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Type System Reference

Type system and schema definitions.

DataType

DataType

Bases: Enum

SQL data types supported by SQLStream.

Source code in sqlstream/core/types.py 
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class DataType(Enum):
    """SQL data types supported by SQLStream."""

    # Numeric types
    INTEGER = "INTEGER"
    FLOAT = "FLOAT"
    DECIMAL = "DECIMAL"

    # String types
    STRING = "STRING"
    JSON = "JSON"

    # Boolean
    BOOLEAN = "BOOLEAN"

    # Temporal types
    DATE = "DATE"
    TIME = "TIME"
    DATETIME = "DATETIME"

    # Special
    NULL = "NULL"

    def __str__(self) -> str:
        return self.value

    def is_numeric(self) -> bool:
        """Check if type is numeric (INTEGER, FLOAT, or DECIMAL)."""
        return self in (DataType.INTEGER, DataType.FLOAT, DataType.DECIMAL)

    def is_temporal(self) -> bool:
        """Check if type is temporal (DATE, TIME, or DATETIME)."""
        return self in (DataType.DATE, DataType.TIME, DataType.DATETIME)

    def is_comparable(self, other: "DataType") -> bool:
        """Check if this type can be compared with another type."""
        # NULL can be compared with anything
        if self == DataType.NULL or other == DataType.NULL:
            return True

        # Same types are always comparable
        if self == other:
            return True

        # Numeric types are comparable with each other
        if self.is_numeric() and other.is_numeric():
            return True

        # Temporal types are comparable with each other
        if self.is_temporal() and other.is_temporal():
            return True

        # Strings and JSON are not comparable with numbers or temporals
        return False

    def coerce_to(self, other: "DataType") -> "DataType":
        """Determine the result type when coercing this type to another.

        Used for type promotion in expressions like INTEGER + FLOAT.
        """
        # NULL coerces to any type
        if self == DataType.NULL:
            return other
        if other == DataType.NULL:
            return self

        # Same type
        if self == other:
            return self

        # Numeric coercion hierarchy: INTEGER < FLOAT < DECIMAL
        numeric_hierarchy = {
            DataType.INTEGER: 1,
            DataType.FLOAT: 2,
            DataType.DECIMAL: 3,
        }
        if self in numeric_hierarchy and other in numeric_hierarchy:
            # Return higher precedence type
            return self if numeric_hierarchy[self] > numeric_hierarchy[other] else other

        # Temporal coercion: DATE/TIME -> DATETIME
        if self == DataType.DATETIME or other == DataType.DATETIME:
            if self.is_temporal() and other.is_temporal():
                return DataType.DATETIME

        # JSON coercion
        if self == DataType.JSON and other == DataType.JSON:
            return DataType.JSON
        if self == DataType.JSON or other == DataType.JSON:
            return DataType.STRING  # Fallback to string

        # Otherwise, no coercion possible - return STRING as fallback
        return DataType.STRING

is_numeric 

is_numeric() -> bool

Check if type is numeric (INTEGER, FLOAT, or DECIMAL).

Source code in sqlstream/core/types.py 
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def is_numeric(self) -> bool:
    """Check if type is numeric (INTEGER, FLOAT, or DECIMAL)."""
    return self in (DataType.INTEGER, DataType.FLOAT, DataType.DECIMAL)

is_temporal 

is_temporal() -> bool

Check if type is temporal (DATE, TIME, or DATETIME).

Source code in sqlstream/core/types.py 
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def is_temporal(self) -> bool:
    """Check if type is temporal (DATE, TIME, or DATETIME)."""
    return self in (DataType.DATE, DataType.TIME, DataType.DATETIME)

is_comparable 

is_comparable(other: DataType) -> bool

Check if this type can be compared with another type.

Source code in sqlstream/core/types.py 
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def is_comparable(self, other: "DataType") -> bool:
    """Check if this type can be compared with another type."""
    # NULL can be compared with anything
    if self == DataType.NULL or other == DataType.NULL:
        return True

    # Same types are always comparable
    if self == other:
        return True

    # Numeric types are comparable with each other
    if self.is_numeric() and other.is_numeric():
        return True

    # Temporal types are comparable with each other
    if self.is_temporal() and other.is_temporal():
        return True

    # Strings and JSON are not comparable with numbers or temporals
    return False

coerce_to 

coerce_to(other: DataType) -> DataType

Determine the result type when coercing this type to another.

