# <center><img height="30px" src="https://em-content.zobj.net/thumbs/120/samsung/78/duck_1f986.png"> Patito<center>
<p align="center">
<em>
Patito combines <a href="https://github.com/samuelcolvin/pydantic">pydantic</a> and <a href="https://github.com/pola-rs/polars">polars</a> in order to write modern, type-annotated data frame logic.
</em>
<br>
<a href="https://patito.readthedocs.io/">
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</a>
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</p>
Patito offers a simple way to declare pydantic data models which double as schema for your polars data frames.
These schema can be used for:
👮 Simple and performant data frame validation.\
🧪 Easy generation of valid mock data frames for tests.\
🐍 Retrieve and represent singular rows in an object-oriented manner.\
🧠 Provide a single source of truth for the core data models in your code base. \
Patito has first-class support for [polars]("https://github.com/pola-rs/polars"), a _"blazingly fast DataFrames library written in Rust"_.
## Installation
```sh
pip install patito
```
## Documentation
The full documentation of Patito can be found [here](https://patito.readthedocs.io).
## 👮 Data validation
Patito allows you to specify the type of each column in your dataframe by creating a type-annotated subclass of `patito.Model`:
```py
# models.py
from typing import Literal
import patito as pt
class Product(pt.Model):
product_id: int = pt.Field(unique=True)
temperature_zone: Literal["dry", "cold", "frozen"]
is_for_sale: bool
```
The **class** `Product` represents the **schema** of the data frame, while **instances** of `Product` represent single **rows** of the dataframe.
Patito can efficiently validate the content of arbitrary data frames and provide human-readable error messages:
```py
import polars as pl
df = pl.DataFrame(
{
"product_id": [1, 1, 3],
"temperature_zone": ["dry", "dry", "oven"],
}
)
try:
Product.validate(df)
except pt.exceptions.DataFrameValidationError as exc:
print(exc)
# 3 validation errors for Product
# is_for_sale
# Missing column (type=type_error.missingcolumns)
# product_id
# 2 rows with duplicated values. (type=value_error.rowvalue)
# temperature_zone
# Rows with invalid values: {'oven'}. (type=value_error.rowvalue)
```
<details>
<summary><b>Click to see a summary of dataframe-compatible type annotations.</b></summary>
* Regular python data types such as `int`, `float`, `bool`, `str`, `date`, which are validated against compatible polars data types.
* Wrapping your type with `typing.Optional` indicates that the given column accepts missing values.
* Model fields annotated with `typing.Literal[...]` check if only a restricted set of values are taken, either as the native dtype (e.g. `pl.Utf8`) or `pl.Categorical`.
Additonally, you can assign `patito.Field` to your class variables in order to specify additional checks:
* `Field(dtype=...)` ensures that a specific dtype is used in those cases where several data types are compliant with the annotated python type, for example `product_id: int = Field(dtype=pl.UInt32)`.
* `Field(unique=True)` checks if every row has a unique value.
* `Field(gt=..., ge=..., le=..., lt=...)` allows you to specify bound checks for any combination of `> gt`, `>= ge`, `<= le` `< lt`, respectively.
* `Field(multiple_of=divisor)` in order to check if a given column only contains values as multiples of the given value.
* `Field(default=default_value, const=True)` indicates that the given column is required and _must_ take the given default value.
* String fields annotated with `Field(regex=r"<regex-pattern>")`, `Field(max_length=bound)`, and/or `Field(min_length)` will be validated with [polars' efficient string processing capabilities](https://pola-rs.github.io/polars-book/user-guide/howcani/data/strings.html).
* Custom constraints can be specified with with `Field(constraints=...)`, either as a single polars expression or a list of expressions. All the rows of the dataframe must satisfy the given constraint(s) in order to be considered valid. Example: `even_field: int = pt.Field(constraints=pl.col("even_field") % 2 == 0)`.
