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[BUG] Unable to create a struct column from an arrow dictionary array #9179

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galipremsagar opened this issue Sep 4, 2021 · 4 comments
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bug Something isn't working Python Affects Python cuDF API.

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@galipremsagar
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Describe the bug
We currently support converting a category column in cudf to a DictionaryArray in pyarrow:

>>> import cudf
>>> s = cudf.Series([1, 2, 3], dtype='category')
>>> s.to_arrow()
<pyarrow.lib.DictionaryArray object at 0x7f7980299ba0>

-- dictionary:
  [
    1,
    2,
    3
  ]
-- indices:
  [
    0,
    1,
    2
  ]
>>> s.to_arrow().type
DictionaryType(dictionary<values=int64, indices=int8, ordered=0>)

But we don't seem to be supporting the construction of StructDtype & StructColumn from our constructors:

>>> import pyarrow as pa
>>> f = pa.array([1, 2, 3])
>>> pa.StructArray.from_arrays([s.to_arrow(), f], names=['a', 'd']).type
StructType(struct<a: dictionary<values=int64, indices=int8, ordered=0>, d: int64>)
>>> pa.StructArray.from_arrays([s.to_arrow(), f], names=['a', 'd'])
<pyarrow.lib.StructArray object at 0x7f7980055760>
-- is_valid: all not null
-- child 0 type: dictionary<values=int64, indices=int8, ordered=0>

  -- dictionary:
    [
      1,
      2,
      3
    ]
  -- indices:
    [
      0,
      1,
      2
    ]
-- child 1 type: int64
  [
    1,
    2,
    3
  ]
>>> cudf.Series.from_arrow(pa.StructArray.from_arrays([s.to_arrow(), f], names=['a', 'd']))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/series.py", line 1111, in __repr__
    preprocess = self.copy()
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/frame.py", line 317, in copy
    new_frame._data = self._data.copy(deep=deep)
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/column_accessor.py", line 315, in copy
    {k: v.copy(deep=True) for k, v in self._data.items()},
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/column_accessor.py", line 315, in <dictcomp>
    {k: v.copy(deep=True) for k, v in self._data.items()},
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/column/struct.py", line 102, in copy
    result = super().copy(deep=deep)
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/column/column.py", line 419, in copy
    return cast(T, result._with_type_metadata(self.dtype))
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/column/struct.py", line 134, in _with_type_metadata
    names=dtype.fields.keys(),
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/dtypes.py", line 289, in fields
    return {
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/dtypes.py", line 290, in <dictcomp>
    field.name: cudf.utils.dtypes.cudf_dtype_from_pa_type(field.type)
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/utils/dtypes.py", line 233, in cudf_dtype_from_pa_type
    return pandas_dtype(typ.to_pandas_dtype())
  File "pyarrow/types.pxi", line 200, in pyarrow.lib.DataType.to_pandas_dtype
NotImplementedError: dictionary<values=int64, indices=int8, ordered=0>
>>> s
0    1
1    2
2    3
dtype: category
Categories (3, int64): [1, 2, 3]
>>> s.to_frame()
   0
0  1
1  2
2  3
>>> s.to_frame().to_struct()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/dataframe.py", line 7001, in to_struct
    col = cudf.core.column.build_struct_column(
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/column/column.py", line 1655, in build_struct_column
    dtype = StructDtype(
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/dtypes.py", line 281, in __init__
    pa_fields = {
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/core/dtypes.py", line 282, in <dictcomp>
    k: cudf.utils.dtypes.cudf_dtype_to_pa_type(v)
  File "/nvme/0/pgali/envs/cudfdev/lib/python3.8/site-packages/cudf/utils/dtypes.py", line 211, in cudf_dtype_to_pa_type
    raise NotImplementedError()
NotImplementedError

Steps/Code to reproduce bug
Follow this guide http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports to craft a minimal bug report. This helps us reproduce the issue you're having and resolve the issue more quickly.

Environment overview (please complete the following information)

  • Environment location: [Bare-metal]
  • Method of cuDF install: [from source]

Environment details
Please run and paste the output of the cudf/print_env.sh script here, to gather any other relevant environment details

Click here to see environment details
 **git***
 commit 8d602b7e99c187db7063529def92a5d343a53a54 (HEAD -> 7618, upstream/branch-21.10, branch-21.10)
 Author: shaneding <[email protected]>
 Date:   Fri Sep 3 18:09:10 2021 -0700
 
 Implemented bindings for `ceil` timestamp operation (#9141)
 
 Closes #8682. Added python bindings for `ceil` operation implemented in #8942.
 
