fenic.api.functions.builtin

Built-in functions for Fenic DataFrames.

Functions:

• array –

  Creates a new array column from multiple input columns.

• array_agg –

  Alias for collect_list().

• array_contains –

  Checks if array column contains a specific value.

• array_size –

  Returns the number of elements in an array column.

• asc –

  Creates a Column expression representing an ascending sort order.

• asc_nulls_first –

  Creates a Column expression representing an ascending sort order with nulls first.

• asc_nulls_last –

  Creates a Column expression representing an ascending sort order with nulls last.

• avg –

  Aggregate function: returns the average (mean) of all values in the specified column.

• coalesce –

  Returns the first non-null value from the given columns for each row.

• collect_list –

  Aggregate function: collects all values from the specified column into a list.

• count –

  Aggregate function: returns the count of non-null values in the specified column.

• desc –

  Creates a Column expression representing a descending sort order.

• desc_nulls_first –

  Creates a Column expression representing a descending sort order with nulls first.

• desc_nulls_last –

  Creates a Column expression representing a descending sort order with nulls last.

• first –

  Aggregate function: returns the first non-null value in the specified column.

• max –

  Aggregate function: returns the maximum value in the specified column.

• mean –

  Aggregate function: returns the mean (average) of all values in the specified column.

• min –

  Aggregate function: returns the minimum value in the specified column.

• stddev –

  Aggregate function: returns the sample standard deviation of the specified column.

• struct –

  Creates a new struct column from multiple input columns.

• sum –

  Aggregate function: returns the sum of all values in the specified column.

• udf –

  A decorator or function for creating user-defined functions (UDFs) that can be applied to DataFrame rows.

• when –

  Evaluates a condition and returns a value if true.

array

array(*args: Union[ColumnOrName, List[ColumnOrName], Tuple[ColumnOrName, ...]]) -> Column

Creates a new array column from multiple input columns.

Parameters:

• *args (Union[ColumnOrName, List[ColumnOrName], Tuple[ColumnOrName, ...]], default: () ) –

  Columns or column names to combine into an array. Can be:

  • Individual arguments
  • Lists of columns/column names
  • Tuples of columns/column names

Returns:

• Column –

  A Column expression representing an array containing values from the input columns

Raises:

• TypeError –

  If any argument is not a Column, string, or collection of Columns/strings

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def array(
    *args: Union[ColumnOrName, List[ColumnOrName], Tuple[ColumnOrName, ...]]
) -> Column:
    """Creates a new array column from multiple input columns.

    Args:
        *args: Columns or column names to combine into an array. Can be:

            - Individual arguments
            - Lists of columns/column names
            - Tuples of columns/column names

    Returns:
        A Column expression representing an array containing values from the input columns

    Raises:
        TypeError: If any argument is not a Column, string, or collection of
            Columns/strings
    """
    flattened_args = []
    for arg in args:
        if isinstance(arg, (list, tuple)):
            flattened_args.extend(arg)
        else:
            flattened_args.append(arg)

    expr_columns = [Column._from_col_or_name(c)._logical_expr for c in flattened_args]

    return Column._from_logical_expr(ArrayExpr(expr_columns))

array_agg

array_agg(column: ColumnOrName) -> Column

Alias for collect_list().

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def array_agg(column: ColumnOrName) -> Column:
    """Alias for collect_list()."""
    return collect_list(column)

array_contains

array_contains(column: ColumnOrName, value: Union[str, int, float, bool, Column]) -> Column

Checks if array column contains a specific value.

This function returns True if the array in the specified column contains the given value, and False otherwise. Returns False if the array is None.

Parameters:

• column (ColumnOrName) –

  Column or column name containing the arrays to check.

• value (Union[str, int, float, bool, Column]) –

  Value to search for in the arrays. Can be: - A literal value (string, number, boolean) - A Column expression

Returns:

• Column –

  A boolean Column expression (True if value is found, False otherwise).

Raises:

• TypeError –

  If value type is incompatible with the array element type.

• TypeError –

  If the column does not contain array data.

Check for values in arrays

# Check if 'python' exists in arrays in the 'tags' column
df.select(array_contains("tags", "python"))

# Check using a value from another column
df.select(array_contains("tags", col("search_term")))

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def array_contains(
    column: ColumnOrName, value: Union[str, int, float, bool, Column]
) -> Column:
    """Checks if array column contains a specific value.

