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BUG GH23282 calling min on series of NaT returns NaT #23289

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Merged
merged 12 commits into from
Oct 28, 2018
Merged

BUG GH23282 calling min on series of NaT returns NaT #23289

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dcce77c
BUG-23282 calling min on series of NaT returns NaT
JustinZhengBC Oct 23, 2018
ef91c24
BUG-23282 Add tests
JustinZhengBC Oct 23, 2018
8caeaed
BUG-23282 Add additional tests
JustinZhengBC Oct 23, 2018
ce0a7ee
BUG-23282 Move check to _wrap_result and add bug number to tests
JustinZhengBC Oct 23, 2018
123ff25
Merge branch 'master' into PR_TOOL_MERGE_PR_23289
jreback Oct 24, 2018
a1bde9c
use fill_value directly
jreback Oct 24, 2018
9d6dca3
BUG-23282 add more underscores
JustinZhengBC Oct 25, 2018
314b2bc
BUG-23282 Fix linting
JustinZhengBC Oct 25, 2018
15e5e87
BUG-23282 Add whatsnew and asserts
JustinZhengBC Oct 26, 2018
e45d908
Merge branch 'master' into BUG-23282
JustinZhengBC Oct 26, 2018
815da44
BUG-23282 Fix whatsnew and assert
JustinZhengBC Oct 26, 2018
a70b903
Fix merge conflict
JustinZhengBC Oct 26, 2018
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use fill_value directly
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@jreback
jreback committed Oct 24, 2018
commit a1bde9c0720146dd5f3ccdddac77c289eaa18fd2
29 changes: 16 additions & 13 deletions pandas/core/nanops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -244,7 +244,7 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
elif is_float_dtype(dtype):
dtype_max = np.float64

return values, mask, dtype, dtype_max
return values, mask, dtype, dtype_max, fill_value


def _isfinite(values):
Expand All @@ -266,19 +266,20 @@ def _view_if_needed(values):
return values


def _wrap_results(result, dtype):
def _wrap_results(result, dtype, fill_value=None):
""" wrap our results if needed """

if (is_integer(result) and is_datetime_or_timedelta_dtype(dtype)
and result == _int64_max):
result = tslibs.iNaT
if is_datetime64_dtype(dtype):
if not isinstance(result, np.ndarray):
if result == fill_value:
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Is it assumed that fill_value is not NA? If not, this will be wrong, since fill_value will never equal fill_value.

Should we assert that it's not NA?

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so this can't be for an i8 type by definition. but yes we can assert it.

result = np.nan
result = tslibs.Timestamp(result)
else:
result = result.view(dtype)
elif is_timedelta64_dtype(dtype):
if not isinstance(result, np.ndarray):
if result == fill_value:
result = np.nan

# raise if we have a timedelta64[ns] which is too large
if np.fabs(result) > _int64_max:
Expand Down Expand Up @@ -451,7 +452,8 @@ def nanmean(values, axis=None, skipna=True, mask=None):
>>> nanops.nanmean(s)
1.5
"""
values, mask, dtype, dtype_max = _get_values(values, skipna, 0, mask=mask)
values, mask, dtype, dtype_max, _ = _get_values(
values, skipna, 0, mask=mask)
dtype_sum = dtype_max
dtype_count = np.float64
if is_integer_dtype(dtype) or is_timedelta64_dtype(dtype):
Expand Down Expand Up @@ -504,7 +506,7 @@ def get_median(x):
return np.nan
return np.nanmedian(x[mask])

values, mask, dtype, dtype_max = _get_values(values, skipna, mask=mask)
values, mask, dtype, dtype_max, _ = _get_values(values, skipna, mask=mask)
if not is_float_dtype(values):
values = values.astype('f8')
values[mask] = np.nan
Expand Down Expand Up @@ -708,7 +710,8 @@ def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
def _nanminmax(meth, fill_value_typ):
@bottleneck_switch()
def reduction(values, axis=None, skipna=True, mask=None):
values, mask, dtype, dtype_max = _get_values(

values, mask, dtype, dtype_max, fill_value = _get_values(
values, skipna, fill_value_typ=fill_value_typ, mask=mask)

if ((axis is not None and values.shape[axis] == 0) or
Expand All @@ -722,7 +725,7 @@ def reduction(values, axis=None, skipna=True, mask=None):
else:
result = getattr(values, meth)(axis)

result = _wrap_results(result, dtype)
result = _wrap_results(result, dtype, fill_value)
return _maybe_null_out(result, axis, mask)

reduction.__name__ = 'nan' + meth
Expand Down Expand Up @@ -756,8 +759,8 @@ def nanargmax(values, axis=None, skipna=True, mask=None):
>>> nanops.nanargmax(s)
4
"""
values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf',
mask=mask)
values, mask, dtype, _, _ = _get_values(
values, skipna, fill_value_typ='-inf', mask=mask)
result = values.argmax(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result
Expand Down Expand Up @@ -786,8 +789,8 @@ def nanargmin(values, axis=None, skipna=True, mask=None):
>>> nanops.nanargmin(s)
0
"""
values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf',
mask=mask)
values, mask, dtype, _, _ = _get_values(
values, skipna, fill_value_typ='+inf', mask=mask)
result = values.argmin(axis)
result = _maybe_arg_null_out(result, axis, mask, skipna)
return result
Expand Down