 Authors:
 - https://github.com/shaneding
 
 Approvers:
 - Ashwin Srinath (https://github.com/shwina)
 - https://github.com/brandon-b-miller
 
 URL: https://github.com/rapidsai/cudf/pull/9141
 **git submodules***
 
 ***OS Information***
 DISTRIB_ID=Ubuntu
 DISTRIB_RELEASE=18.04
 DISTRIB_CODENAME=bionic
 DISTRIB_DESCRIPTION="Ubuntu 18.04.4 LTS"
 NAME="Ubuntu"
 VERSION="18.04.4 LTS (Bionic Beaver)"
 ID=ubuntu
 ID_LIKE=debian
 PRETTY_NAME="Ubuntu 18.04.4 LTS"
 VERSION_ID="18.04"
 HOME_URL="https://www.ubuntu.com/"
 SUPPORT_URL="https://help.ubuntu.com/"
 BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
 PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
 VERSION_CODENAME=bionic
 UBUNTU_CODENAME=bionic
 Linux dt07 4.15.0-76-generic #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux
 
 ***GPU Information***
 Sat Sep  4 14:32:58 2021
 +-----------------------------------------------------------------------------+
 | NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |
 |-------------------------------+----------------------+----------------------+
 | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
 |                               |                      |               MIG M. |
 |===============================+======================+======================|
 |   0  Tesla T4            On   | 00000000:3B:00.0 Off |                    0 |
 | N/A   50C    P0    28W /  70W |    384MiB / 15109MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   1  Tesla T4            On   | 00000000:5E:00.0 Off |                    0 |
 | N/A   37C    P8     9W /  70W |      3MiB / 15109MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   2  Tesla T4            On   | 00000000:AF:00.0 Off |                    0 |
 | N/A   32C    P8    11W /  70W |      3MiB / 15109MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   3  Tesla T4            On   | 00000000:D8:00.0 Off |                    0 |
 | N/A   32C    P8     9W /  70W |      3MiB / 15109MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 
 +-----------------------------------------------------------------------------+
 | Processes:                                                                  |
 |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
 |        ID   ID                                                   Usage      |
 |=============================================================================|
 |    0   N/A  N/A     58965      C   ...i/envs/cudfdev/bin/python      381MiB |
 +-----------------------------------------------------------------------------+
 
 ***CPU***
 Architecture:        x86_64
 CPU op-mode(s):      32-bit, 64-bit
 Byte Order:          Little Endian
 CPU(s):              64
 On-line CPU(s) list: 0-63
 Thread(s) per core:  2
 Core(s) per socket:  16
 Socket(s):           2
 NUMA node(s):        2
 Vendor ID:           GenuineIntel
 CPU family:          6
 Model:               85
 Model name:          Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz
 Stepping:            4
 CPU MHz:             2076.027
 BogoMIPS:            4200.00
 Virtualization:      VT-x
 L1d cache:           32K
 L1i cache:           32K
 L2 cache:            1024K
 L3 cache:            22528K
 NUMA node0 CPU(s):   0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62
 NUMA node1 CPU(s):   1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63
 Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d
 
 ***CMake***
 /nvme/0/pgali/envs/cudfdev/bin/cmake
 cmake version 3.21.2
 
 CMake suite maintained and supported by Kitware (kitware.com/cmake).
 
 ***g++***
 /usr/bin/g++
 g++ (Ubuntu 9.4.0-1ubuntu1~18.04) 9.4.0
 Copyright (C) 2019 Free Software Foundation, Inc.
 This is free software; see the source for copying conditions.  There is NO
 warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 
 
 ***nvcc***
 /usr/local/cuda/bin/nvcc
 nvcc: NVIDIA (R) Cuda compiler driver
 Copyright (c) 2005-2021 NVIDIA Corporation
 Built on Sun_Feb_14_21:12:58_PST_2021
 Cuda compilation tools, release 11.2, V11.2.152
 Build cuda_11.2.r11.2/compiler.29618528_0
 
