feat: Add dy.infer_schema

dataframely/__init__.py

@@ -78,6 +78,7 @@
     "deserialize_schema",
     "read_parquet_metadata_schema",
     "read_parquet_metadata_collection",
+    "infer_schema",
     "Any",
     "Binary",
     "Bool",

dataframely/experimental/__init__.py

@@ -0,0 +1,6 @@
+# Copyright (c) QuantCo 2025-2026
+# SPDX-License-Identifier: BSD-3-Clause
+
+from .infer_schema import infer_schema
+
+__all__ = ["infer_schema"]

dataframely/experimental/infer_schema.py

@@ -0,0 +1,185 @@
+# Copyright (c) QuantCo 2025-2026
+# SPDX-License-Identifier: BSD-3-Clause
+"""Infer schema from a Polars DataFrame."""
+
+from __future__ import annotations
+
+import keyword
+import re
+
+import polars as pl
+
+_POLARS_DTYPE_MAP: dict[type[pl.DataType], str] = {
+    pl.Boolean: "Bool",
+    pl.Int8: "Int8",
+    pl.Int16: "Int16",
+    pl.Int32: "Int32",
+    pl.Int64: "Int64",
+    pl.UInt8: "UInt8",
+    pl.UInt16: "UInt16",
+    pl.UInt32: "UInt32",
+    pl.UInt64: "UInt64",
+    pl.Float32: "Float32",
+    pl.Float64: "Float64",
+    pl.String: "String",
+    pl.Binary: "Binary",
+    pl.Date: "Date",
+    pl.Time: "Time",
+    pl.Object: "Object",
+    pl.Categorical: "Categorical",
+    pl.Duration: "Duration",
+    pl.Datetime: "Datetime",
+    pl.Decimal: "Decimal",
+    pl.Enum: "Enum",
+    pl.List: "List",
+    pl.Array: "Array",
+    pl.Struct: "Struct",
+}
+
+
+def infer_schema(
+    df: pl.DataFrame,
+    schema_name: str = "Schema",
+) -> str:
+    """Infer a dataframely schema from a Polars DataFrame.
+
+    This function inspects a DataFrame's schema and generates corresponding
+    dataframely Schema code as a string.
+
+    Args:
+        df: The Polars DataFrame to infer the schema from.
+        schema_name: The name for the generated schema class.
+
+    Returns:
+        The schema code as a string.
+
+    Example:
+        >>> import polars as pl
+        >>> from dataframely.experimental import infer_schema
+        >>> df = pl.DataFrame({
+        ...     "name": ["Alice", "Bob"],
+        ...     "age": [25, 30],
+        ...     "score": [95.5, None],
+        ... })
+        >>> print(infer_schema(df, "PersonSchema"))
+        class PersonSchema(dy.Schema):
+            name = dy.String()
+            age = dy.Int64()
+            score = dy.Float64(nullable=True)
+
+    Attention:
+        This functionality is considered unstable. It may be changed at any time
+        without it being considered a breaking change.
+
+    Raises:
+        ValueError: If ``schema_name`` is not a valid Python identifier.
+    """
+    if not schema_name.isidentifier():
+        msg = f"schema_name must be a valid Python identifier, got {schema_name!r}"
+        raise ValueError(msg)
+
+    return _generate_schema_code(df, schema_name)
+
+
+def _generate_schema_code(df: pl.DataFrame, schema_name: str) -> str:
+    """Generate schema code string from a DataFrame."""
+    lines = [f"class {schema_name}(dy.Schema):"]
+    used_identifiers: set[str] = set()
+
+    for col_index, (col_name, series) in enumerate(df.to_dict().items()):
+        attr_name = _make_valid_identifier(col_name, col_index)
+        # Make sure yes have no duplicates
+        if attr_name in used_identifiers:
+            # Remove trailing "_" if exists as it will be included in the suffix anyway
+            if attr_name.endswith("_"):
+                attr_name = attr_name[:-1]
+            idx = 1
+            while f"{attr_name}_{idx}" in used_identifiers:
+                idx += 1
+            attr_name = f"{attr_name}_{idx}"
+        used_identifiers.add(attr_name)
+        alias = col_name if attr_name != col_name else None
+        col_code = _dtype_to_column_code(series, alias=alias)
+        lines.append(f"    {attr_name} = {col_code}")
+
+    return "\n".join(lines)
+
+
+def _make_valid_identifier(name: str, col_index: int) -> str:
+    """Convert a string to a valid Python identifier."""
+    # Replace invalid characters with underscores
+    valid_identifier = re.sub(r"[^a-zA-Z0-9_]", "_", name)
+
+    # Handle empty name or name with only special characters ones with simple "_"
+    if set(valid_identifier).issubset({"_"}):
+        return f"column_{col_index}"
+    # Ensure it doesn't start with a digit
+    if valid_identifier[0].isdigit():
+        return "_" + valid_identifier
+    if keyword.iskeyword(valid_identifier):
+        return valid_identifier + "_"
+    return valid_identifier
+
+
+def _get_dtype_args(dtype: pl.DataType, series: pl.Series) -> list[str]:
+    """Get extra arguments for parameterized types."""
+    if isinstance(dtype, pl.Datetime):
+        args = []
+        if dtype.time_zone is not None:
+            args.append(f'time_zone="{dtype.time_zone}"')
+        if dtype.time_unit != "us":
+            args.append(f'time_unit="{dtype.time_unit}"')
+        return args
+
+    if isinstance(dtype, pl.Duration):
+        if dtype.time_unit != "us":  # us is the default
+            return [f'time_unit="{dtype.time_unit}"']
+
+    if isinstance(dtype, pl.Decimal):
+        args = []
+        if dtype.precision is not None:
+            args.append(f"precision={dtype.precision}")
+        if dtype.scale != 0:
+            args.append(f"scale={dtype.scale}")
+        return args
+
+    if isinstance(dtype, pl.Enum):
+        return [repr(dtype.categories.to_list())]
+
+    if isinstance(dtype, pl.List):
+        return [_dtype_to_column_code(series.explode())]
+
+    if isinstance(dtype, pl.Array):
+        return [_dtype_to_column_code(series.explode()), f"shape={dtype.size}"]
+
+    if isinstance(dtype, pl.Struct):
+        fields_parts = []
+        for field in dtype.fields:
+            field_code = _dtype_to_column_code(series.struct.field(field.name))
+            fields_parts.append(f'"{field.name}": {field_code}')
+        return ["{" + ", ".join(fields_parts) + "}"]
+
+    return []
+
+
+def _format_args(*args: str, nullable: bool = False, alias: str | None = None) -> str:
+    """Format arguments for column constructor."""
+    all_args = list(args)
+    if nullable:
+        all_args.append("nullable=True")
+    if alias is not None:
+        all_args.append(f'alias="{alias}"')
+    return ", ".join(all_args)
+
+
+def _dtype_to_column_code(series: pl.Series, *, alias: str | None = None) -> str:
+    """Convert a Polars Series to dataframely column constructor code."""
+    dtype = series.dtype
+    nullable = series.null_count() > 0
+    dy_name = _POLARS_DTYPE_MAP.get(type(dtype))
+
+    if dy_name is None:
+        return f"dy.Any({_format_args(alias=alias)})"
+
+    args = _get_dtype_args(dtype, series)
+    return f"dy.{dy_name}({_format_args(*args, nullable=nullable, alias=alias)})"

