typedframes 0.1.1

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

typedframes

⚠️ Project Status: Proof of Concept

typedframes (v0.1.1) 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 typedframes import BaseSchema, Column
from typedframes.pandas import PandasFrame


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


def process(df: PandasFrame[UserData]) -> None:
  df[UserData.user_id]  # ✓ Schema descriptor — autocomplete, refactor-safe
  df['user_id']  # ✓ String access — also validated by checker
  df['username']  # ✗ Error: Column 'username' not in UserData

Why descriptors? Rename a column in ONE place (user_id = Column(type=int)), and all references — Schema.user_id, str(Schema.user_id), Schema.user_id.col — update automatically. No find-and-replace across string literals.

---

Table of Contents

• Why typedframes?
• Installation
• Quick Start
• Static Analysis
• Static Analysis Performance
• Comparison
• Type Safety With Multiple Backends
• Features
• Advanced Usage
• Pandera Integration
• Examples
• Philosophy
• FAQ

---

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:

• ✅ Static analysis - Catch column errors at lint-time with mypy or the standalone checker
• ✅ Beautiful runtime UX - df[Schema.column_group].mean() (pandas) instead of ugly column lists
• ✅ Works with pandas AND polars - Same schema API, explicit 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

Define Your Schema (Once)

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 typedframes.pandas import PandasFrame

# Load data with schema — one line
df = PandasFrame.read_csv("sales.csv", SalesData)

# Access columns via schema descriptors
print(df[SalesData.revenue].sum())
print(df[SalesData.metrics].mean())  # All metric_* columns


# Type-safe pandas operations
def analyze(data: PandasFrame[SalesData]) -> float:
  data[SalesData.revenue]  # ✓ Validated by type checker
  data['profit']  # ✗ Error at lint-time: 'profit' not in SalesData
  return data[SalesData.revenue].mean()


# Standard pandas access still works
filtered = df[df[SalesData.revenue] > 1000]
grouped = df.groupby(SalesData.customer_id)[str(SalesData.revenue)].sum()

Use With Polars

from typedframes.polars import PolarsFrame
import polars as pl

# Load data with schema — one line
df = PolarsFrame.read_csv("sales.csv", SalesData)

# Use schema column references for type-safe expressions
print(df.select(SalesData.revenue.col).sum())


# Type-safe polars operations
def analyze_polars(data: PolarsFrame[SalesData]) -> pl.DataFrame:
  data.select(SalesData.revenue.col)  # ✓ OK
  data.select(['profit'])  # ✗ Error at lint-time: 'profit' not in SalesData
  return data.select(SalesData.revenue.col).mean()


# Polars methods work as expected
filtered = df.filter(SalesData.revenue.col > 1000)
grouped = df.group_by('customer_id').agg(SalesData.revenue.col.sum())

---

Static Analysis

typedframes provides two ways to check your code:

Option 1: Standalone Checker (Fast)

# Blazing fast Rust-based checker
typedframes check src/

# Output:
# ✓ Checked 47 files in 0.0s
# ✗ src/analysis.py:23 - Column 'profit' not in SalesData
# ✗ src/pipeline.py:56 - Column 'user_name' not in UserData

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
typedframes check src/

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

# JSON output
typedframes check src/ --json

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

---

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 (11 files)	great_expectations (490 files)
typedframes	0.1.1	DataFrame column checker	961µs ±56µs	930µs ±89µs
ruff	0.15.0	Linter (no type checking)	39ms ±12ms	360ms ±18ms
ty	0.0.16	Type checker	146ms ±13ms	1.65s ±26ms
pyrefly	0.52.0	Type checker	152ms ±7ms	693ms ±33ms
mypy	1.19.1	Type checker (no plugin)	9.15s ±218ms	12.13s ±400ms
mypy + typedframes	1.19.1	Type checker + column checker	9.34s ±331ms	13.89s ±491ms
pyright	1.1.408	Type checker	2.34s ±335ms	8.37s ±253ms

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

The typedframes binary performs lexical column name resolution within a single file. It does not perform cross-file type inference. 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.

