we can think about 4 quadrants in the dataframe representation:
1. the region where the names of the indexes are presented
2. the region where the column values are shown
3. the region where the index values are shown
4. the region where the values are shown
there are natural solutions when either row or columns are provided.
the names are column or row scoped headers for the primary axis of the table.
a table with column names is column major while row names are row major.
there are ambiguities about the major axis when both the indexes are named,
this poses specific challenges to region 1 our table representation,
it is likely that under these conditions we will have empty cells.
we'll need principles that help us choose the best conformation under ambiguous conditions.
if empty cells are presented, should they bed in `thead`? `tbody`?
is there an advantage to empty columns vs empty rows.
again this only matters in the single axis use case.
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we can think about 4 quadrants in the dataframe representation:
the region where the names of the indexes are presented
the region where the column values are shown
the region where the index values are shown
the region where the values are shown
there are natural solutions when either row or columns are provided.
the names are column or row scoped headers for the primary axis of the table.
a table with column names is column major while row names are row major.
there are ambiguities about the major axis when both the indexes are named,
this poses specific challenges to region 1 our table representation,
it is likely that under these conditions we will have empty cells.
we'll need principles that help us choose the best conformation under ambiguous conditions.
if empty cells are presented, should they bed in
thead
?
tbody
?
is there an advantage to empty columns vs empty rows.
again this only matters in the single axis use case.