docs/api/experimental/index.rst

@@ -0,0 +1,10 @@
+=============
+Experimental
+=============
+
+.. currentmodule:: dataframely
+.. autosummary::
+    :toctree: _gen/
+    :nosignatures:
+
+    experimental.infer_schema

docs/api/index.rst

@@ -47,3 +47,12 @@ API Reference
            :maxdepth: 1
 
            misc/index
+
+.. grid::
+
+    .. grid-item-card::
+
+        .. toctree::
+           :maxdepth: 1
+
+           experimental/index

docs/guides/faq.md

@@ -5,7 +5,7 @@ thinking, please add it here.
 
 ## How do I define additional unique keys in a {class}`~dataframely.Schema`?
 
-By default, `dataframely` only supports defining a single non-nullable (composite) primary key in :class:
+By default, `dataframely` only supports defining a single non-nullable (composite) primary key in {class}
 `~dataframely.Schema`.
 However, in some scenarios it may be useful to define additional unique keys (which support nullable fields and/or which
 are additionally unique).
@@ -34,6 +34,13 @@ class UserSchema(dy.Schema):
 
 See our documentation on [group rules](./quickstart.md#group-rules).
 
+## How do I get a {class}`~dataframely.Schema` for my dataframe?
+
+You can use {func}`dataframely.experimental.infer_schema` to get a basic {class}`~dataframely.Schema` definition for
+your dataframe. The function infers column names, types and nullability from the dataframe and returns a code
+representation of a {class}`~dataframely.Schema`
+Starting from this definition, you can then refine the schema by adding additional rules, adjusting types, etc.
+
 ## What versions of `polars` does `dataframely` support?
 
 Our CI automatically tests `dataframely` for a minimal supported version of `polars`, which is currently `1.35.*`,

docs/guides/migration/index.md

@@ -37,3 +37,9 @@ Users can disable `FutureWarnings` either through
 builtins from tools
 like [pytest](https://docs.pytest.org/en/stable/how-to/capture-warnings.html#controlling-warnings),
 or by setting the `DATAFRAMELY_NO_FUTURE_WARNINGS` environment variable to `true` or `1`.
+
+## Experimental features
+
+Experimental features are published in a dedicated namespace `dataframely.experimental`.
+The versioning policy above does not apply to this namespace, and we may introduce breaking changes to experimental
+features in minor releases.

docs/guides/quickstart.md

@@ -175,7 +175,7 @@ print(failure.counts())
 ```
 
 In this case, `good` remains to be a `dy.DataFrame[HouseSchema]`, albeit with potentially fewer rows than `df`.
-The `failure` object is of type :class:`~dataframely.FailureInfo` and provides means to inspect
+The `failure` object is of type {class}`~dataframely.FailureInfo` and provides means to inspect
 the reasons for validation failures for invalid rows.
 
 Given the example data above and the schema that we defined, we know that rows 2, 3, 4, and 5 are invalid (0-indexed):
@@ -185,7 +185,7 @@ Given the example data above and the schema that we defined, we know that rows 2
 - Row 4 violates both of the rules above
 - Row 5 violates the reasonable bathroom to bedroom ratio
 
-Using the `counts` method on the :class:`~dataframely.FailureInfo` object will result in the following dictionary:
+Using the `counts` method on the {class}`~dataframely.FailureInfo` object will result in the following dictionary:
 
 ```python
 {