Describe the bug
cudf.read_csv supports two parameters, keep_default_na and na_values. When these two parameters are used in combination they are expected to show a certain behavior in selecting/determining what is/are null values. Pandas doc has detail explanation of the behavior: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html
Here is the relevant part from the doc:
Case 1:
If keep_default_na is True, and na_values are specified, na_values is appended to the default NaN values used for parsing.
Case 2:
If keep_default_na is True, and na_values are not specified, only the default NaN values are used for parsing.
Case 3:
If keep_default_na is False, and na_values are specified, only the NaN values specified na_values are used for parsing.
Case 4:
If keep_default_na is False, and na_values are not specified, no strings will be parsed as NaN.
It looks like we are not actually respecting this set of rules(except case 1 & 2) in our csv reader. See the code sample below.
Steps/Code to reproduce bug
In[152]: import pandas as pd
In[153]: import io
In[154]: import cudf
In[155]: df = pd.DataFrame({'a':['fdsdf',None,'sdffds','','asdf']})
In[156]: csv_buf = df.to_csv()
# Case 3: Even when `keep_default_na` is False, the default set of pre-defined na values
# are not being ignored and the passed `na_values` is not being treated as a null value.
In[168]: pdf = pd.read_csv(io.StringIO(csv_buf), keep_default_na=False, na_values='asdf')
In[169]: gdf = cudf.read_csv(io.StringIO(csv_buf), keep_default_na=False, na_values='asdf')
In[170]: pdf
Out[170]:
Unnamed: 0 a
0 0 fdsdf
1 1
2 2 sdffds
3 3
4 4 NaN
In[171]: pdf['a'][1]
Out[171]: ''
In[172]: pdf['a'].isnull()
Out[172]:
0 False
1 False
2 False
3 False
4 True
Name: a, dtype: bool
In[173]: gdf
Out[173]:
Unnamed: 0 a
0 0 fdsdf
1 1 <NA>
2 2 sdffds
3 3 <NA>
4 4 asdf
In[174]: gdf['a'][1]
In[175]: gdf['a'].isnull()
Out[175]:
0 False
1 True
2 False
3 True
4 False
Name: a, dtype: bool
# Case 4: When `keep_default_na` is False, and `na_values` input is not given, there should be
# no null values at all, the empty places in csv will have to translate to empty strings ('').
In[182]: pdf = pd.read_csv(io.StringIO(csv_buf), keep_default_na=False)
In[183]: gdf = cudf.read_csv(io.StringIO(csv_buf), keep_default_na=False)
In[184]: pdf
Out[184]:
Unnamed: 0 a
0 0 fdsdf
1 1
2 2 sdffds
3 3
4 4 asdf
In[185]: pdf['a'][1]
Out[185]: ''
In[186]: pdf['a'].isnull()
Out[186]:
0 False
1 False
2 False
3 False
4 False
Name: a, dtype: bool
In[187]: gdf
Out[187]:
Unnamed: 0 a
0 0 fdsdf
1 1 <NA>
2 2 sdffds
3 3 <NA>
4 4 asdf
In[188]: gdf['a'][1]
In[189]: gdf['a'].isnull()
Out[189]:
0 False
1 True
2 False
3 True
4 False
Name: a, dtype: bool
Expected behavior
We should be following the above set of rules when these two parameters are used in combination and match pandas behavior.
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 5cf7106c1a86d49ae345eff31868343a894c864a (HEAD -> branch-0.17, upstream/branch-0.17)
Author: David <[email protected]>
Date: Wed Nov 4 09:33:31 2020 -0500
Add cudf::test::dictionary_column_wrapper class (#6635)
**git submodules***
***OS Information***
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=20.04
DISTRIB_CODENAME=focal
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NAME="Ubuntu"
VERSION="20.04.1 LTS (Focal Fossa)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 20.04.1 LTS"
VERSION_ID="20.04"
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=focal
UBUNTU_CODENAME=focal
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***GPU Information***
Wed Nov 4 12:21:45 2020
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|===============================+======================+======================|
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| 33% 29C P8 5W / 260W | 1072MiB / 48601MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 Quadro RTX 8000 Off | 00000000:2D:00.0 On | Off |
| 33% 32C P8 21W / 260W | 446MiB / 48592MiB | 10% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
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| ID ID Usage |
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+-----------------------------------------------------------------------------+
***CPU***
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 12
On-line CPU(s) list: 0-11
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
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Model name: Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz
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Vulnerability Itlb multihit: KVM: Vulnerable
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Full generic retpoline, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable
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 ept_ad 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 hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke md_clear flush_l1d
***CMake***
/home/pgali/anaconda3/envs/cudf_dev/bin/cmake
cmake version 3.18.2
CMake suite maintained and supported by Kitware (kitware.com/cmake).
***g++***
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g++ (Ubuntu 9.3.0-17ubuntu1~20.04) 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) 2005-2020 NVIDIA Corporation
Built on Wed_Jul_22_19:09:09_PDT_2020
Cuda compilation tools, release 11.0, V11.0.221
Build cuda_11.0_bu.TC445_37.28845127_0
***Python***
/home/pgali/anaconda3/envs/cudf_dev/bin/python
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***Environment Variables***
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tornado 6.0.4 py37h8f50634_2 conda-forge
traitlets 5.0.5 py_0 conda-forge
typed-ast 1.4.1 py37h516909a_0 conda-forge
typing_extensions 3.7.4.3 py_0 conda-forge
urllib3 1.25.10 py_0 conda-forge
virtualenv 20.0.35 py37hc8dfbb8_0 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 py_1 conda-forge
wheel 0.35.1 pyh9f0ad1d_0 conda-forge
xz 5.2.5 h516909a_1 conda-forge
yaml 0.2.5 h516909a_0 conda-forge
zeromq 4.3.3 he1b5a44_2 conda-forge
zict 2.0.0 py_0 conda-forge
zipp 3.3.1 py_0 conda-forge
zlib 1.2.11 h516909a_1010 conda-forge
zstd 1.4.5 h6597ccf_2 conda-forge
Additional context
Surfaced while running fuzz tests: #6001
@galipremsagar This should be the same as the other issue - have to use a list for now.
@vuule, that partially solves the issue. Thus ruling out case 1. But case 3 & case 4 are still failing with the fix in this PR: https://github.com/rapidsai/cudf/pull/6693
I have updated the issue with failing cases.
Oh, I see now. This is very similar to https://github.com/rapidsai/cudf/issues/6682 - the empty fields are treated as null regardless of the parameters. I expect that these two issue can be addressed in one PR.
Now I see that empty string is actually one of the default NA values in Pandas. We don't have it in the default _na_values since we detect empty fields separately. This causes all inconsistent behavior here.
The cleanest solution would be to add the empty string to default _na_values and remove the special behavior for empty fields. Need to check if the trie we use to match NA values can support this.
This should have been closed via #6922. Closing now.