rapidsai / cudf

cuDF - GPU DataFrame Library
https://docs.rapids.ai/api/cudf/stable/
Apache License 2.0
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[BUG] Unable to retrieve nulls in float column when reading a cudf created parquet file #8688

Closed galipremsagar closed 3 years ago

galipremsagar commented 3 years ago

Describe the bug This looks like a parquet writer bug. When there is a mix of np.nan & <NA> values in a float column, and that is written to parquet file, we are able to retrieve it correctly from cudf but not in pandas. But pandas is able to write this column data correctly to a parquet file and that can be read from cudf & pandas correctly.

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.

# Working case: Where parquet file is created by pandas
>>> import pyarrow as pa
>>> import numpy as np
>>> arrow_array = pa.array([1, np.nan, None])
>>> arrow_array
<pyarrow.lib.DoubleArray object at 0x7f630c3576a0>
[
  1,
  nan,
  null
]
>>> import pandas as pd
>>> pd_array = pd.Float64Dtype().__from_arrow__(arrow_array)
>>> pd_array
<FloatingArray>
[1.0, nan, <NA>]
Length: 3, dtype: Float64
>>> pd_series = pd.Series(pd_array)
>>> pd_series
0     1.0
1     NaN
2    <NA>
dtype: Float64
>>> pdf = pd.DataFrame({'a':pd_series})
>>> pdf
      a
0   1.0
1   NaN
2  <NA>
>>> pdf.dtypes
a    Float64
dtype: object
>>> pdf.to_parquet('pandas.parquet')
>>> pd.read_parquet('pandas.parquet')
      a
0   1.0
1   NaN
2  <NA>
>>> pd.read_parquet('pandas.parquet').dtypes
a    Float64
dtype: object
>>> cudf.read_parquet('pandas.parquet').dtypes
a    float64
dtype: object
>>> cudf.read_parquet('pandas.parquet')
      a
0   1.0
1   NaN
2  <NA>

# Bug case: Where parquet file is created by cudf.
>>> gdf = cudf.read_parquet('pandas.parquet')
>>> gdf
      a
0   1.0
1   NaN
2  <NA>
>>> gdf.dtypes
a    float64
dtype: object
>>> gdf.to_parquet('cudf.parquet')
>>> cudf.read_parquet('cudf.parquet')
      a
0   1.0
1   NaN
2  <NA>
>>> pd.read_parquet('cudf.parquet')
     a
0  1.0
1  NaN
2  NaN
>>> pd.read_parquet('cudf.parquet', use_nullable_dtypes=True)
     a
0  1.0
1  NaN
2  NaN
>>> pd.read_parquet('cudf.parquet', use_nullable_dtypes=True).dtypes
a    float64
dtype: object

Expected behavior I'd expect the cudf written parquet file (i.e., cudf.parquet) to be able to behave similar to pandas.parquet file when read by both cudf & pandas backends.

Environment overview (please complete the following information)

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 7721819eeed68115fd4d7033cba016830b0afcd8 (HEAD -> branch-21.08)
     Author: Conor Hoekstra <36027403+codereport@users.noreply.github.com>
     Date:   Tue Jul 6 22:10:15 2021 -0400

     Updating Clang Version to 11.0.0 (#6695)

     This resolves: https://github.com/rapidsai/cudf/issues/5187

     PR description copied from: https://github.com/rapidsai/cuml/pull/3121

     Depends on: https://github.com/rapidsai/integration/pull/304

     This PR will upgrade the clang version required to 11.0.0 in order to enable us with running clang-tidy on .cu files, while running on cuda v11. See rapidsai/raft#88 for more details.

     CI will not pass as the underlying conda-env still uses 8.0.1. Once we have the rapids-build-env meta package updated, this should pass.