 ***Python***
 /nvme/0/pgali/envs/cudfdev/bin/python
 Python 3.8.10
 
 ***Environment Variables***
 PATH                            : /nvme/0/pgali/envs/cudfdev/bin:/nvme/0/pgali/envs/cudfdev/bin:/nvme/0/pgali/envs/cudfdev/bin:/usr/share/swift/usr/bin:/home/nfs/pgali/bin:/home/nfs/pgali/.local/bin:/home/nfs/pgali/anaconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/usr/lib/jvm/default-java/bin:/usr/share/sbt-launcher-packaging/bin/sbt-launch.jar/bin:/usr/lib/spark/bin:/usr/lib/spark/sbin:/usr/local/cuda/bin:/usr/local/cuda/bin
 LD_LIBRARY_PATH                 : /usr/local/cuda/lib64:/usr/local/cuda/lib64::/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
 NUMBAPRO_NVVM                   :
 NUMBAPRO_LIBDEVICE              :
 CONDA_PREFIX                    : /nvme/0/pgali/envs/cudfdev
 PYTHON_PATH                     :
 
 ***conda packages***
 /home/nfs/pgali/anaconda3/condabin/conda
 # packages in environment at /nvme/0/pgali/envs/cudfdev:
 #
 # Name                    Version                   Build  Channel
 _libgcc_mutex             0.1                 conda_forge    conda-forge
 _openmp_mutex             4.5                      1_llvm    conda-forge
 abseil-cpp                20210324.2           h9c3ff4c_0    conda-forge
 alabaster                 0.7.12                     py_0    conda-forge
 appdirs                   1.4.4              pyh9f0ad1d_0    conda-forge
 argon2-cffi               20.1.0           py38h497a2fe_2    conda-forge
 arrow-cpp                 5.0.0           py38h327e1ba_4_cuda    conda-forge
 arrow-cpp-proc            3.0.0                      cuda    conda-forge
 async_generator           1.10                       py_0    conda-forge
 attrs                     21.2.0             pyhd8ed1ab_0    conda-forge
 aws-c-cal                 0.5.11               h95a6274_0    conda-forge
 aws-c-common              0.6.2                h7f98852_0    conda-forge
 aws-c-event-stream        0.2.7               h3541f99_13    conda-forge
 aws-c-io                  0.10.5               hfb6a706_0    conda-forge
 aws-checksums             0.1.11               ha31a3da_7    conda-forge
 aws-sdk-cpp               1.8.186              hb4091e7_3    conda-forge
 babel                     2.9.1              pyh44b312d_0    conda-forge
 backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
 backports                 1.0                        py_2    conda-forge
 backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
 beautifulsoup4            4.9.3              pyhb0f4dca_0    conda-forge
 binutils_impl_linux-64    2.36.1               h193b22a_2    conda-forge
 black                     19.10b0                    py_4    conda-forge
 bleach                    4.1.0              pyhd8ed1ab_0    conda-forge
 bokeh                     2.3.3            py38h578d9bd_0    conda-forge
 brotlipy                  0.7.0           py38h497a2fe_1001    conda-forge
 bzip2                     1.0.8                h7f98852_4    conda-forge
 c-ares                    1.17.2               h7f98852_0    conda-forge
 ca-certificates           2021.5.30            ha878542_0    conda-forge
 cachetools                4.2.2              pyhd8ed1ab_0    conda-forge
 certifi                   2021.5.30        py38h578d9bd_0    conda-forge
 cffi                      1.14.6           py38ha65f79e_0    conda-forge
 cfgv                      3.3.1              pyhd8ed1ab_0    conda-forge
 chardet                   4.0.0            py38h578d9bd_1    conda-forge
 clang                     11.0.0               ha770c72_2    conda-forge
 clang-11                  11.0.0          default_ha5c780c_2    conda-forge
 clang-tools               11.0.0          default_ha5c780c_2    conda-forge
 clangxx                   11.0.0          default_ha5c780c_2    conda-forge
 click                     8.0.1            py38h578d9bd_0    conda-forge
 cloudpickle               1.6.0                      py_0    conda-forge
