# DataModel
DataModel is a simple library based on python +3.8 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 (like ORM's), 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
<div class="termy">
```console
$ pip install python-datamodel
---> 100%
Successfully installed datamodel
```
</div>
## Quickstart
```python
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](https://docs.python.org/3/library/datetime.html#available-types)
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](https://docs.python.org/3/library/uuid.html#uuid.UUID) objects. They
are encoded as `str` (JSON string) and decoded back to uuid.UUID objects.
- [Decimal](https://docs.python.org/3/library/decimal.html) objects. They are
also encoded as `float` and decoded back to Decimal.
Also, "custom" encoders are supported.
```python
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())
```
```console
# 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):
```python
import orjson
b = addr.json()
print(b)
```
```console
{"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]}
```
```python
# 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.
```python
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](https://www.conventionalcommits.org/en/v1.0.0/#summary)
## License
This project is licensed under the terms of the BSD v3. license.
Raw data
{
"_id": null,
"home_page": "https://github.com/phenobarbital/python-datamodel",
"name": "python-datamodel",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9.13",
"maintainer_email": null,
"keywords": "asyncio, dataclass, dataclasses, data models",
"author": "Jesus Lara",
"author_email": "jesuslarag@gmail.com",
"download_url": null,
"platform": "any",
"description": "# DataModel\nDataModel is a simple library based on python +3.8 to use Dataclass-syntax for interacting with\nData, using the same syntax of Dataclass, users can write Python Objects\nand work with Data in the same way (like ORM's), is a reimplementation of python Dataclasses supporting true inheritance (without decorators), true composition and other good features.\n\nThe key features are:\n* **Easy to use**: No more using decorators, concerns abour re-ordering attributes or common problems with using dataclasses with inheritance.\n* **Extensibility**: Can use other dataclasses, Data objects or primitives as data-types.\n* **Fast**: DataModel is a replacement 100% full compatible with dataclasses, without any overhead.\n\n\n\n## Requirements\n\nPython 3.8+\n\n## Installation\n\n<div class=\"termy\">\n\n```console\n$ pip install python-datamodel\n---> 100%\nSuccessfully installed datamodel\n```\n\n\n</div>\n\n## Quickstart\n\n\n```python\n\nfrom datamodel import Field, BaseModel\nfrom dataclasses import dataclass, fields, is_dataclass\n\n\n# This pure Dataclass:\n@dataclass\nclass Point:\n x: int = Field(default=0, min=0, max=10)\n y: int = Field(default=0, min=0, max=10)\n\npoint = Point(x=10, y=10)\nprint(point)\nprint(fields(point))\nprint('IS a Dataclass?: ', is_dataclass(point))\n\n# Can be represented by BaseModel\nclass newPoint(BaseModel):\n x: int = Field(default=0, min=0, max=10)\n y: int = Field(default=0, min=0, max=10)\n\n def get_coordinate(self):\n return (self.x, self.y)\n\npoint = newPoint(x=10, y=10)\nprint(point)\nprint(fields(point))\nprint('IS a Dataclass?: ', is_dataclass(point))\nprint(point.get_coordinate())\n```\n## Supported types\n\nDataModel support recursive transformation of fields, so you can easily work with nested dataclasses or complex types.