    This function returns True if the array in the specified column contains the given value,
    and False otherwise. Returns False if the array is None.

    Args:
        column: Column or column name containing the arrays to check.

        value: Value to search for in the arrays. Can be:
            - A literal value (string, number, boolean)
            - A Column expression

    Returns:
        A boolean Column expression (True if value is found, False otherwise).

    Raises:
        TypeError: If value type is incompatible with the array element type.
        TypeError: If the column does not contain array data.

    Example: Check for values in arrays
        ```python
        # Check if 'python' exists in arrays in the 'tags' column
        df.select(array_contains("tags", "python"))

        # Check using a value from another column
        df.select(array_contains("tags", col("search_term")))
        ```
    """
    value_column = None
    if isinstance(value, Column):
        value_column = value
    else:
        value_column = lit(value)
    return Column._from_logical_expr(
        ArrayContainsExpr(
            Column._from_col_or_name(column)._logical_expr, value_column._logical_expr
        )
    )

array_size

array_size(column: ColumnOrName) -> Column

Returns the number of elements in an array column.

This function computes the length of arrays stored in the specified column. Returns None for None arrays.

Parameters:

• column (ColumnOrName) –

  Column or column name containing arrays whose length to compute.

Returns:

• Column –

  A Column expression representing the array length.

Raises:

• TypeError –

  If the column does not contain array data.

Get array sizes

# Get the size of arrays in 'tags' column
df.select(array_size("tags"))

# Use with column reference
df.select(array_size(col("tags")))

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def array_size(column: ColumnOrName) -> Column:
    """Returns the number of elements in an array column.

    This function computes the length of arrays stored in the specified column.
    Returns None for None arrays.

    Args:
        column: Column or column name containing arrays whose length to compute.

    Returns:
        A Column expression representing the array length.

    Raises:
        TypeError: If the column does not contain array data.

    Example: Get array sizes
        ```python
        # Get the size of arrays in 'tags' column
        df.select(array_size("tags"))

        # Use with column reference
        df.select(array_size(col("tags")))
        ```
    """
    return Column._from_logical_expr(
        ArrayLengthExpr(Column._from_col_or_name(column)._logical_expr)
    )

asc

asc(column: ColumnOrName) -> Column

Creates a Column expression representing an ascending sort order.

Parameters:

• column (ColumnOrName) –

  The column to apply the ascending ordering to.

Returns:

• Column –

  A Column expression representing the column and the ascending sort order.

Raises:

• ValueError –

  If the type of the column cannot be inferred.

• Error –

  If this expression is passed to a dataframe operation besides sort() and order_by().

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def asc(column: ColumnOrName) -> Column:
    """Creates a Column expression representing an ascending sort order.

    Args:
        column: The column to apply the ascending ordering to.

    Returns:
        A Column expression representing the column and the ascending sort order.

    Raises:
        ValueError: If the type of the column cannot be inferred.
        Error: If this expression is passed to a dataframe operation besides sort() and order_by().
    """
    return Column._from_col_or_name(column).asc()

asc_nulls_first

asc_nulls_first(column: ColumnOrName) -> Column

Creates a Column expression representing an ascending sort order with nulls first.

Parameters:

• column (ColumnOrName) –

  The column to apply the ascending ordering to.

Returns:

• Column –

  A Column expression representing the column and the ascending sort order with nulls first.

Raises:

• ValueError –

  If the type of the column cannot be inferred.

• Error –

  If this expression is passed to a dataframe operation besides sort() and order_by().

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def asc_nulls_first(column: ColumnOrName) -> Column:
    """Creates a Column expression representing an ascending sort order with nulls first.

    Args:
        column: The column to apply the ascending ordering to.

    Returns:
        A Column expression representing the column and the ascending sort order with nulls first.

    Raises:
        ValueError: If the type of the column cannot be inferred.
        Error: If this expression is passed to a dataframe operation besides sort() and order_by().
    """
    return Column._from_col_or_name(column).asc_nulls_first()

asc_nulls_last

asc_nulls_last(column: ColumnOrName) -> Column

Creates a Column expression representing an ascending sort order with nulls last.

Parameters:

• column (ColumnOrName) –

  The column to apply the ascending ordering to.

Returns:

• Column –

  A Column expression representing the column and the ascending sort order with nulls last.

Raises:

• ValueError –

  If the type of the column cannot be inferred.

