[BUG] `Series.__setitem__` fails with tuple keys on a multiindex
Open
bug
Python
- Dominant language
- C++
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Description
**Describe the bug**
When assigning to a row of a series with multiindex, cudf raises exception.
**Steps/Code to reproduce bug**
```python3
>>> idx= cudf.MultiIndex.from_product([(1, 2), (3, 4)])
>>> gs = cudf.Series([1, 2, 3, 4], index=idx)
>>> gs[(1, 3)] = 101
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
in
4 gs3 = cudf.Series([1, 2, 3, 4], index=gmidx2)
5
----> 6 gs3[(1, 3)] = 101
7 gs3
~/cudf/python/cudf/cudf/core/series.py in __setitem__(self, key, value)
892 self.iloc[key] = value
893 else:
--> 894 self.loc[key] = value
895
896 def take(self, indices, keep_index=True):
~/cudf/python/cudf/cudf/core/indexing.py in __setitem__(self, key, value)
145 def __setitem__(self, key, value):
146 try:
--> 147 key = self._loc_to_iloc(key)
148 except KeyError as e:
149 if (
~/cudf/python/cudf/cudf/core/indexing.py in _loc_to_iloc(self, arg)
190 return arg
191 else:
--> 192 indices = indices_from_labels(self._sr, arg)
193 if indices.null_count > 0:
194 raise KeyError("label scalar is out of bound")
~/cudf/python/cudf/cudf/core/indexing.py in indices_from_labels(obj, labels)
28 labels = column.as_column(labels)
29
---> 30 if is_categorical_dtype(obj.index):
31 labels = labels.astype("category")
32 codes = labels.codes.astype(obj.index._values.codes.dtype)
~/cudf/python/cudf/cudf/utils/dtypes.py in is_categorical_dtype(obj)
213 ),
214 ):
--> 215 return is_categorical_dtype(obj.dtype)
216 if hasattr(obj, "type"):
217 if obj.type is CategoricalDtypeType:
AttributeError: 'MultiIndex' object has no attribute 'dtype'
```
**Expected behavior**
```python3
>>> idx = pd.MultiIndex.from_product([(1, 2), (3, 4)])
>>> s = pd.Series([1, 2, 3, 4], index=idx)
>>> s[(1, 3)] = 101
```
**Environment overview (please complete the following information)**
- Environment location: Docker
- Method of cuDF install: conda
**Environment details**
Click here to see environment details
**git***
commit 7d613f5c4b4a5d75cd8f83383251e4e8b0572bfb (HEAD -> 7290, origin/7290)
Author: Michael Wang
Date: Wed Feb 24 16:43:25 2021 -0800
Update Docstrings
**git submodules***
***OS Information***
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=18.04
DISTRIB_CODENAME=bionic
DISTRIB_DESCRIPTION="Ubuntu 18.04.5 LTS"
NAME="Ubuntu"
VERSION="18.04.5 LTS (Bionic Beaver)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 18.04.5 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 dgx06 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 Feb 24 18:33:21 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla V100-SXM2... On | 00000000:89:00.0 Off | 0 |
| N/A 34C P0 57W / 300W | 1498MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
***CPU***
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 80
On-line CPU(s) list: 0-79
Thread(s) per core: 2
Core(s) per socket: 20
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
Stepping: 1
CPU MHz: 1456.910
CPU max MHz: 3600.0000
CPU min MHz: 1200.0000
BogoMIPS: 4390.16
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 51200K
NUMA node0 CPU(s): 0-19,40-59
NUMA node1 CPU(s): 20-39,60-79
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 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 ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
***CMake***
/raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/envs/rapids/bin/cmake
cmake version 3.18.5
CMake suite maintained and supported by Kitware (kitware.com/cmake).
***g++***
/usr/local/bin/g++
g++ (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 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/bin/nvcc
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
***Python***
/raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/envs/rapids/bin/python
Python 3.7.10
***Environment Variables***
PATH : /raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/envs/rapids/bin:/raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/cuda-10.2/bin
LD_LIBRARY_PATH : /raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/envs/rapids/lib:/raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/i386-linux-gnu:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda-10.2/lib64:/usr/local/lib:/raid/wangm/dev/rapids/rmm/build/release:/raid/wangm/dev/rapids/cudf/cpp/build/release:/raid/wangm/dev/rapids/cudf/cpp/build/release:/raid/wangm/dev/rapids/cuml/cpp/build/release:/raid/wangm/dev/rapids/cugraph/cpp/build/release:/raid/wangm/dev/rapids/cuspatial/cpp/build/release
NUMBAPRO_NVVM :
NUMBAPRO_LIBDEVICE :
CONDA_PREFIX : /raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/envs/rapids
PYTHON_PATH :
***conda packages***
/raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/bin/conda
# packages in environment at /raid/wangm/dev/rapids/compose/etc/conda/cuda_10.2/envs/rapids:
#
# Name Version Build Channel
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apipkg 1.5 py_0 conda-forge
appdirs 1.4.4 pyh9f0ad1d_0 conda-forge
argon2-cffi 20.1.0 py37h8f50634_2 conda-forge
arrow-cpp 1.0.1 py37hb84ec65_31_cuda conda-forge
arrow-cpp-proc 3.0.0 cuda conda-forge
async_generator 1.10 py_0 conda-forge
attrs 20.3.0 pyhd3deb0d_0 conda-forge
aws-c-cal 0.4.5 h2ff795d_6 conda-forge
aws-c-common 0.4.67 h7f98852_0 conda-forge
aws-c-event-stream 0.2.6 h4285e0c_4 conda-forge
aws-c-io 0.8.3 h3b39b8c_1 conda-forge
aws-checksums 0.1.11 h3b39b8c_1 conda-forge
aws-sdk-cpp 1.8.138 h9e0957a_1 conda-forge
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bokeh 2.2.3 py37hc8dfbb8_0 conda-forge
boost-cpp 1.75.0 hc6e9bd1_0 conda-forge
brotli 1.0.9 h9c3ff4c_4 conda-forge
brotlipy 0.7.0 py37hb5d75c8_1001 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
c-ares 1.17.1 h36c2ea0_0 conda-forge
ca-certificates 2021.1.19 h06a4308_0
certifi 2020.12.5 py37h89c1867_1 conda-forge
cffi 1.14.5 py37hc58025e_0 conda-forge
cfgv 3.2.0 py_0 conda-forge
chardet 4.0.0 py37h89c1867_1 conda-forge
clang 8.0.1 hc9558a2_2 conda-forge
clang-tools 8.0.1 hc9558a2_2 conda-forge
clangxx 8.0.1 2 conda-forge
click 7.1.2 pyh9f0ad1d_0 conda-forge
cloudpickle 1.6.0 py_0 conda-forge
cmake 3.18.5 h1f3970d_0 rapidsai-nightly
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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 3.4.4 py37hf1a17b8_0 conda-forge
cudatoolkit 10.2.89 h6bb024c_0 nvidia
cudnn 7.6.5.32 h01f27c4_1 conda-forge
cupy 8.4.0 py37hca4e673_1 conda-forge
cython 0.29.22 py37hcd2ae1e_0 conda-forge
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dask 2021.2.0+31.g895c9541 pypi_0 pypi
dask-core 2021.2.0 pyhd8ed1ab_0 conda-forge
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distlib 0.3.1 pyh9f0ad1d_0 conda-forge
distributed 2021.2.0+12.gd24d62f1 pypi_0 pypi
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zipp 3.4.0 py_0 conda-forge
zlib 1.2.11 h516909a_1010 conda-forge
zstd 1.4.8 ha95c52a_1 conda-forge
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