Collections of pydantic models

Related tags

Data Analysispythonserializationvalidationparsingmodelspydantic
Overview

pydantic-collections

Build Status Coverage Status

The pydantic-collections package provides BaseCollectionModel class that allows you to manipulate collections of pydantic models (and any other types supported by pydantic).

Requirements

  • Python >= 3.7
  • pydantic >= 1.8.2

Installation

pip install pydantic-collections

Usage

Basic usage

from datetime import datetime

from pydantic import BaseModel
from pydantic_collections import BaseCollectionModel


class User(BaseModel):
    id: int
    name: str
    birth_date: datetime


class UserCollection(BaseCollectionModel[User]):
    pass


 user_data = [
        {'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'},
        {'id': 2, 'name': 'Balaganov', 'birth_date': '2020-04-01T12:59:59'},
    ]

users = UserCollection(user_data)
print(users)
#> UserCollection([User(id=1, name='Bender', birth_date=datetime.datetime(2010, 4, 1, 12, 59, 59)), User(id=2, name='Balaganov', birth_date=datetime.datetime(2020, 4, 1, 12, 59, 59))])
print(users.dict())
#> [{'id': 1, 'name': 'Bender', 'birth_date': datetime.datetime(2010, 4, 1, 12, 59, 59)}, {'id': 2, 'name': 'Balaganov', 'birth_date': datetime.datetime(2020, 4, 1, 12, 59, 59)}]
print(users.json())
#> [{"id": 1, "name": "Bender", "birth_date": "2010-04-01T12:59:59"}, {"id": 2, "name": "Balaganov", "birth_date": "2020-04-01T12:59:59"}]

Strict assignment validation

By default BaseCollectionModel has a strict assignment check

...
users = UserCollection()
users.append(User(id=1, name='Bender', birth_date=datetime.utcnow()))  # OK
users.append({'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'})
#> pydantic.error_wrappers.ValidationError: 1 validation error for UserCollection
#> __root__ -> 2
#>  instance of User expected (type=type_error.arbitrary_type; expected_arbitrary_type=User)

This behavior can be changed via Model Config

...
class UserCollection(BaseCollectionModel[User]):
    class Config:
        validate_assignment_strict = False
        
users = UserCollection()
users.append({'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'})  # OK
assert users[0].__class__ is User
assert users[0].id == 1

Using as a model field

BaseCollectionModel is a subclass of BaseModel, so you can use it as a model field

...
class UserContainer(BaseModel):
    users: UserCollection = []
        
data = {
    'users': [
        {'id': 1, 'name': 'Bender', 'birth_date': '2010-04-01T12:59:59'},
        {'id': 2, 'name': 'Balaganov', 'birth_date': '2020-04-01T12:59:59'},
    ]
}

container = UserContainer(**data)
container.users.append(User(...))
...
You might also like...
vartests is a Python library to perform some statistic tests to evaluate Value at Risk (VaR) Models

vartests is a Python library to perform some statistic tests to evaluate Value at Risk (VaR) Models, such as: T-test: verify if mean of distribution i

RAFAEL RODRIGUES 5 Jan 3, 2023
A model checker for verifying properties in epistemic models

Epistemic Model Checker This is a model checker for verifying properties in epistemic models. The goal of the model checker is to check for Pluralisti

Thomas Träff 2 Dec 22, 2021
Fit models to your data in Python with Sherpa.

Table of Contents Sherpa License How To Install Sherpa Using Anaconda Using pip Building from source History Release History Sherpa Sherpa is a modeli

null 134 Jan 7, 2023
pydantic-i18n is an extension to support an i18n for the pydantic error messages.
pydantic-i18n is an extension to support an i18n for the pydantic error messages.

pydantic-i18n is an extension to support an i18n for the pydantic error messages

Boardpack 48 Dec 21, 2022
Python collections that are backended by sqlite3 DB and are compatible with the built-in collections

sqlitecollections Python collections that are backended by sqlite3 DB and are compatible with the built-in collections Installation $ pip install git+

Takeshi OSOEKAWA 11 Feb 3, 2022
Seamlessly integrate pydantic models in your Sphinx documentation.
Seamlessly integrate pydantic models in your Sphinx documentation.

Seamlessly integrate pydantic models in your Sphinx documentation.

Franz Wöllert 71 Dec 26, 2022
🪄 Auto-generate Streamlit UI from Pydantic Models and Dataclasses.
🪄 Auto-generate Streamlit UI from Pydantic Models and Dataclasses.

Streamlit Pydantic Auto-generate Streamlit UI elements from Pydantic models. Getting Started • Documentation • Support • Report a Bug • Contribution •

Lukas Masuch 103 Dec 25, 2022
Hyperlinks for pydantic models

Hyperlinks for pydantic models In a typical web application relationships between resources are modeled by primary and foreign keys in a database (int

Jaakko Moisio 10 Apr 18, 2022
Pydantic models for pywttr and aiopywttr.

Pydantic models for pywttr and aiopywttr.

