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BUG: Perform i8 conversion for datetimelike IntervalTree queries #22988

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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.24.0.txt
Original file line number Diff line number Diff line change
Expand Up @@ -755,7 +755,7 @@ Interval
- Bug in the :class:`IntervalIndex` constructor where the ``closed`` parameter did not always override the inferred ``closed`` (:issue:`19370`)
- Bug in the ``IntervalIndex`` repr where a trailing comma was missing after the list of intervals (:issue:`20611`)
- Bug in :class:`Interval` where scalar arithmetic operations did not retain the ``closed`` value (:issue:`22313`)
-
- Bug in :class:`IntervalIndex` where indexing with datetime-like values raised a ``KeyError`` (:issue:`20636`)

Indexing
^^^^^^^^
Expand Down
57 changes: 50 additions & 7 deletions pandas/core/indexes/interval.py
Original file line number Diff line number Diff line change
Expand Up @@ -6,20 +6,23 @@

from pandas.compat import add_metaclass
from pandas.core.dtypes.missing import isna
from pandas.core.dtypes.cast import find_common_type, maybe_downcast_to_dtype
from pandas.core.dtypes.cast import (
find_common_type, maybe_downcast_to_dtype, infer_dtype_from_scalar)
from pandas.core.dtypes.common import (
ensure_platform_int,
is_list_like,
is_datetime_or_timedelta_dtype,
is_datetime64tz_dtype,
is_dtype_equal,
is_integer_dtype,
is_float_dtype,
is_interval_dtype,
is_object_dtype,
is_scalar,
is_float,
is_number,
is_integer)
is_integer,
needs_i8_conversion)
from pandas.core.indexes.base import (
Index, ensure_index,
default_pprint, _index_shared_docs)
Expand All @@ -29,8 +32,8 @@
Interval, IntervalMixin, IntervalTree,
)

from pandas.core.indexes.datetimes import date_range
from pandas.core.indexes.timedeltas import timedelta_range
from pandas.core.indexes.datetimes import date_range, DatetimeIndex
from pandas.core.indexes.timedeltas import timedelta_range, TimedeltaIndex
from pandas.core.indexes.multi import MultiIndex
import pandas.core.common as com
from pandas.util._decorators import cache_readonly, Appender
Expand Down Expand Up @@ -192,7 +195,9 @@ def _isnan(self):

@cache_readonly
def _engine(self):
return IntervalTree(self.left, self.right, closed=self.closed)
left = self._maybe_convert_i8(self.left)
right = self._maybe_convert_i8(self.right)
return IntervalTree(left, right, closed=self.closed)

def __contains__(self, key):
"""
Expand Down Expand Up @@ -514,6 +519,41 @@ def _maybe_cast_indexed(self, key):

return key

def _maybe_convert_i8(self, key):
if isinstance(key, Interval):
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can you document this a bit, and can you simplify at all?

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Done. Look better?

if not isinstance(key.left, (Timestamp, Timedelta)):
return key
left = self._maybe_convert_i8(key.left)
right = self._maybe_convert_i8(key.right)
return Interval(left, right, key.closed)
elif isinstance(key, (IntervalIndex, IntervalArray)):
if not needs_i8_conversion(key.left):
return key
left = self._maybe_convert_i8(key.left)
right = self._maybe_convert_i8(key.right)
return IntervalIndex.from_arrays(left, right, key.closed)
elif is_list_like(key) and not isinstance(key, Index):
result = self._maybe_convert_i8(ensure_index(key))
if result[0] == key[0]:
# return the list-like key if no conversion
return key
return result

subtype = self.dtype.subtype
msg = ('Cannot index an IntervalIndex of subtype {subtype} with '
'values of dtype {other}')
if isinstance(key, (Timestamp, Timedelta)):
key_dtype, key_i8 = infer_dtype_from_scalar(key, pandas_dtype=True)
if not is_dtype_equal(subtype, key_dtype):
raise ValueError(msg.format(subtype=subtype, other=key_dtype))
return key_i8
elif isinstance(key, (DatetimeIndex, TimedeltaIndex)):
if not is_dtype_equal(subtype, key.dtype):
raise ValueError(msg.format(subtype=subtype, other=key.dtype))
return Index(key.asi8)

return key

def _check_method(self, method):
if method is None:
return
Expand Down Expand Up @@ -648,6 +688,7 @@ def get_loc(self, key, method=None):

else:
# use the interval tree
key = self._maybe_convert_i8(key)
if isinstance(key, Interval):
left, right = _get_interval_closed_bounds(key)
return self._engine.get_loc_interval(left, right)
Expand Down Expand Up @@ -711,8 +752,10 @@ def _get_reindexer(self, target):
"""

# find the left and right indexers
lindexer = self._engine.get_indexer(target.left.values)
rindexer = self._engine.get_indexer(target.right.values)
left = self._maybe_convert_i8(target.left)
right = self._maybe_convert_i8(target.right)
lindexer = self._engine.get_indexer(left.values)
rindexer = self._engine.get_indexer(right.values)

# we want to return an indexer on the intervals
# however, our keys could provide overlapping of multiple
Expand Down
135 changes: 135 additions & 0 deletions pandas/tests/indexes/interval/test_interval.py
Original file line number Diff line number Diff line change
@@ -1,7 +1,9 @@
from __future__ import division

from itertools import permutations
import pytest
import numpy as np
import re
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, date_range, timedelta_range)
Expand Down Expand Up @@ -498,6 +500,48 @@ def test_get_loc_length_one(self, item, closed):
result = index.get_loc(item)
assert result == 0