 cmake                     3.21.2               h8897547_0    conda-forge
 cmake_setuptools          0.1.3                      py_0    rapidsai
 colorama                  0.4.4              pyh9f0ad1d_0    conda-forge
 commonmark                0.9.1                      py_0    conda-forge
 cryptography              3.4.7            py38ha5dfef3_0    conda-forge
 cudatoolkit               11.2.72              h2bc3f7f_0    nvidia
 cudf                      21.10.0a0+267.g8d602b7e99.dirty          pypi_0    pypi
 cupy                      9.4.0            py38h7818112_0    conda-forge
 cython                    0.29.24          py38h709712a_0    conda-forge
 cytoolz                   0.11.0           py38h497a2fe_3    conda-forge
 dask                      2021.9.0           pyhd8ed1ab_0    conda-forge
 dask-core                 2021.9.0           pyhd8ed1ab_0    conda-forge
 dask-cudf                 21.10.0a0+267.g8d602b7e99.dirty          pypi_0    pypi
 dataclasses               0.8                pyhc8e2a94_3    conda-forge
 debugpy                   1.4.1            py38h709712a_0    conda-forge
 decorator                 5.0.9              pyhd8ed1ab_0    conda-forge
 defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
 distlib                   0.3.2              pyhd8ed1ab_0    conda-forge
 distributed               2021.9.0         py38h578d9bd_0    conda-forge
 dlpack                    0.5                  h9c3ff4c_0    conda-forge
 docutils                  0.16             py38h578d9bd_3    conda-forge
 double-conversion         3.1.5                h9c3ff4c_2    conda-forge
 editdistance-s            1.0.0            py38h1fd1430_1    conda-forge
 entrypoints               0.3             pyhd8ed1ab_1003    conda-forge
 execnet                   1.9.0              pyhd8ed1ab_0    conda-forge
 expat                     2.4.1                h9c3ff4c_0    conda-forge
 fastavro                  1.4.4            py38h497a2fe_0    conda-forge
 fastrlock                 0.6              py38h709712a_1    conda-forge
 filelock                  3.0.12             pyh9f0ad1d_0    conda-forge
 flake8                    3.8.3                      py_1    conda-forge
 freetype                  2.10.4               h0708190_1    conda-forge
 fsspec                    2021.8.1           pyhd8ed1ab_0    conda-forge
 future                    0.18.2           py38h578d9bd_3    conda-forge
 gcc_impl_linux-64         11.1.0               h6b5115b_8    conda-forge
 gflags                    2.2.2             he1b5a44_1004    conda-forge
 glog                      0.5.0                h48cff8f_0    conda-forge
 gmp                       6.2.1                h58526e2_0    conda-forge
 grpc-cpp                  1.39.1               h850795e_1    conda-forge
 heapdict                  1.0.1                      py_0    conda-forge
 huggingface_hub           0.0.16             pyhd8ed1ab_0    conda-forge
 hypothesis                6.17.4             pyhd8ed1ab_0    conda-forge
 identify                  2.2.13             pyhd8ed1ab_0    conda-forge
 idna                      2.10               pyh9f0ad1d_0    conda-forge
 imagesize                 1.2.0                      py_0    conda-forge
 importlib-metadata        4.8.1            py38h578d9bd_0    conda-forge
 importlib_metadata        4.8.1                hd8ed1ab_0    conda-forge
 iniconfig                 1.1.1              pyh9f0ad1d_0    conda-forge
 ipykernel                 6.3.1            py38he5a9106_0    conda-forge
 ipython                   7.27.0           py38he5a9106_0    conda-forge
 ipython_genutils          0.2.0                      py_1    conda-forge
 isort                     5.6.4                      py_0    conda-forge
 jbig                      2.1               h7f98852_2003    conda-forge
 jedi                      0.18.0           py38h578d9bd_2    conda-forge
 jinja2                    3.0.1              pyhd8ed1ab_0    conda-forge