Used for type promotion in expressions like INTEGER + FLOAT.

Source code in sqlstream/core/types.py 
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def coerce_to(self, other: "DataType") -> "DataType":
    """Determine the result type when coercing this type to another.

    Used for type promotion in expressions like INTEGER + FLOAT.
    """
    # NULL coerces to any type
    if self == DataType.NULL:
        return other
    if other == DataType.NULL:
        return self

    # Same type
    if self == other:
        return self

    # Numeric coercion hierarchy: INTEGER < FLOAT < DECIMAL
    numeric_hierarchy = {
        DataType.INTEGER: 1,
        DataType.FLOAT: 2,
        DataType.DECIMAL: 3,
    }
    if self in numeric_hierarchy and other in numeric_hierarchy:
        # Return higher precedence type
        return self if numeric_hierarchy[self] > numeric_hierarchy[other] else other

    # Temporal coercion: DATE/TIME -> DATETIME
    if self == DataType.DATETIME or other == DataType.DATETIME:
        if self.is_temporal() and other.is_temporal():
            return DataType.DATETIME

    # JSON coercion
    if self == DataType.JSON and other == DataType.JSON:
        return DataType.JSON
    if self == DataType.JSON or other == DataType.JSON:
        return DataType.STRING  # Fallback to string

    # Otherwise, no coercion possible - return STRING as fallback
    return DataType.STRING

Schema

Schema

Schema definition for a table or query result.

Holds column names and their corresponding data types.

Source code in sqlstream/core/types.py 
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class Schema:
    """Schema definition for a table or query result.

    Holds column names and their corresponding data types.
    """

    def __init__(self, columns: dict[str, DataType]):
        """Initialize schema.

        Args:
            columns: Dictionary mapping column names to data types
        """
        self.columns = columns

    def __getitem__(self, column: str) -> DataType:
        """Get type of a column."""
        return self.columns[column]

    def __contains__(self, column: str) -> bool:
        """Check if column exists in schema."""
        return column in self.columns

    def __len__(self) -> int:
        """Get number of columns."""
        return len(self.columns)

    def __repr__(self) -> str:
        cols = ", ".join(f"{name}: {dtype}" for name, dtype in self.columns.items())
        return f"Schema({cols})"

    def get_column_names(self) -> list[str]:
        """Get list of column names."""
        return list(self.columns.keys())

    def get_column_type(self, column: str) -> DataType | None:
        """Get type of a column, or None if column doesn't exist."""
        return self.columns.get(column)

    def validate_column(self, column: str) -> None:
        """Validate that a column exists in the schema.

        Args:
            column: Column name to validate

        Raises:
            ValueError: If column doesn't exist
        """
        if column not in self.columns:
            available = ", ".join(self.columns.keys())
            raise ValueError(
                f"Column '{column}' not found in schema. Available columns: {available}"
            )

    @staticmethod
    def from_row(row: dict[str, Any]) -> "Schema":
        """Infer schema from a single row.

        Args:
            row: Dictionary representing a row

        Returns:
            Inferred Schema
        """
        columns = {name: infer_type(value) for name, value in row.items()}
        return Schema(columns)

    @staticmethod
    def from_rows(rows: list[dict[str, Any]]) -> "Schema":
        """Infer schema from multiple rows.

        This provides more accurate type inference by looking at multiple values.

        Args:
            rows: List of dictionaries representing rows

        Returns:
            Inferred Schema
        """
        if not rows:
            return Schema({})

        # Get all column names from first row
        column_names = list(rows[0].keys())

        # Collect values for each column
        columns = {}
        for col_name in column_names:
            col_values = [row.get(col_name) for row in rows if col_name in row]
            columns[col_name] = infer_common_type(col_values)

        return Schema(columns)

    def merge(self, other: "Schema") -> "Schema":
        """Merge two schemas, coercing types where needed.

        This is useful for operations like UNION or JOIN where schemas need to be compatible.

