GH-43352: [Docs][Python] Add all tensor classes documentation

docs/source/index.rst

@@ -137,3 +137,4 @@ Examples
    Java cookbook <https://arrow.apache.org/cookbook/java/>
    Python cookbook <https://arrow.apache.org/cookbook/py/>
    R cookbook <https://arrow.apache.org/cookbook/r/>
+

docs/source/python/api/tables.rst

@@ -54,97 +54,7 @@ Dataframe Interchange Protocol
 
    interchange.from_dataframe
 
-.. _api.tensor:
+See Also
+--------
 
-Tensors
--------
-
-PyArrow supports both dense and sparse tensors. Dense tensors store all data values explicitly, while sparse tensors represent only the non-zero elements and their locations, making them efficient for storage and computation.
-
-Dense Tensors
--------------
-
-.. autosummary::
-   :toctree: ../generated/
-
-   Tensor
-
-Sparse Tensors
---------------
-
-PyArrow supports the following sparse tensor formats:
-
-.. autosummary::
-   :toctree: ../generated/
-
-   SparseCOOTensor
-   SparseCSRMatrix
-   SparseCSCMatrix
-   SparseCSFTensor
-
-### SparseCOOTensor
-
-The `SparseCOOTensor` represents a sparse tensor in Coordinate (COO) format, where non-zero elements are stored as tuples of row and column indices.
-
-Example:
-.. code-block:: python
-
-   import pyarrow as pa
-
-   indices = pa.array([[0, 0], [1, 2]])
-   data = pa.array([1, 2])
-   shape = (2, 3)
-
-   tensor = pa.SparseCOOTensor(indices, data, shape)
-   print(tensor.to_dense())
-
-### SparseCSRMatrix
-
-The `SparseCSRMatrix` represents a sparse matrix in Compressed Sparse Row (CSR) format. This format is useful for matrix-vector multiplication.
-
-Example:
-.. code-block:: python
-
-   import pyarrow as pa
-
-   data = pa.array([1, 2, 3])
-   indptr = pa.array([0, 2, 3])
-   indices = pa.array([0, 2, 1])
-   shape = (2, 3)
-
-   sparse_matrix = pa.SparseCSRMatrix.from_numpy(data, indptr, indices, shape)
-   print(sparse_matrix)
-
-### SparseCSCMatrix
-
-The `SparseCSCMatrix` represents a sparse matrix in Compressed Sparse Column (CSC) format, where data is stored by columns.
-
-Example:
-.. code-block:: python
-
-   import pyarrow as pa
-
-   data = pa.array([1, 2, 3])
-   indptr = pa.array([0, 1, 3])
-   indices = pa.array([0, 1, 2])
-   shape = (3, 2)
-
-   sparse_matrix = pa.SparseCSCMatrix.from_numpy(data, indptr, indices, shape)
-   print(sparse_matrix)
-
-### SparseCSFTensor
-
-The `SparseCSFTensor` represents a sparse tensor in Compressed Sparse Fiber (CSF) format, which is a generalization of the CSR format for higher dimensions.
-
-Example:
-.. code-block:: python
-
-   import pyarrow as pa
-
-   data = pa.array([1, 2, 3])
-   indptr = [pa.array([0, 1, 3]), pa.array([0, 2, 3])]
-   indices = [pa.array([0, 1]), pa.array([0, 1, 2])]
-   shape = (2, 3, 2)
-
-   sparse_tensor = pa.SparseCSFTensor.from_numpy(data, indptr, indices, shape)
-   print(sparse_tensor)
+For information about tensors, refer to :doc:`tensors`

docs/source/python/api/tensors.rst

@@ -110,4 +110,5 @@ Example:
    shape = (2, 3, 2)
 
    sparse_tensor = pa.SparseCSFTensor.from_numpy(data, indptr, indices, shape)
-   print(sparse_tensor)
+   print(sparse_tensor)
+