\n\nDataModel supports automatic conversion of:\n\n- [datetime](https://docs.python.org/3/library/datetime.html#available-types)\nobjects. `datetime` objects are encoded to str exactly like orjson conversion, any str typed as datetime is decoded to datetime.\nThe same behavior is used to decoding time, date and timedelta objects.\n\n- [UUID](https://docs.python.org/3/library/uuid.html#uuid.UUID) objects. They\nare encoded as `str` (JSON string) and decoded back to uuid.UUID objects.\n\n- [Decimal](https://docs.python.org/3/library/decimal.html) objects. They are\nalso encoded as `float` and decoded back to Decimal.\n\nAlso, \"custom\" encoders are supported.\n\n```python\n\nimport uuid\nfrom typing import (\n List,\n Optional,\n Union\n)\nfrom dataclasses import dataclass, field\nfrom datamodel import BaseModel, Field\n\n@dataclass\nclass Point:\n x: int = Field(default=0, min=0, max=10)\n y: int = Field(default=0, min=0, max=10)\n\nclass coordinate(BaseModel, intSum):\n latitude: float\n longitude: float\n\n def get_location(self) -> tuple:\n return (self.latitude, self.longitude)\n\ndef auto_uid():\n return uuid.uuid4()\n\ndef default_rect():\n return [0,0,0,0]\n\ndef valid_zipcode(field, value):\n return value > 1000\n\nclass Address(BaseModel):\n id: uuid.UUID = field(default_factory=auto_uid)\n street: str = Field(required=True)\n zipcode: int = Field(required=False, default=1010, validator=valid_zipcode)\n location: Optional[coordinate]\n box: List[Optional[Point]]\n rect: List[int] = Field(factory=default_rect)\n\n\naddr = Address(street=\"Calle Mayor\", location=(18.1, 22.1), zipcode=3021, box=[(2, 10), (4, 8)], rect=[1, 2, 3, 4])\nprint('IS a Dataclass?: ', is_dataclass(addr))\n\nprint(addr.location.get_location())\n```\n```console\n# returns\nAddress(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])\n```\n\n* Fast and convenience conversion from-to JSON (using orjson):\n\n```python\nimport orjson\n\nb = addr.json()\nprint(b)\n```\n```console\n{\"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]}\n```\n\n```python\n# and re-imported from json\nnew_addr = Address.from_json(b) # load directly from json string\n# or using a dictionary decoded by orjson\ndata = orjson.loads(b)\nnew_addr = Address(**data)\n\n```\n\n## Inheritance\n\npython-datamodel supports inheritance of classes.\n\n```python\nimport uuid\nfrom typing import Union, List\nfrom dataclasses import dataclass, field\nfrom datamodel import BaseModel, Column, Field\n\n\ndef auto_uid():\n return uuid.uuid4()\n\nclass User(BaseModel):\n id: uuid.UUID = field(default_factory=auto_uid)\n name: str\n first_name: str\n last_name: str\n\n\n@dataclass\nclass Address:\n street: str\n city: str\n state: str\n zipcode: str\n country: Optional[str] = 'US'\n\n def __str__(self) -> str:\n \"\"\"Provides pretty response of address\"\"\"\n lines = [self.street]\n lines.append(f\"{self.city}, {self.zipcode} {self.state}\")\n lines.append(f\"{self.country}\")\n return \"\\n\".join(lines)\n\nclass Employee(User):\n \"\"\"\n Base Employee.\n \"\"\"\n role: str\n address: Address # composition of a dataclass inside of DataModel is possible.\n\n# Supporting multiple inheritance and composition\n# Wage Policies\nclass MonthlySalary(BaseModel):\n salary: Union[float, int]\n\n def calculate_payroll(self) -> Union[float, int]:\n return self.salary\n\nclass HourlySalary(BaseModel):\n salary: Union[float, int] = Field(default=0)\n hours_worked: Union[float, int] = Field(default=0)\n\n def calculate_payroll(self) -> Union[float, int]:\n return (self.hours_worked * self.salary)\n\n# employee types\nclass Secretary(Employee, MonthlySalary):\n \"\"\"Secretary.\n\n Person with montly salary policy and no commissions.