Although Patito supports [pandas](https://github.com/pandas-dev/pandas), it is highly recommemended to be used in combination with [polars]("https://github.com/pola-rs/polars").
For a much more feature-complete, pandas-first library, take a look at [pandera](https://pandera.readthedocs.io/).
</details>
## 🧪 Synthesize valid test data
Patito encourages you to strictly validate dataframe inputs, thus ensuring correctness at runtime.
But with forced correctness comes friction, especially during testing.
Take the following function as an example:
```py
import polars as pl
def num_products_for_sale(products: pl.DataFrame) -> int:
Product.validate(products)
return products.filter(pl.col("is_for_sale")).height
```
The following test would fail with a `patito.exceptions.DataFrameValidationError`:
```py
def test_num_products_for_sale():
products = pl.DataFrame({"is_for_sale": [True, True, False]})
assert num_products_for_sale(products) == 2
```
In order to make the test pass we would have to add valid dummy data for the `temperature_zone` and `product_id` columns.
This will quickly introduce a lot of boilerplate to all tests involving data frames, obscuring what is actually being tested in each test.
For this reason Patito provides the `examples` constructor for generating test data that is fully compliant with the given model schema.
```py
Product.examples({"is_for_sale": [True, True, False]})
# shape: (3, 3)
# ┌─────────────┬──────────────────┬────────────┐
# │ is_for_sale ┆ temperature_zone ┆ product_id │
# │ --- ┆ --- ┆ --- │
# │ bool ┆ str ┆ i64 │
# ╞═════════════╪══════════════════╪════════════╡
# │ true ┆ dry ┆ 0 │
# ├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
# │ true ┆ dry ┆ 1 │
# ├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
# │ false ┆ dry ┆ 2 │
# └─────────────┴──────────────────┴────────────┘
```
The `examples()` method accepts the same arguments as a regular data frame constructor, the main difference being that it fills in valid dummy data for any unspecified columns.
The test can therefore be rewritten as:
```py
def test_num_products_for_sale():
products = Product.examples({"is_for_sale": [True, True, False]})
assert num_products_for_sale(products) == 2
```
## 🖼️ A model-aware data frame class
Patito offers `patito.DataFrame`, a class that extends `polars.DataFrame` in order to provide utility methods related to `patito.Model`.
The schema of a data frame can be specified at runtime by invoking `patito.DataFrame.set_model(model)`, after which a set of contextualized methods become available:
* `DataFrame.validate()` - Validate the given data frame and return itself.
* `DataFrame.drop()` - Drop all superfluous columns _not_ specified as fields in the model.
* `DataFrame.cast()` - Cast any columns which are not compatible with the given type annotations. When `Field(dtype=...)` is specified, the given dtype will always be forced, even in compatible cases.
* `DataFrame.get(predicate)` - Retrieve a single row from the data frame as an instance of the model. An exception is raised if not exactly one row is yielded from the filter predicate.
* `DataFrame.fill_null(strategy="defaults")` - Fill inn missing values according to the default values set on the model schema.
* `DataFrame.derive()` - A model field annotated with `Field(derived_from=...)` indicates that a column should be defined by some arbitrary polars expression. If `derived_from` is specified as a string, then the given value will be interpreted as a column name with `polars.col()`. These columns are created and populated with data according to the `derived_from` expressions when you invoke `DataFrame.derive()`.