        Args:
            other: Another schema to merge with

        Returns:
            Merged schema with coerced types
        """
        merged = {}

        # Get all column names from both schemas
        all_columns = set(self.columns.keys()) | set(other.columns.keys())

        for col_name in all_columns:
            if col_name in self.columns and col_name in other.columns:
                # Column exists in both - coerce types
                merged[col_name] = self.columns[col_name].coerce_to(other.columns[col_name])
            elif col_name in self.columns:
                # Column only in self
                merged[col_name] = self.columns[col_name]
            else:
                # Column only in other
                merged[col_name] = other.columns[col_name]

        return Schema(merged)

    def to_dict(self) -> dict[str, Any]:
        """Convert schema to dictionary."""
        return {name: dtype.value for name, dtype in self.columns.items()}

__init__ 

__init__(columns: dict[str, DataType])

Initialize schema.

Parameters:

Name Type Description Default
columns dict[str, DataType]

Dictionary mapping column names to data types

 required
Source code in sqlstream/core/types.py 
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def __init__(self, columns: dict[str, DataType]):
    """Initialize schema.

    Args:
        columns: Dictionary mapping column names to data types
    """
    self.columns = columns

__getitem__ 

__getitem__(column: str) -> DataType

Get type of a column.

Source code in sqlstream/core/types.py 
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def __getitem__(self, column: str) -> DataType:
    """Get type of a column."""
    return self.columns[column]

__contains__ 

__contains__(column: str) -> bool

Check if column exists in schema.

Source code in sqlstream/core/types.py 
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def __contains__(self, column: str) -> bool:
    """Check if column exists in schema."""
    return column in self.columns

__len__ 

__len__() -> int

Get number of columns.

Source code in sqlstream/core/types.py 
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def __len__(self) -> int:
    """Get number of columns."""
    return len(self.columns)

get_column_names 

get_column_names() -> list[str]

Get list of column names.

Source code in sqlstream/core/types.py 
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def get_column_names(self) -> list[str]:
    """Get list of column names."""
    return list(self.columns.keys())

get_column_type 

get_column_type(column: str) -> DataType | None

Get type of a column, or None if column doesn't exist.

Source code in sqlstream/core/types.py 
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def get_column_type(self, column: str) -> DataType | None:
    """Get type of a column, or None if column doesn't exist."""
    return self.columns.get(column)

validate_column 

validate_column(column: str) -> None

Validate that a column exists in the schema.

Parameters:

Name Type Description Default
column str

Column name to validate

 required

Raises:

Type Description
ValueError

If column doesn't exist
Source code in sqlstream/core/types.py 
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def validate_column(self, column: str) -> None:
    """Validate that a column exists in the schema.

    Args:
        column: Column name to validate

    Raises:
        ValueError: If column doesn't exist
    """
    if column not in self.columns:
        available = ", ".join(self.columns.keys())
        raise ValueError(
            f"Column '{column}' not found in schema. Available columns: {available}"
        )

from_row staticmethod 

from_row(row: dict[str, Any]) -> Schema

Infer schema from a single row.

Parameters:

Name Type Description Default
row dict[str, Any]

Dictionary representing a row

 required

Returns:

Type Description
Schema

Inferred Schema
Source code in sqlstream/core/types.py 
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@staticmethod
def from_row(row: dict[str, Any]) -> "Schema":
    """Infer schema from a single row.

    Args:
        row: Dictionary representing a row

    Returns:
        Inferred Schema
    """
    columns = {name: infer_type(value) for name, value in row.items()}
    return Schema(columns)

from_rows staticmethod 

from_rows(rows: list[dict[str, Any]]) -> Schema

Infer schema from multiple rows.

This provides more accurate type inference by looking at multiple values.

Parameters:

Name Type Description Default
rows list[dict[str, Any]]

List of dictionaries representing rows

 required

Returns:

Type Description
Schema

Inferred Schema
Source code in sqlstream/core/types.py 
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@staticmethod
def from_rows(rows: list[dict[str, Any]]) -> "Schema":
    """Infer schema from multiple rows.

    This provides more accurate type inference by looking at multiple values.

    Args:
        rows: List of dictionaries representing rows

    Returns:
        Inferred Schema
    """
    if not rows:
        return Schema({})

    # Get all column names from first row
    column_names = list(rows[0].keys())

    # Collect values for each column
    columns = {}
    for col_name in column_names:
        col_values = [row.get(col_name) for row in rows if col_name in row]
        columns[col_name] = infer_common_type(col_values)

    return Schema(columns)

merge 

merge(other: Schema) -> Schema

Merge two schemas, coercing types where needed.

This is useful for operations like UNION or JOIN where schemas need to be compatible.

