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Posted to github@arrow.apache.org by "AlenkaF (via GitHub)" <gi...@apache.org> on 2023/04/07 07:27:10 UTC
[GitHub] [arrow] AlenkaF opened a new pull request, #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
AlenkaF opened a new pull request, #34957:
URL: https://github.com/apache/arrow/pull/34957
### Rationale for this change
This PR adds examples of the use of `FixedShapeTensorType`to the PyArrow user guide. Should be reviewed and merged after https://github.com/apache/arrow/pull/34883 is done.
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[GitHub] [arrow] AlenkaF commented on pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "AlenkaF (via GitHub)" <gi...@apache.org>.
AlenkaF commented on PR #34957:
URL: https://github.com/apache/arrow/pull/34957#issuecomment-1504807634
> I understand that we initially added this to extending_types.rst (when it was an example of implementing it). But now this is actually part of pyarrow, I am wondering if we would want to move it to a different location in our docs.
> Currently, other data types are mentioned at https://arrow.apache.org/docs/dev/python/data.html, but that's already a long page about a lot of things, so also wouldn't just add it there. Maybe for now we can keep it here, and can think about reorganizing it later.
I will open an issue for this as I agree it should be reorganised (and I just remembered we have talked about this before, sorry).
Otherwise I have included the changes with the last two commits.
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[GitHub] [arrow] github-actions[bot] commented on pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #34957:
URL: https://github.com/apache/arrow/pull/34957#issuecomment-1500021809
* Closes: #34956
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[GitHub] [arrow] jorisvandenbossche commented on a diff in pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "jorisvandenbossche (via GitHub)" <gi...@apache.org>.
jorisvandenbossche commented on code in PR #34957:
URL: https://github.com/apache/arrow/pull/34957#discussion_r1163696817
##########
docs/source/python/extending_types.rst:
##########
@@ -357,3 +357,143 @@ pandas ``ExtensionArray``. This method should have the following signature::
This way, you can control the conversion of a pyarrow ``Array`` of your pyarrow
extension type to a pandas ``ExtensionArray`` that can be stored in a DataFrame.
+
+
+Canonical extension types
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can find the official list of canonical extension types in the
+:ref:`format_canonical_extensions` section. Here we add examples on how to
+use them in pyarrow.
+
+Fixed size tensor
+"""""""""""""""""
+
+To create an array of tensors with equal shape (fixed shape tensor array) we
+first need to define a fixed shape tensor extension type with value type
+and shape:
+
+.. code-block:: python
+
+ >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), (2, 2))
+
+Then we need the storage array with :func:`pyarrow.list_` type where ``value_type```
+is the fixed shape tensor value type and list size is a product of ``tensor_type``
+shape elements. Then we can create an array of tensors with
+``pa.ExtensionArray.from_storage()`` method:
+
+.. code-block:: python
+
+ >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
+ >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
+ >>> tensor_array = pa.ExtensionArray.from_storage(tensor_type, storage)
+
+We can also create another array of tensors with different value type:
+
+.. code-block:: python
+
+ >>> tensor_type_2 = pa.fixed_shape_tensor(pa.float32(), (2, 2))
+ >>> storage_2 = pa.array(arr, pa.list_(pa.float32(), 4))
+ >>> tensor_array_2 = pa.ExtensionArray.from_storage(tensor_type_2, storage_2)
+
+Extension arrays can be used as columns in ``pyarrow.Table`` or
+``pyarrow.RecordBatch``:
+
+.. code-block:: python
+
+ >>> data = [
+ ... pa.array([1, 2, 3]),
+ ... pa.array(['foo', 'bar', None]),
+ ... pa.array([True, None, True]),
+ ... tensor_array,
+ ... tensor_array_2
+ ... ]
+ >>> my_schema = pa.schema([('f0', pa.int8()),
+ ... ('f1', pa.string()),
+ ... ('f2', pa.bool_()),
+ ... ('tensors_int', tensor_type),
+ ... ('tensors_float', tensor_type_2)])
+ >>> table = pa.Table.from_arrays(data, schema=my_schema)
+ >>> table
+ pyarrow.Table
+ f0: int8
+ f1: string
+ f2: bool
+ tensors_int: extension<arrow.fixed_size_tensor>
+ tensors_float: extension<arrow.fixed_size_tensor>
+ ----
+ f0: [[1,2,3]]
+ f1: [["foo","bar",null]]
+ f2: [[true,null,true]]
+ tensors_int: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]
+ tensors_float: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]
+
+We can also convert a tensor array to a numpy ndarray:
Review Comment:
```suggestion
We can also convert a tensor array to a single multi-dimensional numpy ndarray:
```
(to contrast it with the 1D result of `to_numpy()`)
##########
docs/source/python/extending_types.rst:
##########
@@ -357,3 +357,143 @@ pandas ``ExtensionArray``. This method should have the following signature::
This way, you can control the conversion of a pyarrow ``Array`` of your pyarrow
extension type to a pandas ``ExtensionArray`` that can be stored in a DataFrame.