These methods are best illustrated with an example:
```py
from typing import Literal
import patito as pt
import polars as pl
class Product(pt.Model):
product_id: int = pt.Field(unique=True)
# Specify a specific dtype to be used
popularity_rank: int = pt.Field(dtype=pl.UInt16)
# Field with default value "for-sale"
status: Literal["draft", "for-sale", "discontinued"] = "for-sale"
# The eurocent cost is extracted from the Euro cost string "€X.Y EUR"
eurocent_cost: int = pt.Field(
derived_from=100 * pl.col("cost").str.extract(r"€(\d+\.+\d+)").cast(float).round(2)
)
products = pt.DataFrame(
{
"product_id": [1, 2],
"popularity_rank": [2, 1],
"status": [None, "discontinued"],
"cost": ["€2.30 EUR", "€1.19 EUR"],
}
)
product = (
products
# Specify the schema of the given data frame
.set_model(Product)
# Derive the `eurocent_cost` int column from the `cost` string column using regex
.derive()
# Drop the `cost` column as it is not part of the model
.drop()
# Cast the popularity rank column to an unsigned 16-bit integer and cents to an integer
.cast()
# Fill missing values with the default values specified in the schema
.fill_null(strategy="defaults")
# Assert that the data frame now complies with the schema
.validate()
# Retrieve a single row and cast it to the model class
.get(pl.col("product_id") == 1)
)
print(repr(product))
# Product(product_id=1, popularity_rank=2, status='for-sale', eurocent_cost=230)
```
Every Patito model automatically gets a `.DataFrame` attribute, a custom data frame subclass where `.set_model()` is invoked at instantiation. With other words, `pt.DataFrame(...).set_model(Product)` is equivalent to `Product.DataFrame(...)`.
## 🐍 Representing rows as classes
Data frames are tailor-made for performing vectorized operations over a _set_ of objects.
But when the time comes to retrieving a _single_ row and operate upon it, the data frame construct naturally falls short.
Patito allows you to embed row-level logic in methods defined on the model.
```py
# models.py
import patito as pt
class Product(pt.Model):
product_id: int = pt.Field(unique=True)
name: str
@property
def url(self) -> str:
return (
"https://example.com/no/products/"
f"{self.product_id}-"
f"{self.name.lower().replace(' ', '-')}"
)
```
The class can be instantiated from a single row of a data frame by using the `from_row()` method:
```py
products = pl.DataFrame(
{
"product_id": [1, 2],
"name": ["Skimmed milk", "Eggs"],
}
)
milk_row = products.filter(pl.col("product_id" == 1))
milk = Product.from_row(milk_row)
print(milk.url)
# https://example.com/no/products/1-skimmed-milk
```
If you "connect" the `Product` model with the `DataFrame` by the use of `patito.DataFrame.set_model()`, or alternatively by using `Product.DataFrame` directly, you can use the `.get()` method in order to filter the data frame down to a single row _and_ cast it to the respective model class:
```py
products = Product.DataFrame(
{
"product_id": [1, 2],
"name": ["Skimmed milk", "Eggs"],
}
)
milk = products.get(pl.col("product_id") == 1)
print(milk.url)
# https://example.com/no/products/1-skimmed-milk
```
Raw data
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"name": "patito",
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"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "validation, dataframe",
"author": "Jakob Gerhard Martinussen",
"author_email": "jakobgm@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/8d/5b/8ac000fef872c5102df20c96207aff58f5d2ddc8e49aec47ed000977368c/patito-0.8.2.tar.gz",