Parameters:

Name Type Description Default
other Schema

Another schema to merge with

 required

Returns:

Type Description
Schema

Merged schema with coerced types
Source code in sqlstream/core/types.py 
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def merge(self, other: "Schema") -> "Schema":
    """Merge two schemas, coercing types where needed.

    This is useful for operations like UNION or JOIN where schemas need to be compatible.

    Args:
        other: Another schema to merge with

    Returns:
        Merged schema with coerced types
    """
    merged = {}

    # Get all column names from both schemas
    all_columns = set(self.columns.keys()) | set(other.columns.keys())

    for col_name in all_columns:
        if col_name in self.columns and col_name in other.columns:
            # Column exists in both - coerce types
            merged[col_name] = self.columns[col_name].coerce_to(other.columns[col_name])
        elif col_name in self.columns:
            # Column only in self
            merged[col_name] = self.columns[col_name]
        else:
            # Column only in other
            merged[col_name] = other.columns[col_name]

    return Schema(merged)

to_dict 

to_dict() -> dict[str, Any]

Convert schema to dictionary.

Source code in sqlstream/core/types.py 
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def to_dict(self) -> dict[str, Any]:
    """Convert schema to dictionary."""
    return {name: dtype.value for name, dtype in self.columns.items()}

infer_common_type

infer_common_type 

infer_common_type(values: list[Any]) -> DataType

Infer a common type from a list of values.

This is useful for schema inference when reading data files.

Parameters:

Name Type Description Default
values list[Any]

List of values to infer type from

 required

Returns:

Type Description
DataType

Common DataType that can represent all values

Examples:

>>> infer_common_type([1, 2, 3])
DataType.INTEGER
>>> infer_common_type([1, 2.5, 3])
DataType.FLOAT
>>> infer_common_type([1, "hello", 3])
DataType.STRING
Source code in sqlstream/core/types.py 
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def infer_common_type(values: list[Any]) -> DataType:
    """Infer a common type from a list of values.

    This is useful for schema inference when reading data files.

    Args:
        values: List of values to infer type from

    Returns:
        Common DataType that can represent all values

    Examples:
        >>> infer_common_type([1, 2, 3])
        DataType.INTEGER
        >>> infer_common_type([1, 2.5, 3])
        DataType.FLOAT
        >>> infer_common_type([1, "hello", 3])
        DataType.STRING
    """
    if not values:
        return DataType.NULL

    # Filter out None values for type inference
    non_null_values = [v for v in values if v is not None]

    if not non_null_values:
        return DataType.NULL

    # Infer type of first value
    common_type = infer_type(non_null_values[0])

    # Check remaining values and coerce as needed
    for value in non_null_values[1:]:
        value_type = infer_type(value)
        common_type = common_type.coerce_to(value_type)

    return common_type

infer_type

infer_type 

infer_type(value: Any) -> DataType

Infer the data type from a Python value.

Parameters:

Name Type Description Default
value Any

Python value to infer type from

 required

Returns:

Type Description
DataType

Inferred DataType

Examples:

>>> infer_type(42)
DataType.INTEGER
>>> infer_type(3.14)
DataType.FLOAT
>>> infer_type("hello")
DataType.STRING
>>> infer_type(None)
DataType.NULL
>>> infer_type(Decimal("19.99"))
DataType.DECIMAL
Source code in sqlstream/core/types.py 
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def infer_type(value: Any) -> DataType:
    """Infer the data type from a Python value.

    Args:
        value: Python value to infer type from

    Returns:
        Inferred DataType

    Examples:
        >>> infer_type(42)
        DataType.INTEGER
        >>> infer_type(3.14)
        DataType.FLOAT
        >>> infer_type("hello")
        DataType.STRING
        >>> infer_type(None)
        DataType.NULL
        >>> infer_type(Decimal("19.99"))
        DataType.DECIMAL
    """
    # 1. NULL check
    if value is None:
        return DataType.NULL

