diff --git a/README.md b/README.md
index eaa4260..fc6fff5 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@ This package is a simple order book matching engine implementation in Python. It
 - price-time priority
 - limit and market orders
 - order cancellation and expiration
-- conversion into pandas DataFrame of orders, executed trades, order book summary
+- conversion into polars LazyFrame of orders, executed trades, order book summary
 
 ## Install
 
@@ -30,7 +30,7 @@ pip install order-matching
 ```python
 >>> from datetime import datetime, timedelta
 >>> from pprint import pp
->>> import pandas as pd
+>>> import polars as pl
 
 >>> from order_matching.matching_engine import MatchingEngine
 >>> from order_matching.order import LimitOrder
diff --git a/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/.openspec.yaml b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/.openspec.yaml
new file mode 100644
index 0000000..8e26fbe
--- /dev/null
+++ b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/.openspec.yaml
@@ -0,0 +1,2 @@
+schema: spec-driven
+created: 2026-07-02
diff --git a/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/design.md b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/design.md
new file mode 100644
index 0000000..46e7c54
--- /dev/null
+++ b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/design.md
@@ -0,0 +1,160 @@
+## Context
+
+The OrderBookMatchingEngine currently uses pandas for DataFrame operations in three key areas:
+1. `Orders.to_frame()` - converting order collections to DataFrames
+2. `ExecutedTrades.to_frame()` - converting trade collections to DataFrames
+3. `OrderBook.summary()` - generating order book summary DataFrames
+
+All DataFrames are validated using pandera schemas (`OrderDataSchema`, `TradeDataSchema`, `OrderBookSummarySchema`) that define column types, constraints, and validation rules. These schemas are critical for downstream code and analysis pipelines.
+
+**Current Implementation:**
+- Uses `pandas.DataFrame.from_records()` to convert dataclass instances
+- Uses pandas-specific operations like `.assign()` with lambda functions
+- Type hints use `pandera.typing.pandas.DataFrame[Schema]`
+- Schema validation uses pandera's pandas backend
+
+**Constraints:**
+- Must maintain 100% backward compatibility with existing DataFrame schemas
+- Cannot break existing tests or downstream code that consumes these DataFrames
+
+## Goals / Non-Goals
+
+**Goals:**
+- Replace pandas with polars for all DataFrame conversion operations
+- Maintain identical DataFrame schemas (column names, types, constraints)
+- Preserve all pandera validation behavior
+- Improve performance through polars' optimized operations
+- Update type hints to reflect polars usage while keeping schema validation
+- Use LazyFrame and lazy evaluation whenever possible
+
+**Non-Goals:**
+- Changing the pandera schema definitions or validation rules
+- Modifying the API surface (method signatures stay the same)
+- Migrating internal data structures from dataclasses
+- Supporting both pandas and polars simultaneously (full migration only)
+- Changing behavior of existing methods beyond the DataFrame type
+
+## Decisions
+
+### Decision 1: Use polars DataFrame construction directly
+**Rationale:** Polars provides native DataFrame construction from dictionaries and records. Instead of using pandas `from_records()`, we'll use `pl.LazyFrame()` constructor.
+
+**Implementation:**
+```python
+# Before (pandas):
+pd.DataFrame.from_records([asdict(order) for order in orders])
+
+# After (polars):
+pl.LazyFrame([asdict(order) for order in orders])
+```
+
+**Alternatives considered:**
+- Converting pandas to polars via arrow - rejected as unnecessary intermediate step
+
+### Decision 2: Handle type conversions with polars expressions
+**Rationale:** Pandas uses `.assign()` with lambda functions for type conversions. Polars uses `.with_columns()` and expressions.
+
+**Implementation:**
+```python
+# Before (pandas):
+.assign(
+    side=lambda df: df['side'].astype(str),
+    timestamp=lambda df: pd.to_datetime(df['timestamp'])
+)
+
+# After (polars):
+.with_columns([
+    pl.col('side').cast(pl.Utf8),
+    pl.col('timestamp').cast(pl.Datetime)
+])
+```
+
+**Alternatives considered:**
+- Keep using assign-style API via polars extensions - rejected for being less idiomatic
+
+### Decision 3: Update pandera imports to support polars
+**Rationale:** Pandera 0.17.0+ supports both pandas and polars backends through separate import paths.
+
+**Implementation:**
+```python
+# Before:
+from pandera.pandas import DataFrameModel
+from pandera.typing.pandas import DataFrame
+
+# After:
+from pandera.polars import DataFrameModel
+from pandera.typing.polars import DataFrame
+from pandera.typing.polars import LazyFrame
+```
+
+**Alternatives considered:**
+- Maintaining dual compatibility - rejected per non-goals (full migration only)
+- Creating polars-specific schema copies - rejected to avoid duplication
+
+### Decision 4: Handle empty DataFrames with explicit schema
+**Rationale:** Empty polars DataFrames need explicit schema definition to maintain type information.
+
+**Implementation:**
+```python
+# Empty DataFrame case:
+if len(collection) == 0:
+    return pl.DataFrame(schema={
+        'column1': pl.Float64,
+        'column2': pl.Utf8,
+        # ... all columns
+    })
+```
+
+**Alternatives considered:**
+- Returning empty DataFrame without schema - rejected because pandera validation would fail
+- Using schema inference from empty records - rejected as unreliable
+
+## Risks / Trade-offs
+
+**Risk:** Pandera polars support may have edge cases or bugs
+- **Mitigation:** Comprehensive test suite covers all schema validation scenarios. Run full test suite before merging.
+
+**Risk:** Polars API differences may cause subtle behavioral changes
+- **Mitigation:** Carefully review all DataFrame operations and validate output schemas match exactly. Add explicit type casts where needed.
+
+**Risk:** Breaking change for downstream code expecting pandas DataFrames
+- **Mitigation:** This is intentional per requirements. Document clearly in CHANGELOG and update README examples.
