typedframes

⚠️ Project Status: Proof of Concept

typedframes (v0.2.1) is currently an experimental proof-of-concept. The core static analysis and mypy/Rust integrations work, but expect rough edges. The codebase prioritizes demonstrating the viability of static DataFrame schema validation over production-grade stability.

Static analysis for pandas and polars DataFrames. Catch column errors at lint-time, not runtime.

from typing import Annotated
import pandas as pd
from typedframes import BaseSchema, Column


class UserData(BaseSchema):
    user_id = Column(type=int)
    email = Column(type=str)
    signup_date = Column(type=str)


df: Annotated[pd.DataFrame, UserData] = pd.read_csv("users.csv")
df['user_id']    # ✓ Validated by checker
df['username']   # ✗ unknown-column: Column 'username' not in UserData

Descriptors as a bridge: define once in Column(type=int), access as df[UserData.user_id.s] (pandas string access) or df.select(UserData.revenue.col) (polars expression). Refactor by changing the descriptor definition — all .s and .col references update automatically. No find-and-replace across string literals.

Why typedframes?

The problem: Many pandas bugs are column mismatches. You access a column that doesn't exist, pass the wrong schema to a function, or make a typo. These errors only surface at runtime, often in production.

The solution: Define your DataFrame schemas as Python classes. Get static type checking that catches column errors before you even run your code.

What you get:

✅ Works without schema annotations - Column inference from usecols=, dtype=, and method chains catches errors on unannotated code
✅ Cross-file awareness - Add BaseSchema and typed return annotations to follow schemas across module boundaries
✅ Static analysis - Catch column errors at lint-time with mypy or the standalone checker
✅ Refactor-safe access - df[Schema.column_group.s].mean() (pandas) or df.select(Schema.col.col) (polars) instead of scattered string literals
✅ Works with pandas AND polars - Same schema API, native backend types
✅ Dynamic column matching - Regex-based ColumnSets for time-series data
✅ Zero runtime overhead - No validation, no slowdown
✅ Type-safe backends - Type checker knows pandas vs polars methods

Installation

pip install typedframes

or

uv add typedframes

The Rust-based checker is included — no separate install needed.

Quick Start

Run on existing code

The checker works from day one without any schema classes. Pass usecols= / columns= to your read calls and column access is validated automatically — no schema classes needed:

import pandas as pd

# Checker infers {order_id, amount, status} from usecols=
orders = pd.read_csv("orders.csv", usecols=["order_id", "amount", "status"])
print(orders["amount"])   # ✓ OK
print(orders["revenue"])  # ✗ unknown-column — 'revenue' not in inferred set

typedframes check src/
# src/pipeline.py:7:8: error[unknown-column] Column 'revenue' not in inferred set {order_id, amount, status}
# ✗ Found 1 error in 12 files (0.0s)

See examples/multi_file_inference/ for a multi-file example with no BaseSchema classes at all.

Define Your Schema (Once)

Add BaseSchema classes when you want cross-file awareness and IDE autocomplete. Schemas travel with function return types across module boundaries — the checker validates call sites even in files that have no usecols= of their own:

from typedframes import BaseSchema, Column, ColumnSet


class SalesData(BaseSchema):
    date = Column(type=str)
    revenue = Column(type=float)
    customer_id = Column(type=int)

    # Dynamic columns with regex
    metrics = ColumnSet(type=float, members=r"metric_\d+", regex=True)

Use With Pandas

from typing import Annotated
import pandas as pd

# Annotate your variable — checker validates all column access below
df: Annotated[pd.DataFrame, SalesData] = pd.read_csv("sales.csv")

# String access — validated by the standalone checker
print(df['revenue'].sum())
print(df['profit'])  # ✗ unknown-column: Column 'profit' not in SalesData

# .s gives a refactor-safe string name from the descriptor
print(df[SalesData.revenue.s].sum())   # same as df['revenue'].sum()


# Type-safe function signature
def analyze(data: Annotated[pd.DataFrame, SalesData]) -> float:
    data['revenue']  # ✓ Validated by checker
    data['profit']   # ✗ unknown-column: 'profit' not in SalesData
    return data[SalesData.revenue.s].mean()