    # 2. Python type checks (non-string)
    if isinstance(value, bool):
        return DataType.BOOLEAN

    if isinstance(value, int):
        return DataType.INTEGER

    if isinstance(value, float):
        return DataType.FLOAT

    if isinstance(value, Decimal):
        return DataType.DECIMAL

    if isinstance(value, datetime):
        return DataType.DATETIME

    if isinstance(value, date):
        return DataType.DATE

    if isinstance(value, time):
        return DataType.TIME

    # 3. String value inference
    if isinstance(value, str):
        value_stripped = value.strip()

        # Empty string → NULL
        if not value_stripped:
            return DataType.NULL

        # Boolean literals
        if value_stripped.lower() in ("true", "false"):
            return DataType.BOOLEAN

        # JSON (must check early - before numeric)
        if is_json_string(value_stripped):
            return DataType.JSON

        # Integer
        try:
            int(value_stripped)
            return DataType.INTEGER
        except ValueError:
            pass

        # Float or Decimal
        try:
            # Check if it has decimal point
            if "." in value_stripped:
                # Check precision - if more than 6 decimal places, use DECIMAL
                decimal_part = value_stripped.split(".")[1]
                # Remove trailing zeros for check
                significant_decimals = decimal_part.rstrip("0")
                if len(significant_decimals) > 6:
                    Decimal(value_stripped)  # Validate
                    return DataType.DECIMAL
            float(value_stripped)
            return DataType.FLOAT
        except (ValueError, InvalidOperation):
            pass

        # DateTime (check before Date to catch timestamps)
        dt = parse_datetime(value_stripped)
        if dt is not None:
            return DataType.DATETIME

        # Date
        d = parse_date(value_stripped)
        if d is not None:
            return DataType.DATE

        # Time
        t = parse_time(value_stripped)
        if t is not None:
            return DataType.TIME

        # Default to STRING
        return DataType.STRING

    # 4. Fallback for unknown types
    return DataType.STRING

infer_type_from_string

infer_type_from_string 

infer_type_from_string(value: str) -> Any

Parse a string value and return the typed Python value.

This is used by readers to convert string data into proper Python types.

Parameters:

Name Type Description Default
value str

String value to parse

 required

Returns:

Type Description
Any

Typed Python value (int, float, Decimal, datetime, bool, str, etc.)

Examples:

>>> infer_type_from_string("42")
42
>>> infer_type_from_string("3.14159265358979")
Decimal("3.14159265358979")
>>> infer_type_from_string("2024-01-15 10:30:00")
datetime(2024, 1, 15, 10, 30, 0)
Source code in sqlstream/core/types.py 
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def infer_type_from_string(value: str) -> Any:
    """Parse a string value and return the typed Python value.

    This is used by readers to convert string data into proper Python types.

    Args:
        value: String value to parse

    Returns:
        Typed Python value (int, float, Decimal, datetime, bool, str, etc.)

    Examples:
        >>> infer_type_from_string("42")
        42
        >>> infer_type_from_string("3.14159265358979")
        Decimal("3.14159265358979")
        >>> infer_type_from_string("2024-01-15 10:30:00")
        datetime(2024, 1, 15, 10, 30, 0)
    """
    if not isinstance(value, str):
        return value

    value_stripped = value.strip()

    # Empty → None
    if not value_stripped:
        return None

    # Boolean
    if value_stripped.lower() == "true":
        return True
    if value_stripped.lower() == "false":
        return False

    # JSON - keep as string for now (DuckDB will handle it)
    if is_json_string(value_stripped):
        return value_stripped

    # Integer
    try:
        return int(value_stripped)
    except ValueError:
        pass

    # Float or Decimal
    try:
        if "." in value_stripped:
            decimal_part = value_stripped.split(".")[1]
            significant_decimals = decimal_part.rstrip("0")
            if len(significant_decimals) > 6:
                return Decimal(value_stripped)
        return float(value_stripped)
    except (ValueError, InvalidOperation):
        pass

    # DateTime
    dt = parse_datetime(value_stripped)
    if dt is not None:
        return dt

    # Date
    d = parse_date(value_stripped)
    if d is not None:
        return d

    # Time
    t = parse_time(value_stripped)
    if t is not None:
        return t

    # Return as string
    return value

is_json_string

is_json_string 

is_json_string(value: str) -> bool

Check if a string contains valid JSON (object or array).

Parameters:

Name Type Description Default
value str

String to check

 required

Returns:

Type Description
bool

True if valid JSON object/array, False otherwise
Source code in sqlstream/core/types.py 
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def is_json_string(value: str) -> bool:
    """Check if a string contains valid JSON (object or array).