Almaz 2 Dec 8, 2022
EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections Ruiqi Zhong, Kristy Lee*, Zheng Zhang*, Dan Klein EMN

Ruiqi Zhong 42 Nov 3, 2022
PyTorch implementation of
PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper.

deep-linear-shapes PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper. If you find this code useful i

Romain Loiseau 27 Sep 24, 2022
flask extension for integration with the awesome pydantic package

Flask-Pydantic Flask extension for integration of the awesome pydantic package with Flask. Installation python3 -m pip install Flask-Pydantic Basics v

null 249 Jan 6, 2023
flask extension for integration with the awesome pydantic package

Flask-Pydantic Flask extension for integration of the awesome pydantic package with Flask. Installation python3 -m pip install Flask-Pydantic Basics v

null 249 Jan 6, 2023
A curated list of awesome things related to Pydantic! 🌪️

Awesome Pydantic A curated list of awesome things related to Pydantic. These packages have not been vetted or approved by the pydantic team. Feel free

Marcelo Trylesinski 186 Jan 5, 2023
Pydantic model support for Django ORM

Pydantic model support for Django ORM

Jordan Eremieff 318 Jan 3, 2023
flask extension for integration with the awesome pydantic package

flask extension for integration with the awesome pydantic package

null 249 Jan 6, 2023
Flask Sugar is a web framework for building APIs with Flask, Pydantic and Python 3.6+ type hints.
Flask Sugar is a web framework for building APIs with Flask, Pydantic and Python 3.6+ type hints.

Flask Sugar is a web framework for building APIs with Flask, Pydantic and Python 3.6+ type hints. check parameters and generate API documents automatically. Flask Sugar是一个基于flask,pyddantic,类型注解的API框架, 可以检查参数并自动生成API文档

null 162 Dec 26, 2022
Pydantic-ish YAML configuration management.
Pydantic-ish YAML configuration management.

Pydantic-ish YAML configuration management.

Dribia Data Research 18 Oct 27, 2022
(A)sync client for sms.ru with pydantic responses

🚧 aioSMSru Send SMS Check SMS status Get SMS cost Get balance Get limit Get free limit Get my senders Check login/password Add to stoplist Remove fro

Eugene Mayer 4 Jul 3, 2022
Comments
  • Bug dict() method: ignore or raised exception when using dict function attribute (ex. include, exclude, etc.)

    Bug dict() method: ignore or raised exception when using dict function attribute (ex. include, exclude, etc.)

    Hi there, I tried to use the method dict but i got an error: KeyError(__root__) Here an example:

    1. Model structure:
    
from datetime import datetime, time
from typing import Optional, Union
from pydantic import Field, validator, BaseModel
from pydantic_collections import BaseCollectionModel

class OpeningTime(BaseModel):
    weekday: int = Field(..., alias="weekday")
    day: Optional[str] = Field(alias="day")  # NB: keep it after number_weekday attribute
    from_time: Optional[time] = Field(alias="fromTime")
    to_time: Optional[time] = Field(alias="toTime")

    @validator("day", pre=True)
    def generate_weekday(cls, weekday: str, values) -> str:
        if weekday is None or len(weekday) == 0:
            return WEEKDAYS[str(values["weekday"])]
        return weekday



class OpeningTimes(BaseCollectionModel[OpeningTime]):
    pass


class PaymentMethod(BaseModel):
    type: str = Field(..., alias="type")
    card_type: str = Field(..., alias="cardType")


class PaymentMethods(BaseCollectionModel[PaymentMethod]):
    pass


class FuelType(BaseModel):
    type: str = Field(..., alias="Fuel")


class FuelTypes(BaseCollectionModel[FuelType]):
    pass


class AdditionalInfoStation(BaseModel):
    opening_times: Optional[OpeningTimes] = Field(alias="openingTimes")
    car_wash_opening_times: Optional[OpeningTimes] = Field(alias="openingTimesCarWash")
    payment_methods: PaymentMethods = Field(..., alias="paymentMethods")
    fuel_types: FuelTypes = Field(..., alias="fuelTypes")


class Example(BaseModel):
    hash_key: int = Field(..., alias="hashKey")
    range_key: str = Field(..., alias="rangeKey")
    location_id: str = Field(..., alias="locationId")
    name: str = Field(..., alias="name")
    street: str = Field(..., alias="street")
    address_number: str = Field(..., alias="addressNumber")
    zip_code: int = Field(..., alias="zipCode")
    city: str = Field(..., alias="city")
    region: str = Field(..., alias="region")
    country: str = Field(..., alias="country")
    additional_info: Union[AdditionalInfoStation] = Field(..., alias="additionalInfo")


class ExampleList(BaseCollectionModel[EniGeoPoint]):
    pass
    1. Imagine that there is an ExampleList populated object and needed filters field during apply of dict method:
    example_list: ExampleList = ExampleList.parse_obj([{......}])

#This istruction raised exception
example_list.dict(by_alias=True, inlcude={"hash_key", "range_key"})
    1. The last istruction raise an error: Message: KeyError('__root__')

    My env is:

    • pydantic==1.9.1
    • pydantic-collections==0.2.0
    • python version 3.9.7

    If you need more info please contact me.

    opened by aferrari94 6
Releases(v0.4.0)
Owner
Roman Snegirev
Roman Snegirev
GitHub Repository
Bearsql allows you to query pandas dataframe with sql syntax.