     -----

     ### Fixes from Clang 8.0.1 to Clang 11.0.0 (that are observed in delta)

     * Missing spaces
     * Incorrect alignment when ternary expression splits across multiple lines
     * Comment alignment on macros
     * Fixed where function signatures have line breaks
     * Aligning macros
     * Always left align pointer/reference
     * Don't allow single line for loops

     -----
     To do list:

     * [x] Update python file
     * [x] Update conda environment files
     * [x] Run formatter to apply all changes from upgrading
     * [x] Add changes from https://github.com/rapidsai/cudf/issues/5187
     * [x] Review list of new changes from 8.0.1 to 11; choose which to incorporate
     * [x] Get working with RAPID compose

     Authors:
     - Conor Hoekstra (https://github.com/codereport)

     Approvers:
     - AJ Schmidt (https://github.com/ajschmidt8)
     - Nghia Truong (https://github.com/ttnghia)
     - Mark Harris (https://github.com/harrism)
     - Dillon Cullinan (https://github.com/dillon-cullinan)

     URL: https://github.com/rapidsai/cudf/pull/6695
     **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***
     Wed Jul  7 19:29:34 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 |    500MiB / 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    10W /  70W |      3MiB / 15109MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   3  Tesla T4            On   | 00000000:D8:00.0 Off |                    0 |
     | N/A   31C    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     37327      C   python                            497MiB |
     +-----------------------------------------------------------------------------+

     ***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:             3255.877
     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 3.20.5

     CMake suite maintained and supported by Kitware (kitware.com/cmake).