+
+**Trade-off:** Lose pandas ecosystem compatibility
+- **Impact:** Code using these DataFrames must work with polars instead of pandas
+- **Benefit:** Significantly better performance, especially for large order books
+
+**Trade-off:** Increased dependency on polars library maturity
+- **Impact:** Polars is newer than pandas, potential for API changes
+- **Benefit:** Polars is stable (1.0+ released), actively developed, and widely adopted
+
+## Migration Plan
+
+**Phase 1: Update schemas**
+1. Change imports in `src/order_matching/schemas.py` from pandas to polars
+2. Update type hints to use `pandera.typing.polars.DataFrame`
+3. Verify schemas still define the same constraints
+
+**Phase 2: Update conversion methods**
+1. Update `Orders.to_frame()` in `orders.py`
+2. Update `ExecutedTrades.to_frame()` in `executed_trades.py`
+3. Update `OrderBook.summary()` in `order_book.py`
+4. Ensure each method handles empty cases with explicit schemas
+
+**Phase 3: Update tests**
+1. Run existing test suite - most should still pass
+2. Fix any tests that check for pandas-specific behavior
+3. Add new tests if needed for polars-specific edge cases
+
+**Phase 4: Validate**
+1. Run full test suite: `uv run pytest`
+2. Run linter and formatter: `uv run prek run -v --show-diff-on-failure --all-files`
+
+**Rollback strategy:**
+- If tests fail after updates, revert changes file by file
+- Git branch allows clean rollback to pandas implementation
+- No database or persistent state changes, so rollback is safe
+
+## Open Questions
+
+None - requirements and approach are clear.
diff --git a/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/proposal.md b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/proposal.md
new file mode 100644
index 0000000..43c370a
--- /dev/null
+++ b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/proposal.md
@@ -0,0 +1,36 @@
+## Why
+
+The project currently uses pandas for DataFrame operations in orders, trades, and order book summaries.
+Polars offers significantly better performance and a more modern API, but the migration must maintain 100% schema compatibility with existing pandera validations to avoid breaking downstream code or analysis pipelines.
+
+## What Changes
+
+- Replace pandas DataFrame operations with polars equivalents across all data conversion methods
+- Maintain pandera schema validation compatibility - all schemas must be converted to work polars DataFrames
+- Update `to_frame()` methods in `Orders`, `ExecutedTrades`, and `OrderBook` classes to return polars DataFrames
+- Ensure column names, dtypes, and constraints remain identical to current pandera-validated schemas
+- Update type hints from `pandera.typing.pandas.DataFrame` to support polars while preserving schema validation
+
+## Capabilities
+
+### New Capabilities
+- `polars-dataframe-conversion`: Converting internal data structures (Orders, ExecutedTrades, OrderBook) to polars DataFrames with schema validation
+- `pandera-polars-validation`: Schema validation using pandera with polars backend to ensure schema compatibility
+
+### Modified Capabilities
+
+## Impact
+
+**Code:**
+- `src/order_matching/orders.py`: Update `to_frame()` method to use polars
+- `src/order_matching/executed_trades.py`: Update `to_frame()` method to use polars
+- `src/order_matching/order_book.py`: Update `summary()` method to use polars
+- `src/order_matching/schemas.py`: Update schema definitions to support polars DataFrames
+
+**Tests:**
+- All existing tests that validate DataFrame schemas must continue to pass
+- Tests in `tests/test_order.py`, `tests/test_trade.py`, `tests/test_executed_trades.py` that check DataFrame output
+- Documentation examples in README.md that demonstrate DataFrame usage
+
+**API:**
+- Return type changes from pandas DataFrame to polars DataFrame, but schema validation ensures column names and dtypes remain compatible
diff --git a/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/specs/pandera-polars-validation/spec.md b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/specs/pandera-polars-validation/spec.md
new file mode 100644
index 0000000..9c871ce
--- /dev/null
+++ b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/specs/pandera-polars-validation/spec.md
@@ -0,0 +1,51 @@
+## ADDED Requirements
+
+### Requirement: Validate polars DataFrames with pandera schemas
+The system SHALL use pandera to validate polars DataFrames against existing schema definitions (`OrderDataSchema`, `TradeDataSchema`, `OrderBookSummarySchema`).
+
+#### Scenario: Valid polars DataFrame passes schema validation
+- **WHEN** a polars DataFrame with correct schema is validated against `OrderDataSchema`, `TradeDataSchema`, or `OrderBookSummarySchema`
+- **THEN** validation succeeds without errors
+
+#### Scenario: Invalid polars DataFrame fails schema validation
+- **WHEN** a polars DataFrame with incorrect schema (wrong columns, wrong types, or constraint violations) is validated
+- **THEN** validation raises a pandera validation error with details
+
+### Requirement: Maintain schema constraint validation
+The system SHALL validate all pandera Field constraints (gt, ge, isin, unique, nullable) on polars DataFrames.
+
+#### Scenario: Price constraint validation
+- **WHEN** a polars DataFrame contains price values <= 0
+- **THEN** validation against schemas with `price: Field(gt=0)` fails
+
+#### Scenario: Enum value constraint validation
+- **WHEN** a polars DataFrame contains invalid enum values in side/execution/status columns
+- **THEN** validation against schemas with `Field(isin=[...])` fails
+
+#### Scenario: Unique constraint validation
+- **WHEN** a polars DataFrame for OrderBookSummary contains duplicate price values
+- **THEN** validation against `OrderBookSummarySchema` with `price: Field(unique=True)` fails
+
+#### Scenario: Nullable constraint validation
+- **WHEN** a polars DataFrame contains null values in non-nullable columns
+- **THEN** validation fails
+- **WHEN** a polars DataFrame contains null values in nullable columns (e.g., expiration)
+- **THEN** validation succeeds
+
+### Requirement: Update schema type hints for polars
+The system SHALL update type hints in schema definitions to support both pandas and polars DataFrames via pandera's typing system.
+
+#### Scenario: Schema works with pandera.typing.polars.DataFrame and pandera.typing.polars.LazyFrame
+- **WHEN** schemas are imported and used with polars DataFrames
+- **THEN** type hints correctly indicate polars DataFrame compatibility
+
+### Requirement: Preserve strict mode behavior
+The system SHALL maintain strict mode validation configured in schema Config classes when validating polars DataFrames.
