
Flat 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 relations Many and One which are used to show how objects are related.
# Customize how dictfier obtains flat obj,
# nested flat obj and nested iterable obj
import dictfier
class Many(object):
def __init__(self, data):
self.data = data
class One(object):
def __init__(self, data):
self.data = data
class Book(object):
def __init__(self, pk, title, publish_date):
self.pk = pk
self.title = title
self.publish_date = publish_date
class Mentor(object):
def __init__(self, pk, name, profession):
self.pk = pk
self.name = name
self.profession = profession
class Course(object):
def __init__(self, pk, code, name, books):
self.pk = pk
self.code = code
self.name = name
self.books = Many(books)
class Student(object):
def __init__(self, pk, name, age, mentor, courses):
self.pk = pk
self.name = name
self.age = age
self.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=lambda obj, parent: obj,
nested_iter_obj=lambda obj, parent: obj.data,
nested_flat_obj=lambda obj, 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"
]
def get_pk(obj, parent, field_name):
if isinstance(obj, One):
return obj.data.pk
elif isinstance(obj, Many):
return [rec.pk for rec in obj.data]
else:
return obj
result = dictfier.dictfy(
student,
query,
flat_obj=get_pk,
nested_iter_obj=lambda obj, parent: obj.data,
nested_flat_obj=lambda obj, parent: obj.data
)
print(result)# Output
{'name': 'Danish', 'age': 24, 'mentor': 1, 'courses': [1, 2]}
I welcome all contributions. Please read CONTRIBUTING.md first. You can submit any ideas as pull requests or as GitHub issues. If you'd like to improve code, check out the Code Style Guide and have a good time!.