jorisvandenbossche commented on code in PR #41870:
URL: https://github.com/apache/arrow/pull/41870#discussion_r1634290749
##########
cpp/src/arrow/table.h:
##########
@@ -101,6 +101,19 @@ class ARROW_EXPORT Table {
static Result<std::shared_ptr<Table>> FromChunkedStructArray(
const std::shared_ptr<ChunkedArray>& array);
+ /// \brief Convert Table to Tensor
+ ///
+ /// Create a Tensor object with shape (number of rows, number of columns) and
+ /// strides (type size in bytes, type size in bytes * number of rows).
Review Comment:
The exact strides depend on whether the result is row major or column major,
I think? So in that case I would maybe just leave that out here.
##########
python/pyarrow/table.pxi:
##########
@@ -4873,6 +4869,81 @@ cdef class Table(_Tabular):
return result
+ def to_tensor(self, c_bool null_to_nan=False, c_bool row_major=True,
MemoryPool memory_pool=None):
+ """
+ Convert to a :class:`~pyarrow.Tensor`.
+
+ Tables that can be converted have fields of type signed or unsigned
integer or float,
+ including all bit-widths.
+
+ ``null_to_nan`` is ``False`` by default and this method will raise an
error in case
+ any nulls are present. Tables with nulls can be converted with
``null_to_nan`` set to
+ ``True``. In this case null values are converted to ``NaN`` and
integer type arrays are
+ promoted to the appropriate float type.
+
+ Parameters
+ ----------
+ null_to_nan : bool, default False
+ Whether to write null values in the result as ``NaN``.
+ row_major : bool, default True
+ Whether resulting Tensor is row-major or column-major
+ memory_pool : MemoryPool, default None
+ For memory allocations, if required, otherwise use default pool
+
+ Examples
+ --------
+ >>> import pyarrow as pa
+ >>> table = pa.table(
+ ... [
+ ... pa.chunked_array([[1, 2], [3, 4, None]], type=pa.int32()),
+ ... pa.chunked_array([[10, 20, 30], [40, None]],
type=pa.float32()),
+ ... ], names = ["a", "b"]
+ ... )
+
+ >>> table
+ pyarrow.Table
+ a: int32
+ b: float
+ ----
+ a: [[1,2],[3,4,null]]
+ b: [[10,20,30],[40,null]]
+
+ Convert a Table to row-major Tensor with null values written as
``NaN``s:
Review Comment:
```suggestion
Convert a Table to row-major Tensor with null values written as
``NaN``:
```
(I am not entirely sure that will render correctly if you have a character
directly after the ``` `` ```)
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