+
+#### Scenario: Strict schema rejects extra columns
+- **WHEN** a polars DataFrame with extra columns is validated against a schema with `strict = True`
+- **THEN** validation fails
+
+#### Scenario: Strict schema accepts exact columns
+- **WHEN** a polars DataFrame with exact required columns is validated against a schema with `strict = True`
+- **THEN** validation succeeds
diff --git a/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/specs/polars-dataframe-conversion/spec.md b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/specs/polars-dataframe-conversion/spec.md
new file mode 100644
index 0000000..25f1790
--- /dev/null
+++ b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/specs/polars-dataframe-conversion/spec.md
@@ -0,0 +1,58 @@
+## ADDED Requirements
+
+### Requirement: Convert Orders to polars DataFrame
+The system SHALL convert `Orders` objects to polars DataFrames using the `to_frame()` method with all columns matching the `OrderDataSchema`.
+
+#### Scenario: Convert empty Orders to DataFrame
+- **WHEN** `to_frame()` is called on an empty `Orders` object
+- **THEN** system returns an empty polars DataFrame
+
+#### Scenario: Convert populated Orders to DataFrame
+- **WHEN** `to_frame()` is called on `Orders` containing one or more orders
+- **THEN** system returns a polars DataFrame with columns: timestamp, expiration, order_id, trader_id, side, execution, status, price, size, price_number_of_digits
+- **THEN** all string enum fields (side, execution, status) are converted to string representations
+- **THEN** timestamp and expiration columns are datetime type
+
+### Requirement: Convert ExecutedTrades to polars DataFrame
+The system SHALL convert `ExecutedTrades` objects to polars DataFrames using the `to_frame()` method with all columns matching the `TradeDataSchema`.
+
+#### Scenario: Convert empty ExecutedTrades to DataFrame
+- **WHEN** `to_frame()` is called on an empty `ExecutedTrades` object
+- **THEN** system returns an empty polars DataFrame
+
+#### Scenario: Convert populated ExecutedTrades to DataFrame
+- **WHEN** `to_frame()` is called on `ExecutedTrades` containing one or more trades
+- **THEN** system returns a polars DataFrame with columns: timestamp, incoming_order_id, book_order_id, trade_id, side, execution, price, size
+- **THEN** string enum fields (side, execution) are converted to string representations
+- **THEN** timestamp column is datetime type
+
+### Requirement: Convert OrderBook to polars DataFrame summary
+The system SHALL convert `OrderBook` objects to polars DataFrames using the `summary()` method with all columns matching the `OrderBookSummarySchema`.
+
+#### Scenario: Convert empty OrderBook to summary DataFrame
+- **WHEN** `summary()` is called on an empty `OrderBook`
+- **THEN** system returns an empty polars DataFrame
+
+#### Scenario: Convert OrderBook with bids and offers to summary
+- **WHEN** `summary()` is called on `OrderBook` containing bids and/or offers
+- **THEN** system returns a polars DataFrame with columns: side, price, size, count
+- **THEN** each unique price level is represented as a single row
+- **THEN** count column is integer type
+- **THEN** side column contains "BUY" or "SELL" string values
+
+### Requirement: Maintain DataFrame schema compatibility
+The system SHALL ensure polars DataFrames produced by conversion methods have identical column names, dtypes, and value formats as the pandas DataFrames they replace.
+
+#### Scenario: Column names match exactly
+- **WHEN** any conversion method produces a polars DataFrame
+- **THEN** column names MUST match the corresponding pandera schema field names exactly
+
+#### Scenario: Data types are compatible
+- **WHEN** any conversion method produces a polars DataFrame
+- **THEN** numeric columns (price, size, count) use appropriate numeric types
+- **THEN** datetime columns use polars datetime type
+- **THEN** string columns (order_id, trader_id, trade_id, side, execution, status) use string type
+
+#### Scenario: Empty DataFrames have correct schema
+- **WHEN** conversion methods are called on empty collections
+- **THEN** resulting empty DataFrames MUST still have the correct column names and types defined
diff --git a/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/tasks.md b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/tasks.md
new file mode 100644
index 0000000..a9569f8
--- /dev/null
+++ b/openspec/changes/archive/2026-07-02-migrate-pandas-to-polars/tasks.md
@@ -0,0 +1,57 @@
+## 1. Update Schema Definitions
+
+- [x] 1.1 Update imports in src/order_matching/schemas.py from `pandera.pandas` to `pandera.polars`
+- [x] 1.2 Update DataFrame type import from `pandera.typing.pandas` to `pandera.typing.polars`
+- [x] 1.3 Ensure all Field constraints (gt, ge, isin, unique, nullable) are preserved
+- [x] 1.4 Verify Config classes with strict=True remain unchanged
+- [x] 1.5 Run mypy to check type annotations are correct
+
+## 2. Update Orders.to_frame() Method
+
+- [x] 2.1 Replace pandas import with polars in src/order_matching/orders.py
+- [x] 2.2 Update empty DataFrame case to return polars DataFrame with explicit OrderDataSchema schema
+- [x] 2.3 Replace `pd.DataFrame.from_records()` with `polars.DataFrame()` constructor
+- [x] 2.4 Replace `.assign()` with `.with_columns()` for type conversions (side, execution, status to string)
+- [x] 2.5 Update timestamp and expiration columns to use polars datetime casting
+- [x] 2.6 Update return type hint to `DataFrame[OrderDataSchema]` using polars typing
+- [x] 2.7 Verify method signature remains unchanged (backward compatible API)
+
+## 3. Update ExecutedTrades.to_frame() Method
+
+- [x] 3.1 Replace pandas import with polars in src/order_matching/executed_trades.py
+- [x] 3.2 Update empty DataFrame case to return polars DataFrame with explicit TradeDataSchema schema
+- [x] 3.3 Replace `pd.DataFrame.from_records()` with `polars.DataFrame()` constructor
+- [x] 3.4 Replace `.assign()` with `.with_columns()` for type conversions (side, execution to string)