• Error –

  If this expression is passed to a dataframe operation besides sort() and order_by().

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def asc_nulls_last(column: ColumnOrName) -> Column:
    """Creates a Column expression representing an ascending sort order with nulls last.

    Args:
        column: The column to apply the ascending ordering to.

    Returns:
        A Column expression representing the column and the ascending sort order with nulls last.

    Raises:
        ValueError: If the type of the column cannot be inferred.
        Error: If this expression is passed to a dataframe operation besides sort() and order_by().
    """
    return Column._from_col_or_name(column).asc_nulls_last()

avg

avg(column: ColumnOrName) -> Column

Aggregate function: returns the average (mean) of all values in the specified column.

Parameters:

• column (ColumnOrName) –

  Column or column name to compute the average of

Returns:

• Column –

  A Column expression representing the average aggregation

Raises:

• TypeError –

  If column is not a Column or string

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def avg(column: ColumnOrName) -> Column:
    """Aggregate function: returns the average (mean) of all values in the specified column.

    Args:
        column: Column or column name to compute the average of

    Returns:
        A Column expression representing the average aggregation

    Raises:
        TypeError: If column is not a Column or string
    """
    return Column._from_logical_expr(
        AvgExpr(Column._from_col_or_name(column)._logical_expr)
    )

coalesce

coalesce(*cols: ColumnOrName) -> Column

Returns the first non-null value from the given columns for each row.

This function mimics the behavior of SQL's COALESCE function. It evaluates the input columns in order and returns the first non-null value encountered. If all values are null, returns null.

Parameters:

• *cols (ColumnOrName, default: () ) –

  Column expressions or column names to evaluate. Can be:

  • Individual arguments
  • Lists of columns/column names
  • Tuples of columns/column names

Returns:

• Column –

  A Column expression containing the first non-null value from the input columns.

Raises:

• ValueError –

  If no columns are provided.

Basic coalesce usage

# Basic usage
df.select(coalesce("col1", "col2", "col3"))

# With nested collections
df.select(coalesce(["col1", "col2"], "col3"))

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def coalesce(*cols: ColumnOrName) -> Column:
    """Returns the first non-null value from the given columns for each row.

    This function mimics the behavior of SQL's COALESCE function. It evaluates the input columns
    in order and returns the first non-null value encountered. If all values are null, returns null.

    Args:
        *cols: Column expressions or column names to evaluate. Can be:

            - Individual arguments
            - Lists of columns/column names
            - Tuples of columns/column names

    Returns:
        A Column expression containing the first non-null value from the input columns.

    Raises:
        ValueError: If no columns are provided.

    Example: Basic coalesce usage
        ```python
        # Basic usage
        df.select(coalesce("col1", "col2", "col3"))

        # With nested collections
        df.select(coalesce(["col1", "col2"], "col3"))
        ```
    """
    if not cols:
        raise ValueError("At least one column must be provided to coalesce method")

    flattened_args = []
    for arg in cols:
        if isinstance(arg, (list, tuple)):
            flattened_args.extend(arg)
        else:
            flattened_args.append(arg)

    flattened_exprs = [
        Column._from_col_or_name(c)._logical_expr for c in flattened_args
    ]
    return Column._from_logical_expr(CoalesceExpr(flattened_exprs))

collect_list

collect_list(column: ColumnOrName) -> Column

Aggregate function: collects all values from the specified column into a list.

Parameters:

• column (ColumnOrName) –

  Column or column name to collect values from

Returns:

• Column –

  A Column expression representing the list aggregation

Raises:

• TypeError –

  If column is not a Column or string

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def collect_list(column: ColumnOrName) -> Column:
    """Aggregate function: collects all values from the specified column into a list.

    Args:
        column: Column or column name to collect values from

    Returns:
        A Column expression representing the list aggregation

    Raises:
        TypeError: If column is not a Column or string
    """
    return Column._from_logical_expr(
        ListExpr(Column._from_col_or_name(column)._logical_expr)
    )

count

count(column: ColumnOrName) -> Column

Aggregate function: returns the count of non-null values in the specified column.

Parameters:

• column (ColumnOrName) –

  Column or column name to count values in

Returns:

• Column –

  A Column expression representing the count aggregation

Raises:

• TypeError –

  If column is not a Column or string

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def count(column: ColumnOrName) -> Column:
    """Aggregate function: returns the count of non-null values in the specified column.