    Args:
        value: String to check

    Returns:
        True if valid JSON object/array, False otherwise
    """
    if not isinstance(value, str):
        return False

    value = value.strip()

    # Must start with { or [
    if not (value.startswith("{") or value.startswith("[")):
        return False

    # Try to parse as JSON
    try:
        parsed = json.loads(value)
        # Must be dict or list (not just a string, number, etc.)
        return isinstance(parsed, (dict, list))
    except (json.JSONDecodeError, ValueError):
        return False

parse_date

parse_date 

parse_date(value: str) -> date | None

Try to parse date from string using multiple formats.

Parameters:

Name Type Description Default
value str

String to parse

 required

Returns:

Type Description
date | None

date object if successful, None otherwise
Source code in sqlstream/core/types.py 
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def parse_date(value: str) -> date | None:
    """Try to parse date from string using multiple formats.

    Args:
        value: String to parse

    Returns:
        date object if successful, None otherwise
    """
    if not isinstance(value, str):
        return None

    value = value.strip()

    formats = [
        "%Y-%m-%d",  # ISO: 2024-01-15
        "%d/%m/%Y",  # EU: 15/01/2024
        "%m/%d/%Y",  # US: 01/15/2024
        "%Y%m%d",  # Compact: 20240115
        "%d-%m-%Y",  # EU with dashes: 15-01-2024
        "%m-%d-%Y",  # US with dashes: 01-15-2024
    ]

    for fmt in formats:
        try:
            return datetime.strptime(value, fmt).date()
        except ValueError:
            continue

    return None

parse_datetime

parse_datetime 

parse_datetime(value: str) -> datetime | None

Try to parse datetime from string using multiple formats.

Parameters:

Name Type Description Default
value str

String to parse

 required

Returns:

Type Description
datetime | None

datetime object if successful, None otherwise
Source code in sqlstream/core/types.py 
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def parse_datetime(value: str) -> datetime | None:
    """Try to parse datetime from string using multiple formats.

    Args:
        value: String to parse

    Returns:
        datetime object if successful, None otherwise
    """
    if not isinstance(value, str):
        return None

    value = value.strip()

    # Try ISO 8601 with timezone (handle Z and +HH:MM)
    if "T" in value:
        # Remove timezone suffix for basic parsing
        base_value = value.replace("Z", "").split("+")[0].split("-")[0:3]
        base_value = "-".join(base_value) if len(base_value) == 3 else value.replace("Z", "")

    formats = [
        "%Y-%m-%dT%H:%M:%S",  # ISO 8601: 2024-01-15T10:30:00
        "%Y-%m-%dT%H:%M:%S.%f",  # ISO with microseconds
        "%Y-%m-%d %H:%M:%S",  # SQL format: 2024-01-15 10:30:00
        "%Y-%m-%d %H:%M:%S.%f",  # SQL with microseconds
        "%d/%m/%Y %H:%M:%S",  # EU format: 15/01/2024 10:30:00
        "%m/%d/%Y %H:%M:%S",  # US format: 01/15/2024 10:30:00
        "%Y%m%d%H%M%S",  # Compact: 20240115103000
        "%Y-%m-%d %H:%M",  # Without seconds
        "%d/%m/%Y %H:%M",  # EU without seconds
        "%m/%d/%Y %H:%M",  # US without seconds
    ]

    for fmt in formats:
        try:
            return datetime.strptime(value, fmt)
        except ValueError:
            continue

    return None

parse_time

parse_time 

parse_time(value: str) -> time | None

Try to parse time from string using multiple formats.

Parameters:

Name Type Description Default
value str

String to parse

 required

Returns:

Type Description
time | None

time object if successful, None otherwise
Source code in sqlstream/core/types.py 
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def parse_time(value: str) -> time | None:
    """Try to parse time from string using multiple formats.

    Args:
        value: String to parse

    Returns:
        time object if successful, None otherwise
    """
    if not isinstance(value, str):
        return None

    value = value.strip()

    formats = [
        "%H:%M:%S",  # 24-hour: 14:30:00
        "%H:%M:%S.%f",  # With microseconds: 14:30:00.123456
        "%H:%M",  # Without seconds: 14:30
        "%I:%M:%S %p",  # 12-hour with seconds: 02:30:00 PM
        "%I:%M %p",  # 12-hour: 02:30 PM
    ]

    for fmt in formats:
        try:
            return datetime.strptime(value, fmt).time()
        except ValueError:
            continue

    return None