+- [x] 3.5 Update timestamp column to use polars datetime casting
+- [x] 3.6 Update return type hint to `DataFrame[TradeDataSchema]` using polars typing
+- [x] 3.7 Verify method signature remains unchanged
+
+## 4. Update OrderBook.summary() Method
+
+- [x] 4.1 Replace pandas import with polars in src/order_matching/order_book.py
+- [x] 4.2 Replace pandas DataFrame construction in summary() with polars DataFrame
+- [x] 4.3 Update count column casting to use polars integer type
+- [x] 4.4 Replace `pd.concat()` with polars concatenation (use `pl.concat()`)
+- [x] 4.5 Update return type hint to `DataFrame[OrderBookSummarySchema]` using polars typing
+- [x] 4.6 Verify empty OrderBook case returns empty polars DataFrame
+- [x] 4.7 Update `get_imbalance()` method to work with polars DataFrames
+
+## 5. Run Tests and Validate
+
+- [x] 5.1 Run full test suite: `uv run pytest`
+- [x] 5.2 Fix any tests in tests/test_order.py that check pandas-specific behavior
+- [x] 5.3 Fix any tests in tests/test_trade.py that check pandas-specific behavior
+- [x] 5.4 Fix any tests in tests/test_executed_trades.py that check pandas-specific behavior
+- [x] 5.5 Verify all pandera schema validations still pass
+- [x] 5.6 Check test coverage remains at current level
+
+## 6. Type Checking and Linting
+
+- [x] 6.1 Run type checker: `uv run prek run -v --show-diff-on-failure --all-files`
+- [x] 6.2 Fix any type errors related to polars DataFrame usage
+
+## 7. Documentation and Examples
+
+- [x] 7.1 Update README.md code examples to reflect polars DataFrames
+- [x] 7.2 Update any docstrings that mention pandas to reference polars
+- [x] 7.3 Verify documentation builds successfully: `uv run mkdocs build`
diff --git a/openspec/config.yaml b/openspec/config.yaml
new file mode 100644
index 0000000..392946c
--- /dev/null
+++ b/openspec/config.yaml
@@ -0,0 +1,20 @@
+schema: spec-driven
+
+# Project context (optional)
+# This is shown to AI when creating artifacts.
+# Add your tech stack, conventions, style guides, domain knowledge, etc.
+# Example:
+#   context: |
+#     Tech stack: TypeScript, React, Node.js
+#     We use conventional commits
+#     Domain: e-commerce platform
+
+# Per-artifact rules (optional)
+# Add custom rules for specific artifacts.
+# Example:
+#   rules:
+#     proposal:
+#       - Keep proposals under 500 words
+#       - Always include a "Non-goals" section
+#     tasks:
+#       - Break tasks into chunks of max 2 hours
diff --git a/openspec/specs/pandera-polars-validation/spec.md b/openspec/specs/pandera-polars-validation/spec.md
new file mode 100644
index 0000000..9c871ce
--- /dev/null
+++ b/openspec/specs/pandera-polars-validation/spec.md
@@ -0,0 +1,51 @@
+## ADDED Requirements
+
+### Requirement: Validate polars DataFrames with pandera schemas
+The system SHALL use pandera to validate polars DataFrames against existing schema definitions (`OrderDataSchema`, `TradeDataSchema`, `OrderBookSummarySchema`).
+
+#### Scenario: Valid polars DataFrame passes schema validation
+- **WHEN** a polars DataFrame with correct schema is validated against `OrderDataSchema`, `TradeDataSchema`, or `OrderBookSummarySchema`
+- **THEN** validation succeeds without errors
+
+#### Scenario: Invalid polars DataFrame fails schema validation
+- **WHEN** a polars DataFrame with incorrect schema (wrong columns, wrong types, or constraint violations) is validated
+- **THEN** validation raises a pandera validation error with details
+
+### Requirement: Maintain schema constraint validation
+The system SHALL validate all pandera Field constraints (gt, ge, isin, unique, nullable) on polars DataFrames.
+
+#### Scenario: Price constraint validation
+- **WHEN** a polars DataFrame contains price values <= 0
+- **THEN** validation against schemas with `price: Field(gt=0)` fails
+
+#### Scenario: Enum value constraint validation
+- **WHEN** a polars DataFrame contains invalid enum values in side/execution/status columns
+- **THEN** validation against schemas with `Field(isin=[...])` fails
+
+#### Scenario: Unique constraint validation
+- **WHEN** a polars DataFrame for OrderBookSummary contains duplicate price values
+- **THEN** validation against `OrderBookSummarySchema` with `price: Field(unique=True)` fails
+
+#### Scenario: Nullable constraint validation
+- **WHEN** a polars DataFrame contains null values in non-nullable columns
+- **THEN** validation fails
+- **WHEN** a polars DataFrame contains null values in nullable columns (e.g., expiration)
+- **THEN** validation succeeds
+
+### Requirement: Update schema type hints for polars
+The system SHALL update type hints in schema definitions to support both pandas and polars DataFrames via pandera's typing system.
+
+#### Scenario: Schema works with pandera.typing.polars.DataFrame and pandera.typing.polars.LazyFrame
+- **WHEN** schemas are imported and used with polars DataFrames
+- **THEN** type hints correctly indicate polars DataFrame compatibility
+
+### Requirement: Preserve strict mode behavior
+The system SHALL maintain strict mode validation configured in schema Config classes when validating polars DataFrames.
+
+#### Scenario: Strict schema rejects extra columns
+- **WHEN** a polars DataFrame with extra columns is validated against a schema with `strict = True`
+- **THEN** validation fails
+
+#### Scenario: Strict schema accepts exact columns
+- **WHEN** a polars DataFrame with exact required columns is validated against a schema with `strict = True`
+- **THEN** validation succeeds
diff --git a/openspec/specs/polars-dataframe-conversion/spec.md b/openspec/specs/polars-dataframe-conversion/spec.md
new file mode 100644
index 0000000..25f1790
--- /dev/null
+++ b/openspec/specs/polars-dataframe-conversion/spec.md
@@ -0,0 +1,58 @@
+## ADDED Requirements
+
+### Requirement: Convert Orders to polars DataFrame
+The system SHALL convert `Orders` objects to polars DataFrames using the `to_frame()` method with all columns matching the `OrderDataSchema`.
