Skip to main content

simple library based on python +3.8 to use Dataclass-syntaxfor interacting with Data

Project description

DataModel

DataModel is a simple library based on python +3.7 to use Dataclass-syntax for interacting with Data, using the same syntax of Dataclass, users can write Python Objects and work with Data in the same way, is a reimplementation of python Dataclasses supporting true inheritance (without decorators), true composition and other good features.

The key features are:

  • Easy to use: No more using decorators, concerns abour re-ordering attributes or common problems with using dataclasses with inheritance.
  • Extensibility: Can use other dataclasses, Data objects or primitives as data-types.
  • Fast: DataModel is a replacement 100% full compatible with dataclasses, without any overhead.

Requirements

Python 3.8+

Installation

$ pip install python-datamodel
---> 100%
Successfully installed datamodel

Quickstart

from datamodel import Field, BaseModel
from dataclasses import dataclass, fields, is_dataclass


# This pure Dataclass:
@dataclass
class Point:
    x: int = Field(default=0, min=0, max=10)
    y: int = Field(default=0, min=0, max=10)

point = Point(x=10, y=10)
print(point)
print(fields(point))
print('IS a Dataclass?: ', is_dataclass(point))

# Can be represented by BaseModel
class newPoint(BaseModel):
    x: int = Field(default=0, min=0, max=10)
    y: int = Field(default=0, min=0, max=10)

    def get_coordinate(self):
        return (self.x, self.y)

point = newPoint(x=10, y=10)
print(point)
print(fields(point))
print('IS a Dataclass?: ', is_dataclass(point))
print(point.get_coordinate())

Supported types

DataModel support recursive transformation of fields, so you can easily work with nested dataclasses or complex types.

DataModel supports automatic conversion of:

  • datetime objects. datetime objects are encoded to str exactly like orjson conversion, any str typed as datetime is decoded to datetime. The same behavior is used to decoding time, date and timedelta objects.

  • UUID objects. They are encoded as str (JSON string) and decoded back to uuid.UUID objects.

  • Decimal objects. They are also encoded as float and decoded back to Decimal.

Also, "custom" encoders are supported.

import uuid
from typing import (
    List,
    Optional,
    Union
)
from dataclasses import dataclass, field
from datamodel import BaseModel, Field

@dataclass
class Point:
    x: int = Field(default=0, min=0, max=10)
    y: int = Field(default=0, min=0, max=10)

class coordinate(BaseModel, intSum):
    latitude: float
    longitude: float

    def get_location(self) -> tuple:
        return (self.latitude, self.longitude)

def auto_uid():
    return uuid.uuid4()

def default_rect():
    return [0,0,0,0]

def valid_zipcode(field, value):
    return value > 1000

class Address(BaseModel):
    id: uuid.UUID = field(default_factory=auto_uid)
    street: str = Field(required=True)
    zipcode: int = Field(required=False, default=1010, validator=valid_zipcode)
    location: Optional[coordinate]
    box: List[Optional[Point]]
    rect: List[int] = Field(factory=default_rect)


addr = Address(street="Calle Mayor", location=(18.1, 22.1), zipcode=3021, box=[(2, 10), (4, 8)], rect=[1, 2, 3, 4])
print('IS a Dataclass?: ', is_dataclass(addr))

print(addr.location.get_location())
# returns
Address(id=UUID('24b34dd5-8d35-4cfd-8916-7876b28cdae3'), street='Calle Mayor', zipcode=3021, location=coordinate(latitude=18.1, longitude=22.1), box=[Point(x=2, y=10), Point(x=4, y=8)], rect=[1, 2, 3, 4])
  • Fast and convenience conversion from-to JSON (using orjson):
import orjson

b = addr.json()
print(b)
{"id":"24b34dd5-8d35-4cfd-8916-7876b28cdae3","street":"Calle Mayor","zipcode":3021,"location":{"latitude":18.1,"longitude":22.1}, "box":[{"x":2,"y":10},{"x":4,"y":8}],"rect":[1,2,3,4]}
# and re-imported from json
new_addr = Address.from_json(b) # load directly from json string
# or using a dictionary decoded by orjson
data = orjson.loads(b)
new_addr = Address(**data)

