Struct Column 

pub struct Column {
    pub name: String,
    pub ty: ColType,
    pub cells: Vec<Value>,
}

One named column with an inferred type and its cells in row order.

Fields

name: String

ty: ColType

cells: Vec<Value>

Implementations

impl Column

pub fn new(name: impl Into<String>, cells: Vec<Value>) -> Self

Builds a column from name and cells, inferring ty by folding each cell’s contributed type through ColType::unify.

pub fn numeric(&self) -> Vec<f64>

The finite numeric projection of this column (nulls and non-numeric cells dropped). Empty for non-numeric columns — honest absence, not zeros.

pub fn null_count(&self) -> usize

Count of null cells.

pub fn len(&self) -> usize

pub fn is_empty(&self) -> bool

impl Column

pub fn role(&self) -> Role

Classifies this column’s Role. Deterministic; order of checks matters: constant first, then identifier (by name), then — for numeric columns — a monotonic sequence else a continuous measurement; non-numeric columns that are none of the above are categorical (labels/codes/free text).

Cardinality is deliberately not used to call a numeric column “categorical”: a column that is one value with a few wild outliers has low cardinality yet is exactly what point detection should catch. Identifiers are caught by name, not by how many distinct values they hold.

Trait Implementations

impl Clone for Column

fn clone(&self) -> Column

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Column

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Column

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for Column

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.