    Args:
        column: Column or column name to count values in

    Returns:
        A Column expression representing the count aggregation

    Raises:
        TypeError: If column is not a Column or string
    """
    if isinstance(column, str) and column == "*":
        return Column._from_logical_expr(CountExpr(lit("*")._logical_expr))
    return Column._from_logical_expr(
        CountExpr(Column._from_col_or_name(column)._logical_expr)
    )

desc

desc(column: ColumnOrName) -> Column

Creates a Column expression representing a descending sort order.

Parameters:

• column (ColumnOrName) –

  The column to apply the descending ordering to.

Returns:

• Column –

  A Column expression representing the column and the descending sort order.

Raises:

• ValueError –

  If the type of the column cannot be inferred.

• Error –

  If this expression is passed to a dataframe operation besides sort() and order_by().

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def desc(column: ColumnOrName) -> Column:
    """Creates a Column expression representing a descending sort order.

    Args:
        column: The column to apply the descending ordering to.

    Returns:
        A Column expression representing the column and the descending sort order.

    Raises:
        ValueError: If the type of the column cannot be inferred.
        Error: If this expression is passed to a dataframe operation besides sort() and order_by().
    """
    return Column._from_col_or_name(column).desc()

desc_nulls_first

desc_nulls_first(column: ColumnOrName) -> Column

Creates a Column expression representing a descending sort order with nulls first.

Parameters:

• column (ColumnOrName) –

  The column to apply the descending ordering to.

Returns:

• Column –

  A Column expression representing the column and the descending sort order with nulls first.

Raises:

• ValueError –

  If the type of the column cannot be inferred.

• Error –

  If this expression is passed to a dataframe operation besides sort() and order_by().

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def desc_nulls_first(column: ColumnOrName) -> Column:
    """Creates a Column expression representing a descending sort order with nulls first.

    Args:
        column: The column to apply the descending ordering to.

    Returns:
        A Column expression representing the column and the descending sort order with nulls first.

    Raises:
        ValueError: If the type of the column cannot be inferred.
        Error: If this expression is passed to a dataframe operation besides sort() and order_by().
    """
    return Column._from_col_or_name(column).desc_nulls_first()

desc_nulls_last

desc_nulls_last(column: ColumnOrName) -> Column

Creates a Column expression representing a descending sort order with nulls last.

Parameters:

• column (ColumnOrName) –

  The column to apply the descending ordering to.

Returns:

• Column –

  A Column expression representing the column and the descending sort order with nulls last.

Raises:

• ValueError –

  If the type of the column cannot be inferred.

• Error –

  If this expression is passed to a dataframe operation besides sort() and order_by().

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def desc_nulls_last(column: ColumnOrName) -> Column:
    """Creates a Column expression representing a descending sort order with nulls last.

    Args:
        column: The column to apply the descending ordering to.

    Returns:
        A Column expression representing the column and the descending sort order with nulls last.

    Raises:
        ValueError: If the type of the column cannot be inferred.
        Error: If this expression is passed to a dataframe operation besides sort() and order_by().
    """
    return Column._from_col_or_name(column).desc_nulls_last()

first

first(column: ColumnOrName) -> Column

Aggregate function: returns the first non-null value in the specified column.

Typically used in aggregations to select the first observed value per group.

Parameters:

• column (ColumnOrName) –

  Column or column name.

Returns:

• Column –

  Column expression for the first value.

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def first(column: ColumnOrName) -> Column:
    """Aggregate function: returns the first non-null value in the specified column.

    Typically used in aggregations to select the first observed value per group.

    Args:
        column: Column or column name.

    Returns:
        Column expression for the first value.
    """
    return Column._from_logical_expr(
        FirstExpr(Column._from_col_or_name(column)._logical_expr)
    )

max

max(column: ColumnOrName) -> Column

Aggregate function: returns the maximum value in the specified column.

Parameters:

• column (ColumnOrName) –

  Column or column name to compute the maximum of

Returns:

• Column –

  A Column expression representing the maximum aggregation

Raises:

• TypeError –

  If column is not a Column or string

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def max(column: ColumnOrName) -> Column:
    """Aggregate function: returns the maximum value in the specified column.