Use With Polars

from typing import Annotated
import polars as pl

# Annotate your variable — checker validates pl.col() references too
df: Annotated[pl.DataFrame, SalesData] = pl.read_csv("sales.csv")

# pl.col() references are now validated by the standalone checker
print(df.filter(pl.col('revenue') > 1000))
print(df.select(pl.col('profit')))  # ✗ unknown-column: Column 'profit' not in SalesData

# .col gives a refactor-safe polars expression from the descriptor
filtered = df.filter(SalesData.revenue.col > 1000)
grouped = df.group_by('customer_id').agg(SalesData.revenue.col.sum())

Column Inference

The standalone checker works without any BaseSchema classes. It infers column sets directly from data loading calls and method chains, so you get column validation even on completely unannotated code. BaseSchema is a progressive enhancement: it adds cross-file awareness and IDE autocomplete, but the checker catches real bugs from day one without it.

Inferred Schemas

When you pass usecols= (pandas) or schema= / columns= (polars), the checker builds an inferred column set and validates all subscript access against it — no schema annotation required:

# Checker infers {user_id, email} from usecols= — no annotation needed
df = pd.read_csv("users.csv", usecols=["user_id", "email"])
print(df["user_id"])   # ✓ OK — in usecols
# print(df["age"])     # ✗ Error: 'age' not in inferred column set

The checker also propagates column sets through method chains. Row-preserving operations (filter, query, head, tail, sort_values, dropna, fillna, ffill, bfill, reset_index) pass the column set through unchanged. Structural operations update it:

from typing import Annotated
df: Annotated[pd.DataFrame, UserData] = pd.read_csv("users.csv")

# Subscript slice — inferred column set {user_id, email}
small = df[["user_id", "email"]]
# print(small["age"])  # ✗ Error: 'age' not in inferred column set

# rename() — old name removed, new name added
renamed = small.rename(columns={"email": "email_address"})
print(renamed["email_address"])  # ✓ OK

# drop() — column removed from inferred set
trimmed = df.drop(columns=["age"])
# print(trimmed["age"])  # ✗ Error: 'age' was dropped

# assign() — new column added to inferred set
augmented = df.assign(created_at="2024-01-01")
print(augmented["created_at"])  # ✓ OK

Inference Gaps and Warnings

untracked-dataframe — unannotated data ingestion (off by default)

By default, typedframes supports permissive Exploratory Data Analysis (EDA). When a DataFrame is loaded via pd.read_csv() without usecols= or a schema annotation, the checker assumes an Unknown state and bypasses strict column validation to avoid nagging you during discovery.

To lock down production CI/CD pipelines, opt in to untracked-dataframe warnings with --strict-ingest:

typedframes check src/ --strict-ingest

With strict ingestion enabled, loading a DataFrame without a schema or usecols= produces:

df = pd.read_csv("users.csv")
# ⚠ untracked-dataframe: columns unknown at lint time; specify `usecols`/`columns` or
#   annotate: `df: Annotated[pd.DataFrame, MySchema] = pd.read_csv(...)`

Fix option 1 — annotate with a schema:

from typing import Annotated
df: Annotated[pd.DataFrame, UserData] = pd.read_csv("users.csv")

Fix option 2 — pass usecols=:

df = pd.read_csv("users.csv", usecols=["user_id", "email"])

dropped-unknown-column — dropped column does not exist

Emitted when drop(columns=[...]) names a column that isn't in the inferred set:

from typing import Annotated
df: Annotated[pd.DataFrame, UserData] = pd.read_csv("users.csv")
trimmed = df.drop(columns=["nonexistent"])
# ⚠ dropped-unknown-column: Dropped column 'nonexistent' does not exist in UserData

Static Analysis

typedframes provides two ways to check your code:

Option 1: Standalone Checker (Fast)

# Blazing fast Rust-based checker
typedframes check src/

# Output (ty-style, auto-colored in terminals):
# src/analysis.py:23:8: error[unknown-column] Column 'profit' not in SalesData
# src/pipeline.py:56:8: error[unknown-column] Column 'user_name' not in UserData
# ✗ Found 2 errors in 47 files (0.0s)

