Collections of pydantic models

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

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

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

 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

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+

Seamlessly integrate pydantic models in your Sphinx documentation.
Seamlessly integrate pydantic models in your Sphinx documentation.

Seamlessly integrate pydantic models in your Sphinx documentation.

🪄 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 •

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

Pydantic models for pywttr and aiopywttr.

Pydantic models for pywttr and aiopywttr.

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

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

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

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

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

Pydantic model support for Django ORM

Pydantic model support for Django ORM

flask extension for integration with the awesome pydantic package

flask extension for integration with the awesome pydantic package

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

Pydantic-ish YAML configuration management.
Pydantic-ish YAML configuration management.

Pydantic-ish YAML configuration management.

(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

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
Python for Data Analysis, 2nd Edition

Python for Data Analysis, 2nd Edition Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media Buy

Wes McKinney 18.6k Jan 08, 2023
A data analysis using python and pandas to showcase trends in school performance.

A data analysis using python and pandas to showcase trends in school performance. A data analysis to showcase trends in school performance using Panda

Jimmy Faccioli 0 Sep 07, 2021
Get mutations in cluster by querying from LAPIS API

Cluster Mutation Script Get mutations appearing within user-defined clusters. Usage Clusters are defined in the clusters dict in main.py: clusters = {

neherlab 1 Oct 22, 2021
Time ranges with python

timeranges Time ranges. Read the Docs Installation pip timeranges is available on pip: pip install timeranges GitHub You can also install the latest v

Micael Jarniac 2 Sep 01, 2022
A meta plugin for processing timelapse data timepoint by timepoint in napari

napari-time-slicer A meta plugin for processing timelapse data timepoint by timepoint. It enables a list of napari plugins to process 2D+t or 3D+t dat

Robert Haase 2 Oct 13, 2022
The Spark Challenge Student Check-In/Out Tracking Script

The Spark Challenge Student Check-In/Out Tracking Script This Python Script uses the Student ID Database to match the entries with the ID Card Swipe a

1 Dec 09, 2021
Retail-Sim is python package to easily create synthetic dataset of retaile store.

Retailer's Sale Data Simulation Retail-Sim is python package to easily create synthetic dataset of retaile store. Simulation Model Simulator consists

Corca AI 7 Sep 30, 2022
Intercepting proxy + analysis toolkit for Second Life compatible virtual worlds

Hippolyzer Hippolyzer is a revival of Linden Lab's PyOGP library targeting modern Python 3, with a focus on debugging issues in Second Life-compatible

Salad Dais 6 Sep 01, 2022
Shot notebooks resuming the main functions of GeoPandas

Shot notebooks resuming the main functions of GeoPandas, 2 notebooks written as Exercises to apply these functions.

1 Jan 12, 2022
Data imputations library to preprocess datasets with missing data

Impyute is a library of missing data imputation algorithms. This library was designed to be super lightweight, here's a sneak peak at what impyute can do.

Elton Law 329 Dec 05, 2022
A real data analysis and modeling project - restaurant inspections

A real data analysis and modeling project - restaurant inspections Jafar Pourbemany 9/27/2021 This project represents data analysis and modeling of re

Jafar Pourbemany 2 Aug 21, 2022
In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift.

ETL Pipeline for AWS Project Description In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift. The data is loaded from S3 t

Mobeen Ahmed 1 Nov 01, 2021
Pypeln is a simple yet powerful Python library for creating concurrent data pipelines.

Pypeln Pypeln (pronounced as "pypeline") is a simple yet powerful Python library for creating concurrent data pipelines. Main Features Simple: Pypeln

Cristian Garcia 1.4k Dec 31, 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
small package with utility functions for analyzing (fly) calcium imaging data

fly2p Tools for analyzing two-photon (2p) imaging data collected with Vidrio Scanimage software and micromanger. Loading scanimage data relies on scan

Hannah Haberkern 3 Dec 14, 2022
CleanX is an open source python library for exploring, cleaning and augmenting large datasets of X-rays, or certain other types of radiological images.

cleanX CleanX is an open source python library for exploring, cleaning and augmenting large datasets of X-rays, or certain other types of radiological

Candace Makeda Moore, MD 20 Jan 05, 2023
MoRecon - A tool for reconstructing missing frames in motion capture data.

MoRecon - A tool for reconstructing missing frames in motion capture data.

Yuki Nishidate 38 Dec 03, 2022
Building house price data pipelines with Apache Beam and Spark on GCP

This project contains the process from building a web crawler to extract the raw data of house price to create ETL pipelines using Google Could Platform services.

1 Nov 22, 2021
simple way to build the declarative and destributed data pipelines with python

unipipeline simple way to build the declarative and distributed data pipelines. Why you should use it Declarative strict config Scaffolding Fully type

aliaksandr-master 0 Jan 26, 2022
Data analysis and visualisation projects from a range of individual projects and applications

Python-Data-Analysis-and-Visualisation-Projects Data analysis and visualisation projects from a range of individual projects and applications. Python

Tom Ritman-Meer 1 Jan 25, 2022