Series.rmod called with scalar returns inconsistent results based on size of series

[kmacdough]

Code Sample, a copy-pastable example if possible

In

s1 = pd.Series([2] * 10001, name='long_series').rmod(-1)
s2 = pd.Series([2] * 10000, name='short_series').rmod(-1)
print(s1.loc[0:9].to_frame().join(s2.loc[0:9]))

Out

   long_series  short_series
0           -1             1
1           -1             1
2           -1             1
3           -1             1
4           -1             1
5           -1             1
6           -1             1
7           -1             1
8           -1             1
9           -1             1

Problem description

Series.rmod called on a scalar behaves differently depending on the size of the series.
On my machine:

• length <= 10,000 sign matches divisor (matches pythons % operator)
• length > 10,000 sign matches dividend

Appears to behave properly when called on other Series.
Series.mod does not appear to have any such issues.

Expected Output

   long_series  short_series
0           1             1
1           1             1
2           1             1
3           1             1
4           1             1
5           1             1
6           1             1
7           1             1
8           1             1
9           1             1

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line]
INSTALLED VERSIONS

commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.1
setuptools : 41.0.1
Cython : 0.29.14
pytest : 5.0.1
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.3.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2

[TomAugspurger]

Seems to be fixed on master.

s1 = pd.Series([2] * 10001, name='long_series').rmod(-1)
s2 = pd.Series([2] * 10000, name='short_series').rmod(-1)
print(s1.loc[0:9].to_frame().join(s2.loc[0:9]))

## -- End pasted text --
   long_series  short_series
0            1             1
1            1             1
2            1             1
3            1             1
4            1             1
5            1             1
6            1             1
7            1             1
8            1             1
9            1             1

@kmacdough can you try with master?

[simonjayhawkins]

Seems to be fixed on master.

i'm getting the same issue on master (and also getting the incorrect behaviour on '0.26.0.dev0+910.g71b78685a' (master on 14/11/2019))

>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1068.g49bc8d8c9'
>>>
>>> s1 = pd.Series([2] * 10001, name="long_series").rmod(-1)
>>> s2 = pd.Series([2] * 10000, name="short_series").rmod(-1)
>>> print(s1.loc[0:9].to_frame().join(s2.loc[0:9]))
   long_series  short_series
0           -1             1
1           -1             1
2           -1             1
3           -1             1
4           -1             1
5           -1             1
6           -1             1
7           -1             1
8           -1             1
9           -1             1

INSTALLED VERSIONS

---

commit : 49bc8d8
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None

pandas : 1.1.0.dev0+1068.g49bc8d8c9
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200311
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.6.0
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0

[simonjayhawkins]

with uint64 casts to float, Int64 works as expected and UInt64 raises TypeError

>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1068.g49bc8d8c9'
>>>
>>> pd.Series([2] * 10001, name="long_series").astype("int64").rmod(-1)
0       -1
1       -1
2       -1
3       -1
4       -1
        ..
9996    -1
9997    -1
9998    -1
9999    -1
10000   -1
Name: long_series, Length: 10001, dtype: int64
>>>
>>> pd.Series([2] * 10001, name="long_series").astype("uint64").rmod(-1)
0        1.0
1        1.0
2        1.0
3        1.0
4        1.0
        ...
9996     1.0
9997     1.0
9998     1.0
9999     1.0
10000    1.0
Name: long_series, Length: 10001, dtype: float64
>>>
>>> pd.Series([2] * 10001, name="long_series").astype("Int64").rmod(-1)
0        1
1        1
2        1
3        1
4        1
        ..
9996     1
9997     1
9998     1
9999     1
10000    1
Name: long_series, Length: 10001, dtype: Int64
>>>
>>> pd.Series([2] * 10001, name="long_series").astype("UInt64").rmod(-1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\simon\pandas\pandas\core\ops\__init__.py", line 518, in flex_wrapper
    return op(self, other)
  File "C:\Users\simon\pandas\pandas\core\ops\roperator.py", line 40, in rmod
    return right % left
  File "C:\Users\simon\pandas\pandas\core\ops\common.py", line 63, in new_method
    return method(self, other)
  File "C:\Users\simon\pandas\pandas\core\ops\__init__.py", line 440, in wrapper
    result = arithmetic_op(lvalues, rvalues, op, str_rep)
  File "C:\Users\simon\pandas\pandas\core\ops\array_ops.py", line 205, in arithmetic_op
    res_values = op(lvalues, rvalues)
  File "C:\Users\simon\pandas\pandas\core\ops\roperator.py", line 40, in rmod
    return right % left
  File "C:\Users\simon\pandas\pandas\core\ops\common.py", line 63, in new_method
    return method(self, other)
  File "C:\Users\simon\pandas\pandas\core\arrays\integer.py", line 679, in integer_arithmetic_method
    return self._maybe_mask_result(result, mask, other, op_name)
  File "C:\Users\simon\pandas\pandas\core\arrays\integer.py", line 608, in _maybe_mask_result
    return type(self)(result, mask, copy=False)
  File "C:\Users\simon\pandas\pandas\core\arrays\integer.py", line 348, in __init__
    "values should be integer numpy array. Use "
TypeError: values should be integer numpy array. Use the 'integer_array' function instead
>>>

[jbrockmendel]

@simonjayhawkins didnt you have a recent numexpr-related PR on a issue similar to this?

[mroeschke]

I'm getting the correct behavior on master again. Maybe worth having a test to see if it's not platform or package related

In [3]: s1 = pd.Series([2] * 10001, name='long_series').rmod(-1)
   ...: s2 = pd.Series([2] * 10000, name='short_series').rmod(-1)
   ...: print(s1.loc[0:9].to_frame().join(s2.loc[0:9]))
   long_series  short_series
0            1             1
1            1             1
2            1             1
3            1             1
4            1             1
5            1             1
6            1             1
7            1             1
8            1             1
9            1             1