Features:

Catches column name errors
Validates schema mismatches between functions
Checks both pandas and polars code
10-100x faster than mypy

Use this for:

Fast feedback during development
CI/CD pipelines
Pre-commit hooks

Configuration:

# Check specific files
typedframes check src/pipeline.py

# Check directory (builds cross-file index automatically)
typedframes check src/

# Fail on any error (for CI)
typedframes check src/ --strict

# JSON output
typedframes check src/ --json

# Skip cross-file index (single-file mode, faster for quick checks)
typedframes check src/ --no-index

# Suppress all warnings (untracked-dataframe, dropped-unknown-column)
typedframes check src/ --no-warnings

To suppress warnings project-wide, add to pyproject.toml:

[tool.typedframes]
enabled = true
warnings = false

Option 2: Mypy Plugin (Comprehensive)

# Add to pyproject.toml
[tool.mypy]
plugins = ["typedframes.mypy"]

# Or mypy.ini
[mypy]
plugins = typedframes.mypy

# Run mypy
mypy src/

Features:

Full type checking across your codebase
Catches column errors AND regular type errors
IDE integration (VSCode, PyCharm)
Works with existing mypy configuration

Use this for:

Comprehensive type checking
Integration with existing mypy setup
IDE error highlighting

Supported Operations

The checker tracks schema changes through rename, drop, assign, select, pop, insert, del, subscript assignment, merge, and concat. Row-passthrough operations like filter, query, head, sort_values, and dropna are validated without schema changes. Operations with runtime-dependent output (join, pivot, melt, groupby, apply, etc.) are left untracked to avoid false positives.

See the full Method Matrix for the complete list of tracked, passthrough, and untracked operations, plus the error code reference.

Static Analysis Performance

Fast feedback reduces development time. The typedframes Rust binary provides near-instant column checking.

Benchmark results (10 runs, 3 warmup, caches cleared between runs):

Tool	Version	What it does	typedframes (13 files)	great_expectations (490 files)†
typedframes	0.2.0	DataFrame column checker	9ms ±2ms	930µs ±89µs
ruff	0.15.4	Linter (no type checking)	64ms ±16ms	360ms ±18ms
ty	0.0.19	Type checker	115ms ±22ms	1.65s ±26ms
pyrefly	0.54.0	Type checker	3.78s ±7.53s	693ms ±33ms
mypy	1.19.1	Type checker (no plugin)	13.85s ±1.08s	12.13s ±400ms
mypy + typedframes	1.19.1	Type checker + column checker	13.51s ±273ms	13.89s ±491ms
pyright	1.1.408	Type checker	2.10s ±422ms	8.37s ±253ms

† great_expectations column from previous benchmark run.

Run uv run python benchmarks/benchmark_checkers.py to reproduce.

The typedframes binary resolves column names within a file and, when a project index is present, across files too. Run typedframes check src/ to build the index automatically and catch errors like df = load_users(); df["typo"] even when load_users is defined in another module. Pass --no-index to skip the index and check each file in isolation. Full type checkers (mypy, pyright, ty) analyze all Python types across your entire codebase. Use both: the binary for fast iteration, mypy for comprehensive checking.

The standalone checker is built with ruff_python_parser for Python AST parsing.

Note: ty (Astral) does not currently support mypy plugins, so use the standalone binary for column checking with ty.

Type Safety With Multiple Backends

typedframes uses native backend types to ensure complete type safety:

from typing import Annotated
import pandas as pd
import polars as pl
from typedframes import BaseSchema, Column


class UserData(BaseSchema):
    user_id = Column(type=int)
    email = Column(type=str)


# Pandas pipeline - type checker knows pandas methods
def pandas_analyze(df: Annotated[pd.DataFrame, UserData]) -> Annotated[pd.DataFrame, UserData]:
    return df[df['user_id'] > 100]  # ✓ Pandas syntax


# Polars pipeline - type checker knows polars methods
def polars_analyze(df: Annotated[pl.DataFrame, UserData]) -> Annotated[pl.DataFrame, UserData]:
    return df.filter(pl.col('user_id') > 100)  # ✓ Polars syntax