\n \"\"\"\n role: str = 'Secretary'\n\nclass FactoryWorker(Employee, HourlySalary):\n \"\"\"\n FactoryWorker is an employee with hourly salary policy and no commissions.\n \"\"\"\n role: str = 'Factory Worker'\n\nclass PayrollSystem:\n def calculate_payroll(self, employees: List[dataclass]) -> None:\n print('=== Calculating Payroll === ')\n for employee in employees:\n print(f\"Payroll for employee {employee.id} - {employee.name}\")\n print(f\"- {employee.role} Amount: {employee.calculate_payroll()}\")\n if employee.address:\n print('- Sent to:')\n print(employee.address)\n print(\"\")\n\njane = Secretary(name='Jane Doe', first_name='Jane', last_name='Doe', salary=1500)\nbob = FactoryWorker(name='Bob Doyle', first_name='Bob', last_name='Doyle', salary=15, hours_worked=40)\nmitch = FactoryWorker(name='Mitch Brian', first_name='Mitch', last_name='Brian', salary=20, hours_worked=35)\n\npayroll = PayrollSystem()\npayroll.calculate_payroll([jane, bob, mitch])\n```\nA sample of output:\n```\n```console\n=== Calculating Payroll ===\nPayroll for employee 745a2623-d4d2-4da6-bf0a-1fa691bafd33 - Jane Doe\n- Secretary Amount: 1500\n- Sent to:\nRodeo Drive, Rd\nLos Angeles, 31050 CA\nUS\n```\n## Contributing\n\nFirst of all, thank you for being interested in contributing to this library.\nI really appreciate you taking the time to work on this project.\n\n- If you're just interested in getting into the code, a good place to start are\nissues tagged as bugs.\n- If introducing a new feature, especially one that modifies the public API,\nconsider submitting an issue for discussion before a PR. Please also take a look\nat existing issues / PRs to see what you're proposing has already been covered\nbefore / exists.\n- I like to follow the commit conventions documented [here](https://www.conventionalcommits.org/en/v1.0.0/#summary)\n\n## License\n\nThis project is licensed under the terms of the BSD v3. license.\n",
"bugtrack_url": null,
"license": "BSD",
"summary": "simple library based on python +3.8 to use Dataclass-syntaxfor interacting with Data",
"version": "0.8.0",
"project_urls": {
"Funding": "https://paypal.me/phenobarbital",
"Homepage": "https://github.com/phenobarbital/python-datamodel",
"Say Thanks!": "https://saythanks.io/to/phenobarbital",
"Source": "https://github.com/phenobarbital/datamodel"
},
"split_keywords": [
"asyncio",
" dataclass",
" dataclasses",
" data models"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e3d1a367e9df2401552b4eb7852c93b82ab8f6b7aa12a2a65b3e825f2782083a",
"md5": "7acfb266d48dbc1dfbecb24f56004954",
"sha256": "b1f2c6eab451e5f9cd48090fce9081a9e5d640bf5962cfbf3624a2ca164f957a"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "7acfb266d48dbc1dfbecb24f56004954",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.9.13",
"size": 2342122,
"upload_time": "2024-12-19T05:04:57",
"upload_time_iso_8601": "2024-12-19T05:04:57.954512Z",
"url": "https://files.pythonhosted.org/packages/e3/d1/a367e9df2401552b4eb7852c93b82ab8f6b7aa12a2a65b3e825f2782083a/python_datamodel-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "63309e9beb86bdfbbef9cfd1dd026e98c737e3e4ed6e43af3e4ccf029c2889e2",
"md5": "d60e5bc4406fa2ad21662b98392a63a7",
"sha256": "ef4565bfd30ff27733397784c142e993996e97ec50e13108a74d61772a3e4466"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp310-cp310-win_amd64.whl",
"has_sig": false,
"md5_digest": "d60e5bc4406fa2ad21662b98392a63a7",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.9.13",
"size": 961004,
"upload_time": "2024-12-19T05:05:15",
"upload_time_iso_8601": "2024-12-19T05:05:15.176216Z",