 joblib                    1.0.1              pyhd8ed1ab_0    conda-forge
 jpeg                      9d                   h36c2ea0_0    conda-forge
 jsonschema                3.2.0              pyhd8ed1ab_3    conda-forge
 jupyter_client            7.0.2              pyhd8ed1ab_0    conda-forge
 jupyter_core              4.7.1            py38h578d9bd_0    conda-forge
 jupyterlab_pygments       0.1.2              pyh9f0ad1d_0    conda-forge
 kernel-headers_linux-64   2.6.32              he073ed8_14    conda-forge
 krb5                      1.19.2               hcc1bbae_0    conda-forge
 lcms2                     2.12                 hddcbb42_0    conda-forge
 ld_impl_linux-64          2.36.1               hea4e1c9_2    conda-forge
 lerc                      2.2.1                h9c3ff4c_0    conda-forge
 libblas                   3.9.0                     8_mkl    conda-forge
 libbrotlicommon           1.0.9                h7f98852_5    conda-forge
 libbrotlidec              1.0.9                h7f98852_5    conda-forge
 libbrotlienc              1.0.9                h7f98852_5    conda-forge
 libcblas                  3.9.0                     8_mkl    conda-forge
 libclang-cpp11            11.0.0          default_ha5c780c_2    conda-forge
 libcurl                   7.78.0               h2574ce0_0    conda-forge
 libdeflate                1.7                  h7f98852_5    conda-forge
 libedit                   3.1.20191231         he28a2e2_2    conda-forge
 libev                     4.33                 h516909a_1    conda-forge
 libevent                  2.1.10               hcdb4288_3    conda-forge
 libffi                    3.3                  h58526e2_2    conda-forge
 libgcc-devel_linux-64     11.1.0               h80e7780_8    conda-forge
 libgcc-ng                 11.1.0               hc902ee8_8    conda-forge
 libgomp                   11.1.0               hc902ee8_8    conda-forge
 liblapack                 3.9.0                     8_mkl    conda-forge
 libllvm10                 10.0.1               he513fc3_3    conda-forge
 libllvm11                 11.0.1               hf817b99_0    conda-forge
 libnghttp2                1.43.0               h812cca2_0    conda-forge
 libpng                    1.6.37               h21135ba_2    conda-forge
 libprotobuf               3.16.0               h780b84a_0    conda-forge
 librmm                    21.10.00a210904 cuda11.2_g8527317_28    rapidsai-nightly
 libsanitizer              11.1.0               h56837e0_8    conda-forge
 libsodium                 1.0.18               h36c2ea0_1    conda-forge
 libssh2                   1.10.0               ha56f1ee_0    conda-forge
 libstdcxx-ng              11.1.0               h56837e0_8    conda-forge
 libthrift                 0.14.2               he6d91bd_1    conda-forge
 libtiff                   4.3.0                hf544144_1    conda-forge
 libutf8proc               2.6.1                h7f98852_0    conda-forge
 libuv                     1.42.0               h7f98852_0    conda-forge
 libwebp-base              1.2.1                h7f98852_0    conda-forge
 llvm-openmp               12.0.1               h4bd325d_1    conda-forge
 llvmlite                  0.36.0           py38h4630a5e_0    conda-forge
 locket                    0.2.0                      py_2    conda-forge
 lz4-c                     1.9.3                h9c3ff4c_1    conda-forge
 markdown                  3.3.4              pyhd8ed1ab_0    conda-forge
 markupsafe                2.0.1            py38h497a2fe_0    conda-forge
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@galipremsagar galipremsagar added bug Something isn't working Needs Triage Need team to review and classify Python Affects Python cuDF API. labels Sep 4, 2021
@beckernick beckernick removed the Needs Triage Need team to review and classify label Sep 7, 2021
@isVoid isVoid self-assigned this Oct 21, 2021
@isVoid
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isVoid commented Oct 23, 2021