# Use native types throughout
df_pandas: Annotated[pd.DataFrame, UserData] = pd.read_csv("data.csv")
df_polars: Annotated[pl.DataFrame, UserData] = pl.read_csv("data.csv")

pandas_analyze(df_pandas)  # ✓ OK
polars_analyze(df_polars)  # ✓ OK

Advanced Usage

Merges, Joins, and Filters

Schema-typed DataFrames preserve their type through common operations:

Pandas:

from typing import Annotated
import pandas as pd
from typedframes import BaseSchema, Column


class UserSchema(BaseSchema):
    user_id = Column(type=int)
    email = Column(type=str)


class OrderSchema(BaseSchema):
    order_id = Column(type=int)
    user_id = Column(type=int)
    total = Column(type=float)


# Schema preserved through filtering
def get_active_users(df: Annotated[pd.DataFrame, UserSchema]) -> Annotated[pd.DataFrame, UserSchema]:
    return df[df['user_id'] > 100]  # ✓ Validated by checker


# Schema preserved through merges
users: Annotated[pd.DataFrame, UserSchema] = pd.read_csv("users.csv")
orders: Annotated[pd.DataFrame, OrderSchema] = pd.read_csv("orders.csv")
merged = users.merge(orders, on=UserSchema.user_id.s)

Polars:

from typing import Annotated
import polars as pl


# Schema columns work in filter expressions
def filter_users(df: Annotated[pl.DataFrame, UserSchema]) -> pl.DataFrame:
    return df.filter(pl.col('user_id') > 100)


# Schema columns work in join expressions
def join_data(
    users: Annotated[pl.DataFrame, UserSchema],
    orders: Annotated[pl.DataFrame, OrderSchema],
) -> pl.DataFrame:
    return users.join(
        orders,
        left_on=UserSchema.user_id.s,
        right_on=OrderSchema.user_id.s,
    )


# Schema columns work in select expressions
def select_columns(df: Annotated[pl.DataFrame, UserSchema]) -> pl.DataFrame:
    return df.select([UserSchema.user_id.s, UserSchema.email.s])

Dynamic Column Matching

Perfect for time-series data where column counts change. Regex ColumnSets document which columns belong to a group and are validated by the static checker. The .s property gives you the list of column names for explicit (non-regex) ColumnSets; for non-regex groups you can also use .cols() for polars expressions.

from typing import Annotated
import pandas as pd
from typedframes import BaseSchema, Column, ColumnSet, ColumnGroup


class SensorReadings(BaseSchema):
    timestamp = Column(type=str)
    # Explicit sensor columns — refactor-safe list access via .s
    sensors = ColumnSet(type=float, members=["sensor_1", "sensor_2", "sensor_3"])


df: Annotated[pd.DataFrame, SensorReadings] = pd.read_csv("readings.csv")
df[SensorReadings.sensors.s].mean()    # ✓ Expands to df[["sensor_1", "sensor_2", "sensor_3"]].mean()

For logical grouping across multiple ColumnSets:

class TimeSeriesData(BaseSchema):
    timestamp = Column(type=str)
    temperature = ColumnSet(type=float, members=["temp_1", "temp_2", "temp_3"])
    pressure = ColumnSet(type=float, members=["pressure_1", "pressure_2"])

    # Group for convenient access to all sensor columns
    sensors = ColumnGroup(members=[temperature, pressure])


df: Annotated[pd.DataFrame, TimeSeriesData] = pd.read_csv("sensors.csv")
avg_temp = df[TimeSeriesData.temperature.s].mean()
all_readings = df[TimeSeriesData.sensors.s].describe()

Schema Composition

Compose upward — build bigger schemas from smaller ones via inheritance. Type checkers see all columns natively.

from typing import Annotated
import pandas as pd
from typedframes import BaseSchema, Column


# Start with the smallest useful schema
class UserPublic(BaseSchema):
    user_id = Column(type=int)
    email = Column(type=str)
    name = Column(type=str)


# Extend it — never strip down
class UserFull(UserPublic):
    password_hash = Column(type=str)


class Orders(BaseSchema):
    order_id = Column(type=int)
    user_id = Column(type=int)
    total = Column(type=float)


# Combine via multiple inheritance
class UserOrders(UserPublic, Orders):
    """Type checkers see all columns from both parents."""
    ...