+
+
+Canonical extension types
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can find the official list of canonical extension types in the
+:ref:`format_canonical_extensions` section. Here we add examples on how to
+use them in pyarrow.
+
+Fixed size tensor
+"""""""""""""""""
+
+To create an array of tensors with equal shape (fixed shape tensor array) we
+first need to define a fixed shape tensor extension type with value type
+and shape:
+
+.. code-block:: python
+
+ >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), (2, 2))
+
+Then we need the storage array with :func:`pyarrow.list_` type where ``value_type```
+is the fixed shape tensor value type and list size is a product of ``tensor_type``
+shape elements. Then we can create an array of tensors with
+``pa.ExtensionArray.from_storage()`` method:
+
+.. code-block:: python
+
+ >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
+ >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
+ >>> tensor_array = pa.ExtensionArray.from_storage(tensor_type, storage)
+
+We can also create another array of tensors with different value type:
+
+.. code-block:: python
+
+ >>> tensor_type_2 = pa.fixed_shape_tensor(pa.float32(), (2, 2))
+ >>> storage_2 = pa.array(arr, pa.list_(pa.float32(), 4))
+ >>> tensor_array_2 = pa.ExtensionArray.from_storage(tensor_type_2, storage_2)
+
+Extension arrays can be used as columns in ``pyarrow.Table`` or
+``pyarrow.RecordBatch``:
+
+.. code-block:: python
+
+ >>> data = [
+ ... pa.array([1, 2, 3]),
+ ... pa.array(['foo', 'bar', None]),
+ ... pa.array([True, None, True]),
+ ... tensor_array,
+ ... tensor_array_2
+ ... ]
+ >>> my_schema = pa.schema([('f0', pa.int8()),
+ ... ('f1', pa.string()),
+ ... ('f2', pa.bool_()),
+ ... ('tensors_int', tensor_type),
+ ... ('tensors_float', tensor_type_2)])
+ >>> table = pa.Table.from_arrays(data, schema=my_schema)
+ >>> table
+ pyarrow.Table
+ f0: int8
+ f1: string
+ f2: bool
+ tensors_int: extension<arrow.fixed_size_tensor>
+ tensors_float: extension<arrow.fixed_size_tensor>
+ ----
+ f0: [[1,2,3]]
+ f1: [["foo","bar",null]]
+ f2: [[true,null,true]]
+ tensors_int: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]
+ tensors_float: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]
+
+We can also convert a tensor array to a numpy ndarray:
+
+.. code-block:: python
+
+ >>> numpy_tensor = tensor_array_2.to_numpy_ndarray()
+ >>> numpy_tensor
+ array([[[ 1., 2.],
+ [ 3., 4.]],
+ [[ 10., 20.],
+ [ 30., 40.]],
+ [[100., 200.],
+ [300., 400.]]])
+
+.. note::
+
+ Both optional parameters, ``permutation`` and ``dim_names``, are meant to provide the user
+ with the information about the logical layout of the data compared to the physical layout.
+
+ The conversion to numpy ndarray is only possible for trivial permutations (``None`` or
+ ``[0, 1, ... N-1]`` where ``N`` is the number of tensor dimensions).
+
+And also the other way around, we can convert a list of numpy ndarrays to a fixed shape tensor
Review Comment:
And I would maybe say something about the first dimension of the ndarray becoming the length of the extension array (and maybe give an example that ndarray of shape (3, 2, 2) becomes an arrow array of length 3 with tensor elements of shape (2, 2))
##########
docs/source/python/extending_types.rst:
##########
@@ -357,3 +357,143 @@ pandas ``ExtensionArray``. This method should have the following signature::
This way, you can control the conversion of a pyarrow ``Array`` of your pyarrow
extension type to a pandas ``ExtensionArray`` that can be stored in a DataFrame.
+
+
+Canonical extension types
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can find the official list of canonical extension types in the
+:ref:`format_canonical_extensions` section. Here we add examples on how to
+use them in pyarrow.