Bearsql adds sql syntax on pandas dataframe. It uses duckdb to speedup the pandas processing and as the sql engine

14 Jun 22, 2022
MS in Data Science capstone project. Studying attacks on autonomous vehicles.

Surveying Attack Models for CAVs Guide to Installing CARLA and Collecting Data Our project focuses on surveying attack models for Connveced Autonomous

Isabela Caetano 1 Dec 09, 2021
Template for a Dataflow Flex Template in Python

Dataflow Flex Template in Python This repository contains a template for a Dataflow Flex Template written in Python that can easily be used to build D

STOIX 5 Apr 28, 2022
Pandas and Dask test helper methods with beautiful error messages.

beavis Pandas and Dask test helper methods with beautiful error messages. test helpers These test helper methods are meant to be used in test suites.

Matthew Powers 18 Nov 28, 2022
A data structure that extends pyspark.sql.DataFrame with metadata information.

MetaFrame A data structure that extends pyspark.sql.DataFrame with metadata info

Invent Analytics 8 Feb 15, 2022
Stream-Kafka-ELK-Stack - Weather data streaming using Apache Kafka and Elastic Stack.

Streaming Data Pipeline - Kafka + ELK Stack Streaming weather data using Apache Kafka and Elastic Stack. Data source: https://openweathermap.org/api O

Felipe Demenech Vasconcelos 2 Jan 20, 2022
Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required)

Binomial Option Pricing Calculator Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required) Background A derivative is a fi

sammuhrai 1 Nov 29, 2021
A tool to compare differences between dataframes and create a differences report in Excel

similarpanda A module to check for differences between pandas Dataframes, and generate a report in Excel format. This is helpful in a workplace settin

Andre Pretorius 9 Sep 15, 2022
Modular analysis tools for neurophysiology data

Neuroanalysis Modular and interactive tools for analysis of neurophysiology data, with emphasis on patch-clamp electrophysiology. Functions for runnin

Allen Institute 5 Dec 22, 2021
Analysiscsv.py for extracting analysis and exporting as CSV

wcc_analysis Lichess page documentation: https://lichess.org/page/world-championships Each WCC has a study, studies are fetched using: https://lichess

32 Apr 25, 2022
Pandas and Spark DataFrame comparison for humans

DataComPy DataComPy is a package to compare two Pandas DataFrames. Originally started to be something of a replacement for SAS's PROC COMPARE for Pand

Capital One 259 Dec 24, 2022
A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms

MatrixProfile MatrixProfile is a Python 3 library, brought to you by the Matrix Profile Foundation, for mining time series data. The Matrix Profile is

Matrix Profile Foundation 302 Dec 29, 2022
Handle, manipulate, and convert data with units in Python

unyt A package for handling numpy arrays with units. Often writing code that deals with data that has units can be confusing. A function might return

The yt project 304 Jan 02, 2023
Useful tool for inserting DataFrames into the Excel sheet.

PyCellFrame Insert Pandas DataFrames into the Excel sheet with a bunch of conditions Install pip install pycellframe Usage Examples Let's suppose that

Luka Sosiashvili 1 Feb 16, 2022
Average time per match by division

HW_02 Unzip matches.rar to access .json files for matches. Get an API key to access their data at: https://developer.riotgames.com/ Average time per m

11 Jan 07, 2022
BIGDATA SIMULATION ONE PIECE WORLD CENSUS

ONE PIECE is a Japanese manga of great international success. The story turns inhabited in a fictional world, tells the adventures of a young man whose body gained rubber properties after accidentall

Maycon Cypriano 3 Jun 30, 2022
PyPDC is a Python package for calculating asymptotic Partial Directed Coherence estimations for brain connectivity analysis.

Python asymptotic Partial Directed Coherence and Directed Coherence estimation package for brain connectivity analysis. Free software: MIT license Doc

Heitor Baldo 3 Nov 26, 2022
ForecastGA is a Python tool to forecast Google Analytics data using several popular time series models.

ForecastGA is a tool that combines a couple of popular libraries, Atspy and googleanalytics, with a few enhancements.

JR Oakes 36 Jan 03, 2023
BinTuner is a cost-efficient auto-tuning framework, which can deliver a near-optimal binary code that reveals much more differences than -Ox settings.

BinTuner is a cost-efficient auto-tuning framework, which can deliver a near-optimal binary code that reveals much more differences than -Ox settings. it also can assist the binary code analysis rese

BinTuner 42 Dec 16, 2022
Geospatial data-science analysis on reasons behind delay in Grab ride-share services

Grab x Pulis Detailed analysis done to investigate possible reasons for delay in Grab services for NUS Data Analytics Competition 2022, to be found in

Keng Hwee 6 Jun 07, 2022