    Args:
        column: Column or column name to compute the maximum of

    Returns:
        A Column expression representing the maximum aggregation

    Raises:
        TypeError: If column is not a Column or string
    """
    return Column._from_logical_expr(
        MaxExpr(Column._from_col_or_name(column)._logical_expr)
    )

mean

mean(column: ColumnOrName) -> Column

Aggregate function: returns the mean (average) of all values in the specified column.

Alias for avg().

Parameters:

• column (ColumnOrName) –

  Column or column name to compute the mean of

Returns:

• Column –

  A Column expression representing the mean aggregation

Raises:

• TypeError –

  If column is not a Column or string

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def mean(column: ColumnOrName) -> Column:
    """Aggregate function: returns the mean (average) of all values in the specified column.

    Alias for avg().

    Args:
        column: Column or column name to compute the mean of

    Returns:
        A Column expression representing the mean aggregation

    Raises:
        TypeError: If column is not a Column or string
    """
    return Column._from_logical_expr(
        AvgExpr(Column._from_col_or_name(column)._logical_expr)
    )

min

min(column: ColumnOrName) -> Column

Aggregate function: returns the minimum value in the specified column.

Parameters:

• column (ColumnOrName) –

  Column or column name to compute the minimum of

Returns:

• Column –

  A Column expression representing the minimum aggregation

Raises:

• TypeError –

  If column is not a Column or string

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def min(column: ColumnOrName) -> Column:
    """Aggregate function: returns the minimum value in the specified column.

    Args:
        column: Column or column name to compute the minimum of

    Returns:
        A Column expression representing the minimum aggregation

    Raises:
        TypeError: If column is not a Column or string
    """
    return Column._from_logical_expr(
        MinExpr(Column._from_col_or_name(column)._logical_expr)
    )

stddev

stddev(column: ColumnOrName) -> Column

Aggregate function: returns the sample standard deviation of the specified column.

Parameters:

• column (ColumnOrName) –

  Column or column name.

Returns:

• Column –

  Column expression for sample standard deviation.

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def stddev(column: ColumnOrName) -> Column:
    """Aggregate function: returns the sample standard deviation of the specified column.

    Args:
        column: Column or column name.

    Returns:
        Column expression for sample standard deviation.
    """
    return Column._from_logical_expr(
        StdDevExpr(Column._from_col_or_name(column)._logical_expr)
    )

struct

struct(*args: Union[ColumnOrName, List[ColumnOrName], Tuple[ColumnOrName, ...]]) -> Column

Creates a new struct column from multiple input columns.

Parameters:

• *args (Union[ColumnOrName, List[ColumnOrName], Tuple[ColumnOrName, ...]], default: () ) –

  Columns or column names to combine into a struct. Can be:

  • Individual arguments
  • Lists of columns/column names
  • Tuples of columns/column names

Returns:

• Column –

  A Column expression representing a struct containing the input columns

Raises:

• TypeError –

  If any argument is not a Column, string, or collection of Columns/strings

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def struct(
    *args: Union[ColumnOrName, List[ColumnOrName], Tuple[ColumnOrName, ...]]
) -> Column:
    """Creates a new struct column from multiple input columns.

    Args:
        *args: Columns or column names to combine into a struct. Can be:

            - Individual arguments
            - Lists of columns/column names
            - Tuples of columns/column names

    Returns:
        A Column expression representing a struct containing the input columns

    Raises:
        TypeError: If any argument is not a Column, string, or collection of
            Columns/strings
    """
    flattened_args = []
    for arg in args:
        if isinstance(arg, (list, tuple)):
            flattened_args.extend(arg)
        else:
            flattened_args.append(arg)

    expr_columns = [Column._from_col_or_name(c)._logical_expr for c in flattened_args]

    return Column._from_logical_expr(StructExpr(expr_columns))

sum

sum(column: ColumnOrName) -> Column

Aggregate function: returns the sum of all values in the specified column.

Parameters:

• column (ColumnOrName) –

  Column or column name to compute the sum of

Returns:

• Column –

  A Column expression representing the sum aggregation

Raises:

• TypeError –

  If column is not a Column or string

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def sum(column: ColumnOrName) -> Column:
    """Aggregate function: returns the sum of all values in the specified column.

    Args:
        column: Column or column name to compute the sum of

    Returns:
        A Column expression representing the sum aggregation

    Raises:
        TypeError: If column is not a Column or string
    """
    return Column._from_logical_expr(
        SumExpr(Column._from_col_or_name(column)._logical_expr)
    )

udf

udf(f: Optional[Callable] = None, *, return_type: DataType)

A decorator or function for creating user-defined functions (UDFs) that can be applied to DataFrame rows.

