Cudf: [BUG] `cudf.read_csv` should not cast to floating types if there are null entries in csv

[galipremsagar]

Describe the bug
We seem to be reading in an integer column with nulls as a floating column. This is similar to pandas behavior but since we have support for nullable dtypes in all dtypes, should we even type-cast to float?

Steps/Code to reproduce bug

In[20]: df = cudf.DataFrame({'a':cudf.Series([1, 2, 3], dtype='uint64')})
In[21]: df.to_csv('temp.csv')
In[22]: cudf.read_csv('temp.csv')
Out[22]: 
   Unnamed: 0  a
0           0  1
1           1  2
2           2  3
In[23]: pd.read_csv('temp.csv')
Out[23]: 
   Unnamed: 0  a
0           0  1
1           1  2
2           2  3
In[24]: df = cudf.DataFrame({'a':cudf.Series([1, 2, 3, None], dtype='uint64')})
In[25]: df.to_csv('temp.csv')
In[26]: cudf.read_csv('temp.csv')
Out[26]: 
   Unnamed: 0     a
0           0   1.0
1           1   2.0
2           2   3.0
3           3  <NA>
In[27]: pd.read_csv('temp.csv')
Out[27]: 
   Unnamed: 0    a
0           0  1.0
1           1  2.0
2           2  3.0
3           3  NaN

Expected behavior
Expected behavior is to not type-cast to float and only cast to the inferred dtype.

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 768b67c37c2e3539f3d80d3c881ff8b1b320e56b (HEAD -> branch-0.16, upstream/branch-0.16)
 Author: GALI PREM SAGAR <[email protected]>
 Date:   Thu Sep 24 09:18:00 2020 -0500

 Implement Fuzz tests for cuIO (#6114)

 * add log reporting for failed tests

 * add random data generation apis for all dtypes

 * make size as mandatory param

 * add random dataframe generation api

 * add Fuzz tests for parquet reader

 * add support for datetime, timedelta and category dtypes datageneration

 * add parquet writer tests

 * add csv writer and reader tests

 * add all dtypes support for random dataframe generation

 * add logging for exception

 * add all dtypes in CSV and parquet testing

 * cleanup

 * code cleanup

 * typo

 * add support for regression run from crash files

 * add json reader writer tests

 * fix issue related to picking random dtypes

 * replace prints with logging

 * exclude timedelta dtypes in CSV reader / writer tests

 * use seed in generate_input apis

 * add max_columns param

 * change param file extension to json from xml

 * add columns param

 * fix issues with regression run

 * add Readme for fuzz tests

 * add call method to fuzzer

 * add readme for fuzz tests

 * change main block code

 * add url

 * fix issue with float dtype data generation

 * add tips in readme

 * Update CHANGELOG.md

 * apply seeds

 * change the format of the dtypes_meta for rand_dataframe

 * re-organize tests into their respective files

 * move common code

 * use 3x faster np.radom apis instead of mimesis

 * make optimizations in _generate_column and fix lambda scope issues

 * remove redundant numpy computation

 * moved common code into utils.py

 * make max string length generation random

 * add todo for list dtype support

 * add copyright and use string buffer

 * change print to an error

 * handle keyboard interrupts and exit

 * replace writing to parquet directly from pyarrow

 * remove skipping of timedelta types in csv

 * give limits to datetime and duration types

 * parametrize max_string_length

 * move common code into a base class implementation

 * push down common logic into IOBase

 * use common logic from IOBase

 * remove unused variables

 * change default max_rows to 100000

 * add utility function to translate to pandas nullable dtypes

 * rename IOBase to IOFuzz

 * copyright

 * change default max_rows_size to 100000

 * change from saving to file to return buffer

 * Update python/cudf/cudf/fuzz_tests/readme.md

 Co-authored-by: Christopher Harris <[email protected]>

 * add comparison of file contents

 * handle deafult param for dirs

 * remove redundant test code

 * change default value for max rows:

 Co-authored-by: Christopher Harris <[email protected]>
 **git submodules***

 ***OS Information***
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 DISTRIB_RELEASE=20.04
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 ***GPU Information***
 Thu Sep 24 15:26:03 2020
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 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   1  Quadro RTX 8000     Off  | 00000000:2D:00.0  On |                  Off |
 | 33%   35C    P8    20W / 260W |    588MiB / 48592MiB |     18%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+

 +-----------------------------------------------------------------------------+
 | Processes:                                                                  |
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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
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 Vendor ID:                       GenuineIntel
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 Model name:                      Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz
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 NUMA node0 CPU(s):               0-11
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 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
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 ***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++***
 /usr/bin/g++
 g++ (Ubuntu 9.3.0-10ubuntu2) 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
 Python 3.7.8

 ***Environment Variables***
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Additional context
Surfaced while running fuzz tests: #6001

[karthikeyann]

This is an issue in pandas as well. Our code is conformant with pandas behavior.
https://github.com/rapidsai/cudf/blob/60d25ea04bf018905836310a18133806f780cb22/cpp/src/io/csv/reader_impl.cu#L583-L587

In [18]: pdf = pd.DataFrame({'a':pd.Series([1, 2, 3, None], dtype='Int64')})

In [19]: pdf.dtypes
Out[19]: 
a    Int64
dtype: object

In [20]: pdf.to_csv('temp.csv')

In [21]: cat temp.csv
,a
0,1
1,2
2,3
3,

In [22]: pd.read_csv('temp.csv').dtypes
Out[22]: 
Unnamed: 0      int64
a             float64
dtype: object

[galipremsagar]

I guess the question is with nullable dtypes support introduced in pandas aswell do we still want to mimic pandas behavior?

[kkraus14]

I guess the question is with nullable dtypes support introduced in pandas aswell do we still want to mimic pandas behavior?

Agreed. This sounds like a bug in Pandas at this point and we shouldn't replicate the behavior. @galipremsagar can you open an issue on Pandas for this?

[galipremsagar]

Agreed. This sounds like a bug in Pandas at this point and we shouldn't replicate the behavior. @galipremsagar can you open an issue on Pandas for this?

Linking for reference: https://github.com/pandas-dev/pandas/issues/36712

[vuule]

From the Pandas issue: Pandas will not change this behavior until they start using nullable types by default.
We can still go ahead with the change.