dataframely

name	dataframely
description	Best practices for polars data processing with dataframely. Covers definitions of Schema and Collection, usage of .validate() and .filter(), type hints, and testing. Use when writing or modifying code involving dataframely or polars data frames.
license	BSD-3-Clause
user-invocable	false

Overview

dataframely provides two types:

• dy.Schema documents and enforces the structure of a single data frame
• dy.Collection documents and enforces the relationships between multiple related data frames that each have their own dy.Schema

dy.Schema

A subclass of dy.Schema describes the structure of a single dataframe.

class MyHouseSchema(dy.Schema):
    """A schema for a dataframe describing houses."""

    street = dy.String(primary_key=True)
    number = dy.UInt16(primary_key=True)
    #: Description on the number of rooms.
    rooms = dy.UInt8()
    #: Description on the area of the house.
    area = dy.UInt16()

The schema can be used in type hints via dy.DataFrame[MyHouseSchema] and dy.LazyFrame[MyHouseSchema] to express schema adherence statically. It can also be used to validate the structure and contents of a data frame at runtime using validation and filtering.

dy.DataFrame[...] and dy.LazyFrame[...] are typically referred to as "typed data frames". They are typing-only wrappers around pl.DataFrame and pl.LazyFrame, respectively, and only express intent. They are never initialized at runtime.

Defining Constraints

Persist all implicit assumptions on the data as constraints in the schema. Use docstrings purely to answer the "what" about the column contents.

• Use the most specific type possible for each column (e.g. dy.Enum instead of dy.String when applicable).

• Use pre-defined arguments (e.g. nullable, min, regex) for column-level constraints if possible.

• Use the check argument for non-standard column-level constraints that cannot be expressed using pre-defined arguments. Prefer the defining the check as a dictionary with keys describing the type of check:

  class MySchema(dy.Schema):
      col = dy.UInt8(check={"divisible_by_two": lambda col: (col % 2) == 0})
  

• Use rules (i.e. methods decorated with @dy.rule) for cross-column constraints. Use expressive names for the rules and use cls to refer to the schema:

  class MySchema(dy.Schema):
      col1 = dy.UInt8()
      col2 = dy.UInt8()
  
      @dy.rule()
      def col1_greater_col2(cls) -> pl.Expr:
          return cls.col1.col > cls.col2.col
  

• Use rules with an over expression for cross-row constraints beyond primary key checks.

Referencing Columns

When referencing columns of the schema anywhere in the code, always reference column as attribute of the schema class:

• Use Schema.column.col instead of pl.col("column") to obtain a pl.Expr referencing the column.
• Use Schema.column.name to reference the column name as a string.

This allows for easier refactorings and enables lookups on column definitions and constraints via LSP.

dy.Collection

A subclass of dy.Collection describes a set of related data frames, each described by a dy.Schema. Data frames in a collection should share at least a subset of their primary key.

class MyStreetSchema(dy.Schema):
    """A schema for a dataframe describing streets."""

    # Shared primary key component with MyHouseSchema
    street = dy.String(primary_key=True)
    city = dy.String()


class MyCollection(dy.Collection):
    """A collection of related dataframes."""

    houses: dy.LazyFrame[MyHouseSchema]
    streets: dy.LazyFrame[MyStreetSchema]

The collection can be used in a standalone manner (much like a dataclass). It can also be used to validate the structure and contents of its members and their relationships at runtime using validation and filtering.

Defining Constraints

Persist all implicit assumptions about the relationships between the collections' data frames as constraints in the collection.