---

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	0.1.1	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 (v0.0.14, Astral): New Rust-based type checker, 10-60x faster than mypy on large codebases. Does not support mypy plugins—use typedframes standalone checker.

• pyrefly (v0.51.1, 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

---

Type Safety With Multiple Backends

typedframes uses explicit backend types to ensure complete type safety:

from typedframes import BaseSchema, Column
from typedframes.pandas import PandasFrame
from typedframes.polars import PolarsFrame


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


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


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


# Type checker prevents mixing backends
df_pandas = PandasFrame.read_csv("data.csv", UserData)
df_polars = PolarsFrame.read_csv("data.csv", UserData)

pandas_analyze(df_pandas)  # ✓ OK
polars_analyze(df_polars)  # ✓ OK
pandas_analyze(df_polars)  # ✗ Type error: Expected PandasFrame, got PolarsFrame

---

Features

Clean Schema Definition

from typedframes import BaseSchema, Column, ColumnSet, ColumnGroup


class TimeSeriesData(BaseSchema):
    timestamp = Column(type=str)
    temperature = ColumnSet(type=float, members=r"temp_sensor_\d+", regex=True)
    pressure = ColumnSet(type=float, members=r"pressure_\d+", regex=True)

    # Logical grouping
    sensors = ColumnGroup(members=[temperature, pressure])

Beautiful Runtime API

from typedframes.pandas import PandasFrame

df = PandasFrame.read_csv("sensors.csv", TimeSeriesData)

# Access column groups as DataFrames
temps = df[TimeSeriesData.temperature]  # All temp_sensor_* columns
all_sensors = df[TimeSeriesData.sensors]  # All sensor columns

# Clean operations
avg_temp = df[TimeSeriesData.temperature].mean()
max_pressure = df[TimeSeriesData.pressure].max()

# Standard pandas access still works
df['timestamp']  # Single column
df[['timestamp', 'temp_sensor_1']]  # Multi-column select

Column-Level Static Checking

from typedframes.pandas import PandasFrame


def daily_summary(data: PandasFrame[TimeSeriesData]) -> PandasFrame[DailySummary]:
    # Type checker validates column access
    data['timestamp']  # ✓ OK - column exists
    data['date']  # ✗ Error: Column 'date' not in TimeSeriesData

    # Type checker validates ColumnSet access
    temps = data[TimeSeriesData.temperature]  # ✓ OK - ColumnSet exists
    summary = temps.mean()
    return summary

Dynamic Column Matching

Perfect for time-series data where column counts change. Regex ColumnSets match actual DataFrame columns at from_schema() time — the matched columns are stored and used when you access df[Schema.sensors]. Static analysis validates that the ColumnSet name exists in the schema, but cannot verify which columns the regex will match at runtime.

class SensorReadings(BaseSchema):
    timestamp = Column(type=str)
    # Matches: sensor_1, sensor_2, ..., sensor_N
    sensors = ColumnSet(type=float, members=r"sensor_\d+", regex=True)

# Works regardless of how many sensor columns exist
df = PandasFrame.read_csv("readings_2024_01.csv", SensorReadings)  # 50 sensors
df[SensorReadings.sensors].mean()  # All sensor columns

df = PandasFrame.read_csv("readings_2024_02.csv", SensorReadings)  # 75 sensors
df[SensorReadings.sensors].mean()  # All sensor columns (different count, same code)

---

Advanced Usage

Merges, Joins, and Filters

Schema-typed DataFrames preserve their type through common operations:

Pandas:

from typedframes import BaseSchema, Column
from typedframes.pandas import PandasFrame
import pandas as pd


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: PandasFrame[UserSchema]) -> PandasFrame[UserSchema]:
  return df[df[UserSchema.user_id] > 100]  # ✓ Still PandasFrame[UserSchema]


# Schema preserved through merges
users: PandasFrame[UserSchema] = ...
orders: PandasFrame[OrderSchema] = ...
merged = users.merge(orders, on=str(UserSchema.user_id))

Polars:

from typedframes.polars import PolarsFrame
import polars as pl


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


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


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

Schema Composition

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

from typedframes import BaseSchema, Column
from typedframes.pandas import PandasFrame


# 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: PandasFrame[UserPublic] = ...
orders: PandasFrame[Orders] = ...
merged: PandasFrame[UserOrders] = PandasFrame.from_schema(
  users.merge(orders, on=str(UserPublic.user_id)), UserOrders
)

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.