+
+#### Scenario: Convert empty Orders to DataFrame
+- **WHEN** `to_frame()` is called on an empty `Orders` object
+- **THEN** system returns an empty polars DataFrame
+
+#### Scenario: Convert populated Orders to DataFrame
+- **WHEN** `to_frame()` is called on `Orders` containing one or more orders
+- **THEN** system returns a polars DataFrame with columns: timestamp, expiration, order_id, trader_id, side, execution, status, price, size, price_number_of_digits
+- **THEN** all string enum fields (side, execution, status) are converted to string representations
+- **THEN** timestamp and expiration columns are datetime type
+
+### Requirement: Convert ExecutedTrades to polars DataFrame
+The system SHALL convert `ExecutedTrades` objects to polars DataFrames using the `to_frame()` method with all columns matching the `TradeDataSchema`.
+
+#### Scenario: Convert empty ExecutedTrades to DataFrame
+- **WHEN** `to_frame()` is called on an empty `ExecutedTrades` object
+- **THEN** system returns an empty polars DataFrame
+
+#### Scenario: Convert populated ExecutedTrades to DataFrame
+- **WHEN** `to_frame()` is called on `ExecutedTrades` containing one or more trades
+- **THEN** system returns a polars DataFrame with columns: timestamp, incoming_order_id, book_order_id, trade_id, side, execution, price, size
+- **THEN** string enum fields (side, execution) are converted to string representations
+- **THEN** timestamp column is datetime type
+
+### Requirement: Convert OrderBook to polars DataFrame summary
+The system SHALL convert `OrderBook` objects to polars DataFrames using the `summary()` method with all columns matching the `OrderBookSummarySchema`.
+
+#### Scenario: Convert empty OrderBook to summary DataFrame
+- **WHEN** `summary()` is called on an empty `OrderBook`
+- **THEN** system returns an empty polars DataFrame
+
+#### Scenario: Convert OrderBook with bids and offers to summary
+- **WHEN** `summary()` is called on `OrderBook` containing bids and/or offers
+- **THEN** system returns a polars DataFrame with columns: side, price, size, count
+- **THEN** each unique price level is represented as a single row
+- **THEN** count column is integer type
+- **THEN** side column contains "BUY" or "SELL" string values
+
+### Requirement: Maintain DataFrame schema compatibility
+The system SHALL ensure polars DataFrames produced by conversion methods have identical column names, dtypes, and value formats as the pandas DataFrames they replace.
+
+#### Scenario: Column names match exactly
+- **WHEN** any conversion method produces a polars DataFrame
+- **THEN** column names MUST match the corresponding pandera schema field names exactly
+
+#### Scenario: Data types are compatible
+- **WHEN** any conversion method produces a polars DataFrame
+- **THEN** numeric columns (price, size, count) use appropriate numeric types
+- **THEN** datetime columns use polars datetime type
+- **THEN** string columns (order_id, trader_id, trade_id, side, execution, status) use string type
+
+#### Scenario: Empty DataFrames have correct schema
+- **WHEN** conversion methods are called on empty collections
+- **THEN** resulting empty DataFrames MUST still have the correct column names and types defined
diff --git a/pyproject.toml b/pyproject.toml
index 8e2e29e..ca17706 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,8 +19,9 @@ classifiers = [
 requires-python = ">=3.10"
 dependencies = [
     "faker>=33.0.0",
-    "pandas>=2.2.3",
-    "pandera>=0.21.0",
+    "numpy>=2.2.6",
+    "pandera[polars]>=0.21.0",
+    "polars>=1.42.1",
 ]
 
 [dependency-groups]
diff --git a/src/order_matching/executed_trades.py b/src/order_matching/executed_trades.py
index 1f0be48..e84f9bc 100644
--- a/src/order_matching/executed_trades.py
+++ b/src/order_matching/executed_trades.py
@@ -5,8 +5,8 @@
 from datetime import datetime
 from typing import cast
 
-import pandas as pd
-from pandera.typing.pandas import DataFrame
+import polars as pl
+from pandera.typing.polars import LazyFrame
 
 from order_matching.schemas import TradeDataSchema
 from order_matching.trade import Trade
@@ -57,27 +57,23 @@ def get(self, timestamp: datetime) -> list[Trade]:
         """
         return self._trades[timestamp]
 
-    def to_frame(self) -> DataFrame[TradeDataSchema]:
-        """Get pandas DataFrame of all stored trades.
+    def to_frame(self) -> LazyFrame[TradeDataSchema]:
+        """Get polars DataFrame of all stored trades.
 
         Returns
         -------
         DataFrame[TradeDataSchema]
-            pandas DataFrame of all stored trades
+            polars DataFrame of all stored trades
         """
         trades = self.trades
         if len(trades) == 0:
-            return cast(DataFrame[TradeDataSchema], pd.DataFrame())
+            return cast(LazyFrame[TradeDataSchema], TradeDataSchema.empty().lazy())
         else:
-            return cast(
-                DataFrame[TradeDataSchema],
-                pd.DataFrame.from_records([asdict(trade) for trade in trades]).assign(
-                    **{
-                        TradeDataSchema.side: lambda df: df[TradeDataSchema.side].astype(str),
-                        TradeDataSchema.execution: lambda df: df[TradeDataSchema.execution].astype(str),
-                    }
-                ),
-            )
+            data = [asdict(trade) for trade in trades]
+            for d in data:
+                d[TradeDataSchema.side] = d[TradeDataSchema.side].name
+                d[TradeDataSchema.execution] = d[TradeDataSchema.execution].name
+            return cast(LazyFrame[TradeDataSchema], pl.LazyFrame(data))
 
     def __add__(self, other: ExecutedTrades) -> ExecutedTrades:
         trades = ExecutedTrades()
diff --git a/src/order_matching/order_book.py b/src/order_matching/order_book.py
index dd2dc3d..d21585b 100644
--- a/src/order_matching/order_book.py
+++ b/src/order_matching/order_book.py
@@ -2,8 +2,8 @@
 from datetime import datetime
 from typing import cast
 
-import pandas as pd
-from pandera.typing import DataFrame
+import polars as pl
+from pandera.typing.polars import LazyFrame
 
 from order_matching.order import Order
 from order_matching.orders import Orders
@@ -49,37 +49,54 @@ def remove(self, incoming_order: Order) -> None:
         if len(self.orders_by_expiration[incoming_order.expiration]) == 0:
             self.orders_by_expiration.pop(incoming_order.expiration)
 
-    def summary(self) -> DataFrame[OrderBookSummarySchema]:
-        """Summary of the order book as a pandas DataFrame.