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"description": "# <center><img height=\"30px\" src=\"https://em-content.zobj.net/thumbs/120/samsung/78/duck_1f986.png\"> Patito<center>\n\n<p align=\"center\">\n <em>\n Patito combines <a href=\"https://github.com/samuelcolvin/pydantic\">pydantic</a> and <a href=\"https://github.com/pola-rs/polars\">polars</a> in order to write modern, type-annotated data frame logic.\n </em>\n <br>\n <a href=\"https://patito.readthedocs.io/\">\n <img src=\"https://readthedocs.org/projects/patito/badge/\" alt=\"Docs status\">\n </a>\n <a href=\"https://github.com/kolonialno/patito/actions?workflow=CI\">\n <img src=\"https://github.com/kolonialno/patito/actions/workflows/ci.yml/badge.svg\" alt=\"CI status\">\n </a>\n <a href=\"https://codecov.io/gh/kolonialno/patito\">\n <img src=\"https://codecov.io/gh/kolonialno/patito/branch/main/graph/badge.svg?token=720LBDYH25\"/>\n </a>\n <a href=\"https://pypi.python.org/pypi/patito\">\n <img src=\"https://img.shields.io/pypi/v/patito.svg\">\n </a>\n <img src=\"https://img.shields.io/pypi/pyversions/patito\">\n <a href=\"https://github.com/kolonialno/patito/blob/master/LICENSE\">\n <img src=\"https://img.shields.io/github/license/kolonialno/patito.svg\">\n </a>\n</p>\n\nPatito offers a simple way to declare pydantic data models which double as schema for your polars data frames.\nThese schema can be used for:\n\n\ud83d\udc6e Simple and performant data frame validation.\\\n\ud83e\uddea Easy generation of valid mock data frames for tests.\\\n\ud83d\udc0d Retrieve and represent singular rows in an object-oriented manner.\\\n\ud83e\udde0 Provide a single source of truth for the core data models in your code base. \\\n\nPatito has first-class support for [polars](\"https://github.com/pola-rs/polars\"), a _\"blazingly fast DataFrames library written in Rust\"_.\n\n## Installation\n\n```sh\npip install patito\n```\n\n## Documentation\n\nThe full documentation of Patito can be found [here](https://patito.readthedocs.io).\n\n## \ud83d\udc6e Data validation\n\nPatito allows you to specify the type of each column in your dataframe by creating a type-annotated subclass of `patito.Model`:\n\n```py\n# models.py\nfrom typing import Literal\n\nimport patito as pt\n\n\nclass Product(pt.Model):\n product_id: int = pt.Field(unique=True)\n temperature_zone: Literal[\"dry\", \"cold\", \"frozen\"]\n is_for_sale: bool\n```\n\nThe **class** `Product` represents the **schema** of the data frame, while **instances** of `Product` represent single **rows** of the dataframe.\nPatito can efficiently validate the content of arbitrary data frames and provide human-readable error messages:\n\n```py\nimport polars as pl\n\ndf = pl.DataFrame(\n {\n \"product_id\": [1, 1, 3],\n \"temperature_zone\": [\"dry\", \"dry\", \"oven\"],\n }\n)\ntry:\n Product.validate(df)\nexcept pt.exceptions.DataFrameValidationError as exc:\n print(exc)\n# 3 validation errors for Product\n# is_for_sale\n# Missing column (type=type_error.missingcolumns)\n# product_id\n# 2 rows with duplicated values. (type=value_error.rowvalue)\n# temperature_zone\n# Rows with invalid values: {'oven'}. (type=value_error.rowvalue)\n```\n\n<details>\n<summary><b>Click to see a summary of dataframe-compatible type annotations.</b></summary>\n\n* Regular python data types such as `int`, `float`, `bool`, `str`, `date`, which are validated against compatible polars data types.\n* Wrapping your type with `typing.Optional` indicates that the given column accepts missing values.\n* Model fields annotated with `typing.Literal[...]` check if only a restricted set of values are taken, either as the native dtype (e.g. `pl.Utf8`) or `pl.Categorical`.\n\nAdditonally, you can assign `patito.Field` to your class variables in order to specify additional checks:\n\n* `Field(dtype=...)` ensures that a specific dtype is used in those cases where several data types are compliant with the annotated python type, for example `product_id: int = Field(dtype=pl.UInt32)`.\n* `Field(unique=True)` checks if every row has a unique value.\n* `Field(gt=..., ge=..., le=..., lt=...)` allows you to specify bound checks for any combination of `> gt`, `>= ge`, `<= le` `< lt`, respectively.\n* `Field(multiple_of=divisor)` in order to check if a given column only contains values as multiples of the given value.\n* `Field(default=default_value, const=True)` indicates that the given column is required and _must_ take the given default value.