# Or use the + operator
UserOrdersDynamic = UserPublic + Orders

users: Annotated[pd.DataFrame, UserPublic] = pd.read_csv("users.csv")
orders: Annotated[pd.DataFrame, Orders] = pd.read_csv("orders.csv")
merged: Annotated[pd.DataFrame, UserOrders] = users.merge(orders, on=UserPublic.user_id.s)

Overlapping columns with the same type are allowed (common after merges). Conflicting types raise SchemaConflictError.

See examples/schema_algebra_example.py for a complete walkthrough.

Comparison

Feature Matrix (Static Analysis Focus)

Comprehensive comparison of pandas/DataFrame typing and validation tools. typedframes focuses on static analysis —catching errors at lint-time before your code runs.

Feature	typedframes	Pandera	Great Expectations	strictly_typed_pandas	pandas-stubs	dataenforce	pandas-type-checks	StaticFrame	narwhals
Version tested	0.2.0	0.29.0	1.4.3	0.3.6	3.0.0	0.1.2	1.1.3	3.7.0	2.16.0
Analysis Type
When errors are caught	Static (lint-time)	Runtime	Runtime	Static + Runtime	Static	Runtime	Runtime	Static + Runtime	Runtime
Static Analysis (our focus)
Mypy plugin	✅ Yes	⚠️ Limited	❌ No	✅ Yes	✅ Yes	❌ No	❌ No	⚠️ Basic	❌ No
Standalone checker	✅ Rust (~1ms)	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No
Column name checking	✅ Yes	⚠️ Limited	❌ No	✅ Yes	❌ No	❌ No	❌ No	✅ Yes	❌ No
Column type checking	✅ Yes	⚠️ Limited	❌ No	✅ Yes	❌ No	❌ No	❌ No	✅ Yes	❌ No
Typo suggestions	✅ Yes	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No
Runtime Validation
Data validation	❌ No	✅ Excellent	✅ Excellent	✅ typeguard	❌ No	✅ Yes	✅ Yes	✅ Yes	❌ No
Value constraints	❌ No	✅ Yes	✅ Excellent	❌ No	❌ No	❌ No	❌ No	✅ Yes	❌ No
Schema Features
Column grouping	✅ ColumnGroup	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No
Regex column matching	✅ Yes	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No	❌ No
Backend Support
Pandas	✅ Yes	✅ Yes	✅ Yes	✅ Yes	✅ Yes	✅ Yes	✅ Yes	❌ Own	✅ Yes
Polars	✅ Yes	✅ Yes	❌ No	❌ No	❌ No	❌ No	❌ No	❌ Own	✅ Yes
DuckDB, cuDF, etc.	❌ No	❌ No	✅ Spark, SQL	❌ No	❌ No	❌ No	❌ No	❌ No	✅ Yes
Project Status (Feb 2026)
Active development	✅ Yes	✅ Yes	✅ Yes	⚠️ Low	✅ Yes	❌ Inactive	⚠️ Low	✅ Yes	✅ Yes

Legend: ✅ Full support | ⚠️ Limited/Partial | ❌ Not supported

Tool Descriptions

Pandera (v0.29.0): Excellent runtime validation. Static analysis support exists but has limitations—column access via df["column"] is not validated, and schema mismatches between functions may not be caught.
strictly_typed_pandas (v0.3.6): Provides DataSet[Schema] type hints with mypy support. No standalone checker. No polars support. Runtime validation via typeguard.
pandas-stubs (v3.0.0): Official pandas type stubs. Provides API-level types but no column-level checking.
dataenforce (v0.1.2): Runtime validation via decorator. Marked as experimental/not production-ready. Appears inactive.
pandas-type-checks (v1.1.3): Runtime validation decorator. No static analysis.
StaticFrame (v3.7.0): Alternative immutable DataFrame library with built-in static typing. Not compatible with pandas/polars—requires using StaticFrame's own DataFrame implementation.
narwhals (v2.16.0): Compatibility layer that provides a unified API across pandas, polars, DuckDB, cuDF, and more. Solves a different problem—write-once-run-anywhere portability, not type safety. See Why Abstraction Layers Don't Solve Type Safety below.
Great Expectations (v1.4.3): Comprehensive data quality framework. Defines "expectations" (assertions) about data values, distributions, and schema properties. Excellent for runtime validation, data documentation, and data quality monitoring. No static analysis or column-level type checking in code. Supports pandas, Spark, and SQL backends.