+    def summary(self) -> LazyFrame[OrderBookSummarySchema]:
+        """Summary of the order book as a polars LazyFrame.
 
         Returns
         -------
-        DataFrame[OrderBookSummarySchema]
-            Summary of the order book as a pandas DataFrame
+        LazyFrame[OrderBookSummarySchema]
+            Summary of the order book as a polars LazyFrame
         """
-        bids = pd.DataFrame(
-            {
-                OrderBookSummarySchema.side: Side.BUY.name,
-                OrderBookSummarySchema.price: self._get_bid_prices(),
-                OrderBookSummarySchema.size: self._get_bid_sizes(),
-                OrderBookSummarySchema.count: self._get_bid_counts(),
-            }
+        bid_prices = self._get_bid_prices()
+        offer_prices = self._get_offer_prices()
+        empty_df = OrderBookSummarySchema.empty().lazy()
+
+        if len(bid_prices) == 0 and len(offer_prices) == 0:
+            return cast(LazyFrame[OrderBookSummarySchema], empty_df)
+
+        dtypes = [
+            pl.col(OrderBookSummarySchema.price).cast(pl.Float64),
+            pl.col(OrderBookSummarySchema.size).cast(pl.Float64),
+            pl.col(OrderBookSummarySchema.count).cast(pl.Int64),
+        ]
+        bids = (
+            pl.LazyFrame(
+                {
+                    OrderBookSummarySchema.side: [Side.BUY.name] * len(bid_prices),
+                    OrderBookSummarySchema.price: bid_prices,
+                    OrderBookSummarySchema.size: self._get_bid_sizes(),
+                    OrderBookSummarySchema.count: self._get_bid_counts(),
+                }
+            ).with_columns(dtypes)
+            if len(bid_prices) > 0
+            else empty_df
         )
-        offers = pd.DataFrame(
-            {
-                OrderBookSummarySchema.side: Side.SELL.name,
-                OrderBookSummarySchema.price: self._get_offer_prices(),
-                OrderBookSummarySchema.size: self._get_offer_sizes(),
-                OrderBookSummarySchema.count: self._get_offer_counts(),
-            }
-        )
-        return cast(
-            DataFrame[OrderBookSummarySchema],
-            pd.concat([bids, offers], ignore_index=True).assign(
-                **{OrderBookSummarySchema.count: lambda df: df[OrderBookSummarySchema.count].astype(int)}
-            ),
+
+        offers = (
+            pl.LazyFrame(
+                {
+                    OrderBookSummarySchema.side: [Side.SELL.name] * len(offer_prices),
+                    OrderBookSummarySchema.price: offer_prices,
+                    OrderBookSummarySchema.size: self._get_offer_sizes(),
+                    OrderBookSummarySchema.count: self._get_offer_counts(),
+                }
+            ).with_columns(dtypes)
+            if len(offer_prices) > 0
+            else empty_df
         )
 
+        return cast(LazyFrame[OrderBookSummarySchema], pl.concat([bids, offers], how="vertical"))
+
     @property
     def current_price(self) -> float:
         """Current market price."""
@@ -186,23 +203,36 @@ def get_imbalance(self, price_range: float = 0.1) -> float:
             Market imbalance indicator
         """
         summary = self.summary()
-        if summary.empty:
+        if self._is_empty(summary):
             return 0
-        elif summary[summary[OrderBookSummarySchema.side] == Side.SELL.name].empty:
+        elif self._is_empty(summary.filter(pl.col(OrderBookSummarySchema.side) == Side.SELL.name)):
             return 1
-        elif summary[summary[OrderBookSummarySchema.side] == Side.BUY.name].empty:
+        elif self._is_empty(summary.filter(pl.col(OrderBookSummarySchema.side) == Side.BUY.name)):
             return -1
         else:
             return self._get_non_trivial_imbalance(price_range=price_range)
 
+    @staticmethod
+    def _is_empty(df: pl.LazyFrame) -> bool:
+        return df.select(pl.col(OrderBookSummarySchema.side).count()).collect().item() == 0
+
     def _get_non_trivial_imbalance(self, price_range: float) -> float:
         schema = OrderBookSummarySchema
         upper_bound = self.current_price + price_range
         lower_bound = self.current_price - price_range
-        summary_subset = self.summary().pipe(lambda df: df[df[schema.price].between(lower_bound, upper_bound)])
-        summary_subset = cast(pd.DataFrame, summary_subset)
-        buy_volume = summary_subset.loc[summary_subset[schema.side] == Side.BUY.name, schema.size].sum()
-        sell_volume = summary_subset.loc[summary_subset[schema.side] == Side.SELL.name, schema.size].sum()
+        summary_subset = self.summary().filter(pl.col(schema.price).is_between(lower_bound, upper_bound))
+        buy_volume = (
+            summary_subset.filter(pl.col(schema.side) == Side.BUY.name)
+            .select(pl.col(schema.size).sum())
+            .collect()
+            .item()
+        )
+        sell_volume = (
+            summary_subset.filter(pl.col(schema.side) == Side.SELL.name)
+            .select(pl.col(schema.size).sum())
+            .collect()
+            .item()
+        )
         if buy_volume + sell_volume > 0:
             return (buy_volume - sell_volume) / (buy_volume + sell_volume)
         else:
diff --git a/src/order_matching/orders.py b/src/order_matching/orders.py
index 156b454..89054dc 100644
--- a/src/order_matching/orders.py
+++ b/src/order_matching/orders.py
@@ -3,8 +3,8 @@
 from dataclasses import asdict
 from typing import Iterator, Sequence, cast
 
-import pandas as pd
-from pandera.typing.pandas import DataFrame
+import polars as pl
+from pandera.typing.polars import LazyFrame
 
 from order_matching.order import Order
 from order_matching.schemas import OrderDataSchema
@@ -55,32 +55,22 @@ def remove(self, orders: list[Order]) -> None:
             if order_to_remove.order_id in self._order_ids:
                 self.orders.remove(self._get_order(order_id=order_to_remove.order_id))
 
-    def to_frame(self) -> DataFrame[OrderDataSchema]:
-        """Get pandas DataFrame with all orders in the storage.
