colnade 0.5.0

A statically type-safe DataFrame abstraction layer

Colnade

A statically type-safe DataFrame abstraction layer for Python.

Colnade replaces string-based column references (pl.col("age")) with typed descriptors (Users.age), so column misspellings, type mismatches, and schema violations are caught by your type checker — before your code runs.

Works with ty, mypy, and pyright. No plugins, no code generation.

Installation

pip install colnade colnade-polars

Colnade requires Python 3.10+. Install the backend adapter for your engine:

Backend	Install
Polars	pip install colnade-polars
Pandas	pip install colnade-pandas
Dask	pip install colnade-dask

Quick Start

1. Define a schema

from colnade import Column, Schema, UInt64, Float64, Utf8

class Users(Schema):
    id: Column[UInt64]
    name: Column[Utf8]
    age: Column[UInt64]
    score: Column[Float64]

2. Create or read typed data

from colnade_polars import from_rows, read_parquet

# From Python data — schema drives dtype coercion
df = from_rows(Users, [
    Users.Row(id=1, name="Alice", age=30, score=85.0),
    Users.Row(id=2, name="Bob", age=25, score=92.5),
])

# Or from files
df = read_parquet("users.parquet", Users)
# df is DataFrame[Users] — the type checker knows the schema

3. Transform with full type safety

# Column references are attributes, not strings
result = (
    df.filter(Users.age > 25)
      .sort(Users.score.desc())
      .select(Users.name, Users.score)
)

4. Bind to an output schema

class UserSummary(Schema):
    name: Column[Utf8]
    score: Column[Float64]

output = result.cast_schema(UserSummary)
# output is DataFrame[UserSummary]

Safety Model

Colnade catches errors at three levels:

1. In your editor — misspelled columns, schema mismatches at function boundaries, and nullability violations are flagged by your type checker (ty, pyright, mypy) before code runs
2. At data boundaries — runtime validation ensures files and external data match your schemas (columns, types, nullability) and that expressions reference columns from the correct schema
3. On your data values — Field() constraints validate domain invariants like ranges, patterns, and uniqueness

Where static safety ends: Static checking covers column references and schema-preserving operations (filter, sort, with_columns). Schema-transforming operations (select, group_by) return DataFrame[Any] — cast_schema() re-binds to a named schema and is a runtime trust boundary. No type checker plugin is needed, but this is a deliberate tradeoff: plugins could theoretically infer output schemas at the cost of type-checker coupling and maintenance burden. See Type Checker Integration for the full list of what is and isn't checked.

Key Features

Type-safe column references

Column references are class attributes verified by the type checker at lint time:

Users.name   # Column[Utf8] — valid
Users.naem   # ty error: Class `Users` has no attribute `naem`

Schema-preserving operations

Operations that don't change the schema (filter, sort, limit, with_columns) preserve the type parameter:

def process(df: DataFrame[Users]) -> DataFrame[Users]:
    return df.filter(Users.age > 25).sort(Users.score.desc())

Typed expressions

Column descriptors build an expression tree with typed operators:

Users.age > 18          # Expr[Bool] — comparison
Users.score * 2         # Expr[Float64] — arithmetic
(Users.age > 18) & (Users.score > 80)  # Expr[Bool] — logical
Users.name.str_starts_with("A")        # Expr[Bool] — string method

Aggregations

result = df.group_by(Users.name).agg(
    Users.score.mean().alias(UserStats.avg_score),
    Users.id.count().alias(UserStats.user_count),
)

Null handling

# Fill nulls, filter nulls, check nulls
df.with_columns(Users.score.fill_null(0.0).alias(Users.score))
df.filter(Users.score.is_not_null())
df.drop_nulls(Users.score)

Joins with typed output

joined = users.join(orders, on=Users.id == Orders.user_id)
# JoinedDataFrame[Users, Orders] — both schemas accessible

class UserOrders(Schema):
    user_name: Column[Utf8] = mapped_from(Users.name)
    amount: Column[Float64]

result = joined.cast_schema(UserOrders)

Schema-polymorphic utility functions

Write generic functions that work with any schema:

from colnade.schema import S

def first_n(df: DataFrame[S], n: int) -> DataFrame[S]:
    return df.head(n)