\n* String fields annotated with `Field(regex=r\"<regex-pattern>\")`, `Field(max_length=bound)`, and/or `Field(min_length)` will be validated with [polars' efficient string processing capabilities](https://pola-rs.github.io/polars-book/user-guide/howcani/data/strings.html).\n* Custom constraints can be specified with with `Field(constraints=...)`, either as a single polars expression or a list of expressions. All the rows of the dataframe must satisfy the given constraint(s) in order to be considered valid. Example: `even_field: int = pt.Field(constraints=pl.col(\"even_field\") % 2 == 0)`.\n\nAlthough Patito supports [pandas](https://github.com/pandas-dev/pandas), it is highly recommemended to be used in combination with [polars](\"https://github.com/pola-rs/polars\").\nFor a much more feature-complete, pandas-first library, take a look at [pandera](https://pandera.readthedocs.io/).\n</details>\n\n## \ud83e\uddea Synthesize valid test data\n\nPatito encourages you to strictly validate dataframe inputs, thus ensuring correctness at runtime.\nBut with forced correctness comes friction, especially during testing.\nTake the following function as an example:\n\n```py\nimport polars as pl\n\ndef num_products_for_sale(products: pl.DataFrame) -> int:\n Product.validate(products)\n return products.filter(pl.col(\"is_for_sale\")).height\n```\n\nThe following test would fail with a `patito.exceptions.DataFrameValidationError`:\n\n```py\ndef test_num_products_for_sale():\n products = pl.DataFrame({\"is_for_sale\": [True, True, False]})\n assert num_products_for_sale(products) == 2\n```\n\nIn order to make the test pass we would have to add valid dummy data for the `temperature_zone` and `product_id` columns.\nThis will quickly introduce a lot of boilerplate to all tests involving data frames, obscuring what is actually being tested in each test.\nFor this reason Patito provides the `examples` constructor for generating test data that is fully compliant with the given model schema.\n\n```py\nProduct.examples({\"is_for_sale\": [True, True, False]})\n# shape: (3, 3)\n# \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n# \u2502 is_for_sale \u2506 temperature_zone \u2506 product_id \u2502\n# \u2502 --- \u2506 --- \u2506 --- \u2502\n# \u2502 bool \u2506 str \u2506 i64 \u2502\n# \u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n# \u2502 true \u2506 dry \u2506 0 \u2502\n# \u251c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u253c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u253c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u2524\n# \u2502 true \u2506 dry \u2506 1 \u2502\n# \u251c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u253c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u253c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u254c\u2524\n# \u2502 false \u2506 dry \u2506 2 \u2502\n# \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\nThe `examples()` method accepts the same arguments as a regular data frame constructor, the main difference being that it fills in valid dummy data for any unspecified columns.\nThe test can therefore be rewritten as:\n\n```py\ndef test_num_products_for_sale():\n products = Product.examples({\"is_for_sale\": [True, True, False]})\n assert num_products_for_sale(products) == 2\n```\n\n## \ud83d\uddbc\ufe0f A model-aware data frame class\nPatito offers `patito.DataFrame`, a class that extends `polars.DataFrame` in order to provide utility methods related to `patito.Model`.\nThe schema of a data frame can be specified at runtime by invoking `patito.DataFrame.set_model(model)`, after which a set of contextualized methods become available:\n\n* `DataFrame.validate()` - Validate the given data frame and return itself.\n* `DataFrame.drop()` - Drop all superfluous columns _not_ specified as fields in the model.\n* `DataFrame.cast()` - Cast any columns which are not compatible with the given type annotations. When `Field(dtype=...)` is specified, the given dtype will always be forced, even in compatible cases.\n* `DataFrame.get(predicate)` - Retrieve a single row from the data frame as an instance of the model. An exception is raised if not exactly one row is yielded from the filter predicate.