+    def to_frame(self) -> LazyFrame[OrderDataSchema]:
+        """Get polars LazyFrame with all orders in the storage.
 
         Returns
         -------
-        DataFrame[OrderDataSchema]
+        LazyFrame[OrderDataSchema]
         """
         if len(self.orders) == 0:
-            return cast(DataFrame[OrderDataSchema], pd.DataFrame())
+            return cast(LazyFrame[OrderDataSchema], OrderDataSchema.empty().lazy())
         else:
-            return cast(
-                DataFrame[OrderDataSchema],
-                pd.DataFrame.from_records([asdict(order) for order in self.orders]).assign(
-                    **{
-                        OrderDataSchema.side: lambda df: df[OrderDataSchema.side].astype(str),
-                        OrderDataSchema.execution: lambda df: df[OrderDataSchema.execution].astype(str),
-                        OrderDataSchema.status: lambda df: df[OrderDataSchema.status].astype(str),
-                        OrderDataSchema.timestamp: lambda df: pd.to_datetime(
-                            df[OrderDataSchema.timestamp], errors="coerce"
-                        ),
-                        OrderDataSchema.expiration: lambda df: pd.to_datetime(
-                            df[OrderDataSchema.expiration], errors="coerce"
-                        ),
-                    }
-                ),
-            )
+            data = [asdict(order) for order in self.orders]
+            for d in data:
+                d[OrderDataSchema.side] = d[OrderDataSchema.side].name
+                d[OrderDataSchema.execution] = d[OrderDataSchema.execution].name
+                d[OrderDataSchema.status] = d[OrderDataSchema.status].name
+            return cast(LazyFrame[OrderDataSchema], pl.LazyFrame(data))
 
     @property
     def is_empty(self) -> bool:
diff --git a/src/order_matching/schemas.py b/src/order_matching/schemas.py
index a842622..8f3a8d2 100644
--- a/src/order_matching/schemas.py
+++ b/src/order_matching/schemas.py
@@ -1,7 +1,7 @@
 from datetime import datetime
 
-from pandera.pandas import DataFrameModel, Field
-from pandera.typing import Series
+from pandera.polars import DataFrameModel, Field
+from pandera.typing.polars import Series
 
 from order_matching.execution import Execution
 from order_matching.side import Side
diff --git a/tests/conftest.py b/tests/conftest.py
index e6ceb33..9bf85a3 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,5 +1,6 @@
+from datetime import datetime, timedelta
+
 import numpy as np
-import pandas as pd
 import pytest
 
 from order_matching.order import LimitOrder
@@ -14,7 +15,7 @@ def random_orders() -> Orders:
     rng = get_random_generator(seed=42)
     orders_per_timestamp, number_of_time_points = 100, 10
     time_intervals = np.array(rng.uniform(low=0, high=1, size=number_of_time_points))
-    random_timestamps = pd.Timestamp(2023, 1, 1) + pd.to_timedelta(time_intervals.cumsum(), unit="D")
+    random_timestamps = [datetime(2023, 1, 1) + timedelta(days=float(days)) for days in time_intervals.cumsum()]
     orders = list()
     for timestamp in random_timestamps:
         prices = rng.lognormal(mean=1, size=orders_per_timestamp).round(decimals=1)
diff --git a/tests/test_executed_trades.py b/tests/test_executed_trades.py
index e33d722..2928deb 100644
--- a/tests/test_executed_trades.py
+++ b/tests/test_executed_trades.py
@@ -1,8 +1,6 @@
 from copy import deepcopy
 from datetime import datetime, timedelta
 
-import pandas as pd
-
 from order_matching.executed_trades import ExecutedTrades
 from order_matching.execution import Execution
 from order_matching.schemas import TradeDataSchema
@@ -55,7 +53,7 @@ def test_get(self) -> None:
     def test_to_frame(self) -> None:
         executed_trades = ExecutedTrades()
 
-        pd.testing.assert_frame_equal(executed_trades.to_frame(), pd.DataFrame())
+        assert executed_trades.to_frame().collect().equals(TradeDataSchema.empty())
 
         first_trade, second_trade = self._get_sample_trades()
         executed_trades.add(trades=[first_trade, second_trade])
diff --git a/tests/test_order.py b/tests/test_order.py
index d3b895f..06671ee 100644
--- a/tests/test_order.py
+++ b/tests/test_order.py
@@ -1,6 +1,6 @@
 from datetime import datetime
 
-import pandas as pd
+import polars as pl
 import pytest
 
 from order_matching.execution import Execution
@@ -13,8 +13,19 @@ class TestOrder:
     @pytest.mark.parametrize("side", [Side.BUY, Side.SELL])
     @pytest.mark.parametrize("price", [1.2, 2.4])
     @pytest.mark.parametrize("size", [10, 4.1])
-    @pytest.mark.parametrize("timestamp", pd.date_range(start="2022", periods=3))
-    @pytest.mark.parametrize("expiration", [None, *pd.date_range(start="2023", periods=3)])
+    @pytest.mark.parametrize(
+        "timestamp",
+        pl.datetime_range(start=datetime(2022, 1, 1), end=datetime(2022, 1, 3), interval="1d", eager=True).to_list(),
+    )
+    @pytest.mark.parametrize(
+        "expiration",
+        [
+            None,
+            *pl.datetime_range(
+                start=datetime(2023, 1, 1), end=datetime(2023, 1, 3), interval="1d", eager=True
+            ).to_list(),
+        ],
+    )
     @pytest.mark.parametrize("order_id", ["a", "b"])
     @pytest.mark.parametrize("trader_id", ["x", "y"])
     @pytest.mark.parametrize("execution", [Execution.LIMIT, Execution.MARKET])
@@ -61,7 +72,10 @@ class TestLimitOrder:
     @pytest.mark.parametrize("side", [Side.BUY, Side.SELL])
     @pytest.mark.parametrize("price", [1.2, 2.4])
     @pytest.mark.parametrize("size", [10, 4.1])