+
+Fixed size tensor
+"""""""""""""""""
+
+To create an array of tensors with equal shape (fixed shape tensor array) we
+first need to define a fixed shape tensor extension type with value type
+and shape:
+
+.. code-block:: python
+
+ >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), (2, 2))
+
+Then we need the storage array with :func:`pyarrow.list_` type where ``value_type```
+is the fixed shape tensor value type and list size is a product of ``tensor_type``
+shape elements. Then we can create an array of tensors with
+``pa.ExtensionArray.from_storage()`` method:
+
+.. code-block:: python
+
+ >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
+ >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
+ >>> tensor_array = pa.ExtensionArray.from_storage(tensor_type, storage)
+
+We can also create another array of tensors with different value type:
+
+.. code-block:: python
+
+ >>> tensor_type_2 = pa.fixed_shape_tensor(pa.float32(), (2, 2))
+ >>> storage_2 = pa.array(arr, pa.list_(pa.float32(), 4))
+ >>> tensor_array_2 = pa.ExtensionArray.from_storage(tensor_type_2, storage_2)
+
+Extension arrays can be used as columns in ``pyarrow.Table`` or
+``pyarrow.RecordBatch``:
+
+.. code-block:: python
+
+ >>> data = [
+ ... pa.array([1, 2, 3]),
+ ... pa.array(['foo', 'bar', None]),
+ ... pa.array([True, None, True]),
+ ... tensor_array,
+ ... tensor_array_2
+ ... ]
+ >>> my_schema = pa.schema([('f0', pa.int8()),
+ ... ('f1', pa.string()),
+ ... ('f2', pa.bool_()),
+ ... ('tensors_int', tensor_type),
+ ... ('tensors_float', tensor_type_2)])
+ >>> table = pa.Table.from_arrays(data, schema=my_schema)
+ >>> table
+ pyarrow.Table
+ f0: int8
+ f1: string
+ f2: bool
+ tensors_int: extension<arrow.fixed_size_tensor>
+ tensors_float: extension<arrow.fixed_size_tensor>
+ ----
+ f0: [[1,2,3]]
+ f1: [["foo","bar",null]]
+ f2: [[true,null,true]]
+ tensors_int: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]
+ tensors_float: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]
+
+We can also convert a tensor array to a numpy ndarray:
+
+.. code-block:: python
+
+ >>> numpy_tensor = tensor_array_2.to_numpy_ndarray()
+ >>> numpy_tensor
+ array([[[ 1., 2.],
+ [ 3., 4.]],
+ [[ 10., 20.],
+ [ 30., 40.]],
+ [[100., 200.],
+ [300., 400.]]])
+
+.. note::
+
+ Both optional parameters, ``permutation`` and ``dim_names``, are meant to provide the user
+ with the information about the logical layout of the data compared to the physical layout.
+
+ The conversion to numpy ndarray is only possible for trivial permutations (``None`` or
+ ``[0, 1, ... N-1]`` where ``N`` is the number of tensor dimensions).
+
+And also the other way around, we can convert a list of numpy ndarrays to a fixed shape tensor
Review Comment:
```suggestion
And also the other way around, we can convert a numpy ndarray to a fixed shape tensor
```
##########
docs/source/python/extending_types.rst:
##########
@@ -357,3 +357,143 @@ pandas ``ExtensionArray``. This method should have the following signature::
This way, you can control the conversion of a pyarrow ``Array`` of your pyarrow
extension type to a pandas ``ExtensionArray`` that can be stored in a DataFrame.
+
+
+Canonical extension types
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can find the official list of canonical extension types in the
+:ref:`format_canonical_extensions` section. Here we add examples on how to
+use them in pyarrow.