\n* `DataFrame.fill_null(strategy=\"defaults\")` - Fill inn missing values according to the default values set on the model schema.\n* `DataFrame.derive()` - A model field annotated with `Field(derived_from=...)` indicates that a column should be defined by some arbitrary polars expression. If `derived_from` is specified as a string, then the given value will be interpreted as a column name with `polars.col()`. These columns are created and populated with data according to the `derived_from` expressions when you invoke `DataFrame.derive()`.\n\nThese methods are best illustrated with an example:\n\n```py\nfrom typing import Literal\n\nimport patito as pt\nimport polars as pl\n\n\nclass Product(pt.Model):\n product_id: int = pt.Field(unique=True)\n # Specify a specific dtype to be used\n popularity_rank: int = pt.Field(dtype=pl.UInt16)\n # Field with default value \"for-sale\"\n status: Literal[\"draft\", \"for-sale\", \"discontinued\"] = \"for-sale\"\n # The eurocent cost is extracted from the Euro cost string \"\u20acX.Y EUR\"\n eurocent_cost: int = pt.Field(\n derived_from=100 * pl.col(\"cost\").str.extract(r\"\u20ac(\\d+\\.+\\d+)\").cast(float).round(2)\n )\n\n\nproducts = pt.DataFrame(\n {\n \"product_id\": [1, 2],\n \"popularity_rank\": [2, 1],\n \"status\": [None, \"discontinued\"],\n \"cost\": [\"\u20ac2.30 EUR\", \"\u20ac1.19 EUR\"],\n }\n)\nproduct = (\n products\n # Specify the schema of the given data frame\n .set_model(Product)\n # Derive the `eurocent_cost` int column from the `cost` string column using regex\n .derive()\n # Drop the `cost` column as it is not part of the model\n .drop()\n # Cast the popularity rank column to an unsigned 16-bit integer and cents to an integer\n .cast()\n # Fill missing values with the default values specified in the schema\n .fill_null(strategy=\"defaults\")\n # Assert that the data frame now complies with the schema\n .validate()\n # Retrieve a single row and cast it to the model class\n .get(pl.col(\"product_id\") == 1)\n)\nprint(repr(product))\n# Product(product_id=1, popularity_rank=2, status='for-sale', eurocent_cost=230)\n```\n\nEvery Patito model automatically gets a `.DataFrame` attribute, a custom data frame subclass where `.set_model()` is invoked at instantiation. With other words, `pt.DataFrame(...).set_model(Product)` is equivalent to `Product.DataFrame(...)`.\n\n## \ud83d\udc0d Representing rows as classes\n\nData frames are tailor-made for performing vectorized operations over a _set_ of objects.\nBut when the time comes to retrieving a _single_ row and operate upon it, the data frame construct naturally falls short.\nPatito allows you to embed row-level logic in methods defined on the model.\n\n\n```py\n# models.py\nimport patito as pt\n\nclass Product(pt.Model):\n product_id: int = pt.Field(unique=True)\n name: str\n\n @property\n def url(self) -> str:\n return (\n \"https://example.com/no/products/\"\n f\"{self.product_id}-\"\n f\"{self.name.lower().replace(' ', '-')}\"\n )\n```\n\nThe class can be instantiated from a single row of a data frame by using the `from_row()` method:\n\n```py\nproducts = pl.DataFrame(\n {\n \"product_id\": [1, 2],\n \"name\": [\"Skimmed milk\", \"Eggs\"],\n }\n)\nmilk_row = products.filter(pl.col(\"product_id\" == 1))\nmilk = Product.from_row(milk_row)\nprint(milk.url)\n# https://example.com/no/products/1-skimmed-milk\n```\n\nIf you \"connect\" the `Product` model with the `DataFrame` by the use of `patito.DataFrame.set_model()`, or alternatively by using `Product.DataFrame` directly, you can use the `.get()` method in order to filter the data frame down to a single row _and_ cast it to the respective model class:\n\n```py\n\nproducts = Product.DataFrame(\n {\n \"product_id\": [1, 2],\n \"name\": [\"Skimmed milk\", \"Eggs\"],\n }\n)\nmilk = products.get(pl.col(\"product_id\") == 1)\nprint(milk.url)\n# https://example.com/no/products/1-skimmed-milk\n```\n",
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