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CLN: avoid try/except in Index methods #37990

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Merged
merged 1 commit into from
Nov 23, 2020
Merged

CLN: avoid try/except in Index methods #37990

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10 changes: 4 additions & 6 deletions pandas/core/indexes/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2490,12 +2490,10 @@ def _get_unique_index(self, dropna: bool = False):
else:
values = self._values

if dropna:
try:
if self.hasnans:
values = values[~isna(values)]
except NotImplementedError:
pass
if dropna and not isinstance(self, ABCMultiIndex):
# isna not defined for MultiIndex
Comment on lines +2493 to +2494
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It is reasonable to consider that ABCMultiIndex is never going to have isna defined?

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This came up on a dev call earlier this year and it didn't sound like there was reason for optimism.

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isna is tricky because a MI can be multi-valued e.g does isna refer to any value on a level, or all levels be null? it would be reasonable to actually return a multi-value here (e.g. same shape as the MI), but would need to see the implications if this is done.

if self.hasnans:
values = values[~isna(values)]

return self._shallow_copy(values)

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63 changes: 29 additions & 34 deletions pandas/core/indexes/datetimelike.py
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Expand Up @@ -27,7 +27,6 @@
from pandas.core import algorithms
from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray
from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
from pandas.core.base import IndexOpsMixin
import pandas.core.common as com
import pandas.core.indexes.base as ibase
from pandas.core.indexes.base import Index, _index_shared_docs
Expand Down Expand Up @@ -217,10 +216,6 @@ def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
result._data._freq = freq
return result

@doc(IndexOpsMixin.searchsorted, klass="Datetime-like Index")
def searchsorted(self, value, side="left", sorter=None):
return self._data.searchsorted(value, side=side, sorter=sorter)

_can_hold_na = True

_na_value = NaT
Expand Down Expand Up @@ -256,23 +251,23 @@ def min(self, axis=None, skipna=True, *args, **kwargs):
return self._na_value

i8 = self.asi8
try:

if len(i8) and self.is_monotonic_increasing:
# quick check
if len(i8) and self.is_monotonic:
if i8[0] != iNaT:
return self._data._box_func(i8[0])

if self.hasnans:
if skipna:
min_stamp = self[~self._isnan].asi8.min()
else:
return self._na_value
else:
min_stamp = i8.min()
return self._data._box_func(min_stamp)
except ValueError:
if i8[0] != iNaT:
return self._data._box_func(i8[0])

if self.hasnans:
if not skipna:
return self._na_value
i8 = i8[~self._isnan]

if not len(i8):
return self._na_value

min_stamp = i8.min()
return self._data._box_func(min_stamp)

def argmin(self, axis=None, skipna=True, *args, **kwargs):
"""
Returns the indices of the minimum values along an axis.
Expand Down Expand Up @@ -313,23 +308,23 @@ def max(self, axis=None, skipna=True, *args, **kwargs):
return self._na_value

i8 = self.asi8
try:

if len(i8) and self.is_monotonic:
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prob can de-duplicate this with min

# quick check
if len(i8) and self.is_monotonic:
if i8[-1] != iNaT:
return self._data._box_func(i8[-1])

if self.hasnans:
if skipna:
max_stamp = self[~self._isnan].asi8.max()
else:
return self._na_value
else:
max_stamp = i8.max()
return self._data._box_func(max_stamp)
except ValueError:
if i8[-1] != iNaT:
return self._data._box_func(i8[-1])

if self.hasnans:
if not skipna:
return self._na_value
i8 = i8[~self._isnan]

if not len(i8):
return self._na_value

max_stamp = i8.max()
return self._data._box_func(max_stamp)

def argmax(self, axis=None, skipna=True, *args, **kwargs):
"""
Returns the indices of the maximum values along an axis.
Expand Down Expand Up @@ -463,7 +458,7 @@ def _partial_date_slice(
vals = self._data._ndarray
unbox = self._data._unbox

if self.is_monotonic:
if self.is_monotonic_increasing:

if len(self) and (
(t1 < self[0] and t2 < self[0]) or (t1 > self[-1] and t2 > self[-1])
Expand Down