• Use filters (i.e. methods decorated with @dy.filter) to enforce assumptions about the relationships (e.g. 1:1, 1:N) between the collections' data frames. Leverage dy.functional for writing filter logic.

  class MyCollection(dy.Collection):
      houses: dy.LazyFrame[MyHouseSchema]
      streets: dy.LazyFrame[MyStreetSchema]
  
      @dy.filter()
      def all_houses_on_known_streets(cls) -> pl.LazyFrame:
          return dy.functional.require_relationship_one_to_at_least_one(
              cls.streets, cls.houses, on="street"
          )
  

Usage Conventions

Clear Interfaces

Structure data processing code with clear interfaces documented using dataframely type hints:

def preprocess(raw: dy.LazyFrame[MyRawSchema]) -> dy.DataFrame[MyPreprocessedSchema]:
    # Internal data frames do not require schemas
    df: pl.LazyFrame = ...
    return MyPreprocessedSchema.validate(df, cast=True)

• Use schemas for all input and output data frames in a function. Omit type hints if the function is a private helper (prefixed with _) unless the schema critically improves readability or testability.
• Omit schemas for short-lived temporary data frames. Never define schemas for function-local data frames.

Validation and Filtering

Both .validate and .filter enforce the schema at runtime. Pass cast=True for safe type-casting.

• Schema.validate — raises on failure. Use when failures are unexpected (e.g. transforming already-validated data).
• Schema.filter — returns valid rows plus a FailureInfo describing filtered-out rows. Use when failures are possible and should be handled gracefully. Failures should either be kept around or logged for introspection. The FailureInfo object provides several utility methods to obtain information about the failures:
  • len(failure) provides the total number of failures
  • failure.counts() provides the number of violations by rule
  • failure.invalid() provides the data frame of invalid rows
  • failure.details() provides the data frame of invalid rows with additional columns providing information on which rules were violated

When performing validation or filtering, prefer using pipe to clarify the flow of data:

result = df.pipe(MySchema.validate)
out, failures = df.pipe(MySchema.filter)

Pure Casting

Use Schema.cast as an escape-hatch when it is already known that the data frame conforms to the schema and the runtime cost of the validation should not be incurred. Generally, prefer using Schema.validate or Schema.filter.

Testing

Unless otherwise specified by the user or the project context, add unit tests for all (non-private) methods performing data transformations.

• Do not test properties already guaranteed by the schema (e.g. data types, nullability, value constraints).

Test structure

Write tests with the following structure:

1. "Arrange": Define synthetic input data and expected output
2. "Act": Execute the transformation
3. "Assert": Compare expected and actual output using assert_frame_equal from polars.testing

from polars.testing import assert_frame_equal


def test_grouped_sum():
    df = pl.DataFrame(
        {
            "col1": [1, 2, 3],
            "col2": ["a", "a", "b"],
        }
    ).pipe(MyInputSchema.validate, cast=True)

    expected = pl.DataFrame(
        {
            "col1": ["a", "b"],
            "col2": [3, 3],
        }
    )

    result = my_code(df)

    assert_frame_equal(expected, result)

Generating Synthetic Test Data

Use dataframely's synthetic data generation for creating inputs to functions requiring typed data frames in their input. Generate synthetic data for schemas as follows:

• Use MySchema.sample(num_rows=...) to generate fully random data when exact contents don't matter.
• Use MySchema.sample(overrides=...) to generate random data with specific columns pinned to certain values for testing specific functionality. Prefer using dicts of lists for overrides unless specifically prompted otherwise.
  • When using dicts of lists: for providing overrides that are constant across all rows, provide scalar values instead of lists of equal values.
• Always use MySchema.create_empty() instead of sampling with empty overrides when an empty data frame is needed.

Synthetic data for collections should be generated as follows:

• Use MyCollection.sample(num_rows=...) to generate fully random data when exact contents don't matter.
• Use MyCollection.sample(overrides=...) to generate random data where certain values of the collection members matter. Use lists of dicts for providing overrides as "objects" spanning the collection members.
  • Values for shared primary keys must be provided at the root of the dictionaries
  • Values for individual collection members must be provided in nested dictionaries under the keys corresponding to the collection member names.
• Always use MyCollection.create_empty() instead of sampling with empty overrides when an empty collection is needed.

Getting more information

dataframely provides clear function signatures, type hints and docstrings for the full public API. For more information, inspect the source code in the site packages. If available, always use the LSP tool to find documentation.