Cudf supports constructing a StructColumn from a StructArray.

In [14]: pa_struct = pa.StructArray.from_arrays([pa.array([1, 2, 3]), pa.array(['a', 'b', 'c'])], names=['num', 'str'])

In [16]: cudf.Series.from_arrow(pa_struct)
Out[16]: 
0    {'num': 1, 'str': 'a'}
1    {'num': 2, 'str': 'b'}
2    {'num': 3, 'str': 'c'}
dtype: struct

I believe the error you see here has to do with having DictionaryArray as one of the fields in the StructArray.

In [5]: r = cudf.Series.from_arrow(pa.StructArray.from_arrays([s.to_arrow(), f], names=['a', 'd']))

In [6]: type(r)
Out[6]: cudf.core.series.Series

In [8]: r._column.children
Out[8]: 
(<cudf.core.column.categorical.CategoricalColumn object at 0x7f6459ceda70>
 
 -- dictionary:
   [
     1,
     2,
     3
   ]
 -- indices:
   [
     0,
     1,
     2
   ]
 dtype: category,
 <cudf.core.column.numerical.NumericalColumn object at 0x7f645a5a53b0>
 [
   1,
   2,
   3
 ]
 dtype: int64)

The result is well formed. But cannot convert the column back to arrow:

In [11]: r.to_arrow()
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-11-77c2df0309ec> in <module>
----> 1 r.to_arrow()

~/cudf/python/cudf/cudf/core/single_column_frame.py in to_arrow(self)
    213         ]
    214         """
--> 215         return self._column.to_arrow()
    216 
    217     @property

~/cudf/python/cudf/cudf/core/column/struct.py in to_arrow(self)
     68             {
     69                 field: child.type
---> 70                 for field, child in zip(self.dtype.fields, children)
     71             }
     72         )

~/cudf/python/cudf/cudf/core/dtypes.py in fields(self)
    289         return {
    290             field.name: cudf.utils.dtypes.cudf_dtype_from_pa_type(field.type)
--> 291             for field in self._typ
    292         }
    293 

~/cudf/python/cudf/cudf/core/dtypes.py in <dictcomp>(.0)
    289         return {
    290             field.name: cudf.utils.dtypes.cudf_dtype_from_pa_type(field.type)
--> 291             for field in self._typ
    292         }
    293 

~/cudf/python/cudf/cudf/utils/dtypes.py in cudf_dtype_from_pa_type(typ)
    215         return cudf.core.dtypes.Decimal64Dtype.from_arrow(typ)
    216     else:
--> 217         return cudf.api.types.pandas_dtype(typ.to_pandas_dtype())
    218 
    219 

~/compose/etc/conda/cuda_11.2/envs/rapids/lib/python3.7/site-packages/pyarrow/types.pxi in pyarrow.lib.DataType.to_pandas_dtype()

NotImplementedError: dictionary<values=int64, indices=int8, ordered=0>

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This issue has been labeled inactive-90d due to no recent activity in the past 90 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.

@GorML
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GorML commented Dec 23, 2024

Still getting error with dask-cudf-cu11==24.10.1.

with dask.config.set({"dataframe.backend": "cudf"}):
    dcdf = dd.read_parquet('data_sample', index='IDNUM')

ERROR:
––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
--> 435 typ = cudf_dtype_from_pa_type(schema.field(col_name).type)
    436 if (
    437     col_name in schema.names
    438     and not isinstance(typ, (cudf.ListDtype, cudf.StructDtype))
    439     and isinstance(col, cudf.core.column.StringColumn)
    440 ):
    441     df._data[col_name] = col.astype(typ)

File [~/share/.../conda_envs/.../lib/python3.10/site-packages/cudf/utils/dtypes.py:190](...), in cudf_dtype_from_pa_type(typ)
    188     return cudf.dtype("str")
    189 else:
--> 190     return cudf.api.types.pandas_dtype(typ.to_pandas_dtype())

File [~/share/.../conda_envs/.../lib/python3.10/site-packages/pyarrow/types.pxi:378](...), in pyarrow.lib.DataType.to_pandas_dtype()

File [~/share/.../conda_envs/.../lib/python3.10/site-packages/pyarrow/types.pxi:183](...), in pyarrow.lib._to_pandas_dtype()

NotImplementedError: dictionary<values=string, indices=int32, ordered=0>

data_sample is folder, partitioned by column, storaged as parquet. I can successfully read folder with read_parquet in pandas, dask, cudf, pyarrow but it failes with dask_cudf. Problem is with CategoricalDtype.

Error is reproducable with the following:

pa.dictionary(pa.int32(), pa.utf8()).to_pandas_dtype()

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[234], line 1
----> 1 pa.dictionary(pa.int32(), pa.utf8()).to_pandas_dtype()

File [~/share/.../conda_envs/.../lib/python3.10/site-packages/pyarrow/types.pxi:378](...), in pyarrow.lib.DataType.to_pandas_dtype()

File [~/share/.../conda_envs/.../lib/python3.10/site-packages/pyarrow/types.pxi:183](...), in pyarrow.lib._to_pandas_dtype()

NotImplementedError: dictionary<values=string, indices=int32, ordered=0>

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