"url": "https://files.pythonhosted.org/packages/63/30/9e9beb86bdfbbef9cfd1dd026e98c737e3e4ed6e43af3e4ccf029c2889e2/python_datamodel-0.8.0-cp310-cp310-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "50ae367918b8871be93fce3da5b81428b250b223ce5598f7835d74d2fc6ba99f",
"md5": "c1f5a68c82422d9a53e12155c3913fcf",
"sha256": "6c306e67bc2fc0a15d1aeff053806be21115e7000453b4a4a9d7129dccaf1492"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "c1f5a68c82422d9a53e12155c3913fcf",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.9.13",
"size": 2466540,
"upload_time": "2024-12-19T05:05:01",
"upload_time_iso_8601": "2024-12-19T05:05:01.573670Z",
"url": "https://files.pythonhosted.org/packages/50/ae/367918b8871be93fce3da5b81428b250b223ce5598f7835d74d2fc6ba99f/python_datamodel-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8f038630dbe5daf6d4b9190512c5fd776fdfe656d68eb8942cdf89ee969a2414",
"md5": "458bb00c48bd076575369b1c0ddbabf3",
"sha256": "51c443ffaa3708bbffff5376fa834741fe889204e03618df2b3a112d8a6d223a"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp311-cp311-win_amd64.whl",
"has_sig": false,
"md5_digest": "458bb00c48bd076575369b1c0ddbabf3",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.9.13",
"size": 962564,
"upload_time": "2024-12-19T05:05:17",
"upload_time_iso_8601": "2024-12-19T05:05:17.817500Z",
"url": "https://files.pythonhosted.org/packages/8f/03/8630dbe5daf6d4b9190512c5fd776fdfe656d68eb8942cdf89ee969a2414/python_datamodel-0.8.0-cp311-cp311-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a45142ac74c62aa281f69a4e91643f391bb8b535e6a3518c1021e3225c5ca087",
"md5": "9f964730fe8175ade4226c976c920a7c",
"sha256": "911fb533f71887f002c28ee32e5aa7b98c0881073b1500678d750a3df3f99869"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "9f964730fe8175ade4226c976c920a7c",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.9.13",
"size": 2629085,
"upload_time": "2024-12-19T05:05:04",
"upload_time_iso_8601": "2024-12-19T05:05:04.272966Z",
"url": "https://files.pythonhosted.org/packages/a4/51/42ac74c62aa281f69a4e91643f391bb8b535e6a3518c1021e3225c5ca087/python_datamodel-0.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "15ea95811465e0464ceccc09ddbd111361f5e05def935374674001284d4b94f6",
"md5": "e9185b84ea4a565f79f9158db45c388c",
"sha256": "0d93b97f8143513aa0f528f47708001921fad0a468000251337d00fd1bfe289c"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp312-cp312-win_amd64.whl",
"has_sig": false,
"md5_digest": "e9185b84ea4a565f79f9158db45c388c",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.9.13",
"size": 956124,
"upload_time": "2024-12-19T05:05:20",
"upload_time_iso_8601": "2024-12-19T05:05:20.300458Z",
"url": "https://files.pythonhosted.org/packages/15/ea/95811465e0464ceccc09ddbd111361f5e05def935374674001284d4b94f6/python_datamodel-0.8.0-cp312-cp312-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "35c884c98014c844c23ecec87eceadbe0295e162b3d65eb3b81c6b2f146bdc3d",
"md5": "ba39f938b861067a07f4aaf2711b45fd",
"sha256": "2d38e2d17aa50d84acab3c6a4e3512e0feae051d4333dd93deb70478de96768c"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "ba39f938b861067a07f4aaf2711b45fd",
"packagetype": "bdist_wheel",
"python_version": "cp313",
"requires_python": ">=3.9.13",
"size": 2567136,
"upload_time": "2024-12-19T05:05:07",
"upload_time_iso_8601": "2024-12-19T05:05:07.319331Z",
"url": "https://files.pythonhosted.org/packages/35/c8/84c98014c844c23ecec87eceadbe0295e162b3d65eb3b81c6b2f146bdc3d/python_datamodel-0.8.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "caada14ff15ed75efc5f9cc391b4006eb443707f5170fdf539c4be151037ba56",
"md5": "77e74d3eba931175df1959a337896dc0",