Inheritance

python-datamodel supports inheritance of classes.

import uuid
from typing import Union, List
from dataclasses import dataclass, field
from datamodel import BaseModel, Column, Field


def auto_uid():
    return uuid.uuid4()

class User(BaseModel):
    id: uuid.UUID = field(default_factory=auto_uid)
    name: str
    first_name: str
    last_name: str


@dataclass
class Address:
    street: str
    city: str
    state: str
    zipcode: str
    country: Optional[str] = 'US'

    def __str__(self) -> str:
        """Provides pretty response of address"""
        lines = [self.street]
        lines.append(f"{self.city}, {self.zipcode} {self.state}")
        lines.append(f"{self.country}")
        return "\n".join(lines)

class Employee(User):
    """
    Base Employee.
    """
    role: str
    address: Address # composition of a dataclass inside of DataModel is possible.

# Supporting multiple inheritance and composition
# Wage Policies
class MonthlySalary(BaseModel):
    salary: Union[float, int]

    def calculate_payroll(self) -> Union[float, int]:
        return self.salary

class HourlySalary(BaseModel):
    salary: Union[float, int] = Field(default=0)
    hours_worked: Union[float, int] = Field(default=0)

    def calculate_payroll(self) -> Union[float, int]:
        return (self.hours_worked * self.salary)

# employee types
class Secretary(Employee, MonthlySalary):
    """Secretary.

    Person with montly salary policy and no commissions.
    """
    role: str = 'Secretary'

class FactoryWorker(Employee, HourlySalary):
    """
    FactoryWorker is an employee with hourly salary policy and no commissions.
    """
    role: str = 'Factory Worker'

class PayrollSystem:
    def calculate_payroll(self, employees: List[dataclass]) -> None:
        print('=== Calculating Payroll === ')
        for employee in employees:
            print(f"Payroll for employee {employee.id} - {employee.name}")
            print(f"- {employee.role} Amount: {employee.calculate_payroll()}")
            if employee.address:
                print('- Sent to:')
                print(employee.address)
            print("")

jane = Secretary(name='Jane Doe', first_name='Jane', last_name='Doe', salary=1500)
bob = FactoryWorker(name='Bob Doyle', first_name='Bob', last_name='Doyle', salary=15, hours_worked=40)
mitch = FactoryWorker(name='Mitch Brian', first_name='Mitch', last_name='Brian', salary=20, hours_worked=35)

payroll = PayrollSystem()
payroll.calculate_payroll([jane, bob, mitch])

A sample of output:

```console
=== Calculating Payroll ===
Payroll for employee 745a2623-d4d2-4da6-bf0a-1fa691bafd33 - Jane Doe
- Secretary Amount: 1500
- Sent to:
Rodeo Drive, Rd
Los Angeles, 31050 CA
US

Contributing

First of all, thank you for being interested in contributing to this library. I really appreciate you taking the time to work on this project.

  • If you're just interested in getting into the code, a good place to start are issues tagged as bugs.
  • If introducing a new feature, especially one that modifies the public API, consider submitting an issue for discussion before a PR. Please also take a look at existing issues / PRs to see what you're proposing has already been covered before / exists.
  • I like to follow the commit conventions documented here

License

This project is licensed under the terms of the BSD v3. license.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

python_datamodel-0.0.33-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (943.8 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

python_datamodel-0.0.33-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (987.3 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

File details

Details for the file python_datamodel-0.0.33-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for python_datamodel-0.0.33-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 39eed11c1ad94053c15e887068b3eb11c13b56eca5f8f2d4479a894e7a1d618d
MD5 7fd2d13120b5f96b953da48e31716a5a
BLAKE2b-256 581ed7a9ccec92e872fff9985240a59f5136772acfd5debe8a3177e2285ae9c9

See more details on using hashes here.

File details

Details for the file python_datamodel-0.0.33-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for python_datamodel-0.0.33-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 bd4b0a4a645bd8275127c98fd6b57e99661dcc167ba19a62a6bcc396a1b27c4f
MD5 2d0274bdf4cd51663fd3534b74027e0c
BLAKE2b-256 9cc0c4d986d5579bc5dafb6f40d3d3955e4ce138abec659ca264328103b27c90

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page