+
+Fixed size tensor
+"""""""""""""""""
+
+To create an array of tensors with equal shape (fixed shape tensor array) we
+first need to define a fixed shape tensor extension type with value type
+and shape:
+
+.. code-block:: python
+
+ >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), (2, 2))
+
+Then we need the storage array with :func:`pyarrow.list_` type where ``value_type```
+is the fixed shape tensor value type and list size is a product of ``tensor_type``
+shape elements. Then we can create an array of tensors with
+``pa.ExtensionArray.from_storage()`` method:
+
+.. code-block:: python
+
+ >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
+ >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
+ >>> tensor_array = pa.ExtensionArray.from_storage(tensor_type, storage)
+
+We can also create another array of tensors with different value type:
+
+.. code-block:: python
+
+ >>> tensor_type_2 = pa.fixed_shape_tensor(pa.float32(), (2, 2))
+ >>> storage_2 = pa.array(arr, pa.list_(pa.float32(), 4))
+ >>> tensor_array_2 = pa.ExtensionArray.from_storage(tensor_type_2, storage_2)
+
+Extension arrays can be used as columns in ``pyarrow.Table`` or
+``pyarrow.RecordBatch``:
+
+.. code-block:: python
+
+ >>> data = [
+ ... pa.array([1, 2, 3]),
+ ... pa.array(['foo', 'bar', None]),
+ ... pa.array([True, None, True]),
+ ... tensor_array,
+ ... tensor_array_2
+ ... ]
+ >>> my_schema = pa.schema([('f0', pa.int8()),
+ ... ('f1', pa.string()),
+ ... ('f2', pa.bool_()),
+ ... ('tensors_int', tensor_type),
+ ... ('tensors_float', tensor_type_2)])
+ >>> table = pa.Table.from_arrays(data, schema=my_schema)
+ >>> table
+ pyarrow.Table
+ f0: int8
+ f1: string
+ f2: bool
+ tensors_int: extension<arrow.fixed_size_tensor>
+ tensors_float: extension<arrow.fixed_size_tensor>
+ ----
+ f0: [[1,2,3]]
+ f1: [["foo","bar",null]]
+ f2: [[true,null,true]]
+ tensors_int: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]
+ tensors_float: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]]
+
+We can also convert a tensor array to a numpy ndarray:
+
+.. code-block:: python
+
+ >>> numpy_tensor = tensor_array_2.to_numpy_ndarray()
+ >>> numpy_tensor
+ array([[[ 1., 2.],
+ [ 3., 4.]],
+ [[ 10., 20.],
Review Comment:
I think the alignment is a bit off here. If I copy past this from a console, it looks like:
```
In [27]: tensor_array_2.to_numpy_ndarray()
Out[27]:
array([[[ 1., 2.],
[ 3., 4.]],
[[ 10., 20.],
[ 30., 40.]],
[[100., 200.],
[300., 400.]]], dtype=float32)
```
So the square brackets are vertically aligned to better show the multiple dimensions. So would try to do that exactly the same here as well
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[GitHub] [arrow] ursabot commented on pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "ursabot (via GitHub)" <gi...@apache.org>.
ursabot commented on PR #34957:
URL: https://github.com/apache/arrow/pull/34957#issuecomment-1507567250
Benchmark runs are scheduled for baseline = 07642fd5da760b2ddf922d7e7c529a260aa3f177 and contender = b8427d391f77454f7009cf7b8091037fd77f01c6. b8427d391f77454f7009cf7b8091037fd77f01c6 is a master commit associated with this PR. Results will be available as each benchmark for each run completes.
Conbench compare runs links:
[Finished :arrow_down:0.0% :arrow_up:0.0%] [ec2-t3-xlarge-us-east-2](https://conbench.ursa.dev/compare/runs/91a1546ac2ec4629b621497979694c7a...f89b5a596ca2411aafbb5b3ea1a74029/)
[Failed] [test-mac-arm](https://conbench.ursa.dev/compare/runs/b377ff979bf749ef8bd8bf58ae168fde...671c76ce67244914982816e59033612c/)
[Finished :arrow_down:4.59% :arrow_up:1.79%] [ursa-i9-9960x](https://conbench.ursa.dev/compare/runs/439959cb62204711a0a959b2f80b4eca...0f6457eb16cd4b04891a1466e48ae177/)
[Finished :arrow_down:0.77% :arrow_up:0.0%] [ursa-thinkcentre-m75q](https://conbench.ursa.dev/compare/runs/66e2fd4f77674f8ab0c0ca6990eb67d9...30d3c0d1f564488cb11e907450d75097/)
Buildkite builds:
[Finished] [`b8427d39` ec2-t3-xlarge-us-east-2](https://buildkite.com/apache-arrow/arrow-bci-benchmark-on-ec2-t3-xlarge-us-east-2/builds/2689)
[Failed] [`b8427d39` test-mac-arm](https://buildkite.com/apache-arrow/arrow-bci-benchmark-on-test-mac-arm/builds/2723)
[Finished] [`b8427d39` ursa-i9-9960x](https://buildkite.com/apache-arrow/arrow-bci-benchmark-on-ursa-i9-9960x/builds/2687)
[Finished] [`b8427d39` ursa-thinkcentre-m75q](https://buildkite.com/apache-arrow/arrow-bci-benchmark-on-ursa-thinkcentre-m75q/builds/2714)
[Finished] [`07642fd5` ec2-t3-xlarge-us-east-2](https://buildkite.com/apache-arrow/arrow-bci-benchmark-on-ec2-t3-xlarge-us-east-2/builds/2688)
[Failed] [`07642fd5` test-mac-arm](https://buildkite.com/apache-arrow/arrow-bci-benchmark-on-test-mac-arm/builds/2722)
[Finished] [`07642fd5` ursa-i9-9960x](https://buildkite.com/apache-arrow/arrow-bci-benchmark-on-ursa-i9-9960x/builds/2686)
[Finished] [`07642fd5` ursa-thinkcentre-m75q](https://buildkite.com/apache-arrow/arrow-bci-benchmark-on-ursa-thinkcentre-m75q/builds/2713)
Supported benchmarks:
ec2-t3-xlarge-us-east-2: Supported benchmark langs: Python, R. Runs only benchmarks with cloud = True
test-mac-arm: Supported benchmark langs: C++, Python, R
ursa-i9-9960x: Supported benchmark langs: Python, R, JavaScript
ursa-thinkcentre-m75q: Supported benchmark langs: C++, Java
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[GitHub] [arrow] rok commented on a diff in pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "rok (via GitHub)" <gi...@apache.org>.