Type Checkers (Not DataFrame-Specific)

These are general Python type checkers. They don't validate DataFrame column names, but they can be used alongside typedframes for comprehensive type checking:

mypy (v1.19.1): The original Python type checker. typedframes provides a mypy plugin for column checking. See performance benchmarks.
ty (v0.0.19, Astral): New Rust-based type checker, 10-60x faster than mypy on large codebases. Does not support mypy plugins—use typedframes standalone checker.
pyrefly (v0.54.0, Meta): Rust-based type checker from Meta, replacement for Pyre. Fast, but no DataFrame column checking.
pyright (v1.1.408, Microsoft): Type checker powering Pylance/VSCode. No mypy plugin support—use typedframes standalone checker.

Not Directly Comparable

These tools serve different purposes:

pandas_lint: Lints pandas code patterns (performance, best practices). Does not check column names/types.
pandas-vet: Flake8 plugin for pandas best practices. Does not check column names/types.

When to Use What

Use Case	Recommended Tool
Static column checking (existing pandas/polars)	typedframes
Runtime data validation	Pandera
Both static + runtime	typedframes + `to_pandera_schema()`
Cross-library portability (write once, run anywhere)	narwhals
Data quality monitoring / pipeline validation	Great Expectations
Immutable DataFrames from scratch	StaticFrame
Pandas API type hints only	pandas-stubs

Pandera Integration

Convert typedframes schemas to Pandera schemas for runtime validation. Define your schema once, get both static and runtime checking.

pip install typedframes[pandera]

from typedframes import BaseSchema, Column
from typedframes.pandera import to_pandera_schema
import pandas as pd


class UserData(BaseSchema):
  user_id = Column(type=int)
  email = Column(type=str)
  age = Column(type=int, nullable=True)


# Convert to pandera schema
pandera_schema = to_pandera_schema(UserData)

# Validate data at runtime
df = pd.read_csv("users.csv")
validated_df = pandera_schema.validate(df)  # Raises SchemaError on failure

The conversion maps:

Column type/nullable/alias to pa.Column dtype/nullable/name
ColumnSet with explicit members to individual pa.Column entries
ColumnSet with regex to pa.Column(regex=True)
allow_extra_columns to pandera's strict mode

Examples

Basic CSV Processing

from typing import Annotated
import pandas as pd
from typedframes import BaseSchema, Column


class Orders(BaseSchema):
    order_id = Column(type=int)
    customer_id = Column(type=int)
    total = Column(type=float)
    date = Column(type=str)


def calculate_revenue(orders: Annotated[pd.DataFrame, Orders]) -> float:
    return orders['total'].sum()


df: Annotated[pd.DataFrame, Orders] = pd.read_csv("orders.csv")
revenue = calculate_revenue(df)

Time Series Analysis

from typing import Annotated
import pandas as pd
from typedframes import BaseSchema, Column, ColumnSet, ColumnGroup


class SensorData(BaseSchema):
    timestamp = Column(type=str)
    temperature = ColumnSet(type=float, members=["temp_1", "temp_2", "temp_3"])
    humidity = ColumnSet(type=float, members=["humidity_1", "humidity_2"])

    all_sensors = ColumnGroup(members=[temperature, humidity])


df: Annotated[pd.DataFrame, SensorData] = pd.read_csv("sensors.csv")

# Clean, type-safe operations using .s for column name lists
avg_temp_per_row = df[SensorData.temperature.s].mean(axis=1)
all_readings_stats = df[SensorData.all_sensors.s].describe()