# Works with any schema — type preserved
users_subset: DataFrame[Users] = first_n(users_df, 10)

Struct and List support

class Address(Schema):
    city: Column[Utf8]
    zip_code: Column[Utf8]

class UserProfile(Schema):
    name: Column[Utf8]
    address: Column[Struct[Address]]
    tags: Column[List[Utf8]]

# Access nested data
df.filter(UserProfile.address.field(Address.city) == "New York")
df.with_columns(UserProfile.tags.list.len().alias(tag_count_col))

Value-level constraints

from colnade import Column, Schema, UInt64, Utf8, Float64, ValidationLevel
from colnade.constraints import Field, schema_check

class Users(Schema):
    id: Column[UInt64] = Field(unique=True)
    age: Column[UInt64] = Field(ge=0, le=150)
    email: Column[Utf8] = Field(pattern=r"^[^@]+@[^@]+\.[^@]+$")
    status: Column[Utf8] = Field(isin=["active", "inactive"])

    @schema_check
    def adult(cls):
        return Users.age >= 18

# Validate with df.validate() or auto-validate at the FULL level
colnade.set_validation(ValidationLevel.FULL)

Lazy execution

from colnade_polars import scan_parquet

lazy = scan_parquet("users.parquet", Users)
# LazyFrame[Users] — builds a query plan

result = lazy.filter(Users.age > 25).sort(Users.score.desc()).collect()
# Executes the optimized query plan

Untyped escape hatch

When you need to drop down to untyped operations:

untyped = df.untyped()  # UntypedDataFrame — string-based columns
retyped = untyped.to_typed(Users)  # Back to DataFrame[Users]

Performance

Colnade adds < 5% overhead for typical Polars operations and < 5% for single Pandas operations (10–25% for multi-step Pandas pipelines at large sizes). Dask overhead is a fixed ~200–300 us per operation on graph construction, negligible compared to compute time. Validation (STRUCTURAL, FULL) adds measurable cost at data boundaries — see the full benchmark results for details.

Type Checker Error Showcase

Colnade catches real errors at lint time. Here are actual error messages from ty:

Misspelled column name

x = Users.agee

error[unresolved-attribute]: Class `Users` has no attribute `agee`

Schema mismatch at function boundary

df: DataFrame[Users] = read_parquet("users.parquet", Users)
wrong: DataFrame[Orders] = df

error[invalid-assignment]: Object of type `DataFrame[Users]` is not assignable
to `DataFrame[Orders]`

Nullability mismatch in mapped_from

class Bad(Schema):
    age: Column[UInt8] = mapped_from(Users.age)  # Users.age is Column[UInt8 | None]

error[invalid-assignment]: Object of type `Column[UInt8 | None]` is not
assignable to `Column[UInt8]`

Comparison with Existing Solutions

Feature	Colnade	Pandera	StaticFrame	Patito	Narwhals
Column refs checked statically	Named attrs	No	Positional types	No	No
Schema preserved through ops	Through ops¹	At boundaries²	No	No	No
Works with existing engines	Polars, Pandas, Dask	Pandas, Polars, others	Own engine	Polars only	Many engines
No plugins or code gen	Yes	Requires mypy plugin	Yes	Yes	Yes
Generic utility functions	Yes	No	No	No	No
Struct/List typed access	Yes	No	No	No	No
Lazy execution support	Yes	No	No	No	Yes
Value-level constraints	Field()	Check	No	Pydantic validators	No

¹ Schema-preserving ops (filter, sort, with_columns) retain DataFrame[S]. Schema-transforming ops (select, group_by) return DataFrame[Any] — use cast_schema() to bind. ² Pandera's @check_types validates schemas at function boundaries via decorator, but column references within function bodies remain unchecked strings.

See Detailed Comparisons for a fuller discussion of tradeoffs.

Documentation

Full documentation is available at colnade.com, including:

• Getting Started — installation and quick start
• User Guide — concepts, schemas, expressions, joins
• Tutorials — worked examples with real data
• API Reference — auto-generated from source

Examples

Runnable examples are in the examples/ directory:

• basic_usage.py — Schema definition, filter, select, aggregate
• null_handling.py — Nullable columns, fill_null, drop_nulls
• joins.py — Joining DataFrames, JoinedDataFrame, cast_schema
• generic_functions.py — Schema-polymorphic utility functions
• nested_types.py — Struct and List column operations
• full_pipeline.py — Complete ETL pipeline example

License

MIT