# Make consistent with test_interval_new.py (see #16316, #16386)
@pytest.mark.parametrize('breaks', [
date_range('20180101', periods=4),
date_range('20180101', periods=4, tz='US/Eastern'),
timedelta_range('0 days', periods=4)], ids=lambda x: str(x.dtype))
def test_get_loc_datetimelike_nonoverlapping(self, breaks):
# GH 20636
# nonoverlapping = IntervalIndex method and no i8 conversion
index = IntervalIndex.from_breaks(breaks)

value = index[0].mid
result = index.get_loc(value)
expected = 0
assert result == expected

interval = Interval(index[0].left, index[1].right)
result = index.get_loc(interval)
expected = slice(0, 2)
assert result == expected

# Make consistent with test_interval_new.py (see #16316, #16386)
@pytest.mark.parametrize('arrays', [
(date_range('20180101', periods=4), date_range('20180103', periods=4)),
(date_range('20180101', periods=4, tz='US/Eastern'),
date_range('20180103', periods=4, tz='US/Eastern')),
(timedelta_range('0 days', periods=4),
timedelta_range('2 days', periods=4))], ids=lambda x: str(x[0].dtype))
def test_get_loc_datetimelike_overlapping(self, arrays):
# GH 20636
# overlapping = IntervalTree method with i8 conversion
index = IntervalIndex.from_arrays(*arrays)

value = index[0].mid + Timedelta('12 hours')
result = np.sort(index.get_loc(value))
expected = np.array([0, 1], dtype='int64')
assert tm.assert_numpy_array_equal(result, expected)

interval = Interval(index[0].left, index[1].right)
result = np.sort(index.get_loc(interval))
expected = np.array([0, 1, 2], dtype='int64')
assert tm.assert_numpy_array_equal(result, expected)

# To be removed, replaced by test_interval_new.py (see #16316, #16386)
def test_get_indexer(self):
actual = self.index.get_indexer([-1, 0, 0.5, 1, 1.5, 2, 3])
Expand Down Expand Up @@ -555,6 +599,97 @@ def test_get_indexer_length_one(self, item, closed):
expected = np.array([0] * len(item), dtype='intp')
tm.assert_numpy_array_equal(result, expected)

# Make consistent with test_interval_new.py (see #16316, #16386)
@pytest.mark.parametrize('arrays', [
(date_range('20180101', periods=4), date_range('20180103', periods=4)),
(date_range('20180101', periods=4, tz='US/Eastern'),
date_range('20180103', periods=4, tz='US/Eastern')),
(timedelta_range('0 days', periods=4),
timedelta_range('2 days', periods=4))], ids=lambda x: str(x[0].dtype))
def test_get_reindexer_datetimelike(self, arrays):
# GH 20636
index = IntervalIndex.from_arrays(*arrays)
tuples = [(index[0].left, index[0].left + pd.Timedelta('12H')),
(index[-1].right - pd.Timedelta('12H'), index[-1].right)]
target = IntervalIndex.from_tuples(tuples)

result = index._get_reindexer(target)
expected = np.array([0, 3], dtype='int64')
tm.assert_numpy_array_equal(result, expected)

@pytest.mark.parametrize('breaks', [
date_range('20180101', periods=4),
date_range('20180101', periods=4, tz='US/Eastern'),
timedelta_range('0 days', periods=4)], ids=lambda x: str(x.dtype))
def test_maybe_convert_i8(self, breaks):
# GH 20636
index = IntervalIndex.from_breaks(breaks)

# intervalindex
result = index._maybe_convert_i8(index)
expected = IntervalIndex.from_breaks(breaks.asi8)
tm.assert_index_equal(result, expected)

# interval
interval = Interval(breaks[0], breaks[1])
result = index._maybe_convert_i8(interval)
expected = Interval(breaks[0].value, breaks[1].value)
assert result == expected

# datetimelike index
result = index._maybe_convert_i8(breaks)
expected = Index(breaks.asi8)
tm.assert_index_equal(result, expected)

# datetimelike scalar
result = index._maybe_convert_i8(breaks[0])
expected = breaks[0].value
assert result == expected

# list-like of datetimelike scalars
result = index._maybe_convert_i8(list(breaks))
expected = Index(breaks.asi8)
tm.assert_index_equal(result, expected)

@pytest.mark.parametrize('breaks', [
np.arange(5, dtype='int64'),
np.arange(5, dtype='float64')], ids=lambda x: str(x.dtype))
def test_maybe_convert_i8_numeric(self, breaks):
# GH 20636
index = IntervalIndex.from_breaks(breaks)
numeric_keys = [
IntervalIndex.from_breaks(breaks),
Interval(breaks[0], breaks[1]),
breaks,
breaks[0],
list(breaks)]

# no conversion occurs for numeric
for key in numeric_keys:
result = index._maybe_convert_i8(key)
assert result is key

@pytest.mark.parametrize('breaks1, breaks2', permutations([
date_range('20180101', periods=4),
date_range('20180101', periods=4, tz='US/Eastern'),
timedelta_range('0 days', periods=4)], 2), ids=lambda x: str(x.dtype))
def test_maybe_convert_i8_errors(self, breaks1, breaks2):
# GH 20636
index = IntervalIndex.from_breaks(breaks1)
invalid_keys = [
IntervalIndex.from_breaks(breaks2),
Interval(breaks2[0], breaks2[1]),
breaks2,
breaks2[0],
list(breaks2)]

msg = ('Cannot index an IntervalIndex of subtype {dtype1} with '
'values of dtype {dtype2}')
msg = re.escape(msg.format(dtype1=breaks1.dtype, dtype2=breaks2.dtype))
for key in invalid_keys:
with tm.assert_raises_regex(ValueError, msg):
index._maybe_convert_i8(key)

# To be removed, replaced by test_interval_new.py (see #16316, #16386)
def test_contains(self):
# Only endpoints are valid.
Expand Down