Multi-Step Pipeline

from typing import Annotated
import pandas as pd
from typedframes import BaseSchema, Column


class RawSales(BaseSchema):
    date = Column(type=str)
    product_id = Column(type=int)
    quantity = Column(type=int)
    price = Column(type=float)


class AggregatedSales(BaseSchema):
    date = Column(type=str)
    total_revenue = Column(type=float)
    total_quantity = Column(type=int)


def aggregate_daily(df: Annotated[pd.DataFrame, RawSales]) -> Annotated[pd.DataFrame, AggregatedSales]:
    result = df.groupby(RawSales.date.s).agg({
        RawSales.price.s: 'sum',
        RawSales.quantity.s: 'sum',
    }).reset_index()
    result.columns = pd.Index(['date', 'total_revenue', 'total_quantity'])
    return result  # type: ignore[return-value]


# Type-safe pipeline
raw: Annotated[pd.DataFrame, RawSales] = pd.read_csv("sales.csv")
aggregated = aggregate_daily(raw)


# Type checker validates schema transformations
def analyze(df: Annotated[pd.DataFrame, AggregatedSales]) -> float:
    df['total_revenue']  # ✓ OK
    df['price']          # ✗ Error: 'price' not in AggregatedSales
    return df[AggregatedSales.total_revenue.s].mean()

Polars Performance Pipeline

from typing import Annotated
import polars as pl
from typedframes import BaseSchema, Column


class LargeDataset(BaseSchema):
    id = Column(type=int)
    value = Column(type=float)
    category = Column(type=str)


def efficient_aggregation(df: Annotated[pl.DataFrame, LargeDataset]) -> pl.DataFrame:
    return (
        df.filter(pl.col('value') > 100)
        .group_by('category')
        .agg(pl.col('value').mean())
    )


# Polars handles large files efficiently
df: Annotated[pl.DataFrame, LargeDataset] = pl.read_csv("huge_file.csv")
result = efficient_aggregation(df)

Philosophy

Type Safety Over Validation

We believe static analysis catches bugs earlier and cheaper than runtime validation.

typedframes focuses on:

✅ Catching errors at lint-time
✅ Zero runtime overhead
✅ Developer experience

We explicitly don't focus on:

❌ Runtime data validation (use Pandera)
❌ Statistical checks (use Pandera)
❌ Data quality monitoring (use Great Expectations)

Important: An Annotated[pd.DataFrame, Schema] type annotation is a trust assertion, not a validation step. It tells the type checker "this DataFrame conforms to this schema" without verifying the actual data. The linter catches mistakes in your code (wrong column names, schema mismatches between functions), but it cannot verify that a CSV file contains the expected columns. For runtime validation of external data, use to_pandera_schema() to convert your typedframes schemas to Pandera schemas.

Native Backend Types

We use native Annotated[pd.DataFrame, Schema] and Annotated[pl.DataFrame, Schema] types because pandas and polars have fundamentally different APIs. By annotating native objects rather than wrapping them in custom classes, typedframes lets you use each library's full, native API while still getting schema-level type safety.

Trade-offs we avoid:

❌ Custom wrapper classes (you lose IDE completion for native methods)
❌ "Universal DataFrame" abstractions (you lose library-specific features)
❌ Lowest-common-denominator APIs

Why Abstraction Layers Don't Solve Type Safety

Tools like narwhals solve a different problem: writing portable code that runs on pandas, polars, DuckDB, cuDF, and other backends. This is useful for library authors who want to support multiple backends without maintaining separate codebases.

However, abstraction layers don't provide column-level type safety:

import narwhals as nw

def process(df: nw.DataFrame) -> nw.DataFrame:
    # No static checking - "revenue" typo won't be caught until runtime
    return df.filter(nw.col("revnue") > 100)  # Typo: "revnue" vs "revenue"

The fundamental issue: Abstraction layers abstract over which library you're using, not what columns your data has. They can't know at lint-time whether "revenue" is a valid column in your DataFrame.

typedframes solves the orthogonal problem of schema safety:

from typing import Annotated
import polars as pl
from typedframes import BaseSchema, Column

class SalesData(BaseSchema):
    revenue = Column(type=float)

def process(df: Annotated[pl.DataFrame, SalesData]) -> pl.DataFrame:
    return df.filter(pl.col('revnue') > 100)  # ✗ Error at lint-time: 'revnue' not in SalesData

Use narwhals when: You're writing a library that needs to work with multiple DataFrame backends.

