As mentioned in the introduction, data frames on the one hand and
vectors and matrices on the other hand or complimentary.
With data frames, the focus is on identifying rows and columns through their keys,
while with vectors and matrices, the focus is on the values.
Still, since matrices may also have row and column indexes,
it makes sense that they may at times be used as data frames.
In particular, vectors and matrices can serve as the data source
for a statistical or machine learning model.
The essence of data frame functionality is captured in the
interface. This interface provides access to the (untyped) row and column
indexes, and to individual columns.
Both the VectorT
type implement this interface.
properties return the row and column indexes of a matrix, if one has been defined.
These properties can be written to as well. The only requirement is that the new index
has the correct number of elements.
A vector acts as a data frame with a single column.
property corresponds to the row index.
property return the column key converted to a string.
The column index can still be accessed through the
Conversions exist between data frames and matrices.
The DataFrameR, C class
has a ToMatrixT(Boolean, Boolean)
method that converts the columns of a data frame to a matrix with element type
specified by the generic type argument. This method takes two arguments, both
Boolean values. The first
specifies whether columns whose element type is incompatible with the element type
of the matrix are to be skipped. The default is .
The second value specifies whether the element types should match exactly.
The default is .
Likewise, the MatrixT
type has a ToDataFrame
method that has two overloads. The first overload converts a matrix which has
row and column indexes. It takes no actual arguments but
does take two generic type arguments that specify the element types of the
row and column index. These types must match the element types of the indexes
of the matrix. The second overload takes two arguments: the row and column index
of the new data frame.