Pydantic v2 Core Knowledge

Installation

pip install pydantic>=2.0
pip install pydantic-settings  # for settings management

BaseModel Basics

from pydantic import BaseModel, Field
from typing import Annotated

class Tag(BaseModel):
    name: str
    area: int
    description: str = ""
    active: bool = True

# Instantiate
tag = Tag(name="11301.FIC.001", area=11301, description="Flow controller")
tag = Tag.model_validate({"name": "11301.FIC.001", "area": 11301})

# Serialize
tag.model_dump()                    # -> dict
tag.model_dump(exclude_none=True)   # skip None fields
tag.model_dump_json()               # -> JSON string
tag.model_json_schema()             # -> JSON Schema dict

Field Configuration

from pydantic import BaseModel, Field
from typing import Annotated

class MotorTag(BaseModel):
    # Required field
    tag: str = Field(..., min_length=1, max_length=50, pattern=r'^\d{5}\.\w+\.\w+$')

    # With alias (for dict keys that differ from Python attr names)
    node_name: str = Field(..., alias="NodeName")

    # With default
    power_kw: float = Field(default=0.0, ge=0.0, le=10000.0)

    # Computed default
    label: str = Field(default_factory=lambda: "")

    # Metadata
    area: int = Field(..., description="ISA-5.1 area code", examples=[11301, 11090])

class Config:
    populate_by_name = True  # allow both alias and field name

Validators (v2 API)

from pydantic import BaseModel, field_validator, model_validator
from typing import Self

class TagModel(BaseModel):
    tag: str
    area: int
    description: str

    # Field-level validator
    @field_validator('tag')
    @classmethod
    def normalize_tag(cls, v: str) -> str:
        v = v.strip().upper()
        if not v:
            raise ValueError('tag cannot be empty')
        return v

    # Validate multiple fields
    @field_validator('area')
    @classmethod
    def valid_area(cls, v: int) -> int:
        if v < 10000 or v > 99999:
            raise ValueError(f'area must be 5-digit code, got {v}')
        return v

    # Cross-field validation (model-level)
    @model_validator(mode='after')
    def check_area_in_tag(self) -> Self:
        if not self.tag.startswith(str(self.area)):
            raise ValueError(f'tag {self.tag!r} does not start with area {self.area}')
        return self

Annotated Types (reusable constraints)

from typing import Annotated
from pydantic import Field, BaseModel

# Define reusable types
AreaCode = Annotated[int, Field(ge=10000, le=99999, description="5-digit area code")]
TagName = Annotated[str, Field(min_length=1, max_length=50, pattern=r'^[\w.]+$')]
PositiveFloat = Annotated[float, Field(gt=0.0)]

class Equipment(BaseModel):
    area: AreaCode
    tag: TagName
    power_kw: PositiveFloat

Handling Optional and Union Types

from pydantic import BaseModel
from typing import Optional

class Reading(BaseModel):
    tag: str
    value: float | None = None          # None by default
    unit: str | None = None
    alarm_high: Optional[float] = None  # equivalent to float | None

# v2: None is only allowed if explicitly typed as Optional/| None

Parsing and Error Handling

from pydantic import ValidationError

try:
    tag = MotorTag(tag="", area=999)
except ValidationError as e:
    print(e.error_count())   # number of errors
    for error in e.errors():
        print(error['loc'])  # field path
        print(error['msg'])  # human-readable message
        print(error['type']) # error type identifier

# Partial validation (collect all errors, not fail-fast)
# ValidationError already collects all field errors by default

DataFrame Row Validation

import pandas as pd
from pydantic import BaseModel, ValidationError

class Row(BaseModel):
    tag: str
    area: int
    power_kw: float

def validate_df(df: pd.DataFrame) -> tuple[list[Row], list[dict]]:
    valid, errors = [], []
    for i, row in df.iterrows():
        try:
            valid.append(Row.model_validate(row.to_dict()))
        except ValidationError as e:
            errors.append({"row": i, "errors": e.errors()})
    return valid, errors

Nested Models

class Signal(BaseModel):
    name: str
    data_type: str
    direction: str  # "IN" | "OUT"

class FunctionBlock(BaseModel):
    name: str
    library: str
    signals: list[Signal] = []

    def get_inputs(self) -> list[Signal]:
        return [s for s in self.signals if s.direction == "IN"]

# Instantiate nested
fb = FunctionBlock(
    name="IDF_1",
    library="BST_LIB_EXT",
    signals=[
        Signal(name="AC", data_type="BOOL", direction="IN"),
        Signal(name="RUN", data_type="BOOL", direction="OUT"),
    ]
)

Pydantic Settings

from pydantic_settings import BaseSettings, SettingsConfigDict

class AppSettings(BaseSettings):
    model_config = SettingsConfigDict(
        env_file=".env",
        env_prefix="APP_",
        case_sensitive=False,
    )

    anthropic_api_key: str
    db_path: str = "data/project.db"
    debug: bool = False

# Reads from env vars APP_ANTHROPIC_API_KEY, APP_DB_PATH, etc.
settings = AppSettings()

Serialization Control

from pydantic import BaseModel, field_serializer
from datetime import datetime

class Record(BaseModel):
    tag: str
    created_at: datetime

    @field_serializer('created_at')
    def serialize_dt(self, dt: datetime) -> str:
        return dt.strftime('%Y-%m-%d %H:%M:%S')

    model_config = {
        'json_encoders': {datetime: lambda v: v.isoformat()}
    }

r = Record(tag="FIC-001", created_at=datetime.now())
r.model_dump()                     # datetime object
r.model_dump(mode='json')          # serialized via field_serializer

Common Patterns