-    @pytest.mark.parametrize("timestamp", pd.date_range(start="2022", periods=3))
+    @pytest.mark.parametrize(
+        "timestamp",
+        pl.datetime_range(start=datetime(2022, 1, 1), end=datetime(2022, 1, 3), interval="1d", eager=True).to_list(),
+    )
     @pytest.mark.parametrize("order_id", ["a", "b"])
     @pytest.mark.parametrize("trader_id", ["x", "y"])
     @pytest.mark.parametrize("price_number_of_digits", [1, 3])
@@ -98,7 +112,10 @@ def test_order_required_defaults(
 class TestMarketOrder:
     @pytest.mark.parametrize("side", [Side.BUY, Side.SELL])
     @pytest.mark.parametrize("size", [10, 4.1])
-    @pytest.mark.parametrize("timestamp", pd.date_range(start="2022", periods=3))
+    @pytest.mark.parametrize(
+        "timestamp",
+        pl.datetime_range(start=datetime(2022, 1, 1), end=datetime(2022, 1, 3), interval="1d", eager=True).to_list(),
+    )
     @pytest.mark.parametrize("order_id", ["a", "b"])
     @pytest.mark.parametrize("trader_id", ["x", "y"])
     def test_order_required_defaults(
diff --git a/tests/test_order_book.py b/tests/test_order_book.py
index 25d1cce..09661eb 100644
--- a/tests/test_order_book.py
+++ b/tests/test_order_book.py
@@ -144,8 +144,7 @@ def test_order_book_summary(self) -> None:
         summary = order_book.summary()
 
         OrderBookSummarySchema.validate(summary, lazy=True)
-        assert summary.index.is_monotonic_increasing
-        assert summary[OrderBookSummarySchema.price].is_monotonic_increasing
+        assert summary.collect()[OrderBookSummarySchema.price].is_sorted()
         assert order_book.current_price == 2.3
 
     def test_order_book_imbalance_one_buy_order(self) -> None:
diff --git a/tests/test_trade.py b/tests/test_trade.py
index 9999a5e..5d13988 100644
--- a/tests/test_trade.py
+++ b/tests/test_trade.py
@@ -1,7 +1,7 @@
 from dataclasses import asdict
 from datetime import datetime
 
-import pandas as pd
+import polars as pl
 import pytest
 
 from order_matching.execution import Execution
@@ -14,7 +14,10 @@ class TestTrade:
     @pytest.mark.parametrize("side", [Side.BUY, Side.SELL])
     @pytest.mark.parametrize("price", [1.2, 2.4])
     @pytest.mark.parametrize("size", [10.0, 4.1])
-    @pytest.mark.parametrize("timestamp", pd.date_range(start="2022", periods=3))
+    @pytest.mark.parametrize(
+        "timestamp",
+        pl.datetime_range(start=datetime(2022, 1, 1), end=datetime(2022, 1, 3), interval="1d", eager=True).to_list(),
+    )
     @pytest.mark.parametrize("incoming_order_id", ["a", "abc"])
     @pytest.mark.parametrize("book_order_id", ["a", "abc"])
     @pytest.mark.parametrize("trade_id", ["t", "tr"])
@@ -48,10 +51,8 @@ def test_trade_required_defaults(
         assert trade.book_order_id == book_order_id
         assert trade.execution == execution
         assert trade.trade_id == trade_id
-        trades = pd.DataFrame(asdict(trade), index=pd.Index([0])).assign(
-            **{
-                TradeDataSchema.side: lambda df: df[TradeDataSchema.side].astype(str),
-                TradeDataSchema.execution: lambda df: df[TradeDataSchema.execution].astype(str),
-            }
-        )
+        trade_dict = asdict(trade)
+        trade_dict[TradeDataSchema.side] = trade_dict[TradeDataSchema.side].name
+        trade_dict[TradeDataSchema.execution] = trade_dict[TradeDataSchema.execution].name
+        trades = pl.LazyFrame([trade_dict])
         TradeDataSchema.validate(trades, lazy=True)
diff --git a/uv.lock b/uv.lock
index bd11148..dcdec96 100644
--- a/uv.lock
+++ b/uv.lock
@@ -881,9 +881,11 @@ version = "0.3.16"
 source = { editable = "." }
 dependencies = [
     { name = "faker" },
-    { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "pandas", version = "3.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "pandera" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.11.*'" },
+    { name = "numpy", version = "2.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "pandera", extra = ["polars"] },
+    { name = "polars" },
 ]
 
 [package.dev-dependencies]
@@ -909,8 +911,9 @@ test = [
 [package.metadata]
 requires-dist = [
     { name = "faker", specifier = ">=33.0.0" },
-    { name = "pandas", specifier = ">=2.2.3" },
-    { name = "pandera", specifier = ">=0.21.0" },
+    { name = "numpy", specifier = ">=2.2.6" },
+    { name = "pandera", extras = ["polars"], specifier = ">=0.21.0" },
+    { name = "polars", specifier = ">=1.42.1" },
 ]
 
 [package.metadata.requires-dev]
@@ -951,142 +954,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/90/96/04b8e52da071d28f5e21a805b19cb9390aa17a47462ac87f5e2696b9566d/paginate-0.5.7-py2.py3-none-any.whl", hash = "sha256:b885e2af73abcf01d9559fd5216b57ef722f8c42affbb63942377668e35c7591", size = 13746, upload-time = "2024-08-25T14:17:22.55Z" },
 ]
 
-[[package]]
-name = "pandas"
-version = "2.3.3"
-source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version < '3.11'",
-]
-dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "python-dateutil", marker = "python_full_version < '3.11'" },
-    { name = "pytz", marker = "python_full_version < '3.11'" },
-    { name = "tzdata", marker = "python_full_version < '3.11'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/33/01/d40b85317f86cf08d853a4f495195c73815fdf205eef3993821720274518/pandas-2.3.3.tar.gz", hash = "sha256:e05e1af93b977f7eafa636d043f9f94c7ee3ac81af99c13508215942e64c993b", size = 4495223, upload-time = "2025-09-29T23:34:51.853Z" }
-wheels = [
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