     ***g++***
     /usr/bin/g++
     g++ (Ubuntu 9.3.0-11ubuntu0~18.04.1) 9.3.0
     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:/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:/nvme/0/pgali/envs/cudfdev/bin
     LD_LIBRARY_PATH                 : /usr/local/cuda/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                 4.0.1           py38hf0991f3_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
     binutils_impl_linux-64    2.36.1               h193b22a_1    conda-forge
     black                     19.10b0                    py_4    conda-forge
     bleach                    3.3.0              pyh44b312d_0    conda-forge
     bokeh                     2.3.2            py38h578d9bd_0    conda-forge
     brotlipy                  0.7.0           py38h497a2fe_1001    conda-forge
     bzip2                     1.0.8                h7f98852_4    conda-forge
     c-ares                    1.17.1               h7f98852_1    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                      1.14.5           py38ha65f79e_0    conda-forge
     cfgv                      3.3.0              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                     3.20.5               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                      21.8.0a0+249.g7721819eee.dirty          pypi_0    pypi
     cupy                      9.2.0            py38ha69542f_0    conda-forge
     cython                    0.29.23          py38h709712a_1    conda-forge
     cytoolz                   0.11.0           py38h497a2fe_3    conda-forge
     dask                      2021.6.2+17.gfcbb4ad7          pypi_0    pypi
     dask-cudf                 21.8.0a0+249.g7721819eee.dirty          pypi_0    pypi
     dataclasses               0.8                pyhc8e2a94_1    conda-forge
     debugpy                   1.3.0            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               2021.6.2+35.g88b99ae2          pypi_0    pypi
     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                  1.4.2            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
     flatbuffers               2.0.0                h9c3ff4c_0    conda-forge
     freetype                  2.10.4               h0708190_1    conda-forge
     fsspec                    2021.6.1           pyhd8ed1ab_0    conda-forge
     future                    0.18.2           py38h578d9bd_3    conda-forge
     gcc_impl_linux-64         9.3.0               h70c0ae5_19    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                  1.38.1               h36ce80c_0    conda-forge
     heapdict                  1.0.1                      py_0    conda-forge
     huggingface_hub           0.0.13             pyhd8ed1ab_0    conda-forge
     hypothesis                6.14.1             pyhd8ed1ab_0    conda-forge
     identify                  2.2.10             pyhd8ed1ab_0    conda-forge
     idna                      2.10               pyh9f0ad1d_0    conda-forge
     imagesize                 1.2.0                      py_0    conda-forge
     importlib-metadata        4.6.1            py38h578d9bd_0    conda-forge
     importlib_metadata        4.6.1                hd8ed1ab_0    conda-forge
     iniconfig                 1.1.1              pyh9f0ad1d_0    conda-forge
     ipykernel                 6.0.1            py38hd0cf306_0    conda-forge
     ipython                   7.25.0           py38hd0cf306_1    conda-forge
     ipython_genutils          0.2.0                      py_1    conda-forge
     isort                     5.0.7            py38h32f6830_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            6.1.12             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              h77966d4_13    conda-forge
     krb5                      1.19.1               hcc1bbae_0    conda-forge
     lcms2                     2.12                 hddcbb42_0    conda-forge
     ld_impl_linux-64          2.36.1               hea4e1c9_1    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                   7.77.0               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     9.3.0               h7864c58_19    conda-forge
     libgcc-ng                 9.3.0               h2828fa1_19    conda-forge
     libgomp                   9.3.0               h2828fa1_19    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                    21.08.00a210707 cuda11.2_geb2b991_34    rapidsai-nightly
     libsodium                 1.0.18               h36c2ea0_1    conda-forge
     libssh2                   1.9.0                ha56f1ee_6    conda-forge
     libstdcxx-ng              9.3.0               h6de172a_19    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                     1.41.0               h7f98852_0    conda-forge
     libwebp-base              1.2.0                h7f98852_2    conda-forge
     llvm-openmp               11.1.0               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_0    conda-forge
     markdown                  3.3.4              pyhd8ed1ab_0    conda-forge
     markupsafe                2.0.1            py38h497a2fe_0    conda-forge
     matplotlib-inline         0.1.2              pyhd8ed1ab_2    conda-forge
     mccabe                    0.6.1                      py_1    conda-forge
     mimesis                   4.0.0              pyh9f0ad1d_0    conda-forge
     mistune                   0.8.4           py38h497a2fe_1004    conda-forge
     mkl                       2020.4             h726a3e6_304    conda-forge
     more-itertools            8.8.0              pyhd8ed1ab_0    conda-forge
     msgpack-python            1.0.2            py38h1fd1430_1    conda-forge
     mypy                      0.782                      py_0    conda-forge
     mypy_extensions           0.4.3            py38h578d9bd_3    conda-forge
     nbclient                  0.5.3              pyhd8ed1ab_0    conda-forge
     nbconvert                 6.1.0            py38h578d9bd_0    conda-forge
     nbformat                  5.1.3              pyhd8ed1ab_0    conda-forge
     nbsphinx                  0.8.6              pyhd8ed1ab_1    conda-forge
     ncurses                   6.2                  h58526e2_4    conda-forge
     nest-asyncio              1.5.1              pyhd8ed1ab_0    conda-forge
     ninja                     1.10.2               h4bd325d_0    conda-forge
     nodeenv                   1.6.0              pyhd8ed1ab_0    conda-forge
     notebook                  6.4.0              pyha770c72_0    conda-forge
     numba                     0.53.1           py38h8b71fd7_1    conda-forge
     numpy                     1.21.0           py38h9894fe3_0    conda-forge
     numpydoc                  1.1.0                      py_1    conda-forge
     nvtx                      0.2.3            py38h497a2fe_0    conda-forge
     olefile                   0.46               pyh9f0ad1d_1    conda-forge
     openjpeg                  2.4.0                hb52868f_1    conda-forge
     openssl                   1.1.1k               h7f98852_0    conda-forge
     orc                       1.6.9                h58a87f1_0    conda-forge
     packaging                 21.0               pyhd8ed1ab_0    conda-forge
     pandas                    1.2.5            py38h1abd341_0    conda-forge
     pandoc                    1.19.2                        0    conda-forge
     pandocfilters             1.4.2                      py_1    conda-forge