---

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 typedframes import BaseSchema, Column
from typedframes.pandas import PandasFrame


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: PandasFrame[Orders]) -> float:
  return orders[Orders.total].sum()


df = PandasFrame.read_csv("orders.csv", Orders)
revenue = calculate_revenue(df)

Time Series Analysis

from typedframes import BaseSchema, Column, ColumnSet, ColumnGroup
from typedframes.pandas import PandasFrame


class SensorData(BaseSchema):
    timestamp = Column(type=str)
    temperature = ColumnSet(type=float, members=r"temp_\d+", regex=True)
    humidity = ColumnSet(type=float, members=r"humidity_\d+", regex=True)

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


df = PandasFrame.read_csv("sensors.csv", SensorData)

# Clean, type-safe operations
avg_temp_per_row = df[SensorData.temperature].mean(axis=1)
all_readings_stats = df[SensorData.all_sensors].describe()

Multi-Step Pipeline

from typedframes import BaseSchema, Column
from typedframes.pandas import PandasFrame


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: PandasFrame[RawSales]) -> PandasFrame[AggregatedSales]:
  result = df.groupby(RawSales.date).agg({
    str(RawSales.price): 'sum',
    str(RawSales.quantity): 'sum',
    }).reset_index()

    result.columns = ['date', 'total_revenue', 'total_quantity']
    return PandasFrame.from_schema(result, AggregatedSales)


# Type-safe pipeline
raw = PandasFrame.read_csv("sales.csv", RawSales)
aggregated = aggregate_daily(raw)


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

Polars Performance Pipeline

from typedframes import BaseSchema, Column
from typedframes.polars import PolarsFrame
import polars as pl


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


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


# Polars handles large files efficiently
df = PolarsFrame.read_csv("huge_file.csv", LargeDataset)
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: PandasFrame.from_schema() 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.

Explicit Backend Types

We use explicit PandasFrame and PolarsFrame types because:

• Pandas and polars have different APIs
• Type safety requires knowing which methods are available
• Being explicit prevents bugs

Trade-offs we avoid:

• ❌ "Universal DataFrame" abstractions (you lose features)
• ❌ Implicit backend detection (runtime errors)
• ❌ Lowest-common-denominator APIs

The reason to choose polars over pandas is its lazy evaluation, native parallelism, and expressive query syntax. Abstraction layers often must expose a lowest-common-denominator API. By using explicit backend types instead, typedframes lets you use each library's full, native API while still getting schema-level type safety.

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 typedframes.polars import PolarsFrame

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

def process(df: PolarsFrame[SalesData]) -> PolarsFrame[SalesData]:
    return df.filter(df['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:

• Schema definition API
• Pandas support
• Polars support
• Mypy plugin
• Standalone checker (Rust)
• Explicit backend types
• Merge/join schema preservation
• Schema Composition (multiple inheritance, SchemaA + SchemaB)
• Column name collision warnings
• Pandera integration (to_pandera_schema())

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 the appropriate type (PandasFrame or PolarsFrame) 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: The __getitem__ overloads on PandasFrame mean that any type checker (mypy, pyright, ty) understands df[Schema.column] returns pd.Series and df[Schema.column_set] returns pd.DataFrame — no plugin or stubs needed. 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: 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.

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.

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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.

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Questions? Issues? Ideas? Open an issue

Ready to catch DataFrame bugs before runtime? pip install typedframes