When applied, UDFs will: - Access StructType columns as Python dictionaries (dict[str, Any]). - Access ArrayType columns as Python lists (list[Any]). - Access primitive types (e.g., int, float, str) as their respective Python types.

Parameters:

• f (Optional[Callable], default: None ) –

  Python function to convert to UDF

• return_type (DataType) –

  Expected return type of the UDF. Required parameter.

UDF with primitive types

# UDF with primitive types
@udf(return_type=IntegerType)
def add_one(x: int):
    return x + 1

# Or
add_one = udf(lambda x: x + 1, return_type=IntegerType)

UDF with nested types

# UDF with nested types
@udf(return_type=StructType([StructField("value1", IntegerType), StructField("value2", IntegerType)]))
def example_udf(x: dict[str, int], y: list[int]):
    return {
        "value1": x["value1"] + x["value2"] + y[0],
        "value2": x["value1"] + x["value2"] + y[1],
    }

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def udf(f: Optional[Callable] = None, *, return_type: DataType):
    """A decorator or function for creating user-defined functions (UDFs) that can be applied to DataFrame rows.

    When applied, UDFs will:
    - Access `StructType` columns as Python dictionaries (`dict[str, Any]`).
    - Access `ArrayType` columns as Python lists (`list[Any]`).
    - Access primitive types (e.g., `int`, `float`, `str`) as their respective Python types.

    Args:
        f: Python function to convert to UDF

        return_type: Expected return type of the UDF. Required parameter.

    Example: UDF with primitive types
        ```python
        # UDF with primitive types
        @udf(return_type=IntegerType)
        def add_one(x: int):
            return x + 1

        # Or
        add_one = udf(lambda x: x + 1, return_type=IntegerType)
        ```

    Example: UDF with nested types
        ```python
        # UDF with nested types
        @udf(return_type=StructType([StructField("value1", IntegerType), StructField("value2", IntegerType)]))
        def example_udf(x: dict[str, int], y: list[int]):
            return {
                "value1": x["value1"] + x["value2"] + y[0],
                "value2": x["value1"] + x["value2"] + y[1],
            }
        ```
    """

    def _create_udf(func: Callable) -> Callable:
        @wraps(func)
        def _udf_wrapper(*cols: ColumnOrName) -> Column:
            col_exprs = [Column._from_col_or_name(c)._logical_expr for c in cols]
            return Column._from_logical_expr(UDFExpr(func, col_exprs, return_type))

        return _udf_wrapper

    if f is not None:
        return _create_udf(f)
    return _create_udf

when

when(condition: Column, value: Column) -> Column

Evaluates a condition and returns a value if true.

This function is used to create conditional expressions. If Column.otherwise() is not invoked, None is returned for unmatched conditions.

Parameters:

• condition (Column) –

  A boolean Column expression to evaluate.

• value (Column) –

  A Column expression to return if the condition is true.

Returns:

• Column –

  A Column expression that evaluates the condition and returns the specified value when true,

• Column –

  and None otherwise.

Raises:

• TypeError –

  If the condition is not a boolean Column expression.

Basic conditional expression

# Basic usage
df.select(when(col("age") > 18, lit("adult")))

# With otherwise
df.select(when(col("age") > 18, lit("adult")).otherwise(lit("minor")))

Source code in src/fenic/api/functions/builtin.py

@validate_call(config=ConfigDict(strict=True, arbitrary_types_allowed=True))
def when(condition: Column, value: Column) -> Column:
    """Evaluates a condition and returns a value if true.

    This function is used to create conditional expressions. If Column.otherwise() is not invoked,
    None is returned for unmatched conditions.

    Args:
        condition: A boolean Column expression to evaluate.

        value: A Column expression to return if the condition is true.

    Returns:
        A Column expression that evaluates the condition and returns the specified value when true,
        and None otherwise.

    Raises:
        TypeError: If the condition is not a boolean Column expression.

    Example: Basic conditional expression
        ```python
        # Basic usage
        df.select(when(col("age") > 18, lit("adult")))

        # With otherwise
        df.select(when(col("age") > 18, lit("adult")).otherwise(lit("minor")))
        ```
    """
    return Column._from_logical_expr(
        WhenExpr(None, condition._logical_expr, value._logical_expr)
    )