     parquet-cpp               1.5.1                         2    conda-forge
     parso                     0.8.2              pyhd8ed1ab_0    conda-forge
     partd                     1.2.0              pyhd8ed1ab_0    conda-forge
     pathspec                  0.8.1              pyhd3deb0d_0    conda-forge
     pexpect                   4.8.0              pyh9f0ad1d_2    conda-forge
     pickleshare               0.7.5                   py_1003    conda-forge
     pillow                    8.3.0            py38h8e6f84c_0    conda-forge
     pip                       21.1.3             pyhd8ed1ab_0    conda-forge
     pluggy                    0.13.1           py38h578d9bd_4    conda-forge
     pre-commit                2.13.0           py38h578d9bd_0    conda-forge
     pre_commit                2.13.0               hd8ed1ab_0    conda-forge
     prometheus_client         0.11.0             pyhd8ed1ab_0    conda-forge
     prompt-toolkit            3.0.19             pyha770c72_0    conda-forge
     protobuf                  3.16.0           py38h709712a_0    conda-forge
     psutil                    5.8.0            py38h497a2fe_1    conda-forge
     ptyprocess                0.7.0              pyhd3deb0d_0    conda-forge
     py                        1.10.0             pyhd3deb0d_0    conda-forge
     py-cpuinfo                8.0.0              pyhd8ed1ab_0    conda-forge
     pyarrow                   4.0.1           py38hb53058b_4_cuda    conda-forge
     pycodestyle               2.6.0              pyh9f0ad1d_0    conda-forge
     pycparser                 2.20               pyh9f0ad1d_2    conda-forge
     pyflakes                  2.2.0              pyh9f0ad1d_0    conda-forge
     pygments                  2.9.0              pyhd8ed1ab_0    conda-forge
     pyopenssl                 20.0.1             pyhd8ed1ab_0    conda-forge
     pyorc                     0.4.0                    pypi_0    pypi
     pyparsing                 2.4.7              pyh9f0ad1d_0    conda-forge
     pyrsistent                0.17.3           py38h497a2fe_2    conda-forge
     pysocks                   1.7.1            py38h578d9bd_3    conda-forge
     pytest                    6.2.4            py38h578d9bd_0    conda-forge
     pytest-benchmark          3.4.1              pyhd8ed1ab_0    conda-forge
     pytest-forked             1.3.0              pyhd3deb0d_0    conda-forge
     pytest-xdist              2.3.0              pyhd8ed1ab_0    conda-forge
     python                    3.8.10          h49503c6_1_cpython    conda-forge
     python-dateutil           2.8.1                      py_0    conda-forge
     python_abi                3.8                      2_cp38    conda-forge
     pytorch                   1.7.1           cpu_py38h36eccb8_2    conda-forge
     pytz                      2021.1             pyhd8ed1ab_0    conda-forge
     pyyaml                    5.4.1            py38h497a2fe_0    conda-forge
     pyzmq                     22.1.0           py38h2035c66_0    conda-forge
     rapidjson                 1.1.0             he1b5a44_1002    conda-forge
     re2                       2021.06.01           h9c3ff4c_0    conda-forge
     readline                  8.1                  h46c0cb4_0    conda-forge
     recommonmark              0.7.1              pyhd8ed1ab_0    conda-forge
     regex                     2021.7.6         py38h497a2fe_0    conda-forge
     requests                  2.25.1             pyhd3deb0d_0    conda-forge
     rhash                     1.4.1                h7f98852_0    conda-forge
     rmm                       21.08.00a210707 cuda_11.2_py38_geb2b991_34    rapidsai-nightly
     s2n                       1.0.10               h9b69904_0    conda-forge
     sacremoses                0.0.43             pyh9f0ad1d_0    conda-forge
     send2trash                1.7.1              pyhd8ed1ab_0    conda-forge
     setuptools                49.6.0           py38h578d9bd_3    conda-forge
     six                       1.16.0             pyh6c4a22f_0    conda-forge
     snappy                    1.1.8                he1b5a44_3    conda-forge
     snowballstemmer           2.1.0              pyhd8ed1ab_0    conda-forge
     sortedcontainers          2.4.0              pyhd8ed1ab_0    conda-forge
     spdlog                    1.8.5                h4bd325d_0    conda-forge
     sphinx                    4.0.3              pyh6c4a22f_0    conda-forge
     sphinx-copybutton         0.4.0              pyhd8ed1ab_0    conda-forge
     sphinx-markdown-tables    0.0.15             pyhd3deb0d_0    conda-forge
     sphinx_rtd_theme          0.5.2              pyhd8ed1ab_1    conda-forge
     sphinxcontrib-applehelp   1.0.2                      py_0    conda-forge
     sphinxcontrib-devhelp     1.0.2                      py_0    conda-forge
     sphinxcontrib-htmlhelp    2.0.0              pyhd8ed1ab_0    conda-forge
     sphinxcontrib-jsmath      1.0.1                      py_0    conda-forge
     sphinxcontrib-qthelp      1.0.3                      py_0    conda-forge
     sphinxcontrib-serializinghtml 1.1.5              pyhd8ed1ab_0    conda-forge
     sphinxcontrib-websupport  1.2.4              pyh9f0ad1d_0    conda-forge
     sqlite                    3.36.0               h9cd32fc_0    conda-forge
     streamz                   0.6.2              pyh44b312d_0    conda-forge
     sysroot_linux-64          2.12                h77966d4_13    conda-forge
     tblib                     1.7.0              pyhd8ed1ab_0    conda-forge
     terminado                 0.10.1           py38h578d9bd_0    conda-forge
     testpath                  0.5.0              pyhd8ed1ab_0    conda-forge
     tk                        8.6.10               h21135ba_1    conda-forge
     tokenizers                0.10.1           py38hb63a372_0    conda-forge
     toml                      0.10.2             pyhd8ed1ab_0    conda-forge
     toolz                     0.11.1                     py_0    conda-forge
     tornado                   6.1              py38h497a2fe_1    conda-forge
     tqdm                      4.61.2             pyhd8ed1ab_1    conda-forge
     traitlets                 5.0.5                      py_0    conda-forge
     transformers              4.8.2              pyhd8ed1ab_0    conda-forge
     typed-ast                 1.4.3            py38h497a2fe_0    conda-forge
     typing-extensions         3.10.0.0             hd8ed1ab_0    conda-forge
     typing_extensions         3.10.0.0           pyha770c72_0    conda-forge
     urllib3                   1.26.6             pyhd8ed1ab_0    conda-forge
     virtualenv                20.4.7           py38h578d9bd_0    conda-forge
     wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge
     webencodings              0.5.1                      py_1    conda-forge
     wheel                     0.36.2             pyhd3deb0d_0    conda-forge
     xz                        5.2.5                h516909a_1    conda-forge
     yaml                      0.2.5                h516909a_0    conda-forge
     zeromq                    4.3.4                h9c3ff4c_0    conda-forge
     zict                      2.0.0                      py_0    conda-forge
     zipp                      3.5.0              pyhd8ed1ab_0    conda-forge
     zlib                      1.2.11            h516909a_1010    conda-forge
     zstd                      1.5.0                ha95c52a_0    conda-forge