rok commented on code in PR #34957:
URL: https://github.com/apache/arrow/pull/34957#discussion_r1163012303
##########
docs/source/python/extending_types.rst:
##########
@@ -357,3 +357,116 @@ pandas ``ExtensionArray``. This method should have the following signature::
This way, you can control the conversion of a pyarrow ``Array`` of your pyarrow
extension type to a pandas ``ExtensionArray`` that can be stored in a DataFrame.
+
+
+Canonical extension types
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In the :ref:`format_canonical_extensions` under the section **Official List**
+there is a list of canonical extension types. Here you can find an example of how
+to implement and how to use the listed canonical extension types.
Review Comment:
Implement seems too strong here? :D
```suggestion
there is a list of canonical extension types. Here we list examples of how to
use the listed canonical extension types.
```
##########
docs/source/python/extending_types.rst:
##########
@@ -357,3 +357,116 @@ pandas ``ExtensionArray``. This method should have the following signature::
This way, you can control the conversion of a pyarrow ``Array`` of your pyarrow
extension type to a pandas ``ExtensionArray`` that can be stored in a DataFrame.
+
+
+Canonical extension types
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In the :ref:`format_canonical_extensions` under the section **Official List**
+there is a list of canonical extension types. Here you can find an example of how
+to implement and how to use the listed canonical extension types.
+
+Fixed size tensor
+"""""""""""""""""
+
+Define fixed shape tensor extension type:
+
+.. code-block:: python
+
+ >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), (2, 2))
+
+Create an array of tensors with storage array and defined fixed shape tensor
+extension type:
+
+.. code-block:: python
+
+ >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
+ >>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
+ >>> tensor = pa.ExtensionArray.from_storage(tensor_type, storage)
Review Comment:
Maybe call these `tensor_array` to not confuse them with actual `Tensor` objects?
```suggestion
>>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]]
>>> storage = pa.array(arr, pa.list_(pa.int32(), 4))
>>> tensor_array = pa.ExtensionArray.from_storage(tensor_type, storage)
```
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[GitHub] [arrow] AlenkaF commented on pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "AlenkaF (via GitHub)" <gi...@apache.org>.
AlenkaF commented on PR #34957:
URL: https://github.com/apache/arrow/pull/34957#issuecomment-1503824763
I am currently adding some more text. Will probably have extra content ready tomorrow morning!
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[GitHub] [arrow] AlenkaF merged pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "AlenkaF (via GitHub)" <gi...@apache.org>.
AlenkaF merged PR #34957:
URL: https://github.com/apache/arrow/pull/34957
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[GitHub] [arrow] ursabot commented on pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "ursabot (via GitHub)" <gi...@apache.org>.
ursabot commented on PR #34957:
URL: https://github.com/apache/arrow/pull/34957#issuecomment-1507568036
['Python', 'R'] benchmarks have high level of regressions.
[ursa-i9-9960x](https://conbench.ursa.dev/compare/runs/439959cb62204711a0a959b2f80b4eca...0f6457eb16cd4b04891a1466e48ae177/)
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[GitHub] [arrow] AlenkaF commented on pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "AlenkaF (via GitHub)" <gi...@apache.org>.
AlenkaF commented on PR #34957:
URL: https://github.com/apache/arrow/pull/34957#issuecomment-1503249091
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[GitHub] [arrow] AlenkaF commented on pull request #34957: GH-34956: [Docs][Python] Add to docs the usage of the FixedShapeTensorType
Posted by "AlenkaF (via GitHub)" <gi...@apache.org>.
AlenkaF commented on PR #34957:
URL: https://github.com/apache/arrow/pull/34957#issuecomment-1504887667
Thank you Joris and Rok for reviews!
The issue to rearrange Data Types user guide section can be found here: https://github.com/apache/arrow/issues/35064
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