Use typedframes when: You want to catch column name/type errors before your code runs.

Why No Built-in Validation?

Ideally, validation happens at the point of data ingestion rather than in Python application code. If you're validating DataFrames in Python, consider whether your data pipeline could enforce constraints earlier. Use Pandera for cases where runtime validation is genuinely necessary.

License

MIT License - see LICENSE

Roadmap

Shipped:

Planned:

Opt-in data loading constraints - Field class with constraints (gt, ge, lt, le), strictly isolated to from_schema() ingestion boundaries

FAQ

Q: Do I need to choose between pandas and polars? A: No. Define your schema once, use it with both. Just use Annotated[pd.DataFrame, Schema] or Annotated[pl.DataFrame, Schema] in your function signatures.

Q: Does this replace Pandera? A: No, it complements it. Use typedframes for static analysis, and to_pandera_schema() to convert your schemas to Pandera for runtime validation. See Pandera Integration.

Q: Is the standalone checker required? A: No. You can use just the mypy plugin, just the standalone checker, or both. They catch the same errors.

Q: What works without any plugin? A: Any type checker (mypy, pyright, ty) understands Annotated[pd.DataFrame, Schema] as a plain pd.DataFrame — no plugin or stubs needed for basic type checking. Column name validation (catching typos like df["revnue"] in string-based access) still requires the standalone checker or mypy plugin.

Q: What about pyright/pylance users? A: The mypy plugin doesn't work with pyright. Use the standalone checker (typedframes check) for column name validation. Schema descriptor access (df[Schema.column]) works natively in pyright without any plugin.

Q: Do I need to write BaseSchema classes to get value? A: No. The standalone checker works entirely from inference: usecols=/columns=/dtype= arguments on read calls give it enough information to validate column access and propagate that knowledge through method chains (rename, drop, assign, select, …). BaseSchema is a progressive enhancement that unlocks cross-file awareness (schemas travel with function return types across module boundaries) and IDE autocomplete via descriptors — but the checker catches real column errors from day one without it. See examples/multi_file_inference/ for a complete demo with no schema classes.

Q: Does this work with existing pandas/polars code? A: Yes. You can gradually adopt typedframes by adding schemas to new code. Existing code continues to work. Start by adding usecols= to your read calls to get immediate column validation, then add BaseSchema classes incrementally where cross-file tracking or autocomplete is most valuable.

Q: What if my column name conflicts with a pandas/polars method? A: No problem. Since column access uses bracket syntax with schema descriptors (df[Schema.mean]), there is no conflict with DataFrame methods (df.mean()). Both work independently.

Credits

Built by developers who believe DataFrame bugs should be caught at lint-time, not in production.

Inspired by the needs of ML/data science teams working with complex data pipelines.

Questions? Issues? Ideas? Open an issue

Ready to catch DataFrame bugs before runtime? pip install typedframes

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typedframes

Table of Contents

Why typedframes?

Installation

Quick Start

Run on existing code

Define Your Schema (Once)

Use With Pandas

Use With Polars

Column Inference

Inferred Schemas

Inference Gaps and Warnings

See Also

Static Analysis

Option 1: Standalone Checker (Fast)

Option 2: Mypy Plugin (Comprehensive)

Supported Operations

Static Analysis Performance

Type Safety With Multiple Backends

Advanced Usage

Merges, Joins, and Filters

Dynamic Column Matching

Schema Composition

Comparison

Feature Matrix (Static Analysis Focus)

Tool Descriptions

Type Checkers (Not DataFrame-Specific)

Not Directly Comparable

When to Use What

Pandera Integration

Examples

Basic CSV Processing

Time Series Analysis

Multi-Step Pipeline

Polars Performance Pipeline

Philosophy

Type Safety Over Validation

Native Backend Types

Why Abstraction Layers Don't Solve Type Safety

Why No Built-in Validation?

License

Roadmap

FAQ

Credits

About

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