humblpatito 0.7.3
A dataframe modelling library built on top of polars and pydantic.
License: MIT
Requires Python: >=3.8,<4.0
Keywords: validation,dataframe

# <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>
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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. \
🦆 Integration with DuckDB for running flexible SQL queries.

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
```

#### DuckDB Integration

Patito can also integrate with [DuckDB](https://duckdb.org/).
In order to enable this integration you must explicitly specify it during installation:

```sh
pip install 'patito[duckdb]'
```


## Documentation

The full documentation of Patio 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, Optional

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.ValidationError 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)` indicates that the given column is required and _must_ take the given default value.
* String fields annotated with `Field(pattern=r"<pattern-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.ValidationError`:

```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 pattern
    .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
```