"sha256": "3f3d93a5dfed5ae7a3665e8f5870bfddb0b8d9b5e6436dae2f98e393d6df511f"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp313-cp313-win_amd64.whl",
"has_sig": false,
"md5_digest": "77e74d3eba931175df1959a337896dc0",
"packagetype": "bdist_wheel",
"python_version": "cp313",
"requires_python": ">=3.9.13",
"size": 953032,
"upload_time": "2024-12-19T05:05:22",
"upload_time_iso_8601": "2024-12-19T05:05:22.918251Z",
"url": "https://files.pythonhosted.org/packages/ca/ad/a14ff15ed75efc5f9cc391b4006eb443707f5170fdf539c4be151037ba56/python_datamodel-0.8.0-cp313-cp313-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "5ff1f5c8bd6c114711c5b5ebc015b403b80f7286861ae19a6a2f58d778f52662",
"md5": "3d17d5366a3d429dd6f17447644a0c2b",
"sha256": "3ae8a701232346d8dd3cbb0dfb46c038600c7d1ec2db1460ef65852a452963cb"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"has_sig": false,
"md5_digest": "3d17d5366a3d429dd6f17447644a0c2b",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.9.13",
"size": 2348423,
"upload_time": "2024-12-19T05:05:10",
"upload_time_iso_8601": "2024-12-19T05:05:10.284043Z",
"url": "https://files.pythonhosted.org/packages/5f/f1/f5c8bd6c114711c5b5ebc015b403b80f7286861ae19a6a2f58d778f52662/python_datamodel-0.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "82df53c9188a69e7b23a568a6038cb4688c454a79d631eccddfece12ffd5ce40",
"md5": "3670dfd7d337cca231e42fbefe544a3d",
"sha256": "5c102ae9f3acb0b605c3811e0615ad583bb770d931ab5cb5b099f3697534b4f3"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-cp39-cp39-win_amd64.whl",
"has_sig": false,
"md5_digest": "3670dfd7d337cca231e42fbefe544a3d",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.9.13",
"size": 962404,
"upload_time": "2024-12-19T05:05:24",
"upload_time_iso_8601": "2024-12-19T05:05:24.208980Z",
"url": "https://files.pythonhosted.org/packages/82/df/53c9188a69e7b23a568a6038cb4688c454a79d631eccddfece12ffd5ce40/python_datamodel-0.8.0-cp39-cp39-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8f050c0e27831412d58e9b724f44cede1342872675ce2e319ea12f46cfe8cb6a",
"md5": "1e7b1844657b4ba87846cf592e042c36",
"sha256": "5fcf0b3aad1aa97bb96f6848f5e6f6b9b86d21aee484eae7a2f8ba809e7539ee"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-pp310-pypy310_pp73-win_amd64.whl",
"has_sig": false,
"md5_digest": "1e7b1844657b4ba87846cf592e042c36",
"packagetype": "bdist_wheel",
"python_version": "pp310",
"requires_python": ">=3.9.13",
"size": 908687,
"upload_time": "2024-12-19T05:05:25",
"upload_time_iso_8601": "2024-12-19T05:05:25.520577Z",
"url": "https://files.pythonhosted.org/packages/8f/05/0c0e27831412d58e9b724f44cede1342872675ce2e319ea12f46cfe8cb6a/python_datamodel-0.8.0-pp310-pypy310_pp73-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0e064109c67c7298faa245b5c6fad2960019bce42d9c249d40724e46c61c10ee",
"md5": "91d42a8bf35791c31e36bad151f109cb",
"sha256": "fa58cd5567241195cc215deddcbd080a523066010704718ed97cc939778f3c55"
},
"downloads": -1,
"filename": "python_datamodel-0.8.0-pp39-pypy39_pp73-win_amd64.whl",
"has_sig": false,
"md5_digest": "91d42a8bf35791c31e36bad151f109cb",
"packagetype": "bdist_wheel",
"python_version": "pp39",
"requires_python": ">=3.9.13",
"size": 908484,
"upload_time": "2024-12-19T05:05:28",
"upload_time_iso_8601": "2024-12-19T05:05:28.444234Z",
"url": "https://files.pythonhosted.org/packages/0e/06/4109c67c7298faa245b5c6fad2960019bce42d9c249d40724e46c61c10ee/python_datamodel-0.8.0-pp39-pypy39_pp73-win_amd64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-19 05:04:57",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "phenobarbital",
"github_project": "python-datamodel",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"tox": true,
"lcname": "python-datamodel"
}
JSON