Additional context Add any other context about the problem here.

devavret commented 3 years ago

Here are my observations: The only difference between the extra metadata of the files written by cudf and pandas is that the one written by cudf has "numpy_type": "float64" while the one written by pandas has "numpy_type": "Float64"

When I added a correction code to utils.pyx:generate_pandas_metadata:

if col_meta["numpy_type"] in ("float64"):
   col_meta["numpy_type"] = "Float64"

it fixed this issue.

The culprit is pa.pandas_compat.construct_metadata

Another area in cudf that uses this pyarrow API also shows the same behaviour:

In [37]: gdf.to_arrow().to_pandas()
Out[37]: 
     a
0  1.0
1  NaN
2  NaN
devavret commented 3 years ago

The difference is in our dtypes. Pandas uses its own Float64Dtype for its numerical columns

In [5]: pdf.a.dtype
Out[5]: Float64Dtype()

In [6]: str(pdf.a.dtype)
Out[6]: 'Float64'

In [12]: type(pdf.a.dtype)
Out[12]: pandas.core.arrays.floating.Float64Dtype

that wraps an np dtype

@register_extension_dtype
class Float64Dtype(FloatingDtype):
    type = np.float64
    name = "Float64"
    __doc__ = _dtype_docstring.format(dtype="float64")

We directly use the np dtype for our numerical columns

In [9]: gdf.a.dtype
Out[9]: dtype('float64')

In [10]: str(gdf.a.dtype)
Out[10]: 'float64'

In [13]: type(gdf.a.dtype)
Out[13]: numpy.dtype

When generating pandas metadata, pyarrow uses str(column.dtype) to generate the aforementioned field.

devavret commented 3 years ago

Pandas used to also use numpy dtype for it's columns until v0.24 when they added null support. Here's the docs from pandas where it explains that the new type used for nullable columns is an "Extension type". Notably the difference between this and the underlying numpy type:

Or the string alias "Int64" (note the capital "I", to differentiate from NumPy’s 'int64' dtype

galipremsagar commented 3 years ago

Here are my observations: The only difference between the extra metadata of the files written by cudf and pandas is that the one written by cudf has "numpy_type": "float64" while the one written by pandas has "numpy_type": "Float64"

When I added a correction code to utils.pyx:generate_pandas_metadata:

if col_meta["numpy_type"] in ("float64"):
   col_meta["numpy_type"] = "Float64"

it fixed this issue.

This looks like a reasonable fix to me, I don't see any downsides to doing this. Are there any that I'm missing?

devavret commented 3 years ago

This looks like a reasonable fix to me, I don't see any downsides to doing this. Are there any that I'm missing?

It fixes the symptom but not the issue. I filed #8707 to explain why we should use a better dtype than np.float64 for a nullable float column.